Vulnerability of children and the developing brain to neurotoxic hazards.

PubMed

Weiss, B

2000-06-01

For much of the history of toxicology, the sensitivity of the developing organism to chemical perturbation attracted limited attention. Several tragic episodes and new insights finally taught us that the course of early brain development incurs unique risks. Although the process is exquisitely controlled, its lability renders it highly susceptible to damage from environmental chemicals. Such disturbances, as recognized by current testing protocols and legislation such as the Food Quality Protection Act, can result in outcomes ranging from death to malformations to functional impairment. The latter are the most difficult to determine. First, they require a variety of measures to assay their extent. Second, adult responses may prove an inadequate guide to the response of the developing brain, which is part of the reason for proposing additional safety factors for children. Third, neuropsychological tests are deployed in complex circumstances in which many factors, including economic status, combine to produce a particular effect such as lowered intelligence quotient score. Fourth, the magnitude of the effect, for most environmental exposure levels, may be relatively small but extremely significant for public health. Fifth, changes in brain function occur throughout life, and some consequences of early damage may not even emerge until advanced age. Such factors need to be addressed in estimating the influence of a particular agent or group of agents on brain development and its functional expression. It is especially important to consider ways of dealing with multiple risks and their combinations in addition to the prevailing practice of estimating risks in isolation.

Parameter estimation of brain tumors using intraoperative thermal imaging based on artificial tactile sensing in conjunction with artificial neural network

NASA Astrophysics Data System (ADS)

Sadeghi-Goughari, M.; Mojra, A.; Sadeghi, S.

2016-02-01

Intraoperative Thermal Imaging (ITI) is a new minimally invasive diagnosis technique that can potentially locate margins of brain tumor in order to achieve maximum tumor resection with least morbidity. This study introduces a new approach to ITI based on artificial tactile sensing (ATS) technology in conjunction with artificial neural networks (ANN) and feasibility and applicability of this method in diagnosis and localization of brain tumors is investigated. In order to analyze validity and reliability of the proposed method, two simulations were performed. (i) An in vitro experimental setup was designed and fabricated using a resistance heater embedded in agar tissue phantom in order to simulate heat generation by a tumor in the brain tissue; and (ii) A case report patient with parafalcine meningioma was presented to simulate ITI in the neurosurgical procedure. In the case report, both brain and tumor geometries were constructed from MRI data and tumor temperature and depth of location were estimated. For experimental tests, a novel assisted surgery robot was developed to palpate the tissue phantom surface to measure temperature variations and ANN was trained to estimate the simulated tumor’s power and depth. Results affirm that ITI based ATS is a non-invasive method which can be useful to detect, localize and characterize brain tumors.

Image updating for brain deformation compensation in tumor resection

NASA Astrophysics Data System (ADS)

Fan, Xiaoyao; Ji, Songbai; Olson, Jonathan D.; Roberts, David W.; Hartov, Alex; Paulsen, Keith D.

2016-03-01

Preoperative magnetic resonance images (pMR) are typically used for intraoperative guidance in image-guided neurosurgery, the accuracy of which can be significantly compromised by brain deformation. Biomechanical finite element models (FEM) have been developed to estimate whole-brain deformation and produce model-updated MR (uMR) that compensates for brain deformation at different surgical stages. Early stages of surgery, such as after craniotomy and after dural opening, have been well studied, whereas later stages after tumor resection begins remain challenging. In this paper, we present a method to simulate tumor resection by incorporating data from intraoperative stereovision (iSV). The amount of tissue resection was estimated from iSV using a "trial-and-error" approach, and the cortical shift was measured from iSV through a surface registration method using projected images and an optical flow (OF) motion tracking algorithm. The measured displacements were employed to drive the biomechanical brain deformation model, and the estimated whole-brain deformation was subsequently used to deform pMR and produce uMR. We illustrate the method using one patient example. The results show that the uMR aligned well with iSV and the overall misfit between model estimates and measured displacements was 1.46 mm. The overall computational time was ~5 min, including iSV image acquisition after resection, surface registration, modeling, and image warping, with minimal interruption to the surgical flow. Furthermore, we compare uMR against intraoperative MR (iMR) that was acquired following iSV acquisition.

Brain perfusion imaging using a Reconstruction-of-Difference (RoD) approach for cone-beam computed tomography

NASA Astrophysics Data System (ADS)

Mow, M.; Zbijewski, W.; Sisniega, A.; Xu, J.; Dang, H.; Stayman, J. W.; Wang, X.; Foos, D. H.; Koliatsos, V.; Aygun, N.; Siewerdsen, J. H.

2017-03-01

Purpose: To improve the timely detection and treatment of intracranial hemorrhage or ischemic stroke, recent efforts include the development of cone-beam CT (CBCT) systems for perfusion imaging and new approaches to estimate perfusion parameters despite slow rotation speeds compared to multi-detector CT (MDCT) systems. This work describes development of a brain perfusion CBCT method using a reconstruction of difference (RoD) approach to enable perfusion imaging on a newly developed CBCT head scanner prototype. Methods: A new reconstruction approach using RoD with a penalized-likelihood framework was developed to image the temporal dynamics of vascular enhancement. A digital perfusion simulation was developed to give a realistic representation of brain anatomy, artifacts, noise, scanner characteristics, and hemo-dynamic properties. This simulation includes a digital brain phantom, time-attenuation curves and noise parameters, a novel forward projection method for improved computational efficiency, and perfusion parameter calculation. Results: Our results show the feasibility of estimating perfusion parameters from a set of images reconstructed from slow scans, sparse data sets, and arc length scans as short as 60 degrees. The RoD framework significantly reduces noise and time-varying artifacts from inconsistent projections. Proper regularization and the use of overlapping reconstructed arcs can potentially further decrease bias and increase temporal resolution, respectively. Conclusions: A digital brain perfusion simulation with RoD imaging approach has been developed and supports the feasibility of using a CBCT head scanner for perfusion imaging. Future work will include testing with data acquired using a 3D-printed perfusion phantom currently and translation to preclinical and clinical studies.

INVESTIGATING DIFFERENCES IN BRAIN FUNCTIONAL NETWORKS USING HIERARCHICAL COVARIATE-ADJUSTED INDEPENDENT COMPONENT ANALYSIS.

PubMed

Shi, Ran; Guo, Ying

2016-12-01

Human brains perform tasks via complex functional networks consisting of separated brain regions. A popular approach to characterize brain functional networks in fMRI studies is independent component analysis (ICA), which is a powerful method to reconstruct latent source signals from their linear mixtures. In many fMRI studies, an important goal is to investigate how brain functional networks change according to specific clinical and demographic variabilities. Existing ICA methods, however, cannot directly incorporate covariate effects in ICA decomposition. Heuristic post-ICA analysis to address this need can be inaccurate and inefficient. In this paper, we propose a hierarchical covariate-adjusted ICA (hc-ICA) model that provides a formal statistical framework for estimating covariate effects and testing differences between brain functional networks. Our method provides a more reliable and powerful statistical tool for evaluating group differences in brain functional networks while appropriately controlling for potential confounding factors. We present an analytically tractable EM algorithm to obtain maximum likelihood estimates of our model. We also develop a subspace-based approximate EM that runs significantly faster while retaining high accuracy. To test the differences in functional networks, we introduce a voxel-wise approximate inference procedure which eliminates the need of computationally expensive covariance matrix estimation and inversion. We demonstrate the advantages of our methods over the existing method via simulation studies. We apply our method to an fMRI study to investigate differences in brain functional networks associated with post-traumatic stress disorder (PTSD).

Development and application of a modified dynamic time warping algorithm (DTW-S) to analyses of primate brain expression time series

PubMed Central

2011-01-01

Background Comparing biological time series data across different conditions, or different specimens, is a common but still challenging task. Algorithms aligning two time series represent a valuable tool for such comparisons. While many powerful computation tools for time series alignment have been developed, they do not provide significance estimates for time shift measurements. Results Here, we present an extended version of the original DTW algorithm that allows us to determine the significance of time shift estimates in time series alignments, the DTW-Significance (DTW-S) algorithm. The DTW-S combines important properties of the original algorithm and other published time series alignment tools: DTW-S calculates the optimal alignment for each time point of each gene, it uses interpolated time points for time shift estimation, and it does not require alignment of the time-series end points. As a new feature, we implement a simulation procedure based on parameters estimated from real time series data, on a series-by-series basis, allowing us to determine the false positive rate (FPR) and the significance of the estimated time shift values. We assess the performance of our method using simulation data and real expression time series from two published primate brain expression datasets. Our results show that this method can provide accurate and robust time shift estimates for each time point on a gene-by-gene basis. Using these estimates, we are able to uncover novel features of the biological processes underlying human brain development and maturation. Conclusions The DTW-S provides a convenient tool for calculating accurate and robust time shift estimates at each time point for each gene, based on time series data. The estimates can be used to uncover novel biological features of the system being studied. The DTW-S is freely available as an R package TimeShift at http://www.picb.ac.cn/Comparative/data.html. PMID:21851598

Development and application of a modified dynamic time warping algorithm (DTW-S) to analyses of primate brain expression time series.

PubMed

Yuan, Yuan; Chen, Yi-Ping Phoebe; Ni, Shengyu; Xu, Augix Guohua; Tang, Lin; Vingron, Martin; Somel, Mehmet; Khaitovich, Philipp

2011-08-18

Comparing biological time series data across different conditions, or different specimens, is a common but still challenging task. Algorithms aligning two time series represent a valuable tool for such comparisons. While many powerful computation tools for time series alignment have been developed, they do not provide significance estimates for time shift measurements. Here, we present an extended version of the original DTW algorithm that allows us to determine the significance of time shift estimates in time series alignments, the DTW-Significance (DTW-S) algorithm. The DTW-S combines important properties of the original algorithm and other published time series alignment tools: DTW-S calculates the optimal alignment for each time point of each gene, it uses interpolated time points for time shift estimation, and it does not require alignment of the time-series end points. As a new feature, we implement a simulation procedure based on parameters estimated from real time series data, on a series-by-series basis, allowing us to determine the false positive rate (FPR) and the significance of the estimated time shift values. We assess the performance of our method using simulation data and real expression time series from two published primate brain expression datasets. Our results show that this method can provide accurate and robust time shift estimates for each time point on a gene-by-gene basis. Using these estimates, we are able to uncover novel features of the biological processes underlying human brain development and maturation. The DTW-S provides a convenient tool for calculating accurate and robust time shift estimates at each time point for each gene, based on time series data. The estimates can be used to uncover novel biological features of the system being studied. The DTW-S is freely available as an R package TimeShift at http://www.picb.ac.cn/Comparative/data.html.

An empirical approach to estimate near-infra-red photon propagation and optically induced drug release in brain tissues

NASA Astrophysics Data System (ADS)

Prabhu Verleker, Akshay; Fang, Qianqian; Choi, Mi-Ran; Clare, Susan; Stantz, Keith M.

2015-03-01

The purpose of this study is to develop an alternate empirical approach to estimate near-infra-red (NIR) photon propagation and quantify optically induced drug release in brain metastasis, without relying on computationally expensive Monte Carlo techniques (gold standard). Targeted drug delivery with optically induced drug release is a noninvasive means to treat cancers and metastasis. This study is part of a larger project to treat brain metastasis by delivering lapatinib-drug-nanocomplexes and activating NIR-induced drug release. The empirical model was developed using a weighted approach to estimate photon scattering in tissues and calibrated using a GPU based 3D Monte Carlo. The empirical model was developed and tested against Monte Carlo in optical brain phantoms for pencil beams (width 1mm) and broad beams (width 10mm). The empirical algorithm was tested against the Monte Carlo for different albedos along with diffusion equation and in simulated brain phantoms resembling white-matter (μs'=8.25mm-1, μa=0.005mm-1) and gray-matter (μs'=2.45mm-1, μa=0.035mm-1) at wavelength 800nm. The goodness of fit between the two models was determined using coefficient of determination (R-squared analysis). Preliminary results show the Empirical algorithm matches Monte Carlo simulated fluence over a wide range of albedo (0.7 to 0.99), while the diffusion equation fails for lower albedo. The photon fluence generated by empirical code matched the Monte Carlo in homogeneous phantoms (R2=0.99). While GPU based Monte Carlo achieved 300X acceleration compared to earlier CPU based models, the empirical code is 700X faster than the Monte Carlo for a typical super-Gaussian laser beam.

Towards real-time diffuse optical tomography for imaging brain functions cooperated with Kalman estimator

NASA Astrophysics Data System (ADS)

Wang, Bingyuan; Zhang, Yao; Liu, Dongyuan; Ding, Xuemei; Dan, Mai; Pan, Tiantian; Wang, Yihan; Li, Jiao; Zhou, Zhongxing; Zhang, Limin; Zhao, Huijuan; Gao, Feng

2018-02-01

Functional near-infrared spectroscopy (fNIRS) is a non-invasive neuroimaging method to monitor the cerebral hemodynamic through the optical changes measured at the scalp surface. It has played a more and more important role in psychology and medical imaging communities. Real-time imaging of brain function using NIRS makes it possible to explore some sophisticated human brain functions unexplored before. Kalman estimator has been frequently used in combination with modified Beer-Lamber Law (MBLL) based optical topology (OT), for real-time brain function imaging. However, the spatial resolution of the OT is low, hampering the application of OT in exploring some complicated brain functions. In this paper, we develop a real-time imaging method combining diffuse optical tomography (DOT) and Kalman estimator, much improving the spatial resolution. Instead of only presenting one spatially distributed image indicating the changes of the absorption coefficients at each time point during the recording process, one real-time updated image using the Kalman estimator is provided. Its each voxel represents the amplitude of the hemodynamic response function (HRF) associated with this voxel. We evaluate this method using some simulation experiments, demonstrating that this method can obtain more reliable spatial resolution images. Furthermore, a statistical analysis is also conducted to help to decide whether a voxel in the field of view is activated or not.

Groupwise registration of MR brain images with tumors.

PubMed

Tang, Zhenyu; Wu, Yihong; Fan, Yong

2017-08-04

A novel groupwise image registration framework is developed for registering MR brain images with tumors. Our method iteratively estimates a normal-appearance counterpart for each tumor image to be registered and constructs a directed graph (digraph) of normal-appearance images to guide the groupwise image registration. Particularly, our method maps each tumor image to its normal appearance counterpart by identifying and inpainting brain tumor regions with intensity information estimated using a low-rank plus sparse matrix decomposition based image representation technique. The estimated normal-appearance images are groupwisely registered to a group center image guided by a digraph of images so that the total length of 'image registration paths' to be the minimum, and then the original tumor images are warped to the group center image using the resulting deformation fields. We have evaluated our method based on both simulated and real MR brain tumor images. The registration results were evaluated with overlap measures of corresponding brain regions and average entropy of image intensity information, and Wilcoxon signed rank tests were adopted to compare different methods with respect to their regional overlap measures. Compared with a groupwise image registration method that is applied to normal-appearance images estimated using the traditional low-rank plus sparse matrix decomposition based image inpainting, our method achieved higher image registration accuracy with statistical significance (p  =  7.02  ×  10 -9 ).

An Efficient and Reliable Statistical Method for Estimating Functional Connectivity in Large Scale Brain Networks Using Partial Correlation

PubMed Central

Wang, Yikai; Kang, Jian; Kemmer, Phebe B.; Guo, Ying

2016-01-01

Currently, network-oriented analysis of fMRI data has become an important tool for understanding brain organization and brain networks. Among the range of network modeling methods, partial correlation has shown great promises in accurately detecting true brain network connections. However, the application of partial correlation in investigating brain connectivity, especially in large-scale brain networks, has been limited so far due to the technical challenges in its estimation. In this paper, we propose an efficient and reliable statistical method for estimating partial correlation in large-scale brain network modeling. Our method derives partial correlation based on the precision matrix estimated via Constrained L1-minimization Approach (CLIME), which is a recently developed statistical method that is more efficient and demonstrates better performance than the existing methods. To help select an appropriate tuning parameter for sparsity control in the network estimation, we propose a new Dens-based selection method that provides a more informative and flexible tool to allow the users to select the tuning parameter based on the desired sparsity level. Another appealing feature of the Dens-based method is that it is much faster than the existing methods, which provides an important advantage in neuroimaging applications. Simulation studies show that the Dens-based method demonstrates comparable or better performance with respect to the existing methods in network estimation. We applied the proposed partial correlation method to investigate resting state functional connectivity using rs-fMRI data from the Philadelphia Neurodevelopmental Cohort (PNC) study. Our results show that partial correlation analysis removed considerable between-module marginal connections identified by full correlation analysis, suggesting these connections were likely caused by global effects or common connection to other nodes. Based on partial correlation, we find that the most significant direct connections are between homologous brain locations in the left and right hemisphere. When comparing partial correlation derived under different sparse tuning parameters, an important finding is that the sparse regularization has more shrinkage effects on negative functional connections than on positive connections, which supports previous findings that many of the negative brain connections are due to non-neurophysiological effects. An R package “DensParcorr” can be downloaded from CRAN for implementing the proposed statistical methods. PMID:27242395

An Efficient and Reliable Statistical Method for Estimating Functional Connectivity in Large Scale Brain Networks Using Partial Correlation.

PubMed

Wang, Yikai; Kang, Jian; Kemmer, Phebe B; Guo, Ying

2016-01-01

Currently, network-oriented analysis of fMRI data has become an important tool for understanding brain organization and brain networks. Among the range of network modeling methods, partial correlation has shown great promises in accurately detecting true brain network connections. However, the application of partial correlation in investigating brain connectivity, especially in large-scale brain networks, has been limited so far due to the technical challenges in its estimation. In this paper, we propose an efficient and reliable statistical method for estimating partial correlation in large-scale brain network modeling. Our method derives partial correlation based on the precision matrix estimated via Constrained L1-minimization Approach (CLIME), which is a recently developed statistical method that is more efficient and demonstrates better performance than the existing methods. To help select an appropriate tuning parameter for sparsity control in the network estimation, we propose a new Dens-based selection method that provides a more informative and flexible tool to allow the users to select the tuning parameter based on the desired sparsity level. Another appealing feature of the Dens-based method is that it is much faster than the existing methods, which provides an important advantage in neuroimaging applications. Simulation studies show that the Dens-based method demonstrates comparable or better performance with respect to the existing methods in network estimation. We applied the proposed partial correlation method to investigate resting state functional connectivity using rs-fMRI data from the Philadelphia Neurodevelopmental Cohort (PNC) study. Our results show that partial correlation analysis removed considerable between-module marginal connections identified by full correlation analysis, suggesting these connections were likely caused by global effects or common connection to other nodes. Based on partial correlation, we find that the most significant direct connections are between homologous brain locations in the left and right hemisphere. When comparing partial correlation derived under different sparse tuning parameters, an important finding is that the sparse regularization has more shrinkage effects on negative functional connections than on positive connections, which supports previous findings that many of the negative brain connections are due to non-neurophysiological effects. An R package "DensParcorr" can be downloaded from CRAN for implementing the proposed statistical methods.

Estimation of Locomotion States of a Rat by Neural Signals from the Motor Cortices Based on a Linear Correlation Model

NASA Astrophysics Data System (ADS)

Fukayama, Osamu; Taniguchi, Noriyuki; Suzuki, Takafumi; Mabuchi, Kunihiko

We are developing a brain-machine interface (BMI) called “RatCar," a small vehicle controlled by the neural signals of a rat's brain. An unconfined adult rat with a set of bundled neural electrodes in the brain rides on the vehicle. Each bundle consists of four tungsten wires isolated with parylene polymer. These bundles were implanted in the primary motor and premotor cortices in both hemispheres of the brain. In this paper, methods and results for estimating locomotion speed and directional changes are described. Neural signals were recorded as the rat moved in a straight line and as it changed direction in a curve. Spike-like waveforms were then detected and classified into several clusters to calculate a firing rate for each neuron. The actual locomotion velocity and directional changes of the rat were recorded concurrently. Finally, the locomotion states were correlated with the neural firing rates using a simple linear model. As a result, the abstract estimation of the locomotion velocity and directional changes were achieved.

Brain-computer interface on the basis of EEG system Encephalan

NASA Astrophysics Data System (ADS)

Maksimenko, Vladimir; Badarin, Artem; Nedaivozov, Vladimir; Kirsanov, Daniil; Hramov, Alexander

2018-04-01

We have proposed brain-computer interface (BCI) for the estimation of the brain response on the presented visual tasks. Proposed BCI is based on the EEG recorder Encephalan-EEGR-19/26 (Medicom MTD, Russia) supplemented by a special home-made developed acquisition software. BCI is tested during experimental session while subject is perceiving the bistable visual stimuli and classifying them according to the interpretation. We have subjected the participant to the different external conditions and observed the significant decrease in the response, associated with the perceiving the bistable visual stimuli, during the presence of distraction. Based on the obtained results we have proposed possibility to use of BCI for estimation of the human alertness during solving the tasks required substantial visual attention.

Relationships between scalp, brain, and skull motion estimated using magnetic resonance elastography.

PubMed

Badachhape, Andrew A; Okamoto, Ruth J; Johnson, Curtis L; Bayly, Philip V

2018-05-17

The objective of this study was to characterize the relationships between motion in the scalp, skull, and brain. In vivo estimates of motion transmission from the skull to the brain may illuminate the mechanics of traumatic brain injury. Because of challenges in directly sensing skull motion, it is useful to know how well motion of soft tissue of the head, i.e., the scalp, can approximate skull motion or predict brain tissue deformation. In this study, motion of the scalp and brain were measured using magnetic resonance elastography (MRE) and separated into components due to rigid-body displacement and dynamic deformation. Displacement estimates in the scalp were calculated using low motion-encoding gradient strength in order to reduce "phase wrapping" (an ambiguity in displacement estimates caused by the 2 π-periodicity of MRE phase contrast). MRE estimates of scalp and brain motion were compared to skull motion estimated from three tri-axial accelerometers. Comparison of the relative amplitudes and phases of harmonic motion in the scalp, skull, and brain of six human subjects indicate that data from scalp-based sensors should be used with caution to estimate skull kinematics, but that fairly consistent relationships exist between scalp, skull, and brain motion. In addition, the measured amplitude and phase relationships of scalp, skull, and brain can be used to evaluate and improve mathematical models of head biomechanics. Copyright © 2018 Elsevier Ltd. All rights reserved.

Accident analysis to support the development of strategies for the prevention of brain injuries in car crashes.

PubMed

Antona-Makoshi, Jacobo; Mikami, Koji; Lindkvist, Mats; Davidsson, Johan; Schick, Sylvia

2018-08-01

This study estimated the frequency and risk of Moderate-to-Maximal traumatic brain injuries sustained by occupants in motor vehicle crashes in the US. National Automotive Sampling System - Crashworthiness Data System crashes that occurred in years 2001-2015 with light vehicles produced 2001 or later were incorporated in the study. Crash type, crash severity, car model year, belt usage and occupant age and sex were controlled for in the analysis. The results showed that Moderate concussions account for 79% of all MAIS brain 2+ injuries. Belted occupants were at lower risks than unbelted occupants for most brain injury categories, including concussions. After controlling for the effects of age and crash severity, belted female occupants involved in frontal crashes were estimated to be 1.5 times more likely to sustain a concussion than male occupants in similar conditions. Belted elderly occupants were found to be at 10.5 and 8 times higher risks for sub-dural haemorrhages than non-elderly belted occupants in frontal and side crashes, respectively. Adopted occupant protection strategies appear to be insufficient to achieve significant decreases in risk of both life-threatening brain injuries and concussions for all car occupants. Further effort to develop occupant and injury specific strategies for the prevention of brain injuries are needed. This study suggests that these strategies may consider prioritization of life-threatening brain vasculature injuries, particularly in elderly occupants, and concussion injuries, particularly in female occupants. Copyright © 2018 Elsevier Ltd. All rights reserved.

Some aspects of measuring levels of potassium in the brain

PubMed Central

Ramirez, L.M.; Coyle, P.; Heymsfield, S.; Zimman, J.

2007-01-01

The general aim of this work is to measure brain potassium (K) levels as a marker of intracellular water content and to test the hypothesis of whether edema in multiple sclerosis (MS) is associated with increased intracellular brain water. For that purpose, a system to measure K in brain is being developed. Our specific aim is to assess the potential contribution to the K photopeak from cranial K located outside the brain. For this, a simplified spherical phantom to represent the brain, a square box to represent the cranium, and a K point source to assess the contributions due to K outside the brain were used. It is estimated that only about 1–2% of the K photopeak might be attributable to K outside the brain. PMID:14618438

A quantitative magnetic resonance histology atlas of postnatal rat brain development with regional estimates of growth and variability.

PubMed

Calabrese, Evan; Badea, Alexandra; Watson, Charles; Johnson, G Allan

2013-05-01

There has been growing interest in the role of postnatal brain development in the etiology of several neurologic diseases. The rat has long been recognized as a powerful model system for studying neuropathology and the safety of pharmacologic treatments. However, the complex spatiotemporal changes that occur during rat neurodevelopment remain to be elucidated. This work establishes the first magnetic resonance histology (MRH) atlas of the developing rat brain, with an emphasis on quantitation. The atlas comprises five specimens at each of nine time points, imaged with eight distinct MR contrasts and segmented into 26 developmentally defined brain regions. The atlas was used to establish a timeline of morphometric changes and variability throughout neurodevelopment and represents a quantitative database of rat neurodevelopment for characterizing rat models of human neurologic disease. Published by Elsevier Inc.

Appraising the Role of Iron in Brain Aging and Cognition: Promises and Limitations of MRI Methods

PubMed Central

Daugherty, Ana M; Raz, Naftali

2015-01-01

Age-related increase in frailty is accompanied by a fundamental shift in cellular iron homeostasis. By promoting oxidative stress, the intracellular accumulation of non-heme iron outside of binding complexes contributes to chronic inflammation and interferes with normal brain metabolism. In the absence of direct non-invasive biomarkers of brain oxidative stress, iron accumulation estimated in vivo may serve as its proxy indicator. Hence, developing reliable in vivo measurements of brain iron content via magnetic resonance imaging (MRI) is of significant interest in human neuroscience. To date, by estimating brain iron content through various MRI methods, significant age differences and age-related increases in iron content of the basal ganglia have been revealed across multiple samples. Less consistent are the findings that pertain to the relationship between elevated brain iron content and systemic indices of vascular and metabolic dysfunction. Only a handful of cross-sectional investigations have linked high iron content in various brain regions and poor performance on assorted cognitive tests. The even fewer longitudinal studies indicate that iron accumulation may precede shrinkage of the basal ganglia and thus predict poor maintenance of cognitive functions. This rapidly developing field will benefit from introduction of higher-field MRI scanners, improvement in iron-sensitive and -specific acquisition sequences and post-processing analytic and computational methods, as well as accumulation of data from long-term longitudinal investigations. This review describes the potential advantages and promises of MRI-based assessment of brain iron, summarizes recent findings and highlights the limitations of the current methodology. PMID:26248580

REVISITING GLYCOGEN CONTENT IN THE HUMAN BRAIN

PubMed Central

Öz, Gülin; DiNuzzo, Mauro; Kumar, Anjali; Moheet, Amir; Seaquist, Elizabeth R.

2015-01-01

Glycogen provides an important glucose reservoir in the brain since the concentration of glucosyl units stored in glycogen is several fold higher than free glucose available in brain tissue. We have previously reported 3–4 µmol/g brain glycogen content using in vivo 13C magnetic resonance spectroscopy (MRS) in conjunction with [1-13C]glucose administration in healthy humans, while higher levels were reported in the rodent brain. Due to the slow turnover of bulk brain glycogen in humans, complete turnover of the glycogen pool, estimated to take 3–5 days, was not observed in these prior studies. In an attempt to reach complete turnover and thereby steady state 13C labeling in glycogen, here we administered [1-13C]glucose to healthy volunteers for 80 hours. To eliminate any net glycogen synthesis during this period and thereby achieve an accurate estimate of glycogen concentration, volunteers were maintained at euglycemic blood glucose levels during [1-13C]glucose administration and 13C-glycogen levels in the occipital lobe were measured by 13C MRS approximately every 12 hours. Finally, we fitted the data with a biophysical model that was recently developed to take into account the tiered structure of the glycogen molecule and additionally incorporated blood glucose levels and isotopic enrichments as input function in the model. We obtained excellent fits of the model to the 13C-glycogen data, and glycogen content in the healthy human brain tissue was found to be 7.8 ± 0.3 µmol/g, a value substantially higher than previous estimates of glycogen content in the human brain. PMID:26202425

Revisiting Glycogen Content in the Human Brain.

PubMed

Öz, Gülin; DiNuzzo, Mauro; Kumar, Anjali; Moheet, Amir; Seaquist, Elizabeth R

2015-12-01

Glycogen provides an important glucose reservoir in the brain since the concentration of glucosyl units stored in glycogen is several fold higher than free glucose available in brain tissue. We have previously reported 3-4 µmol/g brain glycogen content using in vivo (13)C magnetic resonance spectroscopy (MRS) in conjunction with [1-(13)C]glucose administration in healthy humans, while higher levels were reported in the rodent brain. Due to the slow turnover of bulk brain glycogen in humans, complete turnover of the glycogen pool, estimated to take 3-5 days, was not observed in these prior studies. In an attempt to reach complete turnover and thereby steady state (13)C labeling in glycogen, here we administered [1-(13)C]glucose to healthy volunteers for 80 h. To eliminate any net glycogen synthesis during this period and thereby achieve an accurate estimate of glycogen concentration, volunteers were maintained at euglycemic blood glucose levels during [1-(13)C]glucose administration and (13)C-glycogen levels in the occipital lobe were measured by (13)C MRS approximately every 12 h. Finally, we fitted the data with a biophysical model that was recently developed to take into account the tiered structure of the glycogen molecule and additionally incorporated blood glucose levels and isotopic enrichments as input function in the model. We obtained excellent fits of the model to the (13)C-glycogen data, and glycogen content in the healthy human brain tissue was found to be 7.8 ± 0.3 µmol/g, a value substantially higher than previous estimates of glycogen content in the human brain.

Associations between Family Adversity and Brain Volume in Adolescence: Manual vs. Automated Brain Segmentation Yields Different Results.

PubMed

Lyden, Hannah; Gimbel, Sarah I; Del Piero, Larissa; Tsai, A Bryna; Sachs, Matthew E; Kaplan, Jonas T; Margolin, Gayla; Saxbe, Darby

2016-01-01

Associations between brain structure and early adversity have been inconsistent in the literature. These inconsistencies may be partially due to methodological differences. Different methods of brain segmentation may produce different results, obscuring the relationship between early adversity and brain volume. Moreover, adolescence is a time of significant brain growth and certain brain areas have distinct rates of development, which may compromise the accuracy of automated segmentation approaches. In the current study, 23 adolescents participated in two waves of a longitudinal study. Family aggression was measured when the youths were 12 years old, and structural scans were acquired an average of 4 years later. Bilateral amygdalae and hippocampi were segmented using three different methods (manual tracing, FSL, and NeuroQuant). The segmentation estimates were compared, and linear regressions were run to assess the relationship between early family aggression exposure and all three volume segmentation estimates. Manual tracing results showed a positive relationship between family aggression and right amygdala volume, whereas FSL segmentation showed negative relationships between family aggression and both the left and right hippocampi. However, results indicate poor overlap between methods, and different associations were found between early family aggression exposure and brain volume depending on the segmentation method used.

Associations between Family Adversity and Brain Volume in Adolescence: Manual vs. Automated Brain Segmentation Yields Different Results

PubMed Central

Lyden, Hannah; Gimbel, Sarah I.; Del Piero, Larissa; Tsai, A. Bryna; Sachs, Matthew E.; Kaplan, Jonas T.; Margolin, Gayla; Saxbe, Darby

2016-01-01

Associations between brain structure and early adversity have been inconsistent in the literature. These inconsistencies may be partially due to methodological differences. Different methods of brain segmentation may produce different results, obscuring the relationship between early adversity and brain volume. Moreover, adolescence is a time of significant brain growth and certain brain areas have distinct rates of development, which may compromise the accuracy of automated segmentation approaches. In the current study, 23 adolescents participated in two waves of a longitudinal study. Family aggression was measured when the youths were 12 years old, and structural scans were acquired an average of 4 years later. Bilateral amygdalae and hippocampi were segmented using three different methods (manual tracing, FSL, and NeuroQuant). The segmentation estimates were compared, and linear regressions were run to assess the relationship between early family aggression exposure and all three volume segmentation estimates. Manual tracing results showed a positive relationship between family aggression and right amygdala volume, whereas FSL segmentation showed negative relationships between family aggression and both the left and right hippocampi. However, results indicate poor overlap between methods, and different associations were found between early family aggression exposure and brain volume depending on the segmentation method used. PMID:27656121

Estimation of electrical conductivity distribution within the human head from magnetic flux density measurement.

PubMed

Gao, Nuo; Zhu, S A; He, Bin

2005-06-07

We have developed a new algorithm for magnetic resonance electrical impedance tomography (MREIT), which uses only one component of the magnetic flux density to reconstruct the electrical conductivity distribution within the body. The radial basis function (RBF) network and simplex method are used in the present approach to estimate the conductivity distribution by minimizing the errors between the 'measured' and model-predicted magnetic flux densities. Computer simulations were conducted in a realistic-geometry head model to test the feasibility of the proposed approach. Single-variable and three-variable simulations were performed to estimate the brain-skull conductivity ratio and the conductivity values of the brain, skull and scalp layers. When SNR = 15 for magnetic flux density measurements with the target skull-to-brain conductivity ratio being 1/15, the relative error (RE) between the target and estimated conductivity was 0.0737 +/- 0.0746 in the single-variable simulations. In the three-variable simulations, the RE was 0.1676 +/- 0.0317. Effects of electrode position uncertainty were also assessed by computer simulations. The present promising results suggest the feasibility of estimating important conductivity values within the head from noninvasive magnetic flux density measurements.

Automated detection of extradural and subdural hematoma for contrast-enhanced CT images in emergency medical care

NASA Astrophysics Data System (ADS)

Hara, Takeshi; Matoba, Naoto; Zhou, Xiangrong; Yokoi, Shinya; Aizawa, Hiroaki; Fujita, Hiroshi; Sakashita, Keiji; Matsuoka, Tetsuya

2007-03-01

We have been developing the CAD scheme for head and abdominal injuries for emergency medical care. In this work, we have developed an automated method to detect typical head injuries, rupture or strokes of brain. Extradural and subdural hematoma region were detected by comparing technique after the brain areas were registered using warping. We employ 5 normal and 15 stroke cases to estimate the performance after creating the brain model with 50 normal cases. Some of the hematoma regions were detected correctly in all of the stroke cases with no false positive findings on normal cases.

Learning-based subject-specific estimation of dynamic maps of cortical morphology at missing time points in longitudinal infant studies.

PubMed

Meng, Yu; Li, Gang; Gao, Yaozong; Lin, Weili; Shen, Dinggang

2016-11-01

Longitudinal neuroimaging analysis of the dynamic brain development in infants has received increasing attention recently. Many studies expect a complete longitudinal dataset in order to accurately chart the brain developmental trajectories. However, in practice, a large portion of subjects in longitudinal studies often have missing data at certain time points, due to various reasons such as the absence of scan or poor image quality. To make better use of these incomplete longitudinal data, in this paper, we propose a novel machine learning-based method to estimate the subject-specific, vertex-wise cortical morphological attributes at the missing time points in longitudinal infant studies. Specifically, we develop a customized regression forest, named dynamically assembled regression forest (DARF), as the core regression tool. DARF ensures the spatial smoothness of the estimated maps for vertex-wise cortical morphological attributes and also greatly reduces the computational cost. By employing a pairwise estimation followed by a joint refinement, our method is able to fully exploit the available information from both subjects with complete scans and subjects with missing scans for estimation of the missing cortical attribute maps. The proposed method has been applied to estimating the dynamic cortical thickness maps at missing time points in an incomplete longitudinal infant dataset, which includes 31 healthy infant subjects, each having up to five time points in the first postnatal year. The experimental results indicate that our proposed framework can accurately estimate the subject-specific vertex-wise cortical thickness maps at missing time points, with the average error less than 0.23 mm. Hum Brain Mapp 37:4129-4147, 2016. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Extended Survival and Prognostic Factors for Patients With ALK-Rearranged Non–Small-Cell Lung Cancer and Brain Metastasis

PubMed Central

Johung, Kimberly L.; Yeh, Norman; Desai, Neil B.; Williams, Terence M.; Lautenschlaeger, Tim; Arvold, Nils D.; Ning, Matthew S.; Attia, Albert; Lovly, Christine M.; Goldberg, Sarah; Beal, Kathryn; Yu, James B.; Kavanagh, Brian D.; Chiang, Veronica L.; Camidge, D. Ross

2016-01-01

Purpose We performed a multi-institutional study to identify prognostic factors and determine outcomes for patients with ALK-rearranged non–small-cell lung cancer (NSCLC) and brain metastasis. Patients and Methods A total of 90 patients with brain metastases from ALK-rearranged NSCLC were identified from six institutions; 84 of 90 patients received radiotherapy to the brain (stereotactic radiosurgery [SRS] or whole-brain radiotherapy [WBRT]), and 86 of 90 received tyrosine kinase inhibitor (TKI) therapy. Estimates for overall (OS) and intracranial progression-free survival were determined and clinical prognostic factors were identified by Cox proportional hazards modeling. Results Median OS after development of brain metastases was 49.5 months (95% CI, 29.0 months to not reached), and median intracranial progression-free survival was 11.9 months (95% CI, 10.1 to 18.2 months). Forty-five percent of patients with follow-up had progressive brain metastases at death, and repeated interventions for brain metastases were common. Absence of extracranial metastases, Karnofsky performance score ≥ 90, and no history of TKIs before development of brain metastases were associated with improved survival (P = .003, < .001, and < .001, respectively), whereas a single brain metastasis or initial treatment with SRS versus WBRT were not (P = .633 and .666, respectively). Prognostic factors significant by multivariable analysis were used to describe four patient groups with 2-year OS estimates of 33%, 59%, 76%, and 100%, respectively (P < .001). Conclusion Patients with brain metastases from ALK-rearranged NSCLC treated with radiotherapy (SRS and/or WBRT) and TKIs have prolonged survival, suggesting that interventions to control intracranial disease are critical. The refinement of prognosis for this molecular subtype of NSCLC identifies a population of patients likely to benefit from first-line SRS, close CNS observation, and treatment of emergent CNS disease. PMID:26438117

Increased Functional Connectivity Between Subcortical and Cortical Resting-State Networks in Autism Spectrum Disorder

PubMed Central

Cerliani, Leonardo; Mennes, Maarten; Thomas, Rajat M.; Di Martino, Adriana; Thioux, Marc; Keysers, Christian

2016-01-01

Importance Individuals with autism spectrum disorder (ASD) exhibit severe difficulties in social interaction, motor coordination, behavioral flexibility, and atypical sensory processing, with considerable interindividual variability. This heterogeneous set of symptoms recently led to investigating the presence of abnormalities in the interaction across large-scale brain networks. To date, studies have focused either on constrained sets of brain regions or whole-brain analysis, rather than focusing on the interaction between brain networks. Objectives To compare the intrinsic functional connectivity between brain networks in a large sample of individuals with ASD and typically developing control subjects and to estimate to what extent group differences would predict autistic traits and reflect different developmental trajectories. Design, Setting, and Participants We studied 166 male individuals (mean age, 17.6 years; age range, 7-50 years) diagnosed as having DSM-IV-TR autism or Asperger syndrome and 193 typical developing male individuals (mean age, 16.9 years; age range, 6.5-39.4 years) using resting-state functional magnetic resonance imaging (MRI). Participants were matched for age, IQ, head motion, and eye status (open or closed) in the MRI scanner. We analyzed data from the Autism Brain Imaging Data Exchange (ABIDE), an aggregated MRI data set from 17 centers, made public in August 2012. Main Outcomes and Measures We estimated correlations between time courses of brain networks extracted using a data-driven method (independent component analysis). Subsequently, we associated estimates of interaction strength between networks with age and autistic traits indexed by the Social Responsiveness Scale. Results Relative to typically developing control participants, individuals with ASD showed increased functional connectivity between primary sensory networks and subcortical networks (thalamus and basal ganglia) (all t ≥ 3.13, P < .001 corrected). The strength of such connections was associated with the severity of autistic traits in the ASD group (all r ≥ 0.21, P < .0067 corrected). In addition, subcortico-cortical interaction decreased with age in the entire sample (all r ≤ −0.09, P < .012 corrected), although this association was significant only in typically developing participants (all r ≤ −0.13, P < .009 corrected). Conclusions and Relevance Our results showing ASD-related impairment in the interaction between primary sensory cortices and subcortical regions suggest that the sensory processes they subserve abnormally influence brain information processing in individuals with ASD. This might contribute to the occurrence of hyposensitivity or hypersensitivity and of difficulties in top-down regulation of behavior. PMID:26061743

Severe Urban Outdoor Air Pollution and Children's Structural and Functional Brain Development, From Evidence to Precautionary Strategic Action.

PubMed

D'Angiulli, Amedeo

2018-01-01

According to the latest estimates, about 2 billion children around the world are exposed to severe urban outdoor air pollution. Transdisciplinary, multi-method findings from epidemiology, developmental neuroscience, psychology, and pediatrics, show detrimental outcomes associated with pre- and postnatal exposure are found at all ages. Affected brain-related functions include perceptual and sensory information processing, intellectual and cognitive development, memory and executive functions, emotion and self-regulation, and academic achievement. Correspondingly, with the breakdown of natural barriers against entry and translocation of toxic particles in the brain, the most common structural changes are responses promoting neuroinflammation and indicating early neurodegenerative processes. In spite of the gaps in current scientific knowledge and the challenges posed by non-scientific issues that influence policy, the evidence invites the conclusion that urban outdoor air pollution is a serious threat to healthy brain development which may set the conditions for neurodegenerative diseases. Such evidence supports the perspective that urgent strategic precautionary actions, minimizing exposure and attenuating its effects, are needed to protect children and their brain development.

Severe Urban Outdoor Air Pollution and Children’s Structural and Functional Brain Development, From Evidence to Precautionary Strategic Action

PubMed Central

D’Angiulli, Amedeo

2018-01-01

According to the latest estimates, about 2 billion children around the world are exposed to severe urban outdoor air pollution. Transdisciplinary, multi-method findings from epidemiology, developmental neuroscience, psychology, and pediatrics, show detrimental outcomes associated with pre- and postnatal exposure are found at all ages. Affected brain-related functions include perceptual and sensory information processing, intellectual and cognitive development, memory and executive functions, emotion and self-regulation, and academic achievement. Correspondingly, with the breakdown of natural barriers against entry and translocation of toxic particles in the brain, the most common structural changes are responses promoting neuroinflammation and indicating early neurodegenerative processes. In spite of the gaps in current scientific knowledge and the challenges posed by non-scientific issues that influence policy, the evidence invites the conclusion that urban outdoor air pollution is a serious threat to healthy brain development which may set the conditions for neurodegenerative diseases. Such evidence supports the perspective that urgent strategic precautionary actions, minimizing exposure and attenuating its effects, are needed to protect children and their brain development. PMID:29670873

Development of a Physiologically-Based Pharmacokinetic Model of the Rat Central Nervous System

PubMed Central

Badhan, Raj K. Singh; Chenel, Marylore; Penny, Jeffrey I.

2014-01-01

Central nervous system (CNS) drug disposition is dictated by a drug’s physicochemical properties and its ability to permeate physiological barriers. The blood–brain barrier (BBB), blood-cerebrospinal fluid barrier and centrally located drug transporter proteins influence drug disposition within the central nervous system. Attainment of adequate brain-to-plasma and cerebrospinal fluid-to-plasma partitioning is important in determining the efficacy of centrally acting therapeutics. We have developed a physiologically-based pharmacokinetic model of the rat CNS which incorporates brain interstitial fluid (ISF), choroidal epithelial and total cerebrospinal fluid (CSF) compartments and accurately predicts CNS pharmacokinetics. The model yielded reasonable predictions of unbound brain-to-plasma partition ratio (Kpuu,brain) and CSF:plasma ratio (CSF:Plasmau) using a series of in vitro permeability and unbound fraction parameters. When using in vitro permeability data obtained from L-mdr1a cells to estimate rat in vivo permeability, the model successfully predicted, to within 4-fold, Kpuu,brain and CSF:Plasmau for 81.5% of compounds simulated. The model presented allows for simultaneous simulation and analysis of both brain biophase and CSF to accurately predict CNS pharmacokinetics from preclinical drug parameters routinely available during discovery and development pathways. PMID:24647103

Cellular phone use and brain tumor: a meta-analysis.

PubMed

Kan, Peter; Simonsen, Sara E; Lyon, Joseph L; Kestle, John R W

2008-01-01

The dramatic increase in the use of cellular phones has generated concerns about potential adverse effects, especially the development of brain tumors. We conducted a meta-analysis to examine the effect of cellular phone use on the risk of brain tumor development. We searched the literature using MEDLINE to locate case-control studies on cellular phone use and brain tumors. Odds ratios (ORs) for overall effect and stratified ORs associated with specific brain tumors, long-term use, and analog/digital phones were calculated for each study using its original data. A pooled estimator of each OR was then calculated using a random-effects model. Nine case-control studies containing 5,259 cases of primary brain tumors and 12,074 controls were included. All studies reported ORs according to brain tumor subtypes, and five provided ORs on patients with > or =10 years of follow up. Pooled analysis showed an overall OR of 0.90 (95% confidence interval [CI] 0.81-0.99) for cellular phone use and brain tumor development. The pooled OR for long-term users of > or =10 years (5 studies) was 1.25 (95% CI 1.01-1.54). No increased risk was observed in analog or digital cellular phone users. We found no overall increased risk of brain tumors among cellular phone users. The potential elevated risk of brain tumors after long-term cellular phone use awaits confirmation by future studies.

Longitudinal atlas for normative human brain development and aging over the lifespan using quantitative susceptibility mapping.

PubMed

Zhang, Yuyao; Wei, Hongjiang; Cronin, Matthew J; He, Naying; Yan, Fuhua; Liu, Chunlei

2018-05-01

Longitudinal brain atlases play an important role in the study of human brain development and cognition. Existing atlases are mainly based on anatomical features derived from T1-and T2-weighted MRI. A 4D developmental quantitative susceptibility mapping (QSM) atlas may facilitate the estimation of age-related iron changes in deep gray matter nuclei and myelin changes in white matter. To this end, group-wise co-registered QSM templates were generated over various age intervals from age 1-83 years old. Registration was achieved by combining both T1-weighted and QSM images. Based on the proposed template, we created an accurate deep gray matter nuclei parcellation map (DGM map). Notably, we segmented thalamus into 5 sub-regions, i.e. the anterior nuclei, the median nuclei, the lateral nuclei, the pulvinar and the internal medullary lamina. Furthermore, we built a "whole brain QSM parcellation map" by combining existing cortical parcellation and white-matter atlases with the proposed DGM map. Based on the proposed QSM atlas, the segmentation accuracy of iron-rich nuclei using QSM is significantly improved, especially for children and adolescent subjects. The age-related progression of magnetic susceptibility in each of the deep gray matter nuclei, the hippocampus, and the amygdala was estimated. Our automated atlas-based analysis provided a systematic confirmation of previous findings on susceptibility progression with age resulting from manual ROI drawings in deep gray matter nuclei. The susceptibility development in the hippocampus and the amygdala follow an iron accumulation model; while in the thalamus sub-regions, the susceptibility development exhibits a variety of trends. It is envisioned that the newly developed 4D QSM atlas will serve as a template for studying brain iron deposition and myelination/demyelination in both normal aging and various brain diseases. Copyright © 2018 Elsevier Inc. All rights reserved.

A variational Bayes spatiotemporal model for electromagnetic brain mapping.

PubMed

Nathoo, F S; Babul, A; Moiseev, A; Virji-Babul, N; Beg, M F

2014-03-01

In this article, we present a new variational Bayes approach for solving the neuroelectromagnetic inverse problem arising in studies involving electroencephalography (EEG) and magnetoencephalography (MEG). This high-dimensional spatiotemporal estimation problem involves the recovery of time-varying neural activity at a large number of locations within the brain, from electromagnetic signals recorded at a relatively small number of external locations on or near the scalp. Framing this problem within the context of spatial variable selection for an underdetermined functional linear model, we propose a spatial mixture formulation where the profile of electrical activity within the brain is represented through location-specific spike-and-slab priors based on a spatial logistic specification. The prior specification accommodates spatial clustering in brain activation, while also allowing for the inclusion of auxiliary information derived from alternative imaging modalities, such as functional magnetic resonance imaging (fMRI). We develop a variational Bayes approach for computing estimates of neural source activity, and incorporate a nonparametric bootstrap for interval estimation. The proposed methodology is compared with several alternative approaches through simulation studies, and is applied to the analysis of a multimodal neuroimaging study examining the neural response to face perception using EEG, MEG, and fMRI. © 2013, The International Biometric Society.

Predicting individual brain functional connectivity using a Bayesian hierarchical model.

PubMed

Dai, Tian; Guo, Ying

2017-02-15

Network-oriented analysis of functional magnetic resonance imaging (fMRI), especially resting-state fMRI, has revealed important association between abnormal connectivity and brain disorders such as schizophrenia, major depression and Alzheimer's disease. Imaging-based brain connectivity measures have become a useful tool for investigating the pathophysiology, progression and treatment response of psychiatric disorders and neurodegenerative diseases. Recent studies have started to explore the possibility of using functional neuroimaging to help predict disease progression and guide treatment selection for individual patients. These studies provide the impetus to develop statistical methodology that would help provide predictive information on disease progression-related or treatment-related changes in neural connectivity. To this end, we propose a prediction method based on Bayesian hierarchical model that uses individual's baseline fMRI scans, coupled with relevant subject characteristics, to predict the individual's future functional connectivity. A key advantage of the proposed method is that it can improve the accuracy of individualized prediction of connectivity by combining information from both group-level connectivity patterns that are common to subjects with similar characteristics as well as individual-level connectivity features that are particular to the specific subject. Furthermore, our method also offers statistical inference tools such as predictive intervals that help quantify the uncertainty or variability of the predicted outcomes. The proposed prediction method could be a useful approach to predict the changes in individual patient's brain connectivity with the progression of a disease. It can also be used to predict a patient's post-treatment brain connectivity after a specified treatment regimen. Another utility of the proposed method is that it can be applied to test-retest imaging data to develop a more reliable estimator for individual functional connectivity. We show there exists a nice connection between our proposed estimator and a recently developed shrinkage estimator of connectivity measures in the neuroimaging community. We develop an expectation-maximization (EM) algorithm for estimation of the proposed Bayesian hierarchical model. Simulations studies are performed to evaluate the accuracy of our proposed prediction methods. We illustrate the application of the methods with two data examples: the longitudinal resting-state fMRI from ADNI2 study and the test-retest fMRI data from Kirby21 study. In both the simulation studies and the fMRI data applications, we demonstrate that the proposed methods provide more accurate prediction and more reliable estimation of individual functional connectivity as compared with alternative methods. Copyright © 2017 Elsevier Inc. All rights reserved.

Highly adaptive tests for group differences in brain functional connectivity.

PubMed

Kim, Junghi; Pan, Wei

2015-01-01

Resting-state functional magnetic resonance imaging (rs-fMRI) and other technologies have been offering evidence and insights showing that altered brain functional networks are associated with neurological illnesses such as Alzheimer's disease. Exploring brain networks of clinical populations compared to those of controls would be a key inquiry to reveal underlying neurological processes related to such illnesses. For such a purpose, group-level inference is a necessary first step in order to establish whether there are any genuinely disrupted brain subnetworks. Such an analysis is also challenging due to the high dimensionality of the parameters in a network model and high noise levels in neuroimaging data. We are still in the early stage of method development as highlighted by Varoquaux and Craddock (2013) that "there is currently no unique solution, but a spectrum of related methods and analytical strategies" to learn and compare brain connectivity. In practice the important issue of how to choose several critical parameters in estimating a network, such as what association measure to use and what is the sparsity of the estimated network, has not been carefully addressed, largely because the answers are unknown yet. For example, even though the choice of tuning parameters in model estimation has been extensively discussed in the literature, as to be shown here, an optimal choice of a parameter for network estimation may not be optimal in the current context of hypothesis testing. Arbitrarily choosing or mis-specifying such parameters may lead to extremely low-powered tests. Here we develop highly adaptive tests to detect group differences in brain connectivity while accounting for unknown optimal choices of some tuning parameters. The proposed tests combine statistical evidence against a null hypothesis from multiple sources across a range of plausible tuning parameter values reflecting uncertainty with the unknown truth. These highly adaptive tests are not only easy to use, but also high-powered robustly across various scenarios. The usage and advantages of these novel tests are demonstrated on an Alzheimer's disease dataset and simulated data.

Joint eigenvector estimation from mutually anisotropic tensors improves susceptibility tensor imaging of the brain, kidney, and heart.

PubMed

Dibb, Russell; Liu, Chunlei

2017-06-01

To develop a susceptibility-based MRI technique for probing microstructure and fiber architecture of magnetically anisotropic tissues-such as central nervous system white matter, renal tubules, and myocardial fibers-in three dimensions using susceptibility tensor imaging (STI) tools. STI can probe tissue microstructure, but is limited by reconstruction artifacts because of absent phase information outside the tissue and noise. STI accuracy may be improved by estimating a joint eigenvector from mutually anisotropic susceptibility and relaxation tensors. Gradient-recalled echo image data were simulated using a numerical phantom and acquired from the ex vivo mouse brain, kidney, and heart. Susceptibility tensor data were reconstructed using STI, regularized STI, and the proposed algorithm of mutually anisotropic and joint eigenvector STI (MAJESTI). Fiber map and tractography results from each technique were compared with diffusion tensor data. MAJESTI reduced the estimated susceptibility tensor orientation error by 30% in the phantom, 36% in brain white matter, 40% in the inner medulla of the kidney, and 45% in myocardium. This improved the continuity and consistency of susceptibility-based fiber tractography in each tissue. MAJESTI estimation of the susceptibility tensors yields lower orientation errors for susceptibility-based fiber mapping and tractography in the intact brain, kidney, and heart. Magn Reson Med 77:2331-2346, 2017. © 2016 International Society for Magnetic Resonance in Medicine. © 2016 International Society for Magnetic Resonance in Medicine.

Example Based Image Analysis and Synthesis

DTIC Science & Technology

1993-11-01

Technology, 1993 This report describes research done within the Center for Biological and Computational Learning in the Department of Brain and...Fellowship from the Hughes Aircraft Company. A. Shashua is supported by a McDonnell-Pew postdoctoral fellowship from the department of Brain and...graphics has developed sophis- can be estimated from one or more images and then used ticated 3D models and rendering techniques - effectively to

Estimated maximal and current brain volume predict cognitive ability in old age

PubMed Central

Royle, Natalie A.; Booth, Tom; Valdés Hernández, Maria C.; Penke, Lars; Murray, Catherine; Gow, Alan J.; Maniega, Susana Muñoz; Starr, John; Bastin, Mark E.; Deary, Ian J.; Wardlaw, Joanna M.

2013-01-01

Brain tissue deterioration is a significant contributor to lower cognitive ability in later life; however, few studies have appropriate data to establish how much influence prior brain volume and prior cognitive performance have on this association. We investigated the associations between structural brain imaging biomarkers, including an estimate of maximal brain volume, and detailed measures of cognitive ability at age 73 years in a large (N = 620), generally healthy, community-dwelling population. Cognitive ability data were available from age 11 years. We found positive associations (r) between general cognitive ability and estimated brain volume in youth (male, 0.28; females, 0.12), and in measured brain volume in later life (males, 0.27; females, 0.26). Our findings show that cognitive ability in youth is a strong predictor of estimated prior and measured current brain volume in old age but that these effects were the same for both white and gray matter. As 1 of the largest studies of associations between brain volume and cognitive ability with normal aging, this work contributes to the wider understanding of how some early-life factors influence cognitive aging. PMID:23850342

Estimation of locomotion speed and directions changes to control a vehicle using neural signals from the motor cortex of rat.

PubMed

Fukayama, Osamu; Taniguchi, Noriyuki; Suzuki, Takafumi; Mabuchi, Kunihiko

2006-01-01

We have developed a brain-machine interface (BMI) in the form of a small vehicle, which we call the RatCar. In this system, we implanted wire electrodes in the motor cortices of rat's brain to continuously record neural signals. We applied a linear model to estimate the locomotion state (e.g., speed and directions) of a rat using a weighted summation model for the neural firing rates. With this information, we then determined the approximate movement of a rat. Although the estimation is still imprecise, results suggest that our model is able to control the system to some degree. In this paper, we give an overview of our system and describe the methods used, which include continuous neural recording, spike detection and a discrimination algorithm, and a locomotion estimation model minimizes the square error of the locomotion speed and changes in direction.

NETWORK ASSISTED ANALYSIS TO REVEAL THE GENETIC BASIS OF AUTISM1

PubMed Central

Liu, Li; Lei, Jing; Roeder, Kathryn

2016-01-01

While studies show that autism is highly heritable, the nature of the genetic basis of this disorder remains illusive. Based on the idea that highly correlated genes are functionally interrelated and more likely to affect risk, we develop a novel statistical tool to find more potentially autism risk genes by combining the genetic association scores with gene co-expression in specific brain regions and periods of development. The gene dependence network is estimated using a novel partial neighborhood selection (PNS) algorithm, where node specific properties are incorporated into network estimation for improved statistical and computational efficiency. Then we adopt a hidden Markov random field (HMRF) model to combine the estimated network and the genetic association scores in a systematic manner. The proposed modeling framework can be naturally extended to incorporate additional structural information concerning the dependence between genes. Using currently available genetic association data from whole exome sequencing studies and brain gene expression levels, the proposed algorithm successfully identified 333 genes that plausibly affect autism risk. PMID:27134692

Estimating individual contribution from group-based structural correlation networks.

PubMed

Saggar, Manish; Hosseini, S M Hadi; Bruno, Jennifer L; Quintin, Eve-Marie; Raman, Mira M; Kesler, Shelli R; Reiss, Allan L

2015-10-15

Coordinated variations in brain morphology (e.g., cortical thickness) across individuals have been widely used to infer large-scale population brain networks. These structural correlation networks (SCNs) have been shown to reflect synchronized maturational changes in connected brain regions. Further, evidence suggests that SCNs, to some extent, reflect both anatomical and functional connectivity and hence provide a complementary measure of brain connectivity in addition to diffusion weighted networks and resting-state functional networks. Although widely used to study between-group differences in network properties, SCNs are inferred only at the group-level using brain morphology data from a set of participants, thereby not providing any knowledge regarding how the observed differences in SCNs are associated with individual behavioral, cognitive and disorder states. In the present study, we introduce two novel distance-based approaches to extract information regarding individual differences from the group-level SCNs. We applied the proposed approaches to a moderately large dataset (n=100) consisting of individuals with fragile X syndrome (FXS; n=50) and age-matched typically developing individuals (TD; n=50). We tested the stability of proposed approaches using permutation analysis. Lastly, to test the efficacy of our method, individual contributions extracted from the group-level SCNs were examined for associations with intelligence scores and genetic data. The extracted individual contributions were stable and were significantly related to both genetic and intelligence estimates, in both typically developing individuals and participants with FXS. We anticipate that the approaches developed in this work could be used as a putative biomarker for altered connectivity in individuals with neurodevelopmental disorders. Copyright © 2015 Elsevier Inc. All rights reserved.

Brain size growth in wild and captive chimpanzees (Pan troglodytes).

PubMed

Cofran, Zachary

2018-05-24

Despite many studies of chimpanzee brain size growth, intraspecific variation is under-explored. Brain size data from chimpanzees of the Taï Forest and the Yerkes Primate Research Center enable a unique glimpse into brain growth variation as age at death is known for individuals, allowing cross-sectional growth curves to be estimated. Because Taï chimpanzees are from the wild but Yerkes apes are captive, potential environmental effects on neural development can also be explored. Previous research has revealed differences in growth and health between wild and captive primates, but such habitat effects have yet to be investigated for brain growth. Here, I use an iterative curve fitting procedure to estimate brain growth and regression parameters for each population, statistically comparing growth models using bootstrapped confidence intervals. Yerkes and Taï brain sizes overlap at all ages, although the sole Taï newborn is at the low end of captive neonatal variation. Growth rate and duration are statistically indistinguishable between the two populations. Resampling the Yerkes sample to match the Taï sample size and age group composition shows that ontogenetic variation in the two groups are remarkably similar despite the latter's limited size. Best fit growth curves for each sample indicate cessation of brain size growth at around 2 years, earlier than has previously been reported. The overall similarity between wild and captive chimpanzees points to the canalization of brain growth in this species. © 2018 Wiley Periodicals, Inc.

Detection and mapping of delays in early cortical folding derived from in utero MRI

NASA Astrophysics Data System (ADS)

Habas, Piotr A.; Rajagopalan, Vidya; Scott, Julia A.; Kim, Kio; Roosta, Ahmad; Rousseau, Francois; Barkovich, A. James; Glenn, Orit A.; Studholme, Colin

2011-03-01

Understanding human brain development in utero and detecting cortical abnormalities related to specific clinical conditions is an important area of research. In this paper, we describe and evaluate methodology for detection and mapping of delays in early cortical folding from population-based studies of fetal brain anatomies imaged in utero. We use a general linear modeling framework to describe spatiotemporal changes in curvature of the developing brain and explore the ability to detect and localize delays in cortical folding in the presence of uncertainty in estimation of the fetal age. We apply permutation testing to examine which regions of the brain surface provide the most statistical power to detect a given folding delay at a given developmental stage. The presented methodology is evaluated using MR scans of fetuses with normal brain development and gestational ages ranging from 20.57 to 27.86 weeks. This period is critical in early cortical folding and the formation of the primary and secondary sulci. Finally, we demonstrate a clinical application of the framework for detection and localization of folding delays in fetuses with isolated mild ventriculomegaly.

Detection of atypical network development patterns in children with autism spectrum disorder using magnetoencephalography

PubMed Central

Watanabe, Katsumi; Yoshimura, Yuko; Kikuchi, Mitsuru; Minabe, Yoshio; Aihara, Kazuyuki

2017-01-01

Autism spectrum disorder (ASD) is a developmental disorder that involves developmental delays. It has been hypothesized that aberrant neural connectivity in ASD may cause atypical brain network development. Brain graphs not only describe the differences in brain networks between clinical and control groups, but also provide information about network development within each group. In the present study, graph indices of brain networks were estimated in children with ASD and in typically developing (TD) children using magnetoencephalography performed while the children viewed a cartoon video. We examined brain graphs from a developmental point of view, and compared the networks between children with ASD and TD children. Network development patterns (NDPs) were assessed by examining the association between the graph indices and the raw scores on the achievement scale or the age of the children. The ASD and TD groups exhibited different NDPs at both network and nodal levels. In the left frontal areas, the nodal degree and efficiency of the ASD group were negatively correlated with the achievement scores. Reduced network connections were observed in the temporal and posterior areas of TD children. These results suggested that the atypical network developmental trajectory in children with ASD is associated with the development score rather than age. PMID:28886147

How the embryonic chick brain twists.

PubMed

Chen, Zi; Guo, Qiaohang; Dai, Eric; Forsch, Nickolas; Taber, Larry A

2016-11-01

During early development, the tubular embryonic chick brain undergoes a combination of progressive ventral bending and rightward torsion, one of the earliest organ-level left-right asymmetry events in development. Existing evidence suggests that bending is caused by differential growth, but the mechanism for the predominantly rightward torsion of the embryonic brain tube remains poorly understood. Here, we show through a combination of in vitro experiments, a physical model of the embryonic morphology and mechanics analysis that the vitelline membrane (VM) exerts an external load on the brain that drives torsion. Our theoretical analysis showed that the force is of the order of 10 micronewtons. We also designed an experiment to use fluid surface tension to replace the mechanical role of the VM, and the estimated magnitude of the force owing to surface tension was shown to be consistent with the above theoretical analysis. We further discovered that the asymmetry of the looping heart determines the chirality of the twisted brain via physical mechanisms, demonstrating the mechanical transfer of left-right asymmetry between organs. Our experiments also implied that brain flexure is a necessary condition for torsion. Our work clarifies the mechanical origin of torsion and the development of left-right asymmetry in the early embryonic brain. © 2016 The Author(s).

Information spreading by a combination of MEG source estimation and multivariate pattern classification.

PubMed

Sato, Masashi; Yamashita, Okito; Sato, Masa-Aki; Miyawaki, Yoichi

2018-01-01

To understand information representation in human brain activity, it is important to investigate its fine spatial patterns at high temporal resolution. One possible approach is to use source estimation of magnetoencephalography (MEG) signals. Previous studies have mainly quantified accuracy of this technique according to positional deviations and dispersion of estimated sources, but it remains unclear how accurately MEG source estimation restores information content represented by spatial patterns of brain activity. In this study, using simulated MEG signals representing artificial experimental conditions, we performed MEG source estimation and multivariate pattern analysis to examine whether MEG source estimation can restore information content represented by patterns of cortical current in source brain areas. Classification analysis revealed that the corresponding artificial experimental conditions were predicted accurately from patterns of cortical current estimated in the source brain areas. However, accurate predictions were also possible from brain areas whose original sources were not defined. Searchlight decoding further revealed that this unexpected prediction was possible across wide brain areas beyond the original source locations, indicating that information contained in the original sources can spread through MEG source estimation. This phenomenon of "information spreading" may easily lead to false-positive interpretations when MEG source estimation and classification analysis are combined to identify brain areas that represent target information. Real MEG data analyses also showed that presented stimuli were able to be predicted in the higher visual cortex at the same latency as in the primary visual cortex, also suggesting that information spreading took place. These results indicate that careful inspection is necessary to avoid false-positive interpretations when MEG source estimation and multivariate pattern analysis are combined.

Information spreading by a combination of MEG source estimation and multivariate pattern classification

PubMed Central

Sato, Masashi; Yamashita, Okito; Sato, Masa-aki

2018-01-01

To understand information representation in human brain activity, it is important to investigate its fine spatial patterns at high temporal resolution. One possible approach is to use source estimation of magnetoencephalography (MEG) signals. Previous studies have mainly quantified accuracy of this technique according to positional deviations and dispersion of estimated sources, but it remains unclear how accurately MEG source estimation restores information content represented by spatial patterns of brain activity. In this study, using simulated MEG signals representing artificial experimental conditions, we performed MEG source estimation and multivariate pattern analysis to examine whether MEG source estimation can restore information content represented by patterns of cortical current in source brain areas. Classification analysis revealed that the corresponding artificial experimental conditions were predicted accurately from patterns of cortical current estimated in the source brain areas. However, accurate predictions were also possible from brain areas whose original sources were not defined. Searchlight decoding further revealed that this unexpected prediction was possible across wide brain areas beyond the original source locations, indicating that information contained in the original sources can spread through MEG source estimation. This phenomenon of “information spreading” may easily lead to false-positive interpretations when MEG source estimation and classification analysis are combined to identify brain areas that represent target information. Real MEG data analyses also showed that presented stimuli were able to be predicted in the higher visual cortex at the same latency as in the primary visual cortex, also suggesting that information spreading took place. These results indicate that careful inspection is necessary to avoid false-positive interpretations when MEG source estimation and multivariate pattern analysis are combined. PMID:29912968

Brain tumor segmentation from multimodal magnetic resonance images via sparse representation.

PubMed

Li, Yuhong; Jia, Fucang; Qin, Jing

2016-10-01

Accurately segmenting and quantifying brain gliomas from magnetic resonance (MR) images remains a challenging task because of the large spatial and structural variability among brain tumors. To develop a fully automatic and accurate brain tumor segmentation algorithm, we present a probabilistic model of multimodal MR brain tumor segmentation. This model combines sparse representation and the Markov random field (MRF) to solve the spatial and structural variability problem. We formulate the tumor segmentation problem as a multi-classification task by labeling each voxel as the maximum posterior probability. We estimate the maximum a posteriori (MAP) probability by introducing the sparse representation into a likelihood probability and a MRF into the prior probability. Considering the MAP as an NP-hard problem, we convert the maximum posterior probability estimation into a minimum energy optimization problem and employ graph cuts to find the solution to the MAP estimation. Our method is evaluated using the Brain Tumor Segmentation Challenge 2013 database (BRATS 2013) and obtained Dice coefficient metric values of 0.85, 0.75, and 0.69 on the high-grade Challenge data set, 0.73, 0.56, and 0.54 on the high-grade Challenge LeaderBoard data set, and 0.84, 0.54, and 0.57 on the low-grade Challenge data set for the complete, core, and enhancing regions. The experimental results show that the proposed algorithm is valid and ranks 2nd compared with the state-of-the-art tumor segmentation algorithms in the MICCAI BRATS 2013 challenge. Copyright © 2016 Elsevier B.V. All rights reserved.

Estimating the brain pathological age of Alzheimer’s disease patients from MR image data based on the separability distance criterion

NASA Astrophysics Data System (ADS)

Li, Yongming; Li, Fan; Wang, Pin; Zhu, Xueru; Liu, Shujun; Qiu, Mingguo; Zhang, Jingna; Zeng, Xiaoping

2016-10-01

Traditional age estimation methods are based on the same idea that uses the real age as the training label. However, these methods ignore that there is a deviation between the real age and the brain age due to accelerated brain aging. This paper considers this deviation and searches for it by maximizing the separability distance value rather than by minimizing the difference between the estimated brain age and the real age. Firstly, set the search range of the deviation as the deviation candidates according to prior knowledge. Secondly, use the support vector regression (SVR) as the age estimation model to minimize the difference between the estimated age and the real age plus deviation rather than the real age itself. Thirdly, design the fitness function based on the separability distance criterion. Fourthly, conduct age estimation on the validation dataset using the trained age estimation model, put the estimated age into the fitness function, and obtain the fitness value of the deviation candidate. Fifthly, repeat the iteration until all the deviation candidates are involved and get the optimal deviation with maximum fitness values. The real age plus the optimal deviation is taken as the brain pathological age. The experimental results showed that the separability was apparently improved. For normal control-Alzheimer’s disease (NC-AD), normal control-mild cognition impairment (NC-MCI), and MCI-AD, the average improvements were 0.178 (35.11%), 0.033 (14.47%), and 0.017 (39.53%), respectively. For NC-MCI-AD, the average improvement was 0.2287 (64.22%). The estimated brain pathological age could be not only more helpful to the classification of AD but also more precisely reflect accelerated brain aging. In conclusion, this paper offers a new method for brain age estimation that can distinguish different states of AD and can better reflect the extent of accelerated aging.

The role of mechanics during brain development

NASA Astrophysics Data System (ADS)

Budday, Silvia; Steinmann, Paul; Kuhl, Ellen

2014-12-01

Convolutions are a classical hallmark of most mammalian brains. Brain surface morphology is often associated with intelligence and closely correlated with neurological dysfunction. Yet, we know surprisingly little about the underlying mechanisms of cortical folding. Here we identify the role of the key anatomic players during the folding process: cortical thickness, stiffness, and growth. To establish estimates for the critical time, pressure, and the wavelength at the onset of folding, we derive an analytical model using the Föppl-von Kármán theory. Analytical modeling provides a quick first insight into the critical conditions at the onset of folding, yet it fails to predict the evolution of complex instability patterns in the post-critical regime. To predict realistic surface morphologies, we establish a computational model using the continuum theory of finite growth. Computational modeling not only confirms our analytical estimates, but is also capable of predicting the formation of complex surface morphologies with asymmetric patterns and secondary folds. Taken together, our analytical and computational models explain why larger mammalian brains tend to be more convoluted than smaller brains. Both models provide mechanistic interpretations of the classical malformations of lissencephaly and polymicrogyria. Understanding the process of cortical folding in the mammalian brain has direct implications on the diagnostics of neurological disorders including severe retardation, epilepsy, schizophrenia, and autism.

The role of mechanics during brain development

PubMed Central

Budday, Silvia; Steinmann, Paul; Kuhl, Ellen

2014-01-01

Convolutions are a classical hallmark of most mammalian brains. Brain surface morphology is often associated with intelligence and closely correlated to neurological dysfunction. Yet, we know surprisingly little about the underlying mechanisms of cortical folding. Here we identify the role of the key anatomic players during the folding process: cortical thickness, stiffness, and growth. To establish estimates for the critical time, pressure, and the wavelength at the onset of folding, we derive an analytical model using the Föppl-von-Kármán theory. Analytical modeling provides a quick first insight into the critical conditions at the onset of folding, yet it fails to predict the evolution of complex instability patterns in the post-critical regime. To predict realistic surface morphologies, we establish a computational model using the continuum theory of finite growth. Computational modeling not only confirms our analytical estimates, but is also capable of predicting the formation of complex surface morphologies with asymmetric patterns and secondary folds. Taken together, our analytical and computational models explain why larger mammalian brains tend to be more convoluted than smaller brains. Both models provide mechanistic interpretations of the classical malformations of lissencephaly and polymicrogyria. Understanding the process of cortical folding in the mammalian brain has direct implications on the diagnostics of neurological disorders including severe retardation, epilepsy, schizophrenia, and autism. PMID:25202162

Robust generative asymmetric GMM for brain MR image segmentation.

PubMed

Ji, Zexuan; Xia, Yong; Zheng, Yuhui

2017-11-01

Accurate segmentation of brain tissues from magnetic resonance (MR) images based on the unsupervised statistical models such as Gaussian mixture model (GMM) has been widely studied during last decades. However, most GMM based segmentation methods suffer from limited accuracy due to the influences of noise and intensity inhomogeneity in brain MR images. To further improve the accuracy for brain MR image segmentation, this paper presents a Robust Generative Asymmetric GMM (RGAGMM) for simultaneous brain MR image segmentation and intensity inhomogeneity correction. First, we develop an asymmetric distribution to fit the data shapes, and thus construct a spatial constrained asymmetric model. Then, we incorporate two pseudo-likelihood quantities and bias field estimation into the model's log-likelihood, aiming to exploit the neighboring priors of within-cluster and between-cluster and to alleviate the impact of intensity inhomogeneity, respectively. Finally, an expectation maximization algorithm is derived to iteratively maximize the approximation of the data log-likelihood function to overcome the intensity inhomogeneity in the image and segment the brain MR images simultaneously. To demonstrate the performances of the proposed algorithm, we first applied the proposed algorithm to a synthetic brain MR image to show the intermediate illustrations and the estimated distribution of the proposed algorithm. The next group of experiments is carried out in clinical 3T-weighted brain MR images which contain quite serious intensity inhomogeneity and noise. Then we quantitatively compare our algorithm to state-of-the-art segmentation approaches by using Dice coefficient (DC) on benchmark images obtained from IBSR and BrainWeb with different level of noise and intensity inhomogeneity. The comparison results on various brain MR images demonstrate the superior performances of the proposed algorithm in dealing with the noise and intensity inhomogeneity. In this paper, the RGAGMM algorithm is proposed which can simply and efficiently incorporate spatial constraints into an EM framework to simultaneously segment brain MR images and estimate the intensity inhomogeneity. The proposed algorithm is flexible to fit the data shapes, and can simultaneously overcome the influence of noise and intensity inhomogeneity, and hence is capable of improving over 5% segmentation accuracy comparing with several state-of-the-art algorithms. Copyright © 2017 Elsevier B.V. All rights reserved.

Brain Tissue Compartment Density Estimated Using Diffusion-Weighted MRI Yields Tissue Parameters Consistent With Histology

PubMed Central

Sepehrband, Farshid; Clark, Kristi A.; Ullmann, Jeremy F.P.; Kurniawan, Nyoman D.; Leanage, Gayeshika; Reutens, David C.; Yang, Zhengyi

2015-01-01

We examined whether quantitative density measures of cerebral tissue consistent with histology can be obtained from diffusion magnetic resonance imaging (MRI). By incorporating prior knowledge of myelin and cell membrane densities, absolute tissue density values were estimated from relative intra-cellular and intra-neurite density values obtained from diffusion MRI. The NODDI (neurite orientation distribution and density imaging) technique, which can be applied clinically, was used. Myelin density estimates were compared with the results of electron and light microscopy in ex vivo mouse brain and with published density estimates in a healthy human brain. In ex vivo mouse brain, estimated myelin densities in different sub-regions of the mouse corpus callosum were almost identical to values obtained from electron microscopy (Diffusion MRI: 42±6%, 36±4% and 43±5%; electron microscopy: 41±10%, 36±8% and 44±12% in genu, body and splenium, respectively). In the human brain, good agreement was observed between estimated fiber density measurements and previously reported values based on electron microscopy. Estimated density values were unaffected by crossing fibers. PMID:26096639

Estimated maximal and current brain volume predict cognitive ability in old age.

PubMed

Royle, Natalie A; Booth, Tom; Valdés Hernández, Maria C; Penke, Lars; Murray, Catherine; Gow, Alan J; Maniega, Susana Muñoz; Starr, John; Bastin, Mark E; Deary, Ian J; Wardlaw, Joanna M

2013-12-01

Brain tissue deterioration is a significant contributor to lower cognitive ability in later life; however, few studies have appropriate data to establish how much influence prior brain volume and prior cognitive performance have on this association. We investigated the associations between structural brain imaging biomarkers, including an estimate of maximal brain volume, and detailed measures of cognitive ability at age 73 years in a large (N = 620), generally healthy, community-dwelling population. Cognitive ability data were available from age 11 years. We found positive associations (r) between general cognitive ability and estimated brain volume in youth (male, 0.28; females, 0.12), and in measured brain volume in later life (males, 0.27; females, 0.26). Our findings show that cognitive ability in youth is a strong predictor of estimated prior and measured current brain volume in old age but that these effects were the same for both white and gray matter. As 1 of the largest studies of associations between brain volume and cognitive ability with normal aging, this work contributes to the wider understanding of how some early-life factors influence cognitive aging. Copyright © 2013 Elsevier Inc. All rights reserved.

Longitudinal regression analysis of spatial-temporal growth patterns of geometrical diffusion measures in early postnatal brain development with diffusion tensor imaging

PubMed Central

Chen, Yasheng; An, Hongyu; Zhu, Hongtu; Jewells, Valerie; Armao, Diane; Shen, Dinggang; Gilmore, John H.; Lin, Weili

2011-01-01

Although diffusion tensor imaging (DTI) has provided substantial insights into early brain development, most DTI studies based on fractional anisotropy (FA) and mean diffusivity (MD) may not capitalize on the information derived from the three principal diffusivities (e.g. eigenvalues). In this study, we explored the spatial and temporal evolution of white matter structures during early brain development using two geometrical diffusion measures, namely, linear (Cl) and planar (Cp) diffusion anisotropies, from 71 longitudinal datasets acquired from 29 healthy, full-term pediatric subjects. The growth trajectories were estimated with generalized estimating equations (GEE) using linear fitting with logarithm of age (days). The presence of the white matter structures in Cl and Cp was observed in neonates, suggesting that both the cylindrical and fanning or crossing structures in various white matter regions may already have been formed at birth. Moreover, we found that both Cl and Cp evolved in a temporally nonlinear and spatially inhomogeneous manner. The growth velocities of Cl in central white matter were significantly higher when compared to peripheral, or more laterally located, white matter: central growth velocity Cl = 0.0465±0.0273/log(days), versus peripheral growth velocity Cl=0.0198±0.0127/log(days), p<10−6. In contrast, the growth velocities of Cp in central white matter were significantly lower than that in peripheral white matter: central growth velocity Cp= 0.0014±0.0058/log(days), versus peripheral growth velocity Cp = 0.0289±0.0101/log(days), p<10−6. Depending on the underlying white matter site which is analyzed, our findings suggest that ongoing physiologic and microstructural changes in the developing brain may exert different effects on the temporal evolution of these two geometrical diffusion measures. Thus, future studies utilizing DTI with correlative histological analysis in the study of early brain development are warranted. PMID:21784163

Chronic neurodegenerative consequences of traumatic brain injury.

PubMed

Chauhan, Neelima B

2014-01-01

Traumatic brain injury (TBI) is a serious public health concern and a major cause of death and disability worldwide. Each year, an estimated 1.7 million Americans sustain TBI of which ~52,000 people die, ~275,000 people are hospitalized and 1,365,000 people are treated as emergency outpatients. Currently there are ~5.3 million Americans living with TBI. TBI is more of a disease process than of an event that is associated with immediate and long-term sensomotor, psychological and cognitive impairments. TBI is the best known established epigenetic risk factor for later development of neurodegenerative diseases and dementia. People sustaining TBI are ~4 times more likely to develop dementia at a later stage than people without TBI. Single brain injury is linked to later development of symptoms resembling Alzheimer's disease while repetitive brain injuries are linked to later development of chronic traumatic encephalopathy (CTE) and/or Dementia Pugilistica (DP). Furthermore, genetic background of ß-amyloid precursor protein (APP), Apolipoprotein E (ApoE), presenilin (PS) and neprilysin (NEP) genes is associated with exacerbation of neurodegenerative process after TBI. This review encompasses acute effects and chronic neurodegenerative consequences after TBI.

Adaptive estimation of hand movement trajectory in an EEG based brain-computer interface system

NASA Astrophysics Data System (ADS)

Robinson, Neethu; Guan, Cuntai; Vinod, A. P.

2015-12-01

Objective. The various parameters that define a hand movement such as its trajectory, speed, etc, are encoded in distinct brain activities. Decoding this information from neurophysiological recordings is a less explored area of brain-computer interface (BCI) research. Applying non-invasive recordings such as electroencephalography (EEG) for decoding makes the problem more challenging, as the encoding is assumed to be deep within the brain and not easily accessible by scalp recordings. Approach. EEG based BCI systems can be developed to identify the neural features underlying movement parameters that can be further utilized to provide a detailed and well defined control command set to a BCI output device. A real-time continuous control is better suited for practical BCI systems, and can be achieved by continuous adaptive reconstruction of movement trajectory than discrete brain activity classifications. In this work, we adaptively reconstruct/estimate the parameters of two-dimensional hand movement trajectory, namely movement speed and position, from multi-channel EEG recordings. The data for analysis is collected by performing an experiment that involved center-out right-hand movement tasks in four different directions at two different speeds in random order. We estimate movement trajectory using a Kalman filter that models the relation between brain activity and recorded parameters based on a set of defined predictors. We propose a method to define these predictor variables that includes spatial, spectral and temporally localized neural information and to select optimally informative variables. Main results. The proposed method yielded correlation of (0.60 ± 0.07) between recorded and estimated data. Further, incorporating the proposed predictor subset selection, the correlation achieved is (0.57 ± 0.07, p {\\lt }0.004) with significant gain in stability of the system, as well as dramatic reduction in number of predictors (76%) for the savings of computational time. Significance. The proposed system provides a real time movement control system using EEG-BCI with control over movement speed and position. These results are higher and statistically significant compared to existing techniques in EEG based systems and thus promise the applicability of the proposed method for efficient estimation of movement parameters and for continuous motor control.

A validation framework for brain tumor segmentation.

PubMed

Archip, Neculai; Jolesz, Ferenc A; Warfield, Simon K

2007-10-01

We introduce a validation framework for the segmentation of brain tumors from magnetic resonance (MR) images. A novel unsupervised semiautomatic brain tumor segmentation algorithm is also presented. The proposed framework consists of 1) T1-weighted MR images of patients with brain tumors, 2) segmentation of brain tumors performed by four independent experts, 3) segmentation of brain tumors generated by a semiautomatic algorithm, and 4) a software tool that estimates the performance of segmentation algorithms. We demonstrate the validation of the novel segmentation algorithm within the proposed framework. We show its performance and compare it with existent segmentation. The image datasets and software are available at http://www.brain-tumor-repository.org/. We present an Internet resource that provides access to MR brain tumor image data and segmentation that can be openly used by the research community. Its purpose is to encourage the development and evaluation of segmentation methods by providing raw test and image data, human expert segmentation results, and methods for comparing segmentation results.

Non-signalling energy use in the developing rat brain

PubMed Central

Engl, Elisabeth; Jolivet, Renaud; Hall, Catherine N

2016-01-01

Energy use in the brain constrains its information processing power, but only about half the brain's energy consumption is directly related to information processing. Evidence for which non-signalling processes consume the rest of the brain's energy has been scarce. For the first time, we investigated the energy use of the brain's main non-signalling tasks with a single method. After blocking each non-signalling process, we measured oxygen level changes in juvenile rat brain slices with an oxygen-sensing microelectrode and calculated changes in oxygen consumption throughout the slice using a modified diffusion equation. We found that the turnover of the actin and microtubule cytoskeleton, followed by lipid synthesis, are significant energy drains, contributing 25%, 22% and 18%, respectively, to the rate of oxygen consumption. In contrast, protein synthesis is energetically inexpensive. We assess how these estimates of energy expenditure relate to brain energy use in vivo, and how they might differ in the mature brain. PMID:27170699

Statistical Signal Processing and the Motor Cortex

PubMed Central

Brockwell, A.E.; Kass, R.E.; Schwartz, A.B.

2011-01-01

Over the past few decades, developments in technology have significantly improved the ability to measure activity in the brain. This has spurred a great deal of research into brain function and its relation to external stimuli, and has important implications in medicine and other fields. As a result of improved understanding of brain function, it is now possible to build devices that provide direct interfaces between the brain and the external world. We describe some of the current understanding of function of the motor cortex region. We then discuss a typical likelihood-based state-space model and filtering based approach to address the problems associated with building a motor cortical-controlled cursor or robotic prosthetic device. As a variation on previous work using this approach, we introduce the idea of using Markov chain Monte Carlo methods for parameter estimation in this context. By doing this instead of performing maximum likelihood estimation, it is possible to expand the range of possible models that can be explored, at a cost in terms of computational load. We demonstrate results obtained applying this methodology to experimental data gathered from a monkey. PMID:21765538

Test suite for image-based motion estimation of the brain and tongue

NASA Astrophysics Data System (ADS)

Ramsey, Jordan; Prince, Jerry L.; Gomez, Arnold D.

2017-03-01

Noninvasive analysis of motion has important uses as qualitative markers for organ function and to validate biomechanical computer simulations relative to experimental observations. Tagged MRI is considered the gold standard for noninvasive tissue motion estimation in the heart, and this has inspired multiple studies focusing on other organs, including the brain under mild acceleration and the tongue during speech. As with other motion estimation approaches, using tagged MRI to measure 3D motion includes several preprocessing steps that affect the quality and accuracy of estimation. Benchmarks, or test suites, are datasets of known geometries and displacements that act as tools to tune tracking parameters or to compare different motion estimation approaches. Because motion estimation was originally developed to study the heart, existing test suites focus on cardiac motion. However, many fundamental differences exist between the heart and other organs, such that parameter tuning (or other optimization) with respect to a cardiac database may not be appropriate. Therefore, the objective of this research was to design and construct motion benchmarks by adopting an "image synthesis" test suite to study brain deformation due to mild rotational accelerations, and a benchmark to model motion of the tongue during speech. To obtain a realistic representation of mechanical behavior, kinematics were obtained from finite-element (FE) models. These results were combined with an approximation of the acquisition process of tagged MRI (including tag generation, slice thickness, and inconsistent motion repetition). To demonstrate an application of the presented methodology, the effect of motion inconsistency on synthetic measurements of head- brain rotation and deformation was evaluated. The results indicated that acquisition inconsistency is roughly proportional to head rotation estimation error. Furthermore, when evaluating non-rigid deformation, the results suggest that inconsistent motion can yield "ghost" shear strains, which are a function of slice acquisition viability as opposed to a true physical deformation.

Test Suite for Image-Based Motion Estimation of the Brain and Tongue

PubMed Central

Ramsey, Jordan; Prince, Jerry L.; Gomez, Arnold D.

2017-01-01

Noninvasive analysis of motion has important uses as qualitative markers for organ function and to validate biomechanical computer simulations relative to experimental observations. Tagged MRI is considered the gold standard for noninvasive tissue motion estimation in the heart, and this has inspired multiple studies focusing on other organs, including the brain under mild acceleration and the tongue during speech. As with other motion estimation approaches, using tagged MRI to measure 3D motion includes several preprocessing steps that affect the quality and accuracy of estimation. Benchmarks, or test suites, are datasets of known geometries and displacements that act as tools to tune tracking parameters or to compare different motion estimation approaches. Because motion estimation was originally developed to study the heart, existing test suites focus on cardiac motion. However, many fundamental differences exist between the heart and other organs, such that parameter tuning (or other optimization) with respect to a cardiac database may not be appropriate. Therefore, the objective of this research was to design and construct motion benchmarks by adopting an “image synthesis” test suite to study brain deformation due to mild rotational accelerations, and a benchmark to model motion of the tongue during speech. To obtain a realistic representation of mechanical behavior, kinematics were obtained from finite-element (FE) models. These results were combined with an approximation of the acquisition process of tagged MRI (including tag generation, slice thickness, and inconsistent motion repetition). To demonstrate an application of the presented methodology, the effect of motion inconsistency on synthetic measurements of head-brain rotation and deformation was evaluated. The results indicated that acquisition inconsistency is roughly proportional to head rotation estimation error. Furthermore, when evaluating non-rigid deformation, the results suggest that inconsistent motion can yield “ghost” shear strains, which are a function of slice acquisition viability as opposed to a true physical deformation. PMID:28781414

Estimation of RF energy absorbed in the brain from mobile phones in the Interphone Study.

PubMed

Cardis, E; Varsier, N; Bowman, J D; Deltour, I; Figuerola, J; Mann, S; Moissonnier, M; Taki, M; Vecchia, P; Villegas, R; Vrijheid, M; Wake, K; Wiart, J

2011-09-01

The objective of this study was to develop an estimate of a radio frequency (RF) dose as the amount of mobile phone RF energy absorbed at the location of a brain tumour, for use in the Interphone Epidemiological Study. We systematically evaluated and quantified all the main parameters thought to influence the amount of specific RF energy absorbed in the brain from mobile telephone use. For this, we identified the likely important determinants of RF specific energy absorption rate during protocol and questionnaire design, we collected information from study subjects, network operators and laboratories involved in specific energy absorption rate measurements and we studied potential modifiers of phone output through the use of software-modified phones. Data collected were analysed to assess the relative importance of the different factors, leading to the development of an algorithm to evaluate the total cumulative specific RF energy (in joules per kilogram), or dose, absorbed at a particular location in the brain. This algorithm was applied to Interphone Study subjects in five countries. The main determinants of total cumulative specific RF energy from mobile phones were communication system and frequency band, location in the brain and amount and duration of mobile phone use. Though there was substantial agreement between categorisation of subjects by cumulative specific RF energy and cumulative call time, misclassification was non-negligible, particularly at higher frequency bands. Factors such as adaptive power control (except in Code Division Multiple Access networks), discontinuous transmission and conditions of phone use were found to have a relatively minor influence on total cumulative specific RF energy. While amount and duration of use are important determinants of RF dose in the brain, their impact can be substantially modified by communication system, frequency band and location in the brain. It is important to take these into account in analyses of risk of brain tumours from RF exposure from mobile phones.

Estimation of RF energy absorbed in the brain from mobile phones in the Interphone Study

PubMed Central

Varsier, N; Bowman, J D; Deltour, I; Figuerola, J; Mann, S; Moissonnier, M; Taki, M; Vecchia, P; Villegas, R; Vrijheid, M; Wake, K; Wiart, J

2011-01-01

Objectives The objective of this study was to develop an estimate of a radio frequency (RF) dose as the amount of mobile phone RF energy absorbed at the location of a brain tumour, for use in the Interphone Epidemiological Study. Methods We systematically evaluated and quantified all the main parameters thought to influence the amount of specific RF energy absorbed in the brain from mobile telephone use. For this, we identified the likely important determinants of RF specific energy absorption rate during protocol and questionnaire design, we collected information from study subjects, network operators and laboratories involved in specific energy absorption rate measurements and we studied potential modifiers of phone output through the use of software-modified phones. Data collected were analysed to assess the relative importance of the different factors, leading to the development of an algorithm to evaluate the total cumulative specific RF energy (in joules per kilogram), or dose, absorbed at a particular location in the brain. This algorithm was applied to Interphone Study subjects in five countries. Results The main determinants of total cumulative specific RF energy from mobile phones were communication system and frequency band, location in the brain and amount and duration of mobile phone use. Though there was substantial agreement between categorisation of subjects by cumulative specific RF energy and cumulative call time, misclassification was non-negligible, particularly at higher frequency bands. Factors such as adaptive power control (except in Code Division Multiple Access networks), discontinuous transmission and conditions of phone use were found to have a relatively minor influence on total cumulative specific RF energy. Conclusions While amount and duration of use are important determinants of RF dose in the brain, their impact can be substantially modified by communication system, frequency band and location in the brain. It is important to take these into account in analyses of risk of brain tumours from RF exposure from mobile phones. PMID:21659468

Brain Volume Estimation Enhancement by Morphological Image Processing Tools.

PubMed

Zeinali, R; Keshtkar, A; Zamani, A; Gharehaghaji, N

2017-12-01

Volume estimation of brain is important for many neurological applications. It is necessary in measuring brain growth and changes in brain in normal/abnormal patients. Thus, accurate brain volume measurement is very important. Magnetic resonance imaging (MRI) is the method of choice for volume quantification due to excellent levels of image resolution and between-tissue contrast. Stereology method is a good method for estimating volume but it requires to segment enough MRI slices and have a good resolution. In this study, it is desired to enhance stereology method for volume estimation of brain using less MRI slices with less resolution. In this study, a program for calculating volume using stereology method has been introduced. After morphologic method, dilation was applied and the stereology method enhanced. For the evaluation of this method, we used T1-wighted MR images from digital phantom in BrainWeb which had ground truth. The volume of 20 normal brain extracted from BrainWeb, was calculated. The volumes of white matter, gray matter and cerebrospinal fluid with given dimension were estimated correctly. Volume calculation from Stereology method in different cases was made. In three cases, Root Mean Square Error (RMSE) was measured. Case I with T=5, d=5, Case II with T=10, D=10 and Case III with T=20, d=20 (T=slice thickness, d=resolution as stereology parameters). By comparing these results of two methods, it is obvious that RMSE values for our proposed method are smaller than Stereology method. Using morphological operation, dilation allows to enhance the estimation volume method, Stereology. In the case with less MRI slices and less test points, this method works much better compared to Stereology method.

Removal of interfering nucleotides from brain extracts containing substance p. Effect of drugs on brain concentrations of substance p

PubMed Central

Laszlo, I.

1963-01-01

Several methods for removing interfering nucleotides, adenosine-5'-monophosphate and adenosine 5'-triphosphate from brain extracts have been studied. An enzymic method, using adenylic acid deaminase, has been found suitable. This deaminates adenosine monophosphate to 5'-inosinic acid, an inactive compound which does not influence the estimations of substance P. Owing to the adenosine triphosphatase content of the enzyme extract, adenosine triphosphate was also inactivated. For the estimation of adenosine monophosphate-deaminase activity, a simple colorimetric method is described which measures the ammonia liberated from adenosine monophosphate. Substance P in mouse brain extracts was estimated after treatment of the animals with various drugs, and after the enzymic removal of interfering nucleotides from the brain extracts. The drugs had no effect on the substance P content of mouse brain. The effect of drugs on the contractions of the guinea-pig ileum induced by substance P was also investigated, and the effect of drugs on the estimations of substance P in brain extracts is discussed. PMID:14066136

Electrophysiological Source Imaging: A Noninvasive Window to Brain Dynamics.

PubMed

He, Bin; Sohrabpour, Abbas; Brown, Emery; Liu, Zhongming

2018-06-04

Brain activity and connectivity are distributed in the three-dimensional space and evolve in time. It is important to image brain dynamics with high spatial and temporal resolution. Electroencephalography (EEG) and magnetoencephalography (MEG) are noninvasive measurements associated with complex neural activations and interactions that encode brain functions. Electrophysiological source imaging estimates the underlying brain electrical sources from EEG and MEG measurements. It offers increasingly improved spatial resolution and intrinsically high temporal resolution for imaging large-scale brain activity and connectivity on a wide range of timescales. Integration of electrophysiological source imaging and functional magnetic resonance imaging could further enhance spatiotemporal resolution and specificity to an extent that is not attainable with either technique alone. We review methodological developments in electrophysiological source imaging over the past three decades and envision its future advancement into a powerful functional neuroimaging technology for basic and clinical neuroscience applications.

Development and Validation of a HPTLC Method for Simultaneous Estimation of L-Glutamic Acid and γ-Aminobutyric Acid in Mice Brain

PubMed Central

Sancheti, J. S.; Shaikh, M. F.; Khatwani, P. F.; Kulkarni, Savita R.; Sathaye, Sadhana

2013-01-01

A new robust, simple and economic high performance thin layer chromatographic method was developed for simultaneous estimation of L-glutamic acid and γ-amino butyric acid in brain homogenate. The high performance thin layer chromatographic separation of these amino acid was achieved using n-butanol:glacial acetic acid:water (22:3:5 v/v/v) as mobile phase and ninhydrin as a derivatising agent. Quantitation of the method was achieved by densitometric method at 550 nm over the concentration range of 10-100 ng/spot. This method showed good separation of amino acids in the brain homogenate with Rf value of L-glutamic acid and γ-amino butyric acid as 21.67±0.58 and 33.67±0.58, respectively. The limit of detection and limit of quantification for L-glutamic acid was found to be 10 and 20 ng and for γ-amino butyric acid it was 4 and 10 ng, respectively. The method was also validated in terms of accuracy, precision and repeatability. The developed method was found to be precise and accurate with good reproducibility and shows promising applicability for studying pathological status of disease and therapeutic significance of drug treatment. PMID:24591747

Development and Validation of a HPTLC Method for Simultaneous Estimation of L-Glutamic Acid and γ-Aminobutyric Acid in Mice Brain.

PubMed

Sancheti, J S; Shaikh, M F; Khatwani, P F; Kulkarni, Savita R; Sathaye, Sadhana

2013-11-01

A new robust, simple and economic high performance thin layer chromatographic method was developed for simultaneous estimation of L-glutamic acid and γ-amino butyric acid in brain homogenate. The high performance thin layer chromatographic separation of these amino acid was achieved using n-butanol:glacial acetic acid:water (22:3:5 v/v/v) as mobile phase and ninhydrin as a derivatising agent. Quantitation of the method was achieved by densitometric method at 550 nm over the concentration range of 10-100 ng/spot. This method showed good separation of amino acids in the brain homogenate with Rf value of L-glutamic acid and γ-amino butyric acid as 21.67±0.58 and 33.67±0.58, respectively. The limit of detection and limit of quantification for L-glutamic acid was found to be 10 and 20 ng and for γ-amino butyric acid it was 4 and 10 ng, respectively. The method was also validated in terms of accuracy, precision and repeatability. The developed method was found to be precise and accurate with good reproducibility and shows promising applicability for studying pathological status of disease and therapeutic significance of drug treatment.

A database for estimating organ dose for coronary angiography and brain perfusion CT scans for arbitrary spectra and angular tube current modulation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Rupcich, Franco; Badal, Andreu; Kyprianou, Iacovos

Purpose: The purpose of this study was to develop a database for estimating organ dose in a voxelized patient model for coronary angiography and brain perfusion CT acquisitions with any spectra and angular tube current modulation setting. The database enables organ dose estimation for existing and novel acquisition techniques without requiring Monte Carlo simulations. Methods: The study simulated transport of monoenergetic photons between 5 and 150 keV for 1000 projections over 360 Degree-Sign through anthropomorphic voxelized female chest and head (0 Degree-Sign and 30 Degree-Sign tilt) phantoms and standard head and body CTDI dosimetry cylinders. The simulations resulted in tablesmore » of normalized dose deposition for several radiosensitive organs quantifying the organ dose per emitted photon for each incident photon energy and projection angle for coronary angiography and brain perfusion acquisitions. The values in a table can be multiplied by an incident spectrum and number of photons at each projection angle and then summed across all energies and angles to estimate total organ dose. Scanner-specific organ dose may be approximated by normalizing the database-estimated organ dose by the database-estimated CTDI{sub vol} and multiplying by a physical CTDI{sub vol} measurement. Two examples are provided demonstrating how to use the tables to estimate relative organ dose. In the first, the change in breast and lung dose during coronary angiography CT scans is calculated for reduced kVp, angular tube current modulation, and partial angle scanning protocols relative to a reference protocol. In the second example, the change in dose to the eye lens is calculated for a brain perfusion CT acquisition in which the gantry is tilted 30 Degree-Sign relative to a nontilted scan. Results: Our database provides tables of normalized dose deposition for several radiosensitive organs irradiated during coronary angiography and brain perfusion CT scans. Validation results indicate total organ doses calculated using our database are within 1% of those calculated using Monte Carlo simulations with the same geometry and scan parameters for all organs except red bone marrow (within 6%), and within 23% of published estimates for different voxelized phantoms. Results from the example of using the database to estimate organ dose for coronary angiography CT acquisitions show 2.1%, 1.1%, and -32% change in breast dose and 2.1%, -0.74%, and 4.7% change in lung dose for reduced kVp, tube current modulated, and partial angle protocols, respectively, relative to the reference protocol. Results show -19.2% difference in dose to eye lens for a tilted scan relative to a nontilted scan. The reported relative changes in organ doses are presented without quantification of image quality and are for the sole purpose of demonstrating the use of the proposed database. Conclusions: The proposed database and calculation method enable the estimation of organ dose for coronary angiography and brain perfusion CT scans utilizing any spectral shape and angular tube current modulation scheme by taking advantage of the precalculated Monte Carlo simulation results. The database can be used in conjunction with image quality studies to develop optimized acquisition techniques and may be particularly beneficial for optimizing dual kVp acquisitions for which numerous kV, mA, and filtration combinations may be investigated.« less

Targeted Lipidomic Analysis of Oxysterols in the Embryonic Central Nervous System

PubMed Central

Wang, Yuqin; Sousa, Kyle M.; Bodin, Karl; Theofilopoulos, Spyridon; Sacchetti, Paola; Hornshaw, Martin; Woffendin, Gary; Karu, Kersti; Sjövall, Jan; Arenas, Ernest; Griffiths, William J.

2009-01-01

Summary In this study two regions of embryonic (E11) mouse central nervous system (CNS) have been profiled for their unesterified sterol content. Using high-performance liquid chromatography (HPLC) – mass spectrometry (MS) and tandem mass spectrometry (MSn) low levels of oxysterols (estimated 2 – 165 ng/g wet weight) were identified in cortex (Ctx) and spinal cord (Sc). The identified oxysterols include 7α-, 7β-, 22R-, 24S-, 25- and 27-hydroxycholesterol; 24,25- and 24,27-dihydroxycholesterol; and 24S,25-epoxycholesterol. Of these, 24S-hydroxycholesterol is biosynthesised exclusively in brain. In comparison to adult mouse where the 24S-hydroxycholesterol level is about 40 μg/g in brain the level of 24S-hydroxycholesterol reported here (estimated 26 ng/g in Ctx and 13 ng/g in Sc) is extremely low. Interestingly, the level of 24S,25-epoxycholesterol in both CNS regions (estimated 165 ng/g in Ctx and 91 ng/g in Sc) is somewhat higher than the levels of the hydroxycholesterols. This oxysterol is formed in parallel to cholesterol via a shunt of the mevalonate pathway and its comparatively high abundance may be a reflection of a high rate of cholesterol synthesis at this stage of development. Levels of cholesterol (estimated 1.25 mg/g in Ctx and 1.15 mg/g in Sc) and its precursors were determined by gas chromatography – mass spectrometry (GC-MS). In both CNS regions cholesterol levels were found to be lower than those reported in the adult, but in relation to cholesterol the levels of cholesterol precursors were higher than found in adult indicating a high rate of cholesterol synthesis. In summary, our data provide evidence for the presence of endogenous oxysterols in two brain regions of the developing CNS. Moreover, while most of the enzymes involved in hydroxysterol synthesis are minimally active at E11, our results suggest that the mevalonate pathway is significantly active, opening up the possibility for a function of 24S,25-epoxycholesterol during brain development. PMID:19381367

Prevalence estimates for primary brain tumors in the United States by age, gender, behavior, and histology.

PubMed

Porter, Kimberly R; McCarthy, Bridget J; Freels, Sally; Kim, Yoonsang; Davis, Faith G

2010-06-01

Prevalence is the best indicator of cancer survivorship in the population, but few studies have focused on brain tumor prevalence because of previous data limitations. Hence, the full impact of primary brain tumors on the healthcare system in the United States is not completely described. The present study provides an estimate of the prevalence of disease in the United States, updating an earlier prevalence study. Incidence data for 2004 and survival data for 1985-2005 were obtained by the Central Brain Tumor Registry of the United States from selected regions, modeled under 2 different survival assumptions, to estimate prevalence rates for the year 2004 and projected estimates for 2010. The overall incidence rate for primary brain tumors was 18.1 per 100 000 person-years with 2-, 5-, 10-, and 20-year observed survival rates of 62%, 54%, 45%, and 30%, respectively. On the basis of the sum of nonmalignant and averaged malignant estimates, the overall prevalence rate of individuals with a brain tumor was estimated to be 209.0 per 100 000 in 2004 and 221.8 per 100 000 in 2010. The female prevalence rate (264.8 per 100 000) was higher than that in males (158.7 per 100 000). The averaged prevalence rate for malignant tumors (42.5 per 100 000) was lower than the prevalence for nonmalignant tumors (166.5 per 100 000). This study provides estimates of the 2004 (n = 612 770) and 2010 (n = 688 096) expected number of individuals living with primary brain tumor diagnoses in the United States, providing more current and robust estimates for aiding healthcare planning and patient advocacy for an aging US population.

Multispectral imaging of absorption and scattering properties of in vivo exposed rat brain using a digital red-green-blue camera.

PubMed

Yoshida, Keiichiro; Nishidate, Izumi; Ishizuka, Tomohiro; Kawauchi, Satoko; Sato, Shunichi; Sato, Manabu

2015-05-01

In order to estimate multispectral images of the absorption and scattering properties in the cerebral cortex of in vivo rat brain, we investigated spectral reflectance images estimated by the Wiener estimation method using a digital RGB camera. A Monte Carlo simulation-based multiple regression analysis for the corresponding spectral absorbance images at nine wavelengths (500, 520, 540, 560, 570, 580, 600, 730, and 760 nm) was then used to specify the absorption and scattering parameters of brain tissue. In this analysis, the concentrations of oxygenated hemoglobin and that of deoxygenated hemoglobin were estimated as the absorption parameters, whereas the coefficient a and the exponent b of the reduced scattering coefficient spectrum approximated by a power law function were estimated as the scattering parameters. The spectra of absorption and reduced scattering coefficients were reconstructed from the absorption and scattering parameters, and the spectral images of absorption and reduced scattering coefficients were then estimated. In order to confirm the feasibility of this method, we performed in vivo experiments on exposed rat brain. The estimated images of the absorption coefficients were dominated by the spectral characteristics of hemoglobin. The estimated spectral images of the reduced scattering coefficients had a broad scattering spectrum, exhibiting a larger magnitude at shorter wavelengths, corresponding to the typical spectrum of brain tissue published in the literature. The changes in the estimated absorption and scattering parameters during normoxia, hyperoxia, and anoxia indicate the potential applicability of the method by which to evaluate the pathophysiological conditions of in vivo brain due to the loss of tissue viability.

On robust parameter estimation in brain-computer interfacing

NASA Astrophysics Data System (ADS)

Samek, Wojciech; Nakajima, Shinichi; Kawanabe, Motoaki; Müller, Klaus-Robert

2017-12-01

Objective. The reliable estimation of parameters such as mean or covariance matrix from noisy and high-dimensional observations is a prerequisite for successful application of signal processing and machine learning algorithms in brain-computer interfacing (BCI). This challenging task becomes significantly more difficult if the data set contains outliers, e.g. due to subject movements, eye blinks or loose electrodes, as they may heavily bias the estimation and the subsequent statistical analysis. Although various robust estimators have been developed to tackle the outlier problem, they ignore important structural information in the data and thus may not be optimal. Typical structural elements in BCI data are the trials consisting of a few hundred EEG samples and indicating the start and end of a task. Approach. This work discusses the parameter estimation problem in BCI and introduces a novel hierarchical view on robustness which naturally comprises different types of outlierness occurring in structured data. Furthermore, the class of minimum divergence estimators is reviewed and a robust mean and covariance estimator for structured data is derived and evaluated with simulations and on a benchmark data set. Main results. The results show that state-of-the-art BCI algorithms benefit from robustly estimated parameters. Significance. Since parameter estimation is an integral part of various machine learning algorithms, the presented techniques are applicable to many problems beyond BCI.

Age estimation using cortical surface pattern combining thickness with curvatures

PubMed Central

Wang, Jieqiong; Li, Wenjing; Miao, Wen; Dai, Dai; Hua, Jing; He, Huiguang

2014-01-01

Brain development and healthy aging have been proved to follow a specific pattern, which, in turn, can be applied to help doctors diagnose mental diseases. In this paper, we design a cortical surface pattern (CSP) combining the cortical thickness with curvatures, which constructs an accurate human age estimation model with relevance vector regression. We test our model with two public databases. One is the IXI database (360 healthy subjects aging from 20 to 82 years old were selected), and the other is the INDI database (303 subjects aging from 7 to 22 years old were selected). The results show that our model can achieve as small as 4.57 years deviation in the IXI database and 1.38 years deviation in the INDI database. Furthermore, we employ this surface pattern to age groups classification, and get a remarkably high accuracy (97.77%) and a significantly high sensitivity/specificity (97.30%/98.10%). These results suggest that our designed CSP combining thickness with curvatures is stable and sensitive to brain development, and it is much more powerful than voxel-based morphometry used in previous methods for age estimation. PMID:24395657

Assessment of blood-brain barrier penetration: in silico, in vitro and in vivo.

PubMed

Feng, Meihua Rose

2002-12-01

The amount of drug achieved and maintained in the brain after systemic administration is determined by the agent's permeability at blood-brain barrier (BBB), potential involvement of transport systems, and the distribution, metabolism and elimination properties. Passive diffusion permeability may be predicted by an in silico method based on a molecule's structure property. In vitro cell culture is another useful tool for the assessment of passive permeability and BBB transports (e.g. PGP, MRP). In situ or in vivo techniques like carotid artery single injection or perfusion, brain microdialysis, autoradiography, and others are used at various stages of drug discovery and development to estimate CNS penetration and PK/PD correlation. Each technique has its own application with specific advantages and limitations.

ERP Reliability Analysis (ERA) Toolbox: An open-source toolbox for analyzing the reliability of event-related brain potentials.

PubMed

Clayson, Peter E; Miller, Gregory A

2017-01-01

Generalizability theory (G theory) provides a flexible, multifaceted approach to estimating score reliability. G theory's approach to estimating score reliability has important advantages over classical test theory that are relevant for research using event-related brain potentials (ERPs). For example, G theory does not require parallel forms (i.e., equal means, variances, and covariances), can handle unbalanced designs, and provides a single reliability estimate for designs with multiple sources of error. This monograph provides a detailed description of the conceptual framework of G theory using examples relevant to ERP researchers, presents the algorithms needed to estimate ERP score reliability, and provides a detailed walkthrough of newly-developed software, the ERP Reliability Analysis (ERA) Toolbox, that calculates score reliability using G theory. The ERA Toolbox is open-source, Matlab software that uses G theory to estimate the contribution of the number of trials retained for averaging, group, and/or event types on ERP score reliability. The toolbox facilitates the rigorous evaluation of psychometric properties of ERP scores recommended elsewhere in this special issue. Copyright © 2016 Elsevier B.V. All rights reserved.

On improving the speed and reliability of T2-Relaxation-Under-Spin-Tagging (TRUST) MRI

PubMed Central

Xu, Feng; Uh, Jinsoo; Liu, Peiying; Lu, Hanzhang

2011-01-01

A T2-Relaxation-Under-Spin-Tagging (TRUST) technique was recently developed to estimate cerebral blood oxygenation, providing potentials for non-invasive assessment of the brain's oxygen consumption. A limitation of the current sequence is the need for long TR, as shorter TR causes an over-estimation in blood R2. The present study proposes a post-saturation TRUST by placing a non-selective 90° pulse after the signal acquisition to reset magnetization in the whole brain. This scheme was found to eliminate estimation bias at a slight cost of precision. To improve the precision, TE of the sequence was optimized and it was found that a modest TE shortening of 3.4ms can reduce the estimation error by 49%. We recommend the use of post-saturation TRUST sequence with a TR of 3000ms and a TE of 3.6ms, which allows the determination of global venous oxygenation with scan duration of 1 minute 12 seconds and an estimation precision of ±1% (in units of oxygen saturation percentage). PMID:22127845

RatCar system for estimating locomotion states using neural signals with parameter monitoring: Vehicle-formed brain-machine interfaces for rat.

PubMed

Fukayama, Osamu; Taniguchi, Noriyuki; Suzuki, Takafumi; Mabuchi, Kunihiko

2008-01-01

An online brain-machine interface (BMI) in the form of a small vehicle, the 'RatCar,' has been developed. A rat had neural electrodes implanted in its primary motor cortex and basal ganglia regions to continuously record neural signals. Then, a linear state space model represents a correlation between the recorded neural signals and locomotion states (i.e., moving velocity and azimuthal variances) of the rat. The model parameters were set so as to minimize estimation errors, and the locomotion states were estimated from neural firing rates using a Kalman filter algorithm. The results showed a small oscillation to achieve smooth control of the vehicle in spite of fluctuating firing rates with noises applied to the model. Major variation of the model variables converged in a first 30 seconds of the experiments and lasted for the entire one hour session.

Using the Wiener estimator to determine optimal imaging parameters in a synthetic-collimator SPECT system used for small animal imaging

NASA Astrophysics Data System (ADS)

Lin, Alexander; Johnson, Lindsay C.; Shokouhi, Sepideh; Peterson, Todd E.; Kupinski, Matthew A.

2015-03-01

In synthetic-collimator SPECT imaging, two detectors are placed at different distances behind a multi-pinhole aperture. This configuration allows for image detection at different magnifications and photon energies, resulting in higher overall sensitivity while maintaining high resolution. Image multiplexing the undesired overlapping between images due to photon origin uncertainty may occur in both detector planes and is often present in the second detector plane due to greater magnification. However, artifact-free image reconstruction is possible by combining data from both the front detector (little to no multiplexing) and the back detector (noticeable multiplexing). When the two detectors are used in tandem, spatial resolution is increased, allowing for a higher sensitivity-to-detector-area ratio. Due to variability in detector distances and pinhole spacings found in synthetic-collimator SPECT systems, a large parameter space must be examined to determine optimal imaging configurations. We chose to assess image quality based on the task of estimating activity in various regions of a mouse brain. Phantom objects were simulated using mouse brain data from the Magnetic Resonance Microimaging Neurological Atlas (MRM NeAt) and projected at different angles through models of a synthetic-collimator SPECT system, which was developed by collaborators at Vanderbilt University. Uptake in the different brain regions was modeled as being normally distributed about predetermined means and variances. We computed the performance of the Wiener estimator for the task of estimating activity in different regions of the mouse brain. Our results demonstrate the utility of the method for optimizing synthetic-collimator system design.

A nomogram to predict brain metastasis as the first relapse in curatively resected non-small cell lung cancer patients.

PubMed

Won, Young-Woong; Joo, Jungnam; Yun, Tak; Lee, Geon-Kook; Han, Ji-Youn; Kim, Heung Tae; Lee, Jin Soo; Kim, Moon Soo; Lee, Jong Mog; Lee, Hyun-Sung; Zo, Jae Ill; Kim, Sohee

2015-05-01

Development of brain metastasis results in a significant reduction in overall survival. However, there is no an effective tool to predict brain metastasis in non-small cell lung cancer (NSCLC) patients. We conducted this study to develop a feasible nomogram that can predict metastasis to the brain as the first relapse site in patients with curatively resected NSCLC. A retrospective review of NSCLC patients who had received curative surgery at National Cancer Center (Goyang, South Korea) between 2001 and 2008 was performed. We chose metastasis to the brain as the first relapse site after curative surgery as the primary endpoint of the study. A nomogram was modeled using logistic regression. Among 1218 patients, brain metastasis as the first relapse developed in 87 patients (7.14%) during the median follow-up of 43.6 months. Occurrence rates of brain metastasis were higher in patients with adenocarcinoma or those with a high pT and pN stage. Younger age appeared to be associated with brain metastasis, but this result was not statistically significant. The final prediction model included histology, smoking status, pT stage, and the interaction between adenocarcinoma and pN stage. The model showed fairly good discriminatory ability with a C-statistic of 69.3% and 69.8% for predicting brain metastasis within 2 years and 5 years, respectively. Internal validation using 2000 bootstrap samples resulted in C-statistics of 67.0% and 67.4% which still indicated good discriminatory performances. The nomogram presented here provides the individual risk estimate of developing metastasis to the brain as the first relapse site in patients with NSCLC who have undergone curative surgery. Surveillance programs or preventive treatment strategies for brain metastasis could be established based on this nomogram. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Estimation of effective brain connectivity with dual Kalman filter and EEG source localization methods.

PubMed

Rajabioun, Mehdi; Nasrabadi, Ali Motie; Shamsollahi, Mohammad Bagher

2017-09-01

Effective connectivity is one of the most important considerations in brain functional mapping via EEG. It demonstrates the effects of a particular active brain region on others. In this paper, a new method is proposed which is based on dual Kalman filter. In this method, firstly by using a brain active localization method (standardized low resolution brain electromagnetic tomography) and applying it to EEG signal, active regions are extracted, and appropriate time model (multivariate autoregressive model) is fitted to extracted brain active sources for evaluating the activity and time dependence between sources. Then, dual Kalman filter is used to estimate model parameters or effective connectivity between active regions. The advantage of this method is the estimation of different brain parts activity simultaneously with the calculation of effective connectivity between active regions. By combining dual Kalman filter with brain source localization methods, in addition to the connectivity estimation between parts, source activity is updated during the time. The proposed method performance has been evaluated firstly by applying it to simulated EEG signals with interacting connectivity simulation between active parts. Noisy simulated signals with different signal to noise ratios are used for evaluating method sensitivity to noise and comparing proposed method performance with other methods. Then the method is applied to real signals and the estimation error during a sweeping window is calculated. By comparing proposed method results in different simulation (simulated and real signals), proposed method gives acceptable results with least mean square error in noisy or real conditions.

Acute Brain Dysfunction: Development and Validation of a Daily Prediction Model.

PubMed

Marra, Annachiara; Pandharipande, Pratik P; Shotwell, Matthew S; Chandrasekhar, Rameela; Girard, Timothy D; Shintani, Ayumi K; Peelen, Linda M; Moons, Karl G M; Dittus, Robert S; Ely, E Wesley; Vasilevskis, Eduard E

2018-03-24

The goal of this study was to develop and validate a dynamic risk model to predict daily changes in acute brain dysfunction (ie, delirium and coma), discharge, and mortality in ICU patients. Using data from a multicenter prospective ICU cohort, a daily acute brain dysfunction-prediction model (ABD-pm) was developed by using multinomial logistic regression that estimated 15 transition probabilities (from one of three brain function states [normal, delirious, or comatose] to one of five possible outcomes [normal, delirious, comatose, ICU discharge, or died]) using baseline and daily risk factors. Model discrimination was assessed by using predictive characteristics such as negative predictive value (NPV). Calibration was assessed by plotting empirical vs model-estimated probabilities. Internal validation was performed by using a bootstrap procedure. Data were analyzed from 810 patients (6,711 daily transitions). The ABD-pm included individual risk factors: mental status, age, preexisting cognitive impairment, baseline and daily severity of illness, and daily administration of sedatives. The model yielded very high NPVs for "next day" delirium (NPV: 0.823), coma (NPV: 0.892), normal cognitive state (NPV: 0.875), ICU discharge (NPV: 0.905), and mortality (NPV: 0.981). The model demonstrated outstanding calibration when predicting the total number of patients expected to be in any given state across predicted risk. We developed and internally validated a dynamic risk model that predicts the daily risk for one of three cognitive states, ICU discharge, or mortality. The ABD-pm may be useful for predicting the proportion of patients for each outcome state across entire ICU populations to guide quality, safety, and care delivery activities. Copyright © 2018 American College of Chest Physicians. Published by Elsevier Inc. All rights reserved.

Heritability of volumetric brain changes and height in children entering puberty.

PubMed

van Soelen, Inge L C; Brouwer, Rachel M; van Baal, G Caroline M; Schnack, Hugo G; Peper, Jiska S; Chen, Lei; Kahn, René S; Boomsma, Dorret I; Hulshoff Pol, Hilleke E

2013-03-01

The human brain undergoes structural changes in children entering puberty, while simultaneously children increase in height. It is not known if brain changes are under genetic control, and whether they are related to genetic factors influencing the amount of overall increase in height. Twins underwent magnetic resonance imaging brain scans at age 9 (N = 190) and 12 (N = 125). High heritability estimates were found at both ages for height and brain volumes (49-96%), and high genetic correlation between ages were observed (r(g) > 0.89). With increasing age, whole brain (+1.1%), cerebellum (+4.2%), cerebral white matter (+5.1%), and lateral ventricle (+9.4%) volumes increased, and third ventricle (-4.0%) and cerebral gray matter (-1.6%) volumes decreased. Children increased on average 13.8 cm in height (9.9%). Genetic influences on individual difference in volumetric brain and height changes were estimated, both within and across traits. The same genetic factors influenced both cerebral (20% heritable) and cerebellar volumetric changes (45%). Thus, the extent to which changes in cerebral and cerebellar volumes are heritable in children entering puberty are due to the same genes that influence change in both structures. The increase in height was heritable (73%), and not associated with cerebral volumetric change, but positively associated with cerebellar volume change (r(p) = 0.24). This association was explained by a genetic correlation (r(g) = 0.48) between height and cerebellar change. Brain and body each expand at their own pace and through separate genetic pathways. There are distinct genetic processes acting on structural brain development, which cannot be explained by genetic increase in height. Copyright © 2011 Wiley Periodicals, Inc.

Relations between Brain Structure and Attentional Function in Spina Bifida: Utilization of Robust Statistical Approaches

PubMed Central

Kulesz, Paulina A.; Tian, Siva; Juranek, Jenifer; Fletcher, Jack M.; Francis, David J.

2015-01-01

Objective Weak structure-function relations for brain and behavior may stem from problems in estimating these relations in small clinical samples with frequently occurring outliers. In the current project, we focused on the utility of using alternative statistics to estimate these relations. Method Fifty-four children with spina bifida meningomyelocele performed attention tasks and received MRI of the brain. Using a bootstrap sampling process, the Pearson product moment correlation was compared with four robust correlations: the percentage bend correlation, the Winsorized correlation, the skipped correlation using the Donoho-Gasko median, and the skipped correlation using the minimum volume ellipsoid estimator Results All methods yielded similar estimates of the relations between measures of brain volume and attention performance. The similarity of estimates across correlation methods suggested that the weak structure-function relations previously found in many studies are not readily attributable to the presence of outlying observations and other factors that violate the assumptions behind the Pearson correlation. Conclusions Given the difficulty of assembling large samples for brain-behavior studies, estimating correlations using multiple, robust methods may enhance the statistical conclusion validity of studies yielding small, but often clinically significant, correlations. PMID:25495830

Relations between volumetric measures of brain structure and attentional function in spina bifida: utilization of robust statistical approaches.

PubMed

Kulesz, Paulina A; Tian, Siva; Juranek, Jenifer; Fletcher, Jack M; Francis, David J

2015-03-01

Weak structure-function relations for brain and behavior may stem from problems in estimating these relations in small clinical samples with frequently occurring outliers. In the current project, we focused on the utility of using alternative statistics to estimate these relations. Fifty-four children with spina bifida meningomyelocele performed attention tasks and received MRI of the brain. Using a bootstrap sampling process, the Pearson product-moment correlation was compared with 4 robust correlations: the percentage bend correlation, the Winsorized correlation, the skipped correlation using the Donoho-Gasko median, and the skipped correlation using the minimum volume ellipsoid estimator. All methods yielded similar estimates of the relations between measures of brain volume and attention performance. The similarity of estimates across correlation methods suggested that the weak structure-function relations previously found in many studies are not readily attributable to the presence of outlying observations and other factors that violate the assumptions behind the Pearson correlation. Given the difficulty of assembling large samples for brain-behavior studies, estimating correlations using multiple, robust methods may enhance the statistical conclusion validity of studies yielding small, but often clinically significant, correlations. PsycINFO Database Record (c) 2015 APA, all rights reserved.

Estimating repetitive spatiotemporal patterns from resting-state brain activity data.

PubMed

Takeda, Yusuke; Hiroe, Nobuo; Yamashita, Okito; Sato, Masa-Aki

2016-06-01

Repetitive spatiotemporal patterns in spontaneous brain activities have been widely examined in non-human studies. These studies have reported that such patterns reflect past experiences embedded in neural circuits. In human magnetoencephalography (MEG) and electroencephalography (EEG) studies, however, spatiotemporal patterns in resting-state brain activities have not been extensively examined. This is because estimating spatiotemporal patterns from resting-state MEG/EEG data is difficult due to their unknown onsets. Here, we propose a method to estimate repetitive spatiotemporal patterns from resting-state brain activity data, including MEG/EEG. Without the information of onsets, the proposed method can estimate several spatiotemporal patterns, even if they are overlapping. We verified the performance of the method by detailed simulation tests. Furthermore, we examined whether the proposed method could estimate the visual evoked magnetic fields (VEFs) without using stimulus onset information. The proposed method successfully detected the stimulus onsets and estimated the VEFs, implying the applicability of this method to real MEG data. The proposed method was applied to resting-state functional magnetic resonance imaging (fMRI) data and MEG data. The results revealed informative spatiotemporal patterns representing consecutive brain activities that dynamically change with time. Using this method, it is possible to reveal discrete events spontaneously occurring in our brains, such as memory retrieval. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

Optimal hemodynamic response model for functional near-infrared spectroscopy

PubMed Central

Kamran, Muhammad A.; Jeong, Myung Yung; Mannan, Malik M. N.

2015-01-01

Functional near-infrared spectroscopy (fNIRS) is an emerging non-invasive brain imaging technique and measures brain activities by means of near-infrared light of 650–950 nm wavelengths. The cortical hemodynamic response (HR) differs in attributes at different brain regions and on repetition of trials, even if the experimental paradigm is kept exactly the same. Therefore, an HR model that can estimate such variations in the response is the objective of this research. The canonical hemodynamic response function (cHRF) is modeled by two Gamma functions with six unknown parameters (four of them to model the shape and other two to scale and baseline respectively). The HRF model is supposed to be a linear combination of HRF, baseline, and physiological noises (amplitudes and frequencies of physiological noises are supposed to be unknown). An objective function is developed as a square of the residuals with constraints on 12 free parameters. The formulated problem is solved by using an iterative optimization algorithm to estimate the unknown parameters in the model. Inter-subject variations in HRF and physiological noises have been estimated for better cortical functional maps. The accuracy of the algorithm has been verified using 10 real and 15 simulated data sets. Ten healthy subjects participated in the experiment and their HRF for finger-tapping tasks have been estimated and analyzed. The statistical significance of the estimated activity strength parameters has been verified by employing statistical analysis (i.e., t-value > tcritical and p-value < 0.05). PMID:26136668

Optimal hemodynamic response model for functional near-infrared spectroscopy.

PubMed

Kamran, Muhammad A; Jeong, Myung Yung; Mannan, Malik M N

2015-01-01

Functional near-infrared spectroscopy (fNIRS) is an emerging non-invasive brain imaging technique and measures brain activities by means of near-infrared light of 650-950 nm wavelengths. The cortical hemodynamic response (HR) differs in attributes at different brain regions and on repetition of trials, even if the experimental paradigm is kept exactly the same. Therefore, an HR model that can estimate such variations in the response is the objective of this research. The canonical hemodynamic response function (cHRF) is modeled by two Gamma functions with six unknown parameters (four of them to model the shape and other two to scale and baseline respectively). The HRF model is supposed to be a linear combination of HRF, baseline, and physiological noises (amplitudes and frequencies of physiological noises are supposed to be unknown). An objective function is developed as a square of the residuals with constraints on 12 free parameters. The formulated problem is solved by using an iterative optimization algorithm to estimate the unknown parameters in the model. Inter-subject variations in HRF and physiological noises have been estimated for better cortical functional maps. The accuracy of the algorithm has been verified using 10 real and 15 simulated data sets. Ten healthy subjects participated in the experiment and their HRF for finger-tapping tasks have been estimated and analyzed. The statistical significance of the estimated activity strength parameters has been verified by employing statistical analysis (i.e., t-value > t critical and p-value < 0.05).

F18 EF5 PET/CT Imaging in Patients with Brain Metastases from Breast Cancer

DTIC Science & Technology

2013-07-01

control and survival in select patients after WBRT . At present we do not have any method of determining a priori which patients may benefit from RS...boost. The development of a noninvasive imaging biomarker to identify patients that are at highest risk of local relapse after WBRT would represent a...detect residual tumor hypoxia in patients receiving WBRT . Body: Task 1. To estimate the degree of hypoxia after WBRT in patients with brain

F18 EF5 PET/CT Imaging in Patients with Brain Metastases from Breast Cancer

DTIC Science & Technology

2014-09-01

patients after WBRT . At present we do not have any method of determining a priori which patients may benefit from RS boost. The development of a...noninvasive imaging biomarker to identify patients that are at highest risk of local relapse after WBRT would represent a significant step forward in...residual tumor hypoxia in patients receiving WBRT . Body: Task 1. To estimate the degree of hypoxia after WBRT in patients with brain metastases from

Improved recovery of the hemodynamic response in Diffuse Optical Imaging using short optode separations and state-space modeling

PubMed Central

Gagnon, Louis; Perdue, Katherine; Greve, Douglas N.; Goldenholz, Daniel; Kaskhedikar, Gayatri; Boas, David A.

2011-01-01

Diffuse Optical Imaging (DOI) allows the recovery of the hemodynamic response associated with evoked brain activity. The signal is contaminated with systemic physiological interference which occurs in the superficial layers of the head as well as in the brain tissue. The back-reflection geometry of the measurement makes the DOI signal strongly contaminated by systemic interference occurring in the superficial layers. A recent development has been the use of signals from small source-detector separation (1 cm) optodes as regressors. Since those additional measurements are mainly sensitive to superficial layers in adult humans, they help in removing the systemic interference present in longer separation measurements (3 cm). Encouraged by those findings, we developed a dynamic estimation procedure to remove global interference using small optode separations and to estimate simultaneously the hemodynamic response. The algorithm was tested by recovering a simulated synthetic hemodynamic response added over baseline DOI data acquired from 6 human subjects at rest. The performance of the algorithm was quantified by the Pearson R2 coefficient and the mean square error (MSE) between the recovered and the simulated hemodynamic responses. Our dynamic estimator was also compared with a static estimator and the traditional adaptive filtering method. We observed a significant improvement (two-tailed paired t-test, p < 0.05) in both HbO and HbR recovery using our Kalman filter dynamic estimator compared to the traditional adaptive filter, the static estimator and the standard GLM technique. PMID:21385616

Estimating and Testing the Sources of Evoked Potentials in the Brain.

ERIC Educational Resources Information Center

Huizenga, Hilde M.; Molenaar, Peter C. M.

1994-01-01

The source of an event-related brain potential (ERP) is estimated from multivariate measures of ERP on the head under several mathematical and physical constraints on the parameters of the source model. Statistical aspects of estimation are discussed, and new tests are proposed. (SLD)

Segmentation of human brain using structural MRI.

PubMed

Helms, Gunther

2016-04-01

Segmentation of human brain using structural MRI is a key step of processing in imaging neuroscience. The methods have undergone a rapid development in the past two decades and are now widely available. This non-technical review aims at providing an overview and basic understanding of the most common software. Starting with the basis of structural MRI contrast in brain and imaging protocols, the concepts of voxel-based and surface-based segmentation are discussed. Special emphasis is given to the typical contrast features and morphological constraints of cortical and sub-cortical grey matter. In addition to the use for voxel-based morphometry, basic applications in quantitative MRI, cortical thickness estimations, and atrophy measurements as well as assignment of cortical regions and deep brain nuclei are briefly discussed. Finally, some fields for clinical applications are given.

Analysis of fMRI data using noise-diffusion network models: a new covariance-coding perspective.

PubMed

Gilson, Matthieu

2018-04-01

Since the middle of the 1990s, studies of resting-state fMRI/BOLD data have explored the correlation patterns of activity across the whole brain, which is referred to as functional connectivity (FC). Among the many methods that have been developed to interpret FC, a recently proposed model-based approach describes the propagation of fluctuating BOLD activity within the recurrently connected brain network by inferring the effective connectivity (EC). In this model, EC quantifies the strengths of directional interactions between brain regions, viewed from the proxy of BOLD activity. In addition, the tuning procedure for the model provides estimates for the local variability (input variances) to explain how the observed FC is generated. Generalizing, the network dynamics can be studied in the context of an input-output mapping-determined by EC-for the second-order statistics of fluctuating nodal activities. The present paper focuses on the following detection paradigm: observing output covariances, how discriminative is the (estimated) network model with respect to various input covariance patterns? An application with the model fitted to experimental fMRI data-movie viewing versus resting state-illustrates that changes in local variability and changes in brain coordination go hand in hand.

Image-Guided Intraoperative Cortical Deformation Recovery Using Game Theory: Application to Neocortical Epilepsy Surgery

PubMed Central

DeLorenzo, Christine; Papademetris, Xenophon; Staib, Lawrence H.; Vives, Kenneth P.; Spencer, Dennis D.; Duncan, James S.

2010-01-01

During neurosurgery, nonrigid brain deformation prevents preoperatively-acquired images from accurately depicting the intraoperative brain. Stereo vision systems can be used to track intraoperative cortical surface deformation and update preoperative brain images in conjunction with a biomechanical model. However, these stereo systems are often plagued with calibration error, which can corrupt the deformation estimation. In order to decouple the effects of camera calibration from the surface deformation estimation, a framework that can solve for disparate and often competing variables is needed. Game theory, which was developed to handle decision making in this type of competitive environment, has been applied to various fields from economics to biology. In this paper, game theory is applied to cortical surface tracking during neocortical epilepsy surgery and used to infer information about the physical processes of brain surface deformation and image acquisition. The method is successfully applied to eight in vivo cases, resulting in an 81% decrease in mean surface displacement error. This includes a case in which some of the initial camera calibration parameters had errors of 70%. Additionally, the advantages of using a game theoretic approach in neocortical epilepsy surgery are clearly demonstrated in its robustness to initial conditions. PMID:20129844

Imaging brain microstructure with diffusion MRI: practicality and applications.

PubMed

Alexander, Daniel C; Dyrby, Tim B; Nilsson, Markus; Zhang, Hui

2017-11-29

This article gives an overview of microstructure imaging of the brain with diffusion MRI and reviews the state of the art. The microstructure-imaging paradigm aims to estimate and map microscopic properties of tissue using a model that links these properties to the voxel scale MR signal. Imaging techniques of this type are just starting to make the transition from the technical research domain to wide application in biomedical studies. We focus here on the practicalities of both implementing such techniques and using them in applications. Specifically, the article summarizes the relevant aspects of brain microanatomy and the range of diffusion-weighted MR measurements that provide sensitivity to them. It then reviews the evolution of mathematical and computational models that relate the diffusion MR signal to brain tissue microstructure, as well as the expanding areas of application. Next we focus on practicalities of designing a working microstructure imaging technique: model selection, experiment design, parameter estimation, validation, and the pipeline of development of this class of technique. The article concludes with some future perspectives on opportunities in this topic and expectations on how the field will evolve in the short-to-medium term. Copyright © 2017 John Wiley & Sons, Ltd.

Probabilistic Air Segmentation and Sparse Regression Estimated Pseudo CT for PET/MR Attenuation Correction

PubMed Central

Chen, Yasheng; Juttukonda, Meher; Su, Yi; Benzinger, Tammie; Rubin, Brian G.; Lee, Yueh Z.; Lin, Weili; Shen, Dinggang; Lalush, David

2015-01-01

Purpose To develop a positron emission tomography (PET) attenuation correction method for brain PET/magnetic resonance (MR) imaging by estimating pseudo computed tomographic (CT) images from T1-weighted MR and atlas CT images. Materials and Methods In this institutional review board–approved and HIPAA-compliant study, PET/MR/CT images were acquired in 20 subjects after obtaining written consent. A probabilistic air segmentation and sparse regression (PASSR) method was developed for pseudo CT estimation. Air segmentation was performed with assistance from a probabilistic air map. For nonair regions, the pseudo CT numbers were estimated via sparse regression by using atlas MR patches. The mean absolute percentage error (MAPE) on PET images was computed as the normalized mean absolute difference in PET signal intensity between a method and the reference standard continuous CT attenuation correction method. Friedman analysis of variance and Wilcoxon matched-pairs tests were performed for statistical comparison of MAPE between the PASSR method and Dixon segmentation, CT segmentation, and population averaged CT atlas (mean atlas) methods. Results The PASSR method yielded a mean MAPE ± standard deviation of 2.42% ± 1.0, 3.28% ± 0.93, and 2.16% ± 1.75, respectively, in the whole brain, gray matter, and white matter, which were significantly lower than the Dixon, CT segmentation, and mean atlas values (P < .01). Moreover, 68.0% ± 16.5, 85.8% ± 12.9, and 96.0% ± 2.5 of whole-brain volume had within ±2%, ±5%, and ±10% percentage error by using PASSR, respectively, which was significantly higher than other methods (P < .01). Conclusion PASSR outperformed the Dixon, CT segmentation, and mean atlas methods by reducing PET error owing to attenuation correction. © RSNA, 2014 PMID:25521778

Nonlocal Intracranial Cavity Extraction

PubMed Central

Manjón, José V.; Eskildsen, Simon F.; Coupé, Pierrick; Romero, José E.; Collins, D. Louis; Robles, Montserrat

2014-01-01

Automatic and accurate methods to estimate normalized regional brain volumes from MRI data are valuable tools which may help to obtain an objective diagnosis and followup of many neurological diseases. To estimate such regional brain volumes, the intracranial cavity volume (ICV) is often used for normalization. However, the high variability of brain shape and size due to normal intersubject variability, normal changes occurring over the lifespan, and abnormal changes due to disease makes the ICV estimation problem challenging. In this paper, we present a new approach to perform ICV extraction based on the use of a library of prelabeled brain images to capture the large variability of brain shapes. To this end, an improved nonlocal label fusion scheme based on BEaST technique is proposed to increase the accuracy of the ICV estimation. The proposed method is compared with recent state-of-the-art methods and the results demonstrate an improved performance both in terms of accuracy and reproducibility while maintaining a reduced computational burden. PMID:25328511

Developmental basis for telencephalon expansion in waterfowl: enlargement prior to neurogenesis

PubMed Central

Charvet, Christine J.; Striedter, Georg F.

2009-01-01

Some altricial and some precocial species of birds have evolved enlarged telencephalons compared with other birds. Previous work has shown that finches and parakeets, two species that hatch in an immature (i.e. altricial) state, enlarged their telencephalon by delaying telencephalic neurogenesis. To determine whether species that hatch in a relatively mature (i.e. precocial) state also enlarged their telencephalon by delaying telencephalic neurogenesis, we examined brain development in geese, ducks, turkeys and chickens, which are all precocial. Whereas the telencephalon occupies less than 55 per cent of the brain in chickens and turkeys, it occupies more than 65 per cent in ducks and geese. To determine how these species differences in adult brain region proportions arise during development, we examined brain maturation (i.e. neurogenesis timing) and estimated telencephalon, tectum and medulla volumes from serial Nissl-stained sections in the four species. We found that incubation time predicts the timing of neurogenesis in all major brain regions and that the telencephalon is proportionally larger in ducks and geese before telencephalic neurogenesis begins. These findings demonstrate that the expansion of the telencephalon in ducks and geese is achieved by altering development prior to neurogenesis onset. Thus, precocial and altricial species evolved different developmental strategies to expand their telencephalon. PMID:19605398

Developmental basis for telencephalon expansion in waterfowl: enlargement prior to neurogenesis.

PubMed

Charvet, Christine J; Striedter, Georg F

2009-10-07

Some altricial and some precocial species of birds have evolved enlarged telencephalons compared with other birds. Previous work has shown that finches and parakeets, two species that hatch in an immature (i.e. altricial) state, enlarged their telencephalon by delaying telencephalic neurogenesis. To determine whether species that hatch in a relatively mature (i.e. precocial) state also enlarged their telencephalon by delaying telencephalic neurogenesis, we examined brain development in geese, ducks, turkeys and chickens, which are all precocial. Whereas the telencephalon occupies less than 55 per cent of the brain in chickens and turkeys, it occupies more than 65 per cent in ducks and geese. To determine how these species differences in adult brain region proportions arise during development, we examined brain maturation (i.e. neurogenesis timing) and estimated telencephalon, tectum and medulla volumes from serial Nissl-stained sections in the four species. We found that incubation time predicts the timing of neurogenesis in all major brain regions and that the telencephalon is proportionally larger in ducks and geese before telencephalic neurogenesis begins. These findings demonstrate that the expansion of the telencephalon in ducks and geese is achieved by altering development prior to neurogenesis onset. Thus, precocial and altricial species evolved different developmental strategies to expand their telencephalon.

Biomechanical Analysis of Normal Brain Development during the First Year of Life Using Finite Strain Theory.

PubMed

Kim, Jeong Chul; Wang, Li; Shen, Dinggang; Lin, Weili

2016-12-02

The first year of life is the most critical time period for structural and functional development of the human brain. Combining longitudinal MR imaging and finite strain theory, this study aimed to provide new insights into normal brain development through a biomechanical framework. Thirty-three normal infants were longitudinally imaged using MRI from 2 weeks to 1 year of age. Voxel-wise Jacobian determinant was estimated to elucidate volumetric changes while Lagrange strains (both normal and shear strains) were measured to reveal directional growth information every 3 months during the first year of life. Directional normal strain maps revealed that, during the first 6 months, the growth pattern of gray matter is anisotropic and spatially inhomogeneous with higher left-right stretch around the temporal lobe and interhemispheric fissure, anterior-posterior stretch in the frontal and occipital lobes, and superior-inferior stretch in right inferior occipital and right inferior temporal gyri. In contrast, anterior lateral ventricles and insula showed an isotropic stretch pattern. Volumetric and directional growth rates were linearly decreased with age for most of the cortical regions. Our results revealed anisotropic and inhomogeneous brain growth patterns of the human brain during the first year of life using longitudinal MRI and a biomechanical framework.

Design of electrodes and current limits for low frequency electrical impedance tomography of the brain.

PubMed

Gilad, O; Horesh, L; Holder, D S

2007-07-01

For the novel application of recording of resistivity changes related to neuronal depolarization in the brain with electrical impedance tomography, optimal recording is with applied currents below 100 Hz, which might cause neural stimulation of skin or underlying brain. The purpose of this work was to develop a method for application of low frequency currents to the scalp, which delivered the maximum current without significant stimulation of skin or underlying brain. We propose a recessed electrode design which enabled current injection with an acceptable skin sensation to be increased from 100 muA using EEG electrodes, to 1 mA in 16 normal volunteers. The effect of current delivered to the brain was assessed with an anatomically realistic finite element model of the adult head. The modelled peak cerebral current density was 0.3 A/m(2), which was 5 to 25-fold less than the threshold for stimulation of the brain estimated from literature review.

Transcranial magnetic stimulation of mouse brain using high-resolution anatomical models

NASA Astrophysics Data System (ADS)

Crowther, L. J.; Hadimani, R. L.; Kanthasamy, A. G.; Jiles, D. C.

2014-05-01

Transcranial magnetic stimulation (TMS) offers the possibility of non-invasive treatment of brain disorders in humans. Studies on animals can allow rapid progress of the research including exploring a variety of different treatment conditions. Numerical calculations using animal models are needed to help design suitable TMS coils for use in animal experiments, in particular, to estimate the electric field induced in animal brains. In this paper, we have implemented a high-resolution anatomical MRI-derived mouse model consisting of 50 tissue types to accurately calculate induced electric field in the mouse brain. Magnetic field measurements have been performed on the surface of the coil and compared with the calculations in order to validate the calculated magnetic and induced electric fields in the brain. Results show how the induced electric field is distributed in a mouse brain and allow investigation of how this could be improved for TMS studies using mice. The findings have important implications in further preclinical development of TMS for treatment of human diseases.

A mathematical model for human brain cooling during cold-water near-drowning.

PubMed

Xu, X; Tikuisis, P; Giesbrecht, G

1999-01-01

A two-dimensional mathematical model was developed to estimate the contributions of different mechanisms of brain cooling during cold-water near-drowning. Mechanisms include 1) conductive heat loss through tissue to the water at the head surface and in the upper airway and 2) circulatory cooling to aspirated water via the lung and via venous return from the scalp. The model accounts for changes in boundary conditions, blood circulation, respiratory ventilation of water, and head size. Results indicate that conductive heat loss through the skull surface or the upper airways is minimal, although a small child-sized head will conductively cool faster than a large adult-sized head. However, ventilation of cold water may provide substantial brain cooling through circulatory cooling. Although it seems that water breathing is required for rapid "whole" brain cooling, it is possible that conductive cooling may provide some advantage by cooling the brain cortex peripherally and the brain stem centrally via the upper airway.

In vivo imaging of scattering and absorption properties of exposed brain using a digital red-green-blue camera

NASA Astrophysics Data System (ADS)

Nishidate, Izumi; Yoshida, Keiichiro; Kawauchi, Satoko; Sato, Shunichi; Sato, Manabu

2014-03-01

We investigate a method to estimate the spectral images of reduced scattering coefficients and the absorption coefficients of in vivo exposed brain tissues in the range from visible to near-infrared wavelength (500-760 nm) based on diffuse reflectance spectroscopy using a digital RGB camera. In the proposed method, the multi-spectral reflectance images of in vivo exposed brain are reconstructed from the digital red, green blue images using the Wiener estimation algorithm. The Monte Carlo simulation-based multiple regression analysis for the absorbance spectra is then used to specify the absorption and scattering parameters of brain tissue. In this analysis, the concentration of oxygenated hemoglobin and that of deoxygenated hemoglobin are estimated as the absorption parameters whereas the scattering amplitude a and the scattering power b in the expression of μs'=aλ-b as the scattering parameters, respectively. The spectra of absorption and reduced scattering coefficients are reconstructed from the absorption and scattering parameters, and finally, the spectral images of absorption and reduced scattering coefficients are estimated. The estimated images of absorption coefficients were dominated by the spectral characteristics of hemoglobin. The estimated spectral images of reduced scattering coefficients showed a broad scattering spectrum, exhibiting larger magnitude at shorter wavelengths, corresponding to the typical spectrum of brain tissue published in the literature. In vivo experiments with exposed brain of rats during CSD confirmed the possibility of the method to evaluate both hemodynamics and changes in tissue morphology due to electrical depolarization.

Effect of Magnitude Estimation of Pleasantness and Intensity on fMRI Activation to Taste

PubMed Central

Cerf-Ducastel, B.; Haase, L.; Murphy, C.

2012-01-01

The goal of the present study was to investigate whether the psychophysical evaluation of taste stimuli using magnitude estimation influences the pattern of cortical activation observed with neuroimaging. That is, whether different brain areas are involved in the magnitude estimation of pleasantness relative to the magnitude estimation of intensity. fMRI was utilized to examine the patterns of cortical activation involved in magnitude estimation of pleasantness and intensity during hunger in response to taste stimuli. During scanning, subjects were administered taste stimuli orally and were asked to evaluate the perceived pleasantness or intensity using the general Labeled Magnitude Scale (Green 1996, Bartoshuk et al. 2004). Image analysis was conducted using AFNI. Magnitude estimation of intensity and pleasantness shared common activations in the insula, rolandic operculum, and the medio dorsal nucleus of the thalamus. Globally, magnitude estimation of pleasantness produced significantly more activation than magnitude estimation of intensity. Areas differentially activated during magnitude estimation of pleasantness versus intensity included, e.g., the insula, the anterior cingulate gyrus, and putamen; suggesting that different brain areas were recruited when subjects made magnitude estimates of intensity and pleasantness. These findings demonstrate significant differences in brain activation during magnitude estimation of intensity and pleasantness to taste stimuli. An appreciation for the complexity of brain response to taste stimuli may facilitate a clearer understanding of the neural mechanisms underlying eating behavior and over consumption. PMID:23227271

Effects of valerian consumption during pregnancy on cortical volume and the levels of zinc and copper in the brain tissue of mouse fetus.

PubMed

Mahmoudian, Alireza; Rajaei, Ziba; Haghir, Hossein; Banihashemian, Shahaboldin; Hami, Javad

2012-04-01

The aim of the present study was to determine the effects of valerian (Valeriana officinalis) consumption in pregnancy on cortical volume and the levels of zinc and copper, two essential elements that affect brain development and function, in the brain tissues of mouse fetuses. Pregnant female mice were treated with either saline or 1.2 g/kg body weight valerian extract intraperitoneally daily on gestation days (GD) 7 to 17. On GD 20, mice were sacrificed and their fetuses were collected. Fetal brains were dissected, weighed and processed for histological analysis. The volume of cerebral cortex was estimated by the Cavalieri principle. The levels of zinc and copper in the brain tissues were measured by atomic absorption spectroscopy. The results indicated that valerian consumption in pregnancy had no significant effect on brain weight, cerebral cortex volume and copper level in fetal brain. However,it significantly decreased the level of zinc in the brain (P<0.05). Using valerian during midgestation do not have an adverse effect on cerebral cortex; however,it caused a significant decrease in zinc level in the fetal brain. This suggests that valerian use should be limited during pregnancy.

Effects of diquat, an aquatic herbicide, on the development of mallard embryos

USGS Publications Warehouse

Sewalk, C.J.; Brewer, G.L.; Hoffman, D.J.

2001-01-01

Bipyridylium herbicides produce embryotoxic and teratogenic effects in dipteran, amphibian, avian, and mammalian organisms. Diquat dibromide, a bipyridylium compound, is commonly used as an aquatic herbicide. Mallard (Anas platyrhynchos) eggs were exposed to diquat by immersing the eggs for 10s in solutions of 0.88, 3.5, 7, 14, or 56 g/L on either the fourth or twenty-first day of incubation. Application of diquat on day 4 yielded an estimated LC50 of 19.5 g/L through 18 days of incubation, and 9.6 g/L through hatching. Body and organ weights, and bone lengths of hatchlings did not differ between control and treatment groups with the exception of a slight increase in brain weight in the 14 g/L group. Malformations in diquat-treated embryos included defects of the brain, eye, bill, limb, and pelvis; skeletal scoliosis; and incomplete ossification. Subcutaneous edema was also present. Significant manifestations of oxidative stress were apparent in hatchlings and included increased hepatic thiobarbituric acid reactive substances (TBARS) (lipid peroxidation) and decreased brain reduced glutathione (GSH). Brain protein-bound sulfhydryls (PBSH) increased. Diquat applied on day 21 of incubation yielded an estimated LC50 of 12.6 g/L through hatching. Exposure at this late stage of development did not produce deformities. Body and organ weights, and, bone lengths of hatchlings did not differ between control and treatment groups. Significant manifestations of oxidative stress in hatchlings included decreased brain GSH, increased oxidized glutathione (GSSG) and ratio of GSSG:GSH. This study suggests that concentrations of diquat commonly used for aquatic weed control, when based upon the expected dilution effect of average water depth of the application area, would probably have little impact on mallard embryos. However, concentrations applied above ground to weeds and cattails along the edge of waters and ditches could adversely affect the survival and development of mallard embryos, and presumably other avian species nesting in such habitats.

Chemotherapy in the management of brain metastases: the emerging role of fotemustine for patients with melanoma and NSCLC.

PubMed

Addeo, Raffaele; Zappavigna, Silvia; Luce, Amalia; Facchini, Sergio; Caraglia, Michele

2013-09-01

An estimated 20 - 40% of cancer patients will develop brain metastases that are the most common intracranial tumors in adults. Patients with cerebral metastases represent a variegate group where selection of the most appropriate treatment depends on many patient- and disease-related factors. The impact of therapeutic option on overall survival is lacking and it is important to consider quality of life (QOL) when treating patients with brain metastases. A considerable proportion of patients are treated with palliative approaches such as whole-brain radiotherapy. The role of chemotherapy was limited in the past. Recently, several chemotherapeutic agents have been identified as potentially useful. This article examines the pharmacokinetics, efficacy and safety and tolerability of fotemustine (FTM) for the management of patients with cerebral metastasis from melanoma and non-small cell lung cancer (NSCLC). FTM is a third-generation nitrosourea that has proved its efficacy on brain metastases of melanoma and showed promising results for the treatment of brain metastasis of NSCLC because of its ability to pass the blood-brain barrier.

Brain metabolite differences in one-year-old infants born small at term and association with neurodevelopmental outcome.

PubMed

Simões, Rui V; Cruz-Lemini, Mónica; Bargalló, Núria; Gratacós, Eduard; Sanz-Cortés, Magdalena

2015-08-01

We assessed brain metabolite levels by magnetic resonance spectroscopy (MRS) in 1-year-old infants born small at term, as compared with infants born appropriate for gestational age (AGA), and their association with neurodevelopment at 2 years of age. A total of 40 infants born small (birthweight <10th centile for gestational age) and 30 AGA infants underwent brain MRS at age 1 year on a 3-T scanner. Small-born infants were subclassified as late intrauterine growth restriction or as small for gestational age, based on the presence or absence of prenatal Doppler and birthweight predictors of an adverse perinatal outcome, respectively. Single-voxel proton magnetic resonance spectroscopy ((1)H-MRS) data were acquired from the frontal lobe at short echo time. Neurodevelopment was evaluated at 2 years of age using the Bayley Scales of Infant and Toddler Development, Third Edition, assessing cognitive, language, motor, social-emotional, and adaptive behavior scales. As compared with AGA controls, infants born small showed significantly higher levels of glutamate and total N-acetylaspartate (NAAt) to creatine (Cr) ratio at age 1 year, and lower Bayley Scales of Infant and Toddler Development, Third Edition scores at 2 years. The subgroup with late intrauterine growth restriction further showed lower estimated glutathione levels at age 1 year. Significant correlations were observed for estimated glutathione levels with adaptive scores, and for myo-inositol with language scores. Significant associations were also noticed for NAA/Cr with cognitive scores, and for glutamate/Cr with motor scores. Infants born small show brain metabolite differences at 1 year of age, which are correlated with later neurodevelopment. These results support further research on MRS to develop imaging biomarkers of abnormal neurodevelopment. Copyright © 2015 Elsevier Inc. All rights reserved.

Increased brain-predicted aging in treated HIV disease

PubMed Central

Underwood, Jonathan; Caan, Matthan W.A.; De Francesco, Davide; van Zoest, Rosan A.; Leech, Robert; Wit, Ferdinand W.N.M.; Portegies, Peter; Geurtsen, Gert J.; Schmand, Ben A.; Schim van der Loeff, Maarten F.; Franceschi, Claudio; Sabin, Caroline A.; Majoie, Charles B.L.M.; Winston, Alan; Reiss, Peter; Sharp, David J.

2017-01-01

Objective: To establish whether HIV disease is associated with abnormal levels of age-related brain atrophy, by estimating apparent brain age using neuroimaging and exploring whether these estimates related to HIV status, age, cognitive performance, and HIV-related clinical parameters. Methods: A large sample of virologically suppressed HIV-positive adults (n = 162, age 45–82 years) and highly comparable HIV-negative controls (n = 105) were recruited as part of the Comorbidity in Relation to AIDS (COBRA) collaboration. Using T1-weighted MRI scans, a machine-learning model of healthy brain aging was defined in an independent cohort (n = 2,001, aged 18–90 years). Neuroimaging data from HIV-positive and HIV-negative individuals were then used to estimate brain-predicted age; then brain-predicted age difference (brain-PAD = brain-predicted brain age − chronological age) scores were calculated. Neuropsychological and clinical assessments were also carried out. Results: HIV-positive individuals had greater brain-PAD score (mean ± SD 2.15 ± 7.79 years) compared to HIV-negative individuals (−0.87 ± 8.40 years; b = 3.48, p < 0.01). Increased brain-PAD score was associated with decreased performance in multiple cognitive domains (information processing speed, executive function, memory) and general cognitive performance across all participants. Brain-PAD score was not associated with age, duration of HIV infection, or other HIV-related measures. Conclusion: Increased apparent brain aging, predicted using neuroimaging, was observed in HIV-positive adults, despite effective viral suppression. Furthermore, the magnitude of increased apparent brain aging related to cognitive deficits. However, predicted brain age difference did not correlate with chronological age or duration of HIV infection, suggesting that HIV disease may accentuate rather than accelerate brain aging. PMID:28258081

Increased brain-predicted aging in treated HIV disease.

PubMed

Cole, James H; Underwood, Jonathan; Caan, Matthan W A; De Francesco, Davide; van Zoest, Rosan A; Leech, Robert; Wit, Ferdinand W N M; Portegies, Peter; Geurtsen, Gert J; Schmand, Ben A; Schim van der Loeff, Maarten F; Franceschi, Claudio; Sabin, Caroline A; Majoie, Charles B L M; Winston, Alan; Reiss, Peter; Sharp, David J

2017-04-04

To establish whether HIV disease is associated with abnormal levels of age-related brain atrophy, by estimating apparent brain age using neuroimaging and exploring whether these estimates related to HIV status, age, cognitive performance, and HIV-related clinical parameters. A large sample of virologically suppressed HIV-positive adults (n = 162, age 45-82 years) and highly comparable HIV-negative controls (n = 105) were recruited as part of the Comorbidity in Relation to AIDS (COBRA) collaboration. Using T1-weighted MRI scans, a machine-learning model of healthy brain aging was defined in an independent cohort (n = 2,001, aged 18-90 years). Neuroimaging data from HIV-positive and HIV-negative individuals were then used to estimate brain-predicted age; then brain-predicted age difference (brain-PAD = brain-predicted brain age - chronological age) scores were calculated. Neuropsychological and clinical assessments were also carried out. HIV-positive individuals had greater brain-PAD score (mean ± SD 2.15 ± 7.79 years) compared to HIV-negative individuals (-0.87 ± 8.40 years; b = 3.48, p < 0.01). Increased brain-PAD score was associated with decreased performance in multiple cognitive domains (information processing speed, executive function, memory) and general cognitive performance across all participants. Brain-PAD score was not associated with age, duration of HIV infection, or other HIV-related measures. Increased apparent brain aging, predicted using neuroimaging, was observed in HIV-positive adults, despite effective viral suppression. Furthermore, the magnitude of increased apparent brain aging related to cognitive deficits. However, predicted brain age difference did not correlate with chronological age or duration of HIV infection, suggesting that HIV disease may accentuate rather than accelerate brain aging. Copyright © 2017 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the American Academy of Neurology.

The CONNECT project: Combining macro- and micro-structure.

PubMed

Assaf, Yaniv; Alexander, Daniel C; Jones, Derek K; Bizzi, Albero; Behrens, Tim E J; Clark, Chris A; Cohen, Yoram; Dyrby, Tim B; Huppi, Petra S; Knoesche, Thomas R; Lebihan, Denis; Parker, Geoff J M; Poupon, Cyril; Anaby, Debbie; Anwander, Alfred; Bar, Leah; Barazany, Daniel; Blumenfeld-Katzir, Tamar; De-Santis, Silvia; Duclap, Delphine; Figini, Matteo; Fischi, Elda; Guevara, Pamela; Hubbard, Penny; Hofstetter, Shir; Jbabdi, Saad; Kunz, Nicolas; Lazeyras, Francois; Lebois, Alice; Liptrot, Matthew G; Lundell, Henrik; Mangin, Jean-François; Dominguez, David Moreno; Morozov, Darya; Schreiber, Jan; Seunarine, Kiran; Nava, Simone; Poupon, Cyril; Riffert, Till; Sasson, Efrat; Schmitt, Benoit; Shemesh, Noam; Sotiropoulos, Stam N; Tavor, Ido; Zhang, Hui Gary; Zhou, Feng-Lei

2013-10-15

In recent years, diffusion MRI has become an extremely important tool for studying the morphology of living brain tissue, as it provides unique insights into both its macrostructure and microstructure. Recent applications of diffusion MRI aimed to characterize the structural connectome using tractography to infer connectivity between brain regions. In parallel to the development of tractography, additional diffusion MRI based frameworks (CHARMED, AxCaliber, ActiveAx) were developed enabling the extraction of a multitude of micro-structural parameters (axon diameter distribution, mean axonal diameter and axonal density). This unique insight into both tissue microstructure and connectivity has enormous potential value in understanding the structure and organization of the brain as well as providing unique insights to abnormalities that underpin disease states. The CONNECT (Consortium Of Neuroimagers for the Non-invasive Exploration of brain Connectivity and Tracts) project aimed to combine tractography and micro-structural measures of the living human brain in order to obtain a better estimate of the connectome, while also striving to extend validation of these measurements. This paper summarizes the project and describes the perspective of using micro-structural measures to study the connectome. Copyright © 2013 Elsevier Inc. All rights reserved.

Comparison of LCModel and SAGE in Analysis of Brain Metabolite Concentrations-A study of Patients with Mild Cognitive Impairment.

PubMed

Shih, Chiu-Ming; Lai, Jui-Jen; Chang, Chin-Ching; Chen, Cheng-Sheng; Yeh, Yi-Chun; Jaw, Twei-Shiun; Hsu, Jui-Sheng; Li, Chun-Wei

2017-03-15

The purpose of this study was to compare brain metabolite concentration ratios determined by LCModel and Spectroscopy Analysis by General Electric (SAGE) quantitative methods to elucidate the advantages and disadvantages of each method. A total of 10 healthy volunteers and 10 patients with mild cognitive impairment (MCI) were recruited in this study. A point-resolved spectroscopy (PRESS) sequence was used to obtain the brain magnetic resonance spectroscopy (MRS) spectra of the volunteers and patients, as well as the General Electric (GE) MRS-HD-sphere phantom. The brain metabolite concentration ratios were estimated based on the peak area obtained from both LCModel and SAGE software. Three brain regions were sampled for each volunteer or patient, and 20 replicates were acquired at different times for the phantom analysis. The metabolite ratios of the GE phantom were estimated to be myo-inositol (mI)/creatine (Cr): 0.70 ± 0.01, choline (Cho)/Cr: 0.37 ± 0.00, N-acetylaspartate (NAA)/Cr: 1.26 ± 0.02, and NAA/mI: 1.81 ± 0.04 by LCModel, and mI/Cr: 0.88 ± 0.15, Cho/Cr: 0.35 ± 0.01, NAA/Cr: 1.33 ± 0.03, and NAA/mI: 1.55 ± 0.26 by SAGE. In the healthy volunteers and MCI patients, the ratios of mI/Cr and Cho/Cr estimated by LCModel were higher than those estimated by SAGE. In contrast, the ratio of NAA/Cr estimated by LCModel was lower than that estimated by SAGE. Both methods were acceptable in estimating brain metabolite concentration ratios. However, LCModel was marginally more accurate than SAGE because of its full automation, basis set, and user independency.

Hierarchical and symmetric infant image registration by robust longitudinal-example-guided correspondence detection

PubMed Central

Wu, Yao; Wu, Guorong; Wang, Li; Munsell, Brent C.; Wang, Qian; Lin, Weili; Feng, Qianjin; Chen, Wufan; Shen, Dinggang

2015-01-01

Purpose: To investigate anatomical differences across individual subjects, or longitudinal changes in early brain development, it is important to perform accurate image registration. However, due to fast brain development and dynamic tissue appearance changes, it is very difficult to align infant brain images acquired from birth to 1-yr-old. Methods: To solve this challenging problem, a novel image registration method is proposed to align two infant brain images, regardless of age at acquisition. The main idea is to utilize the growth trajectories, or spatial-temporal correspondences, learned from a set of longitudinal training images, for guiding the registration of two different time-point images with different image appearances. Specifically, in the training stage, an intrinsic growth trajectory is first estimated for each training subject using the longitudinal images. To register two new infant images with potentially a large age gap, the corresponding images patches between each new image and its respective training images with similar age are identified. Finally, the registration between the two new images can be assisted by the learned growth trajectories from one time point to another time point that have been established in the training stage. To further improve registration accuracy, the proposed method is combined with a hierarchical and symmetric registration framework that can iteratively add new key points in both images to steer the estimation of the deformation between the two infant brain images under registration. Results: To evaluate image registration accuracy, the proposed method is used to align 24 infant subjects at five different time points (2-week-old, 3-month-old, 6-month-old, 9-month-old, and 12-month-old). Compared to the state-of-the-art methods, the proposed method demonstrated superior registration performance. Conclusions: The proposed method addresses the difficulties in the infant brain registration and produces better results compared to existing state-of-the-art registration methods. PMID:26133617

Onset of multiple sclerosis before adulthood leads to failure of age-expected brain growth

PubMed Central

Aubert-Broche, Bérengère; Fonov, Vladimir; Narayanan, Sridar; Arnold, Douglas L.; Araujo, David; Fetco, Dumitru; Till, Christine; Sled, John G.; Collins, D. Louis

2014-01-01

Objective: To determine the impact of pediatric-onset multiple sclerosis (MS) on age-expected brain growth. Methods: Whole brain and regional volumes of 36 patients with relapsing-remitting MS onset prior to 18 years of age were segmented in 185 longitudinal MRI scans (2–11 scans per participant, 3-month to 2-year scan intervals). MRI scans of 25 age- and sex-matched healthy normal controls (NC) were also acquired at baseline and 2 years later on the same scanner as the MS group. A total of 874 scans from 339 participants from the NIH-funded MRI study of normal brain development acquired at 2-year intervals were used as an age-expected healthy growth reference. All data were analyzed with an automatic image processing pipeline to estimate the volume of brain and brain substructures. Mixed-effect models were built using age, sex, and group as fixed effects. Results: Significant group and age interactions were found with the adjusted models fitting brain volumes and normalized thalamus volumes (p < 10−4). These findings indicate a failure of age-normative brain growth for the MS group, and an even greater failure of thalamic growth. In patients with MS, T2 lesion volume correlated with a greater reduction in age-expected thalamic volume. To exclude any scanner-related influence on our data, we confirmed no significant interaction of group in the adjusted models between the NC and NIH MRI Study of Normal Brain Development groups. Conclusions: Our results provide evidence that the onset of MS during childhood and adolescence limits age-expected primary brain growth and leads to subsequent brain atrophy, implicating an early onset of the neurodegenerative aspect of MS. PMID:25378667

Dental development in Megaladapis edwardsi (Primates, Lemuriformes): implications for understanding life history variation in subfossil lemurs.

PubMed

Schwartz, Gary T; Mahoney, Patrick; Godfrey, Laurie R; Cuozzo, Frank P; Jungers, William L; Randria, Gisèle F N

2005-12-01

Teeth grow incrementally and preserve within them a record of that incremental growth in the form of microscopic growth lines. Studying dental development in extinct and extant primates, and its relationship to adult brain and body size as well as other life history and ecological parameters (e.g., diet, somatic growth rates, gestation length, age at weaning), holds the potential to yield unparalleled insights into the life history profiles of fossil primates. Here, we address the absolute pace of dental development in Megaladapis edwardsi, a giant extinct lemur of Madagascar. By examining the microstructure of the first and developing second molars in a juvenile individual, we establish a chronology of molar crown development for this specimen (M1 CFT = 1.04 years; M2 CFT = 1.42 years) and determine its age at death (1.39 years). Microstructural data on prenatal M1 crown formation time allow us to calculate a minimum gestation length of 0.54 years for this species. Postnatal crown and root formation data allow us to estimate its age at M1 emergence (approximately 0.9 years) and to establish a minimum age for M2 emergence (>1.39 years). Finally, using reconstructions or estimates (drawn elsewhere) of adult body mass, brain size, and diet in Megaladapis, as well as the eruption sequence of its permanent teeth, we explore the efficacy of these variables in predicting the absolute pace of dental development in this fossil species. We test competing explanations of variation in crown formation timing across the order Primates. Brain size is the best single predictor of crown formation time in primates, but other variables help to explain the variation.

Linear regression analysis of survival data with missing censoring indicators.

PubMed

Wang, Qihua; Dinse, Gregg E

2011-04-01

Linear regression analysis has been studied extensively in a random censorship setting, but typically all of the censoring indicators are assumed to be observed. In this paper, we develop synthetic data methods for estimating regression parameters in a linear model when some censoring indicators are missing. We define estimators based on regression calibration, imputation, and inverse probability weighting techniques, and we prove all three estimators are asymptotically normal. The finite-sample performance of each estimator is evaluated via simulation. We illustrate our methods by assessing the effects of sex and age on the time to non-ambulatory progression for patients in a brain cancer clinical trial.

White matter tractography using diffusion tensor deflection.

PubMed

Lazar, Mariana; Weinstein, David M; Tsuruda, Jay S; Hasan, Khader M; Arfanakis, Konstantinos; Meyerand, M Elizabeth; Badie, Benham; Rowley, Howard A; Haughton, Victor; Field, Aaron; Alexander, Andrew L

2003-04-01

Diffusion tensor MRI provides unique directional diffusion information that can be used to estimate the patterns of white matter connectivity in the human brain. In this study, the behavior of an algorithm for white matter tractography is examined. The algorithm, called TEND, uses the entire diffusion tensor to deflect the estimated fiber trajectory. Simulations and imaging experiments on in vivo human brains were performed to investigate the behavior of the tractography algorithm. The simulations show that the deflection term is less sensitive than the major eigenvector to image noise. In the human brain imaging experiments, estimated tracts were generated in corpus callosum, corticospinal tract, internal capsule, corona radiata, superior longitudinal fasciculus, inferior longitudinal fasciculus, fronto-occipital fasciculus, and uncinate fasciculus. This approach is promising for mapping the organizational patterns of white matter in the human brain as well as mapping the relationship between major fiber trajectories and the location and extent of brain lesions. Copyright 2003 Wiley-Liss, Inc.

3D brain tumor localization and parameter estimation using thermographic approach on GPU.

PubMed

Bousselham, Abdelmajid; Bouattane, Omar; Youssfi, Mohamed; Raihani, Abdelhadi

2018-01-01

The aim of this paper is to present a GPU parallel algorithm for brain tumor detection to estimate its size and location from surface temperature distribution obtained by thermography. The normal brain tissue is modeled as a rectangular cube including spherical tumor. The temperature distribution is calculated using forward three dimensional Pennes bioheat transfer equation, it's solved using massively parallel Finite Difference Method (FDM) and implemented on Graphics Processing Unit (GPU). Genetic Algorithm (GA) was used to solve the inverse problem and estimate the tumor size and location by minimizing an objective function involving measured temperature on the surface to those obtained by numerical simulation. The parallel implementation of Finite Difference Method reduces significantly the time of bioheat transfer and greatly accelerates the inverse identification of brain tumor thermophysical and geometrical properties. Experimental results show significant gains in the computational speed on GPU and achieve a speedup of around 41 compared to the CPU. The analysis performance of the estimation based on tumor size inside brain tissue also presented. Copyright © 2017 Elsevier Ltd. All rights reserved.

Estimation of hyper-parameters for a hierarchical model of combined cortical and extra-brain current sources in the MEG inverse problem.

PubMed

Morishige, Ken-ichi; Yoshioka, Taku; Kawawaki, Dai; Hiroe, Nobuo; Sato, Masa-aki; Kawato, Mitsuo

2014-11-01

One of the major obstacles in estimating cortical currents from MEG signals is the disturbance caused by magnetic artifacts derived from extra-cortical current sources such as heartbeats and eye movements. To remove the effect of such extra-brain sources, we improved the hybrid hierarchical variational Bayesian method (hyVBED) proposed by Fujiwara et al. (NeuroImage, 2009). hyVBED simultaneously estimates cortical and extra-brain source currents by placing dipoles on cortical surfaces as well as extra-brain sources. This method requires EOG data for an EOG forward model that describes the relationship between eye dipoles and electric potentials. In contrast, our improved approach requires no EOG and less a priori knowledge about the current variance of extra-brain sources. We propose a new method, "extra-dipole," that optimally selects hyper-parameter values regarding current variances of the cortical surface and extra-brain source dipoles. With the selected parameter values, the cortical and extra-brain dipole currents were accurately estimated from the simulated MEG data. The performance of this method was demonstrated to be better than conventional approaches, such as principal component analysis and independent component analysis, which use only statistical properties of MEG signals. Furthermore, we applied our proposed method to measured MEG data during covert pursuit of a smoothly moving target and confirmed its effectiveness. Copyright © 2014 Elsevier Inc. All rights reserved.

Performance study of a PET scanner based on monolithic scintillators for different DoI-dependent methods

NASA Astrophysics Data System (ADS)

Preziosi, E.; Sánchez, S.; González, A. J.; Pani, R.; Borrazzo, C.; Bettiol, M.; Rodriguez-Alvarez, M. J.; González-Montoro, A.; Moliner, L.; Benlloch, J. M.

2016-12-01

One of the technical objectives of the MindView project is developing a brain-dedicated PET insert based on monolithic scintillation crystals. It will be inserted in MRI systems with the purpose to obtain simultaneous PET and MRI brain images. High sensitivity, high image quality performance and accurate detection of the Depth-of-Interaction (DoI) of the 511keV photons are required. We have developed a DoI estimation method, dedicated to monolithic scintillators, allowing continuous DoI estimation and a DoI-dependent algorithm for the estimation of the photon planar impact position, able to improve the single module imaging capabilities. In this work, through experimental measurements, the proposed methods have been used for the estimation of the impact positions within the monolithic crystal block. We have evaluated the PET system performance following the NEMA NU 4-2008 protocol by reconstructing the images using the STIR 3D platform. The results obtained with two different methods, providing discrete and continuous DoI information, are compared with those obtained from an algorithm without DoI capabilities and with the ideal response of the detector. The proposed DoI-dependent imaging methods show clear improvements in the spatial resolution (FWHM) of reconstructed images, allowing to obtain values from 2mm (at the center FoV) to 3mm (at the FoV edges).

The protons of space and brain tumors: I. Clinical and dosimetric considerations

NASA Astrophysics Data System (ADS)

Dalrymple, G. V.; Nagle, W. A.; Moss, A. J.; Cavin, L. A.; Broadwater, J. R.; McGuire, E. L.; Eason, C. S.; Mitchell, J. C.; Hardy, K. A.; Wood, D. H.; Salmon, Y. A.; Yochmowitz, M. G.

1989-05-01

Almost 25 years ago a large group of Rhesus monkeys were irradiated with protons (32-2300 MeV). The experiments were designed: 1) To estimate the RBE of protons, per se, and 2) To provide some estimate of the hazards of the radiation environment of space. The initial results showed the RBE to be about 1.0 for acute radiation effects (mortality, hematologic changes, etc). The colony has been maintained at Brooks AFB, TX since irradiation. The survivors of 55 MeV proton irradiation have developed a very high incidence of Glioblastoma multiforme, a highly malignant primary brain tumor. These tumors appeared 1-20 yrs after surface doses of 400-800 rads. Reconstruction of the dosimetry suggests that some areas within the brain may have received doses of 1500-2500 rads. More than 30 radiation induced Glioblastomas have been reported in human patients who had received therapeutic head irradiation. The radiation doses required to induce Glioblastoma were of the same order of magnitude as required to induce Glioblastoma in the Rhesus monkey.

The role of long-range connectivity for the characterization of the functional-anatomical organization of the cortex.

PubMed

Knösche, Thomas R; Tittgemeyer, Marc

2011-01-01

This review focuses on the role of long-range connectivity as one element of brain structure that is of key importance for the functional-anatomical organization of the cortex. In this context, we discuss the putative guiding principles for mapping brain function and structure onto the cortical surface. Such mappings reveal a high degree of functional-anatomical segregation. Given that brain regions frequently maintain characteristic connectivity profiles and the functional repertoire of a cortical area is closely related to its anatomical connections, long-range connectivity may be used to define segregated cortical areas. This methodology is called connectivity-based parcellation. Within this framework, we investigate different techniques to estimate connectivity profiles with emphasis given to non-invasive methods based on diffusion magnetic resonance imaging (dMRI) and diffusion tractography. Cortical parcellation is then defined based on similarity between diffusion tractograms, and different clustering approaches are discussed. We conclude that the use of non-invasively acquired connectivity estimates to characterize the functional-anatomical organization of the brain is a valid, relevant, and necessary endeavor. Current and future developments in dMRI technology, tractography algorithms, and models of the similarity structure hold great potential for a substantial improvement and enrichment of the results of the technique.

The retention time of inorganic mercury in the brain — A systematic review of the evidence

DOE Office of Scientific and Technical Information (OSTI.GOV)

Rooney, James P.K., E-mail: jrooney@rcsi.ie

2014-02-01

Reports from human case studies indicate a half-life for inorganic mercury in the brain in the order of years—contradicting older radioisotope studies that estimated half-lives in the order of weeks to months in duration. This study systematically reviews available evidence on the retention time of inorganic mercury in humans and primates to better understand this conflicting evidence. A broad search strategy was used to capture 16,539 abstracts on the Pubmed database. Abstracts were screened to include only study types containing relevant information. 131 studies of interest were identified. Only 1 primate study made a numeric estimate for the half-life ofmore » inorganic mercury (227–540 days). Eighteen human mercury poisoning cases were followed up long term including autopsy. Brain inorganic mercury concentrations at death were consistent with a half-life of several years or longer. 5 radionucleotide studies were found, one of which estimated head half-life (21 days). This estimate has sometimes been misinterpreted to be equivalent to brain half-life—which ignores several confounding factors including limited radioactive half-life and radioactive decay from surrounding tissues including circulating blood. No autopsy cohort study estimated a half-life for inorganic mercury, although some noted bioaccumulation of brain mercury with age. Modelling studies provided some extreme estimates (69 days vs 22 years). Estimates from modelling studies appear sensitive to model assumptions, however predications based on a long half-life (27.4 years) are consistent with autopsy findings. In summary, shorter estimates of half-life are not supported by evidence from animal studies, human case studies, or modelling studies based on appropriate assumptions. Evidence from such studies point to a half-life of inorganic mercury in human brains of several years to several decades. This finding carries important implications for pharmcokinetic modelling of mercury and potentially for the regulatory toxicology of mercury.« less

Selection of independent components based on cortical mapping of electromagnetic activity

NASA Astrophysics Data System (ADS)

Chan, Hui-Ling; Chen, Yong-Sheng; Chen, Li-Fen

2012-10-01

Independent component analysis (ICA) has been widely used to attenuate interference caused by noise components from the electromagnetic recordings of brain activity. However, the scalp topographies and associated temporal waveforms provided by ICA may be insufficient to distinguish functional components from artifactual ones. In this work, we proposed two component selection methods, both of which first estimate the cortical distribution of the brain activity for each component, and then determine the functional components based on the parcellation of brain activity mapped onto the cortical surface. Among all independent components, the first method can identify the dominant components, which have strong activity in the selected dominant brain regions, whereas the second method can identify those inter-regional associating components, which have similar component spectra between a pair of regions. For a targeted region, its component spectrum enumerates the amplitudes of its parceled brain activity across all components. The selected functional components can be remixed to reconstruct the focused electromagnetic signals for further analysis, such as source estimation. Moreover, the inter-regional associating components can be used to estimate the functional brain network. The accuracy of the cortical activation estimation was evaluated on the data from simulation studies, whereas the usefulness and feasibility of the component selection methods were demonstrated on the magnetoencephalography data recorded from a gender discrimination study.

Waking up too early - the consequences of preterm birth on sleep development.

PubMed

Bennet, Laura; Walker, David W; Horne, Rosemary S C

2018-04-24

Good quality sleep of sufficient duration is vital for optimal physiological function and our health. Sleep deprivation is associated with impaired neurocognitive function and emotional control, and increases the risk for cardiometabolic diseases, obesity and cancer. Sleep develops during fetal life with the emergence of a recognisable pattern of sleep states in the preterm fetus associated with the development, maturation, and connectivity within neural networks in the brain. Despite the physiological importance of sleep, surprisingly little is known about how sleep develops in individuals born preterm. Globally, an estimated 15 million babies are born preterm (<37 weeks gestation), and these babies are at significant risk of neural injury and impaired brain development. This review discusses how sleep develops during fetal and neonatal life, how preterm birth impacts on sleep development to adulthood, and the factors which may contribute to impaired brain and sleep development, leading to altered neurocognitive, behavioural and motor capabilities in the infant and child. Going forward, the challenge is to identify specific risk factors for impaired sleep development in preterm babies to allow for the design of interventions that will improve the quality and quantity of sleep throughout life. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

The inverse problem of brain energetics: ketone bodies as alternative substrates

NASA Astrophysics Data System (ADS)

Calvetti, D.; Occhipinti, R.; Somersalo, E.

2008-07-01

Little is known about brain energy metabolism under ketosis, although there is evidence that ketone bodies have a neuroprotective role in several neurological disorders. We investigate the inverse problem of estimating reaction fluxes and transport rates in the different cellular compartments of the brain, when the data amounts to a few measured arterial venous concentration differences. By using a recently developed methodology to perform Bayesian Flux Balance Analysis and a new five compartment model of the astrocyte-glutamatergic neuron cellular complex, we are able to identify the preferred biochemical pathways during shortage of glucose and in the presence of ketone bodies in the arterial blood. The analysis is performed in a minimally biased way, therefore revealing the potential of this methodology for hypothesis testing.

Long-term exposure to ambient air pollution and incidence of brain tumours: The Danish Nurse Cohort.

PubMed

Jørgensen, Jeanette Therming; Johansen, Martin Søes; Ravnskjær, Line; Andersen, Klaus Kaae; Bräuner, Elvira Vaclavik; Loft, Steffen; Ketzel, Matthias; Becker, Thomas; Brandt, Jørgen; Hertel, Ole; Andersen, Zorana Jovanovic

2016-07-01

Air pollution has been considered a potent environmental risk factor for neuropathology through neuroinflammation and oxidative stress, which might also cause brain tumour formation. However, epidemiological evidence on the association between air pollution and brain tumours in humans is sparse, with no data on exposure to particles. In this study we aim to examine associations between long-term exposure to ambient air pollution and risk for development of brain tumours. We used the Danish Nurse Cohort with 28,731 female nurses (age≥44years) recruited in 1993 or 1999 when self-reported information on lifestyle was collected. We obtained data on the incidence of brain tumours until 2013 from the Danish Cancer Register, and estimated annual mean concentrations of particulate matter with diameter<2.5μm (PM2.5), particulate matter with diameter<10μm (PM10), nitrogen oxides (NOx) and nitrogen dioxide (NO2) at the residence since 1990 using an atmospheric integrated chemistry-transport models system, and examined the association between the 3-year running mean of pollutants and brain tumour incidence using time-varying Cox regression, separately for total brain tumours, and for tumour subtypes by location (brain or meninges), and by malignancy (malignant or benign), and estimated hazard ratios and 95% confidence intervals per increase in interquartile range of exposure. Of 25,143 tumour-free nurses at recruitment, 121 developed brain cancer during 15.7 years of follow-up. We found a weak positive association between total brain tumours and PM2.5 (1.06; 0.80-1.40 per 3.37μg/m(3)), NO2 (1.09; 0.91-1.29) per 7.5μg/m(3), and NOx (1.02; 0.93-1.12 per 10.22μg/m(3)), and none with PM10 (0.93; 0.70-1.23 per 3.31μg/m(3)). Associations with PM2.5 and NO2 were stronger for tumours located in meninges than in brain, and for benign than for malignant tumours. Finally, association of total brain tumours with PM2.5 was modified by BMI, and was statistically significantly enhanced in obese women (2.03; 1.35-3.05). We found weak evidence for association between risk of brain tumours and long-term exposure to air pollution in women older than 44 years. However, we present novel results that obese women may be susceptible, as well as a positive tendency towards elevated risk for meninges and benign tumours, which require further investigation. Copyright © 2016 Elsevier B.V. All rights reserved.

Childhood adversity is linked to differential brain volumes in adolescents with alcohol use disorder: a voxel-based morphometry study.

PubMed

Brooks, Samantha J; Dalvie, Shareefa; Cuzen, Natalie L; Cardenas, Valerie; Fein, George; Stein, Dan J

2014-06-01

Previous neuroimaging studies link both alcohol use disorder (AUD) and early adversity to neurobiological differences in the adult brain. However, the association between AUD and childhood adversity and effects on the developing adolescent brain are less clear, due in part to the confound of psychiatric comorbidity. Here we examine early life adversity and its association with brain volume in a unique sample of 116 South African adolescents (aged 12-16) with AUD but without psychiatric comorbidity. Participants were 58 adolescents with DSM-IV alcohol dependence and with no other psychiatric comorbidities, and 58 age-, gender- and protocol-matched light/non-drinking controls (HC). Assessments included the Childhood Trauma Questionnaire (CTQ). MR images were acquired on a 3T Siemens Magnetom Allegra scanner. Volumes of global and regional structures were estimated using SPM8 Voxel Based Morphometry (VBM), with analysis of covariance (ANCOVA) and regression analyses. In whole brain ANCOVA analyses, a main effect of group when examining the AUD effect after covarying out CTQ was observed on brain volume in bilateral superior temporal gyrus. Subsequent regression analyses to examine how childhood trauma scores are linked to brain volumes in the total cohort revealed a negative correlation in the left hippocampus and right precentral gyrus. Furthermore, bilateral (but most significantly left) hippocampal volume was negatively associated with sub-scores on the CTQ in the total cohort. These findings support our view that some alterations found in brain volumes in studies of adolescent AUD may reflect the impact of confounding factors such as psychiatric comorbidity rather than the effects of alcohol per se. In particular, early life adversity may influence the developing adolescent brain in specific brain regions, such as the hippocampus.

Hyperspectral imaging solutions for brain tissue metabolic and hemodynamic monitoring: past, current and future developments

NASA Astrophysics Data System (ADS)

Giannoni, Luca; Lange, Frédéric; Tachtsidis, Ilias

2018-04-01

Hyperspectral imaging (HSI) technologies have been used extensively in medical research, targeting various biological phenomena and multiple tissue types. Their high spectral resolution over a wide range of wavelengths enables acquisition of spatial information corresponding to different light-interacting biological compounds. This review focuses on the application of HSI to monitor brain tissue metabolism and hemodynamics in life sciences. Different approaches involving HSI have been investigated to assess and quantify cerebral activity, mainly focusing on: (1) mapping tissue oxygen delivery through measurement of changes in oxygenated (HbO2) and deoxygenated (HHb) hemoglobin; and (2) the assessment of the cerebral metabolic rate of oxygen (CMRO2) to estimate oxygen consumption by brain tissue. Finally, we introduce future perspectives of HSI of brain metabolism, including its potential use for imaging optical signals from molecules directly involved in cellular energy production. HSI solutions can provide remarkable insight in understanding cerebral tissue metabolism and oxygenation, aiding investigation on brain tissue physiological processes.

Bivariate Heritability of Total and Regional Brain Volumes: the Framingham Study

PubMed Central

DeStefano, Anita L.; Seshadri, Sudha; Beiser, Alexa; Atwood, Larry D.; Massaro, Joe M.; Au, Rhoda; Wolf, Philip A.; DeCarli, Charles

2009-01-01

Heritability and genetic and environmental correlations of total and regional brain volumes were estimated from a large, generally healthy, community-based sample, to determine if there are common elements to the genetic influence of brain volumes and white matter hyperintensity volume. There were 1538 Framingham Heart Study participants with brain volume measures from quantitative magnetic resonance imaging (MRI) who were free of stroke and other neurological disorders that might influence brain volumes and who were members of families with at least two Framingham Heart Study participants. Heritability was estimated using variance component methodology and adjusting for the components of the Framingham stroke risk profile. Genetic and environmental correlations between traits were obtained from bivariate analysis. Heritability estimates ranging from 0.46 to 0.60, were observed for total brain, white matter hyperintensity, hippocampal, temporal lobe, and lateral ventricular volumes. Moderate, yet significant, heritability was observed for the other measures. Bivariate analyses demonstrated that relationships between brain volume measures, except for white matter hyperintensity, reflected both moderate to strong shared genetic and shared environmental influences. This study confirms strong genetic effects on brain and white matter hyperintensity volumes. These data extend current knowledge by showing that these two different types of MRI measures do not share underlying genetic or environmental influences. PMID:19812462

A method for monitoring of oxygen saturation changes in brain tissue using diffuse reflectance spectroscopy.

PubMed

Rejmstad, Peter; Johansson, Johannes D; Haj-Hosseini, Neda; Wårdell, Karin

2017-03-01

Continuous measurement of local brain oxygen saturation (SO 2 ) can be used to monitor the status of brain trauma patients in the neurocritical care unit. Currently, micro-oxygen-electrodes are considered as the "gold standard" in measuring cerebral oxygen pressure (pO 2 ), which is closely related to SO 2 through the oxygen dissociation curve (ODC) of hemoglobin, but with the drawback of slow in response time. The present study suggests estimation of SO 2 in brain tissue using diffuse reflectance spectroscopy (DRS) for finding an analytical relation between measured spectra and the SO 2 for different blood concentrations. The P 3 diffusion approximation is used to generate a set of spectra simulating brain tissue for various levels of blood concentrations in order to estimate SO 2 . The algorithm is evaluated on optical phantoms mimicking white brain matter (blood volume of 0.5-2%) where pO 2 and temperature is controlled and on clinical data collected during brain surgery. The suggested method is capable of estimating the blood fraction and oxygen saturation changes from the spectroscopic signal and the hemoglobin absorption profile. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Car Accident Reconstruction and Head Injury Correlation

NASA Astrophysics Data System (ADS)

Chawla, A.; Grover, V.; Mukherjee, S.; Hassan, A. M.

2013-04-01

Estimation of brain damage remains an elusive issue and controlled tests leading to brain damage cannot be carried out on volunteers. This study reconstructs real-world car accidents to estimate the kinematics of the head impact. This data is to be used to estimate the head injury measures through computer simulations and then correlate reported skull as well as brain damage to impact measures; whence validating the head FE model (Willinger, IJCrash 8:605-617, 2003). In this study, two crash cases were reconstructed. Injury correlation was successful in one of these cases in that the injuries to the brain of one of the car drivers could be correlated in terms of type, location and severity when compared with the tolerance limits of relevant injury parameters (Willinger, IJCrash 8:605-617, 2003).

Predicting the Risk of Developing New Cerebral Lesions After Stereotactic Radiosurgery or Fractionated Stereotactic Radiotherapy for Brain Metastases from Renal Cell Carcinoma.

PubMed

Rades, Dirk; Dziggel, Liesa; Blanck, Oliver; Gebauer, Niklas; Bartscht, Tobias; Schild, Steven E

2018-05-01

To create an instrument for estimating the risk of new brain metastases after stereotactic radiosurgery (SRS) or fractionated stereotactic radiotherapy (FSRT) alone in patients with renal cell carcinoma (RCC). In 45 patients with 1-3 brain metastases, seven characteristics were analyzed for association with freedom from new brain metastases (age, gender, performance score, number and sites of brain metastases, extra-cerebral metastasis, interval from RCC diagnosis to SRS/FSRT). Lower risk of subsequent brain lesions after RT was associated with single metastasis (p=0.043) and supratentorial involvement only (p=0.018). Scoring points were: One metastasis=1, 2-3 metastases=0, supratentorial alone=1, infratentorial with/without supratentorial=0. Scores of 0, 1 and 2 points were associated with 6-month rates of freedom from subsequent brain lesions of 25%, 74% and 92% (p=0.008). After combining groups with 1 and 2 points, 6-month rates were 25% for those with 0 points and 83% for those with 1-2 points (p=0.002). Two groups were identified with different risks of new brain metastases after SRS or FSRT alone. High-risk patients may benefit from additional whole-brain irradiation. Copyright© 2018, International Institute of Anticancer Research (Dr. George J. Delinasios), All rights reserved.

Pattern of brain injury and depressed heart rate variability in newborns with hypoxic ischemic encephalopathy.

PubMed

Metzler, Marina; Govindan, Rathinaswamy; Al-Shargabi, Tareq; Vezina, Gilbert; Andescavage, Nickie; Wang, Yunfei; du Plessis, Adre; Massaro, An N

2017-09-01

BackgroundDecreased heart rate variability (HRV) is a measure of autonomic dysfunction and brain injury in newborns with hypoxic ischemic encephalopathy (HIE). This study aimed to characterize the relationship between HRV and brain injury pattern using magnetic resonance imaging (MRI) in newborns with HIE undergoing therapeutic hypothermia.MethodsHRV metrics were quantified in the time domain (α S , α L , and root mean square at short (RMS S ) and long (RMS L ) timescales) and frequency domain (relative low-(LF) and high-frequency (HF) power) over 24-27 h of life. The brain injury pattern shown by MRI was classified as no injury, pure cortical/white matter injury, mixed watershed/mild basal ganglia injury, predominant basal ganglia or global injury, and death. HRV metrics were compared across brain injury pattern groups using a random-effects mixed model.ResultsData from 74 infants were analyzed. Brain injury pattern was significantly associated with the degree of HRV suppression. Specifically, negative associations were observed between the pattern of brain injury and RMS S (estimate -0.224, SE 0.082, P=0.006), RMS L (estimate -0.189, SE 0.082, P=0.021), and LF power (estimate -0.044, SE 0.016, P=0.006).ConclusionDegree of HRV depression is related to the pattern of brain injury. HRV monitoring may provide insights into the pattern of brain injury at the bedside.

PATTERN OF BRAIN INJURY AND DEPRESSED HEART RATE VARIABILITY IN NEWBORNS WITH HYPOXIC ISCHEMIC ENCEPHALOPATHY

PubMed Central

Metzler, Marina; Govindan, Rathinaswamy; Al-Shargabi, Tareq; Vezina, Gilbert; Andescavage, Nickie; Wang, Yunfei; du Plessis, Adre; Massaro, An N

2017-01-01

Background Decreased heart rate variability (HRV) is a measure of autonomic dysfunction and brain injury in newborns with hypoxic ischemic encephalopathy (HIE). This study aimed to characterize the relationship between HRV and brain injury pattern by MRI in newborns with HIE undergoing therapeutic hypothermia. Methods HRV metrics were quantified in the time domain (αS, αL, and root mean square at short [RMSS] and long [RMSL] time scales) and frequency domain (relative low-[LF] and high-frequency [HF] power) during the time period 24–27 hours of life. Brain injury pattern by MRI was classified as no injury, pure cortical/white matter injury, mixed watershed/mild basal nuclei injury, predominant basal nuclei or global injury, and died. HRV metrics were compared across brain injury pattern groups using a random effects mixed model. Results Data from 74 infants were analyzed. Brain injury pattern was significantly associated with degree of HRV suppression. Specifically, negative associations were observed between pattern of brain injury and RMSS (estimate −0.224, SE 0.082, p=0.006), RMSL (estimate −0.189, SE 0.082, p=0.021), and LF power (estimate −0.044, SE 0.016, p=0.006). Conclusion Degree of HRV depression is related to pattern of brain injury. HRV monitoring may provide insights into pattern of brain injury at the bedside. PMID:28376079

Nicotine increases brain functional network efficiency.

PubMed

Wylie, Korey P; Rojas, Donald C; Tanabe, Jody; Martin, Laura F; Tregellas, Jason R

2012-10-15

Despite the use of cholinergic therapies in Alzheimer's disease and the development of cholinergic strategies for schizophrenia, relatively little is known about how the system modulates the connectivity and structure of large-scale brain networks. To better understand how nicotinic cholinergic systems alter these networks, this study examined the effects of nicotine on measures of whole-brain network communication efficiency. Resting state fMRI was acquired from fifteen healthy subjects before and after the application of nicotine or placebo transdermal patches in a single blind, crossover design. Data, which were previously examined for default network activity, were analyzed with network topology techniques to measure changes in the communication efficiency of whole-brain networks. Nicotine significantly increased local efficiency, a parameter that estimates the network's tolerance to local errors in communication. Nicotine also significantly enhanced the regional efficiency of limbic and paralimbic areas of the brain, areas which are especially altered in diseases such as Alzheimer's disease and schizophrenia. These changes in network topology may be one mechanism by which cholinergic therapies improve brain function. Published by Elsevier Inc.

Nicotine Increases Brain Functional Network Efficiency

PubMed Central

Wylie, Korey P.; Rojas, Donald C.; Tanabe, Jody; Martin, Laura F.; Tregellas, Jason R.

2012-01-01

Despite the use of cholinergic therapies in Alzheimer’s disease and the development of cholinergic strategies for schizophrenia, relatively little is known about how the system modulates the connectivity and structure of large-scale brain networks. To better understand how nicotinic cholinergic systems alter these networks, this study examined the effects of nicotine on measures of whole-brain network communication efficiency. Resting-state fMRI was acquired from fifteen healthy subjects before and after the application of nicotine or placebo transdermal patches in a single blind, crossover design. Data, which were previously examined for default network activity, were analyzed with network topology techniques to measure changes in the communication efficiency of whole-brain networks. Nicotine significantly increased local efficiency, a parameter that estimates the network’s tolerance to local errors in communication. Nicotine also significantly enhanced the regional efficiency of limbic and paralimbic areas of the brain, areas which are especially altered in diseases such as Alzheimer’s disease and schizophrenia. These changes in network topology may be one mechanism by which cholinergic therapies improve brain function. PMID:22796985

Joint penalized-likelihood reconstruction of time-activity curves and regions-of-interest from projection data in brain PET

NASA Astrophysics Data System (ADS)

Krestyannikov, E.; Tohka, J.; Ruotsalainen, U.

2008-06-01

This paper presents a novel statistical approach for joint estimation of regions-of-interest (ROIs) and the corresponding time-activity curves (TACs) from dynamic positron emission tomography (PET) brain projection data. It is based on optimizing the joint objective function that consists of a data log-likelihood term and two penalty terms reflecting the available a priori information about the human brain anatomy. The developed local optimization strategy iteratively updates both the ROI and TAC parameters and is guaranteed to monotonically increase the objective function. The quantitative evaluation of the algorithm is performed with numerically and Monte Carlo-simulated dynamic PET brain data of the 11C-Raclopride and 18F-FDG tracers. The results demonstrate that the method outperforms the existing sequential ROI quantification approaches in terms of accuracy, and can noticeably reduce the errors in TACs arising due to the finite spatial resolution and ROI delineation.

Complete prevalence of malignant primary brain tumors registry data in the United States compared with other common cancers, 2010

PubMed Central

Zhang, Adah S.; Ostrom, Quinn T.; Kruchko, Carol; Rogers, Lisa; Peereboom, David M.

2017-01-01

Abstract Background. Complete prevalence proportions illustrate the burden of disease in a population. This study estimates the 2010 complete prevalence of malignant primary brain tumors overall and by Central Brain Tumor Registry of the United States (CBTRUS) histology groups, and compares the brain tumor prevalence estimates to the complete prevalence of other common cancers as determined by the Surveillance, Epidemiology, and End Results Program (SEER) by age at prevalence (2010): children (0–14 y), adolescent and young adult (AYA) (15–39 y), and adult (40+ y). Methods. Complete prevalence proportions were estimated using a novel regression method extended from the Completeness Index Method, which combines survival and incidence data from multiple sources. In this study, two datasets, CBTRUS and SEER, were used to calculate complete prevalence estimates of interest. Results. Complete prevalence for malignant primary brain tumors was 47.59/100000 population (22.31, 48.49, and 57.75/100000 for child, AYA, and adult populations). The most prevalent cancers by age were childhood leukemia (36.65/100000), AYA melanoma of the skin (66.21/100000), and adult female breast (1949.00/100000). The most prevalent CBTRUS histologies in children and AYA were pilocytic astrocytoma (6.82/100000, 5.92/100000), and glioblastoma (12.76/100000) in adults. Conclusions. The relative impact of malignant primary brain tumors is higher among children than any other age group; it emerges as the second most prevalent cancer among children. Complete prevalence estimates for primary malignant brain tumors fills a gap in overall cancer knowledge, which provides critical information toward public health and health care planning, including treatment, decision making, funding, and advocacy programs. PMID:28039365

A Novel Application for the Cavalieri Principle: A Stereological and Methodological Study

PubMed Central

Altunkaynak, Berrin Zuhal; Altunkaynak, Eyup; Unal, Deniz; Unal, Bunyamin

2009-01-01

Objective The Cavalieri principle was applied to consecutive pathology sections that were photographed at the same magnification and used to estimate tissue volumes via superimposing a point counting grid on these images. The goal of this study was to perform the Cavalieri method quickly and practically. Materials and Methods In this study, 10 adult female Sprague Dawley rats were used. Brain tissue was removed and sampled both systematically and randomly. Brain volumes were estimated using two different methods. First, all brain slices were scanned with an HP ScanJet 3400C scanner, and their images were shown on a PC monitor. Brain volume was then calculated based on these images. Second, all brain slices were photographed in 10× magnification with a microscope camera, and brain volumes were estimated based on these micrographs. Results There was no statistically significant difference between the volume measurements of the two techniques (P>0.05; Paired Samples t Test). Conclusion This study demonstrates that personal computer scanning of serial tissue sections allows for easy and reliable volume determination based on the Cavalieri method. PMID:25610077

A novel application for the cavalieri principle: a stereological and methodological study.

PubMed

Altunkaynak, Berrin Zuhal; Altunkaynak, Eyup; Unal, Deniz; Unal, Bunyamin

2009-08-01

The Cavalieri principle was applied to consecutive pathology sections that were photographed at the same magnification and used to estimate tissue volumes via superimposing a point counting grid on these images. The goal of this study was to perform the Cavalieri method quickly and practically. In this study, 10 adult female Sprague Dawley rats were used. Brain tissue was removed and sampled both systematically and randomly. Brain volumes were estimated using two different methods. First, all brain slices were scanned with an HP ScanJet 3400C scanner, and their images were shown on a PC monitor. Brain volume was then calculated based on these images. Second, all brain slices were photographed in 10× magnification with a microscope camera, and brain volumes were estimated based on these micrographs. There was no statistically significant difference between the volume measurements of the two techniques (P>0.05; Paired Samples t Test). This study demonstrates that personal computer scanning of serial tissue sections allows for easy and reliable volume determination based on the Cavalieri method.

Estimating direction in brain-behavior interactions: Proactive and reactive brain states in driving.

PubMed

Garcia, Javier O; Brooks, Justin; Kerick, Scott; Johnson, Tony; Mullen, Tim R; Vettel, Jean M

2017-04-15

Conventional neuroimaging analyses have ascribed function to particular brain regions, exploiting the power of the subtraction technique in fMRI and event-related potential analyses in EEG. Moving beyond this convention, many researchers have begun exploring network-based neurodynamics and coordination between brain regions as a function of behavioral parameters or environmental statistics; however, most approaches average evoked activity across the experimental session to study task-dependent networks. Here, we examined on-going oscillatory activity as measured with EEG and use a methodology to estimate directionality in brain-behavior interactions. After source reconstruction, activity within specific frequency bands (delta: 2-3Hz; theta: 4-7Hz; alpha: 8-12Hz; beta: 13-25Hz) in a priori regions of interest was linked to continuous behavioral measurements, and we used a predictive filtering scheme to estimate the asymmetry between brain-to-behavior and behavior-to-brain prediction using a variant of Granger causality. We applied this approach to a simulated driving task and examined directed relationships between brain activity and continuous driving performance (steering behavior or vehicle heading error). Our results indicated that two neuro-behavioral states may be explored with this methodology: a Proactive brain state that actively plans the response to the sensory information and is characterized by delta-beta activity, and a Reactive brain state that processes incoming information and reacts to environmental statistics primarily within the alpha band. Published by Elsevier Inc.

Fiber estimation and tractography in diffusion MRI: Development of simulated brain images and comparison of multi-fiber analysis methods at clinical b-values

PubMed Central

Wilkins, Bryce; Lee, Namgyun; Gajawelli, Niharika; Law, Meng; Leporé, Natasha

2015-01-01

Advances in diffusion-weighted magnetic resonance imaging (DW-MRI) have led to many alternative diffusion sampling strategies and analysis methodologies. A common objective among methods is estimation of white matter fiber orientations within each voxel, as doing so permits in-vivo fiber-tracking and the ability to study brain connectivity and networks. Knowledge of how DW-MRI sampling schemes affect fiber estimation accuracy, and consequently tractography and the ability to recover complex white-matter pathways, as well as differences between results due to choice of analysis method and which method(s) perform optimally for specific data sets, all remain important problems, especially as tractography-based studies become common. In this work we begin to address these concerns by developing sets of simulated diffusion-weighted brain images which we then use to quantitatively evaluate the performance of six DW-MRI analysis methods in terms of estimated fiber orientation accuracy, false-positive (spurious) and false-negative (missing) fiber rates, and fiber-tracking. The analysis methods studied are: 1) a two-compartment “ball and stick” model (BSM) (Behrens et al., 2003); 2) a non-negativity constrained spherical deconvolution (CSD) approach (Tournier et al., 2007); 3) analytical q-ball imaging (QBI) (Descoteaux et al., 2007); 4) q-ball imaging with Funk-Radon and Cosine Transform (FRACT) (Haldar and Leahy, 2013); 5) q-ball imaging within constant solid angle (CSA) (Aganj et al., 2010); and 6) a generalized Fourier transform approach known as generalized q-sampling imaging (GQI) (Yeh et al., 2010). We investigate these methods using 20, 30, 40, 60, 90 and 120 evenly distributed q-space samples of a single shell, and focus on a signal-to-noise ratio (SNR = 18) and diffusion-weighting (b = 1000 s/mm2) common to clinical studies. We found the BSM and CSD methods consistently yielded the least fiber orientation error and simultaneously greatest detection rate of fibers. Fiber detection rate was found to be the most distinguishing characteristic between the methods, and a significant factor for complete recovery of tractography through complex white-matter pathways. For example, while all methods recovered similar tractography of prominent white matter pathways of limited fiber crossing, CSD (which had the highest fiber detection rate, especially for voxels containing three fibers) recovered the greatest number of fibers and largest fraction of correct tractography for a complex three-fiber crossing region. The synthetic data sets, ground-truth, and tools for quantitative evaluation are publically available on the NITRC website as the project “Simulated DW-MRI Brain Data Sets for Quantitative Evaluation of Estimated Fiber Orientations” at http://www.nitrc.org/projects/sim_dwi_brain PMID:25555998

Fiber estimation and tractography in diffusion MRI: development of simulated brain images and comparison of multi-fiber analysis methods at clinical b-values.

PubMed

Wilkins, Bryce; Lee, Namgyun; Gajawelli, Niharika; Law, Meng; Leporé, Natasha

2015-04-01

Advances in diffusion-weighted magnetic resonance imaging (DW-MRI) have led to many alternative diffusion sampling strategies and analysis methodologies. A common objective among methods is estimation of white matter fiber orientations within each voxel, as doing so permits in-vivo fiber-tracking and the ability to study brain connectivity and networks. Knowledge of how DW-MRI sampling schemes affect fiber estimation accuracy, tractography and the ability to recover complex white-matter pathways, differences between results due to choice of analysis method, and which method(s) perform optimally for specific data sets, all remain important problems, especially as tractography-based studies become common. In this work, we begin to address these concerns by developing sets of simulated diffusion-weighted brain images which we then use to quantitatively evaluate the performance of six DW-MRI analysis methods in terms of estimated fiber orientation accuracy, false-positive (spurious) and false-negative (missing) fiber rates, and fiber-tracking. The analysis methods studied are: 1) a two-compartment "ball and stick" model (BSM) (Behrens et al., 2003); 2) a non-negativity constrained spherical deconvolution (CSD) approach (Tournier et al., 2007); 3) analytical q-ball imaging (QBI) (Descoteaux et al., 2007); 4) q-ball imaging with Funk-Radon and Cosine Transform (FRACT) (Haldar and Leahy, 2013); 5) q-ball imaging within constant solid angle (CSA) (Aganj et al., 2010); and 6) a generalized Fourier transform approach known as generalized q-sampling imaging (GQI) (Yeh et al., 2010). We investigate these methods using 20, 30, 40, 60, 90 and 120 evenly distributed q-space samples of a single shell, and focus on a signal-to-noise ratio (SNR = 18) and diffusion-weighting (b = 1000 s/mm(2)) common to clinical studies. We found that the BSM and CSD methods consistently yielded the least fiber orientation error and simultaneously greatest detection rate of fibers. Fiber detection rate was found to be the most distinguishing characteristic between the methods, and a significant factor for complete recovery of tractography through complex white-matter pathways. For example, while all methods recovered similar tractography of prominent white matter pathways of limited fiber crossing, CSD (which had the highest fiber detection rate, especially for voxels containing three fibers) recovered the greatest number of fibers and largest fraction of correct tractography for complex three-fiber crossing regions. The synthetic data sets, ground-truth, and tools for quantitative evaluation are publically available on the NITRC website as the project "Simulated DW-MRI Brain Data Sets for Quantitative Evaluation of Estimated Fiber Orientations" at http://www.nitrc.org/projects/sim_dwi_brain. Copyright © 2014 Elsevier Inc. All rights reserved.

Background field removal technique based on non-regularized variable kernels sophisticated harmonic artifact reduction for phase data for quantitative susceptibility mapping.

PubMed

Kan, Hirohito; Arai, Nobuyuki; Takizawa, Masahiro; Omori, Kazuyoshi; Kasai, Harumasa; Kunitomo, Hiroshi; Hirose, Yasujiro; Shibamoto, Yuta

2018-06-11

We developed a non-regularized, variable kernel, sophisticated harmonic artifact reduction for phase data (NR-VSHARP) method to accurately estimate local tissue fields without regularization for quantitative susceptibility mapping (QSM). We then used a digital brain phantom to evaluate the accuracy of the NR-VSHARP method, and compared it with the VSHARP and iterative spherical mean value (iSMV) methods through in vivo human brain experiments. Our proposed NR-VSHARP method, which uses variable spherical mean value (SMV) kernels, minimizes L2 norms only within the volume of interest to reduce phase errors and save cortical information without regularization. In a numerical phantom study, relative local field and susceptibility map errors were determined using NR-VSHARP, VSHARP, and iSMV. Additionally, various background field elimination methods were used to image the human brain. In a numerical phantom study, the use of NR-VSHARP considerably reduced the relative local field and susceptibility map errors throughout a digital whole brain phantom, compared with VSHARP and iSMV. In the in vivo experiment, the NR-VSHARP-estimated local field could sufficiently achieve minimal boundary losses and phase error suppression throughout the brain. Moreover, the susceptibility map generated using NR-VSHARP minimized the occurrence of streaking artifacts caused by insufficient background field removal. Our proposed NR-VSHARP method yields minimal boundary losses and highly precise phase data. Our results suggest that this technique may facilitate high-quality QSM. Copyright © 2017. Published by Elsevier Inc.

Speech perception in autism spectrum disorder: An activation likelihood estimation meta-analysis.

PubMed

Tryfon, Ana; Foster, Nicholas E V; Sharda, Megha; Hyde, Krista L

2018-02-15

Autism spectrum disorder (ASD) is often characterized by atypical language profiles and auditory and speech processing. These can contribute to aberrant language and social communication skills in ASD. The study of the neural basis of speech perception in ASD can serve as a potential neurobiological marker of ASD early on, but mixed results across studies renders it difficult to find a reliable neural characterization of speech processing in ASD. To this aim, the present study examined the functional neural basis of speech perception in ASD versus typical development (TD) using an activation likelihood estimation (ALE) meta-analysis of 18 qualifying studies. The present study included separate analyses for TD and ASD, which allowed us to examine patterns of within-group brain activation as well as both common and distinct patterns of brain activation across the ASD and TD groups. Overall, ASD and TD showed mostly common brain activation of speech processing in bilateral superior temporal gyrus (STG) and left inferior frontal gyrus (IFG). However, the results revealed trends for some distinct activation in the TD group showing additional activation in higher-order brain areas including left superior frontal gyrus (SFG), left medial frontal gyrus (MFG), and right IFG. These results provide a more reliable neural characterization of speech processing in ASD relative to previous single neuroimaging studies and motivate future work to investigate how these brain signatures relate to behavioral measures of speech processing in ASD. Copyright © 2017 Elsevier B.V. All rights reserved.

Determination of surgical variables for a brain shift correction pipeline using an Android application

NASA Astrophysics Data System (ADS)

Vijayan, Rohan; Conley, Rebekah H.; Thompson, Reid C.; Clements, Logan W.; Miga, Michael I.

2016-03-01

Brain shift describes the deformation that the brain undergoes from mechanical and physiological effects typically during a neurosurgical or neurointerventional procedure. With respect to image guidance techniques, brain shift has been shown to compromise the fidelity of these approaches. In recent work, a computational pipeline has been developed to predict "brain shift" based on preoperatively determined surgical variables (such as head orientation), and subsequently correct preoperative images to more closely match the intraoperative state of the brain. However, a clinical workflow difficulty in the execution of this pipeline has been acquiring the surgical variables by the neurosurgeon prior to surgery. In order to simplify and expedite this process, an Android, Java-based application designed for tablets was developed to provide the neurosurgeon with the ability to orient 3D computer graphic models of the patient's head, determine expected location and size of the craniotomy, and provide the trajectory into the tumor. These variables are exported for use as inputs for the biomechanical models of the preoperative computing phase for the brain shift correction pipeline. The accuracy of the application's exported data was determined by comparing it to data acquired from the physical execution of the surgeon's plan on a phantom head. Results indicated good overlap of craniotomy predictions, craniotomy centroid locations, and estimates of patient's head orientation with respect to gravity. However, improvements in the app interface and mock surgical setup are needed to minimize error.

Complete prevalence of malignant primary brain tumors registry data in the United States compared with other common cancers, 2010

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, Adah S.; Ostrom, Quinn T.; Kruchko, Carol

Complete prevalence proportions illustrate the burden of disease in a population. Here, this study estimates the 2010 complete prevalence of malignant primary brain tumors overall and by Central Brain Tumor Registry of the United States (CBTRUS) histology groups, and compares the brain tumor prevalence estimates to the complete prevalence of other common cancers as determined by the Surveillance, Epidemiology, and End Results Program (SEER) by age at prevalence (2010): children (0–14 y), adolescent and young adult (AYA) (15–39 y), and adult (40+ y).

Complete prevalence of malignant primary brain tumors registry data in the United States compared with other common cancers, 2010

DOE PAGES

Zhang, Adah S.; Ostrom, Quinn T.; Kruchko, Carol; ...

2016-12-29

Complete prevalence proportions illustrate the burden of disease in a population. Here, this study estimates the 2010 complete prevalence of malignant primary brain tumors overall and by Central Brain Tumor Registry of the United States (CBTRUS) histology groups, and compares the brain tumor prevalence estimates to the complete prevalence of other common cancers as determined by the Surveillance, Epidemiology, and End Results Program (SEER) by age at prevalence (2010): children (0–14 y), adolescent and young adult (AYA) (15–39 y), and adult (40+ y).

Dynamic Granger-Geweke causality modeling with application to interictal spike propagation

PubMed Central

Lin, Fa-Hsuan; Hara, Keiko; Solo, Victor; Vangel, Mark; Belliveau, John W.; Stufflebeam, Steven M.; Hamalainen, Matti S.

2010-01-01

A persistent problem in developing plausible neurophysiological models of perception, cognition, and action is the difficulty of characterizing the interactions between different neural systems. Previous studies have approached this problem by estimating causal influences across brain areas activated during cognitive processing using Structural Equation Modeling and, more recently, with Granger-Geweke causality. While SEM is complicated by the need for a priori directional connectivity information, the temporal resolution of dynamic Granger-Geweke estimates is limited because the underlying autoregressive (AR) models assume stationarity over the period of analysis. We have developed a novel optimal method for obtaining data-driven directional causality estimates with high temporal resolution in both time and frequency domains. This is achieved by simultaneously optimizing the length of the analysis window and the chosen AR model order using the SURE criterion. Dynamic Granger-Geweke causality in time and frequency domains is subsequently calculated within a moving analysis window. We tested our algorithm by calculating the Granger-Geweke causality of epileptic spike propagation from the right frontal lobe to the left frontal lobe. The results quantitatively suggested the epileptic activity at the left frontal lobe was propagated from the right frontal lobe, in agreement with the clinical diagnosis. Our novel computational tool can be used to help elucidate complex directional interactions in the human brain. PMID:19378280

Patient dose estimation from CT scans at the Mexican National Neurology and Neurosurgery Institute

DOE Office of Scientific and Technical Information (OSTI.GOV)

Alva-Sánchez, Héctor, E-mail: halva@ciencias.unam.mx; Reynoso-Mejía, Alberto; Casares-Cruz, Katiuzka

In the radiology department of the Mexican National Institute of Neurology and Neurosurgery, a dedicated institute in Mexico City, on average 19.3 computed tomography (CT) examinations are performed daily on hospitalized patients for neurological disease diagnosis, control scans and follow-up imaging. The purpose of this work was to estimate the effective dose received by hospitalized patients who underwent a diagnostic CT scan using typical effective dose values for all CT types and to obtain the estimated effective dose distributions received by surgical and non-surgical patients. Effective patient doses were estimated from values per study type reported in the applications guidemore » provided by the scanner manufacturer. This retrospective study included all hospitalized patients who underwent a diagnostic CT scan between 1 January 2011 and 31 December 2012. A total of 8777 CT scans were performed in this two-year period. Simple brain scan was the CT type performed the most (74.3%) followed by contrasted brain scan (6.1%) and head angiotomography (5.7%). The average number of CT scans per patient was 2.83; the average effective dose per patient was 7.9 mSv; the mean estimated radiation dose was significantly higher for surgical (9.1 mSv) than non-surgical patients (6.0 mSv). Three percent of the patients had 10 or more brain CT scans and exceeded the organ radiation dose threshold set by the International Commission on Radiological Protection for deterministic effects of the eye-lens. Although radiation patient doses from CT scans were in general relatively low, 187 patients received a high effective dose (>20 mSv) and 3% might develop cataract from cumulative doses to the eye lens.« less

Patient dose estimation from CT scans at the Mexican National Neurology and Neurosurgery Institute

NASA Astrophysics Data System (ADS)

Alva-Sánchez, Héctor; Reynoso-Mejía, Alberto; Casares-Cruz, Katiuzka; Taboada-Barajas, Jesús

2014-11-01

In the radiology department of the Mexican National Institute of Neurology and Neurosurgery, a dedicated institute in Mexico City, on average 19.3 computed tomography (CT) examinations are performed daily on hospitalized patients for neurological disease diagnosis, control scans and follow-up imaging. The purpose of this work was to estimate the effective dose received by hospitalized patients who underwent a diagnostic CT scan using typical effective dose values for all CT types and to obtain the estimated effective dose distributions received by surgical and non-surgical patients. Effective patient doses were estimated from values per study type reported in the applications guide provided by the scanner manufacturer. This retrospective study included all hospitalized patients who underwent a diagnostic CT scan between 1 January 2011 and 31 December 2012. A total of 8777 CT scans were performed in this two-year period. Simple brain scan was the CT type performed the most (74.3%) followed by contrasted brain scan (6.1%) and head angiotomography (5.7%). The average number of CT scans per patient was 2.83; the average effective dose per patient was 7.9 mSv; the mean estimated radiation dose was significantly higher for surgical (9.1 mSv) than non-surgical patients (6.0 mSv). Three percent of the patients had 10 or more brain CT scans and exceeded the organ radiation dose threshold set by the International Commission on Radiological Protection for deterministic effects of the eye-lens. Although radiation patient doses from CT scans were in general relatively low, 187 patients received a high effective dose (>20 mSv) and 3% might develop cataract from cumulative doses to the eye lens.

Measurement of the Dynamic Shear Modulus of Mouse Brain Tissue In Vivo By Magnetic Resonance Elastography

PubMed Central

Atay, Stefan M.; Kroenke, Christopher D.; Sabet, Arash; Bayly, Philip V.

2008-01-01

In this study, the magnetic resonance elastography (MRE) technique was used to estimate the dynamic shear modulus of mouse brain tissue in vivo. The technique allows visualization and measurement of mechanical shear waves excited by lateral vibration of the skull. Quantitative measurements of displacement in three dimensions (3-D) during vibration at 1200 Hz were obtained by applying oscillatory magnetic field gradients at the same frequency during an MR imaging sequence. Contrast in the resulting phase images of the mouse brain is proportional to displacement. To obtain estimates of shear modulus, measured displacement fields were fitted to the shear wave equation. Validation of the procedure was performed on gel characterized by independent rheometry tests and on data from finite element simulations. Brain tissue is, in reality, viscoelastic and nonlinear. The current estimates of dynamic shear modulus are strictly relevant only to small oscillations at a specific frequency, but these estimates may be obtained at high frequencies (and thus high deformation rates), non-invasively throughout the brain. These data complement measurements of nonlinear viscoelastic properties obtained by others at slower rates, either ex vivo or invasively. PMID:18412500

Bayesian estimation inherent in a Mexican-hat-type neural network

NASA Astrophysics Data System (ADS)

Takiyama, Ken

2016-05-01

Brain functions, such as perception, motor control and learning, and decision making, have been explained based on a Bayesian framework, i.e., to decrease the effects of noise inherent in the human nervous system or external environment, our brain integrates sensory and a priori information in a Bayesian optimal manner. However, it remains unclear how Bayesian computations are implemented in the brain. Herein, I address this issue by analyzing a Mexican-hat-type neural network, which was used as a model of the visual cortex, motor cortex, and prefrontal cortex. I analytically demonstrate that the dynamics of an order parameter in the model corresponds exactly to a variational inference of a linear Gaussian state-space model, a Bayesian estimation, when the strength of recurrent synaptic connectivity is appropriately stronger than that of an external stimulus, a plausible condition in the brain. This exact correspondence can reveal the relationship between the parameters in the Bayesian estimation and those in the neural network, providing insight for understanding brain functions.

The roadmap for estimation of cell-type-specific neuronal activity from non-invasive measurements

PubMed Central

Uhlirova, Hana; Kılıç, Kıvılcım; Tian, Peifang; Sakadžić, Sava; Thunemann, Martin; Desjardins, Michèle; Saisan, Payam A.; Nizar, Krystal; Yaseen, Mohammad A.; Hagler, Donald J.; Vandenberghe, Matthieu; Djurovic, Srdjan; Andreassen, Ole A.; Silva, Gabriel A.; Masliah, Eliezer; Vinogradov, Sergei; Buxton, Richard B.; Einevoll, Gaute T.; Boas, David A.; Dale, Anders M.; Devor, Anna

2016-01-01

The computational properties of the human brain arise from an intricate interplay between billions of neurons connected in complex networks. However, our ability to study these networks in healthy human brain is limited by the necessity to use non-invasive technologies. This is in contrast to animal models where a rich, detailed view of cellular-level brain function with cell-type-specific molecular identity has become available due to recent advances in microscopic optical imaging and genetics. Thus, a central challenge facing neuroscience today is leveraging these mechanistic insights from animal studies to accurately draw physiological inferences from non-invasive signals in humans. On the essential path towards this goal is the development of a detailed ‘bottom-up’ forward model bridging neuronal activity at the level of cell-type-specific populations to non-invasive imaging signals. The general idea is that specific neuronal cell types have identifiable signatures in the way they drive changes in cerebral blood flow, cerebral metabolic rate of O2 (measurable with quantitative functional Magnetic Resonance Imaging), and electrical currents/potentials (measurable with magneto/electroencephalography). This forward model would then provide the ‘ground truth’ for the development of new tools for tackling the inverse problem—estimation of neuronal activity from multimodal non-invasive imaging data. This article is part of the themed issue ‘Interpreting BOLD: a dialogue between cognitive and cellular neuroscience’. PMID:27574309

SISSY: An efficient and automatic algorithm for the analysis of EEG sources based on structured sparsity.

PubMed

Becker, H; Albera, L; Comon, P; Nunes, J-C; Gribonval, R; Fleureau, J; Guillotel, P; Merlet, I

2017-08-15

Over the past decades, a multitude of different brain source imaging algorithms have been developed to identify the neural generators underlying the surface electroencephalography measurements. While most of these techniques focus on determining the source positions, only a small number of recently developed algorithms provides an indication of the spatial extent of the distributed sources. In a recent comparison of brain source imaging approaches, the VB-SCCD algorithm has been shown to be one of the most promising algorithms among these methods. However, this technique suffers from several problems: it leads to amplitude-biased source estimates, it has difficulties in separating close sources, and it has a high computational complexity due to its implementation using second order cone programming. To overcome these problems, we propose to include an additional regularization term that imposes sparsity in the original source domain and to solve the resulting optimization problem using the alternating direction method of multipliers. Furthermore, we show that the algorithm yields more robust solutions by taking into account the temporal structure of the data. We also propose a new method to automatically threshold the estimated source distribution, which permits to delineate the active brain regions. The new algorithm, called Source Imaging based on Structured Sparsity (SISSY), is analyzed by means of realistic computer simulations and is validated on the clinical data of four patients. Copyright © 2017 Elsevier Inc. All rights reserved.

Brain Network Analysis from High-Resolution EEG Signals

NASA Astrophysics Data System (ADS)

de Vico Fallani, Fabrizio; Babiloni, Fabio

Over the last decade, there has been a growing interest in the detection of the functional connectivity in the brain from different neuroelectromagnetic and hemodynamic signals recorded by several neuro-imaging devices such as the functional Magnetic Resonance Imaging (fMRI) scanner, electroencephalography (EEG) and magnetoencephalography (MEG) apparatus. Many methods have been proposed and discussed in the literature with the aim of estimating the functional relationships among different cerebral structures. However, the necessity of an objective comprehension of the network composed by the functional links of different brain regions is assuming an essential role in the Neuroscience. Consequently, there is a wide interest in the development and validation of mathematical tools that are appropriate to spot significant features that could describe concisely the structure of the estimated cerebral networks. The extraction of salient characteristics from brain connectivity patterns is an open challenging topic, since often the estimated cerebral networks have a relative large size and complex structure. Recently, it was realized that the functional connectivity networks estimated from actual brain-imaging technologies (MEG, fMRI and EEG) can be analyzed by means of the graph theory. Since a graph is a mathematical representation of a network, which is essentially reduced to nodes and connections between them, the use of a theoretical graph approach seems relevant and useful as firstly demonstrated on a set of anatomical brain networks. In those studies, the authors have employed two characteristic measures, the average shortest path L and the clustering index C, to extract respectively the global and local properties of the network structure. They have found that anatomical brain networks exhibit many local connections (i.e. a high C) and few random long distance connections (i.e. a low L). These values identify a particular model that interpolate between a regular lattice and a random structure. Such a model has been designated as "small-world" network in analogy with the concept of the small-world phenomenon observed more than 30 years ago in social systems. In a similar way, many types of functional brain networks have been analyzed according to this mathematical approach. In particular, several studies based on different imaging techniques (fMRI, MEG and EEG) have found that the estimated functional networks showed small-world characteristics. In the functional brain connectivity context, these properties have been demonstrated to reflect an optimal architecture for the information processing and propagation among the involved cerebral structures. However, the performance of cognitive and motor tasks as well as the presence of neural diseases has been demonstrated to affect such a small-world topology, as revealed by the significant changes of L and C. Moreover, some functional brain networks have been mostly found to be very unlike the random graphs in their degree-distribution, which gives information about the allocation of the functional links within the connectivity pattern. It was demonstrated that the degree distributions of these networks follow a power-law trend. For this reason those networks are called "scale-free". They still exhibit the small-world phenomenon but tend to contain few nodes that act as highly connected "hubs". Scale-free networks are known to show resistance to failure, facility of synchronization and fast signal processing. Hence, it would be important to see whether the scaling properties of the functional brain networks are altered under various pathologies or experimental tasks. The present Chapter proposes a theoretical graph approach in order to evaluate the functional connectivity patterns obtained from high-resolution EEG signals. In this way, the "Brain Network Analysis" (in analogy with the Social Network Analysis that has emerged as a key technique in modern sociology) represents an effective methodology improving the comprehension of the complex interactions in the brain.

Effects of exogenous agents on brain development: stress, abuse and therapeutic compounds.

PubMed

Archer, Trevor

2011-10-01

The range of exogenous agents likely to affect, generally detrimentally, the normal development of the brain and central nervous system defies estimation although the amount of accumulated evidence is enormous. The present review is limited to certain types of chemotherapeutic and "use-and-abuse" compounds and environmental agents, exemplified by anesthetic, antiepileptic, sleep-inducing and anxiolytic compounds, nicotine and alcohol, and stress as well as agents of infection; each of these agents have been investigated quite extensively and have been shown to contribute to the etiopathogenesis of serious neuropsychiatric disorders. To greater or lesser extent, all of the exogenous agents discussed in the present treatise have been investigated for their influence upon neurodevelopmental processes during the period of the brain growth spurt and during other phases uptill adulthood, thereby maintaining the notion of critical phases for the outcome of treatment whether prenatal, postnatal, or adolescent. Several of these agents have contributed to the developmental disruptions underlying structural and functional brain abnormalities that are observed in the symptom and biomarker profiles of the schizophrenia spectrum disorders and the fetal alcohol spectrum disorders. In each case, the effects of the exogenous agents upon the status of the affected brain, within defined parameters and conditions, is generally permanent and irreversible. © 2010 Blackwell Publishing Ltd.

Assessing dynamic brain graphs of time-varying connectivity in fMRI data: application to healthy controls and patients with schizophrenia

PubMed Central

Yu, Qingbao; Erhardt, Erik B.; Sui, Jing; Du, Yuhui; He, Hao; Hjelm, Devon; Cetin, Mustafa S.; Rachakonda, Srinivas; Miller, Robyn L.; Pearlson, Godfrey; Calhoun, Vince D.

2014-01-01

Graph theory-based analysis has been widely employed in brain imaging studies, and altered topological properties of brain connectivity have emerged as important features of mental diseases such as schizophrenia. However, most previous studies have focused on graph metrics of stationary brain graphs, ignoring that brain connectivity exhibits fluctuations over time. Here we develop a new framework for accessing dynamic graph properties of time-varying functional brain connectivity in resting state fMRI data and apply it to healthy controls (HCs) and patients with schizophrenia (SZs). Specifically, nodes of brain graphs are defined by intrinsic connectivity networks (ICNs) identified by group independent component analysis (ICA). Dynamic graph metrics of the time-varying brain connectivity estimated by the correlation of sliding time-windowed ICA time courses of ICNs are calculated. First- and second-level connectivity states are detected based on the correlation of nodal connectivity strength between time-varying brain graphs. Our results indicate that SZs show decreased variance in the dynamic graph metrics. Consistent with prior stationary functional brain connectivity works, graph measures of identified first-level connectivity states show lower values in SZs. In addition, more first-level connectivity states are disassociated with the second-level connectivity state which resembles the stationary connectivity pattern computed by the entire scan. Collectively, the findings provide new evidence about altered dynamic brain graphs in schizophrenia which may underscore the abnormal brain performance in this mental illness. PMID:25514514

Sampling theory and automated simulations for vertical sections, applied to human brain.

PubMed

Cruz-Orive, L M; Gelšvartas, J; Roberts, N

2014-02-01

In recent years, there have been substantial developments in both magnetic resonance imaging techniques and automatic image analysis software. The purpose of this paper is to develop stereological image sampling theory (i.e. unbiased sampling rules) that can be used by image analysts for estimating geometric quantities such as surface area and volume, and to illustrate its implementation. The methods will ideally be applied automatically on segmented, properly sampled 2D images - although convenient manual application is always an option - and they are of wide applicability in many disciplines. In particular, the vertical sections design to estimate surface area is described in detail and applied to estimate the area of the pial surface and of the boundary between cortex and underlying white matter (i.e. subcortical surface area). For completeness, cortical volume and mean cortical thickness are also estimated. The aforementioned surfaces were triangulated in 3D with the aid of FreeSurfer software, which provided accurate surface area measures that served as gold standards. Furthermore, a software was developed to produce digitized trace curves of the triangulated target surfaces automatically from virtual sections. From such traces, a new method (called the 'lambda method') is presented to estimate surface area automatically. In addition, with the new software, intersections could be counted automatically between the relevant surface traces and a cycloid test grid for the classical design. This capability, together with the aforementioned gold standard, enabled us to thoroughly check the performance and the variability of the different estimators by Monte Carlo simulations for studying the human brain. In particular, new methods are offered to split the total error variance into the orientations, sectioning and cycloid components. The latter prediction was hitherto unavailable--one is proposed here and checked by way of simulations on a given set of digitized vertical sections with automatically superimposed cycloid grids of three different sizes. Concrete and detailed recommendations are given to implement the methods. © 2013 The Authors Journal of Microscopy © 2013 Royal Microscopical Society.

Function-specific and Enhanced Brain Structural Connectivity Mapping via Joint Modeling of Diffusion and Functional MRI.

PubMed

Chu, Shu-Hsien; Parhi, Keshab K; Lenglet, Christophe

2018-03-16

A joint structural-functional brain network model is presented, which enables the discovery of function-specific brain circuits, and recovers structural connections that are under-estimated by diffusion MRI (dMRI). Incorporating information from functional MRI (fMRI) into diffusion MRI to estimate brain circuits is a challenging task. Usually, seed regions for tractography are selected from fMRI activation maps to extract the white matter pathways of interest. The proposed method jointly analyzes whole brain dMRI and fMRI data, allowing the estimation of complete function-specific structural networks instead of interactively investigating the connectivity of individual cortical/sub-cortical areas. Additionally, tractography techniques are prone to limitations, which can result in erroneous pathways. The proposed framework explicitly models the interactions between structural and functional connectivity measures thereby improving anatomical circuit estimation. Results on Human Connectome Project (HCP) data demonstrate the benefits of the approach by successfully identifying function-specific anatomical circuits, such as the language and resting-state networks. In contrast to correlation-based or independent component analysis (ICA) functional connectivity mapping, detailed anatomical connectivity patterns are revealed for each functional module. Results on a phantom (Fibercup) also indicate improvements in structural connectivity mapping by rejecting false-positive connections with insufficient support from fMRI, and enhancing under-estimated connectivity with strong functional correlation.

Modeling Dynamic Functional Neuroimaging Data Using Structural Equation Modeling

ERIC Educational Resources Information Center

Price, Larry R.; Laird, Angela R.; Fox, Peter T.; Ingham, Roger J.

2009-01-01

The aims of this study were to present a method for developing a path analytic network model using data acquired from positron emission tomography. Regions of interest within the human brain were identified through quantitative activation likelihood estimation meta-analysis. Using this information, a "true" or population path model was then…

Volumetric evaluation of the relations among the cerebrum, cerebellum and brain stem in young subjects: a combination of stereology and magnetic resonance imaging.

PubMed

Ekinci, Nihat; Acer, Niyazi; Akkaya, Akcan; Sankur, Seref; Kabadayi, Taner; Sahin, Bünyamin

2008-08-01

The Cavalieri estimator using a point grid is used to estimate the volume of three-dimensional structures based on two-dimensional slices of the object. The size of the components of intracranial neural structures should have proportional relations among them. The volume fraction approach of stereological methods provides information about volumetric relations of the components of structures. The purpose of our study is to estimate the volume and volume fraction data related to the cerebrum, cerebellum and brain stem. In this study, volume of the total brain, cerebrum, cerebellum and brain stem were estimated in 24 young Turkish volunteers (12 males and 12 females) who are free of any neurological symptoms and signs. The volume and volume fraction of the total brain, cerebrum, cerebellum and brain stem were determined on magnetic resonance (MR) images using the point-counting approach of stereological methods. The mean (+/-SD) total brain, cerebrum and cerebellum volumes were 1,202.05 +/- 103.51, 1,143.65 +/- 106.25 cm3 in males and females, 1,060.0 +/- 94.6, 1,008.9 +/- 104.3 cm3 in males and females, 117.75 +/- 10.7, 111.83 +/- 8.0 cm3 in males and females, respectively. The mean brain stem volumes were 24.3 +/- 2.89, 22.9 +/- 4.49 cm3 in males and females, respectively. Our results revealed that female subjects have less cerebral, cerebellar and brain stem volumes compared to males, although there was no statistically significant difference between genders (P > 0.05). The volume ratio of the cerebrum to total brain volume (TBV), cerebellum to TBV and brain stem to TBV were 88.16 and 88.13% in males and females, 9.8 and 9.8% in males and females, 2.03 and 2.03% in males and females, respectively. The volume ratio of the cerebellum to cerebrum, brain stem to cerebrum and brain stem to cerebellum were 11.12 and 11.16% in males and females, 2.30 and 2.31% in males and females, 20.7 and 20.6% in males and females, respectively. The difference between the genders was not statistically significant (P > 0.05). Our results revealed that the volumetric composition of the cerebrum, cerebellum and brain stem does not show sexual dimorphism.

Geometric Metamorphosis

PubMed Central

Niethammer, Marc; Hart, Gabriel L.; Pace, Danielle F.; Vespa, Paul M.; Irimia, Andrei; Van Horn, John D.; Aylward, Stephen R.

2013-01-01

Standard image registration methods do not account for changes in image appearance. Hence, metamorphosis approaches have been developed which jointly estimate a space deformation and a change in image appearance to construct a spatio-temporal trajectory smoothly transforming a source to a target image. For standard metamorphosis, geometric changes are not explicitly modeled. We propose a geometric metamorphosis formulation, which explains changes in image appearance by a global deformation, a deformation of a geometric model, and an image composition model. This work is motivated by the clinical challenge of predicting the long-term effects of traumatic brain injuries based on time-series images. This work is also applicable to the quantification of tumor progression (e.g., estimating its infiltrating and displacing components) and predicting chronic blood perfusion changes after stroke. We demonstrate the utility of the method using simulated data as well as scans from a clinical traumatic brain injury patient. PMID:21995083

Cost of traumatic brain injury in New Zealand: evidence from a population-based study.

PubMed

Te Ao, Braden; Brown, Paul; Tobias, Martin; Ameratunga, Shanthi; Barker-Collo, Suzanne; Theadom, Alice; McPherson, Kathryn; Starkey, Nicola; Dowell, Anthony; Jones, Kelly; Feigin, Valery L

2014-10-28

We aimed to estimate from a societal perspective the 1-year and lifetime direct and indirect costs of traumatic brain injury (TBI) for New Zealand (NZ) in 2010 projected to 2020. An incidence-based cost of illness model was developed using data from the Brain Injury Outcomes New Zealand in the Community Study. Details of TBI-related resource use during the first 12 months after injury were obtained for 725 cases using resource utilization information from participant surveys and medical records. Total costs are presented in US dollars year 2010 value. In 2010, 11,301 first-ever TBI cases were estimated to have occurred in NZ; total first-year cost of all new TBI cases was estimated to be US $47.9 million with total prevalence costs of US $101.4 million. The average cost per new TBI case during the first 12 months and over a lifetime was US $5,922 (95% confidence interval [CI] $4,777-$7,858), varying from US $4,636 (95% CI $3,756-$5,561) for mild cases to US $36,648 (95% CI $16,348-$65,350) for moderate/severe cases. Because of the unexpectedly large number of mild TBI cases (95% of all TBI cases), the total cost of treating these cases is nearly 3 times that of moderate/severe. The total lifetime cost of all TBI survivors in 2010 was US $146.5 million and is expected to increase to US $177.1 million in 2020. The results suggest that there is an urgent need to develop effective interventions to prevent both mild and moderate/severe TBI. © 2014 American Academy of Neurology.

Validated Automatic Brain Extraction of Head CT Images

PubMed Central

Muschelli, John; Ullman, Natalie L.; Mould, W. Andrew; Vespa, Paul; Hanley, Daniel F.; Crainiceanu, Ciprian M.

2015-01-01

Background X-ray Computed Tomography (CT) imaging of the brain is commonly used in diagnostic settings. Although CT scans are primarily used in clinical practice, they are increasingly used in research. A fundamental processing step in brain imaging research is brain extraction – the process of separating the brain tissue from all other tissues. Methods for brain extraction have either been 1) validated but not fully automated, or 2) fully automated and informally proposed, but never formally validated. Aim To systematically analyze and validate the performance of FSL's brain extraction tool (BET) on head CT images of patients with intracranial hemorrhage. This was done by comparing the manual gold standard with the results of several versions of automatic brain extraction and by estimating the reliability of automated segmentation of longitudinal scans. The effects of the choice of BET parameters and data smoothing is studied and reported. Methods All images were thresholded using a 0 – 100 Hounsfield units (HU) range. In one variant of the pipeline, data were smoothed using a 3-dimensional Gaussian kernel (σ = 1mm3) and re-thresholded to 0 – 100 HU; in the other, data were not smoothed. BET was applied using 1 of 3 fractional intensity (FI) thresholds: 0.01, 0.1, or 0.35 and any holes in the brain mask were filled. For validation against a manual segmentation, 36 images from patients with intracranial hemorrhage were selected from 19 different centers from the MISTIE (Minimally Invasive Surgery plus recombinant-tissue plasminogen activator for Intracerebral Evacuation) stroke trial. Intracranial masks of the brain were manually created by one expert CT reader. The resulting brain tissue masks were quantitatively compared to the manual segmentations using sensitivity, specificity, accuracy, and the Dice Similarity Index (DSI). Brain extraction performance across smoothing and FI thresholds was compared using the Wilcoxon signed-rank test. The intracranial volume (ICV) of each scan was estimated by multiplying the number of voxels in the brain mask by the dimensions of each voxel for that scan. From this, we calculated the ICV ratio comparing manual and automated segmentation: ICVautomatedICVmanual. To estimate the performance in a large number of scans, brain masks were generated from the 6 BET pipelines for 1095 longitudinal scans from 129 patients. Failure rates were estimated from visual inspection. ICV of each scan was estimated and and an intraclass correlation (ICC) was estimated using a one-way ANOVA. Results Smoothing images improves brain extraction results using BET for all measures except specificity (all p < 0.01, uncorrected), irrespective of the FI threshold. Using an FI of 0.01 or 0.1 performed better than 0.35. Thus, all reported results refer only to smoothed data using an FI of 0.01 or 0.1. Using an FI of 0.01 had a higher median sensitivity (0.9901) than an FI of 0.1 (0.9884, median difference: 0.0014, p < 0.001), accuracy (0.9971 vs. 0.9971; median difference: 0.0001, p < 0.001), and DSI (0.9895 vs. 0.9894; median difference: 0.0004, p < 0.001) and lower specificity (0.9981 vs. 0.9982; median difference: −0.0001, p < 0.001). These measures are all very high indicating that a range of FI values may produce visually indistinguishable brain extractions. Using smoothed data and an FI of 0.01, the mean (SD) ICV ratio was 1.002 (0.008); the mean being close to 1 indicates the ICV estimates are similar for automated and manual segmentation. In the 1095 longitudinal scans, this pipeline had a low failure rate (5.2%) and the ICC estimate was high (0.929, 95% CI: 0.91, 0.945) for successfully extracted brains. Conclusion BET performs well at brain extraction on thresholded, 1mm3 smoothed CT images with an FI of 0.01 or 0.1. Smoothing before applying BET is an important step not previously discussed in the literature. Analysis code is provided. PMID:25862260

Early postnatal myelin content estimate of white matter via T1w/T2w ratio

NASA Astrophysics Data System (ADS)

Lee, Kevin; Cherel, Marie; Budin, Francois; Gilmore, John; Zaldarriaga Consing, Kirsten; Rasmussen, Jerod; Wadhwa, Pathik D.; Entringer, Sonja; Glasser, Matthew F.; Van Essen, David C.; Buss, Claudia; Styner, Martin

2015-03-01

To develop and evaluate a novel processing framework for the relative quantification of myelin content in cerebral white matter (WM) regions from brain MRI data via a computed ratio of T1 to T2 weighted intensity values. We employed high resolution (1mm3 isotropic) T1 and T2 weighted MRI from 46 (28 male, 18 female) neonate subjects (typically developing controls) scanned on a Siemens Tim Trio 3T at UC Irvine. We developed a novel, yet relatively straightforward image processing framework for WM myelin content estimation based on earlier work by Glasser, et al. We first co-register the structural MRI data to correct for motion. Then, background areas are masked out via a joint T1w and T2 foreground mask computed. Raw T1w/T2w-ratios images are computed next. For purpose of calibration across subjects, we first coarsely segment the fat-rich facial regions via an atlas co-registration. Linear intensity rescaling based on median T1w/T2w-ratio values in those facial regions yields calibrated T1w/T2wratio images. Mean values in lobar regions are evaluated using standard statistical analysis to investigate their interaction with age at scan. Several lobes have strongly positive significant interactions of age at scan with the computed T1w/T2w-ratio. Most regions do not show sex effects. A few regions show no measurable effects of change in myelin content change within the first few weeks of postnatal development, such as cingulate and CC areas, which we attribute to sample size and measurement variability. We developed and evaluated a novel way to estimate white matter myelin content for use in studies of brain white matter development.

Perceived object stability depends on multisensory estimates of gravity.

PubMed

Barnett-Cowan, Michael; Fleming, Roland W; Singh, Manish; Bülthoff, Heinrich H

2011-04-27

How does the brain estimate object stability? Objects fall over when the gravity-projected centre-of-mass lies outside the point or area of support. To estimate an object's stability visually, the brain must integrate information across the shape and compare its orientation to gravity. When observers lie on their sides, gravity is perceived as tilted toward body orientation, consistent with a representation of gravity derived from multisensory information. We exploited this to test whether vestibular and kinesthetic information affect this visual task or whether the brain estimates object stability solely from visual information. In three body orientations, participants viewed images of objects close to a table edge. We measured the critical angle at which each object appeared equally likely to fall over or right itself. Perceived gravity was measured using the subjective visual vertical. The results show that the perceived critical angle was significantly biased in the same direction as the subjective visual vertical (i.e., towards the multisensory estimate of gravity). Our results rule out a general explanation that the brain depends solely on visual heuristics and assumptions about object stability. Instead, they suggest that multisensory estimates of gravity govern the perceived stability of objects, resulting in objects appearing more stable than they are when the head is tilted in the same direction in which they fall.

Investigation of the electric field distribution in the human brain based on MRI and EEG data

NASA Astrophysics Data System (ADS)

Kistenev, Yu. V.; Borisov, A. V.; Knyazkova, A. I.; Shapovalova, A. V.; Ilyasova, E. E.; Sandykova, E. A.

2018-04-01

This work is devoted to the development of the approach to restoration of the spatial-temporal distribution of electric field in the human brain. This field was estimated from the model derived from the Maxwell's equations with boundary conditions corresponding to electric potentials at the EEG electrodes, which are located on the surface of the head according to the standard "10-20" scheme. The MRI data were used for calculation of the spatial distribution of the electrical conductivity of biotissues in the human brain. The study of the electric field distribution using our approach was carried out for the healthy child and the child with autism. The research was carried out using the equipment of the Tomsk Regional Common Use Center of Tomsk State University.

Multi-Scale Computational Models for Electrical Brain Stimulation

PubMed Central

Seo, Hyeon; Jun, Sung C.

2017-01-01

Electrical brain stimulation (EBS) is an appealing method to treat neurological disorders. To achieve optimal stimulation effects and a better understanding of the underlying brain mechanisms, neuroscientists have proposed computational modeling studies for a decade. Recently, multi-scale models that combine a volume conductor head model and multi-compartmental models of cortical neurons have been developed to predict stimulation effects on the macroscopic and microscopic levels more precisely. As the need for better computational models continues to increase, we overview here recent multi-scale modeling studies; we focused on approaches that coupled a simplified or high-resolution volume conductor head model and multi-compartmental models of cortical neurons, and constructed realistic fiber models using diffusion tensor imaging (DTI). Further implications for achieving better precision in estimating cellular responses are discussed. PMID:29123476

Learning to Estimate Dynamical State with Probabilistic Population Codes.

PubMed

Makin, Joseph G; Dichter, Benjamin K; Sabes, Philip N

2015-11-01

Tracking moving objects, including one's own body, is a fundamental ability of higher organisms, playing a central role in many perceptual and motor tasks. While it is unknown how the brain learns to follow and predict the dynamics of objects, it is known that this process of state estimation can be learned purely from the statistics of noisy observations. When the dynamics are simply linear with additive Gaussian noise, the optimal solution is the well known Kalman filter (KF), the parameters of which can be learned via latent-variable density estimation (the EM algorithm). The brain does not, however, directly manipulate matrices and vectors, but instead appears to represent probability distributions with the firing rates of population of neurons, "probabilistic population codes." We show that a recurrent neural network-a modified form of an exponential family harmonium (EFH)-that takes a linear probabilistic population code as input can learn, without supervision, to estimate the state of a linear dynamical system. After observing a series of population responses (spike counts) to the position of a moving object, the network learns to represent the velocity of the object and forms nearly optimal predictions about the position at the next time-step. This result builds on our previous work showing that a similar network can learn to perform multisensory integration and coordinate transformations for static stimuli. The receptive fields of the trained network also make qualitative predictions about the developing and learning brain: tuning gradually emerges for higher-order dynamical states not explicitly present in the inputs, appearing as delayed tuning for the lower-order states.

Learning to Estimate Dynamical State with Probabilistic Population Codes

PubMed Central

Sabes, Philip N.

2015-01-01

Tracking moving objects, including one’s own body, is a fundamental ability of higher organisms, playing a central role in many perceptual and motor tasks. While it is unknown how the brain learns to follow and predict the dynamics of objects, it is known that this process of state estimation can be learned purely from the statistics of noisy observations. When the dynamics are simply linear with additive Gaussian noise, the optimal solution is the well known Kalman filter (KF), the parameters of which can be learned via latent-variable density estimation (the EM algorithm). The brain does not, however, directly manipulate matrices and vectors, but instead appears to represent probability distributions with the firing rates of population of neurons, “probabilistic population codes.” We show that a recurrent neural network—a modified form of an exponential family harmonium (EFH)—that takes a linear probabilistic population code as input can learn, without supervision, to estimate the state of a linear dynamical system. After observing a series of population responses (spike counts) to the position of a moving object, the network learns to represent the velocity of the object and forms nearly optimal predictions about the position at the next time-step. This result builds on our previous work showing that a similar network can learn to perform multisensory integration and coordinate transformations for static stimuli. The receptive fields of the trained network also make qualitative predictions about the developing and learning brain: tuning gradually emerges for higher-order dynamical states not explicitly present in the inputs, appearing as delayed tuning for the lower-order states. PMID:26540152

A statistical framework for neuroimaging data analysis based on mutual information estimated via a gaussian copula.

PubMed

Ince, Robin A A; Giordano, Bruno L; Kayser, Christoph; Rousselet, Guillaume A; Gross, Joachim; Schyns, Philippe G

2017-03-01

We begin by reviewing the statistical framework of information theory as applicable to neuroimaging data analysis. A major factor hindering wider adoption of this framework in neuroimaging is the difficulty of estimating information theoretic quantities in practice. We present a novel estimation technique that combines the statistical theory of copulas with the closed form solution for the entropy of Gaussian variables. This results in a general, computationally efficient, flexible, and robust multivariate statistical framework that provides effect sizes on a common meaningful scale, allows for unified treatment of discrete, continuous, unidimensional and multidimensional variables, and enables direct comparisons of representations from behavioral and brain responses across any recording modality. We validate the use of this estimate as a statistical test within a neuroimaging context, considering both discrete stimulus classes and continuous stimulus features. We also present examples of analyses facilitated by these developments, including application of multivariate analyses to MEG planar magnetic field gradients, and pairwise temporal interactions in evoked EEG responses. We show the benefit of considering the instantaneous temporal derivative together with the raw values of M/EEG signals as a multivariate response, how we can separately quantify modulations of amplitude and direction for vector quantities, and how we can measure the emergence of novel information over time in evoked responses. Open-source Matlab and Python code implementing the new methods accompanies this article. Hum Brain Mapp 38:1541-1573, 2017. © 2016 Wiley Periodicals, Inc. 2016 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.

The natural history of West Nile virus infection presenting with West Nile virus meningoencephalitis in a man with a prolonged illness: a case report.

PubMed

Mainali, Shraddha; Afshani, Mansoor; Wood, James B; Levin, Michael C

2011-05-25

Estimates indicate that West Nile virus infects approximately one and a half million people in the United States of America. Up to 1% may develop West Nile virus neuroinvasive disease, in which infected patients develop any combination of meningitis, encephalitis, or acute paralysis. A 56-year-old African-American man presented to our hospital with headache, restlessness, fever, myalgias, decreased appetite, and progressive confusion. A cerebrospinal fluid examination showed mild leukocytosis and an elevated protein level. Testing for routine infections was negative. Brain T2-weighted magnetic resonance imaging scans showed marked enlargement of caudate nuclei and increased intensity within the basal ganglia and thalami. A West Nile virus titer was positive, and serial brain magnetic resonance imaging scans showed resolving abnormalities that paralleled his neurological examination. This report is unusual as it portrays the natural history and long-term consequences of West Nile virus meningoencephalitis diagnosed on the basis of serial brain images.

Comparison of analytical methods of brain [18F]FDG-PET after severe traumatic brain injury.

PubMed

Madsen, Karine; Hesby, Sara; Poulsen, Ingrid; Fuglsang, Stefan; Graff, Jesper; Larsen, Karen B; Kammersgaard, Lars P; Law, Ian; Siebner, Hartwig R

2017-11-01

Loss of consciousness has been shown to reduce cerebral metabolic rates of glucose (CMRglc) measured by brain [ 18 F]FDG-PET. Measurements of regional metabolic patterns by normalization to global cerebral metabolism or cerebellum may underestimate widespread reductions. The aim of this study was to compare quantification methods of whole brain glucose metabolism, including whole brain [18F]FDG uptake normalized to uptake in cerebellum, normalized to injected activity, normalized to plasma tracer concentration, and two methods for estimating CMRglc. Six patients suffering from severe traumatic brain injury (TBI) and ten healthy controls (HC) underwent a 10min static [ 18 F]FDG-PET scan and venous blood sampling. Except from normalizing to cerebellum, all quantification methods found significant lower level of whole brain glucose metabolism of 25-33% in TBI patients compared to HC. In accordance these measurements correlated to level of consciousness. Our study demonstrates that the analysis method of the [ 18 F]FDG PET data has a substantial impact on the estimated whole brain cerebral glucose metabolism in patients with severe TBI. Importantly, the SUVR method which is often used in a clinical setting was not able to distinguish patients with severe TBI from HC at the whole-brain level. We recommend supplementing a static [ 18 F]FDG scan with a single venous blood sample in future studies of patients with severe TBI or reduced level of consciousness. This can be used for simple semi-quantitative uptake values by normalizing brain activity uptake to plasma tracer concentration, or quantitative estimates of CMRglc. Copyright © 2017 Elsevier B.V. All rights reserved.

Implementation of a computer-aided detection tool for quantification of intracranial radiologic markers on brain CT images

NASA Astrophysics Data System (ADS)

Aghaei, Faranak; Ross, Stephen R.; Wang, Yunzhi; Wu, Dee H.; Cornwell, Benjamin O.; Ray, Bappaditya; Zheng, Bin

2017-03-01

Aneurysmal subarachnoid hemorrhage (aSAH) is a form of hemorrhagic stroke that affects middle-aged individuals and associated with significant morbidity and/or mortality especially those presenting with higher clinical and radiologic grades at the time of admission. Previous studies suggested that blood extravasated after aneurysmal rupture was a potentially clinical prognosis factor. But all such studies used qualitative scales to predict prognosis. The purpose of this study is to develop and test a new interactive computer-aided detection (CAD) tool to detect, segment and quantify brain hemorrhage and ventricular cerebrospinal fluid on non-contrasted brain CT images. First, CAD segments brain skull using a multilayer region growing algorithm with adaptively adjusted thresholds. Second, CAD assigns pixels inside the segmented brain region into one of three classes namely, normal brain tissue, blood and fluid. Third, to avoid "black-box" approach and increase accuracy in quantification of these two image markers using CT images with large noise variation in different cases, a graphic User Interface (GUI) was implemented and allows users to visually examine segmentation results. If a user likes to correct any errors (i.e., deleting clinically irrelevant blood or fluid regions, or fill in the holes inside the relevant blood or fluid regions), he/she can manually define the region and select a corresponding correction function. CAD will automatically perform correction and update the computed data. The new CAD tool is now being used in clinical and research settings to estimate various quantitatively radiological parameters/markers to determine radiological severity of aSAH at presentation and correlate the estimations with various homeostatic/metabolic derangements and predict clinical outcome.

Scoring Systems to Estimate Intracerebral Control and Survival Rates of Patients Irradiated for Brain Metastases;Brain metastases; Radiation therapy; Local control; Survival; Prognostic scores

DOE Office of Scientific and Technical Information (OSTI.GOV)

Rades, Dirk, E-mail: Rades.Dirk@gmx.net; Dziggel, Liesa; Haatanen, Tiina

2011-07-15

Purpose: To create and validate scoring systems for intracerebral control (IC) and overall survival (OS) of patients irradiated for brain metastases. Methods and Materials: In this study, 1,797 patients were randomly assigned to the test (n = 1,198) or the validation group (n = 599). Two scoring systems were developed, one for IC and another for OS. The scores included prognostic factors found significant on multivariate analyses. Age, performance status, extracerebral metastases, interval tumor diagnosis to RT, and number of brain metastases were associated with OS. Tumor type, performance status, interval, and number of brain metastases were associated with IC.more » The score for each factor was determined by dividing the 6-month IC or OS rate (given in percent) by 10. The total score represented the sum of the scores for each factor. The score groups of the test group were compared with the corresponding score groups of the validation group. Results: In the test group, 6-month IC rates were 17% for 14-18 points, 49% for 19-23 points, and 77% for 24-27 points (p < 0.0001). IC rates in the validation group were 19%, 52%, and 77%, respectively (p < 0.0001). In the test group, 6-month OS rates were 9% for 15-19 points, 41% for 20-25 points, and 78% for 26-30 points (p < 0.0001). OS rates in the validation group were 7%, 39%, and 79%, respectively (p < 0.0001). Conclusions: Patients irradiated for brain metastases can be given scores to estimate OS and IC. IC and OS rates of the validation group were similar to the test group demonstrating the validity and reproducibility of both scores.« less

Improved Determination of the Myelin Water Fraction in Human Brain using Magnetic Resonance Imaging through Bayesian Analysis of mcDESPOT

PubMed Central

Bouhrara, Mustapha; Spencer, Richard G.

2015-01-01

Myelin water fraction (MWF) mapping with magnetic resonance imaging has led to the ability to directly observe myelination and demyelination in both the developing brain and in disease. Multicomponent driven equilibrium single pulse observation of T1 and T2 (mcDESPOT) has been proposed as a rapid approach for multicomponent relaxometry and has been applied to map MWF in human brain. However, even for the simplest two-pool signal model consisting of MWF and non-myelin-associated water, the dimensionality of the parameter space for obtaining MWF estimates remains high. This renders parameter estimation difficult, especially at low-to-moderate signal-to-noise ratios (SNR), due to the presence of local minima and the flatness of the fit residual energy surface used for parameter determination using conventional nonlinear least squares (NLLS)-based algorithms. In this study, we introduce three Bayesian approaches for analysis of the mcDESPOT signal model to determine MWF. Given the high dimensional nature of mcDESPOT signal model, and, thereby, the high dimensional marginalizations over nuisance parameters needed to derive the posterior probability distribution of MWF parameter, the introduced Bayesian analyses use different approaches to reduce the dimensionality of the parameter space. The first approach uses normalization by average signal amplitude, and assumes that noise can be accurately estimated from signal-free regions of the image. The second approach likewise uses average amplitude normalization, but incorporates a full treatment of noise as an unknown variable through marginalization. The third approach does not use amplitude normalization and incorporates marginalization over both noise and signal amplitude. Through extensive Monte Carlo numerical simulations and analysis of in-vivo human brain datasets exhibiting a range of SNR and spatial resolution, we demonstrated the markedly improved accuracy and precision in the estimation of MWF using these Bayesian methods as compared to the stochastic region contraction (SRC) implementation of NLLS. PMID:26499810

Automatic Measurement of Fetal Brain Development from Magnetic Resonance Imaging: New Reference Data.

PubMed

Link, Daphna; Braginsky, Michael B; Joskowicz, Leo; Ben Sira, Liat; Harel, Shaul; Many, Ariel; Tarrasch, Ricardo; Malinger, Gustavo; Artzi, Moran; Kapoor, Cassandra; Miller, Elka; Ben Bashat, Dafna

2018-01-01

Accurate fetal brain volume estimation is of paramount importance in evaluating fetal development. The aim of this study was to develop an automatic method for fetal brain segmentation from magnetic resonance imaging (MRI) data, and to create for the first time a normal volumetric growth chart based on a large cohort. A semi-automatic segmentation method based on Seeded Region Growing algorithm was developed and applied to MRI data of 199 typically developed fetuses between 18 and 37 weeks' gestation. The accuracy of the algorithm was tested against a sub-cohort of ground truth manual segmentations. A quadratic regression analysis was used to create normal growth charts. The sensitivity of the method to identify developmental disorders was demonstrated on 9 fetuses with intrauterine growth restriction (IUGR). The developed method showed high correlation with manual segmentation (r2 = 0.9183, p < 0.001) as well as mean volume and volume overlap differences of 4.77 and 18.13%, respectively. New reference data on 199 normal fetuses were created, and all 9 IUGR fetuses were at or below the third percentile of the normal growth chart. The proposed method is fast, accurate, reproducible, user independent, applicable with retrospective data, and is suggested for use in routine clinical practice. © 2017 S. Karger AG, Basel.

Prenatal caffeine intake differently affects synaptic proteins during fetal brain development.

PubMed

Mioranzza, Sabrina; Nunes, Fernanda; Marques, Daniela M; Fioreze, Gabriela T; Rocha, Andréia S; Botton, Paulo Henrique S; Costa, Marcelo S; Porciúncula, Lisiane O

2014-08-01

Caffeine is the psychostimulant most consumed worldwide. However, little is known about its effects during fetal brain development. In this study, adult female Wistar rats received caffeine in drinking water (0.1, 0.3 and 1.0 g/L) during the active cycle in weekdays, two weeks before mating and throughout pregnancy. Cerebral cortex and hippocampus from embryonic stages 18 or 20 (E18 or E20, respectively) were collected for immunodetection of the following synaptic proteins: brain-derived neurotrophic factor (BDNF), TrkB receptor, Sonic Hedgehog (Shh), Growth Associated Protein 43 (GAP-43) and Synaptosomal-associated Protein 25 (SNAP-25). Besides, the estimation of NeuN-stained nuclei (mature neurons) and non-neuronal nuclei was verified in both brain regions and embryonic periods. Caffeine (1.0 g/L) decreased the body weight of embryos at E20. Cortical BDNF at E18 was decreased by caffeine (1.0 g/L), while it increased at E20, with no major effects on TrkB receptors. In the hippocampus, caffeine decreased TrkB receptor only at E18, with no effects on BDNF. Moderate and high doses of caffeine promoted an increase in Shh in both brain regions at E18, and in the hippocampus at E20. Caffeine (0.3g/L) decreased GAP-43 only in the hippocampus at E18. The NeuN-stained nuclei increased in the cortex at E20 by lower dose and in the hippocampus at E18 by moderate dose. Our data revealed that caffeine transitorily affect synaptic proteins during fetal brain development. The increased number of NeuN-stained nuclei by prenatal caffeine suggests a possible acceleration of the telencephalon maturation. Although some modifications in the synaptic proteins were transient, our data suggest that caffeine even in lower doses may alter the fetal brain development. Copyright © 2014 ISDN. Published by Elsevier Ltd. All rights reserved.

Brain catechol synthesis - Control by brain tyrosine concentration

NASA Technical Reports Server (NTRS)

Wurtman, R. J.; Larin, F.; Mostafapour, S.; Fernstrom, J. D.

1974-01-01

Brain catechol synthesis was estimated by measuring the rate at which brain dopa levels rose following decarboxylase inhibition. Dopa accumulation was accelerated by tyrosine administration, and decreased by treatments that lowered brain tyrosine concentrations (for example, intraperitoneal tryptophan, leucine, or parachlorophenylalanine). A low dose of phenylalanine elevated brain tyrosine without accelerating dopa synthesis. Our findings raise the possibility that nutritional and endocrine factors might influence brain catecholamine synthesis by controlling the availability of tyrosine.

Positive parenting predicts the development of adolescent brain structure: a longitudinal study.

PubMed

Whittle, Sarah; Simmons, Julian G; Dennison, Meg; Vijayakumar, Nandita; Schwartz, Orli; Yap, Marie B H; Sheeber, Lisa; Allen, Nicholas B

2014-04-01

Little work has been conducted that examines the effects of positive environmental experiences on brain development to date. The aim of this study was to prospectively investigate the effects of positive (warm and supportive) maternal behavior on structural brain development during adolescence, using longitudinal structural MRI. Participants were 188 (92 female) adolescents, who were part of a longitudinal adolescent development study that involved mother-adolescent interactions and MRI scans at approximately 12 years old, and follow-up MRI scans approximately 4 years later. FreeSurfer software was used to estimate the volume of limbic-striatal regions (amygdala, hippocampus, caudate, putamen, pallidum, and nucleus accumbens) and the thickness of prefrontal regions (anterior cingulate and orbitofrontal cortices) across both time points. Higher frequency of positive maternal behavior during the interactions predicted attenuated volumetric growth in the right amygdala, and accelerated cortical thinning in the right anterior cingulate (males only) and left and right orbitofrontal cortices, between baseline and follow up. These results have implications for understanding the biological mediators of risk and protective factors for mental disorders that have onset during adolescence. Copyright © 2013 The Authors. Published by Elsevier Ltd.. All rights reserved.

Psychosis following traumatic brain injury and cannabis use in late adolescence.

PubMed

Rabner, Jonathan; Gottlieb, Sarah; Lazdowsky, Lori; LeBel, Alyssa

2016-03-01

Both cannabis and traumatic brain injury (TBI) pose risks on the developing brain, including a potential increased vulnerability for developing psychosis. Recent reports detail an upward trend in both adolescent cannabis use and the concentration of THC, the most potent psychoactive component in cannabis. Similarly, it is estimated that 1.7 million Americans incur a TBI each year. Previously trivialized as a minor nuisance, attitudes towards TBIs are changing as researchers and the public recognize TBIs' possible long-lasting sequelae. Two cases are presented of adolescent patients with histories of TBI and self-reported heavy, recreational cannabis use who developed symptoms of psychosis. Similar neuronal signaling pathways involved in cannabis ingestion and TBI recovery, specifically CB1 receptors of the endocannabinoid system, as well as the allostatic load model provide context for the two presented cases. Given the cases and theories presented, we believe that cannabis use may act as a neurological stressor and risk factor for psychosis outweighing its possible benefits as a therapeutic solution for pain in late adolescent and young adult populations. The presented cases provide further support for the compounded risk of developing psychosis following TBI and cannabis use. © American Academy of Addiction Psychiatry.

Bayesian switching factor analysis for estimating time-varying functional connectivity in fMRI.

PubMed

Taghia, Jalil; Ryali, Srikanth; Chen, Tianwen; Supekar, Kaustubh; Cai, Weidong; Menon, Vinod

2017-07-15

There is growing interest in understanding the dynamical properties of functional interactions between distributed brain regions. However, robust estimation of temporal dynamics from functional magnetic resonance imaging (fMRI) data remains challenging due to limitations in extant multivariate methods for modeling time-varying functional interactions between multiple brain areas. Here, we develop a Bayesian generative model for fMRI time-series within the framework of hidden Markov models (HMMs). The model is a dynamic variant of the static factor analysis model (Ghahramani and Beal, 2000). We refer to this model as Bayesian switching factor analysis (BSFA) as it integrates factor analysis into a generative HMM in a unified Bayesian framework. In BSFA, brain dynamic functional networks are represented by latent states which are learnt from the data. Crucially, BSFA is a generative model which estimates the temporal evolution of brain states and transition probabilities between states as a function of time. An attractive feature of BSFA is the automatic determination of the number of latent states via Bayesian model selection arising from penalization of excessively complex models. Key features of BSFA are validated using extensive simulations on carefully designed synthetic data. We further validate BSFA using fingerprint analysis of multisession resting-state fMRI data from the Human Connectome Project (HCP). Our results show that modeling temporal dependencies in the generative model of BSFA results in improved fingerprinting of individual participants. Finally, we apply BSFA to elucidate the dynamic functional organization of the salience, central-executive, and default mode networks-three core neurocognitive systems with central role in cognitive and affective information processing (Menon, 2011). Across two HCP sessions, we demonstrate a high level of dynamic interactions between these networks and determine that the salience network has the highest temporal flexibility among the three networks. Our proposed methods provide a novel and powerful generative model for investigating dynamic brain connectivity. Copyright © 2017 Elsevier Inc. All rights reserved.

SU-F-T-116: Predicting IQ and the Risk of Hearing Loss Following Proton Versus Photon Radiotherapy for Pediatric Brain Tumor Patients

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fortin, D; Sharpe, M; Laperriere, N

Purpose: The increased sparing of normal tissues in intensity modulated proton therapy (IMPT) compared to photon intensity modulated radiotherapy (IMRT) in brain tumor treatments should translate into improved neurocognitive outcomes. Models were used to estimate the intelligence quotient (IQ) and the risk of hearing loss 5 years post radiotherapy and to compare outcomes of proton against photon in pediatric brain tumors. Methods: Patients who had received radical IMRT were randomly selected from our retrospective database: 10 cases each of craniopharyngioma, ependymoma and medulloblastoma, and 20 cases of glioma. The existing planning CT and contours were used to generate IMPT plans.more » The RBE-corrected dose to brain structures and cochleas were calculated for both IMPT and IMRT. A model was applied to estimate IQ using a Markov chain Monte Carlo technique. The reported incidence of hearing loss as a function of cochlear dose was used to estimate the rate of occurrence. Results: The average brain dose was less in all IMPT plans compared to IMRT: ranging from a 6.7% reduction (P=0.003) in the case of medulloblastoma to 38% (P=0.007) for craniopharyngioma. This dose reduction translated into a gain in IQ of 1.9 points on average for protons vs photons for the whole cohort at 5 years post-treatment (P=0.011). In terms of specific diseases, the gains in IQ ranged from 0.8 points for medulloblastoma, to 2.7 points for craniopharyngioma. Hearing loss probability was evaluated on a per-ear-basis and was found to be systematically less for proton versus photon: overall 2.9% versus 7.2% (P < 0.001). Conclusion: A novel method was developed to predict neurocognitive outcomes in pediatric brain tumor patients on a case-by-case basis. A modest gain in IQ was systematically observed for proton in all patients. Given the uncertainties within the model used and our reinterpretation, these gains may be underestimated.« less

Direct estimation of tracer-kinetic parameter maps from highly undersampled brain dynamic contrast enhanced MRI.

PubMed

Guo, Yi; Lingala, Sajan Goud; Zhu, Yinghua; Lebel, R Marc; Nayak, Krishna S

2017-10-01

The purpose of this work was to develop and evaluate a T 1 -weighted dynamic contrast enhanced (DCE) MRI methodology where tracer-kinetic (TK) parameter maps are directly estimated from undersampled (k,t)-space data. The proposed reconstruction involves solving a nonlinear least squares optimization problem that includes explicit use of a full forward model to convert parameter maps to (k,t)-space, utilizing the Patlak TK model. The proposed scheme is compared against an indirect method that creates intermediate images by parallel imaging and compressed sensing before to TK modeling. Thirteen fully sampled brain tumor DCE-MRI scans with 5-second temporal resolution are retrospectively undersampled at rates R = 20, 40, 60, 80, and 100 for each dynamic frame. TK maps are quantitatively compared based on root mean-squared-error (rMSE) and Bland-Altman analysis. The approach is also applied to four prospectively R = 30 undersampled whole-brain DCE-MRI data sets. In the retrospective study, the proposed method performed statistically better than indirect method at R ≥ 80 for all 13 cases. This approach provided restoration of TK parameter values with less errors in tumor regions of interest, an improvement compared to a state-of-the-art indirect method. Applied prospectively, the proposed method provided whole-brain, high-resolution TK maps with good image quality. Model-based direct estimation of TK maps from k,t-space DCE-MRI data is feasible and is compatible up to 100-fold undersampling. Magn Reson Med 78:1566-1578, 2017. © 2016 International Society for Magnetic Resonance in Medicine. © 2016 International Society for Magnetic Resonance in Medicine.

Keeping brains young with making music.

PubMed

Rogenmoser, Lars; Kernbach, Julius; Schlaug, Gottfried; Gaser, Christian

2018-01-01

Music-making is a widespread leisure and professional activity that has garnered interest over the years due to its effect on brain and cognitive development and its potential as a rehabilitative and restorative therapy of brain dysfunctions. We investigated whether music-making has a potential age-protecting effect on the brain. For this, we studied anatomical magnetic resonance images obtained from three matched groups of subjects who differed in their lifetime dose of music-making activities (i.e., professional musicians, amateur musicians, and non-musicians). For each subject, we calculated a so-called BrainAGE score which corresponds to the discrepancy (in years) between chronological age and the "age of the brain", with negative values reflecting an age-decelerating brain and positive values an age-accelerating brain, respectively. The index of "brain age" was estimated using a machine-learning algorithm that was trained in a large independent sample to identify anatomical correlates of brain-aging. Compared to non-musicians, musicians overall had lower BrainAGE scores, with amateur musicians having the lowest scores suggesting that music-making has an age-decelerating effect on the brain. Unlike the amateur musicians, the professional musicians showed a positive correlation between their BrainAGE scores and years of music-making, possibly indicating that engaging more intensely in just one otherwise enriching activity might not be as beneficial than if the activity is one of several that an amateur musician engages in. Intense music-making activities at a professional level could also lead to stress-related interferences and a less enriched environment than that of amateur musicians, possibly somewhat diminishing the otherwise positive effect of music-making.

Development of the Cell Population in the Brain White Matter of Young Children.

PubMed

Sigaard, Rasmus Krarup; Kjær, Majken; Pakkenberg, Bente

2016-01-01

While brain gray matter is primarily associated with sensorimotor processing and cognition, white matter modulates the distribution of action potentials, coordinates communication between different brain regions, and acts as a relay for input/output signals. Previous studies have described morphological changes in gray and white matter during childhood and adolescence, which are consistent with cellular genesis and maturation, but corresponding events in infants are poorly documented. In the present study, we estimated the total number of cells (neurons, oligodendrocytes, astrocytes, and microglia) in the cerebral white matter of 9 infants aged 0-33 months, using design-based stereological methods to obtain quantitative data about brain development. There were linear increases with age in the numbers of oligodendrocytes (7-28 billion) and astrocytes (1.5-6.7 billion) during the first 3 years of life, thus attaining two-thirds of the corresponding numbers in adults. The numbers of neurons (0.7 billion) and microglia (0.2 billion) in the white matter did not increase during the first 3 years of life, but showed large biological variation. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Decoding of visual activity patterns from fMRI responses using multivariate pattern analyses and convolutional neural network.

PubMed

Zafar, Raheel; Kamel, Nidal; Naufal, Mohamad; Malik, Aamir Saeed; Dass, Sarat C; Ahmad, Rana Fayyaz; Abdullah, Jafri M; Reza, Faruque

2017-01-01

Decoding of human brain activity has always been a primary goal in neuroscience especially with functional magnetic resonance imaging (fMRI) data. In recent years, Convolutional neural network (CNN) has become a popular method for the extraction of features due to its higher accuracy, however it needs a lot of computation and training data. In this study, an algorithm is developed using Multivariate pattern analysis (MVPA) and modified CNN to decode the behavior of brain for different images with limited data set. Selection of significant features is an important part of fMRI data analysis, since it reduces the computational burden and improves the prediction performance; significant features are selected using t-test. MVPA uses machine learning algorithms to classify different brain states and helps in prediction during the task. General linear model (GLM) is used to find the unknown parameters of every individual voxel and the classification is done using multi-class support vector machine (SVM). MVPA-CNN based proposed algorithm is compared with region of interest (ROI) based method and MVPA based estimated values. The proposed method showed better overall accuracy (68.6%) compared to ROI (61.88%) and estimation values (64.17%).

Head circumference and brain size in autism spectrum disorder: A systematic review and meta-analysis.

PubMed

Sacco, Roberto; Gabriele, Stefano; Persico, Antonio M

2015-11-30

Macrocephaly and brain overgrowth have been associated with autism spectrum disorder. We performed a systematic review and meta-analysis to provide an overall estimate of effect size and statistical significance for both head circumference and total brain volume in autism. Our literature search strategy identified 261 and 391 records, respectively; 27 studies defining percentages of macrocephalic patients and 44 structural brain imaging studies providing total brain volumes for patients and controls were included in our meta-analyses. Head circumference was significantly larger in autistic compared to control individuals, with 822/5225 (15.7%) autistic individuals displaying macrocephaly. Structural brain imaging studies measuring brain volume estimated effect size. The effect size is higher in low functioning autistics compared to high functioning and ASD individuals. Brain overgrowth was recorded in 142/1558 (9.1%) autistic patients. Finally, we found a significant interaction between age and total brain volume, resulting in larger head circumference and brain size during early childhood. Our results provide conclusive effect sizes and prevalence rates for macrocephaly and brain overgrowth in autism, confirm the variation of abnormal brain growth with age, and support the inclusion of this endophenotype in multi-biomarker diagnostic panels for clinical use. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Functional Brain Networks: Does the Choice of Dependency Estimator and Binarization Method Matter?

NASA Astrophysics Data System (ADS)

Jalili, Mahdi

2016-07-01

The human brain can be modelled as a complex networked structure with brain regions as individual nodes and their anatomical/functional links as edges. Functional brain networks are constructed by first extracting weighted connectivity matrices, and then binarizing them to minimize the noise level. Different methods have been used to estimate the dependency values between the nodes and to obtain a binary network from a weighted connectivity matrix. In this work we study topological properties of EEG-based functional networks in Alzheimer’s Disease (AD). To estimate the connectivity strength between two time series, we use Pearson correlation, coherence, phase order parameter and synchronization likelihood. In order to binarize the weighted connectivity matrices, we use Minimum Spanning Tree (MST), Minimum Connected Component (MCC), uniform threshold and density-preserving methods. We find that the detected AD-related abnormalities highly depend on the methods used for dependency estimation and binarization. Topological properties of networks constructed using coherence method and MCC binarization show more significant differences between AD and healthy subjects than the other methods. These results might explain contradictory results reported in the literature for network properties specific to AD symptoms. The analysis method should be seriously taken into account in the interpretation of network-based analysis of brain signals.

The development and investigation of a prototype three-dimensional compensator for whole brain radiation therapy

NASA Astrophysics Data System (ADS)

Keall, Paul; Arief, Isti; Shamas, Sofia; Weiss, Elisabeth; Castle, Steven

2008-05-01

Whole brain radiation therapy (WBRT) is the standard treatment for patients with brain metastases, and is often used in conjunction with stereotactic radiotherapy for patients with a limited number of brain metastases, as well as prophylactic cranial irradiation. The use of open fields (conventionally used for WBRT) leads to higher doses to the brain periphery if dose is prescribed to the brain center at the largest lateral radius. These dose variations potentially compromise treatment efficacy and translate to increased side effects. The goal of this research was to design and construct a 3D 'brain wedge' to compensate dose heterogeneities in WBRT. Radiation transport theory was invoked to calculate the desired shape of a wedge to achieve a uniform dose distribution at the sagittal plane for an ellipsoid irradiated medium. The calculations yielded a smooth 3D wedge design to account for the missing tissue at the peripheral areas of the brain. A wedge was machined based on the calculation results. Three ellipsoid phantoms, spanning the mean and ± two standard deviations from the mean cranial dimensions were constructed, representing 95% of the adult population. Film was placed at the sagittal plane for each of the three phantoms and irradiated with 6 MV photons, with the wedge in place. Sagittal plane isodose plots for the three phantoms demonstrated the feasibility of this wedge to create a homogeneous distribution with similar results observed for the three phantom sizes, indicating that a single wedge may be sufficient to cover 95% of the adult population. The sagittal dose is a reasonable estimate of the off-axis dose for whole brain radiation therapy. Comparing the dose with and without the wedge the average minimum dose was higher (90% versus 86%), the maximum dose was lower (107% versus 113%) and the dose variation was lower (one standard deviation 2.7% versus 4.6%). In summary, a simple and effective 3D wedge for whole brain radiotherapy has been developed. The wedge gives a more uniform dose distribution than commonly used techniques. Further development and shape optimization may be necessary prior to clinical implementation.

The Association between Lifelong Greenspace Exposure and 3-Dimensional Brain Magnetic Resonance Imaging in Barcelona Schoolchildren.

PubMed

Dadvand, Payam; Pujol, Jesus; Macià, Dídac; Martínez-Vilavella, Gerard; Blanco-Hinojo, Laura; Mortamais, Marion; Alvarez-Pedrerol, Mar; Fenoll, Raquel; Esnaola, Mikel; Dalmau-Bueno, Albert; López-Vicente, Mónica; Basagaña, Xavier; Jerrett, Michael; Nieuwenhuijsen, Mark J; Sunyer, Jordi

2018-02-23

Proponents of the biophilia hypothesis believe that contact with nature, including green spaces, has a crucial role in brain development in children. Currently, however, we are not aware of evidence linking such exposure with potential effects on brain structure. We determined whether lifelong exposure to residential surrounding greenness is associated with regional differences in brain volume based on 3-dimensional magnetic resonance imaging (3D MRI) among children attending primary school. We performed a series of analyses using data from a subcohort of 253 Barcelona schoolchildren from the Brain Development and Air Pollution Ultrafine Particles in School Children (BREATHE) project. We averaged satellite-based normalized difference vegetation index (NDVI) across 100-m buffers around all residential addresses since birth to estimate each participant's lifelong exposure to residential surrounding greenness, and we used high-resolution 3D MRIs of brain anatomy to identify regional differences in voxel-wise brain volume associated with greenness exposure. In addition, we performed a supporting substudy to identify regional differences in brain volume associated with measures of working memory ( d' from computerized n -back tests) and inattentiveness (hit reaction time standard error from the Attentional Network Task instrument) that were repeated four times over one year. We also performed a second supporting substudy to determine whether peak voxel tissue volumes in brain regions associated with residential greenness predicted cognitive function test scores. Lifelong exposure to greenness was positively associated with gray matter volume in the left and right prefrontal cortex and in the left premotor cortex and with white matter volume in the right prefrontal region, in the left premotor region, and in both cerebellar hemispheres. Some of these regions partly overlapped with regions associated with cognitive test scores (prefrontal cortex and cerebellar and premotor white matter), and peak volumes in these regions predicted better working memory and reduced inattentiveness. Our findings from a study population of urban schoolchildren in Barcelona require confirmation, but they suggest that being raised in greener neighborhoods may have beneficial effects on brain development and cognitive function. https://doi.org/10.1289/EHP1876.

Assessing Occupational Exposure to Chemicals in an International Epidemiological Study of Brain Tumours

PubMed Central

van Tongeren, Martie

2013-01-01

The INTEROCC project is a multi-centre case–control study investigating the risk of developing brain cancer due to occupational chemical and electromagnetic field exposures. To estimate chemical exposures, the Finnish Job Exposure Matrix (FINJEM) was modified to improve its performance in the INTEROCC study and to address some of its limitations, resulting in the development of the INTEROCC JEM. An international team of occupational hygienists developed a crosswalk between the Finnish occupational codes used in FINJEM and the International Standard Classification of Occupations 1968 (ISCO68). For ISCO68 codes linked to multiple Finnish codes, weighted means of the exposure estimates were calculated. Similarly, multiple ISCO68 codes linked to a single Finnish code with evidence of heterogeneous exposure were refined. One of the key time periods in FINJEM (1960–1984) was split into two periods (1960–1974 and 1975–1984). Benzene exposure estimates in early periods were modified upwards. The internal consistency of hydrocarbon exposures and exposures to engine exhaust fumes was improved. Finally, exposure to polycyclic aromatic hydrocarbon and benzo(a)pyrene was modified to include the contribution from second-hand smoke. The crosswalk ensured that the FINJEM exposure estimates could be applied to the INTEROCC study subjects. The modifications generally resulted in an increased prevalence of exposure to chemical agents. This increased prevalence of exposure was not restricted to the lowest categories of cumulative exposure, but was seen across all levels for some agents. Although this work has produced a JEM with important improvements compared to FINJEM, further improvements are possible with the expansion of agents and additional external data. PMID:23467593

Brain-heart linear and nonlinear dynamics during visual emotional elicitation in healthy subjects.

PubMed

Valenza, G; Greco, A; Gentili, C; Lanata, A; Toschi, N; Barbieri, R; Sebastiani, L; Menicucci, D; Gemignani, A; Scilingo, E P

2016-08-01

This study investigates brain-heart dynamics during visual emotional elicitation in healthy subjects through linear and nonlinear coupling measures of EEG spectrogram and instantaneous heart rate estimates. To this extent, affective pictures including different combinations of arousal and valence levels, gathered from the International Affective Picture System, were administered to twenty-two healthy subjects. Time-varying maps of cortical activation were obtained through EEG spectral analysis, whereas the associated instantaneous heartbeat dynamics was estimated using inhomogeneous point-process linear models. Brain-Heart linear and nonlinear coupling was estimated through the Maximal Information Coefficient (MIC), considering EEG time-varying spectra and point-process estimates defined in the time and frequency domains. As a proof of concept, we here show preliminary results considering EEG oscillations in the θ band (4-8 Hz). This band, indeed, is known in the literature to be involved in emotional processes. MIC highlighted significant arousal-dependent changes, mediated by the prefrontal cortex interplay especially occurring at intermediate arousing levels. Furthermore, lower and higher arousing elicitations were associated to not significant brain-heart coupling changes in response to pleasant/unpleasant elicitations.

Brain development during adolescence: A mixed-longitudinal investigation of cortical thickness, surface area, and volume.

PubMed

Vijayakumar, Nandita; Allen, Nicholas B; Youssef, George; Dennison, Meg; Yücel, Murat; Simmons, Julian G; Whittle, Sarah

2016-06-01

What we know about cortical development during adolescence largely stems from analyses of cross-sectional or cohort-sequential samples, with few studies investigating brain development using a longitudinal design. Further, cortical volume is a product of two evolutionarily and genetically distinct features of the cortex - thickness and surface area, and few studies have investigated development of these three characteristics within the same sample. The current study examined maturation of cortical thickness, surface area and volume during adolescence, as well as sex differences in development, using a mixed longitudinal design. 192 MRI scans were obtained from 90 healthy (i.e., free from lifetime psychopathology) adolescents (11-20 years) at three time points (with different MRI scanners used at time 1 compared to 2 and 3). Developmental trajectories were estimated using linear mixed models. Non-linear increases were present across most of the cortex for surface area. In comparison, thickness and volume were both characterised by a combination of non-linear decreasing and increasing trajectories. While sex differences in volume and surface area were observed across time, no differences in thickness were identified. Furthermore, few regions exhibited sex differences in the cortical development. Our findings clearly illustrate that volume is a product of surface area and thickness, with each exhibiting differential patterns of development during adolescence, particularly in regions known to contribute to the development of social-cognition and behavioral regulation. These findings suggest that thickness and surface area may be driven by different underlying mechanisms, with each measure potentially providing independent information about brain development. Hum Brain Mapp 37:2027-2038, 2016. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Decreased frontal white-matter volume in chronic substance abuse.

PubMed

Schlaepfer, Thomas E; Lancaster, Eric; Heidbreder, Rebecca; Strain, Eric C; Kosel, Markus; Fisch, Hans-Ulrich; Pearlson, Godfrey D

2006-04-01

There is quite a body of work assessing functional brain changes in chronic substance abuse, much less is known about structural brain abnormalities in this patient population. In this study we used magnetic resonance imaging (MRI) to determine if structural brain differences exist in patients abusing illicit drugs compared to healthy controls. Sixteen substance abusers who abused heroin, cocaine and cannabis but not alcohol and 16 age-, sex- and race-matched controls were imaged on a MRI scanner. Contiguous, 5-mm-thick axial slices were acquired with simultaneous T2 and proton density sequences. Volumes were estimated for total grey and white matter, frontal grey and white matter, ventricles, and CSF using two different methods: a conventional segmentation and a stereological method based on the Cavalieri principle. Overall brain volume differences were corrected for by expressing the volumes of interest as a percentage of total brain volume. Volume measures obtained with the two methods were highly correlated (r=0.65, p<0.001). Substance abusers had significantly less frontal white-matter volume percentage than controls. There were no significant differences in any of the other brain volumes measured. This difference in frontal lobe white matter might be explained by a direct neurotoxic effect of drug use on white matter, a pre-existing abnormality in the development of the frontal lobe or a combination of both effects. This last explanation might be compelling based on the fact that newer concepts on shared aspects of some neuropsychiatric disorders focus on the promotion and inhibition of the process of myelination throughout brain development and subsequent degeneration.

Altered brain activation and connectivity during anticipation of uncertain threat in trait anxiety.

PubMed

Geng, Haiyang; Wang, Yi; Gu, Ruolei; Luo, Yue-Jia; Xu, Pengfei; Huang, Yuxia; Li, Xuebing

2018-06-08

In the research field of anxiety, previous studies generally focus on emotional responses following threat. A recent model of anxiety proposes that altered anticipation prior to uncertain threat is related with the development of anxiety. Behavioral findings have built the relationship between anxiety and distinct anticipatory processes including attention, estimation of threat, and emotional responses. However, few studies have characterized the brain organization underlying anticipation of uncertain threat and its role in anxiety. In the present study, we used an emotional anticipation paradigm with functional magnetic resonance imaging (fMRI) to examine the aforementioned topics by employing brain activation and general psychophysiological interactions (gPPI) analysis. In the activation analysis, we found that high trait anxious individuals showed significantly increased activation in the thalamus, middle temporal gyrus (MTG), and dorsomedial prefrontal cortex (dmPFC), as well as decreased activation in the precuneus, during anticipation of uncertain threat compared to the certain condition. In the gPPI analysis, the key regions including the amygdala, dmPFC, and precuneus showed altered connections with distributed brain areas including the ventromedial prefrontal cortex (vmPFC), dorsolateral prefrontal cortex (dlPFC), inferior parietal sulcus (IPS), insula, para-hippocampus gyrus (PHA), thalamus, and MTG involved in anticipation of uncertain threat in anxious individuals. Taken together, our findings indicate that during the anticipation of uncertain threat, anxious individuals showed altered activations and functional connectivity in widely distributed brain areas, which may be critical for abnormal perception, estimation, and emotion reactions during the anticipation of uncertain threat. © 2018 Wiley Periodicals, Inc.

Poster - 31: Predicting IQ and hearing loss following radiotherapy in pediatric brain tumors: proton vs photon

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fortin, Dominique; Ng, Angela; Tsang, Derek

Purpose: The increased sparing of normal tissues in intensity modulated proton therapy (IMPT) in pediatric brain tumor treatments should translate into improved neurocognitive outcomes. Models were used to estimate the intelligence quotient (IQ) and the risk of hearing loss 5 years post radiotherapy and to compare outcomes of proton against photon in pediatric brain tumors. Methods: Patients who had received intensity modulated radiotherapy (IMRT) were randomly selected from our retrospective database. The existing planning CT and contours were used to generate IMPT plans. The RBE-corrected dose was calculated for both IMPT and IMRT. For each patient, the IQ was estimatedmore » via a Monte Carlo technique, whereas the reported incidence of hearing loss as a function of cochlear dose was used to estimate the probability of occurrence. Results: The integrated brain dose was reduced in all IMPT plans, translating into a gain of 2 IQ points on average for protons for the whole cohort at 5 years post-treatment. In terms of specific diseases, the gains in IQ ranged from 0.8 points for medulloblastoma, to 2.7 points for craniopharyngioma. Hearing loss probability was evaluated on a per-ear-basis and was found to be systematically less for proton versus photon: overall 2.9% versus 7.2%. Conclusions: A method was developed to predict IQ and hearing outcomes in pediatric brain tumor patients on a case-by-case basis. A modest gain was systematically observed for proton in all patients. Given the uncertainties within the model used and our reinterpretation, these gains may be underestimated.« less

Detecting event-related changes of multivariate phase coupling in dynamic brain networks.

PubMed

Canolty, Ryan T; Cadieu, Charles F; Koepsell, Kilian; Ganguly, Karunesh; Knight, Robert T; Carmena, Jose M

2012-04-01

Oscillatory phase coupling within large-scale brain networks is a topic of increasing interest within systems, cognitive, and theoretical neuroscience. Evidence shows that brain rhythms play a role in controlling neuronal excitability and response modulation (Haider B, McCormick D. Neuron 62: 171-189, 2009) and regulate the efficacy of communication between cortical regions (Fries P. Trends Cogn Sci 9: 474-480, 2005) and distinct spatiotemporal scales (Canolty RT, Knight RT. Trends Cogn Sci 14: 506-515, 2010). In this view, anatomically connected brain areas form the scaffolding upon which neuronal oscillations rapidly create and dissolve transient functional networks (Lakatos P, Karmos G, Mehta A, Ulbert I, Schroeder C. Science 320: 110-113, 2008). Importantly, testing these hypotheses requires methods designed to accurately reflect dynamic changes in multivariate phase coupling within brain networks. Unfortunately, phase coupling between neurophysiological signals is commonly investigated using suboptimal techniques. Here we describe how a recently developed probabilistic model, phase coupling estimation (PCE; Cadieu C, Koepsell K Neural Comput 44: 3107-3126, 2010), can be used to investigate changes in multivariate phase coupling, and we detail the advantages of this model over the commonly employed phase-locking value (PLV; Lachaux JP, Rodriguez E, Martinerie J, Varela F. Human Brain Map 8: 194-208, 1999). We show that the N-dimensional PCE is a natural generalization of the inherently bivariate PLV. Using simulations, we show that PCE accurately captures both direct and indirect (network mediated) coupling between network elements in situations where PLV produces erroneous results. We present empirical results on recordings from humans and nonhuman primates and show that the PCE-estimated coupling values are different from those using the bivariate PLV. Critically on these empirical recordings, PCE output tends to be sparser than the PLVs, indicating fewer significant interactions and perhaps a more parsimonious description of the data. Finally, the physical interpretation of PCE parameters is straightforward: the PCE parameters correspond to interaction terms in a network of coupled oscillators. Forward modeling of a network of coupled oscillators with parameters estimated by PCE generates synthetic data with statistical characteristics identical to empirical signals. Given these advantages over the PLV, PCE is a useful tool for investigating multivariate phase coupling in distributed brain networks.

Longitudinal connectome-based predictive modeling for REM sleep behavior disorder from structural brain connectivity

NASA Astrophysics Data System (ADS)

Giancardo, Luca; Ellmore, Timothy M.; Suescun, Jessika; Ocasio, Laura; Kamali, Arash; Riascos-Castaneda, Roy; Schiess, Mya C.

2018-02-01

Methods to identify neuroplasticity patterns in human brains are of the utmost importance in understanding and potentially treating neurodegenerative diseases. Parkinson disease (PD) research will greatly benefit and advance from the discovery of biomarkers to quantify brain changes in the early stages of the disease, a prodromal period when subjects show no obvious clinical symptoms. Diffusion tensor imaging (DTI) allows for an in-vivo estimation of the structural connectome inside the brain and may serve to quantify the degenerative process before the appearance of clinical symptoms. In this work, we introduce a novel strategy to compute longitudinal structural connectomes in the context of a whole-brain data-driven pipeline. In these initial tests, we show that our predictive models are able to distinguish controls from asymptomatic subjects at high risk of developing PD (REM sleep behavior disorder, RBD) with an area under the receiving operating characteristic curve of 0.90 (p<0.001) and a longitudinal dataset of 46 subjects part of the Parkinson's Progression Markers Initiative. By analyzing the brain connections most relevant for the predictive ability of the best performing model, we find connections that are biologically relevant to the disease.

The Hierarchy of Brain Networks Is Related to Insulin Growth Factor-1 in a Large, Middle-Aged, Healthy Cohort: An Exploratory Magnetoencephalography Study.

PubMed

Sorrentino, Pierpaolo; Nieboer, Dagmar; Twisk, Jos W R; Stam, Cornelis J; Douw, Linda; Hillebrand, Arjan

2017-06-01

Recently, a large study demonstrated that lower serum levels of insulin growth factor-1 (IGF-1) relate to brain atrophy and to a greater risk for developing Alzheimer's disease in a healthy elderly population. We set out to test if functional brain networks relate to IGF-1 levels in the middle aged. Hence, we studied the association between IGF-1 and magnetoencephalography-based functional network characteristics in a middle-aged population. The functional connections between brain areas were estimated for six frequency bands (delta, theta, alpha1, alpha2, beta, gamma) using the phase lag index. Subsequently, the topology of the frequency-specific functional networks was characterized using the minimum spanning tree. Our results showed that lower levels of serum IGF-1 relate to a globally less integrated functional network in the beta and theta band. The associations remained significant when correcting for gender and systemic effects of IGF-1 that might indirectly affect the brain. The value of this exploratory study is the demonstration that lower levels of IGF-1 are associated with brain network topology in the middle aged.

Relaxed genetic control of cortical organization in human brains compared with chimpanzees

PubMed Central

Gómez-Robles, Aida; Hopkins, William D.; Schapiro, Steven J.; Sherwood, Chet C.

2015-01-01

The study of hominin brain evolution has focused largely on the neocortical expansion and reorganization undergone by humans as inferred from the endocranial fossil record. Comparisons of modern human brains with those of chimpanzees provide an additional line of evidence to define key neural traits that have emerged in human evolution and that underlie our unique behavioral specializations. In an attempt to identify fundamental developmental differences, we have estimated the genetic bases of brain size and cortical organization in chimpanzees and humans by studying phenotypic similarities between individuals with known kinship relationships. We show that, although heritability for brain size and cortical organization is high in chimpanzees, cerebral cortical anatomy is substantially less genetically heritable than brain size in humans, indicating greater plasticity and increased environmental influence on neurodevelopment in our species. This relaxed genetic control on cortical organization is especially marked in association areas and likely is related to underlying microstructural changes in neural circuitry. A major result of increased plasticity is that the development of neural circuits that underlie behavior is shaped by the environmental, social, and cultural context more intensively in humans than in other primate species, thus providing an anatomical basis for behavioral and cognitive evolution. PMID:26627234

Permeability and route of entry for lipid-insoluble molecules across brain barriers in developing Monodelphis domestica

PubMed Central

Ek, C Joakim; Habgood, Mark D; Dziegielewska, Katarzyna M; Potter, Ann; Saunders, Norman R

2001-01-01

We have studied the permeability of blood-brain barriers to small molecules such as [14C]sucrose, [3H]inulin, [14C]l-glucose and [3H]glycerol from early stages of development (postnatal day 6, P6) in South American opossums (Monodelphis domestica), using a litter-based method for estimating steady-state cerebrospinal fluid (CSF)/plasma and brain/plasma ratios of markers that were injected i.p.. Steady-state ratios for l-glucose, sucrose and inulin all showed progressive decreases during development. The rate of uptake of l-glucose into the brain and CSF, in short time course experiments (7–24 min) when age-related differences in CSF production can be considered negligible also decreased during development. These results indicate that there is a significant decrease in the permeability of brain barriers to small lipid-insoluble molecules during brain development. The steady-state blood/CSF ratio for 3000 Da lysine-fixable biotin-dextran following i.p. injection was shown to be consistent with diffusion from blood to CSF. It was therefore used to visualise the route of penetration for small lipid-insoluble molecules across brain barriers at P 0–30. The proportion of biotin-dextran-positive cells in the choroid plexuses declined in parallel with the age-related decline in permeability to the small-molecular-weight markers; the paracellular (tight junction) pathway for biotin-dextran appeared to be blocked, but biotin-dextran was easily detectable in the CSF. A transcellular route from blood to CSF was suggested by the finding that some choroid plexus epithelial cells contained biotin-dextran. Biotin-dextran was also taken up by cerebral endothelial cells in the youngest brains studied (P0), but in contrast to the CSF, could not be detected in the brain extracellular space (i.e. a significant blood-brain barrier to small-sized lipid-insoluble compounds was already present). However, in immature brains (P0–13) biotin-dextran was taken up by some cells in the brain. These cells generally had contact with the CSF, suggesting that it is likely to have been the 2source of their biotin-dextran. Since the quantitative permeability data suggest that biotin-dextran behaves similarly to the radiolabelled markers used in this study, it is suggested that these markers in the more immature brains were also present intracellularly. Thus, brain/plasma ratios may be a misleading indicator of blood-brain barrier permeability in very immature animals. The immunocytochemical staining for biotin-dextran in the CSF, in contrast to the lack of staining in the brain extracellular space, together with the quantitative permeability data showing that the radiolabelled markers penetrated more rapidly and to a much higher steady-state level in CSF than in the brain, suggests that lipid-insoluble molecules such as sucrose and inulin reach the immature brain predominantly via the CSF rather than directly across the very few blood vessels that are present at that time. PMID:11691876

Complex network analysis of resting-state fMRI of the brain.

PubMed

Anwar, Abdul Rauf; Hashmy, Muhammad Yousaf; Imran, Bilal; Riaz, Muhammad Hussnain; Mehdi, Sabtain Muhammad Muntazir; Muthalib, Makii; Perrey, Stephane; Deuschl, Gunther; Groppa, Sergiu; Muthuraman, Muthuraman

2016-08-01

Due to the fact that the brain activity hardly ever diminishes in healthy individuals, analysis of resting state functionality of the brain seems pertinent. Various resting state networks are active inside the idle brain at any time. Based on various neuro-imaging studies, it is understood that various structurally distant regions of the brain could be functionally connected. Regions of the brain, that are functionally connected, during rest constitutes to the resting state network. In the present study, we employed the complex network measures to estimate the presence of community structures within a network. Such estimate is named as modularity. Instead of using a traditional correlation matrix, we used a coherence matrix taken from the causality measure between different nodes. Our results show that in prolonged resting state the modularity starts to decrease. This decrease was observed in all the resting state networks and on both sides of the brain. Our study highlights the usage of coherence matrix instead of correlation matrix for complex network analysis.

The cost of health professionals' brain drain in Kenya.

PubMed

Kirigia, Joses Muthuri; Gbary, Akpa Raphael; Muthuri, Lenity Kainyu; Nyoni, Jennifer; Seddoh, Anthony

2006-07-17

Past attempts to estimate the cost of migration were limited to education costs only and did not include the lost returns from investment. The objectives of this study were: (i) to estimate the financial cost of emigration of Kenyan doctors to the United Kingdom (UK) and the United States of America (USA); (ii) to estimate the financial cost of emigration of nurses to seven OECD countries (Canada, Denmark, Finland, Ireland, Portugal, UK, USA); and (iii) to describe other losses from brain drain. The costs of primary, secondary, medical and nursing schools were estimated in 2005. The cost information used in this study was obtained from one non-profit primary and secondary school and one public university in Kenya. The cost estimates represent unsubsidized cost. The loss incurred by Kenya through emigration was obtained by compounding the cost of educating a medical doctor and a nurse over the period between the average age of emigration (30 years) and the age of retirement (62 years) in recipient countries. The total cost of educating a single medical doctor from primary school to university is 65,997 US dollars; and for every doctor who emigrates, a country loses about 517,931 US dollars worth of returns from investment. The total cost of educating one nurse from primary school to college of health sciences is 43,180 US dollars; and for every nurse that emigrates, a country loses about 338,868 US dollars worth of returns from investment. Developed countries continue to deprive Kenya of millions of dollars worth of investments embodied in her human resources for health. If the current trend of poaching of scarce human resources for health (and other professionals) from Kenya is not curtailed, the chances of achieving the Millennium Development Goals would remain bleak. Such continued plunder of investments embodied in human resources contributes to further underdevelopment of Kenya and to keeping a majority of her people in the vicious circle of ill-health and poverty. Therefore, both developed and developing countries need to urgently develop and implement strategies for addressing the health human resource crisis.

The cost of health professionals' brain drain in Kenya

PubMed Central

Kirigia, Joses Muthuri; Gbary, Akpa Raphael; Muthuri, Lenity Kainyu; Nyoni, Jennifer; Seddoh, Anthony

2006-01-01

Background Past attempts to estimate the cost of migration were limited to education costs only and did not include the lost returns from investment. The objectives of this study were: (i) to estimate the financial cost of emigration of Kenyan doctors to the United Kingdom (UK) and the United States of America (USA); (ii) to estimate the financial cost of emigration of nurses to seven OECD countries (Canada, Denmark, Finland, Ireland, Portugal, UK, USA); and (iii) to describe other losses from brain drain. Methods The costs of primary, secondary, medical and nursing schools were estimated in 2005. The cost information used in this study was obtained from one non-profit primary and secondary school and one public university in Kenya. The cost estimates represent unsubsidized cost. The loss incurred by Kenya through emigration was obtained by compounding the cost of educating a medical doctor and a nurse over the period between the average age of emigration (30 years) and the age of retirement (62 years) in recipient countries. Results The total cost of educating a single medical doctor from primary school to university is US$ 65,997; and for every doctor who emigrates, a country loses about US$ 517,931 worth of returns from investment. The total cost of educating one nurse from primary school to college of health sciences is US$ 43,180; and for every nurse that emigrates, a country loses about US$ 338,868 worth of returns from investment. Conclusion Developed countries continue to deprive Kenya of millions of dollars worth of investments embodied in her human resources for health. If the current trend of poaching of scarce human resources for health (and other professionals) from Kenya is not curtailed, the chances of achieving the Millennium Development Goals would remain bleak. Such continued plunder of investments embodied in human resources contributes to further underdevelopment of Kenya and to keeping a majority of her people in the vicious circle of ill-health and poverty. Therefore, both developed and developing countries need to urgently develop and implement strategies for addressing the health human resource crisis. PMID:16846492

Optimal-mass-transfer-based estimation of glymphatic transport in living brain

NASA Astrophysics Data System (ADS)

Ratner, Vadim; Zhu, Liangjia; Kolesov, Ivan; Nedergaard, Maiken; Benveniste, Helene; Tannenbaum, Allen

2015-03-01

It was recently shown that the brain-wide cerebrospinal fluid (CSF) and interstitial fluid exchange system designated the `glymphatic pathway' plays a key role in removing waste products from the brain, similarly to the lymphatic system in other body organs . It is therefore important to study the flow patterns of glymphatic transport through the live brain in order to better understand its functionality in normal and pathological states. Unlike blood, the CSF does not flow rapidly through a network of dedicated vessels, but rather through para-vascular channels and brain parenchyma in a slower time-domain, and thus conventional fMRI or other blood-flow sensitive MRI sequences do not provide much useful information about the desired flow patterns. We have accordingly analyzed a series of MRI images, taken at different times, of the brain of a live rat, which was injected with a paramagnetic tracer into the CSF via the lumbar intrathecal space of the spine. Our goal is twofold: (a) find glymphatic (tracer) flow directions in the live rodent brain; and (b) provide a model of a (healthy) brain that will allow the prediction of tracer concentrations given initial conditions. We model the liquid flow through the brain by the diffusion equation. We then use the Optimal Mass Transfer (OMT) approach to derive the glymphatic flow vector field, and estimate the diffusion tensors by analyzing the (changes in the) flow. Simulations show that the resulting model successfully reproduces the dominant features of the experimental data. Keywords: inverse problem, optimal mass transport, diffusion equation, cerebrospinal fluid flow in brain, optical flow, liquid flow modeling, Monge Kantorovich problem, diffusion tensor estimation

Implementation of magnetic resonance elastography for the investigation of traumatic brain injuries

NASA Astrophysics Data System (ADS)

Boulet, Thomas

Magnetic resonance elastography (MRE) is a potentially transformative imaging modality allowing local and non-invasive measurement of biological tissue mechanical properties. It uses a specific phase contrast MR pulse sequence to measure induced vibratory motion in soft material, from which material properties can be estimated. Compared to other imaging techniques, MRE is able to detect tissue pathology at early stages by quantifying the changes in tissue stiffness associated with diseases. In an effort to develop the technique and improve its capabilities, two inversion algorithms were written to evaluate viscoelastic properties from the measured displacements fields. The first one was based on a direct algebraic inversion of the differential equation of motion, which decouples under certain simplifying assumptions, and featured a spatio-temporal multi-directional filter. The second one relies on a finite element discretization of the governing equations to perform a direct inversion. Several applications of this technique have also been investigated, including the estimation of mechanical parameters in various gel phantoms and polymers, as well as the use of MRE as a diagnostic tools for brain disorders. In this respect, the particular interest was to investigate traumatic brain injury (TBI), a complex and diverse injury affecting 1.7 million Americans annually. The sensitivity of MRE to TBI was first assessed on excised rat brains subjected to a controlled cortical impact (CCI) injury, before execution of in vivo experiments in mice. MRE was also applied in vivo on mouse models of medulloblastoma tumors and multiple sclerosis. These studies showed the potential of MRE in mapping the brain mechanically and providing non-invasive in vivo imaging markers for neuropathology and pathogenesis of brain diseases. Furthermore, MRE can easily be translatable to clinical settings; thus, while this technique may not be used directly to diagnose different abnormalities in the brain at this time, it may be helpful to detect abnormalities, follow therapies, and trace macroscopic changes that are not seen by conventional methods with clinical relevance.

Disrupted global metastability and static and dynamic brain connectivity across individuals in the Alzheimer’s disease continuum

NASA Astrophysics Data System (ADS)

Córdova-Palomera, Aldo; Kaufmann, Tobias; Persson, Karin; Alnæs, Dag; Doan, Nhat Trung; Moberget, Torgeir; Lund, Martina Jonette; Barca, Maria Lage; Engvig, Andreas; Brækhus, Anne; Engedal, Knut; Andreassen, Ole A.; Selbæk, Geir; Westlye, Lars T.

2017-01-01

As findings on the neuropathological and behavioral components of Alzheimer’s disease (AD) continue to accrue, converging evidence suggests that macroscale brain functional disruptions may mediate their association. Recent developments on theoretical neuroscience indicate that instantaneous patterns of brain connectivity and metastability may be a key mechanism in neural communication underlying cognitive performance. However, the potential significance of these patterns across the AD spectrum remains virtually unexplored. We assessed the clinical sensitivity of static and dynamic functional brain disruptions across the AD spectrum using resting-state fMRI in a sample consisting of AD patients (n = 80) and subjects with either mild (n = 44) or subjective (n = 26) cognitive impairment (MCI, SCI). Spatial maps constituting the nodes in the functional brain network and their associated time-series were estimated using spatial group independent component analysis and dual regression, and whole-brain oscillatory activity was analyzed both globally (metastability) and locally (static and dynamic connectivity). Instantaneous phase metrics showed functional coupling alterations in AD compared to MCI and SCI, both static (putamen, dorsal and default-mode) and dynamic (temporal, frontal-superior and default-mode), along with decreased global metastability. The results suggest that brains of AD patients display altered oscillatory patterns, in agreement with theoretical premises on cognitive dynamics.

Nanoparticle transport across in vitro olfactory cell monolayers.

PubMed

Gartziandia, Oihane; Egusquiaguirre, Susana Patricia; Bianco, John; Pedraz, José Luis; Igartua, Manoli; Hernandez, Rosa Maria; Préat, Véronique; Beloqui, Ana

2016-02-29

Drug access to the CNS is hindered by the presence of the blood-brain barrier (BBB), and the intranasal route has risen as a non-invasive route to transport drugs directly from nose-to-brain avoiding the BBB. In addition, nanoparticles (NPs) have been described as efficient shuttles for direct nose-to-brain delivery of drugs. Nevertheless, there are few studies describing NP nose-to-brain transport. Thus, the aim of this work was (i) to develop, characterize and validate in vitro olfactory cell monolayers and (ii) to study the transport of polymeric- and lipid-based NPs across these monolayers in order to estimate NP access into the brain using cell penetrating peptide (CPPs) moieties: Tat and Penetratin (Pen). All tested poly(d,l-lactide-co-glycolide) (PLGA) and nanostructured lipid carrier (NLC) formulations were stable in transport buffer and biocompatible with the olfactory mucosa cells. Nevertheless, 0.7% of PLGA NPs was able to cross the olfactory cell monolayers, whereas 8% and 22% of NLC and chitosan-coated NLC (CS-NLC) were transported across them, respectively. Moreover, the incorporation of CPPs to NLC surface significantly increased their transport, reaching 46% of transported NPs. We conclude that CPP-CS-NLC represent a promising brain shuttle via nose-to-brain for drug delivery. Copyright © 2015 Elsevier B.V. All rights reserved.

A Multidisciplinary Breast Cancer Brain Metastases Clinic: The University of North Carolina Experience.

PubMed

McKee, Megan J; Keith, Kevin; Deal, Allison M; Garrett, Amy L; Wheless, Amy A; Green, Rebecca L; Benbow, Julie M; Dees, E Claire; Carey, Lisa A; Ewend, Matthew G; Anders, Carey K; Zagar, Timothy M

2016-01-01

Breast cancer brain metastasis (BCBM) confers a poor prognosis and is unusual in requiring multidisciplinary care in the metastatic setting. The University of North Carolina at Chapel Hill (UNC-CH) has created a BCBM clinic to provide medical and radiation oncology, neurosurgical, and supportive services to this complex patient population. We describe organization and design of the clinic as well as characteristics, treatments, and outcomes of the patients seen in its first 3 years. Clinical and demographic data were collected from patients in a prospectively maintained database. Descriptive statistics are reported as percentages and means. The Kaplan-Meier method was used to estimate time-to-event outcomes. Sixty-five patients were seen between January 2012 and January 2015. At the time of presentation to the BCBM clinic, most patients (74%) had multiple (≥2) brain metastases and had received prior systemic (77%) and whole-brain radiation therapy and/or central nervous system stereotactic radiosurgery (65%) in the metastatic setting. Seventy-eight percent returned for a follow-up visit; 32% were enrolled in a clinical trial. Median time from diagnosis of brain metastasis to death was 2.11 years (95% confidence interval [CI] 1.31-2.47) for all patients, 1.15 years (95% CI 0.4-2.43) for triple-negative breast cancer, 1.31 years (95% CI 0.51-2.52) for hormone receptor-positive/HER2- breast cancer, and 3.03 years (95% CI lower limit 1.94, upper limit not estimable) for HER2+ breast cancer (p = .0037). Patients with BCBM have unique and complex needs that require input from several oncologic disciplines. The development of the UNC-CH multidisciplinary BCBM clinic is a model that can be adapted at other centers to provide coordinated care for patients with a challenging and complex disease. Patients with breast cancer brain metastases often require unique multidisciplinary care to meet the numerous and uncommon challenges associated with their conditions. Here, the development and characteristics of a clinic designed specifically to provide for the multidisciplinary needs of patients with breast cancer brain metastases are described. This clinic may serve as a model for other institutions interested in creating specialty clinics with similar objectives. ©AlphaMed Press.

Revisiting atenolol as a low passive permeability marker.

PubMed

Chen, Xiaomei; Slättengren, Tim; de Lange, Elizabeth C M; Smith, David E; Hammarlund-Udenaes, Margareta

2017-10-31

Atenolol, a hydrophilic beta blocker, has been used as a model drug for studying passive permeability of biological membranes such as the blood-brain barrier (BBB) and the intestinal epithelium. However, the extent of S-atenolol (the active enantiomer) distribution in brain has never been evaluated, at equilibrium, to confirm that no transporters are involved in its transport at the BBB. To assess whether S-atenolol, in fact, depicts the characteristics of a low passive permeable drug at the BBB, a microdialysis study was performed in rats to monitor the unbound concentrations of S-atenolol in brain extracellular fluid (ECF) and plasma during and after intravenous infusion. A pharmacokinetic model was developed, based on the microdialysis data, to estimate the permeability clearance of S-atenolol into and out of brain. In addition, the nonspecific binding of S-atenolol in brain homogenate was evaluated using equilibrium dialysis. The steady-state ratio of unbound S-atenolol concentrations in brain ECF to that in plasma (i.e., K p,uu,brain ) was 3.5% ± 0.4%, a value much less than unity. The unbound volume of distribution in brain (V u, brain ) of S-atenolol was also calculated as 0.69 ± 0.10 mL/g brain, indicating that S-atenolol is evenly distributed within brain parenchyma. Lastly, equilibrium dialysis showed limited nonspecific binding of S-atenolol in brain homogenate with an unbound fraction (f u,brain ) of 0.88 ± 0.07. It is concluded, based on K p,uu,brain being much smaller than unity, that S-atenolol is actively effluxed at the BBB, indicating the need to re-consider S-atenolol as a model drug for passive permeability studies of BBB transport or intestinal absorption.

Perfusion CT of the Brain and Liver and of Lung Tumors: Use of Monte Carlo Simulation for Patient Dose Estimation for Examinations With a Cone-Beam 320-MDCT Scanner.

PubMed

Cros, Maria; Geleijns, Jacob; Joemai, Raoul M S; Salvadó, Marçal

2016-01-01

The purpose of this study was to estimate the patient dose from perfusion CT examinations of the brain, lung tumors, and the liver on a cone-beam 320-MDCT scanner using a Monte Carlo simulation and the recommendations of the International Commission on Radiological Protection (ICRP). A Monte Carlo simulation based on the Electron Gamma Shower Version 4 package code was used to calculate organ doses and the effective dose in the reference computational phantoms for an adult man and adult woman as published by the ICRP. Three perfusion CT acquisition protocols--brain, lung tumor, and liver perfusion--were evaluated. Additionally, dose assessments were performed for the skin and for the eye lens. Conversion factors were obtained to estimate effective doses and organ doses from the volume CT dose index and dose-length product. The sex-averaged effective doses were approximately 4 mSv for perfusion CT of the brain and were between 23 and 26 mSv for the perfusion CT body protocols. The eye lens dose from the brain perfusion CT examination was approximately 153 mGy. The sex-averaged peak entrance skin dose (ESD) was 255 mGy for the brain perfusion CT studies, 157 mGy for the lung tumor perfusion CT studies, and 172 mGy for the liver perfusion CT studies. The perfusion CT protocols for imaging the brain, lung tumors, and the liver performed on a 320-MDCT scanner yielded patient doses that are safely below the threshold doses for deterministic effects. The eye lens dose, peak ESD, and effective doses can be estimated for other clinical perfusion CT examinations from the conversion factors that were derived in this study.

Brain-computer interface for alertness estimation and improving

NASA Astrophysics Data System (ADS)

Hramov, Alexander; Maksimenko, Vladimir; Hramova, Marina

2018-02-01

Using wavelet analysis of the signals of electrical brain activity (EEG), we study the processes of neural activity, associated with perception of visual stimuli. We demonstrate that the brain can process visual stimuli in two scenarios: (i) perception is characterized by destruction of the alpha-waves and increase in the high-frequency (beta) activity, (ii) the beta-rhythm is not well pronounced, while the alpha-wave energy remains unchanged. The special experiments show that the motivation factor initiates the first scenario, explained by the increasing alertness. Based on the obtained results we build the brain-computer interface and demonstrate how the degree of the alertness can be estimated and controlled in real experiment.

Brain Tumor Hyperthermia with Static and Moving Seeds

NASA Astrophysics Data System (ADS)

Molloy, Janelle Arlene

1990-01-01

Thermodynamic studies are presented for both static and moving ferromagnetic "seeds" imbedded in biological media. These studies were performed in support of the development of a system which delivers localized hyperthermia to deep-seated brain tumors. In this system, a magnetic "seed" of approximately 5 mm dimension (length and diameter) is remotely repositioned within the brain by an externally produced magnetic field. The seed is inductively heated and repositioned throughout the tumor volume. An induction heating system was built for experimental use with tissue phantoms and animals. The maximum level of direct tissue heating produced by this system was measured in vivo in three animals. An upper limit on the power absorption was placed at 0.46 mW cm^{ -3}, a factor of 10^{-4 } below the power density produced in ferromagnetic seeds by the same system. Measurements were made of the temporal and spatial dependence of the temperature rise in the vicinity of a statically placed 6 mm diameter nickel sphere, in vivo in four pigs, and in one which was euthanized. These results were compared to a theroetical model which was based on a point source solution to the thermal diffusion equation and estimates of blood flow rates, tissue thermal conductivity and seed power absorption were found using a parameter estimation algorithm. Studies were also made of the temperature gradients produced by a heated iron ellipsoid of 4.8 mm diameter and 9.6 mm length in a brain tissue phantom. Temperature measurements were made both with the seed statically imbedded in the tissue phantom and with the phantom moving at a constant velocity of 0.11 mm s^{-1 } with respect to the seed. These static and moving data were compared to obtain an estimate for the thermal field and convective cooling of a moving seed. In addition, an exploratory study was performed in which the dependence of seed heating efficiency on material and geometry were tested. A "hybrid" seed was developed consisting of a permanent magnet core surrounded by a non -magnetic spacing material and a 0.5 mm thick ferromagnetic outer sleeve. This seed was designed to accommodate potentially conflicting magnetic force and induction heating requirements.

Using Copula Distributions to Support More Accurate Imaging-Based Diagnostic Classifiers for Neuropsychiatric Disorders

PubMed Central

Bansal, Ravi; Hao, Xuejun; Liu, Jun; Peterson, Bradley S.

2014-01-01

Many investigators have tried to apply machine learning techniques to magnetic resonance images (MRIs) of the brain in order to diagnose neuropsychiatric disorders. Usually the number of brain imaging measures (such as measures of cortical thickness and measures of local surface morphology) derived from the MRIs (i.e., their dimensionality) has been large (e.g. >10) relative to the number of participants who provide the MRI data (<100). Sparse data in a high dimensional space increases the variability of the classification rules that machine learning algorithms generate, thereby limiting the validity, reproducibility, and generalizability of those classifiers. The accuracy and stability of the classifiers can improve significantly if the multivariate distributions of the imaging measures can be estimated accurately. To accurately estimate the multivariate distributions using sparse data, we propose to estimate first the univariate distributions of imaging data and then combine them using a Copula to generate more accurate estimates of their multivariate distributions. We then sample the estimated Copula distributions to generate dense sets of imaging measures and use those measures to train classifiers. We hypothesize that the dense sets of brain imaging measures will generate classifiers that are stable to variations in brain imaging measures, thereby improving the reproducibility, validity, and generalizability of diagnostic classification algorithms in imaging datasets from clinical populations. In our experiments, we used both computer-generated and real-world brain imaging datasets to assess the accuracy of multivariate Copula distributions in estimating the corresponding multivariate distributions of real-world imaging data. Our experiments showed that diagnostic classifiers generated using imaging measures sampled from the Copula were significantly more accurate and more reproducible than were the classifiers generated using either the real-world imaging measures or their multivariate Gaussian distributions. Thus, our findings demonstrate that estimated multivariate Copula distributions can generate dense sets of brain imaging measures that can in turn be used to train classifiers, and those classifiers are significantly more accurate and more reproducible than are those generated using real-world imaging measures alone. PMID:25093634

Joint Estimation of Effective Brain Wave Activation Modes Using EEG/MEG Sensor Arrays and Multimodal MRI Volumes.

PubMed

Galinsky, Vitaly L; Martinez, Antigona; Paulus, Martin P; Frank, Lawrence R

2018-04-13

In this letter, we present a new method for integration of sensor-based multifrequency bands of electroencephalography and magnetoencephalography data sets into a voxel-based structural-temporal magnetic resonance imaging analysis by utilizing the general joint estimation using entropy regularization (JESTER) framework. This allows enhancement of the spatial-temporal localization of brain function and the ability to relate it to morphological features and structural connectivity. This method has broad implications for both basic neuroscience research and clinical neuroscience focused on identifying disease-relevant biomarkers by enhancing the spatial-temporal resolution of the estimates derived from current neuroimaging modalities, thereby providing a better picture of the normal human brain in basic neuroimaging experiments and variations associated with disease states.

Robust Brain-Machine Interface Design Using Optimal Feedback Control Modeling and Adaptive Point Process Filtering

PubMed Central

Carmena, Jose M.

2016-01-01

Much progress has been made in brain-machine interfaces (BMI) using decoders such as Kalman filters and finding their parameters with closed-loop decoder adaptation (CLDA). However, current decoders do not model the spikes directly, and hence may limit the processing time-scale of BMI control and adaptation. Moreover, while specialized CLDA techniques for intention estimation and assisted training exist, a unified and systematic CLDA framework that generalizes across different setups is lacking. Here we develop a novel closed-loop BMI training architecture that allows for processing, control, and adaptation using spike events, enables robust control and extends to various tasks. Moreover, we develop a unified control-theoretic CLDA framework within which intention estimation, assisted training, and adaptation are performed. The architecture incorporates an infinite-horizon optimal feedback-control (OFC) model of the brain’s behavior in closed-loop BMI control, and a point process model of spikes. The OFC model infers the user’s motor intention during CLDA—a process termed intention estimation. OFC is also used to design an autonomous and dynamic assisted training technique. The point process model allows for neural processing, control and decoder adaptation with every spike event and at a faster time-scale than current decoders; it also enables dynamic spike-event-based parameter adaptation unlike current CLDA methods that use batch-based adaptation on much slower adaptation time-scales. We conducted closed-loop experiments in a non-human primate over tens of days to dissociate the effects of these novel CLDA components. The OFC intention estimation improved BMI performance compared with current intention estimation techniques. OFC assisted training allowed the subject to consistently achieve proficient control. Spike-event-based adaptation resulted in faster and more consistent performance convergence compared with batch-based methods, and was robust to parameter initialization. Finally, the architecture extended control to tasks beyond those used for CLDA training. These results have significant implications towards the development of clinically-viable neuroprosthetics. PMID:27035820

Creating an anthropomorphic digital MR phantom—an extensible tool for comparing and evaluating quantitative imaging algorithms

NASA Astrophysics Data System (ADS)

Bosca, Ryan J.; Jackson, Edward F.

2016-01-01

Assessing and mitigating the various sources of bias and variance associated with image quantification algorithms is essential to the use of such algorithms in clinical research and practice. Assessment is usually accomplished with grid-based digital reference objects (DRO) or, more recently, digital anthropomorphic phantoms based on normal human anatomy. Publicly available digital anthropomorphic phantoms can provide a basis for generating realistic model-based DROs that incorporate the heterogeneity commonly found in pathology. Using a publicly available vascular input function (VIF) and digital anthropomorphic phantom of a normal human brain, a methodology was developed to generate a DRO based on the general kinetic model (GKM) that represented realistic and heterogeneously enhancing pathology. GKM parameters were estimated from a deidentified clinical dynamic contrast-enhanced (DCE) MRI exam. This clinical imaging volume was co-registered with a discrete tissue model, and model parameters estimated from clinical images were used to synthesize a DCE-MRI exam that consisted of normal brain tissues and a heterogeneously enhancing brain tumor. An example application of spatial smoothing was used to illustrate potential applications in assessing quantitative imaging algorithms. A voxel-wise Bland-Altman analysis demonstrated negligible differences between the parameters estimated with and without spatial smoothing (using a small radius Gaussian kernel). In this work, we reported an extensible methodology for generating model-based anthropomorphic DROs containing normal and pathological tissue that can be used to assess quantitative imaging algorithms.

Improved estimates of partial volume coefficients from noisy brain MRI using spatial context.

PubMed

Manjón, José V; Tohka, Jussi; Robles, Montserrat

2010-11-01

This paper addresses the problem of accurate voxel-level estimation of tissue proportions in the human brain magnetic resonance imaging (MRI). Due to the finite resolution of acquisition systems, MRI voxels can contain contributions from more than a single tissue type. The voxel-level estimation of this fractional content is known as partial volume coefficient estimation. In the present work, two new methods to calculate the partial volume coefficients under noisy conditions are introduced and compared with current similar methods. Concretely, a novel Markov Random Field model allowing sharp transitions between partial volume coefficients of neighbouring voxels and an advanced non-local means filtering technique are proposed to reduce the errors due to random noise in the partial volume coefficient estimation. In addition, a comparison was made to find out how the different methodologies affect the measurement of the brain tissue type volumes. Based on the obtained results, the main conclusions are that (1) both Markov Random Field modelling and non-local means filtering improved the partial volume coefficient estimation results, and (2) non-local means filtering was the better of the two strategies for partial volume coefficient estimation. Copyright 2010 Elsevier Inc. All rights reserved.

Prolonged maternal separation attenuates BDNF-ERK signaling correlated with spine formation in the hippocampus during early brain development.

PubMed

Ohta, Ken-Ichi; Suzuki, Shingo; Warita, Katsuhiko; Kaji, Tomohiro; Kusaka, Takashi; Miki, Takanori

2017-04-01

Maternal separation (MS) is known to affect hippocampal function such as learning and memory, yet the molecular mechanism remains unknown. We hypothesized that these impairments are attributed to abnormities of neural circuit formation by MS, and focused on brain-derived neurotrophic factor (BDNF) as key factor because BDNF signaling has an essential role in synapse formation during early brain development. Using rat offspring exposed to MS for 6 h/day during postnatal days (PD) 2-20, we estimated BDNF signaling in the hippocampus during brain development. Our results show that MS attenuated BDNF expression and activation of extracellular signal-regulated kinase (ERK) around PD 7. Moreover, plasticity-related immediate early genes, which are transcriptionally regulated by BDNF-ERK signaling, were also reduced by MS around PD 7. Interestingly, detailed analysis revealed that MS particularly reduced expression of BDNF gene and immediate early genes in the cornu ammonis 1 (CA1) of hippocampus at PD 7. Considering that BDNF-ERK signaling is involved in spine formation, we next evaluated spine formation in the hippocampus during the weaning period. Our results show that MS particularly reduced mature spine density in proximal apical dendrites of CA1 pyramidal neurons at PD 21. These results suggest that MS could attenuate BDNF-ERK signaling during primary synaptogenesis with a region-specific manner, which is likely to lead to decreased spine formation and maturation observed in the hippocampal CA1 region. It is speculated that this incomplete spine formation during early brain development has an influence on learning capabilities throughout adulthood. © 2017 International Society for Neurochemistry.

Uncertainty analysis for absorbed dose from a brain receptor imaging agent

DOE Office of Scientific and Technical Information (OSTI.GOV)

Aydogan, B.; Miller, L.F.; Sparks, R.B.

Absorbed dose estimates are known to contain uncertainties. A recent literature search indicates that prior to this study no rigorous investigation of uncertainty associated with absorbed dose has been undertaken. A method of uncertainty analysis for absorbed dose calculations has been developed and implemented for the brain receptor imaging agent {sup 123}I-IPT. The two major sources of uncertainty considered were the uncertainty associated with the determination of residence time and that associated with the determination of the S values. There are many sources of uncertainty in the determination of the S values, but only the inter-patient organ mass variation wasmore » considered in this work. The absorbed dose uncertainties were determined for lung, liver, heart and brain. Ninety-five percent confidence intervals of the organ absorbed dose distributions for each patient and for a seven-patient population group were determined by the ``Latin Hypercube Sampling`` method. For an individual patient, the upper bound of the 95% confidence interval of the absorbed dose was found to be about 2.5 times larger than the estimated mean absorbed dose. For the seven-patient population the upper bound of the 95% confidence interval of the absorbed dose distribution was around 45% more than the estimated population mean. For example, the 95% confidence interval of the population liver dose distribution was found to be between 1.49E+0.7 Gy/MBq and 4.65E+07 Gy/MBq with a mean of 2.52E+07 Gy/MBq. This study concluded that patients in a population receiving {sup 123}I-IPT could receive absorbed doses as much as twice as large as the standard estimated absorbed dose due to these uncertainties.« less

A Penalized Likelihood Framework For High-Dimensional Phylogenetic Comparative Methods And An Application To New-World Monkeys Brain Evolution.

PubMed

Julien, Clavel; Leandro, Aristide; Hélène, Morlon

2018-06-19

Working with high-dimensional phylogenetic comparative datasets is challenging because likelihood-based multivariate methods suffer from low statistical performances as the number of traits p approaches the number of species n and because some computational complications occur when p exceeds n. Alternative phylogenetic comparative methods have recently been proposed to deal with the large p small n scenario but their use and performances are limited. Here we develop a penalized likelihood framework to deal with high-dimensional comparative datasets. We propose various penalizations and methods for selecting the intensity of the penalties. We apply this general framework to the estimation of parameters (the evolutionary trait covariance matrix and parameters of the evolutionary model) and model comparison for the high-dimensional multivariate Brownian (BM), Early-burst (EB), Ornstein-Uhlenbeck (OU) and Pagel's lambda models. We show using simulations that our penalized likelihood approach dramatically improves the estimation of evolutionary trait covariance matrices and model parameters when p approaches n, and allows for their accurate estimation when p equals or exceeds n. In addition, we show that penalized likelihood models can be efficiently compared using Generalized Information Criterion (GIC). We implement these methods, as well as the related estimation of ancestral states and the computation of phylogenetic PCA in the R package RPANDA and mvMORPH. Finally, we illustrate the utility of the new proposed framework by evaluating evolutionary models fit, analyzing integration patterns, and reconstructing evolutionary trajectories for a high-dimensional 3-D dataset of brain shape in the New World monkeys. We find a clear support for an Early-burst model suggesting an early diversification of brain morphology during the ecological radiation of the clade. Penalized likelihood offers an efficient way to deal with high-dimensional multivariate comparative data.

Combining the boundary shift integral and tensor-based morphometry for brain atrophy estimation

NASA Astrophysics Data System (ADS)

Michalkiewicz, Mateusz; Pai, Akshay; Leung, Kelvin K.; Sommer, Stefan; Darkner, Sune; Sørensen, Lauge; Sporring, Jon; Nielsen, Mads

2016-03-01

Brain atrophy from structural magnetic resonance images (MRIs) is widely used as an imaging surrogate marker for Alzheimers disease. Their utility has been limited due to the large degree of variance and subsequently high sample size estimates. The only consistent and reasonably powerful atrophy estimation methods has been the boundary shift integral (BSI). In this paper, we first propose a tensor-based morphometry (TBM) method to measure voxel-wise atrophy that we combine with BSI. The combined model decreases the sample size estimates significantly when compared to BSI and TBM alone.

Capturing intraoperative deformations: research experience at Brigham and Women's Hospital.

PubMed

Warfield, Simon K; Haker, Steven J; Talos, Ion-Florin; Kemper, Corey A; Weisenfeld, Neil; Mewes, Andrea U J; Goldberg-Zimring, Daniel; Zou, Kelly H; Westin, Carl-Fredrik; Wells, William M; Tempany, Clare M C; Golby, Alexandra; Black, Peter M; Jolesz, Ferenc A; Kikinis, Ron

2005-04-01

During neurosurgical procedures the objective of the neurosurgeon is to achieve the resection of as much diseased tissue as possible while achieving the preservation of healthy brain tissue. The restricted capacity of the conventional operating room to enable the surgeon to visualize critical healthy brain structures and tumor margin has lead, over the past decade, to the development of sophisticated intraoperative imaging techniques to enhance visualization. However, both rigid motion due to patient placement and nonrigid deformations occurring as a consequence of the surgical intervention disrupt the correspondence between preoperative data used to plan surgery and the intraoperative configuration of the patient's brain. Similar challenges are faced in other interventional therapies, such as in cryoablation of the liver, or biopsy of the prostate. We have developed algorithms to model the motion of key anatomical structures and system implementations that enable us to estimate the deformation of the critical anatomy from sequences of volumetric images and to prepare updated fused visualizations of preoperative and intraoperative images at a rate compatible with surgical decision making. This paper reviews the experience at Brigham and Women's Hospital through the process of developing and applying novel algorithms for capturing intraoperative deformations in support of image guided therapy.

Scalable Joint Segmentation and Registration Framework for Infant Brain Images.

PubMed

Dong, Pei; Wang, Li; Lin, Weili; Shen, Dinggang; Wu, Guorong

2017-03-15

The first year of life is the most dynamic and perhaps the most critical phase of postnatal brain development. The ability to accurately measure structure changes is critical in early brain development study, which highly relies on the performances of image segmentation and registration techniques. However, either infant image segmentation or registration, if deployed independently, encounters much more challenges than segmentation/registration of adult brains due to dynamic appearance change with rapid brain development. In fact, image segmentation and registration of infant images can assists each other to overcome the above challenges by using the growth trajectories (i.e., temporal correspondences) learned from a large set of training subjects with complete longitudinal data. Specifically, a one-year-old image with ground-truth tissue segmentation can be first set as the reference domain. Then, to register the infant image of a new subject at earlier age, we can estimate its tissue probability maps, i.e., with sparse patch-based multi-atlas label fusion technique, where only the training images at the respective age are considered as atlases since they have similar image appearance. Next, these probability maps can be fused as a good initialization to guide the level set segmentation. Thus, image registration between the new infant image and the reference image is free of difficulty of appearance changes, by establishing correspondences upon the reasonably segmented images. Importantly, the segmentation of new infant image can be further enhanced by propagating the much more reliable label fusion heuristics at the reference domain to the corresponding location of the new infant image via the learned growth trajectories, which brings image segmentation and registration to assist each other. It is worth noting that our joint segmentation and registration framework is also flexible to handle the registration of any two infant images even with significant age gap in the first year of life, by linking their joint segmentation and registration through the reference domain. Thus, our proposed joint segmentation and registration method is scalable to various registration tasks in early brain development studies. Promising segmentation and registration results have been achieved for infant brain MR images aged from 2-week-old to 1-year-old, indicating the applicability of our method in early brain development study.

[Assessment of motor and sensory pathways of the brain using diffusion-tensor tractography in children with cerebral palsy].

PubMed

Memedyarov, A M; Namazova-Baranova, L S; Ermolina, Y V; Anikin, A V; Maslova, O I; Karkashadze, M Z; Klochkova, O A

2014-01-01

Diffusion tensor tractography--a new method of magnetic resonance imaging, that allows to visualize the pathways of the brain and to study their structural-functional state. The authors investigated the changes in motor and sensory pathways of brain in children with cerebral palsy using routine magnetic resonance imaging and diffusion-tensor tractography. The main group consisted of 26 patients with various forms of cerebral palsy and the comparison group was 25 people with normal psychomotor development (aged 2 to 6 years) and MR-picture of the brain. Magnetic resonance imaging was performed on the scanner with the induction of a magnetic field of 1,5 Tesla. Coefficients of fractional anisotropy and average diffusion coefficient estimated in regions of the brain containing the motor and sensory pathways: precentral gyrus, posterior limb of the internal capsule, thalamus, posterior thalamic radiation and corpus callosum. Statistically significant differences (p < 0.05) values of fractional anisotropy and average diffusion coefficient in patients with cerebral palsy in relation to the comparison group. All investigated regions, the coefficients of fractional anisotropy in children with cerebral palsy were significantly lower, and the average diffusion coefficient, respectively, higher. These changes indicate a lower degree of ordering of the white matter tracts associated with damage and subsequent development of gliosis of varying severity in children with cerebral palsy. It is shown that microstructural damage localized in both motor and sensory tracts that plays a leading role in the development of the clinical picture of cerebral palsy.

Initial Validation for the Estimation of Resting-State fMRI Effective Connectivity by a Generalization of the Correlation Approach.

PubMed

Xu, Nan; Spreng, R Nathan; Doerschuk, Peter C

2017-01-01

Resting-state functional MRI (rs-fMRI) is widely used to noninvasively study human brain networks. Network functional connectivity is often estimated by calculating the timeseries correlation between blood-oxygen-level dependent (BOLD) signal from different regions of interest (ROIs). However, standard correlation cannot characterize the direction of information flow between regions. In this paper, we introduce and test a new concept, prediction correlation, to estimate effective connectivity in functional brain networks from rs-fMRI. In this approach, the correlation between two BOLD signals is replaced by a correlation between one BOLD signal and a prediction of this signal via a causal system driven by another BOLD signal. Three validations are described: (1) Prediction correlation performed well on simulated data where the ground truth was known, and outperformed four other methods. (2) On simulated data designed to display the "common driver" problem, prediction correlation did not introduce false connections between non-interacting driven ROIs. (3) On experimental data, prediction correlation recovered the previously identified network organization of human brain. Prediction correlation scales well to work with hundreds of ROIs, enabling it to assess whole brain interregional connectivity at the single subject level. These results provide an initial validation that prediction correlation can capture the direction of information flow and estimate the duration of extended temporal delays in information flow between regions of interest ROIs based on BOLD signal. This approach not only maintains the high sensitivity to network connectivity provided by the correlation analysis, but also performs well in the estimation of causal information flow in the brain.

Visualization of hemodynamics and light scattering in exposed brain of rat using multispectral image reconstruction based on Wiener estimation method

NASA Astrophysics Data System (ADS)

Nishidate, Izumi; Ishizuka, Tomohiro; Yoshida, Keiichiro; Kawauchi, Satoko; Sato, Shunichi; Sato, Manabu

2015-07-01

We investigate a method to estimate the spectral images of reduced scattering coefficients and the absorption coefficients of in vivo exposed brain tissues in the range from visible to near-infrared wavelength (500-760 nm) based on diffuse reflectance spectroscopy using a digital RGB camera. In the proposed method, the multi-spectral reflectance images of in vivo exposed brain are reconstructed from the digital red, green, blue images using the Wiener estimation algorithm. The Monte Carlo simulation-based multiple regression analysis for the absorbance spectra is then used to specify the absorption and scattering parameters of brain tissue. In this analysis, the concentration of oxygenated hemoglobin and that of deoxygenated hemoglobin are estimated as the absorption parameters whereas the scattering amplitude a and the scattering power b in the expression of μs'=aλ-b as the scattering parameters, respectively. The spectra of absorption and reduced scattering coefficients are reconstructed from the absorption and scattering parameters, and finally, the spectral images of absorption and reduced scattering coefficients are estimated. We performed simultaneous recordings of spectral diffuse reflectance images and of the electrophysiological signals for in vivo exposed rat brain during the cortical spreading depression evoked by the topical application of KCl. Changes in the total hemoglobin concentration and the tissue oxygen saturation imply the temporary change in cerebral blood flow during CSD. Change in the reduced scattering coefficient was observed before the profound increase in the total hemoglobin concentration, and its occurrence was synchronized with the negative dc shift of the local field potential.

Brain mass estimation by head circumference and body mass methods in neonatal glycaemic modelling and control.

PubMed

Gunn, Cameron Allan; Dickson, Jennifer L; Pretty, Christopher G; Alsweiler, Jane M; Lynn, Adrienne; Shaw, Geoffrey M; Chase, J Geoffrey

2014-07-01

Hyperglycaemia is a common complication of stress and prematurity in extremely low-birth-weight infants. Model-based insulin therapy protocols have the ability to safely improve glycaemic control for this group. Estimating non-insulin-mediated brain glucose uptake by the central nervous system in these models is typically done using population-based body weight models, which may not be ideal. A head circumference-based model that separately treats small-for-gestational-age (SGA) and appropriate-for-gestational-age (AGA) infants is compared to a body weight model in a retrospective analysis of 48 patients with a median birth weight of 750g and median gestational age of 25 weeks. Estimated brain mass, model-based insulin sensitivity (SI) profiles, and projected glycaemic control outcomes are investigated. SGA infants (5) are also analyzed as a separate cohort. Across the entire cohort, estimated brain mass deviated by a median 10% between models, with a per-patient median difference in SI of 3.5%. For the SGA group, brain mass deviation was 42%, and per-patient SI deviation 13.7%. In virtual trials, 87-93% of recommended insulin rates were equal or slightly reduced (Δ<0.16mU/h) under the head circumference method, while glycaemic control outcomes showed little change. The results suggest that body weight methods are not as accurate as head circumference methods. Head circumference-based estimates may offer improved modelling accuracy and a small reduction in insulin administration, particularly for SGA infants. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Neural activity in the posterior superior temporal region during eye contact perception correlates with autistic traits.

PubMed

Hasegawa, Naoya; Kitamura, Hideaki; Murakami, Hiroatsu; Kameyama, Shigeki; Sasagawa, Mutsuo; Egawa, Jun; Endo, Taro; Someya, Toshiyuki

2013-08-09

The present study investigated the relationship between neural activity associated with gaze processing and autistic traits in typically developed subjects using magnetoencephalography. Autistic traits in 24 typically developed college students with normal intelligence were assessed using the Autism Spectrum Quotient (AQ). The Minimum Current Estimates method was applied to estimate the cortical sources of magnetic responses to gaze stimuli. These stimuli consisted of apparent motion of the eyes, displaying direct or averted gaze motion. Results revealed gaze-related brain activations in the 150-250 ms time window in the right posterior superior temporal sulcus (pSTS), and in the 150-450 ms time window in medial prefrontal regions. In addition, the mean amplitude in the 150-250 ms time window in the right pSTS region was modulated by gaze direction, and its activity in response to direct gaze stimuli correlated with AQ score. pSTS activation in response to direct gaze is thought to be related to higher-order social processes. Thus, these results suggest that brain activity linking eye contact and social signals is associated with autistic traits in a typical population. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Conditional survival of all primary brain tumor patients by age, behavior, and histology.

PubMed

Porter, Kimberly R; McCarthy, Bridget J; Berbaum, Michael L; Davis, Faith G

2011-01-01

Survival statistics commonly reflect survival from the time of diagnosis but do not take into account survival already achieved after a diagnosis. The objective of this study was to provide conditional survival estimates for brain tumor patients as a more accurate measure of survival for those who have already survived for a specified amount of time after diagnosis. Data on primary malignant and nonmalignant brain tumor cases diagnosed from 1985-2005 from selected SEER state cancer registries were obtained. Relative survival up to 15 years postdiagnosis and varying relative conditional survival rates were computed using the life-table method. The overall 1-year relative survival estimate derived from time of diagnosis was 67.8% compared to the 6-month relative conditional survival rate of 85.7% for 6-month survivors (the probability of surviving to 1 year given survival to 6 months). The 10-year overall relative survival rate was 49.5% from time of diagnosis compared to the 8-year relative conditional survival rate of 79.2% for 2-year survivors. Conditional survival estimates and standard survival estimates varied by histology, behavior, and age at diagnosis. The 5-year relative survival estimate derived from time of diagnosis for glioblastoma was 3.6% compared to the 3-year relative conditional survival rate of 36.4% for 2-year survivors. For most nonmalignant tumors, the difference between relative survival and the corresponding conditional survival estimates were minimal. Older age groups had greater numeric gains in survival but lower conditional survival estimates than other age groups. Similar findings were seen for other conditional survival intervals. Conditional survival is a useful disease surveillance measure for clinicians and brain tumor survivors to provide them with better 'real-time' estimates and hope. Copyright © 2011 S. Karger AG, Basel.

Effects of peripubertal gonadotropin-releasing hormone agonist on brain development in sheep--a magnetic resonance imaging study.

PubMed

Nuruddin, Syed; Bruchhage, Muriel; Ropstad, Erik; Krogenæs, Anette; Evans, Neil P; Robinson, Jane E; Endestad, Tor; Westlye, Lars T; Madison, Cindee; Haraldsen, Ira Ronit Hebold

2013-10-01

In many species sexual dimorphisms in brain structures and functions have been documented. In ovine model, we have previously demonstrated that peri-pubertal pharmacological blockade of gonadotropin releasing hormone (GnRH) action increased sex-differences of executive emotional behavior. The structural substrate of this behavioral alteration however is unknown. In this magnetic resonance image (MRI) study on the same animals, we investigated the effects of GnRH agonist (GnRHa) treatment on the volume of total brain, hippocampus and amygdala. In total 41 brains (17 treated; 10 females and 7 males, and 24 controls; 11 females and 13 males) were included in the MRI study. Image acquisition was performed with 3-T MRI scanner. Segmentation of the amygdala and the hippocampus was done by manual tracing and total gray and white matter volumes were estimated by means of automated brain volume segmentation of the individual T2-weighted MRI volumes. Statistical comparisons were performed with general linear models. Highly significant GnRHa treatment effects were found on the volume of left and right amygdala, indicating larger amygdalae in treated animals. Significant sex differences were found for total gray matter and right amygdala, indicating larger volumes in male compared to female animals. Additionally, we observed a significant interaction between sex and treatment on left amygdala volume, indicating stronger effects of treatment in female compared to male animals. The effects of GnRHa treatment on amygdala volumes indicate that increasing GnRH concentration during puberty may have an important impact on normal brain development in mammals. These novel findings substantiate the need for further studies investigating potential neurobiological side effects of GnRHa treatment on the brains of young animals and humans. Copyright © 2013 Elsevier Ltd. All rights reserved.

Hemodynamic resuscitation with arginine vasopressin reduces lung injury after brain death in the transplant donor.

PubMed

Rostron, Anthony J; Avlonitis, Vassilios S; Cork, David M W; Grenade, Danielle S; Kirby, John A; Dark, John H

2008-02-27

The autonomic storm accompanying brain death leads to neurogenic pulmonary edema and triggers development of systemic and pulmonary inflammatory responses. Neurogenic vasoplegia exacerbates the pulmonary injury caused by brain death and primes the lung for ischemia reperfusion injury and primary graft dysfunction in the recipient. Donor resuscitation with norepinephrine ameliorates the inflammatory response to brain death, however norepinephrine has deleterious effects, particularly on the heart. We tested the hypothesis that arginine vasopressin is a suitable alternative to norepinephrine in managing the hypotensive brain dead donor. Brain death was induced in Wistar rats by intracranial balloon inflation. Pulmonary capillary leak was estimated using radioiodinated albumin. Development of pulmonary edema was assessed by measurement of wet and dry lung weights. Cell surface expression of CD11b/CD18 by neutrophils was determined using flow cytometry. Enzyme-linked immunosorbent assays were used to measure the levels of TNFalpha, IL-1beta, CINC-1, and CINC-3 in serum and bronchoalveolar lavage. Quantitative reverse-transcription polymerase chain reaction was used to determine the expression of cytokine mRNA (IL-1beta, CINC-1 and CINC-3) in lung tissue. There was a significant increase in pulmonary capillary permeability, wet/dry lung weight ratios, neutrophil integrin expression and pro-inflammatory cytokines in serum (TNFalpha, IL-1beta, CINC-1 and CINC-3), bronchoalveolar lavage (TNFalpha and IL-1beta) and lung tissue (IL-1beta and CINC-1) in braindead animals compared to controls. Correction of neurogenic hypotension with either arginine vasopressin or norepinephrine limits edema, reduces pulmonary capillary leak, and modulates systemic and pulmonary inflammatory responses to brain death. Arginine vasopressin and norepinephrine are equally effective in treating the hypotensive pulmonary donor in this rodent model.

Using a cost-benefit analysis to estimate outcomes of a clinical treatment guideline: testing theBrain Trauma Foundation guidelines for the treatment of severe traumatic brain injury.

PubMed

Faul, Mark; Wald, Marlena M; Rutland-Brown, Wesley; Sullivent, Ernest E; Sattin, Richard W

2007-12-01

A decade after promulgation of treatment guidelines by the Brain Trauma Foundation (BTF), few studies exist that examine the application of these guidelines for severe traumatic brain injury (TBI) patients. These studies have reported both cost savings and reduced mortality. We projected the results of previous studies of BTF guideline adoption to estimate the impact of widespread adoption across the United States. We used surveillance systems and national surveys to estimate the number of severely injured TBI patients and compared the lifetime costs of BTF adoption to the current state of treatment. After examining the health outcomes and costs, we estimated that a substantial savings in annual medical costs ($262 million), annual rehabilitation costs ($43 million) and lifetime societal costs ($3.84 billion) would be achieved if treatment guidelines were used more routinely. Implementation costs were estimated to be $61 million. The net savings were primarily because of better health outcomes and a decreased burden on lifetime social support systems. We also estimate that mortality would be reduced by 3,607 lives if the guidelines were followed. Widespread adoption of the BTF guidelines for the treatment of severe TBI would result in substantial savings in costs and lives. The majority of cost savings are societal costs. Further validation work to identify the most effective aspects of the BTF guidelines is warranted.

Comparison of cumulant expansion and q-space imaging estimates for diffusional kurtosis in brain.

PubMed

Mohanty, Vaibhav; McKinnon, Emilie T; Helpern, Joseph A; Jensen, Jens H

2018-05-01

To compare estimates for the diffusional kurtosis in brain as obtained from a cumulant expansion (CE) of the diffusion MRI (dMRI) signal and from q-space (QS) imaging. For the CE estimates of the kurtosis, the CE was truncated to quadratic order in the b-value and fit to the dMRI signal for b-values from 0 up to 2000s/mm 2 . For the QS estimates, b-values ranging from 0 up to 10,000s/mm 2 were used to determine the diffusion displacement probability density function (dPDF) via Stejskal's formula. The kurtosis was then calculated directly from the second and fourth order moments of the dPDF. These two approximations were studied for in vivo human data obtained on a 3T MRI scanner using three orthogonal diffusion encoding directions. The whole brain mean values for the CE and QS kurtosis estimates differed by 16% or less in each of the considered diffusion encoding directions, and the Pearson correlation coefficients all exceeded 0.85. Nonetheless, there were large discrepancies in many voxels, particularly those with either very high or very low kurtoses relative to the mean values. Estimates of the diffusional kurtosis in brain obtained using CE and QS approximations are strongly correlated, suggesting that they encode similar information. However, for the choice of b-values employed here, there may be substantial differences, depending on the properties of the diffusion microenvironment in each voxel. Copyright © 2018 Elsevier Inc. All rights reserved.

Alterations of functional connectivities from early to middle adulthood: Clues from multivariate pattern analysis of resting-state fMRI data.

PubMed

Tian, Lixia; Ma, Lin; Wang, Linlin

2016-04-01

In contrast to extended research interests in the maturation and aging of human brain, alterations of brain structure and function from early to middle adulthood have been much less studied. The aim of the present study was to investigate the extent and pattern of the alterations of functional interactions between brain regions from early to middle adulthood. We carried out the study by multivariate pattern analysis of resting-state fMRI (RS-fMRI) data of 63 adults aged 18 to 45 years. Specifically, using elastic net, we performed brain age estimation and age-group classification (young adults aged 18-28 years vs. middle-aged adults aged 35-45 years) based on the resting-state functional connectivities (RSFCs) between 160 regions of interest (ROIs) evaluated on the RS-fMRI data of each subject. The results indicate that the estimated brain ages were significantly correlated with the chronological age (R=0.78, MAE=4.81), and a classification rate of 94.44% and area under the receiver operating characteristic curve (AUC) of 0.99 were obtained when classifying the young and middle-aged adults. These results provide strong evidence that functional interactions between brain regions undergo notable alterations from early to middle adulthood. By analyzing the RSFCs that contribute to brain age estimation/age-group classification, we found that a majority of the RSFCs were inter-network, and we speculate that inter-network RSFCs might mature late but age early as compared to intra-network ones. In addition, the strengthening/weakening of the RSFCs associated with the left/right hemispheric ROIs, the weakening of cortico-cerebellar RSFCs and the strengthening of the RSFCs between the default mode network and other networks contributed much to both brain age estimation and age-group classification. All these alterations might reflect that aging of brain function is already in progress in middle adulthood. Overall, the present study indicated that the RSFCs undergo notable alterations from early to middle adulthood and highlighted the necessity of careful considerations of possible influences of these alterations in related studies. Copyright © 2016 Elsevier Inc. All rights reserved.

Task performance in virtual environments used for cognitive rehabilitation after traumatic brain injury.

PubMed

Christiansen, C; Abreu, B; Ottenbacher, K; Huffman, K; Masel, B; Culpepper, R

1998-08-01

This report describes a reliability study using a prototype computer-simulated virtual environment to assess basic daily living skills in a sample of persons with traumatic brain injury (TBI). The benefits of using virtual reality in training for situations where safety is a factor have been established in defense and industry, but have not been demonstrated in rehabilitation. Thirty subjects with TBI receiving comprehensive rehabilitation services at a residential facility. An immersive virtual kitchen was developed in which a meal preparation task involving multiple steps could be performed. The prototype was tested using subjects who completed the task twice within 7 days. The stability of performance was estimated using intraclass correlation coefficients (ICCs). The ICC value for total performance based on all steps involved in the meal preparation task was .73. When three items with low variance were removed the ICC improved to .81. Little evidence of vestibular optical side-effects was noted in the subjects tested. Adequate initial reliability exists to continue development of the environment as an assessment and training prototype for persons with brain injury.

Estimation of in-vivo neurotransmitter release by brain microdialysis: the issue of validity.

PubMed

Di Chiara, G.; Tanda, G.; Carboni, E.

1996-11-01

Although microdialysis is commonly understood as a method of sampling low molecular weight compounds in the extracellular compartment of tissues, this definition appears insufficient to specifically describe brain microdialysis of neurotransmitters. In fact, transmitter overflow from the brain into dialysates is critically dependent upon the composition of the perfusing Ringer. Therefore, the dialysing Ringer not only recovers the transmitter from the extracellular brain fluid but is a main determinant of its in-vivo release. Two types of brain microdialysis are distinguished: quantitative micro-dialysis and conventional microdialysis. Quantitative microdialysis provides an estimate of neurotransmitter concentrations in the extracellular fluid in contact with the probe. However, this information might poorly reflect the kinetics of neurotransmitter release in vivo. Conventional microdialysis involves perfusion at a constant rate with a transmitter-free Ringer, resulting in the formation of a steep neurotransmitter concentration gradient extending from the Ringer into the extracellular fluid. This artificial gradient might be critical for the ability of conventional microdialysis to detect and resolve phasic changes in neurotransmitter release taking place in the implanted area. On the basis of these characteristics, conventional microdialysis of neurotransmitters can be conceptualized as a model of the in-vivo release of neurotransmitters in the brain. As such, the criteria of face-validity, construct-validity and predictive-validity should be applied to select the most appropriate experimental conditions for estimating neurotransmitter release in specific brain areas in relation to behaviour.

A hierarchical model for probabilistic independent component analysis of multi-subject fMRI studies

PubMed Central

Tang, Li

2014-01-01

Summary An important goal in fMRI studies is to decompose the observed series of brain images to identify and characterize underlying brain functional networks. Independent component analysis (ICA) has been shown to be a powerful computational tool for this purpose. Classic ICA has been successfully applied to single-subject fMRI data. The extension of ICA to group inferences in neuroimaging studies, however, is challenging due to the unavailability of a pre-specified group design matrix. Existing group ICA methods generally concatenate observed fMRI data across subjects on the temporal domain and then decompose multi-subject data in a similar manner to single-subject ICA. The major limitation of existing methods is that they ignore between-subject variability in spatial distributions of brain functional networks in group ICA. In this paper, we propose a new hierarchical probabilistic group ICA method to formally model subject-specific effects in both temporal and spatial domains when decomposing multi-subject fMRI data. The proposed method provides model-based estimation of brain functional networks at both the population and subject level. An important advantage of the hierarchical model is that it provides a formal statistical framework to investigate similarities and differences in brain functional networks across subjects, e.g., subjects with mental disorders or neurodegenerative diseases such as Parkinson’s as compared to normal subjects. We develop an EM algorithm for model estimation where both the E-step and M-step have explicit forms. We compare the performance of the proposed hierarchical model with that of two popular group ICA methods via simulation studies. We illustrate our method with application to an fMRI study of Zen meditation. PMID:24033125

Sparsity enables estimation of both subcortical and cortical activity from MEG and EEG

PubMed Central

Krishnaswamy, Pavitra; Obregon-Henao, Gabriel; Ahveninen, Jyrki; Khan, Sheraz; Iglesias, Juan Eugenio; Hämäläinen, Matti S.; Purdon, Patrick L.

2017-01-01

Subcortical structures play a critical role in brain function. However, options for assessing electrophysiological activity in these structures are limited. Electromagnetic fields generated by neuronal activity in subcortical structures can be recorded noninvasively, using magnetoencephalography (MEG) and electroencephalography (EEG). However, these subcortical signals are much weaker than those generated by cortical activity. In addition, we show here that it is difficult to resolve subcortical sources because distributed cortical activity can explain the MEG and EEG patterns generated by deep sources. We then demonstrate that if the cortical activity is spatially sparse, both cortical and subcortical sources can be resolved with M/EEG. Building on this insight, we develop a hierarchical sparse inverse solution for M/EEG. We assess the performance of this algorithm on realistic simulations and auditory evoked response data, and show that thalamic and brainstem sources can be correctly estimated in the presence of cortical activity. Our work provides alternative perspectives and tools for characterizing electrophysiological activity in subcortical structures in the human brain. PMID:29138310

Caring for Patients with traumatic brain injury: a survey of nurses' perceptions.

PubMed

Oyesanya, Tolu O; Brown, Roger L; Turkstra, Lyn S

2017-06-01

The purpose of this study was to determine nurses' perceptions about caring for patients with traumatic brain injury. Annually, it is estimated that over 10 million people sustain a traumatic brain injury around the world. Patients with traumatic brain injury and their families are often concerned with expectations about recovery and seek information from nurses. Nurses' perceptions of care might influence information provided to patients and families, particularly if inaccurate knowledge and perceptions are held. Thus, nurses must be knowledgeable about care of these patients. A cross-sectional survey, the Perceptions of Brain Injury Survey (PBIS), was completed electronically by 513 nurses between October and December 2014. Data were analysed with structural equation modelling, factor analysis, and pairwise comparisons. Using latent class analysis, authors were able to divide nurses into three homogeneous sub-groups based on perceived knowledge: low, moderate and high. Findings showed that nurses who care for patients with traumatic brain injury the most have the highest perceived confidence but the lowest perceived knowledge. Nurses also had significant variations in training. As there is limited literature on nurses' perceptions of caring for patients with traumatic brain injury, these findings have implications for training and educating nurses, including direction for development of nursing educational interventions. As the incidence of traumatic brain injury is growing, it is imperative that nurses be knowledgeable about care of patients with these injuries. The traumatic brain injury PBIS can be used to determine inaccurate perceptions about caring for patients with traumatic brain injury before educating and training nurses. © 2016 John Wiley & Sons Ltd.

Discrimination surfaces with application to region-specific brain asymmetry analysis.

PubMed

Martos, Gabriel; de Carvalho, Miguel

2018-05-20

Discrimination surfaces are here introduced as a diagnostic tool for localizing brain regions where discrimination between diseased and nondiseased participants is higher. To estimate discrimination surfaces, we introduce a Mann-Whitney type of statistic for random fields and present large-sample results characterizing its asymptotic behavior. Simulation results demonstrate that our estimator accurately recovers the true surface and corresponding interval of maximal discrimination. The empirical analysis suggests that in the anterior region of the brain, schizophrenic patients tend to present lower local asymmetry scores in comparison with participants in the control group. Copyright © 2018 John Wiley & Sons, Ltd.

Concussion classification via deep learning using whole-brain white matter fiber strains

PubMed Central

Cai, Yunliang; Wu, Shaoju; Zhao, Wei; Li, Zhigang; Wu, Zheyang

2018-01-01

Developing an accurate and reliable injury predictor is central to the biomechanical studies of traumatic brain injury. State-of-the-art efforts continue to rely on empirical, scalar metrics based on kinematics or model-estimated tissue responses explicitly pre-defined in a specific brain region of interest. They could suffer from loss of information. A single training dataset has also been used to evaluate performance but without cross-validation. In this study, we developed a deep learning approach for concussion classification using implicit features of the entire voxel-wise white matter fiber strains. Using reconstructed American National Football League (NFL) injury cases, leave-one-out cross-validation was employed to objectively compare injury prediction performances against two baseline machine learning classifiers (support vector machine (SVM) and random forest (RF)) and four scalar metrics via univariate logistic regression (Brain Injury Criterion (BrIC), cumulative strain damage measure of the whole brain (CSDM-WB) and the corpus callosum (CSDM-CC), and peak fiber strain in the CC). Feature-based machine learning classifiers including deep learning, SVM, and RF consistently outperformed all scalar injury metrics across all performance categories (e.g., leave-one-out accuracy of 0.828–0.862 vs. 0.690–0.776, and .632+ error of 0.148–0.176 vs. 0.207–0.292). Further, deep learning achieved the best cross-validation accuracy, sensitivity, AUC, and .632+ error. These findings demonstrate the superior performances of deep learning in concussion prediction and suggest its promise for future applications in biomechanical investigations of traumatic brain injury. PMID:29795640

Concussion classification via deep learning using whole-brain white matter fiber strains.

PubMed

Cai, Yunliang; Wu, Shaoju; Zhao, Wei; Li, Zhigang; Wu, Zheyang; Ji, Songbai

2018-01-01

Developing an accurate and reliable injury predictor is central to the biomechanical studies of traumatic brain injury. State-of-the-art efforts continue to rely on empirical, scalar metrics based on kinematics or model-estimated tissue responses explicitly pre-defined in a specific brain region of interest. They could suffer from loss of information. A single training dataset has also been used to evaluate performance but without cross-validation. In this study, we developed a deep learning approach for concussion classification using implicit features of the entire voxel-wise white matter fiber strains. Using reconstructed American National Football League (NFL) injury cases, leave-one-out cross-validation was employed to objectively compare injury prediction performances against two baseline machine learning classifiers (support vector machine (SVM) and random forest (RF)) and four scalar metrics via univariate logistic regression (Brain Injury Criterion (BrIC), cumulative strain damage measure of the whole brain (CSDM-WB) and the corpus callosum (CSDM-CC), and peak fiber strain in the CC). Feature-based machine learning classifiers including deep learning, SVM, and RF consistently outperformed all scalar injury metrics across all performance categories (e.g., leave-one-out accuracy of 0.828-0.862 vs. 0.690-0.776, and .632+ error of 0.148-0.176 vs. 0.207-0.292). Further, deep learning achieved the best cross-validation accuracy, sensitivity, AUC, and .632+ error. These findings demonstrate the superior performances of deep learning in concussion prediction and suggest its promise for future applications in biomechanical investigations of traumatic brain injury.

Chromatographic behaviour predicts the ability of potential nootropics to permeate the blood-brain barrier.

PubMed

Farsa, Oldřich

2013-01-01

The log BB parameter is the logarithm of the ratio of a compound's equilibrium concentrations in the brain tissue versus the blood plasma. This parameter is a useful descriptor in assessing the ability of a compound to permeate the blood-brain barrier. The aim of this study was to develop a Hansch-type linear regression QSAR model that correlates the parameter log BB and the retention time of drugs and other organic compounds on a reversed-phase HPLC containing an embedded amide moiety. The retention time was expressed by the capacity factor log k'. The second aim was to estimate the brain's absorption of 2-(azacycloalkyl)acetamidophenoxyacetic acids, which are analogues of piracetam, nefiracetam, and meclofenoxate. Notably, these acids may be novel nootropics. Two simple regression models that relate log BB and log k' were developed from an assay performed using a reversed-phase HPLC that contained an embedded amide moiety. Both the quadratic and linear models yielded statistical parameters comparable to previously published models of log BB dependence on various structural characteristics. The models predict that four members of the substituted phenoxyacetic acid series have a strong chance of permeating the barrier and being absorbed in the brain. The results of this study show that a reversed-phase HPLC system containing an embedded amide moiety is a functional in vitro surrogate of the blood-brain barrier. These results suggest that racetam-type nootropic drugs containing a carboxylic moiety could be more poorly absorbed than analogues devoid of the carboxyl group, especially if the compounds penetrate the barrier by a simple diffusion mechanism.

Validation of model-based brain shift correction in neurosurgery via intraoperative magnetic resonance imaging: preliminary results

NASA Astrophysics Data System (ADS)

Luo, Ma; Frisken, Sarah F.; Weis, Jared A.; Clements, Logan W.; Unadkat, Prashin; Thompson, Reid C.; Golby, Alexandra J.; Miga, Michael I.

2017-03-01

The quality of brain tumor resection surgery is dependent on the spatial agreement between preoperative image and intraoperative anatomy. However, brain shift compromises the aforementioned alignment. Currently, the clinical standard to monitor brain shift is intraoperative magnetic resonance (iMR). While iMR provides better understanding of brain shift, its cost and encumbrance is a consideration for medical centers. Hence, we are developing a model-based method that can be a complementary technology to address brain shift in standard resections, with resource-intensive cases as referrals for iMR facilities. Our strategy constructs a deformation `atlas' containing potential deformation solutions derived from a biomechanical model that account for variables such as cerebrospinal fluid drainage and mannitol effects. Volumetric deformation is estimated with an inverse approach that determines the optimal combinatory `atlas' solution fit to best match measured surface deformation. Accordingly, preoperative image is updated based on the computed deformation field. This study is the latest development to validate our methodology with iMR. Briefly, preoperative and intraoperative MR images of 2 patients were acquired. Homologous surface points were selected on preoperative and intraoperative scans as measurement of surface deformation and used to drive the inverse problem. To assess the model accuracy, subsurface shift of targets between preoperative and intraoperative states was measured and compared to model prediction. Considering subsurface shift above 3 mm, the proposed strategy provides an average shift correction of 59% across 2 cases. While further improvements in both the model and ability to validate with iMR are desired, the results reported are encouraging.

Cerebral glycogen in humans following acute and recurrent hypoglycemia: Implications on a role in hypoglycemia unawareness.

PubMed

Öz, Gülin; DiNuzzo, Mauro; Kumar, Anjali; Moheet, Amir; Khowaja, Ameer; Kubisiak, Kristine; Eberly, Lynn E; Seaquist, Elizabeth R

2017-08-01

Supercompensated brain glycogen levels may contribute to the development of hypoglycemia-associated autonomic failure (HAAF) following recurrent hypoglycemia (RH) by providing energy for the brain during subsequent periods of hypoglycemia. To assess the role of glycogen supercompensation in the generation of HAAF, we estimated the level of brain glycogen following RH and acute hypoglycemia (AH). After undergoing 3 hyperinsulinemic, euglycemic and 3 hyperinsulinemic, hypoglycemic clamps (RH) on separate occasions at least 1 month apart, five healthy volunteers received [1- 13 C]glucose intravenously over 80+ h while maintaining euglycemia. 13 C-glycogen levels in the occipital lobe were measured by 13 C magnetic resonance spectroscopy at ∼8, 20, 32, 44, 56, 68 and 80 h at 4 T and glycogen levels estimated by fitting the data with a biophysical model that takes into account the tiered glycogen structure. Similarly, prior 13 C-glycogen data obtained following a single hypoglycemic episode (AH) were fitted with the same model. Glycogen levels did not significantly increase after RH relative to after euglycemia, while they increased by ∼16% after AH relative to after euglycemia. These data suggest that glycogen supercompensation may be blunted with repeated hypoglycemic episodes. A causal relationship between glycogen supercompensation and generation of HAAF remains to be established.

Cognitive timing: neuropsychology and anatomic basis.

PubMed

Coslett, H Branch; Shenton, Jeff; Dyer, Tamarah; Wiener, Martin

2009-02-13

We report data from 31 subjects with focal hemisphere lesions (15 left hemisphere) as well as 16 normal controls on a battery of tasks assessing the estimation, production and reproduction of time intervals ranging from 2-12 s. Both visual and auditory stimuli were employed for the estimation and production tasks. First, ANOVAs were performed to assess the effect of stimulus modality on estimation and production tasks; a significant effect of stimulus modality was observed for the production but not the estimation task. Second, accuracy was significantly different for the 2 s interval as compared to longer intervals. Subsequent analyses of the data from 4-12 s stimuli demonstrated that patients with brain lesions were more variable than controls on the estimation and reproduction tasks. Additionally, patients with brain lesions but not controls exhibited significant differences in performance on the different tasks; patients with brain lesions under-produced but over-estimated time intervals of 4-12 s but performed relatively well on the reproduction task, a pattern of performance consistent with a "fast clock". There was a significant correlation between impaired performance and lesions of the parietal lobe but there was no effect of laterality of lesion or correlation between lateral frontal lobe lesions and impairment on any task.

Improved frame-based estimation of head motion in PET brain imaging.

PubMed

Mukherjee, J M; Lindsay, C; Mukherjee, A; Olivier, P; Shao, L; King, M A; Licho, R

2016-05-01

Head motion during PET brain imaging can cause significant degradation of image quality. Several authors have proposed ways to compensate for PET brain motion to restore image quality and improve quantitation. Head restraints can reduce movement but are unreliable; thus the need for alternative strategies such as data-driven motion estimation or external motion tracking. Herein, the authors present a data-driven motion estimation method using a preprocessing technique that allows the usage of very short duration frames, thus reducing the intraframe motion problem commonly observed in the multiple frame acquisition method. The list mode data for PET acquisition is uniformly divided into 5-s frames and images are reconstructed without attenuation correction. Interframe motion is estimated using a 3D multiresolution registration algorithm and subsequently compensated for. For this study, the authors used 8 PET brain studies that used F-18 FDG as the tracer and contained minor or no initial motion. After reconstruction and prior to motion estimation, known motion was introduced to each frame to simulate head motion during a PET acquisition. To investigate the trade-off in motion estimation and compensation with respect to frames of different length, the authors summed 5-s frames accordingly to produce 10 and 60 s frames. Summed images generated from the motion-compensated reconstructed frames were then compared to the original PET image reconstruction without motion compensation. The authors found that our method is able to compensate for both gradual and step-like motions using frame times as short as 5 s with a spatial accuracy of 0.2 mm on average. Complex volunteer motion involving all six degrees of freedom was estimated with lower accuracy (0.3 mm on average) than the other types investigated. Preprocessing of 5-s images was necessary for successful image registration. Since their method utilizes nonattenuation corrected frames, it is not susceptible to motion introduced between CT and PET acquisitions. The authors have shown that they can estimate motion for frames with time intervals as short as 5 s using nonattenuation corrected reconstructed FDG PET brain images. Intraframe motion in 60-s frames causes degradation of accuracy to about 2 mm based on the motion type.

Multifractal texture estimation for detection and segmentation of brain tumors.

PubMed

Islam, Atiq; Reza, Syed M S; Iftekharuddin, Khan M

2013-11-01

A stochastic model for characterizing tumor texture in brain magnetic resonance (MR) images is proposed. The efficacy of the model is demonstrated in patient-independent brain tumor texture feature extraction and tumor segmentation in magnetic resonance images (MRIs). Due to complex appearance in MRI, brain tumor texture is formulated using a multiresolution-fractal model known as multifractional Brownian motion (mBm). Detailed mathematical derivation for mBm model and corresponding novel algorithm to extract spatially varying multifractal features are proposed. A multifractal feature-based brain tumor segmentation method is developed next. To evaluate efficacy, tumor segmentation performance using proposed multifractal feature is compared with that using Gabor-like multiscale texton feature. Furthermore, novel patient-independent tumor segmentation scheme is proposed by extending the well-known AdaBoost algorithm. The modification of AdaBoost algorithm involves assigning weights to component classifiers based on their ability to classify difficult samples and confidence in such classification. Experimental results for 14 patients with over 300 MRIs show the efficacy of the proposed technique in automatic segmentation of tumors in brain MRIs. Finally, comparison with other state-of-the art brain tumor segmentation works with publicly available low-grade glioma BRATS2012 dataset show that our segmentation results are more consistent and on the average outperforms these methods for the patients where ground truth is made available.

Multifractal Texture Estimation for Detection and Segmentation of Brain Tumors

PubMed Central

Islam, Atiq; Reza, Syed M. S.

2016-01-01

A stochastic model for characterizing tumor texture in brain magnetic resonance (MR) images is proposed. The efficacy of the model is demonstrated in patient-independent brain tumor texture feature extraction and tumor segmentation in magnetic resonance images (MRIs). Due to complex appearance in MRI, brain tumor texture is formulated using a multiresolution-fractal model known as multifractional Brownian motion (mBm). Detailed mathematical derivation for mBm model and corresponding novel algorithm to extract spatially varying multifractal features are proposed. A multifractal feature-based brain tumor segmentation method is developed next. To evaluate efficacy, tumor segmentation performance using proposed multifractal feature is compared with that using Gabor-like multiscale texton feature. Furthermore, novel patient-independent tumor segmentation scheme is proposed by extending the well-known AdaBoost algorithm. The modification of AdaBoost algorithm involves assigning weights to component classifiers based on their ability to classify difficult samples and confidence in such classification. Experimental results for 14 patients with over 300 MRIs show the efficacy of the proposed technique in automatic segmentation of tumors in brain MRIs. Finally, comparison with other state-of-the art brain tumor segmentation works with publicly available low-grade glioma BRATS2012 dataset show that our segmentation results are more consistent and on the average outperforms these methods for the patients where ground truth is made available. PMID:23807424

Neuroimaging of the Injured Pediatric Brain: Methods and New Lessons.

PubMed

Dennis, Emily L; Babikian, Talin; Giza, Christopher C; Thompson, Paul M; Asarnow, Robert F

2018-02-01

Traumatic brain injury (TBI) is a significant public health problem in the United States, especially for children and adolescents. Current epidemiological data estimate over 600,000 patients younger than 20 years are treated for TBI in emergency rooms annually. While many patients experience a full recovery, for others there can be long-lasting cognitive, neurological, psychological, and behavioral disruptions. TBI in youth can disrupt ongoing brain development and create added family stress during a formative period. The neuroimaging methods used to assess brain injury improve each year, providing researchers a more detailed characterization of the injury and recovery process. In this review, we cover current imaging methods used to quantify brain disruption post-injury, including structural magnetic resonance imaging (MRI), diffusion MRI, functional MRI, resting state fMRI, and magnetic resonance spectroscopy (MRS), with brief coverage of other methods, including electroencephalography (EEG), single-photon emission computed tomography (SPECT), and positron emission tomography (PET). We include studies focusing on pediatric moderate-severe TBI from 2 months post-injury and beyond. While the morbidity of pediatric TBI is considerable, continuing advances in imaging methods have the potential to identify new treatment targets that can lead to significant improvements in outcome.

Real-time estimation of paracellular permeability of cerebral endothelial cells by capacitance sensor array

NASA Astrophysics Data System (ADS)

Hyun Jo, Dong; Lee, Rimi; Hyoung Kim, Jin; Oh Jun, Hyoung; Geol Lee, Tae; Hun Kim, Jeong

2015-06-01

Vascular integrity is important in maintaining homeostasis of brain microenvironments. In various brain diseases including Alzheimer’s disease, stroke, and multiple sclerosis, increased paracellular permeability due to breakdown of blood-brain barrier is linked with initiation and progression of pathological conditions. We developed a capacitance sensor array to monitor dielectric responses of cerebral endothelial cell monolayer, which could be utilized to evaluate the integrity of brain microvasculature. Our system measured real-time capacitance values which demonstrated frequency- and time-dependent variations. With the measurement of capacitance at the frequency of 100 Hz, we could differentiate the effects of vascular endothelial growth factor (VEGF), a representative permeability-inducing factor, on endothelial cells and quantitatively analyse the normalized values. Interestingly, we showed differential capacitance values according to the status of endothelial cell monolayer, confluent or sparse, evidencing that the integrity of monolayer was associated with capacitance values. Another notable feature was that we could evaluate the expression of molecules in samples in our system with the reference of real-time capacitance values. We suggest that this dielectric spectroscopy system could be successfully implanted as a novel in vitro assay in the investigation of the roles of paracellular permeability in various brain diseases.

Low dose radiation effects on the brain - from mechanisms and behavioral outcomes to mitigation strategies.

PubMed

Kovalchuk, Anna; Kolb, Bryan

2017-07-03

Based on the most recent estimates by the Canadian Cancer Society, 2 in 5 Canadians will develop cancer in their lifetimes. More than half of all cancer patients receive some type of radiation therapy, and all patients undergo radiation-based diagnostics. While radiation is one of the most important diagnostic and treatments modalities, high-dose cranial radiation therapy causes numerous central nervous system side-effects, including declines in cognitive function, memory, and attention. While the mechanisms of these effects have been studies, they still need to be further elucidated. On the other hand, the effects of low dose radiation as well as indirect radiation bystander effects on the brain remain elusive. We pioneered analysis of the molecular and cellular effects of low dose direct, bystander and scatter radiation on the brain. Using a rat model, we showed that low dose radiation exposures cause molecular and cellular changes in the brain and impacts animal behavior. Here we reflect upon our recent findings and current state of knowledge in the field, and suggest novel radiation effect biomarkers and means of prevention. We propose strategies and interventions to prevent and mitigate radiation effects on the brain.

Data-driven models of dominantly-inherited Alzheimer's disease progression.

PubMed

Oxtoby, Neil P; Young, Alexandra L; Cash, David M; Benzinger, Tammie L S; Fagan, Anne M; Morris, John C; Bateman, Randall J; Fox, Nick C; Schott, Jonathan M; Alexander, Daniel C

2018-05-01

See Li and Donohue (doi:10.1093/brain/awy089) for a scientific commentary on this article.Dominantly-inherited Alzheimer's disease is widely hoped to hold the key to developing interventions for sporadic late onset Alzheimer's disease. We use emerging techniques in generative data-driven disease progression modelling to characterize dominantly-inherited Alzheimer's disease progression with unprecedented resolution, and without relying upon familial estimates of years until symptom onset. We retrospectively analysed biomarker data from the sixth data freeze of the Dominantly Inherited Alzheimer Network observational study, including measures of amyloid proteins and neurofibrillary tangles in the brain, regional brain volumes and cortical thicknesses, brain glucose hypometabolism, and cognitive performance from the Mini-Mental State Examination (all adjusted for age, years of education, sex, and head size, as appropriate). Data included 338 participants with known mutation status (211 mutation carriers in three subtypes: 163 PSEN1, 17 PSEN2, and 31 APP) and a baseline visit (age 19-66; up to four visits each, 1.1 ± 1.9 years in duration; spanning 30 years before, to 21 years after, parental age of symptom onset). We used an event-based model to estimate sequences of biomarker changes from baseline data across disease subtypes (mutation groups), and a differential equation model to estimate biomarker trajectories from longitudinal data (up to 66 mutation carriers, all subtypes combined). The two models concur that biomarker abnormality proceeds as follows: amyloid deposition in cortical then subcortical regions (∼24 ± 11 years before onset); phosphorylated tau (17 ± 8 years), tau and amyloid-β changes in cerebrospinal fluid; neurodegeneration first in the putamen and nucleus accumbens (up to 6 ± 2 years); then cognitive decline (7 ± 6 years), cerebral hypometabolism (4 ± 4 years), and further regional neurodegeneration. Our models predicted symptom onset more accurately than predictions that used familial estimates: root mean squared error of 1.35 years versus 5.54 years. The models reveal hidden detail on dominantly-inherited Alzheimer's disease progression, as well as providing data-driven systems for fine-grained patient staging and prediction of symptom onset with great potential utility in clinical trials.

TH-A-18C-09: Ultra-Fast Monte Carlo Simulation for Cone Beam CT Imaging of Brain Trauma

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sisniega, A; Zbijewski, W; Stayman, J

Purpose: Application of cone-beam CT (CBCT) to low-contrast soft tissue imaging, such as in detection of traumatic brain injury, is challenged by high levels of scatter. A fast, accurate scatter correction method based on Monte Carlo (MC) estimation is developed for application in high-quality CBCT imaging of acute brain injury. Methods: The correction involves MC scatter estimation executed on an NVIDIA GTX 780 GPU (MC-GPU), with baseline simulation speed of ~1e7 photons/sec. MC-GPU is accelerated by a novel, GPU-optimized implementation of variance reduction (VR) techniques (forced detection and photon splitting). The number of simulated tracks and projections is reduced formore » additional speed-up. Residual noise is removed and the missing scatter projections are estimated via kernel smoothing (KS) in projection plane and across gantry angles. The method is assessed using CBCT images of a head phantom presenting a realistic simulation of fresh intracranial hemorrhage (100 kVp, 180 mAs, 720 projections, source-detector distance 700 mm, source-axis distance 480 mm). Results: For a fixed run-time of ~1 sec/projection, GPU-optimized VR reduces the noise in MC-GPU scatter estimates by a factor of 4. For scatter correction, MC-GPU with VR is executed with 4-fold angular downsampling and 1e5 photons/projection, yielding 3.5 minute run-time per scan, and de-noised with optimized KS. Corrected CBCT images demonstrate uniformity improvement of 18 HU and contrast improvement of 26 HU compared to no correction, and a 52% increase in contrast-tonoise ratio in simulated hemorrhage compared to “oracle” constant fraction correction. Conclusion: Acceleration of MC-GPU achieved through GPU-optimized variance reduction and kernel smoothing yields an efficient (<5 min/scan) and accurate scatter correction that does not rely on additional hardware or simplifying assumptions about the scatter distribution. The method is undergoing implementation in a novel CBCT dedicated to brain trauma imaging at the point of care in sports and military applications. Research grant from Carestream Health. JY is an employee of Carestream Health.« less

Imaging decision about whether to benefit self by harming others: Adolescents with conduct and substance problems, with or without callous-unemotionality, or developing typically.

PubMed

Sakai, Joseph T; Dalwani, Manish S; Mikulich-Gilbertson, Susan K; Raymond, Kristen; McWilliams, Shannon; Tanabe, Jody; Rojas, Don; Regner, Michael; Banich, Marie T; Crowley, Thomas J

2017-05-30

We sought to identify brain activation differences in conduct-problem youth with limited prosocial emotions (LPE) compared to conduct-problem youth without LPE and community adolescents, and to test associations between brain activation and severity of callous-unemotional traits. We utilized a novel task, which asks subjects to repeatedly decide whether to accept offers where they will benefit but a beneficent other will be harmed. Behavior on this task has been previously associated with levels of prosocial emotions and severity of callous-unemotional traits, and is related to empathic concern. During fMRI acquisition, 66 male adolescents (21 conduct-problem patients with LPE, 21 without, and 24 typically-developing controls) played this novel game. Within typically-developing controls, we identified a network engaged during decision involving bilateral insula, and inferior parietal and medial frontal cortices, among other regions. Group comparisons using non-parametric (distribution-free) permutation tests demonstrated LPE patients had lower activation estimates than typically-developing adolescents in right anterior insula. Additional significant group differences emerged with our a priori parametric cluster-wise inference threshold. These results suggest measurable functional brain activation differences in conduct-problem adolescents with LPE compared to typically-developing adolescents. Such differences may underscore differential treatment needs for conduct-problem males with and without LPE. Copyright © 2017 Elsevier Ireland Ltd. All rights reserved.

MRI Estimates of Brain Iron Concentration in Normal Aging Using Quantitative Susceptibility Mapping

PubMed Central

Bilgic, Berkin; Pfefferbaum, Adolf; Rohlfing, Torsten; Sullivan, Edith V.; Adalsteinsson, Elfar

2011-01-01

Quantifying tissue iron concentration in vivo is instrumental for understanding the role of iron in physiology and in neurological diseases associated with abnormal iron distribution. Herein, we use recently-developed Quantitative Susceptibility Mapping (QSM) methodology to estimate the tissue magnetic susceptibility based on MRI signal phase. To investigate the effect of different regularization choices, we implement and compare ℓ1 and ℓ2 norm regularized QSM algorithms. These regularized approaches solve for the underlying magnetic susceptibility distribution, a sensitive measure of the tissue iron concentration, that gives rise to the observed signal phase. Regularized QSM methodology also involves a pre-processing step that removes, by dipole fitting, unwanted background phase effects due to bulk susceptibility variations between air and tissue and requires data acquisition only at a single field strength. For validation, performances of the two QSM methods were measured against published estimates of regional brain iron from postmortem and in vivo data. The in vivo comparison was based on data previously acquired using Field-Dependent Relaxation Rate Increase (FDRI), an estimate of MRI relaxivity enhancement due to increased main magnetic field strength, requiring data acquired at two different field strengths. The QSM analysis was based on susceptibility-weighted images acquired at 1.5T, whereas FDRI analysis used Multi-Shot Echo-Planar Spin Echo images collected at 1.5T and 3.0T. Both datasets were collected in the same healthy young and elderly adults. The in vivo estimates of regional iron concentration comported well with published postmortem measurements; both QSM approaches yielded the same rank ordering of iron concentration by brain structure, with the lowest in white matter and the highest in globus pallidus. Further validation was provided by comparison of the in vivo measurements, ℓ1-regularized QSM versus FDRI and ℓ2-regularized QSM versus FDRI, which again yielded perfect rank ordering of iron by brain structure. The final means of validation was to assess how well each in vivo method detected known age-related differences in regional iron concentrations measured in the same young and elderly healthy adults. Both QSM methods and FDRI were consistent in identifying higher iron concentrations in striatal and brain stem ROIs (i.e., caudate nucleus, putamen, globus pallidus, red nucleus, and substantia nigra) in the older than in the young group. The two QSM methods appeared more sensitive in detecting age differences in brain stem structures as they revealed differences of much higher statistical significance between the young and elderly groups than did FDRI. However, QSM values are influenced by factors such as the myelin content, whereas FDRI is a more specific indicator of iron content. Hence, FDRI demonstrated higher specificity to iron yet yielded noisier data despite longer scan times and lower spatial resolution than QSM. The robustness, practicality, and demonstrated ability of predicting the change in iron deposition in adult aging suggest that regularized QSM algorithms using single-field-strength data are possible alternatives to tissue iron estimation requiring two field strengths. PMID:21925274

The cost of brain diseases: a burden or a challenge?

PubMed

DiLuca, Monica; Olesen, Jes

2014-06-18

Brain diseases represent a considerable social and economic burden in Europe. With yearly costs of about 800 billion euros and an estimated 179 million people afflicted in 2010, brain diseases are an unquestionable emergency and a grand challenge for neuroscientists. Copyright © 2014 Elsevier Inc. All rights reserved.

Multiparametric estimation of brain hemodynamics with MR fingerprinting ASL.

PubMed

Su, Pan; Mao, Deng; Liu, Peiying; Li, Yang; Pinho, Marco C; Welch, Babu G; Lu, Hanzhang

2017-11-01

Assessment of brain hemodynamics without exogenous contrast agents is of increasing importance in clinical applications. This study aims to develop an MR perfusion technique that can provide noncontrast and multiparametric estimation of hemodynamic markers. We devised an arterial spin labeling (ASL) method based on the principle of MR fingerprinting (MRF), referred to as MRF-ASL. By taking advantage of the rich information contained in MRF sequence, up to seven hemodynamic parameters can be estimated concomitantly. Feasibility demonstration, flip angle optimization, comparison with Look-Locker ASL, reproducibility test, sensitivity to hypercapnia challenge, and initial clinical application in an intracranial steno-occlusive process, Moyamoya disease, were performed to evaluate this technique. Magnetic resonance fingerprinting ASL provided estimation of up to seven parameters, including B1+, tissue T 1 , cerebral blood flow (CBF), tissue bolus arrival time (BAT), pass-through arterial BAT, pass-through blood volume, and pass-through blood travel time. Coefficients of variation of the estimated parameters ranged from 0.2 to 9.6%. Hypercapnia resulted in an increase in CBF by 57.7%, and a decrease in BAT by 13.7 and 24.8% in tissue and vessels, respectively. Patients with Moyamoya disease showed diminished CBF and lengthened BAT that could not be detected with regular ASL. Magnetic resonance fingerprinting ASL is a promising technique for noncontrast, multiparametric perfusion assessment. Magn Reson Med 78:1812-1823, 2017. © 2016 International Society for Magnetic Resonance in Medicine. © 2016 International Society for Magnetic Resonance in Medicine.

Socioeconomic status moderates age-related differences in the brain's functional network organization and anatomy across the adult lifespan.

PubMed

Chan, Micaela Y; Na, Jinkyung; Agres, Phillip F; Savalia, Neil K; Park, Denise C; Wig, Gagan S

2018-05-14

An individual's environmental surroundings interact with the development and maturation of their brain. An important aspect of an individual's environment is his or her socioeconomic status (SES), which estimates access to material resources and social prestige. Previous characterizations of the relation between SES and the brain have primarily focused on earlier or later epochs of the lifespan (i.e., childhood, older age). We broaden this work to examine the relationship between SES and the brain across a wide range of human adulthood (20-89 years), including individuals from the less studied middle-age range. SES, defined by education attainment and occupational socioeconomic characteristics, moderates previously reported age-related differences in the brain's functional network organization and whole-brain cortical structure. Across middle age (35-64 years), lower SES is associated with reduced resting-state system segregation (a measure of effective functional network organization). A similar but less robust relationship exists between SES and age with respect to brain anatomy: Lower SES is associated with reduced cortical gray matter thickness in middle age. Conversely, younger and older adulthood do not exhibit consistent SES-related difference in the brain measures. The SES-brain relationships persist after controlling for measures of physical and mental health, cognitive ability, and participant demographics. Critically, an individual's childhood SES cannot account for the relationship between their current SES and functional network organization. These findings provide evidence that SES relates to the brain's functional network organization and anatomy across adult middle age, and that higher SES may be a protective factor against age-related brain decline. Copyright © 2018 the Author(s). Published by PNAS.

Initial Validation for the Estimation of Resting-State fMRI Effective Connectivity by a Generalization of the Correlation Approach

PubMed Central

Xu, Nan; Spreng, R. Nathan; Doerschuk, Peter C.

2017-01-01

Resting-state functional MRI (rs-fMRI) is widely used to noninvasively study human brain networks. Network functional connectivity is often estimated by calculating the timeseries correlation between blood-oxygen-level dependent (BOLD) signal from different regions of interest (ROIs). However, standard correlation cannot characterize the direction of information flow between regions. In this paper, we introduce and test a new concept, prediction correlation, to estimate effective connectivity in functional brain networks from rs-fMRI. In this approach, the correlation between two BOLD signals is replaced by a correlation between one BOLD signal and a prediction of this signal via a causal system driven by another BOLD signal. Three validations are described: (1) Prediction correlation performed well on simulated data where the ground truth was known, and outperformed four other methods. (2) On simulated data designed to display the “common driver” problem, prediction correlation did not introduce false connections between non-interacting driven ROIs. (3) On experimental data, prediction correlation recovered the previously identified network organization of human brain. Prediction correlation scales well to work with hundreds of ROIs, enabling it to assess whole brain interregional connectivity at the single subject level. These results provide an initial validation that prediction correlation can capture the direction of information flow and estimate the duration of extended temporal delays in information flow between regions of interest ROIs based on BOLD signal. This approach not only maintains the high sensitivity to network connectivity provided by the correlation analysis, but also performs well in the estimation of causal information flow in the brain. PMID:28559793

Coupling BCI and cortical stimulation for brain-state-dependent stimulation: methods for spectral estimation in the presence of stimulation after-effects

PubMed Central

Walter, Armin; Murguialday, Ander R.; Rosenstiel, Wolfgang; Birbaumer, Niels; Bogdan, Martin

2012-01-01

Brain-state-dependent stimulation (BSDS) combines brain-computer interfaces (BCIs) and cortical stimulation into one paradigm that allows the online decoding for example of movement intention from brain signals while simultaneously applying stimulation. If the BCI decoding is performed by spectral features, stimulation after-effects such as artefacts and evoked activity present a challenge for a successful implementation of BSDS because they can impair the detection of targeted brain states. Therefore, efficient and robust methods are needed to minimize the influence of the stimulation-induced effects on spectral estimation without violating the real-time constraints of the BCI. In this work, we compared four methods for spectral estimation with autoregressive (AR) models in the presence of pulsed cortical stimulation. Using combined EEG-TMS (electroencephalography-transcranial magnetic stimulation) as well as combined electrocorticography (ECoG) and epidural electrical stimulation, three patients performed a motor task using a sensorimotor-rhythm BCI. Three stimulation paradigms were varied between sessions: (1) no stimulation, (2) single stimulation pulses applied independently (open-loop), or (3) coupled to the BCI output (closed-loop) such that stimulation was given only while an intention to move was detected using neural data. We found that removing the stimulation after-effects by linear interpolation can introduce a bias in the estimation of the spectral power of the sensorimotor rhythm, leading to an overestimation of decoding performance in the closed-loop setting. We propose the use of the Burg algorithm for segmented data to deal with stimulation after-effects. This work shows that the combination of BCIs controlled with spectral features and cortical stimulation in a closed-loop fashion is possible when the influence of stimulation after-effects on spectral estimation is minimized. PMID:23162436

Trend of brain tumor incidence by histological subtypes in Japan: estimation from the Brain Tumor Registry of Japan, 1973-1993.

PubMed

Kaneko, Satoshi; Nomura, Kazuhiro; Yoshimura, Takesumi; Yamaguchi, Naohito

2002-10-01

In order to estimate the risk of primary brain tumor (PBT), we attempted to estimate the national incidence rates of PBT by histological subtypes using the Brain Tumor Registry of Japan (BTR). The number of deaths due to PBT in a certain year is the sum of the deaths among patients diagnosed in different years. Registered cases in the BTR represent incident cases of PBT in the whole country multiplied by a cover rate. The cover rate is defined as the proportions of PBT cases that the Registry counts in relation to all the cases in the country in a given year. If the survival experience among the registered cases represents the survival experience of all cases, then the rate of registered deaths represents all deaths due to PBT in Japan. By this logic, we estimated the cover rates and incidence rates from 1973 to 1993 using the BTR and National Vital Statistics data. Our estimates showed three patterns of time trends: (1) a gradual linear increasing trend before the 1980s followed by a plateau (total PBT, gliomas, meningioma, and hemangioblastoma), (2) a trend with a step-up increase in the 1980s followed by a plateau (germ cell tumor and pituitary tumor), and (3) a linear increasing trend throughout the observation period with no plateau (malignant lymphoma and neurinoma). Furthermore, obvious sex differences in time trends were observed in rates of meningioma, germ cell tumor, and pituitary tumor. The results of this study demonstrated several distinctive patterns in time trends, which give us insight into the possible etiologies of brain tumors. Further epidemiological study is needed to elucidate these findings.

HomER: a review of time-series analysis methods for near-infrared spectroscopy of the brain

PubMed Central

Huppert, Theodore J.; Diamond, Solomon G.; Franceschini, Maria A.; Boas, David A.

2009-01-01

Near-infrared spectroscopy (NIRS) is a noninvasive neuroimaging tool for studying evoked hemodynamic changes within the brain. By this technique, changes in the optical absorption of light are recorded over time and are used to estimate the functionally evoked changes in cerebral oxyhemoglobin and deoxyhemoglobin concentrations that result from local cerebral vascular and oxygen metabolic effects during brain activity. Over the past three decades this technology has continued to grow, and today NIRS studies have found many niche applications in the fields of psychology, physiology, and cerebral pathology. The growing popularity of this technique is in part associated with a lower cost and increased portability of NIRS equipment when compared with other imaging modalities, such as functional magnetic resonance imaging and positron emission tomography. With this increasing number of applications, new techniques for the processing, analysis, and interpretation of NIRS data are continually being developed. We review some of the time-series and functional analysis techniques that are currently used in NIRS studies, we describe the practical implementation of various signal processing techniques for removing physiological, instrumental, and motion-artifact noise from optical data, and we discuss the unique aspects of NIRS analysis in comparison with other brain imaging modalities. These methods are described within the context of the MATLAB-based graphical user interface program, HomER, which we have developed and distributed to facilitate the processing of optical functional brain data. PMID:19340120

The Track of Brain Activity during the Observation of TV Commercials with the High-Resolution EEG Technology

PubMed Central

Astolfi, Laura; Vecchiato, Giovanni; De Vico Fallani, Fabrizio; Salinari, Serenella; Cincotti, Febo; Aloise, Fabio; Mattia, Donatella; Marciani, Maria Grazia; Bianchi, Luigi; Soranzo, Ramon; Babiloni, Fabio

2009-01-01

We estimate cortical activity in normal subjects during the observation of TV commercials inserted within a movie by using high-resolution EEG techniques. The brain activity was evaluated in both time and frequency domains by solving the associate inverse problem of EEG with the use of realistic head models. In particular, we recover statistically significant information about cortical areas engaged by particular scenes inserted within the TV commercial proposed with respect to the brain activity estimated while watching a documentary. Results obtained in the population investigated suggest that the statistically significant brain activity during the observation of the TV commercial was mainly concentrated in frontoparietal cortical areas, roughly coincident with the Brodmann areas 8, 9, and 7, in the analyzed population. PMID:19584910

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sawada, Y.; Kawai, R.; McManaway, M.

(3H)Cyclofoxy (CF: 17-cyclopropylmethyl-3,14-dihydroxy-4,5-alpha-epoxy-6-beta-fluoromorp hinan) is an opioid antagonist with affinity to both mu and kappa subtypes that was synthesized for quantitative evaluation of opioid receptor binding in vivo. Two sets of experiments in rats were analyzed. The first involved determining the metabolite-corrected blood concentration and tissue distribution of CF in brain 1 to 60 min after i.v. bolus injection. The second involved measuring brain washout for 15 to 120 s following intracarotid artery injection of CF. A physiologically based model and a classical compartmental pharmacokinetic model were compared. The models included different assumptions for transport across the blood-brain barrier (BBB);more » estimates of nonspecific tissue binding and specific binding to a single opiate receptor site were found to be essentially the same with both models. The nonspecific binding equilibrium constant varied modestly in different brain structures (Keq = 3-9), whereas the binding potential (BP) varied over a much broader range (BP = 0.6-32). In vivo estimates of the opioid receptor dissociation constant were similar for different brain structures (KD = 2.1-5.2 nM), whereas the apparent receptor density (Bmax) varied between 1 (cerebellum) and 78 (thalamus) pmol/g of brain. The receptor dissociation rate constants in cerebrum (k4 = 0.08-0.16 min-1; koff = 0.16-0.23 min-1) and brain vascular permeability (PS = 1.3-3.4 ml/min/g) are sufficiently high to achieve equilibrium conditions within a reasonable period of time. Graphical analysis of the data is inappropriate due to the high tissue-loss rate constant for CF in brain. From these findings, CF should be a very useful opioid receptor ligand for the estimation of the receptor binding parameters in human subjects using (18F)CF and positron emission tomography.« less

Residential Radon and Brain Tumour Incidence in a Danish Cohort

PubMed Central

Bräuner, Elvira V.; Andersen, Zorana J.; Andersen, Claus E.; Pedersen, Camilla; Gravesen, Peter; Ulbak, Kaare; Hertel, Ole; Loft, Steffen; Raaschou-Nielsen, Ole

2013-01-01

Background Increased brain tumour incidence over recent decades may reflect improved diagnostic methods and clinical practice, but remain unexplained. Although estimated doses are low a relationship between radon and brain tumours may exist. Objective To investigate the long-term effect of exposure to residential radon on the risk of primary brain tumour in a prospective Danish cohort. Methods During 1993–1997 we recruited 57,053 persons. We followed each cohort member for cancer occurrence from enrolment until 31 December 2009, identifying 121 primary brain tumour cases. We traced residential addresses from 1 January 1971 until 31 December 2009 and calculated radon concentrations at each address using information from central databases regarding geology and house construction. Cox proportional hazards models were used to estimate incidence rate-ratios (IRR) and 95% confidence intervals (CI) for the risk of primary brain tumours associated with residential radon exposure with adjustment for age, sex, occupation, fruit and vegetable consumption and traffic-related air pollution. Effect modification by air pollution was assessed. Results Median estimated radon was 40.5 Bq/m3. The adjusted IRR for primary brain tumour associated with each 100 Bq/m3 increment in average residential radon levels was 1.96 (95% CI: 1.07; 3.58) and this was exposure-dependently higher over the four radon exposure quartiles. This association was not modified by air pollution. Conclusions We found significant associations and exposure-response patterns between long-term residential radon exposure radon in a general population and risk of primary brain tumours, adding new knowledge to this field. This finding could be chance and needs to be challenged in future studies. PMID:24066143

A Computerized Microelectrode Recording to Magnetic Resonance Imaging Mapping System for Subthalamic Nucleus Deep Brain Stimulation Surgery.

PubMed

Dodani, Sunjay S; Lu, Charles W; Aldridge, J Wayne; Chou, Kelvin L; Patil, Parag G

2018-06-01

Accurate electrode placement is critical to the success of deep brain stimulation (DBS) surgery. Suboptimal targeting may arise from poor initial target localization, frame-based targeting error, or intraoperative brain shift. These uncertainties can make DBS surgery challenging. To develop a computerized system to guide subthalamic nucleus (STN) DBS electrode localization and to estimate the trajectory of intraoperative microelectrode recording (MER) on magnetic resonance (MR) images algorithmically during DBS surgery. Our method is based upon the relationship between the high-frequency band (HFB; 500-2000 Hz) signal from MER and voxel intensity on MR images. The HFB profile along an MER trajectory recorded during surgery is compared to voxel intensity profiles along many potential trajectories in the region of the surgically planned trajectory. From these comparisons of HFB recordings and potential trajectories, an estimate of the MER trajectory is calculated. This calculated trajectory is then compared to actual trajectory, as estimated by postoperative high-resolution computed tomography. We compared 20 planned, calculated, and actual trajectories in 13 patients who underwent STN DBS surgery. Targeting errors for our calculated trajectories (2.33 mm ± 0.2 mm) were significantly less than errors for surgically planned trajectories (2.83 mm ± 0.2 mm; P = .01), improving targeting prediction in 70% of individual cases (14/20). Moreover, in 4 of 4 initial MER trajectories that missed the STN, our method correctly indicated the required direction of targeting adjustment for the DBS lead to intersect the STN. A computer-based algorithm simultaneously utilizing MER and MR information potentially eases electrode localization during STN DBS surgery.

Radiobiological and treatment planning study of a simultaneously integrated boost for canine nasal tumors using helical tomotherapy.

PubMed

Gutíerrez, Alonso N; Deveau, Michael; Forrest, Lisa J; Tomé, Wolfgang A; Mackie, Thomas R

2007-01-01

Feasibility of delivering a simultaneously integrated boost to canine nasal tumors using helical tomotherapy to improve tumor control probability (TCP) via an increase in total biological equivalent uniform dose (EUD) was evaluated. Eight dogs with varying size nasal tumors (5.8-110.9 cc) were replanned to 42 Gy to the nasal cavity and integrated dose boosts to gross disease of 45.2, 48.3, and 51.3 Gy in 10 fractions. EUD values were calculated for tumors and mean normalized total doses (NTD(mean)) for organs at risk (OAR). Normal Tissue Complication Probability (NTCP) values were obtained for OARs, and estimated TCP values were computed using a logistic dose-response model and based on deliverable EUD boost doses. Significant increases in estimated TCP to 54%, 74%, and 86% can be achieved with 10%, 23%, and 37% mean relative EUD boosts to the gross disease, respectively. NTCP values for blindness of either eye and for brain necrosis were < 0.01% for all boosts. Values for cataract development were 31%, 42%, and 46% for studied boost schemas, respectively. Average NTD(mean) to eyes and brain for mean EUD boosts were 10.2, 11.3, and 12.1 Gy3, and 7.5, 7.2, and 7.9 Gy2, respectively. Using helical tomotherapy, simultaneously integrated dose boosts can be delivered to increase the estimated TCP at 1-year without significantly increasing the NTD(mean) to eyes and brain. Delivery of these treatments in a prospective trial may allow quantification of a dose-response relationship in canine nasal tumors.

Robust electroencephalogram phase estimation with applications in brain-computer interface systems.

PubMed

Seraj, Esmaeil; Sameni, Reza

2017-03-01

In this study, a robust method is developed for frequency-specific electroencephalogram (EEG) phase extraction using the analytic representation of the EEG. Based on recent theoretical findings in this area, it is shown that some of the phase variations-previously associated to the brain response-are systematic side-effects of the methods used for EEG phase calculation, especially during low analytical amplitude segments of the EEG. With this insight, the proposed method generates randomized ensembles of the EEG phase using minor perturbations in the zero-pole loci of narrow-band filters, followed by phase estimation using the signal's analytical form and ensemble averaging over the randomized ensembles to obtain a robust EEG phase and frequency. This Monte Carlo estimation method is shown to be very robust to noise and minor changes of the filter parameters and reduces the effect of fake EEG phase jumps, which do not have a cerebral origin. As proof of concept, the proposed method is used for extracting EEG phase features for a brain computer interface (BCI) application. The results show significant improvement in classification rates using rather simple phase-related features and a standard K-nearest neighbors and random forest classifiers, over a standard BCI dataset. The average performance was improved between 4-7% (in absence of additive noise) and 8-12% (in presence of additive noise). The significance of these improvements was statistically confirmed by a paired sample t-test, with 0.01 and 0.03 p-values, respectively. The proposed method for EEG phase calculation is very generic and may be applied to other EEG phase-based studies.

Brain noise is task dependent and region specific.

PubMed

Misić, Bratislav; Mills, Travis; Taylor, Margot J; McIntosh, Anthony R

2010-11-01

The emerging organization of anatomical and functional connections during human brain development is thought to facilitate global integration of information. Recent empirical and computational studies have shown that this enhanced capacity for information processing enables a diversified dynamic repertoire that manifests in neural activity as irregularity and noise. However, transient functional networks unfold over multiple time, scales and the embedding of a particular region depends not only on development, but also on the manner in which sensory and cognitive systems are engaged. Here we show that noise is a facet of neural activity that is also sensitive to the task context and is highly region specific. Children (6-16 yr) and adults (20-41 yr) performed a one-back face recognition task with inverted and upright faces. Neuromagnetic activity was estimated at several hundred sources in the brain by applying a beamforming technique to the magnetoencephalogram (MEG). During development, neural activity became more variable across the whole brain, with most robust increases in medial parietal regions, such as the precuneus and posterior cingulate cortex. For young children and adults, activity evoked by upright faces was more variable and noisy compared with inverted faces, and this effect was reliable only in the right fusiform gyrus. These results are consistent with the notion that upright faces engender a variety of integrative neural computations, such as the relations among facial features and their holistic constitution. This study shows that transient changes in functional integration modulated by task demand are evident in the variability of regional neural activity.

[Expression of c-jun protein after experimental rat brain concussion].

PubMed

Wang, Feng; Li, Yong-hong

2010-02-01

To observe e-jun protein expression after rat brain concussion and explore the forensic pathologic markers following brain concussion. Fifty-five rats were randomly divided into brain concussion group and control group. The expression of c-jun protein was observed by immunohistochemistry. There were weak positive expression of c-jun protein in control group. In brain concussion group, however, some neutrons showed positive expression of c-jun protein at 15 min after brain concussion, and reach to the peak at 3 h after brain concussion. The research results suggest that detection of c-jun protein could be a marker to determine brain concussion and estimate injury time after brain concussion.

Brain Tumor Statistics

MedlinePlus

... Scientific Advisory Council & Reviewers The International Low Grade Glioma Registry Get Involved Advocacy Breakthrough for Brain Tumors ... an estimated 29,320 new cases in 2018. Gliomas , a broad term which includes all tumors arising ...

Efficacy, safety and outcome of frameless image-guided robotic radiosurgery for brain metastases after whole brain radiotherapy.

PubMed

Lohkamp, Laura-Nanna; Vajkoczy, Peter; Budach, Volker; Kufeld, Markus

2018-05-01

Estimating efficacy, safety and outcome of frameless image-guided robotic radiosurgery for the treatment of recurrent brain metastases after whole brain radiotherapy (WBRT). We performed a retrospective single-center analysis including patients with recurrent brain metastases after WBRT, who have been treated with single session radiosurgery, using the CyberKnife® Radiosurgery System (CKRS) (Accuray Inc., CA) between 2011 and 2016. The primary end point was local tumor control, whereas secondary end points were distant tumor control, treatment-related toxicity and overall survival. 36 patients with 140 recurrent brain metastases underwent 46 single session CKRS treatments. Twenty one patients had multiple brain metastases (58%). The mean interval between WBRT and CKRS accounted for 2 years (range 0.2-7 years). The median number of treated metastases per treatment session was five (range 1-12) with a tumor volume of 1.26 ccm (mean) and a median tumor dose of 18 Gy prescribed to the 70% isodose line. Two patients experienced local tumor recurrence within the 1st year after treatment and 13 patients (36%) developed novel brain metastases. Nine of these patients underwent additional one to three CKRS treatments. Eight patients (22.2%) showed treatment-related radiation reactions on MRI, three with clinical symptoms. Median overall survival was 19 months after CKRS. The actuarial 1-year local control rate was 94.2%. CKRS has proven to be locally effective and safe due to high local tumor control rates and low toxicity. Thus CKRS offers a reliable salvage treatment option for recurrent brain metastases after WBRT.

Disrupted Structural Brain Network in AD and aMCI: A Finding of Long Fiber Degeneration.

PubMed

Fang, Rong; Yan, Xiao-Xiao; Wu, Zhi-Yuan; Sun, Yu; Yin, Qi-Hua; Wang, Ying; Tang, Hui-Dong; Sun, Jun-Feng; Miao, Fei; Chen, Sheng-Di

2015-01-01

Although recent evidence has emerged that Alzheimer's disease (AD) and amnestic mild cognitive impairment (aMCI) patients show both regional brain abnormalities and topological degeneration in brain networks, our understanding of the effects of white matter fiber aberrations on brain network topology in AD and aMCI is still rudimentary. In this study, we investigated the regional volumetric aberrations and the global topological abnormalities in AD and aMCI patients. The results showed a widely distributed atrophy in both gray and white matters in the AD and aMCI groups. In particular, AD patients had weaker connectivity with long fiber length than aMCI and normal control (NC) groups, as assessed by fractional anisotropy (FA). Furthermore, the brain networks of all three groups exhibited prominent economical small-world properties. Interestingly, the topological characteristics estimated from binary brain networks showed no significant group effect, indicating a tendency of preserving an optimal topological architecture in AD and aMCI during degeneration. However, significantly longer characteristic path length was observed in the FA weighted brain networks of AD and aMCI patients, suggesting dysfunctional global integration. Moreover, the abnormality of the characteristic path length was negatively correlated with the clinical ratings of cognitive impairment. Thus, the results therefore suggested that the topological alterations in weighted brain networks of AD are induced by the loss of connectivity with long fiber lengths. Our findings provide new insights into the alterations of the brain network in AD and may indicate the predictive value of the network metrics as biomarkers of disease development.

Development of a brain MRI-based hidden Markov model for dementia recognition.

PubMed

Chen, Ying; Pham, Tuan D

2013-01-01

Dementia is an age-related cognitive decline which is indicated by an early degeneration of cortical and sub-cortical structures. Characterizing those morphological changes can help to understand the disease development and contribute to disease early prediction and prevention. But modeling that can best capture brain structural variability and can be valid in both disease classification and interpretation is extremely challenging. The current study aimed to establish a computational approach for modeling the magnetic resonance imaging (MRI)-based structural complexity of the brain using the framework of hidden Markov models (HMMs) for dementia recognition. Regularity dimension and semi-variogram were used to extract structural features of the brains, and vector quantization method was applied to convert extracted feature vectors to prototype vectors. The output VQ indices were then utilized to estimate parameters for HMMs. To validate its accuracy and robustness, experiments were carried out on individuals who were characterized as non-demented and mild Alzheimer's diseased. Four HMMs were constructed based on the cohort of non-demented young, middle-aged, elder and demented elder subjects separately. Classification was carried out using a data set including both non-demented and demented individuals with a wide age range. The proposed HMMs have succeeded in recognition of individual who has mild Alzheimer's disease and achieved a better classification accuracy compared to other related works using different classifiers. Results have shown the ability of the proposed modeling for recognition of early dementia. The findings from this research will allow individual classification to support the early diagnosis and prediction of dementia. By using the brain MRI-based HMMs developed in our proposed research, it will be more efficient, robust and can be easily used by clinicians as a computer-aid tool for validating imaging bio-markers for early prediction of dementia.

Neurocytotoxic effects of iron-ions on the developing brain measured in vivo using medaka (Oryzias latipes), a vertebrate model

PubMed Central

Yasuda, Takako; Oda, Shoji; Yasuda, Hiroshi; Hibi, Yusuke; Anzai, Kazunori; Mitani, Hiroshi

2011-01-01

Purpose: Exposure to heavy-ion radiation is considered a critical health risk on long-term space missions. The developing central nervous system (CNS) is a highly radiosensitive tissue; however, the biological effects of heavy-ion radiation, which are greater than those of low-linear energy transfer (LET) radiation, are not well studied, especially in vivo in intact organisms. Here, we examined the effects of iron-ions on the developing CNS using vertebrate organism, fish embryos of medaka (Oryzias latipes). Materials and methods: Medaka embryos at developmental stage 28 were irradiated with iron-ions at various doses of 0-1.5 Gy. At 24 h after irradiation, radiation-induced apoptosis was examined using an acridine orange (AO) assay and histo-logically. To estimate the relative biological effectiveness (RBE), we quantified only characteristic AO-stained rosette-shaped apoptosis in the developing optic tectum (OT). At the time of hatching, morphological abnormalities in the irradiated brain were examined histologically. Results: The dose-response curve utilizing an apoptotic index for the iron-ion irradiated embryos was much steeper than that for X-ray irradiated embryos, with RBE values of 3.7-4.2. Histological examinations of irradiated medaka brain at 24 h after irradiation showed AO-positive rosette-shaped clusters as aggregates of condensed nuclei, exhibiting a circular hole, mainly in the marginal area of the OT and in the retina. However, all of the irradiated embryos hatched normally without apparent histological abnormalities in their brains. Conclusion: Our present study indicates that the medaka embryo is a useful model for evaluating neurocytotoxic effects on the developing CNS induced by exposure to heavy iron-ions relevant to the aerospace radiation environment. PMID:21770703

AGE-RELATED BRAIN CHOLINESTERASE INHIBITION KINETICS FOLLOWING IN VITRO INCUBATION WITH CHLORPYRIFOS-OXON AND DIAZINON-OXON

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kousba, Ahmed A.; Poet, Torka S.; Timchalk, Chuck

2007-01-01

Chlorpyrifos and diazinon are two commonly used organophosphorus (OP) insecticides, and their primary mechanism of action involves the inhibition of acetylcholinesterase (AChE) by their metabolites chlorpyrifos-oxon (CPO) and diazinon-oxon (DZO), respectively. The study objectives were to assess the in vitro age-related inhibition kinetics of neonatal rat brain cholinesterase (ChE) by estimating the bimolecular inhibitory rate constant (ki) values for CPO and DZO. Brain ChE inhibition and ki values following CPO and DZO incubation with neonatal Sprague-Dawley rats rat brain homogenates were determined at post natal day (PND) -5, -12 and -17 and compared with the corresponding inhibition and ki valuesmore » obtained in the adult rat. A modified Ellman method was utilized for measuring the ChE activity. Chlorpyrifos-oxon resulted in greater ChE inhibition than DZO consistent with the estimated ki values of both compounds. Neonatal brain ChE inhibition kinetics exhibited a marked age-related sensitivity to CPO, where the order of ChE inhibition was PND-5 > PND-7 > PND-17 with ki values of 0.95, 0.50 and 0.22 nM-1hr-1, respectively. In contrast, DZO did not exhibit an age-related inhibition of neonatal brain ChE, and the estimated ki value at all PND ages was 0.02 nM-1hr-1. These results demonstrated an age- and chemical-related OP-selective inhibition of rat brain ChE which may be critically important in understanding the potential sensitivity of juvenile humans to specific OP exposures.« less

Brain CT scan indexes in the normal pressure hydrocephalus: predictive value in the outcome of patients and correlation to the clinical symptoms.

PubMed

Chatzidakis, Emmanuel M; Barlas, George; Condilis, Nicolas; Bouramas, Dimos; Anagnostopoulos, Demetrios; Volikas, Zacharias; Simopoulos, Konstantinos

2008-01-01

The aim of this study is to find out the correlation of the ventricular size of the brain, as it is estimated using brain computed tomography (CT) scan indexes in patients with normal pressure hydrocephalus (NPH), to: a) the clinical symptoms, and b) the results of cerebrospinal fluid (CSF) shunting procedures. We looked for any predictive value in the estimation of brain CT scan indexes, in patients as above, in whom a shunt is going to be placed. It is well known that it is very difficult to decide who is going to improve after shunting. We studied 40 cases of patients with the diagnosis "NPH" in whom the ventricular shunts were placed. Every symptom (motor disturbance, deficit of memory, incontinence) was separately evaluated preoperatively. The outcome of shunting was also evaluated and the patients were graded. The following CT scan indexes were estimated from the preoperative CT scans of the brain in every case: the ventricle-brain ratio (VBR), the bi-caudate and bi-frontal ratios, the third ventricle-Sylvian fissure (3V-SF) ratio, and the four largest cortical gyri. The method we have used for statistics is "one way analysis of variance", correlating the CT scan indexes to the symptoms of the patients preoperatively, and the outcome of them postoperatively. The main conclusion is that the size of the lateral ventricles of the brain preoperatively is not correlated to the outcome after CSF shunting surgery, but it is correlated to the symptoms of NPH preoperatively.

Spatial distribution of resting-state BOLD regional homogeneity as a predictor of brain glucose uptake: A study in healthy aging.

PubMed

Bernier, Michaël; Croteau, Etienne; Castellano, Christian-Alexandre; Cunnane, Stephen C; Whittingstall, Kevin

2017-04-15

Positron emission tomography using [18F]-fluorodeoxyglucose (PET-FDG) is the primary imaging modality used to measure glucose metabolism in the brain (CMRGlu). CMRGlu has been used as a biomarker of brain aging and neurodegenerative diseases, but the complexity and invasive nature of PET often limits its use in research. There is therefore great interest in developing non-invasive metrics for estimating brain CMRGlu. We therefore investigated resting state fMRI metrics such as regional homogeneity (ReHo), amplitude of low-frequency fluctuations (ALFF) and regional global connectivity (Closeness) with multiple analytical approaches to determine their relationship to CMRGlu. We investigated this relation in two distinct cognitively healthy populations separated by age (27 young adults and 35 older adults). Overall, we found that both regionally and across participants, ReHo strongly correlated with CMRGlu in healthy young and older adults. Moreover, ReHo demonstrated the same age-related differences as CMRGlu throughout all cortical regions, particularly in the default network and frontal areas. Copyright © 2017 Elsevier Inc. All rights reserved.

Three-dimensional brain MRI for DBS patients within ultra-low radiofrequency power limits.

PubMed

Sarkar, Subhendra N; Papavassiliou, Efstathios; Hackney, David B; Alsop, David C; Shih, Ludy C; Madhuranthakam, Ananth J; Busse, Reed F; La Ruche, Susan; Bhadelia, Rafeeque A

2014-04-01

For patients with deep brain stimulators (DBS), local absorbed radiofrequency (RF) power is unknown and is much higher than what the system estimates. We developed a comprehensive, high-quality brain magnetic resonance imaging (MRI) protocol for DBS patients utilizing three-dimensional (3D) magnetic resonance sequences at very low RF power. Six patients with DBS were imaged (10 sessions) using a transmit/receive head coil at 1.5 Tesla with modified 3D sequences within ultra-low specific absorption rate (SAR) limits (0.1 W/kg) using T2 , fast fluid-attenuated inversion recovery (FLAIR) and T1 -weighted image contrast. Tissue signal and tissue contrast from the low-SAR images were subjectively and objectively compared with routine clinical images of six age-matched controls. Low-SAR images of DBS patients demonstrated tissue contrast comparable to high-SAR images and were of diagnostic quality except for slightly reduced signal. Although preliminary, we demonstrated diagnostic quality brain MRI with optimized, volumetric sequences in DBS patients within very conservative RF safety guidelines offering a greater safety margin. © 2014 International Parkinson and Movement Disorder Society.

In vivo characterization of 3D skull and brain motion during dynamic head vibration using magnetic resonance elastography.

PubMed

Yin, Ziying; Sui, Yi; Trzasko, Joshua D; Rossman, Phillip J; Manduca, Armando; Ehman, Richard L; Huston, John

2018-05-17

To introduce newly developed MR elastography (MRE)-based dual-saturation imaging and dual-sensitivity motion encoding schemes to directly measure in vivo skull-brain motion, and to study the skull-brain coupling in volunteers with these approaches. Six volunteers were scanned with a high-performance compact 3T-MRI scanner. The skull-brain MRE images were obtained with a dual-saturation imaging where the skull and brain motion were acquired with fat- and water-suppression scans, respectively. A dual-sensitivity motion encoding scheme was applied to estimate the heavily wrapped phase in skull by the simultaneous acquisition of both low- and high-sensitivity phase during a single MRE exam. The low-sensitivity phase was used to guide unwrapping of the high-sensitivity phase. The amplitude and temporal phase delay of the rigid-body motion between the skull and brain was measured, and the skull-brain interface was visualized by slip interface imaging (SII). Both skull and brain motion can be successfully acquired and unwrapped. The skull-brain motion analysis demonstrated the motion transmission from the skull to the brain is attenuated in amplitude and delayed. However, this attenuation (%) and delay (rad) were considerably greater with rotation (59 ± 7%, 0.68 ± 0.14 rad) than with translation (92 ± 5%, 0.04 ± 0.02 rad). With SII the skull-brain slip interface was not completely evident, and the slip pattern was spatially heterogeneous. This study provides a framework for acquiring in vivo voxel-based skull and brain displacement using MRE that can be used to characterize the skull-brain coupling system for understanding of mechanical brain protection mechanisms, which has potential to facilitate risk management for future injury. © 2018 International Society for Magnetic Resonance in Medicine.

Automatic Brain Tumor Detection in T2-weighted Magnetic Resonance Images

NASA Astrophysics Data System (ADS)

Dvořák, P.; Kropatsch, W. G.; Bartušek, K.

2013-10-01

This work focuses on fully automatic detection of brain tumors. The first aim is to determine, whether the image contains a brain with a tumor, and if it does, localize it. The goal of this work is not the exact segmentation of tumors, but the localization of their approximate position. The test database contains 203 T2-weighted images of which 131 are images of healthy brain and the remaining 72 images contain brain with pathological area. The estimation, whether the image shows an afflicted brain and where a pathological area is, is done by multi resolution symmetry analysis. The first goal was tested by five-fold cross-validation technique with 100 repetitions to avoid the result dependency on sample order. This part of the proposed method reaches the true positive rate of 87.52% and the true negative rate of 93.14% for an afflicted brain detection. The evaluation of the second part of the algorithm was carried out by comparing the estimated location to the true tumor location. The detection of the tumor location reaches the rate of 95.83% of correct anomaly detection and the rate 87.5% of correct tumor location.

Somatosensory evoked changes in cerebral oxygen consumption measured non-invasively in premature neonates.

PubMed

Roche-Labarbe, Nadege; Fenoglio, Angela; Radhakrishnan, Harsha; Kocienski-Filip, Marcia; Carp, Stefan A; Dubb, Jay; Boas, David A; Grant, P Ellen; Franceschini, Maria Angela

2014-01-15

The hemodynamic functional response is used as a reliable marker of neuronal activity in countless studies of brain function and cognition. In newborns and infants, however, conflicting results have appeared in the literature concerning the typical response, and there is little information on brain metabolism and functional activation. Measurement of all hemodynamic components and oxygen metabolism is critical for understanding neurovascular coupling in the developing brain. To this end, we combined multiple near infrared spectroscopy techniques to measure oxy- and deoxy-hemoglobin concentrations, cerebral blood volume (CBV), and relative cerebral blood flow (CBF) in the somatosensory cortex of 6 preterm neonates during passive tactile stimulation of the hand. By combining these measures we estimated relative changes in the cerebral metabolic rate of oxygen consumption (rCMRO2). CBF starts increasing immediately after stimulus onset, and returns to baseline before blood volume. This is consistent with the model of pre-capillary arteriole active dilation driving the CBF response, with a subsequent CBV increase influenced by capillaries and veins dilating passively to accommodate the extra blood. rCMRO2 estimated using the steady-state formulation shows a biphasic pattern: an increase immediately after stimulus onset, followed by a post-stimulus undershoot due to blood flow returning faster to baseline than oxygenation. However, assuming a longer mean transit time from the arterial to the venous compartment, due to the immature vascular system of premature infants, reduces the post-stimulus undershoot and increases the flow/consumption ratio to values closer to adult values reported in the literature. We are the first to report changes in local rCBF and rCMRO2 during functional activation in preterm infants. The ability to measure these variables in addition to hemoglobin concentration changes is critical for understanding neurovascular coupling in the developing brain, and for using this coupling as a reliable functional imaging marker in neonates. Copyright © 2013 Elsevier Inc. All rights reserved.

Altered small-world topology of structural brain networks in infants with intrauterine growth restriction and its association with later neurodevelopmental outcome.

PubMed

Batalle, Dafnis; Eixarch, Elisenda; Figueras, Francesc; Muñoz-Moreno, Emma; Bargallo, Nuria; Illa, Miriam; Acosta-Rojas, Ruthy; Amat-Roldan, Ivan; Gratacos, Eduard

2012-04-02

Intrauterine growth restriction (IUGR) due to placental insufficiency affects 5-10% of all pregnancies and it is associated with a wide range of short- and long-term neurodevelopmental disorders. Prediction of neurodevelopmental outcomes in IUGR is among the clinical challenges of modern fetal medicine and pediatrics. In recent years several studies have used magnetic resonance imaging (MRI) to demonstrate differences in brain structure in IUGR subjects, but the ability to use MRI for individual predictive purposes in IUGR is limited. Recent research suggests that MRI in vivo access to brain connectivity might have the potential to help understanding cognitive and neurodevelopment processes. Specifically, MRI based connectomics is an emerging approach to extract information from MRI data that exhaustively maps inter-regional connectivity within the brain to build a graph model of its neural circuitry known as brain network. In the present study we used diffusion MRI based connectomics to obtain structural brain networks of a prospective cohort of one year old infants (32 controls and 24 IUGR) and analyze the existence of quantifiable brain reorganization of white matter circuitry in IUGR group by means of global and regional graph theory features of brain networks. Based on global and regional analyses of the brain network topology we demonstrated brain reorganization in IUGR infants at one year of age. Specifically, IUGR infants presented decreased global and local weighted efficiency, and a pattern of altered regional graph theory features. By means of binomial logistic regression, we also demonstrated that connectivity measures were associated with abnormal performance in later neurodevelopmental outcome as measured by Bayley Scale for Infant and Toddler Development, Third edition (BSID-III) at two years of age. These findings show the potential of diffusion MRI based connectomics and graph theory based network characteristics for estimating differences in the architecture of neural circuitry and developing imaging biomarkers of poor neurodevelopment outcome in infants with prenatal diseases. Copyright © 2012 Elsevier Inc. All rights reserved.

Big-Brained People are Smarter: A Meta-Analysis of the Relationship between In Vivo Brain Volume and Intelligence

ERIC Educational Resources Information Center

McDaniel, Michael A.

2005-01-01

The relationship between brain volume and intelligence has been a topic of a scientific debate since at least the 1830s. To address the debate, a meta-analysis of the relationship between in vivo brain volume and intelligence was conducted. Based on 37 samples across 1530 people, the population correlation was estimated at 0.33. The correlation is…

Brain Mechanical Property Measurement Using MRE with Intrinsic Activation

PubMed Central

Pattison, Adam J.; McGarry, Matthew D.; Perreard, Irina M.; Swienckowski, Jessica G.; Eskey, Clifford J.; Lollis, S. Scott; Paulsen, Keith D.

2013-01-01

Problem Addressed Many pathologies alter the mechanical properties of tissue. Magnetic resonance elastography (MRE) has been developed to noninvasively characterize these quantities in vivo. Typically, small vibrations are induced in the tissue of interest with an external mechanical actuator. The resulting displacements are measured with phase contrast sequences and are then used to estimate the underlying mechanical property distribution. Several MRE studies have quantified brain tissue properties. However, the cranium and meninges, especially the dura, are very effective at damping externally applied vibrations from penetrating deeply into the brain. Here, we report a method, termed ‘intrinsic activation’, that eliminates the requirement for external vibrations by measuring the motion generated by natural blood vessel pulsation. Methodology A retrospectively gated phase contrast MR angiography sequence was used to record the tissue velocity at eight phases of the cardiac cycle. The velocities were numerically integrated via the Fourier transform to produce the harmonic displacements at each position within the brain. The displacements were then reconstructed into images of the shear modulus based on both linear elastic and poroelastic models. Results, Significance and Potential Impact The mechanical properties produced fall within the range of brain tissue estimates reported in the literature and, equally important, the technique yielded highly reproducible results. The mean shear modulus was 8.1 kPa for linear elastic reconstructions and 2.4 kPa for poroelastic reconstructions where fluid pressure carries a portion of the stress. Gross structures of the brain were visualized, particularly in the poroelastic reconstructions. Intra-subject variability was significantly less than the inter-subject variability in a study of 6 asymptomatic individuals. Further, larger changes in mechanical properties were observed in individuals when examined over time than when the MRE procedures were repeated on the same day. Cardiac pulsation, termed intrinsic activation, produces sufficient motion to allow mechanical properties to be recovered. The poroelastic model is more consistent with the measured data from brain at low frequencies than the linear elastic model. Intrinsic activation allows MR elastography to be performed without a device shaking the head so the patient notices no differences between it and the other sequences in an MR examination. PMID:23079508

Brain mechanical property measurement using MRE with intrinsic activation

NASA Astrophysics Data System (ADS)

Weaver, John B.; Pattison, Adam J.; McGarry, Matthew D.; Perreard, Irina M.; Swienckowski, Jessica G.; Eskey, Clifford J.; Lollis, S. Scott; Paulsen, Keith D.

2012-11-01

Many pathologies alter the mechanical properties of tissue. Magnetic resonance elastography (MRE) has been developed to noninvasively characterize these quantities in vivo. Typically, small vibrations are induced in the tissue of interest with an external mechanical actuator. The resulting displacements are measured with phase contrast sequences and are then used to estimate the underlying mechanical property distribution. Several MRE studies have quantified brain tissue properties. However, the cranium and meninges, especially the dura, are very effective at damping externally applied vibrations from penetrating deeply into the brain. Here, we report a method, termed ‘intrinsic activation’, that eliminates the requirement for external vibrations by measuring the motion generated by natural blood vessel pulsation. A retrospectively gated phase contrast MR angiography sequence was used to record the tissue velocity at eight phases of the cardiac cycle. The velocities were numerically integrated via the Fourier transform to produce the harmonic displacements at each position within the brain. The displacements were then reconstructed into images of the shear modulus based on both linear elastic and poroelastic models. The mechanical properties produced fall within the range of brain tissue estimates reported in the literature and, equally important, the technique yielded highly reproducible results. The mean shear modulus was 8.1 kPa for linear elastic reconstructions and 2.4 kPa for poroelastic reconstructions where fluid pressure carries a portion of the stress. Gross structures of the brain were visualized, particularly in the poroelastic reconstructions. Intra-subject variability was significantly less than the inter-subject variability in a study of six asymptomatic individuals. Further, larger changes in mechanical properties were observed in individuals when examined over time than when the MRE procedures were repeated on the same day. Cardiac pulsation, termed intrinsic activation, produces sufficient motion to allow mechanical properties to be recovered. The poroelastic model is more consistent with the measured data from brain at low frequencies than the linear elastic model. Intrinsic activation allows MRE to be performed without a device shaking the head so the patient notices no differences between it and the other sequences in an MR examination.

Locally adaptive MR intensity models and MRF-based segmentation of multiple sclerosis lesions

NASA Astrophysics Data System (ADS)

Galimzianova, Alfiia; Lesjak, Žiga; Likar, Boštjan; Pernuš, Franjo; Špiclin, Žiga

2015-03-01

Neuroimaging biomarkers are an important paraclinical tool used to characterize a number of neurological diseases, however, their extraction requires accurate and reliable segmentation of normal and pathological brain structures. For MR images of healthy brains the intensity models of normal-appearing brain tissue (NABT) in combination with Markov random field (MRF) models are known to give reliable and smooth NABT segmentation. However, the presence of pathology, MR intensity bias and natural tissue-dependent intensity variability altogether represent difficult challenges for a reliable estimation of NABT intensity model based on MR images. In this paper, we propose a novel method for segmentation of normal and pathological structures in brain MR images of multiple sclerosis (MS) patients that is based on locally-adaptive NABT model, a robust method for the estimation of model parameters and a MRF-based segmentation framework. Experiments on multi-sequence brain MR images of 27 MS patients show that, compared to whole-brain model and compared to the widely used Expectation-Maximization Segmentation (EMS) method, the locally-adaptive NABT model increases the accuracy of MS lesion segmentation.

Automatic segmentation of cortical vessels in pre- and post-tumor resection laser range scan images

NASA Astrophysics Data System (ADS)

Ding, Siyi; Miga, Michael I.; Thompson, Reid C.; Garg, Ishita; Dawant, Benoit M.

2009-02-01

Measurement of intra-operative cortical brain movement is necessary to drive mechanical models developed to predict sub-cortical shift. At our institution, this is done with a tracked laser range scanner. This device acquires both 3D range data and 2D photographic images. 3D cortical brain movement can be estimated if 2D photographic images acquired over time can be registered. Previously, we have developed a method, which permits this registration using vessels visible in the images. But, vessel segmentation required the localization of starting and ending points for each vessel segment. Here, we propose a method, which automates the segmentation process further. This method involves several steps: (1) correction of lighting artifacts, (2) vessel enhancement, and (3) vessels' centerline extraction. Result obtained on 5 images obtained in the operating room suggests that our method is robust and is able to segment vessels reliably.

Evaluation method for in situ electric field in standardized human brain for different transcranial magnetic stimulation coils

NASA Astrophysics Data System (ADS)

Iwahashi, Masahiro; Gomez-Tames, Jose; Laakso, Ilkka; Hirata, Akimasa

2017-03-01

This study proposes a method to evaluate the electric field induced in the brain by transcranial magnetic stimulation (TMS) to realize focal stimulation in the target area considering the inter-subject difference of the brain anatomy. The TMS is a non-invasive technique used for treatment/diagnosis, and it works by inducing an electric field in a specific area of the brain via a coil-induced magnetic field. Recent studies that report on the electric field distribution in the brain induced by TMS coils have been limited to simplified human brain models or a small number of detailed human brain models. Until now, no method has been developed that appropriately evaluates the coil performance for a group of subjects. In this study, we first compare the magnetic field and the magnetic vector potential distributions to determine if they can be used as predictors of the TMS focality derived from the electric field distribution. Next, the hotspots of the electric field on the brain surface of ten subjects using six coils are compared. Further, decisive physical factors affecting the focality of the induced electric field by different coils are discussed by registering the computed electric field in a standard brain space for the first time, so as to evaluate coil characteristics for a large population of subjects. The computational results suggest that the induced electric field in the target area cannot be generalized without considering the morphological variability of the human brain. Moreover, there was no remarkable difference between the various coils, although focality could be improved to a certain extent by modifying the coil design (e.g., coil radius). Finally, the focality estimated by the electric field was more correlated with the magnetic vector potential than the magnetic field in a homogeneous sphere.

Evaluation method for in situ electric field in standardized human brain for different transcranial magnetic stimulation coils.

PubMed

Iwahashi, Masahiro; Gomez-Tames, Jose; Laakso, Ilkka; Hirata, Akimasa

2017-03-21

This study proposes a method to evaluate the electric field induced in the brain by transcranial magnetic stimulation (TMS) to realize focal stimulation in the target area considering the inter-subject difference of the brain anatomy. The TMS is a non-invasive technique used for treatment/diagnosis, and it works by inducing an electric field in a specific area of the brain via a coil-induced magnetic field. Recent studies that report on the electric field distribution in the brain induced by TMS coils have been limited to simplified human brain models or a small number of detailed human brain models. Until now, no method has been developed that appropriately evaluates the coil performance for a group of subjects. In this study, we first compare the magnetic field and the magnetic vector potential distributions to determine if they can be used as predictors of the TMS focality derived from the electric field distribution. Next, the hotspots of the electric field on the brain surface of ten subjects using six coils are compared. Further, decisive physical factors affecting the focality of the induced electric field by different coils are discussed by registering the computed electric field in a standard brain space for the first time, so as to evaluate coil characteristics for a large population of subjects. The computational results suggest that the induced electric field in the target area cannot be generalized without considering the morphological variability of the human brain. Moreover, there was no remarkable difference between the various coils, although focality could be improved to a certain extent by modifying the coil design (e.g., coil radius). Finally, the focality estimated by the electric field was more correlated with the magnetic vector potential than the magnetic field in a homogeneous sphere.

Devices for noninvasive transcranial electrostimulation of the brain endorphinergic system: application for improvement of human psycho-physiological status.

PubMed

Lebedev, Valery P; Malygin, A V; Kovalevski, A V; Rychkova, S V; Sisoev, V N; Kropotov, S P; Krupitski, E M; Gerasimova, L I; Glukhov, D V; Kozlowski, G P

2002-03-01

It is well known that deficit of endorphins plays an important role in disturbances of human psycho-physiological status. Previously, we revealed that brain endorphinergic structures have quasiresonance characteristics. On the basis of these data, a method of activation of the brain endorphinergic structures by means of noninvasive and rather selective transcranial electrostimulation (TES) as a kind of functional electrical stimulation (FES) was elaborated. New models of TES devices (TRANSAIR) were developed for indoor and outdoor usage. To increase the efficacy of TES, the frequency modulation according to normal distribution in the limits of the quasiresonance characteristics was put into operation. The blind and placebo-controlled (passive and active placebo) study was produced to estimate the TES effects on stress events and accompanied psycho-physiological and autonomic disturbances of different intensities on volunteers and patients in the following groups: everyday stress and fatigue; stress in regular military service and in field conditions; stress in the relatives of those lost in mass disaster; posttraumatic stress (thermal burns); and affective disorders in a postabstinence period. Some subjective verbal and nonverbal tests and objective tests (including heart rate variability) were used for estimation of the initial level of psycho-physiological status, which changes after TES sessions. It was demonstrated that fatigue, stress, and other accompanied psycho-physiological disturbances were significantly improved or abolished after 2-5 TES sessions. The TES effects were more pronounced in cases of heavier disturbances. In conclusion, activation of the brain endorphinergic structures by TES is an effective homeostatic method of FES that sufficiently improves quality of life.

Estimation of cerebral metabolic rate of oxygen consumption using combined multiwavelength photoacoustic microscopy and Doppler microultrasound

NASA Astrophysics Data System (ADS)

Jiang, Yan; Zemp, Roger

2018-01-01

The metabolic rate of oxygen consumption is an important metric of tissue oxygen metabolism and is especially critical in the brain, yet few methods are available for measuring it. We use a custom combined photoacoustic-microultrasound system and demonstrate cerebral oxygen consumption estimation in vivo. In particular, the cerebral metabolic rate of oxygen consumption was estimated in a murine model during variation of inhaled oxygen from hypoxia to hyperoxia. The hypothesis of brain autoregulation was confirmed with our method even though oxygen saturation and flow in vessels changed.

A Subspace Pursuit–based Iterative Greedy Hierarchical Solution to the Neuromagnetic Inverse Problem

PubMed Central

Babadi, Behtash; Obregon-Henao, Gabriel; Lamus, Camilo; Hämäläinen, Matti S.; Brown, Emery N.; Purdon, Patrick L.

2013-01-01

Magnetoencephalography (MEG) is an important non-invasive method for studying activity within the human brain. Source localization methods can be used to estimate spatiotemporal activity from MEG measurements with high temporal resolution, but the spatial resolution of these estimates is poor due to the ill-posed nature of the MEG inverse problem. Recent developments in source localization methodology have emphasized temporal as well as spatial constraints to improve source localization accuracy, but these methods can be computationally intense. Solutions emphasizing spatial sparsity hold tremendous promise, since the underlying neurophysiological processes generating MEG signals are often sparse in nature, whether in the form of focal sources, or distributed sources representing large-scale functional networks. Recent developments in the theory of compressed sensing (CS) provide a rigorous framework to estimate signals with sparse structure. In particular, a class of CS algorithms referred to as greedy pursuit algorithms can provide both high recovery accuracy and low computational complexity. Greedy pursuit algorithms are difficult to apply directly to the MEG inverse problem because of the high-dimensional structure of the MEG source space and the high spatial correlation in MEG measurements. In this paper, we develop a novel greedy pursuit algorithm for sparse MEG source localization that overcomes these fundamental problems. This algorithm, which we refer to as the Subspace Pursuit-based Iterative Greedy Hierarchical (SPIGH) inverse solution, exhibits very low computational complexity while achieving very high localization accuracy. We evaluate the performance of the proposed algorithm using comprehensive simulations, as well as the analysis of human MEG data during spontaneous brain activity and somatosensory stimuli. These studies reveal substantial performance gains provided by the SPIGH algorithm in terms of computational complexity, localization accuracy, and robustness. PMID:24055554

Structural MRI biomarkers of shared pathogenesis in autism spectrum disorder and epilepsy.

PubMed

Blackmon, Karen

2015-06-01

Etiological factors that contribute to a high comorbidity between autism spectrum disorder (ASD) and epilepsy are the subject of much debate. Does epilepsy cause ASD or are there common underlying brain abnormalities that increase the risk of developing both disorders? This review summarizes evidence from quantitative MRI studies to suggest that abnormalities of brain structure are not necessarily the consequence of ASD and epilepsy but are antecedent to disease expression. Abnormal gray and white matter volumes are present prior to onset of ASD and evident at the time of onset in pediatric epilepsy. Aberrant brain growth trajectories are also common in both disorders, as evidenced by blunted gray matter maturation and white matter maturation. Although the etiological factors that explain these abnormalities are unclear, high heritability estimates for gray matter volume and white matter microstructure demonstrate that genetic factors assert a strong influence on brain structure. In addition, histopathological studies of ASD and epilepsy brain tissue reveal elevated rates of malformations of cortical development (MCDs), such as focal cortical dysplasia and heterotopias, which supports disruption of neuronal migration as a contributing factor. Although MCDs are not always visible on MRI with conventional radiological analysis, quantitative MRI detection methods show high sensitivity to subtle malformations in epilepsy and can be potentially applied to MCD detection in ASD. Such an approach is critical for establishing quantitative neuroanatomic endophenotypes that can be used in genetic research. In the context of emerging drug treatments for seizures and autism symptoms, such as rapamycin and rapalogs, in vivo neuroimaging markers of subtle structural brain abnormalities could improve sample stratification in human clinical trials and potentially extend the range of patients that might benefit from treatment. This article is part of a Special Issue entitled "Autism and Epilepsy". Copyright © 2015 Elsevier Inc. All rights reserved.

Segmentation propagation for the automated quantification of ventricle volume from serial MRI

NASA Astrophysics Data System (ADS)

Linguraru, Marius George; Butman, John A.

2009-02-01

Accurate ventricle volume estimates could potentially improve the understanding and diagnosis of communicating hydrocephalus. Postoperative communicating hydrocephalus has been recognized in patients with brain tumors where the changes in ventricle volume can be difficult to identify, particularly over short time intervals. Because of the complex alterations of brain morphology in these patients, the segmentation of brain ventricles is challenging. Our method evaluates ventricle size from serial brain MRI examinations; we (i) combined serial images to increase SNR, (ii) automatically segmented this image to generate a ventricle template using fast marching methods and geodesic active contours, and (iii) propagated the segmentation using deformable registration of the original MRI datasets. By applying this deformation to the ventricle template, serial volume estimates were obtained in a robust manner from routine clinical images (0.93 overlap) and their variation analyzed.

Brain iron homeostasis, the choroid plexus, and localization of iron transport proteins.

PubMed

Rouault, Tracey A; Zhang, De-Liang; Jeong, Suh Young

2009-12-01

Maintenance of appropriate iron homeostasis in the brain is important, but the mechanisms involved in brain iron uptake are incompletely understood. Here, we have analyzed where messenger RNAs that encode iron transport proteins are expressed in the brain, using the Allen Brain atlas, and we conclude that several important iron transporters are highly expressed in the choroid plexus. Based on recent estimates of the surface area of the choroid plexus and on MRI imaging studies of manganese uptake in the brain, we propose that the choroid plexus may have a much greater role than has been previously appreciated in brain iron transport.

A Simple Noise Correction Scheme for Diffusional Kurtosis Imaging

PubMed Central

Glenn, G. Russell; Tabesh, Ali; Jensen, Jens H.

2014-01-01

Purpose Diffusional kurtosis imaging (DKI) is sensitive to the effects of signal noise due to strong diffusion weightings and higher order modeling of the diffusion weighted signal. A simple noise correction scheme is proposed to remove the majority of the noise bias in the estimated diffusional kurtosis. Methods Weighted linear least squares (WLLS) fitting together with a voxel-wise, subtraction-based noise correction from multiple, independent acquisitions are employed to reduce noise bias in DKI data. The method is validated in phantom experiments and demonstrated for in vivo human brain for DKI-derived parameter estimates. Results As long as the signal-to-noise ratio (SNR) for the most heavily diffusion weighted images is greater than 2.1, errors in phantom diffusional kurtosis estimates are found to be less than 5 percent with noise correction, but as high as 44 percent for uncorrected estimates. In human brain, noise correction is also shown to improve diffusional kurtosis estimates derived from measurements made with low SNR. Conclusion The proposed correction technique removes the majority of noise bias from diffusional kurtosis estimates in noisy phantom data and is applicable to DKI of human brain. Features of the method include computational simplicity and ease of integration into standard WLLS DKI post-processing algorithms. PMID:25172990

Biases in measuring the brain: the trouble with the telencephalon.

PubMed

LaDage, Lara D; Roth, Timothy C; Pravosudov, Vladimir V

2009-01-01

When correlating behavior with particular brain regions thought responsible for the behavior, a different region of the brain is usually measured as a control region. This technique is often used to relate spatial processes with the hippocampus, while concomitantly controlling for overall brain changes by measuring the remainder of the telencephalon. We have identified two methods in the literature (the HOM and TTM) that estimate the volume of the telencephalon, although the majority of studies are ambiguous regarding the method employed in measuring the telencephalon. Of these two methods, the HOM might produce an artificial correlation between the telencephalon and the hippocampus, and this bias could result in a significant overestimation of the relative hippocampal volume and a significant underestimation of the telencephalon volume, both of which are regularly used in large comparative analyses. We suggest that future studies should avoid this method and all studies should explicitly delineate the procedures used when estimating brain volumes. Copyright 2009 S. Karger AG, Basel.

Decoding of four movement directions using hybrid NIRS-EEG brain-computer interface

PubMed Central

Khan, M. Jawad; Hong, Melissa Jiyoun; Hong, Keum-Shik

2014-01-01

The hybrid brain-computer interface (BCI)'s multimodal technology enables precision brain-signal classification that can be used in the formulation of control commands. In the present study, an experimental hybrid near-infrared spectroscopy-electroencephalography (NIRS-EEG) technique was used to extract and decode four different types of brain signals. The NIRS setup was positioned over the prefrontal brain region, and the EEG over the left and right motor cortex regions. Twelve subjects participating in the experiment were shown four direction symbols, namely, “forward,” “backward,” “left,” and “right.” The control commands for forward and backward movement were estimated by performing arithmetic mental tasks related to oxy-hemoglobin (HbO) changes. The left and right directions commands were associated with right and left hand tapping, respectively. The high classification accuracies achieved showed that the four different control signals can be accurately estimated using the hybrid NIRS-EEG technology. PMID:24808844

Expensive Brains: "Brainy" Rodents have Higher Metabolic Rate.

PubMed

Sobrero, Raúl; May-Collado, Laura J; Agnarsson, Ingi; Hernández, Cristián E

2011-01-01

Brains are the centers of the nervous system of animals, controlling the organ systems of the body and coordinating responses to changes in the ecological and social environment. The evolution of traits that correlate with cognitive ability, such as relative brain size is thus of broad interest. Brain mass relative to body mass (BM) varies among mammals, and diverse factors have been proposed to explain this variation. A recent study provided evidence that energetics play an important role in brain evolution (Isler and van Schaik, 2006). Using composite phylogenies and data drawn from multiple sources, these authors showed that basal metabolic rate (BMR) correlates with brain mass across mammals. However, no such relationship was found within rodents. Here we re-examined the relationship between BMR and brain mass within Rodentia using a novel species-level phylogeny. Our results are sensitive to parameter evaluation; in particular how species mass is estimated. We detect no pattern when applying an approach used by previous studies, where each species BM is represented by two different numbers, one being the individual that happened to be used for BMR estimates of that species. However, this approach may compromise the analysis. When using a single value of BM for each species, whether representing a single individual, or available species mean, our findings provide evidence that brain mass (independent of BM) and BMR are correlated. These findings are thus consistent with the hypothesis that large brains evolve when the payoff for increased brain mass is greater than the energetic cost they incur.

Non-invasive imaging of the levels and effects of glutathione on the redox status of mouse brain using electron paramagnetic resonance imaging

DOE Office of Scientific and Technical Information (OSTI.GOV)

Emoto, Miho C.; Department of Neurology, Sapporo Medical University School of Medicine, Sapporo, Hokkaido 060-8556; Matsuoka, Yuta

Glutathione (GSH) is the most abundant non-protein thiol that buffers reactive oxygen species in the brain. GSH does not reduce nitroxides directly, but in the presence of ascorbates, addition of GSH increases ascorbate-induced reduction of nitroxides. In this study, we used electron paramagnetic resonance (EPR) imaging and the nitroxide imaging probe, 3-methoxycarbonyl-2,2,5,5-tetramethyl-piperidine-1-oxyl (MCP), to non-invasively obtain spatially resolved redox data from mouse brains depleted of GSH with diethyl maleate compared to control. Based on the pharmacokinetics of the reduction reaction of MCP in the mouse heads, the pixel-based rate constant of its reduction reaction was calculated as an index ofmore » the redox status in vivo and mapped as a “redox map”. The obtained redox maps from control and GSH-depleted mouse brains showed a clear change in the brain redox status, which was due to the decreased levels of GSH in brains as measured by a biochemical assay. We observed a linear relationship between the reduction rate constant of MCP and the level of GSH for both control and GSH-depleted mouse brains. Using this relationship, the GSH level in the brain can be estimated from the redox map obtained with EPR imaging. - Highlights: • Redox status of glutathione-depleted mouse brain was examined with EPR imaging. • Redox status of mouse brain changed depending on glutathione (GSH) levels in brains. • Linear relationship between GSH levels and redox status in brains was found. • Using this relation, estimation of GSH levels in brains is possible from EPR images.« less

MNE software for processing MEG and EEG data

PubMed Central

Gramfort, A.; Luessi, M.; Larson, E.; Engemann, D.; Strohmeier, D.; Brodbeck, C.; Parkkonen, L.; Hämäläinen, M.

2013-01-01

Magnetoencephalography and electroencephalography (M/EEG) measure the weak electromagnetic signals originating from neural currents in the brain. Using these signals to characterize and locate brain activity is a challenging task, as evidenced by several decades of methodological contributions. MNE, whose name stems from its capability to compute cortically-constrained minimum-norm current estimates from M/EEG data, is a software package that provides comprehensive analysis tools and workflows including preprocessing, source estimation, time–frequency analysis, statistical analysis, and several methods to estimate functional connectivity between distributed brain regions. The present paper gives detailed information about the MNE package and describes typical use cases while also warning about potential caveats in analysis. The MNE package is a collaborative effort of multiple institutes striving to implement and share best methods and to facilitate distribution of analysis pipelines to advance reproducibility of research. Full documentation is available at http://martinos.org/mne. PMID:24161808

Multi-subject hierarchical inverse covariance modelling improves estimation of functional brain networks.

PubMed

Colclough, Giles L; Woolrich, Mark W; Harrison, Samuel J; Rojas López, Pedro A; Valdes-Sosa, Pedro A; Smith, Stephen M

2018-05-07

A Bayesian model for sparse, hierarchical, inver-covariance estimation is presented, and applied to multi-subject functional connectivity estimation in the human brain. It enables simultaneous inference of the strength of connectivity between brain regions at both subject and population level, and is applicable to fMRI, MEG and EEG data. Two versions of the model can encourage sparse connectivity, either using continuous priors to suppress irrelevant connections, or using an explicit description of the network structure to estimate the connection probability between each pair of regions. A large evaluation of this model, and thirteen methods that represent the state of the art of inverse covariance modelling, is conducted using both simulated and resting-state functional imaging datasets. Our novel Bayesian approach has similar performance to the best extant alternative, Ng et al.'s Sparse Group Gaussian Graphical Model algorithm, which also is based on a hierarchical structure. Using data from the Human Connectome Project, we show that these hierarchical models are able to reduce the measurement error in MEG beta-band functional networks by 10%, producing concomitant increases in estimates of the genetic influence on functional connectivity. Copyright © 2018. Published by Elsevier Inc.

Motion correction of PET brain images through deconvolution: I. Theoretical development and analysis in software simulations

NASA Astrophysics Data System (ADS)

Faber, T. L.; Raghunath, N.; Tudorascu, D.; Votaw, J. R.

2009-02-01

Image quality is significantly degraded even by small amounts of patient motion in very high-resolution PET scanners. Existing correction methods that use known patient motion obtained from tracking devices either require multi-frame acquisitions, detailed knowledge of the scanner, or specialized reconstruction algorithms. A deconvolution algorithm has been developed that alleviates these drawbacks by using the reconstructed image to estimate the original non-blurred image using maximum likelihood estimation maximization (MLEM) techniques. A high-resolution digital phantom was created by shape-based interpolation of the digital Hoffman brain phantom. Three different sets of 20 movements were applied to the phantom. For each frame of the motion, sinograms with attenuation and three levels of noise were simulated and then reconstructed using filtered backprojection. The average of the 20 frames was considered the motion blurred image, which was restored with the deconvolution algorithm. After correction, contrast increased from a mean of 2.0, 1.8 and 1.4 in the motion blurred images, for the three increasing amounts of movement, to a mean of 2.5, 2.4 and 2.2. Mean error was reduced by an average of 55% with motion correction. In conclusion, deconvolution can be used for correction of motion blur when subject motion is known.

Spatial and temporal single-cell volume estimation by a fluorescence imaging technique with application to astrocytes in primary culture

NASA Astrophysics Data System (ADS)

Khatibi, Siamak; Allansson, Louise; Gustavsson, Tomas; Blomstrand, Fredrik; Hansson, Elisabeth; Olsson, Torsten

1999-05-01

Cell volume changes are often associated with important physiological and pathological processes in the cell. These changes may be the means by which the cell interacts with its surrounding. Astroglial cells change their volume and shape under several circumstances that affect the central nervous system. Following an incidence of brain damage, such as a stroke or a traumatic brain injury, one of the first events seen is swelling of the astroglial cells. In order to study this and other similar phenomena, it is desirable to develop technical instrumentation and analysis methods capable of detecting and characterizing dynamic cell shape changes in a quantitative and robust way. We have developed a technique to monitor and to quantify the spatial and temporal volume changes in a single cell in primary culture. The technique is based on two- and three-dimensional fluorescence imaging. The temporal information is obtained from a sequence of microscope images, which are analyzed in real time. The spatial data is collected in a sequence of images from the microscope, which is automatically focused up and down through the specimen. The analysis of spatial data is performed off-line and consists of photobleaching compensation, focus restoration, filtering, segmentation and spatial volume estimation.

Magnetic resonance spectroscopic analysis of neurometabolite changes in the developing rat brain at 7T.

PubMed

Ramu, Jaivijay; Konak, Tetyana; Liachenko, Serguei

2016-11-15

We utilized proton magnetic resonance spectroscopy to evaluate the metabolic profile of the hippocampus and anterior cingulate cortex of the developing rat brain from postnatal days 14-70. Measured metabolite concentrations were modeled using linear, exponential, or logarithmic functions and the time point at which the data reached plateau (i.e. when the portion of the data could be fit to horizontal line) was estimated and was interpreted as the time when the brain has reached maturity with respect to that metabolite. N-acetyl-aspartate and myo-inositol increased within the observed period. Gluthathione did not vary significantly, while taurine decreased initially and then stabilized. Phosphocreatine and total creatine had a tendency to increase towards the end of the experiment. Some differences between our data and the published literature were observed in the concentrations and dynamics of phosphocreatine, myo-inositol, and GABA in the hippocampus and creatine, GABA, glutamine, choline and N-acetyl-aspartate in the cortex. Such differences may be attributed to experimental conditions, analysis approaches and animal species. The latter is supported by differences between in-house rat colony and rats from Charles River Labs. Spectroscopy provides a valuable tool for non-invasive brain neurochemical profiling for use in developmental neurobiology research. Special attention needs to be paid to important sources of variation like animal strain and commercial source. Published by Elsevier B.V.

Application of intracerebral microdialysis to study regional distribution kinetics of drugs in rat brain.

PubMed Central

de Lange, E. C.; Bouw, M. R.; Mandema, J. W.; Danhof, M.; de Boer, A. G.; Breimer, D. D.

1995-01-01

1. The purpose of the present study was to determine whether intracerebral microdialysis can be used for the assessment of local differences in drug concentrations within the brain. 2. Two transversal microdialysis probes were implanted in parallel into the frontal cortex of male Wistar rats, and used as a local infusion and detection device respectively. Within one rat, three different concentrations of atenolol or acetaminophen were infused in randomized order. By means of the detection probe, concentration-time profiles of the drug in the brain were measured at interprobe distances between 1 and 2 mm. 3. Drug concentrations were found to be dependent on the drug as well as on the interprobe distance. It was found that the outflow concentration from the detection probe decreased with increasing lateral spacing between the probes and this decay was much steeper for acetaminophen than for atenolol. A model was developed which allows estimation of kbp/Deff (transfer coefficient from brain to blood/effective diffusion coefficient in brain extracellular fluid), which was considerably larger for the more lipohilic drug, acetaminophen. In addition, in vivo recovery values for both drugs were determined. 4. The results show that intracerebral microdialysis is able to detect local differences in drug concentrations following infusion into the brain. Furthermore, the potential use of intracerebral microdialysis to obtain pharmacokinetic parameters of drug distribution in brain by means of monitoring local concentrations of drugs in time is demonstrated. PMID:8581296

Comparison of effectiveness between cork-screw and peg-screw electrodes for transcranial motor evoked potential monitoring using the finite element method.

PubMed

Tomio, Ryosuke; Akiyama, Takenori; Ohira, Takayuki; Yoshida, Kazunari

2016-01-01

Intraoperative monitoring of motor evoked potentials by transcranial electric stimulation is popular in neurosurgery for monitoring motor function preservation. Some authors have reported that the peg-screw electrodes screwed into the skull can more effectively conduct current to the brain compared to subdermal cork-screw electrodes screwed into the skin. The aim of this study was to investigate the influence of electrode design on transcranial motor evoked potential monitoring. We estimated differences in effectiveness between the cork-screw electrode, peg-screw electrode, and cortical electrode to produce electric fields in the brain. We used the finite element method to visualize electric fields in the brain generated by transcranial electric stimulation using realistic three-dimensional head models developed from T1-weighted images. Surfaces from five layers of the head were separated as accurately as possible. We created the "cork-screws model," "1 peg-screw model," "peg-screws model," and "cortical electrode model". Electric fields in the brain radially diffused from the brain surface at a maximum just below the electrodes in coronal sections. The coronal sections and surface views of the brain showed higher electric field distributions under the peg-screw compared to the cork-screw. An extremely high electric field was observed under cortical electrodes. Our main finding was that the intensity of electric fields in the brain are higher in the peg-screw model than the cork-screw model.

Intellectual enrichment lessens the effect of brain atrophy on learning and memory in multiple sclerosis.

PubMed

Sumowski, James F; Wylie, Glenn R; Chiaravalloti, Nancy; DeLuca, John

2010-06-15

Learning and memory impairments are prevalent among persons with multiple sclerosis (MS); however, such deficits are only weakly associated with MS disease severity (brain atrophy). The cognitive reserve hypothesis states that greater lifetime intellectual enrichment lessens the negative impact of brain disease on cognition, thereby helping to explain the incomplete relationship between brain disease and cognitive status in neurologic populations. The literature on cognitive reserve has focused mainly on Alzheimer disease. The current research examines whether greater intellectual enrichment lessens the negative effect of brain atrophy on learning and memory in patients with MS. Forty-four persons with MS completed neuropsychological measures of verbal learning and memory, and a vocabulary-based estimate of lifetime intellectual enrichment. Brain atrophy was estimated with third ventricle width measured from 3-T magnetization-prepared rapid gradient echo MRIs. Hierarchical regression was used to predict learning and memory with brain atrophy, intellectual enrichment, and the interaction between brain atrophy and intellectual enrichment. Brain atrophy predicted worse learning and memory, and intellectual enrichment predicted better learning; however, these effects were moderated by interactions between brain atrophy and intellectual enrichment. Specifically, higher intellectual enrichment lessened the negative impact of brain atrophy on both learning and memory. These findings help to explain the incomplete relationship between multiple sclerosis disease severity and cognition, as the effect of disease on cognition is attenuated among patients with higher intellectual enrichment. As such, intellectual enrichment is supported as a protective factor against disease-related cognitive impairment in persons with multiple sclerosis.

An animal-to-human scaling law for blast-induced traumatic brain injury risk assessment.

PubMed

Jean, Aurélie; Nyein, Michelle K; Zheng, James Q; Moore, David F; Joannopoulos, John D; Radovitzky, Raúl

2014-10-28

Despite recent efforts to understand blast effects on the human brain, there are still no widely accepted injury criteria for humans. Recent animal studies have resulted in important advances in the understanding of brain injury due to intense dynamic loads. However, the applicability of animal brain injury results to humans remains uncertain. Here, we use advanced computational models to derive a scaling law relating blast wave intensity to the mechanical response of brain tissue across species. Detailed simulations of blast effects on the brain are conducted for different mammals using image-based biofidelic models. The intensity of the stress waves computed for different external blast conditions is compared across species. It is found that mass scaling, which successfully estimates blast tolerance of the thorax, fails to capture the brain mechanical response to blast across mammals. Instead, we show that an appropriate scaling variable must account for the mass of protective tissues relative to the brain, as well as their acoustic impedance. Peak stresses transmitted to the brain tissue by the blast are then shown to be a power function of the scaling parameter for a range of blast conditions relevant to TBI. In particular, it is found that human brain vulnerability to blast is higher than for any other mammalian species, which is in distinct contrast to previously proposed scaling laws based on body or brain mass. An application of the scaling law to recent experiments on rabbits furnishes the first physics-based injury estimate for blast-induced TBI in humans.

The study of evolution and depression of the alpha-rhythm in the human brain EEG by means of wavelet-based methods

NASA Astrophysics Data System (ADS)

Runnova, A. E.; Zhuravlev, M. O.; Khramova, M. V.; Pysarchik, A. N.

2017-04-01

We study the appearance, development and depression of the alpha-rhythm in human EEG data during a psychophysiological experiment by stimulating cognitive activity with the perception of ambiguous object. The new method based on continuous wavelet transform allows to estimate the energy contribution of various components, including the alpha rhythm, in the general dynamics of the electrical activity of the projections of various areas of the brain. The decision-making process by observe ambiguous images is characterized by specific oscillatory alfa-rhytm patterns in the multi-channel EEG data. We have shown the repeatability of detected principles of the alpha-rhythm evolution in a data of group of 12 healthy male volunteers.

Segmentation of brain volume based on 3D region growing by integrating intensity and edge for image-guided surgery

NASA Astrophysics Data System (ADS)

Tsagaan, Baigalmaa; Abe, Keiichi; Goto, Masahiro; Yamamoto, Seiji; Terakawa, Susumu

2006-03-01

This paper presents a segmentation method of brain tissues from MR images, invented for our image-guided neurosurgery system under development. Our goal is to segment brain tissues for creating biomechanical model. The proposed segmentation method is based on 3-D region growing and outperforms conventional approaches by stepwise usage of intensity similarities between voxels in conjunction with edge information. Since the intensity and the edge information are complementary to each other in the region-based segmentation, we use them twice by performing a coarse-to-fine extraction. First, the edge information in an appropriate neighborhood of the voxel being considered is examined to constrain the region growing. The expanded region of the first extraction result is then used as the domain for the next processing. The intensity and the edge information of the current voxel only are utilized in the final extraction. Before segmentation, the intensity parameters of the brain tissues as well as partial volume effect are estimated by using expectation-maximization (EM) algorithm in order to provide an accurate data interpretation into the extraction. We tested the proposed method on T1-weighted MR images of brain and evaluated the segmentation effectiveness comparing the results with ground truths. Also, the generated meshes from the segmented brain volume by using mesh generating software are shown in this paper.

Using Structural Equation Modeling to Assess Functional Connectivity in the Brain: Power and Sample Size Considerations

ERIC Educational Resources Information Center

Sideridis, Georgios; Simos, Panagiotis; Papanicolaou, Andrew; Fletcher, Jack

2014-01-01

The present study assessed the impact of sample size on the power and fit of structural equation modeling applied to functional brain connectivity hypotheses. The data consisted of time-constrained minimum norm estimates of regional brain activity during performance of a reading task obtained with magnetoencephalography. Power analysis was first…

The Evolution of Human Intelligence and the Coefficient of Additive Genetic Variance in Human Brain Size

ERIC Educational Resources Information Center

Miller, Geoffrey F.; Penke, Lars

2007-01-01

Most theories of human mental evolution assume that selection favored higher intelligence and larger brains, which should have reduced genetic variance in both. However, adult human intelligence remains highly heritable, and is genetically correlated with brain size. This conflict might be resolved by estimating the coefficient of additive genetic…

Cerebral palsy characterization by estimating ocular motion

NASA Astrophysics Data System (ADS)

González, Jully; Atehortúa, Angélica; Moncayo, Ricardo; Romero, Eduardo

2017-11-01

Cerebral palsy (CP) is a large group of motion and posture disorders caused during the fetal or infant brain development. Sensorial impairment is commonly found in children with CP, i.e., between 40-75 percent presents some form of vision problems or disabilities. An automatic characterization of the cerebral palsy is herein presented by estimating the ocular motion during a gaze pursuing task. Specifically, After automatically detecting the eye location, an optical flow algorithm tracks the eye motion following a pre-established visual assignment. Subsequently, the optical flow trajectories are characterized in the velocity-acceleration phase plane. Differences are quantified in a small set of patients between four to ten years.

Age-related apparent diffusion coefficient changes in the normal brain.

PubMed

Watanabe, Memi; Sakai, Osamu; Ozonoff, Al; Kussman, Steven; Jara, Hernán

2013-02-01

To measure the mean diffusional age-related changes of the brain over the full human life span by using diffusion-weighted spin-echo single-shot echo-planar magnetic resonance (MR) imaging and sequential whole-brain apparent diffusion coefficient (ADC) histogram analysis and, secondarily, to build mathematical models of these normal age-related changes throughout human life. After obtaining institutional review board approval, a HIPAA-compliant retrospective search was conducted for brain MR imaging studies performed in 2007 for various clinical indications. Informed consent was waived. The brain data of 414 healthy subjects (189 males and 225 females; mean age, 33.7 years; age range, 2 days to 89.3 years) were obtained with diffusion-weighted spin-echo single-shot echo-planar MR imaging. ADC histograms of the whole brain were generated. ADC peak values, histogram widths, and intracranial volumes were plotted against age, and model parameters were estimated by using nonlinear regression. Four different stages were identified for aging changes in ADC peak values, as characterized by specific mathematical terms: There were age-associated exponential decays for the maturation period and the development period, a constant term for adulthood, and a linear increase for the senescence period. The age dependency of ADC peak value was simulated by using four-term six-coefficient function, including biexponential and linear terms. This model fit the data very closely (R(2) = 0.91). Brain diffusivity as a whole demonstrated age-related changes through four distinct periods of life. These results could contribute to establishing an ADC baseline of the normal brain, covering the full human life span.

Practice on an augmented reality/haptic simulator and library of virtual brains improves residents' ability to perform a ventriculostomy.

PubMed

Yudkowsky, Rachel; Luciano, Cristian; Banerjee, Pat; Schwartz, Alan; Alaraj, Ali; Lemole, G Michael; Charbel, Fady; Smith, Kelly; Rizzi, Silvio; Byrne, Richard; Bendok, Bernard; Frim, David

2013-02-01

Ventriculostomy is a neurosurgical procedure for providing therapeutic cerebrospinal fluid drainage. Complications may arise during repeated attempts at placing the catheter in the ventricle. We studied the impact of simulation-based practice with a library of virtual brains on neurosurgery residents' performance in simulated and live surgical ventriculostomies. Using computed tomographic scans of actual patients, we developed a library of 15 virtual brains for the ImmersiveTouch system, a head- and hand-tracked augmented reality and haptic simulator. The virtual brains represent a range of anatomies including normal, shifted, and compressed ventricles. Neurosurgery residents participated in individual simulator practice on the library of brains including visualizing the 3-dimensional location of the catheter within the brain immediately after each insertion. Performance of participants on novel brains in the simulator and during actual surgery before and after intervention was analyzed using generalized linear mixed models. Simulator cannulation success rates increased after intervention, and live procedure outcomes showed improvement in the rate of successful cannulation on the first pass. However, the incidence of deeper, contralateral (simulator) and third-ventricle (live) placements increased after intervention. Residents reported that simulations were realistic and helpful in improving procedural skills such as aiming the probe, sensing the pressure change when entering the ventricle, and estimating how far the catheter should be advanced within the ventricle. Simulator practice with a library of virtual brains representing a range of anatomies and difficulty levels may improve performance, potentially decreasing complications due to inexpert technique.

Improved frame-based estimation of head motion in PET brain imaging

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mukherjee, J. M., E-mail: joyeeta.mitra@umassmed.edu; Lindsay, C.; King, M. A.

Purpose: Head motion during PET brain imaging can cause significant degradation of image quality. Several authors have proposed ways to compensate for PET brain motion to restore image quality and improve quantitation. Head restraints can reduce movement but are unreliable; thus the need for alternative strategies such as data-driven motion estimation or external motion tracking. Herein, the authors present a data-driven motion estimation method using a preprocessing technique that allows the usage of very short duration frames, thus reducing the intraframe motion problem commonly observed in the multiple frame acquisition method. Methods: The list mode data for PET acquisition ismore » uniformly divided into 5-s frames and images are reconstructed without attenuation correction. Interframe motion is estimated using a 3D multiresolution registration algorithm and subsequently compensated for. For this study, the authors used 8 PET brain studies that used F-18 FDG as the tracer and contained minor or no initial motion. After reconstruction and prior to motion estimation, known motion was introduced to each frame to simulate head motion during a PET acquisition. To investigate the trade-off in motion estimation and compensation with respect to frames of different length, the authors summed 5-s frames accordingly to produce 10 and 60 s frames. Summed images generated from the motion-compensated reconstructed frames were then compared to the original PET image reconstruction without motion compensation. Results: The authors found that our method is able to compensate for both gradual and step-like motions using frame times as short as 5 s with a spatial accuracy of 0.2 mm on average. Complex volunteer motion involving all six degrees of freedom was estimated with lower accuracy (0.3 mm on average) than the other types investigated. Preprocessing of 5-s images was necessary for successful image registration. Since their method utilizes nonattenuation corrected frames, it is not susceptible to motion introduced between CT and PET acquisitions. Conclusions: The authors have shown that they can estimate motion for frames with time intervals as short as 5 s using nonattenuation corrected reconstructed FDG PET brain images. Intraframe motion in 60-s frames causes degradation of accuracy to about 2 mm based on the motion type.« less

Improved frame-based estimation of head motion in PET brain imaging

PubMed Central

Mukherjee, J. M.; Lindsay, C.; Mukherjee, A.; Olivier, P.; Shao, L.; King, M. A.; Licho, R.

2016-01-01

Purpose: Head motion during PET brain imaging can cause significant degradation of image quality. Several authors have proposed ways to compensate for PET brain motion to restore image quality and improve quantitation. Head restraints can reduce movement but are unreliable; thus the need for alternative strategies such as data-driven motion estimation or external motion tracking. Herein, the authors present a data-driven motion estimation method using a preprocessing technique that allows the usage of very short duration frames, thus reducing the intraframe motion problem commonly observed in the multiple frame acquisition method. Methods: The list mode data for PET acquisition is uniformly divided into 5-s frames and images are reconstructed without attenuation correction. Interframe motion is estimated using a 3D multiresolution registration algorithm and subsequently compensated for. For this study, the authors used 8 PET brain studies that used F-18 FDG as the tracer and contained minor or no initial motion. After reconstruction and prior to motion estimation, known motion was introduced to each frame to simulate head motion during a PET acquisition. To investigate the trade-off in motion estimation and compensation with respect to frames of different length, the authors summed 5-s frames accordingly to produce 10 and 60 s frames. Summed images generated from the motion-compensated reconstructed frames were then compared to the original PET image reconstruction without motion compensation. Results: The authors found that our method is able to compensate for both gradual and step-like motions using frame times as short as 5 s with a spatial accuracy of 0.2 mm on average. Complex volunteer motion involving all six degrees of freedom was estimated with lower accuracy (0.3 mm on average) than the other types investigated. Preprocessing of 5-s images was necessary for successful image registration. Since their method utilizes nonattenuation corrected frames, it is not susceptible to motion introduced between CT and PET acquisitions. Conclusions: The authors have shown that they can estimate motion for frames with time intervals as short as 5 s using nonattenuation corrected reconstructed FDG PET brain images. Intraframe motion in 60-s frames causes degradation of accuracy to about 2 mm based on the motion type. PMID:27147355

Identifying Seizure Onset Zone From the Causal Connectivity Inferred Using Directed Information

NASA Astrophysics Data System (ADS)

Malladi, Rakesh; Kalamangalam, Giridhar; Tandon, Nitin; Aazhang, Behnaam

2016-10-01

In this paper, we developed a model-based and a data-driven estimator for directed information (DI) to infer the causal connectivity graph between electrocorticographic (ECoG) signals recorded from brain and to identify the seizure onset zone (SOZ) in epileptic patients. Directed information, an information theoretic quantity, is a general metric to infer causal connectivity between time-series and is not restricted to a particular class of models unlike the popular metrics based on Granger causality or transfer entropy. The proposed estimators are shown to be almost surely convergent. Causal connectivity between ECoG electrodes in five epileptic patients is inferred using the proposed DI estimators, after validating their performance on simulated data. We then proposed a model-based and a data-driven SOZ identification algorithm to identify SOZ from the causal connectivity inferred using model-based and data-driven DI estimators respectively. The data-driven SOZ identification outperforms the model-based SOZ identification algorithm when benchmarked against visual analysis by neurologist, the current clinical gold standard. The causal connectivity analysis presented here is the first step towards developing novel non-surgical treatments for epilepsy.

Dipole estimation errors due to not incorporating anisotropic conductivities in realistic head models for EEG source analysis

NASA Astrophysics Data System (ADS)

Hallez, Hans; Staelens, Steven; Lemahieu, Ignace

2009-10-01

EEG source analysis is a valuable tool for brain functionality research and for diagnosing neurological disorders, such as epilepsy. It requires a geometrical representation of the human head or a head model, which is often modeled as an isotropic conductor. However, it is known that some brain tissues, such as the skull or white matter, have an anisotropic conductivity. Many studies reported that the anisotropic conductivities have an influence on the calculated electrode potentials. However, few studies have assessed the influence of anisotropic conductivities on the dipole estimations. In this study, we want to determine the dipole estimation errors due to not taking into account the anisotropic conductivities of the skull and/or brain tissues. Therefore, head models are constructed with the same geometry, but with an anisotropically conducting skull and/or brain tissue compartment. These head models are used in simulation studies where the dipole location and orientation error is calculated due to neglecting anisotropic conductivities of the skull and brain tissue. Results show that not taking into account the anisotropic conductivities of the skull yields a dipole location error between 2 and 25 mm, with an average of 10 mm. When the anisotropic conductivities of the brain tissues are neglected, the dipole location error ranges between 0 and 5 mm. In this case, the average dipole location error was 2.3 mm. In all simulations, the dipole orientation error was smaller than 10°. We can conclude that the anisotropic conductivities of the skull have to be incorporated to improve the accuracy of EEG source analysis. The results of the simulation, as presented here, also suggest that incorporation of the anisotropic conductivities of brain tissues is not necessary. However, more studies are needed to confirm these suggestions.

First demonstration of in vivo mapping for regional brain monoacylglycerol lipase using PET with [11C]SAR127303.

PubMed

Yamasaki, Tomoteru; Mori, Wakana; Zhang, Yiding; Hatori, Akiko; Fujinaga, Masayuki; Wakizaka, Hidekatsu; Kurihara, Yusuke; Wang, Lu; Nengaki, Nobuki; Ohya, Tomoyuki; Liang, Steven H; Zhang, Ming-Rong

2018-08-01

Monoacylglycerol lipase (MAGL) is a main regulator of the endocannabinoid system within the central nervous system (CNS). Recently, [ 11 C]SAR127303 was developed as a promising radioligand for MAGL imaging. In this study, we aimed to quantify regional MAGL concentrations in the rat brain using positron emission tomography (PET) with [ 11 C]SAR127303. An irreversible two-tissue compartment model (2-TCMi, k 4  = 0) analysis was conducted to estimate quantitative parameters (k 3 , K i 2-TCMi , and λk 3 ). These parameters were successfully obtained with high identifiability (<10 %COV) for the following regions ranked in order from highest to lowest: cingulate cortex > striatum > hippocampus > thalamus > cerebellum > hypothalamus ≈ pons. In vitro autoradiographs using [ 11 C]SAR127303 showed a heterogeneous distribution of radioactivity, as seen in the PET images. The K i 2-TCMi and λk 3 values correlated relatively highly with in vitro binding (r > 0.4, P < 0.005). The K i 2-TCMi values showed high correlation and low underestimation (<10%) compared with the slope of a Patlak plot analysis with linear regression (K i Patlak ). In conclusion, we successfully estimated regional net uptake value of [ 11 C]SAR127303 reflecting MAGL concentrations in rat brain regions for the first time. Copyright © 2018 Elsevier Inc. All rights reserved.

A task-related and resting state realistic fMRI simulator for fMRI data validation

NASA Astrophysics Data System (ADS)

Hill, Jason E.; Liu, Xiangyu; Nutter, Brian; Mitra, Sunanda

2017-02-01

After more than 25 years of published functional magnetic resonance imaging (fMRI) studies, careful scrutiny reveals that most of the reported results lack fully decisive validation. The complex nature of fMRI data generation and acquisition results in unavoidable uncertainties in the true estimation and interpretation of both task-related activation maps and resting state functional connectivity networks, despite the use of various statistical data analysis methodologies. The goal of developing the proposed STANCE (Spontaneous and Task-related Activation of Neuronally Correlated Events) simulator is to generate realistic task-related and/or resting-state 4D blood oxygenation level dependent (BOLD) signals, given the experimental paradigm and scan protocol, by using digital phantoms of twenty normal brains available from BrainWeb (http://brainweb.bic.mni.mcgill.ca/brainweb/). The proposed simulator will include estimated system and modelled physiological noise as well as motion to serve as a reference to measured brain activities. In its current form, STANCE is a MATLAB toolbox with command line functions serving as an open-source add-on to SPM8 (http://www.fil.ion.ucl.ac.uk/spm/software/spm8/). The STANCE simulator has been designed in a modular framework so that the hemodynamic response (HR) and various noise models can be iteratively improved to include evolving knowledge about such models.

Estimate the effective connectivity in multi-coupled neural mass model using particle swarm optimization

NASA Astrophysics Data System (ADS)

Shan, Bonan; Wang, Jiang; Deng, Bin; Zhang, Zhen; Wei, Xile

2017-03-01

Assessment of the effective connectivity among different brain regions during seizure is a crucial problem in neuroscience today. As a consequence, a new model inversion framework of brain function imaging is introduced in this manuscript. This framework is based on approximating brain networks using a multi-coupled neural mass model (NMM). NMM describes the excitatory and inhibitory neural interactions, capturing the mechanisms involved in seizure initiation, evolution and termination. Particle swarm optimization method is used to estimate the effective connectivity variation (the parameters of NMM) and the epileptiform dynamics (the states of NMM) that cannot be directly measured using electrophysiological measurement alone. The estimated effective connectivity includes both the local connectivity parameters within a single region NMM and the remote connectivity parameters between multi-coupled NMMs. When the epileptiform activities are estimated, a proportional-integral controller outputs control signal so that the epileptiform spikes can be inhibited immediately. Numerical simulations are carried out to illustrate the effectiveness of the proposed framework. The framework and the results have a profound impact on the way we detect and treat epilepsy.

Robust Estimation of Electron Density From Anatomic Magnetic Resonance Imaging of the Brain Using a Unifying Multi-Atlas Approach

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ren, Shangjie; Department of Radiation Oncology, Stanford University School of Medicine, Palo Alto, California; Hara, Wendy

Purpose: To develop a reliable method to estimate electron density based on anatomic magnetic resonance imaging (MRI) of the brain. Methods and Materials: We proposed a unifying multi-atlas approach for electron density estimation based on standard T1- and T2-weighted MRI. First, a composite atlas was constructed through a voxelwise matching process using multiple atlases, with the goal of mitigating effects of inherent anatomic variations between patients. Next we computed for each voxel 2 kinds of conditional probabilities: (1) electron density given its image intensity on T1- and T2-weighted MR images; and (2) electron density given its spatial location in a referencemore » anatomy, obtained by deformable image registration. These were combined into a unifying posterior probability density function using the Bayesian formalism, which provided the optimal estimates for electron density. We evaluated the method on 10 patients using leave-one-patient-out cross-validation. Receiver operating characteristic analyses for detecting different tissue types were performed. Results: The proposed method significantly reduced the errors in electron density estimation, with a mean absolute Hounsfield unit error of 119, compared with 140 and 144 (P<.0001) using conventional T1-weighted intensity and geometry-based approaches, respectively. For detection of bony anatomy, the proposed method achieved an 89% area under the curve, 86% sensitivity, 88% specificity, and 90% accuracy, which improved upon intensity and geometry-based approaches (area under the curve: 79% and 80%, respectively). Conclusion: The proposed multi-atlas approach provides robust electron density estimation and bone detection based on anatomic MRI. If validated on a larger population, our work could enable the use of MRI as a primary modality for radiation treatment planning.« less

Estimating the functional dimensionality of neural representations.

PubMed

Ahlheim, Christiane; Love, Bradley C

2018-06-07

Recent advances in multivariate fMRI analysis stress the importance of information inherent to voxel patterns. Key to interpreting these patterns is estimating the underlying dimensionality of neural representations. Dimensions may correspond to psychological dimensions, such as length and orientation, or involve other coding schemes. Unfortunately, the noise structure of fMRI data inflates dimensionality estimates and thus makes it difficult to assess the true underlying dimensionality of a pattern. To address this challenge, we developed a novel approach to identify brain regions that carry reliable task-modulated signal and to derive an estimate of the signal's functional dimensionality. We combined singular value decomposition with cross-validation to find the best low-dimensional projection of a pattern of voxel-responses at a single-subject level. Goodness of the low-dimensional reconstruction is measured as Pearson correlation with a test set, which allows to test for significance of the low-dimensional reconstruction across participants. Using hierarchical Bayesian modeling, we derive the best estimate and associated uncertainty of underlying dimensionality across participants. We validated our method on simulated data of varying underlying dimensionality, showing that recovered dimensionalities match closely true dimensionalities. We then applied our method to three published fMRI data sets all involving processing of visual stimuli. The results highlight three possible applications of estimating the functional dimensionality of neural data. Firstly, it can aid evaluation of model-based analyses by revealing which areas express reliable, task-modulated signal that could be missed by specific models. Secondly, it can reveal functional differences across brain regions. Thirdly, knowing the functional dimensionality allows assessing task-related differences in the complexity of neural patterns. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

Pharmacokinetics of pericyte involvement in small-molecular drug transport across the blood-brain barrier.

PubMed

Mihajlica, Nebojsa; Betsholtz, Christer; Hammarlund-Udenaes, Margareta

2018-06-19

Pericytes are perivascular cells that play important roles in the regulation of the blood-brain barrier (BBB) properties. Pericyte-deficiency causes compromised BBB integrity and increase in permeability to different macromolecules mainly by upregulated transcytosis. The aim of the present study was to investigate pericyte involvement in the extent of small-molecular drug transport across the BBB. This was performed with five compounds: diazepam, digoxin, levofloxacin, oxycodone and paliperidone. Compounds were administered at low doses via subcutaneous injections as a cassette (simultaneously) to pericyte-deficient Pdgfb ret/ret mice and corresponding WT controls. Total drug partitioning across the BBB was calculated as the ratio of total drug exposures in brain tissue and plasma (K p,brain ). In addition, equilibrium dialysis experiments were performed to estimate unbound drug fractions in brain (f u,brain ) and plasma (f u,plasma ). This enabled estimation of unbound drug partitioning coefficients (K p,uu,brain ). The results indicated slight tendencies towards increase of total brain exposures in Pdgfb ret/ret mice as reflected in K p,brain values, which were within the 2-fold limit. Part of these differences could be explained by differences in plasma protein binding. No difference was found in brain tissue binding. The combined in vivo and in vitro data resulted in no differences in BBB transport in pericyte-deficiency, as described by similar K p,uu,brain values in Pdgfb ret/ret and control mice. In conclusion, these findings imply no influence of pericytes on the extent of BBB transport of small-molecular drugs, and suggest preserved BBB features relevant for handling of this type of molecules irrespective of pericyte presence at the brain endothelium. Copyright © 2018. Published by Elsevier B.V.

Integration of Brain and Skull in Prenatal Mouse Models of Apert and Crouzon Syndromes

PubMed Central

Motch Perrine, Susan M.; Stecko, Tim; Neuberger, Thomas; Jabs, Ethylin W.; Ryan, Timothy M.; Richtsmeier, Joan T.

2017-01-01

The brain and skull represent a complex arrangement of integrated anatomical structures composed of various cell and tissue types that maintain structural and functional association throughout development. Morphological integration, a concept developed in vertebrate morphology and evolutionary biology, describes the coordinated variation of functionally and developmentally related traits of organisms. Syndromic craniosynostosis is characterized by distinctive changes in skull morphology and perceptible, though less well studied, changes in brain structure and morphology. Using mouse models for craniosynostosis conditions, our group has precisely defined how unique craniosynostosis causing mutations in fibroblast growth factor receptors affect brain and skull morphology and dysgenesis involving coordinated tissue-specific effects of these mutations. Here we examine integration of brain and skull in two mouse models for craniosynostosis: one carrying the FGFR2c C342Y mutation associated with Pfeiffer and Crouzon syndromes and a mouse model carrying the FGFR2 S252W mutation, one of two mutations responsible for two-thirds of Apert syndrome cases. Using linear distances estimated from three-dimensional coordinates of landmarks acquired from dual modality imaging of skull (high resolution micro-computed tomography and magnetic resonance microscopy) of mice at embryonic day 17.5, we confirm variation in brain and skull morphology in Fgfr2cC342Y/+ mice, Fgfr2+/S252W mice, and their unaffected littermates. Mutation-specific variation in neural and cranial tissue notwithstanding, patterns of integration of brain and skull differed only subtly between mice carrying either the FGFR2c C342Y or the FGFR2 S252W mutation and their unaffected littermates. However, statistically significant and substantial differences in morphological integration of brain and skull were revealed between the two mutant mouse models, each maintained on a different strain. Relative to the effects of disease-associated mutations, our results reveal a stronger influence of the background genome on patterns of brain-skull integration and suggest robust genetic, developmental, and evolutionary relationships between neural and skeletal tissues of the head. PMID:28790902

Brain properties predict proximity to symptom onset in sporadic Alzheimer's disease.

PubMed

Vogel, Jacob W; Vachon-Presseau, Etienne; Pichet Binette, Alexa; Tam, Angela; Orban, Pierre; La Joie, Renaud; Savard, Mélissa; Picard, Cynthia; Poirier, Judes; Bellec, Pierre; Breitner, John C S; Villeneuve, Sylvia

2018-06-01

See Tijms and Visser (doi:10.1093/brain/awy113) for a scientific commentary on this article.Alzheimer's disease is preceded by a lengthy 'preclinical' stage spanning many years, during which subtle brain changes occur in the absence of overt cognitive symptoms. Predicting when the onset of disease symptoms will occur is an unsolved challenge in individuals with sporadic Alzheimer's disease. In individuals with autosomal dominant genetic Alzheimer's disease, the age of symptom onset is similar across generations, allowing the prediction of individual onset times with some accuracy. We extend this concept to persons with a parental history of sporadic Alzheimer's disease to test whether an individual's symptom onset age can be informed by the onset age of their affected parent, and whether this estimated onset age can be predicted using only MRI. Structural and functional MRIs were acquired from 255 ageing cognitively healthy subjects with a parental history of sporadic Alzheimer's disease from the PREVENT-AD cohort. Years to estimated symptom onset was calculated as participant age minus age of parental symptom onset. Grey matter volume was extracted from T1-weighted images and whole-brain resting state functional connectivity was evaluated using degree count. Both modalities were summarized using a 444-region cortical-subcortical atlas. The entire sample was divided into training (n = 138) and testing (n = 68) sets. Within the training set, individuals closer to or beyond their parent's symptom onset demonstrated reduced grey matter volume and altered functional connectivity, specifically in regions known to be vulnerable in Alzheimer's disease. Machine learning was used to identify a weighted set of imaging features trained to predict years to estimated symptom onset. This feature set alone significantly predicted years to estimated symptom onset in the unseen testing data. This model, using only neuroimaging features, significantly outperformed a similar model instead trained with cognitive, genetic, imaging and demographic features used in a traditional clinical setting. We next tested if these brain properties could be generalized to predict time to clinical progression in a subgroup of 26 individuals from the Alzheimer's Disease Neuroimaging Initiative, who eventually converted either to mild cognitive impairment or to Alzheimer's dementia. The feature set trained on years to estimated symptom onset in the PREVENT-AD predicted variance in time to clinical conversion in this separate longitudinal dataset. Adjusting for participant age did not impact any of the results. These findings demonstrate that years to estimated symptom onset or similar measures can be predicted from brain features and may help estimate presymptomatic disease progression in at-risk individuals.

Organotypic cultures as tools for testing neuroactive drugs - link between in-vitro and in-vivo experiments.

PubMed

Drexler, B; Hentschke, H; Antkowiak, B; Grasshoff, C

2010-01-01

The development of neuroactive drugs is a time consuming procedure. Candidate drugs must be run through a battery of tests, including receptor studies and behavioural tests on animals. As a rule, numerous substances with promising properties as assessed in receptor studies must be eliminated from the development pipeline in advanced test phases because of unforeseen problems like intolerable side-effects or unsatisfactory performance in the whole organism. Clearly, test systems of intermediate complexity would alleviate this inefficiency. In this review, we propose cultured organotypic brain slices as model systems that could bridge the 'interpolation gap' between receptors and the brain, with a focus on the development of new general anaesthetics with lesser side effects. General anaesthesia is based on the modulation of neurotransmitter receptors and other conductances located on neurons in diverse brain regions, including cerebral cortex and spinal cord. It is well known that different components of general anaesthesia, e.g. hypnosis and immobility, are produced by the depression of neuronal activity in distinct brain regions. The ventral horn of the spinal cord is an important structure for the induction of immobility. Thus, the potentially immobilizing effects of a newly designed drug can be estimated from its depressant effect on neuronal network activity in cultured spinal slices. A drug's sedative and hypnotic potential can be examined in cortical cultures. Combined with genetically engineered mice, this approach can point to receptor subtypes most relevant to the drug's intended net effect and in return can help in the design of more selective drugs. In conclusion, the use of organotypic cultures permits predictions of neuroactive properties of newly designed drugs on an intermediate level, and should therefore open up avenues for a more creative and economic drug development process.

Genome-wide scan of healthy human connectome discovers SPON1 gene variant influencing dementia severity.

PubMed

Jahanshad, Neda; Rajagopalan, Priya; Hua, Xue; Hibar, Derrek P; Nir, Talia M; Toga, Arthur W; Jack, Clifford R; Saykin, Andrew J; Green, Robert C; Weiner, Michael W; Medland, Sarah E; Montgomery, Grant W; Hansell, Narelle K; McMahon, Katie L; de Zubicaray, Greig I; Martin, Nicholas G; Wright, Margaret J; Thompson, Paul M

2013-03-19

Aberrant connectivity is implicated in many neurological and psychiatric disorders, including Alzheimer's disease and schizophrenia. However, other than a few disease-associated candidate genes, we know little about the degree to which genetics play a role in the brain networks; we know even less about specific genes that influence brain connections. Twin and family-based studies can generate estimates of overall genetic influences on a trait, but genome-wide association scans (GWASs) can screen the genome for specific variants influencing the brain or risk for disease. To identify the heritability of various brain connections, we scanned healthy young adult twins with high-field, high-angular resolution diffusion MRI. We adapted GWASs to screen the brain's connectivity pattern, allowing us to discover genetic variants that affect the human brain's wiring. The association of connectivity with the SPON1 variant at rs2618516 on chromosome 11 (11p15.2) reached connectome-wide, genome-wide significance after stringent statistical corrections were enforced, and it was replicated in an independent subsample. rs2618516 was shown to affect brain structure in an elderly population with varying degrees of dementia. Older people who carried the connectivity variant had significantly milder clinical dementia scores and lower risk of Alzheimer's disease. As a posthoc analysis, we conducted GWASs on several organizational and topological network measures derived from the matrices to discover variants in and around genes associated with autism (MACROD2), development (NEDD4), and mental retardation (UBE2A) significantly associated with connectivity. Connectome-wide, genome-wide screening offers substantial promise to discover genes affecting brain connectivity and risk for brain diseases.

Computational deconvolution of genome wide expression data from Parkinson's and Huntington's disease brain tissues using population-specific expression analysis

PubMed Central

Capurro, Alberto; Bodea, Liviu-Gabriel; Schaefer, Patrick; Luthi-Carter, Ruth; Perreau, Victoria M.

2015-01-01

The characterization of molecular changes in diseased tissues gives insight into pathophysiological mechanisms and is important for therapeutic development. Genome-wide gene expression analysis has proven valuable for identifying biological processes in neurodegenerative diseases using post mortem human brain tissue and numerous datasets are publically available. However, many studies utilize heterogeneous tissue samples consisting of multiple cell types, all of which contribute to global gene expression values, confounding biological interpretation of the data. In particular, changes in numbers of neuronal and glial cells occurring in neurodegeneration confound transcriptomic analyses, particularly in human brain tissues where sample availability and controls are limited. To identify cell specific gene expression changes in neurodegenerative disease, we have applied our recently published computational deconvolution method, population specific expression analysis (PSEA). PSEA estimates cell-type-specific expression values using reference expression measures, which in the case of brain tissue comprises mRNAs with cell-type-specific expression in neurons, astrocytes, oligodendrocytes and microglia. As an exercise in PSEA implementation and hypothesis development regarding neurodegenerative diseases, we applied PSEA to Parkinson's and Huntington's disease (PD, HD) datasets. Genes identified as differentially expressed in substantia nigra pars compacta neurons by PSEA were validated using external laser capture microdissection data. Network analysis and Annotation Clustering (DAVID) identified molecular processes implicated by differential gene expression in specific cell types. The results of these analyses provided new insights into the implementation of PSEA in brain tissues and additional refinement of molecular signatures in human HD and PD. PMID:25620908

Resting State Network Estimation in Individual Subjects

PubMed Central

Hacker, Carl D.; Laumann, Timothy O.; Szrama, Nicholas P.; Baldassarre, Antonello; Snyder, Abraham Z.

2014-01-01

Resting-state functional magnetic resonance imaging (fMRI) has been used to study brain networks associated with both normal and pathological cognitive function. The objective of this work is to reliably compute resting state network (RSN) topography in single participants. We trained a supervised classifier (multi-layer perceptron; MLP) to associate blood oxygen level dependent (BOLD) correlation maps corresponding to pre-defined seeds with specific RSN identities. Hard classification of maps obtained from a priori seeds was highly reliable across new participants. Interestingly, continuous estimates of RSN membership retained substantial residual error. This result is consistent with the view that RSNs are hierarchically organized, and therefore not fully separable into spatially independent components. After training on a priori seed-based maps, we propagated voxel-wise correlation maps through the MLP to produce estimates of RSN membership throughout the brain. The MLP generated RSN topography estimates in individuals consistent with previous studies, even in brain regions not represented in the training data. This method could be used in future studies to relate RSN topography to other measures of functional brain organization (e.g., task-evoked responses, stimulation mapping, and deficits associated with lesions) in individuals. The multi-layer perceptron was directly compared to two alternative voxel classification procedures, specifically, dual regression and linear discriminant analysis; the perceptron generated more spatially specific RSN maps than either alternative. PMID:23735260

When instructions fail. The effects of stimulus control training on brain injury survivors' attending and reporting during hearing screenings.

PubMed

Schlund, M W

2000-10-01

Bedside hearing screenings are routinely conducted by speech and language pathologists for brain injury survivors during rehabilitation. Cognitive deficits resulting from brain injury, however, may interfere with obtaining estimates of auditory thresholds. Poor comprehension or attention deficits often compromise patient abilities to follow procedural instructions. This article describes the effects of jointly applying behavioral methods and psychophysical methods to improve two severely brain-injured survivors' attending and reporting on auditory test stimuli presentation. Treatment consisted of stimulus control training that involved differentially reinforcing responding in the presence and absence of an auditory test tone. Subsequent hearing screenings were conducted with novel auditory test tones and a common titration procedure. Results showed that prior stimulus control training improved attending and reporting such that hearing screenings were conducted and estimates of auditory thresholds were obtained.

Prevalence of traumatic brain injury in incarcerated groups compared to the general population: a meta-analysis.

PubMed

Farrer, Thomas J; Hedges, Dawson W

2011-03-30

Traumatic brain injury can cause numerous behavioral abnormalities including aggression, violence, impulsivity, and apathy, factors that can be associated with criminal behavior and incarceration. To better characterize the association between traumatic brain injury and incarceration, we pooled reported frequencies of lifetime traumatic brain injury of any severity among incarcerated samples and compared the pooled frequency to estimates of the lifetime prevalence of traumatic brain injury in the general population. We found a significantly higher prevalence of traumatic brain injury in the incarcerated groups compared to the general population. As such, there appears to be an association between traumatic brain injury and incarceration. Copyright © 2011 Elsevier Inc. All rights reserved.

Optimal-mass-transfer-based estimation of glymphatic transport in living brain.

PubMed

Ratner, Vadim; Zhu, Liangjia; Kolesov, Ivan; Nedergaard, Maiken; Benveniste, Helene; Tannenbaum, Allen

2015-02-21

It was recently shown that the brain-wide cerebrospinal fluid (CSF) and interstitial fluid exchange system designated the 'glymphatic pathway' plays a key role in removing waste products from the brain, similarly to the lymphatic system in other body organs 1,2 . It is therefore important to study the flow patterns of glymphatic transport through the live brain in order to better understand its functionality in normal and pathological states. Unlike blood, the CSF does not flow rapidly through a network of dedicated vessels, but rather through para-vascular channels and brain parenchyma in a slower time-domain, and thus conventional fMRI or other blood-flow sensitive MRI sequences do not provide much useful information about the desired flow patterns. We have accordingly analyzed a series of MRI images, taken at different times, of the brain of a live rat, which was injected with a paramagnetic tracer into the CSF via the lumbar intrathecal space of the spine. Our goal is twofold: (a) find glymphatic (tracer) flow directions in the live rodent brain; and (b) provide a model of a (healthy) brain that will allow the prediction of tracer concentrations given initial conditions. We model the liquid flow through the brain by the diffusion equation. We then use the Optimal Mass Transfer (OMT) approach 3 to derive the glymphatic flow vector field, and estimate the diffusion tensors by analyzing the (changes in the) flow. Simulations show that the resulting model successfully reproduces the dominant features of the experimental data.

Australian Cerebral Palsy Child Study: protocol of a prospective population based study of motor and brain development of preschool aged children with cerebral palsy.

PubMed

Boyd, Roslyn N; Jordan, Rachel; Pareezer, Laura; Moodie, Anne; Finn, Christine; Luther, Belinda; Arnfield, Evyn; Pym, Aaron; Craven, Alex; Beall, Paula; Weir, Kelly; Kentish, Megan; Wynter, Meredith; Ware, Robert; Fahey, Michael; Rawicki, Barry; McKinlay, Lynne; Guzzetta, Andrea

2013-06-11

Cerebral palsy (CP) results from a static brain lesion during pregnancy or early life and remains the most common cause of physical disability in children (1 in 500). While the brain lesion is static, the physical manifestations and medical issues may progress resulting in altered motor patterns. To date, there are no prospective longitudinal studies of CP that follow a birth cohort to track early gross and fine motor development and use Magnetic Resonance Imaging (MRI) to determine the anatomical pattern and likely timing of the brain lesion. Existing studies do not consider treatment costs and outcomes. This study aims to determine the pathway(s) to motor outcome from diagnosis at 18 months corrected age (c.a.) to outcome at 5 years in relation to the nature of the brain lesion (using structural MRI). This prospective cohort study aims to recruit a total of 240 children diagnosed with CP born in Victoria (birth years 2004 and 2005) and Queensland (birth years 2006-2009). Children can enter the study at any time between 18 months to 5 years of age and will be assessed at 18, 24, 30, 36, 48 and 60 months c.a. Outcomes include gross motor function (GMFM-66 & GMFM-88), Gross Motor Function Classification System (GMFCS); musculoskeletal development (hip displacement, spasticity, muscle contracture), upper limb function (Manual Ability Classification System), communication difficulties using Communication and Symbolic Behaviour Scales-Developmental Profile (CSBS-DP), participation using the Paediatric Evaluation of Disability Inventory (PEDI), parent reported quality of life and classification of medical and allied health resource use and determination of the aetiology of CP using clinical evaluation combined with MRI. The relationship between the pathways to motor outcome and the nature of the brain lesion will be analysed using multiple methods including non-linear modelling, multilevel mixed-effects models and generalised estimating equations. This protocol describes a large population-based study of early motor development and brain structure in a representative sample of preschool aged children with CP, using direct clinical assessment. The results of this study will be published in peer reviewed journals and presented at relevant international conferences. Australia and New Zealand Clinical Trials Register (ACTRN1261200169820).

Australian Cerebral Palsy Child Study: protocol of a prospective population based study of motor and brain development of preschool aged children with cerebral palsy

PubMed Central

2013-01-01

Background Cerebral palsy (CP) results from a static brain lesion during pregnancy or early life and remains the most common cause of physical disability in children (1 in 500). While the brain lesion is static, the physical manifestations and medical issues may progress resulting in altered motor patterns. To date, there are no prospective longitudinal studies of CP that follow a birth cohort to track early gross and fine motor development and use Magnetic Resonance Imaging (MRI) to determine the anatomical pattern and likely timing of the brain lesion. Existing studies do not consider treatment costs and outcomes. This study aims to determine the pathway(s) to motor outcome from diagnosis at 18 months corrected age (c.a.) to outcome at 5 years in relation to the nature of the brain lesion (using structural MRI). Methods This prospective cohort study aims to recruit a total of 240 children diagnosed with CP born in Victoria (birth years 2004 and 2005) and Queensland (birth years 2006–2009). Children can enter the study at any time between 18 months to 5 years of age and will be assessed at 18, 24, 30, 36, 48 and 60 months c.a. Outcomes include gross motor function (GMFM-66 & GMFM-88), Gross Motor Function Classification System (GMFCS); musculoskeletal development (hip displacement, spasticity, muscle contracture), upper limb function (Manual Ability Classification System), communication difficulties using Communication and Symbolic Behaviour Scales-Developmental Profile (CSBS-DP), participation using the Paediatric Evaluation of Disability Inventory (PEDI), parent reported quality of life and classification of medical and allied health resource use and determination of the aetiology of CP using clinical evaluation combined with MRI. The relationship between the pathways to motor outcome and the nature of the brain lesion will be analysed using multiple methods including non-linear modelling, multilevel mixed-effects models and generalised estimating equations. Discussion This protocol describes a large population-based study of early motor development and brain structure in a representative sample of preschool aged children with CP, using direct clinical assessment. The results of this study will be published in peer reviewed journals and presented at relevant international conferences. Trial registration Australia and New Zealand Clinical Trials Register (ACTRN1261200169820) PMID:23758951

Dynamic patterns of cortical expansion during folding of the preterm human brain.

PubMed

Garcia, Kara E; Robinson, Emma C; Alexopoulos, Dimitrios; Dierker, Donna L; Glasser, Matthew F; Coalson, Timothy S; Ortinau, Cynthia M; Rueckert, Daniel; Taber, Larry A; Van Essen, David C; Rogers, Cynthia E; Smyser, Christopher D; Bayly, Philip V

2018-03-20

During the third trimester of human brain development, the cerebral cortex undergoes dramatic surface expansion and folding. Physical models suggest that relatively rapid growth of the cortical gray matter helps drive this folding, and structural data suggest that growth may vary in both space (by region on the cortical surface) and time. In this study, we propose a unique method to estimate local growth from sequential cortical reconstructions. Using anatomically constrained multimodal surface matching (aMSM), we obtain accurate, physically guided point correspondence between younger and older cortical reconstructions of the same individual. From each pair of surfaces, we calculate continuous, smooth maps of cortical expansion with unprecedented precision. By considering 30 preterm infants scanned two to four times during the period of rapid cortical expansion (28-38 wk postmenstrual age), we observe significant regional differences in growth across the cortical surface that are consistent with the emergence of new folds. Furthermore, these growth patterns shift over the course of development, with noninjured subjects following a highly consistent trajectory. This information provides a detailed picture of dynamic changes in cortical growth, connecting what is known about patterns of development at the microscopic (cellular) and macroscopic (folding) scales. Since our method provides specific growth maps for individual brains, we are also able to detect alterations due to injury. This fully automated surface analysis, based on tools freely available to the brain-mapping community, may also serve as a useful approach for future studies of abnormal growth due to genetic disorders, injury, or other environmental variables.

More insights into early brain development through statistical analyses of eigen-structural elements of diffusion tensor imaging using multivariate adaptive regression splines

PubMed Central

Chen, Yasheng; Zhu, Hongtu; An, Hongyu; Armao, Diane; Shen, Dinggang; Gilmore, John H.; Lin, Weili

2013-01-01

The aim of this study was to characterize the maturational changes of the three eigenvalues (λ1 ≥ λ2 ≥ λ3) of diffusion tensor imaging (DTI) during early postnatal life for more insights into early brain development. In order to overcome the limitations of using presumed growth trajectories for regression analysis, we employed Multivariate Adaptive Regression Splines (MARS) to derive data-driven growth trajectories for the three eigenvalues. We further employed Generalized Estimating Equations (GEE) to carry out statistical inferences on the growth trajectories obtained with MARS. With a total of 71 longitudinal datasets acquired from 29 healthy, full-term pediatric subjects, we found that the growth velocities of the three eigenvalues were highly correlated, but significantly different from each other. This paradox suggested the existence of mechanisms coordinating the maturations of the three eigenvalues even though different physiological origins may be responsible for their temporal evolutions. Furthermore, our results revealed the limitations of using the average of λ2 and λ3 as the radial diffusivity in interpreting DTI findings during early brain development because these two eigenvalues had significantly different growth velocities even in central white matter. In addition, based upon the three eigenvalues, we have documented the growth trajectory differences between central and peripheral white matter, between anterior and posterior limbs of internal capsule, and between inferior and superior longitudinal fasciculus. Taken together, we have demonstrated that more insights into early brain maturation can be gained through analyzing eigen-structural elements of DTI. PMID:23455648

Amyloid-β Plaques in Clinical Alzheimer’s Disease Brain Incorporate Stable Isotope Tracer In Vivo and Exhibit Nanoscale Heterogeneity

PubMed Central

Wildburger, Norelle C.; Gyngard, Frank; Guillermier, Christelle; Patterson, Bruce W.; Elbert, Donald; Mawuenyega, Kwasi G.; Schneider, Theresa; Green, Karen; Roth, Robyn; Schmidt, Robert E.; Cairns, Nigel J.; Benzinger, Tammie L. S.; Steinhauser, Matthew L.; Bateman, Randall J.

2018-01-01

Alzheimer’s disease (AD) is a neurodegenerative disorder with clinical manifestations of progressive memory decline and loss of executive function and language. AD affects an estimated 5.3 million Americans alone and is the most common form of age-related dementia with a rapidly growing prevalence among the aging population—those 65 years of age or older. AD is characterized by accumulation of aggregated amyloid-beta (Aβ) in the brain, which leads to one of the pathological hallmarks of AD—Aβ plaques. As a result, Aβ plaques have been extensively studied after being first described over a century ago. Advances in brain imaging and quantitative measures of Aβ in biological fluids have yielded insight into the time course of plaque development decades before and after AD symptom onset. However, despite the fundamental role of Aβ plaques in AD, in vivo measures of individual plaque growth, growth distribution, and dynamics are still lacking. To address this question, we combined stable isotope labeling kinetics (SILK) and nanoscale secondary ion mass spectrometry (NanoSIMS) imaging in an approach termed SILK–SIMS to resolve plaque dynamics in three human AD brains. In human AD brain, plaques exhibit incorporation of a stable isotope tracer. Tracer enrichment was highly variable between plaques and the spatial distribution asymmetric with both quiescent and active nanometer sub-regions of tracer incorporation. These data reveal that Aβ plaques are dynamic structures with deposition rates over days indicating a highly active process. Here, we report the first, direct quantitative measures of in vivo deposition into plaques in human AD brain. Our SILK–SIMS studies will provide invaluable information on plaque dynamics in the normal and diseased brain and offer many new avenues for investigation into pathological mechanisms of the disease, with implications for therapeutic development. PMID:29623063

Amyloid-β Plaques in Clinical Alzheimer's Disease Brain Incorporate Stable Isotope Tracer In Vivo and Exhibit Nanoscale Heterogeneity.

PubMed

Wildburger, Norelle C; Gyngard, Frank; Guillermier, Christelle; Patterson, Bruce W; Elbert, Donald; Mawuenyega, Kwasi G; Schneider, Theresa; Green, Karen; Roth, Robyn; Schmidt, Robert E; Cairns, Nigel J; Benzinger, Tammie L S; Steinhauser, Matthew L; Bateman, Randall J

2018-01-01

Alzheimer's disease (AD) is a neurodegenerative disorder with clinical manifestations of progressive memory decline and loss of executive function and language. AD affects an estimated 5.3 million Americans alone and is the most common form of age-related dementia with a rapidly growing prevalence among the aging population-those 65 years of age or older. AD is characterized by accumulation of aggregated amyloid-beta (Aβ) in the brain, which leads to one of the pathological hallmarks of AD-Aβ plaques. As a result, Aβ plaques have been extensively studied after being first described over a century ago. Advances in brain imaging and quantitative measures of Aβ in biological fluids have yielded insight into the time course of plaque development decades before and after AD symptom onset. However, despite the fundamental role of Aβ plaques in AD, in vivo measures of individual plaque growth, growth distribution, and dynamics are still lacking. To address this question, we combined stable isotope labeling kinetics (SILK) and nanoscale secondary ion mass spectrometry (NanoSIMS) imaging in an approach termed SILK-SIMS to resolve plaque dynamics in three human AD brains. In human AD brain, plaques exhibit incorporation of a stable isotope tracer. Tracer enrichment was highly variable between plaques and the spatial distribution asymmetric with both quiescent and active nanometer sub-regions of tracer incorporation. These data reveal that Aβ plaques are dynamic structures with deposition rates over days indicating a highly active process. Here, we report the first, direct quantitative measures of in vivo deposition into plaques in human AD brain. Our SILK-SIMS studies will provide invaluable information on plaque dynamics in the normal and diseased brain and offer many new avenues for investigation into pathological mechanisms of the disease, with implications for therapeutic development.

Micro Electrochemical pH Sensor Applicable for Real-Time Ratiometric Monitoring of pH Values in Rat Brains.

PubMed

Zhou, Jie; Zhang, Limin; Tian, Yang

2016-02-16

To develop in vivo monitoring meter for pH measurements is still the bottleneck for understanding the role of pH plays in the brain diseases. In this work, a selective and sensitive electrochemical pH meter was developed for real-time ratiometric monitoring of pH in different regions of rat brains upon ischemia. First, 1,2-naphthoquinone (1,2-NQ) was employed and optimized as a selective pH recognition element to establish a 2H(+)/2e(-) approach over a wide range of pH from 5.8 to 8.0. The pH meter demonstrated remarkable selectivity toward pH detection against metal ions, amino acids, reactive oxygen species, and other biological species in the brain. Meanwhile, an inner reference, 6-(ferrocenyl)hexanethiol (FcHT), was selected as a built-in correction to avoid the environmental effect through coimmobilization with 1,2-NQ. In addition, three-dimensional gold nanoleaves were electrodeposited onto the electrode surface to amplify the signal by ∼4.0-fold and the measurement was achieved down to 0.07 pH. Finally, combined with the microelectrode technique, the microelectrochemical pH meter was directly implanted into brain regions including the striatum, hippocampus, and cortex and successfully applied in real-time monitoring of pH values in these regions of brain followed by global cerebral ischemia. The results demonstrated that pH values were estimated to 7.21 ± 0.05, 7.13 ± 0.09, and 7.27 ± 0.06 in the striatum, hippocampus, and cortex in the rat brains, respectively, in normal conditions. However, pH decreased to 6.75 ± 0.07 and 6.52 ± 0.03 in the striatum and hippocampus, upon global cerebral ischemia, while a negligible pH change was obtained in the cortex.

A Symbiotic Brain-Machine Interface through Value-Based Decision Making

PubMed Central

Mahmoudi, Babak; Sanchez, Justin C.

2011-01-01

Background In the development of Brain Machine Interfaces (BMIs), there is a great need to enable users to interact with changing environments during the activities of daily life. It is expected that the number and scope of the learning tasks encountered during interaction with the environment as well as the pattern of brain activity will vary over time. These conditions, in addition to neural reorganization, pose a challenge to decoding neural commands for BMIs. We have developed a new BMI framework in which a computational agent symbiotically decoded users' intended actions by utilizing both motor commands and goal information directly from the brain through a continuous Perception-Action-Reward Cycle (PARC). Methodology The control architecture designed was based on Actor-Critic learning, which is a PARC-based reinforcement learning method. Our neurophysiology studies in rat models suggested that Nucleus Accumbens (NAcc) contained a rich representation of goal information in terms of predicting the probability of earning reward and it could be translated into an evaluative feedback for adaptation of the decoder with high precision. Simulated neural control experiments showed that the system was able to maintain high performance in decoding neural motor commands during novel tasks or in the presence of reorganization in the neural input. We then implanted a dual micro-wire array in the primary motor cortex (M1) and the NAcc of rat brain and implemented a full closed-loop system in which robot actions were decoded from the single unit activity in M1 based on an evaluative feedback that was estimated from NAcc. Conclusions Our results suggest that adapting the BMI decoder with an evaluative feedback that is directly extracted from the brain is a possible solution to the problem of operating BMIs in changing environments with dynamic neural signals. During closed-loop control, the agent was able to solve a reaching task by capturing the action and reward interdependency in the brain. PMID:21423797

Estimating the Integrated Information Measure Phi from High-Density Electroencephalography during States of Consciousness in Humans

PubMed Central

Kim, Hyoungkyu; Hudetz, Anthony G.; Lee, Joseph; Mashour, George A.; Lee, UnCheol; Avidan, Michael S.

2018-01-01

The integrated information theory (IIT) proposes a quantitative measure, denoted as Φ, of the amount of integrated information in a physical system, which is postulated to have an identity relationship with consciousness. IIT predicts that the value of Φ estimated from brain activities represents the level of consciousness across phylogeny and functional states. Practical limitations, such as the explosive computational demands required to estimate Φ for real systems, have hindered its application to the brain and raised questions about the utility of IIT in general. To achieve practical relevance for studying the human brain, it will be beneficial to establish the reliable estimation of Φ from multichannel electroencephalogram (EEG) and define the relationship of Φ to EEG properties conventionally used to define states of consciousness. In this study, we introduce a practical method to estimate Φ from high-density (128-channel) EEG and determine the contribution of each channel to Φ. We examine the correlation of power, frequency, functional connectivity, and modularity of EEG with regional Φ in various states of consciousness as modulated by diverse anesthetics. We find that our approximation of Φ alone is insufficient to discriminate certain states of anesthesia. However, a multi-dimensional parameter space extended by four parameters related to Φ and EEG connectivity is able to differentiate all states of consciousness. The association of Φ with EEG connectivity during clinically defined anesthetic states represents a new practical approach to the application of IIT, which may be used to characterize various physiological (sleep), pharmacological (anesthesia), and pathological (coma) states of consciousness in the human brain. PMID:29503611

Estimating the Integrated Information Measure Phi from High-Density Electroencephalography during States of Consciousness in Humans.

PubMed

Kim, Hyoungkyu; Hudetz, Anthony G; Lee, Joseph; Mashour, George A; Lee, UnCheol

2018-01-01

The integrated information theory (IIT) proposes a quantitative measure, denoted as Φ, of the amount of integrated information in a physical system, which is postulated to have an identity relationship with consciousness. IIT predicts that the value of Φ estimated from brain activities represents the level of consciousness across phylogeny and functional states. Practical limitations, such as the explosive computational demands required to estimate Φ for real systems, have hindered its application to the brain and raised questions about the utility of IIT in general. To achieve practical relevance for studying the human brain, it will be beneficial to establish the reliable estimation of Φ from multichannel electroencephalogram (EEG) and define the relationship of Φ to EEG properties conventionally used to define states of consciousness. In this study, we introduce a practical method to estimate Φ from high-density (128-channel) EEG and determine the contribution of each channel to Φ. We examine the correlation of power, frequency, functional connectivity, and modularity of EEG with regional Φ in various states of consciousness as modulated by diverse anesthetics. We find that our approximation of Φ alone is insufficient to discriminate certain states of anesthesia. However, a multi-dimensional parameter space extended by four parameters related to Φ and EEG connectivity is able to differentiate all states of consciousness. The association of Φ with EEG connectivity during clinically defined anesthetic states represents a new practical approach to the application of IIT, which may be used to characterize various physiological (sleep), pharmacological (anesthesia), and pathological (coma) states of consciousness in the human brain.

Clinical and Biochemical Characteristics of Brain-Dead Donors as Predictors of Early- and Long-Term Renal Function After Transplant.

PubMed

Kwiatkowska, Ewa; Domański, Leszek; Bober, Joanna; Safranow, Krzysztof; Pawlik, Andrzej; Ciechanowski, Kazimierz; Wiśniewska, Magda; Kędzierska, Karolina

2017-08-01

Organs from brain-dead donors are the main source of allografts for transplant. Comparisons between living-donor and brain-dead donor kidneys show that the latter are more likely to demonstrate delayed graft function and lower long-term survival. This study aimed to assess the effects of various clinical and biochemical factors of donors on early- and long-term renal function after transplant. We analyzed data from kidney recipients treated between 2006 and 2008 who received organs from brain-dead donors. Data from 54 donors and 89 recipients were analyzed. No relation was observed between donor sodium concentration and the presence of delayed graft function. Donor height was positively correlated with creatinine clearance in recipients in the 1 to 3 months after renal transplant. Donor diastolic blood pressure was negatively correlated with estimated glomerular filtration rate throughout the observation period. Donor age was negatively correlated with the allograft recipient's estimated glomerular filtration rate throughout 4 years of observation. Donor estimated glomerular filtration rate was positively correlated with that of the recipient throughout 3 years of observation. The results of this study indicate that various factors associated with allograft donors may influence graft function.

Gold-nanorod contrast-enhanced photoacoustic micro-imaging of focused-ultrasound induced blood-brain-barrier opening in a rat model

NASA Astrophysics Data System (ADS)

Wang, Po-Hsun; Liu, Hao-Li; Hsu, Po-Hung; Lin, Chia-Yu; Chris Wang, Churng-Ren; Chen, Pin-Yuan; Wei, Kuo-Chen; Yen, Tzu-Chen; Li, Meng-Lin

2012-06-01

In this study, we develop a novel photoacoustic imaging technique based on gold nanorods (AuNRs) for quantitatively monitoring focused-ultrasound (FUS) induced blood-brain barrier (BBB) opening in a rat model in vivo. This study takes advantage of the strong near-infrared absorption (peak at ~800 nm) of AuNRs and the extravasation tendency from BBB opening foci due to their nano-scale size to passively label the BBB disruption area. Experimental results show that AuNR contrast-enhanced photoacoustic microscopy (PAM) successfully reveals the spatial distribution and temporal response of BBB disruption area in the rat brains. The quantitative measurement of contrast enhancement has potential to estimate the local concentration of AuNRs and even the dosage of therapeutic molecules when AuNRs are further used as nano-carrier for drug delivery or photothermal therapy. The photoacoustic results also provide complementary information to MRI, being helpful to discover more details about FUS induced BBB opening in small animal models.

Use of spectral analysis with iterative filter for voxelwise determination of regional rates of cerebral protein synthesis with L-[1-11C]leucine PET.

PubMed

Veronese, Mattia; Schmidt, Kathleen C; Smith, Carolyn Beebe; Bertoldo, Alessandra

2012-06-01

A spectral analysis approach was used to estimate kinetic parameters of the L-[1-(11)C]leucine positron emission tomography (PET) method and regional rates of cerebral protein synthesis (rCPS) on a voxel-by-voxel basis. Spectral analysis applies to both heterogeneous and homogeneous tissues; it does not require prior assumptions concerning number of tissue compartments. Parameters estimated with spectral analysis can be strongly affected by noise, but numerical filters improve estimation performance. Spectral analysis with iterative filter (SAIF) was originally developed to improve estimation of leucine kinetic parameters and rCPS in region-of-interest (ROI) data analyses. In the present study, we optimized SAIF for application at the voxel level. In measured L-[1-(11)C]leucine PET data, voxel-level SAIF parameter estimates averaged over all voxels within a ROI (mean voxel-SAIF) generally agreed well with corresponding estimates derived by applying the originally developed SAIF to ROI time-activity curves (ROI-SAIF). Region-of-interest-SAIF and mean voxel-SAIF estimates of rCPS were highly correlated. Simulations showed that mean voxel-SAIF rCPS estimates were less biased and less variable than ROI-SAIF estimates in the whole brain and cortex; biases were similar in white matter. We conclude that estimation of rCPS with SAIF is improved when the method is applied at voxel level than in ROI analysis.

Tumors of the brain and nervous system after radiotherapy in childhood

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ron, E.; Modan, B.; Boice, J.D. Jr.

1988-10-20

We investigated the relation between radiotherapy in childhood for tinea capitis and the later development of tumors of the brain and nervous system among 10,834 patients treated between 1948 and 1960 in Israel. Benign and malignant tumors were identified from the pathology records of all Israeli hospitals and from Israeli national cancer and death registries. Doses of radiation to the neural tissue were retrospectively estimated for each patient (mean, 1.5 Gy). Sixty neural tumors developed in the patients exposed as children, and the 30-year cumulative risk (+/- SE) was 0.8 +/- 0.2 percent. The incidence of tumors was 1.8 permore » 10,000 persons per year. The estimated relative risk as compared with that for 10,834 matched general-population controls and 5392 siblings who had not been irradiated was 6.9 (95 percent confidence interval, 4.1 to 11.6) for all tumors and 8.4 (confidence interval, 4.8 to 14.8) when the analysis was restricted to neural tumors of the head and neck. Increased risks were apparent for meningiomas (relative risk, 9.5; n = 19), gliomas (relative risk, 2.6; n = 7), nerve-sheath tumors (relative risk, 18.8; n = 25), and other neural tumors (relative risk, 3.4; n = 9). A strong dose--response relation was found, with the relative risk approaching 20 after estimated doses of approximately 2.5 Gy. Our study confirms that radiation doses on the order of 1 to 2 Gy can significantly increase the risk of neural tumors.« less

Physician Expectations of Treatment Outcomes for Patients With Brain Metastases Referred for Whole Brain Radiotherapy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Barnes, Elizabeth A., E-mail: toni.barnes@sunnybrook.c; Chow, Edward; Tsao, May N.

2010-01-15

Purpose: Patients with advanced cancer are referred to our Rapid Response Radiotherapy Program for quick access to palliative radiotherapy. The primary objective of this prospective study was to determine the physician expectations of the treatment outcomes for patients with brain metastases referred for whole brain radiotherapy (WBRT). The secondary objectives were to determine the factors influencing the expectations and to examine the accuracy of the physician-estimated patient survival. Methods and Materials: Patients were identified during a 17-month period. The referring physicians were sent a survey by facsimile to be completed and returned before the patient consultation. Information was sought onmore » the patient's disease status, the physician's expectations of WBRT, the estimated patient survival and performance status, and physician demographic data. Results: A total of 137 surveys were sent out, and the overall response rate was 57.7%. The median patient age was 66 years (range, 35-87), 78.5% had multiple brain metastases, 42.3% had a controlled primary tumor, and 62.3% had extracranial disease. WBRT was thought to stabilize neurologic symptoms, improve quality of life, and allow for a Decadron (dexamethasone) taper by >=94.9% of the referring physicians; 87.0% thought WBRT would improve performance status; 77.9% thought it would improve neurologic symptoms; and 40.8% thought it would improve survival. The referring physicians estimated patient survival as a median of 6.0 months; however, the actual survival was a median of 2.5 months, for a median individual difference of 1.9 months (p < .0001). Conclusion: Physicians referring patients with brain metastases for consideration of WBRT are often overly optimistic when estimating the clinical benefit of the treatment and overestimate patient survival. These findings highlight the need for education and additional research in this field.« less

MO-E-17A-08: Attenuation-Based Size Adjusted, Scanner-Independent Organ Dose Estimates for Head CT Exams: TG 204 for Head CT

DOE Office of Scientific and Technical Information (OSTI.GOV)

McMillan, K; Bostani, M; Cagnon, C

Purpose: AAPM Task Group 204 described size specific dose estimates (SSDE) for body scans. The purpose of this work is to use a similar approach to develop patient-specific, scanner-independent organ dose estimates for head CT exams using an attenuation-based size metric. Methods: For eight patient models from the GSF family of voxelized phantoms, dose to brain and lens of the eye was estimated using Monte Carlo simulations of contiguous axial scans for 64-slice MDCT scanners from four major manufacturers. Organ doses were normalized by scannerspecific 16 cm CTDIvol values and averaged across all scanners to obtain scanner-independent CTDIvol-to-organ-dose conversion coefficientsmore » for each patient model. Head size was measured at the first slice superior to the eyes; patient perimeter and effective diameter (ED) were measured directly from the GSF data. Because the GSF models use organ identification codes instead of Hounsfield units, water equivalent diameter (WED) was estimated indirectly. Using the image data from 42 patients ranging from 2 weeks old to adult, the perimeter, ED and WED size metrics were obtained and correlations between each metric were established. Applying these correlations to the GSF perimeter and ED measurements, WED was calculated for each model. The relationship between the various patient size metrics and CTDIvol-to-organ-dose conversion coefficients was then described. Results: The analysis of patient images demonstrated the correlation between WED and ED across a wide range of patient sizes. When applied to the GSF patient models, an exponential relationship between CTDIvol-to-organ-dose conversion coefficients and the WED size metric was observed with correlation coefficients of 0.93 and 0.77 for the brain and lens of the eye, respectively. Conclusion: Strong correlation exists between CTDIvol normalized brain dose and WED. For the lens of the eye, a lower correlation is observed, primarily due to surface dose variations. Funding Support: Siemens-UCLA Radiology Master Research Agreement; Disclosures - Michael McNitt-Gray: Institutional Research Agreement, Siemens AG; Research Support, Siemens AG; Consultant, Flaherty Sensabaugh Bonasso PLLC; Consultant, Fulbright and Jaworski.« less

Mortality among Workers Exposed to Polychlorinated Biphenyls (PCBs) in an Electrical Capacitor Manufacturing Plant in Indiana: An Update

PubMed Central

Ruder, Avima M.; Hein, Misty J.; Nilsen, Nancy; Waters, Martha A.; Laber, Patricia; Davis-King, Karen; Prince, Mary M.; Whelan, Elizabeth

2006-01-01

An Indiana capacitor-manufacturing cohort (n = 3,569) was exposed to polychlorinated biphenyls (PCBs) from 1957 to 1977. The original study of mortality through 1984 found excess melanoma and brain cancer; other studies of PCB-exposed individuals have found excess non-Hodgkin lymphoma and rectal, liver, biliary tract, and gallbladder cancer. Mortality was updated through 1998. Analyses have included standardized mortality ratios (SMRs) and 95% confidence intervals (CIs) using rates for Indiana and the United States, standardized rate ratios (SRRs), and Poisson regression rate ratios (RRs). Estimated cumulative exposure calculations used a new job–exposure matrix. Mortality overall was reduced (547 deaths; SMR, 0.81; 95% CI, 0.7–0.9). Non-Hodgkin lymphoma mortality was elevated (9 deaths; SMR, 1.23; 95% CI, 0.6–2.3). Melanoma remained in excess (9 deaths; SMR, 2.43; 95% CI, 1.1–4.6), especially in the lowest tertile of estimated cumulative exposure (5 deaths; SMR, 3.72; 95% CI, 1.2–8.7). Seven of the 12 brain cancer deaths (SMR, 1.91; 95% CI, 1.0–3.3) occurred after the original study. Brain cancer mortality increased with exposure (in the highest tertile, 5 deaths; SMR, 2.71; 95% CI, 0.9–6.3); the SRR dose–response trend was significant (p = 0.016). Among those working ≥90 days, both melanoma (8 deaths; SMR, 2.66; 95% CI, 1.1–5.2) and brain cancer (11 deaths; SMR, 2.12; 95% CI, 1.1–3.8) were elevated, especially for women: melanoma, 3 deaths (SMR, 5.99; 95% CI, 1.2–17.5); brain cancer, 3 deaths (SMR, 2.87; 95% CI, 0.6–8.4). These findings of excess melanoma and brain cancer mortality confirm results of the original study. Melanoma mortality was not associated with estimated cumulative exposure. Brain cancer mortality did not demonstrate a clear dose–response relationship with estimated cumulative exposure. PMID:16393652

Back-Projection Cortical Potential Imaging: Theory and Results.

PubMed

Haor, Dror; Shavit, Reuven; Shapiro, Moshe; Geva, Amir B

2017-07-01

Electroencephalography (EEG) is the single brain monitoring technique that is non-invasive, portable, passive, exhibits high-temporal resolution, and gives a directmeasurement of the scalp electrical potential. Amajor disadvantage of the EEG is its low-spatial resolution, which is the result of the low-conductive skull that "smears" the currents coming from within the brain. Recording brain activity with both high temporal and spatial resolution is crucial for the localization of confined brain activations and the study of brainmechanismfunctionality, whichis then followed by diagnosis of brain-related diseases. In this paper, a new cortical potential imaging (CPI) method is presented. The new method gives an estimation of the electrical activity on the cortex surface and thus removes the "smearing effect" caused by the skull. The scalp potentials are back-projected CPI (BP-CPI) onto the cortex surface by building a well-posed problem to the Laplace equation that is solved by means of the finite elements method on a realistic head model. A unique solution to the CPI problem is obtained by introducing a cortical normal current estimation technique. The technique is based on the same mechanism used in the well-known surface Laplacian calculation, followed by a scalp-cortex back-projection routine. The BP-CPI passed four stages of validation, including validation on spherical and realistic head models, probabilistic analysis (Monte Carlo simulation), and noise sensitivity tests. In addition, the BP-CPI was compared with the minimum norm estimate CPI approach and found superior for multi-source cortical potential distributions with very good estimation results (CC >0.97) on a realistic head model in the regions of interest, for two representative cases. The BP-CPI can be easily incorporated in different monitoring tools and help researchers by maintaining an accurate estimation for the cortical potential of ongoing or event-related potentials in order to have better neurological inferences from the EEG.

Decoding of Ankle Flexion and Extension from Cortical Current Sources Estimated from Non-invasive Brain Activity Recording Methods.

PubMed

Mejia Tobar, Alejandra; Hyoudou, Rikiya; Kita, Kahori; Nakamura, Tatsuhiro; Kambara, Hiroyuki; Ogata, Yousuke; Hanakawa, Takashi; Koike, Yasuharu; Yoshimura, Natsue

2017-01-01

The classification of ankle movements from non-invasive brain recordings can be applied to a brain-computer interface (BCI) to control exoskeletons, prosthesis, and functional electrical stimulators for the benefit of patients with walking impairments. In this research, ankle flexion and extension tasks at two force levels in both legs, were classified from cortical current sources estimated by a hierarchical variational Bayesian method, using electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) recordings. The hierarchical prior for the current source estimation from EEG was obtained from activated brain areas and their intensities from an fMRI group (second-level) analysis. The fMRI group analysis was performed on regions of interest defined over the primary motor cortex, the supplementary motor area, and the somatosensory area, which are well-known to contribute to movement control. A sparse logistic regression method was applied for a nine-class classification (eight active tasks and a resting control task) obtaining a mean accuracy of 65.64% for time series of current sources, estimated from the EEG and the fMRI signals using a variational Bayesian method, and a mean accuracy of 22.19% for the classification of the pre-processed of EEG sensor signals, with a chance level of 11.11%. The higher classification accuracy of current sources, when compared to EEG classification accuracy, was attributed to the high number of sources and the different signal patterns obtained in the same vertex for different motor tasks. Since the inverse filter estimation for current sources can be done offline with the present method, the present method is applicable to real-time BCIs. Finally, due to the highly enhanced spatial distribution of current sources over the brain cortex, this method has the potential to identify activation patterns to design BCIs for the control of an affected limb in patients with stroke, or BCIs from motor imagery in patients with spinal cord injury.

A statistical framework for neuroimaging data analysis based on mutual information estimated via a gaussian copula

PubMed Central

Giordano, Bruno L.; Kayser, Christoph; Rousselet, Guillaume A.; Gross, Joachim; Schyns, Philippe G.

2016-01-01

Abstract We begin by reviewing the statistical framework of information theory as applicable to neuroimaging data analysis. A major factor hindering wider adoption of this framework in neuroimaging is the difficulty of estimating information theoretic quantities in practice. We present a novel estimation technique that combines the statistical theory of copulas with the closed form solution for the entropy of Gaussian variables. This results in a general, computationally efficient, flexible, and robust multivariate statistical framework that provides effect sizes on a common meaningful scale, allows for unified treatment of discrete, continuous, unidimensional and multidimensional variables, and enables direct comparisons of representations from behavioral and brain responses across any recording modality. We validate the use of this estimate as a statistical test within a neuroimaging context, considering both discrete stimulus classes and continuous stimulus features. We also present examples of analyses facilitated by these developments, including application of multivariate analyses to MEG planar magnetic field gradients, and pairwise temporal interactions in evoked EEG responses. We show the benefit of considering the instantaneous temporal derivative together with the raw values of M/EEG signals as a multivariate response, how we can separately quantify modulations of amplitude and direction for vector quantities, and how we can measure the emergence of novel information over time in evoked responses. Open‐source Matlab and Python code implementing the new methods accompanies this article. Hum Brain Mapp 38:1541–1573, 2017. © 2016 Wiley Periodicals, Inc. PMID:27860095

Confounding of the Association between Radiation Exposure from CT Scans and Risk of Leukemia and Brain Tumors by Cancer Susceptibility Syndromes.

PubMed

Meulepas, Johanna M; Ronckers, Cécile M; Merks, Johannes; Weijerman, Michel E; Lubin, Jay H; Hauptmann, Michael

2016-01-01

Recent studies linking radiation exposure from pediatric computed tomography (CT) to increased risks of leukemia and brain tumors lacked data to control for cancer susceptibility syndromes (CSS). These syndromes might be confounders because they are associated with an increased cancer risk and may increase the likelihood of CT scans performed in children. We identify CSS predisposing to leukemia and brain tumors through a systematic literature search and summarize prevalence and risk estimates. Because there is virtually no empirical evidence in published literature on patterns of CT use for most types of CSS, we estimate confounding bias of relative risks (RR) for categories of radiation exposure based on expert opinion about the current and previous patterns of CT scans among CSS patients. We estimate that radiation-related RRs for leukemia are not meaningfully confounded by Down syndrome, Noonan syndrome, or other CSS. In contrast, RRs for brain tumors may be overestimated due to confounding by tuberous sclerosis complex (TSC) while von Hippel-Lindau disease, neurofibromatosis type 1, or other CSS do not meaningfully confound. Empirical data on the use of CT scans among CSS patients are urgently needed. Our assessment indicates that associations with leukemia reported in previous studies are unlikely to be substantially confounded by unmeasured CSS, whereas brain tumor risks might have been overestimated due to confounding by TSC. Future studies should identify TSC patients in order to avoid overestimation of brain tumor risks due to radiation exposure from CT scans. ©2015 American Association for Cancer Research.

Multi-Family Group Intervention for OEF/OIF Traumatic Brain Injury Survivors and Their Families

DTIC Science & Technology

2010-10-01

information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden...considerable layer of complexity to recruitment, especially as the PI and study clinicians were based in psychiatry. It has taken many months to develop...coordination or recruitment efforts by psychiatry with the services diagnosing and treating the vets is complex and time-consuming. In New Jersey

Estimating brain connectivity when few data points are available: Perspectives and limitations.

PubMed

Antonacci, Yuri; Toppi, Jlenia; Caschera, Stefano; Anzolin, Alessandra; Mattia, Donatella; Astolfi, Laura

2017-07-01

Methods based on the use of multivariate autoregressive modeling (MVAR) have proved to be an accurate and flexible tool for the estimation of brain functional connectivity. The multivariate approach, however, implies the use of a model whose complexity (in terms of number of parameters) increases quadratically with the number of signals included in the problem. This can often lead to an underdetermined problem and to the condition of multicollinearity. The aim of this paper is to introduce and test an approach based on Ridge Regression combined with a modified version of the statistics usually adopted for these methods, to broaden the estimation of brain connectivity to those conditions in which current methods fail, due to the lack of enough data points. We tested the performances of this new approach, in comparison with the classical approach based on ordinary least squares (OLS), by means of a simulation study implementing different ground-truth networks, under different network sizes and different levels of data points. Simulation results showed that the new approach provides better performances, in terms of accuracy of the parameters estimation and false positives/false negatives rates, in all conditions related to a low data points/model dimension ratio, and may thus be exploited to estimate and validate estimated patterns at single-trial level or when short time data segments are available.

Radiation dosimetry predicts IQ after conformal radiation therapy in pediatric patients with localized ependymoma

DOE Office of Scientific and Technical Information (OSTI.GOV)

Merchant, Thomas E.; Kiehna, Erin N.; Li Chenghong

2005-12-01

Purpose: To assess the effects of radiation dose-volume distribution on the trajectory of IQ development after conformal radiation therapy (CRT) in pediatric patients with ependymoma. Methods and Materials: The study included 88 patients (median age, 2.8 years {+-} 4.5 years) with localized ependymoma who received CRT (54-59.4 Gy) that used a 1-cm margin on the postoperative tumor bed. Patients were evaluated with tests that included IQ measures at baseline (before CRT) and at 6, 12, 24, 36, 48, and 60 months. Differential dose-volume histograms (DVH) were derived for total-brain, supratentorial-brain, and right and left temporal-lobe volumes. The data were partitionedmore » into three dose intervals and integrated to create variables that represent the fractional volume that received dose over the specified intervals (e.g., V{sub 0-20Gy}, V{sub 20-40Gy}, V{sub 40-65Gy}) and modeled with clinical variables to develop a regression equation to estimate IQ after CRT. Results: A total of 327 IQ tests were performed in 66 patients with infratentorial tumors and 20 with supratentorial tumors. The median follow-up was 29.4 months. For all patients, IQ was best estimated by age (years) at CRT; percent volume of the supratentorial brain that received doses between 0 and 20 Gy, 20 and 40 Gy, and 40 and 65 Gy; and time (months) after CRT. Age contributed significantly to the intercept (p > 0.0001), and the dose-volume coefficients were statistically significant (V{sub 0-20Gy}, p = 0.01; V{sub 20-40Gy}, p < 0.001; V{sub 40-65Gy}, p = 0.04). A similar model was developed exclusively for patients with infratentorial tumors but not supratentorial tumors. Conclusion: Radiation dosimetry can be used to predict IQ after CRT in patients with localized ependymoma. The specificity of models may be enhanced by grouping according to tumor location.« less

Intellectual enrichment lessens the effect of brain atrophy on learning and memory in multiple sclerosis

PubMed Central

Sumowski, James F.; Wylie, Glenn R.; Chiaravalloti, Nancy; DeLuca, John

2010-01-01

Objective: Learning and memory impairments are prevalent among persons with multiple sclerosis (MS); however, such deficits are only weakly associated with MS disease severity (brain atrophy). The cognitive reserve hypothesis states that greater lifetime intellectual enrichment lessens the negative impact of brain disease on cognition, thereby helping to explain the incomplete relationship between brain disease and cognitive status in neurologic populations. The literature on cognitive reserve has focused mainly on Alzheimer disease. The current research examines whether greater intellectual enrichment lessens the negative effect of brain atrophy on learning and memory in patients with MS. Methods: Forty-four persons with MS completed neuropsychological measures of verbal learning and memory, and a vocabulary-based estimate of lifetime intellectual enrichment. Brain atrophy was estimated with third ventricle width measured from 3-T magnetization-prepared rapid gradient echo MRIs. Hierarchical regression was used to predict learning and memory with brain atrophy, intellectual enrichment, and the interaction between brain atrophy and intellectual enrichment. Results: Brain atrophy predicted worse learning and memory, and intellectual enrichment predicted better learning; however, these effects were moderated by interactions between brain atrophy and intellectual enrichment. Specifically, higher intellectual enrichment lessened the negative impact of brain atrophy on both learning and memory. Conclusion: These findings help to explain the incomplete relationship between multiple sclerosis disease severity and cognition, as the effect of disease on cognition is attenuated among patients with higher intellectual enrichment. As such, intellectual enrichment is supported as a protective factor against disease-related cognitive impairment in persons with multiple sclerosis. GLOSSARY AD = Alzheimer disease; ANOVA = analysis of variance; MPRAGE = magnetization-prepared rapid gradient echo; MS = multiple sclerosis; SRT = Selective Reminding Test; TVW = third ventricle width; WASI = Wechsler Abbreviated Scale of Intelligence. PMID:20548040

Estimation of effective connectivity using multi-layer perceptron artificial neural network.

PubMed

Talebi, Nasibeh; Nasrabadi, Ali Motie; Mohammad-Rezazadeh, Iman

2018-02-01

Studies on interactions between brain regions estimate effective connectivity, (usually) based on the causality inferences made on the basis of temporal precedence. In this study, the causal relationship is modeled by a multi-layer perceptron feed-forward artificial neural network, because of the ANN's ability to generate appropriate input-output mapping and to learn from training examples without the need of detailed knowledge of the underlying system. At any time instant, the past samples of data are placed in the network input, and the subsequent values are predicted at its output. To estimate the strength of interactions, the measure of " Causality coefficient " is defined based on the network structure, the connecting weights and the parameters of hidden layer activation function. Simulation analysis demonstrates that the method, called "CREANN" (Causal Relationship Estimation by Artificial Neural Network), can estimate time-invariant and time-varying effective connectivity in terms of MVAR coefficients. The method shows robustness with respect to noise level of data. Furthermore, the estimations are not significantly influenced by the model order (considered time-lag), and the different initial conditions (initial random weights and parameters of the network). CREANN is also applied to EEG data collected during a memory recognition task. The results implicate that it can show changes in the information flow between brain regions, involving in the episodic memory retrieval process. These convincing results emphasize that CREANN can be used as an appropriate method to estimate the causal relationship among brain signals.

Integration of EEG source imaging and fMRI during continuous viewing of natural movies.

PubMed

Whittingstall, Kevin; Bartels, Andreas; Singh, Vanessa; Kwon, Soyoung; Logothetis, Nikos K

2010-10-01

Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) are noninvasive neuroimaging tools which can be used to measure brain activity with excellent temporal and spatial resolution, respectively. By combining the neural and hemodynamic recordings from these modalities, we can gain better insight into how and where the brain processes complex stimuli, which may be especially useful in patients with different neural diseases. However, due to their vastly different spatial and temporal resolutions, the integration of EEG and fMRI recordings is not always straightforward. One fundamental obstacle has been that paradigms used for EEG experiments usually rely on event-related paradigms, while fMRI is not limited in this regard. Therefore, here we ask whether one can reliably localize stimulus-driven EEG activity using the continuously varying feature intensities occurring in natural movie stimuli presented over relatively long periods of time. Specifically, we asked whether stimulus-driven aspects in the EEG signal would be co-localized with the corresponding stimulus-driven BOLD signal during free viewing of a movie. Secondly, we wanted to integrate the EEG signal directly with the BOLD signal, by estimating the underlying impulse response function (IRF) that relates the BOLD signal to the underlying current density in the primary visual area (V1). We made sequential fMRI and 64-channel EEG recordings in seven subjects who passively watched 2-min-long segments of a James Bond movie. To analyze EEG data in this natural setting, we developed a method based on independent component analysis (ICA) to reject EEG artifacts due to blinks, subject movement, etc., in a way unbiased by human judgment. We then calculated the EEG source strength of this artifact-free data at each time point of the movie within the entire brain volume using low-resolution electromagnetic tomography (LORETA). This provided for every voxel in the brain (i.e., in 3D space) an estimate of the current density at every time point. We then carried out a correlation between the time series of visual contrast changes in the movie with that of EEG voxels. We found the most significant correlations in visual area V1, just as seen in previous fMRI studies (Bartels A, Zeki, S, Logothetis NK. Natural vision reveals regional specialization to local motion and to contrast-invariant, global flow in the human brain. Cereb Cortex 2008;18(3):705-717), but on the time scale of milliseconds rather than of seconds. To obtain an estimate of how the EEG signal relates to the BOLD signal, we calculated the IRF between the BOLD signal and the estimated current density in area V1. We found that this IRF was very similar to that observed using combined intracortical recordings and fMRI experiments in nonhuman primates. Taken together, these findings open a new approach to noninvasive mapping of the brain. It allows, firstly, the localization of feature-selective brain areas during natural viewing conditions with the temporal resolution of EEG. Secondly, it provides a tool to assess EEG/BOLD transfer functions during processing of more natural stimuli. This is especially useful in combined EEG/fMRI experiments, where one can now potentially study neural-hemodynamic relationships across the whole brain volume in a noninvasive manner. Copyright © 2010 Elsevier Inc. All rights reserved.

White Matter Fiber-based Analysis of T1w/T2w Ratio Map.

PubMed

Chen, Haiwei; Budin, Francois; Noel, Jean; Prieto, Juan Carlos; Gilmore, John; Rasmussen, Jerod; Wadhwa, Pathik D; Entringer, Sonja; Buss, Claudia; Styner, Martin

2017-02-01

To develop, test, evaluate and apply a novel tool for the white matter fiber-based analysis of T1w/T2w ratio maps quantifying myelin content. The cerebral white matter in the human brain develops from a mostly non-myelinated state to a nearly fully mature white matter myelination within the first few years of life. High resolution T1w/T2w ratio maps are believed to be effective in quantitatively estimating myelin content on a voxel-wise basis. We propose the use of a fiber-tract-based analysis of such T1w/T2w ratio data, as it allows us to separate fiber bundles that a common regional analysis imprecisely groups together, and to associate effects to specific tracts rather than large, broad regions. We developed an intuitive, open source tool to facilitate such fiber-based studies of T1w/T2w ratio maps. Via its Graphical User Interface (GUI) the tool is accessible to non-technical users. The framework uses calibrated T1w/T2w ratio maps and a prior fiber atlas as an input to generate profiles of T1w/T2w values. The resulting fiber profiles are used in a statistical analysis that performs along-tract functional statistical analysis. We applied this approach to a preliminary study of early brain development in neonates. We developed an open-source tool for the fiber based analysis of T1w/T2w ratio maps and tested it in a study of brain development.

White matter fiber-based analysis of T1w/T2w ratio map

NASA Astrophysics Data System (ADS)

Chen, Haiwei; Budin, Francois; Noel, Jean; Prieto, Juan Carlos; Gilmore, John; Rasmussen, Jerod; Wadhwa, Pathik D.; Entringer, Sonja; Buss, Claudia; Styner, Martin

2017-02-01

Purpose: To develop, test, evaluate and apply a novel tool for the white matter fiber-based analysis of T1w/T2w ratio maps quantifying myelin content. Background: The cerebral white matter in the human brain develops from a mostly non-myelinated state to a nearly fully mature white matter myelination within the first few years of life. High resolution T1w/T2w ratio maps are believed to be effective in quantitatively estimating myelin content on a voxel-wise basis. We propose the use of a fiber-tract-based analysis of such T1w/T2w ratio data, as it allows us to separate fiber bundles that a common regional analysis imprecisely groups together, and to associate effects to specific tracts rather than large, broad regions. Methods: We developed an intuitive, open source tool to facilitate such fiber-based studies of T1w/T2w ratio maps. Via its Graphical User Interface (GUI) the tool is accessible to non-technical users. The framework uses calibrated T1w/T2w ratio maps and a prior fiber atlas as an input to generate profiles of T1w/T2w values. The resulting fiber profiles are used in a statistical analysis that performs along-tract functional statistical analysis. We applied this approach to a preliminary study of early brain development in neonates. Results: We developed an open-source tool for the fiber based analysis of T1w/T2w ratio maps and tested it in a study of brain development.

Five-band microwave radiometer system for noninvasive brain temperature measurement in newborn babies: Phantom experiment and confidence interval

NASA Astrophysics Data System (ADS)

Sugiura, T.; Hirata, H.; Hand, J. W.; van Leeuwen, J. M. J.; Mizushina, S.

2011-10-01

Clinical trials of hypothermic brain treatment for newborn babies are currently hindered by the difficulty in measuring deep brain temperatures. As one of the possible methods for noninvasive and continuous temperature monitoring that is completely passive and inherently safe is passive microwave radiometry (MWR). We have developed a five-band microwave radiometer system with a single dual-polarized, rectangular waveguide antenna operating within the 1-4 GHz range and a method for retrieving the temperature profile from five radiometric brightness temperatures. This paper addresses (1) the temperature calibration for five microwave receivers, (2) the measurement experiment using a phantom model that mimics the temperature profile in a newborn baby, and (3) the feasibility for noninvasive monitoring of deep brain temperatures. Temperature resolutions were 0.103, 0.129, 0.138, 0.105 and 0.111 K for 1.2, 1.65, 2.3, 3.0 and 3.6 GHz receivers, respectively. The precision of temperature estimation (2σ confidence interval) was about 0.7°C at a 5-cm depth from the phantom surface. Accuracy, which is the difference between the estimated temperature using this system and the measured temperature by a thermocouple at a depth of 5 cm, was about 2°C. The current result is not satisfactory for clinical application because the clinical requirement for accuracy must be better than 1°C for both precision and accuracy at a depth of 5 cm. Since a couple of possible causes for this inaccuracy have been identified, we believe that the system can take a step closer to the clinical application of MWR for hypothermic rescue treatment.

Occupational risk factors for low grade and high grade glioma: results from an international case control study of adult brain tumours.

PubMed

Schlehofer, Brigitte; Hettinger, Iris; Ryan, Philip; Blettner, Maria; Preston-Martin, Susan; Little, Julian; Arslan, Annie; Ahlbom, Anders; Giles, Graham G; Howe, Geoffrey R; Ménégoz, Francoise; Rodvall, Ylva; Choi, Won N; Wahrendorf, Jürgen

2005-01-01

The majority of suspected occupational risk factors for adult brain tumours have yet to be confirmed as etiologically relevant. Within an international case-control study on brain tumours, lifelong occupational histories and information on exposures to specific substances were obtained by direct interviews to further investigate occupational risk factors for glioma. This is one of the largest studies of brain tumours in adults, including 1,178 cases and 1987 population controls from 8 collaborating study centres matched for age, gender and centre. All occupational information, was aggregated into 16 occupational categories. In a pooled analysis, odds ratios (OR), adjusted for education, were estimated separately for men and women and for high-grade glioma (HGG) and low-grade glioma (LGG), focusing especially on 6 categories defined a priori: agricultural, chemical, construction, metal, electrical/electronic and transport. For men, an elevated OR of glioma associated with the category "metal" (OR = 1.24, 95% CI 0.96-1.62) was seen, which appeared to be largely accounted for by LGG (OR = 1.59, 95% CI 1.00-2.52). For the other 5 occupational categories, no elevated risks for glioma were observed. For women the only noteworthy observation for the 6 a priori categories was an inverse association with the "agriculture" category (OR = 0.60, 95% CI 0.36-0.99). Apart from the 6 major categories, women working in food production or food processing (category "food") showed an increased OR of 1.95 (95% CI 1.04-3.68). None of the 20 substance groups was positively associated with glioma risk. Although some other point estimates were elevated, they lacked statistical significance. The results do not provide evidence of a strong association between occupational exposures and glioma development.

Decoding fMRI events in sensorimotor motor network using sparse paradigm free mapping and activation likelihood estimates.

PubMed

Tan, Francisca M; Caballero-Gaudes, César; Mullinger, Karen J; Cho, Siu-Yeung; Zhang, Yaping; Dryden, Ian L; Francis, Susan T; Gowland, Penny A

2017-11-01

Most functional MRI (fMRI) studies map task-driven brain activity using a block or event-related paradigm. Sparse paradigm free mapping (SPFM) can detect the onset and spatial distribution of BOLD events in the brain without prior timing information, but relating the detected events to brain function remains a challenge. In this study, we developed a decoding method for SPFM using a coordinate-based meta-analysis method of activation likelihood estimation (ALE). We defined meta-maps of statistically significant ALE values that correspond to types of events and calculated a summation overlap between the normalized meta-maps and SPFM maps. As a proof of concept, this framework was applied to relate SPFM-detected events in the sensorimotor network (SMN) to six motor functions (left/right fingers, left/right toes, swallowing, and eye blinks). We validated the framework using simultaneous electromyography (EMG)-fMRI experiments and motor tasks with short and long duration, and random interstimulus interval. The decoding scores were considerably lower for eye movements relative to other movement types tested. The average successful rate for short and long motor events were 77 ± 13% and 74 ± 16%, respectively, excluding eye movements. We found good agreement between the decoding results and EMG for most events and subjects, with a range in sensitivity between 55% and 100%, excluding eye movements. The proposed method was then used to classify the movement types of spontaneous single-trial events in the SMN during resting state, which produced an average successful rate of 22 ± 12%. Finally, this article discusses methodological implications and improvements to increase the decoding performance. Hum Brain Mapp 38:5778-5794, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Robertson, Janeen Denise

In a mortality study of white males who had worked at the Rocky Flats Nuclear Weapons Plant between 1952 and 1979, an increased number of deaths from benign and unspecified intracranial neoplasms was found. A case-control study nested within this cohort investigated the hypothesis that an association existed between brain tumor death and exposure to either internally deposited plutonium or external ionizing radiation. There was no statistically significant association found between estimated radiation exposure from internally deposited plutonium and the development of brain tumors. Exposure by job or work area showed no significant difference between the cohort and the controlmore » groups. An update of the study found elevated risk estimates for (1) all lymphopoietic neoplasms, and (2) all causes of death in employees with body burdens greater than or equal to two nanocuries of plutonium. There was an excess of brain tumors for the entire cohort. Similar cohort studies conducted on worker populations from other plutonium handling facilities have not yet shown any elevated risks for brain tumors. Historically, the Rocky Flats Nuclear Weapons Plant used large quantities of chemicals in their production operations. The use of solvents, particularly carbon tetrachloride, was unique to Rocky Flats. No investigation of the possible confounding effects of chemical exposures was done in the initial studies. The objectives of the present study are to (1) investigate the history of chemical use at the Rocky Flats facility; (2) locate and analyze chemical monitoring information in order to assess employee exposure to the chemicals that were used in the highest volume; and (3) determine the feasibility of establishing a chemical exposure assessment model that could be used in future epidemiology studies.« less

Individualized statistical learning from medical image databases: application to identification of brain lesions.

PubMed

Erus, Guray; Zacharaki, Evangelia I; Davatzikos, Christos

2014-04-01

This paper presents a method for capturing statistical variation of normal imaging phenotypes, with emphasis on brain structure. The method aims to estimate the statistical variation of a normative set of images from healthy individuals, and identify abnormalities as deviations from normality. A direct estimation of the statistical variation of the entire volumetric image is challenged by the high-dimensionality of images relative to smaller sample sizes. To overcome this limitation, we iteratively sample a large number of lower dimensional subspaces that capture image characteristics ranging from fine and localized to coarser and more global. Within each subspace, a "target-specific" feature selection strategy is applied to further reduce the dimensionality, by considering only imaging characteristics present in a test subject's images. Marginal probability density functions of selected features are estimated through PCA models, in conjunction with an "estimability" criterion that limits the dimensionality of estimated probability densities according to available sample size and underlying anatomy variation. A test sample is iteratively projected to the subspaces of these marginals as determined by PCA models, and its trajectory delineates potential abnormalities. The method is applied to segmentation of various brain lesion types, and to simulated data on which superiority of the iterative method over straight PCA is demonstrated. Copyright © 2014 Elsevier B.V. All rights reserved.

A review of anisotropic conductivity models of brain white matter based on diffusion tensor imaging.

PubMed

Wu, Zhanxiong; Liu, Yang; Hong, Ming; Yu, Xiaohui

2018-06-01

The conductivity of brain tissues is not only essential for electromagnetic source estimation (ESI), but also a key reflector of the brain functional changes. Different from the other brain tissues, the conductivity of whiter matter (WM) is highly anisotropic and a tensor is needed to describe it. The traditional electrical property imaging methods, such as electrical impedance tomography (EIT) and magnetic resonance electrical impedance tomography (MREIT), usually fail to image the anisotropic conductivity tensor of WM with high spatial resolution. The diffusion tensor imaging (DTI) is a newly developed technique that can fulfill this purpose. This paper reviews the existing anisotropic conductivity models of WM based on the DTI and discusses their advantages and disadvantages, as well as identifies opportunities for future research on this subject. It is crucial to obtain the linear conversion coefficient between the eigenvalues of anisotropic conductivity tensor and diffusion tensor, since they share the same eigenvectors. We conclude that the electrochemical model is suitable for ESI analysis because the conversion coefficient can be directly obtained from the concentration of ions in extracellular liquid and that the volume fraction model is appropriate to study the influence of WM structural changes on electrical conductivity. Graphical abstract ᅟ.

Investigation of the cortical activation by touching fabric actively using fingers.

PubMed

Wang, Q; Yu, W; He, N; Chen, K

2015-11-01

Human subjects can tactually estimate the perception of touching fabric. Although many psychophysical and neurophysiological experiments have elucidated the peripheral neural mechanisms that underlie fabric hand estimation, the associated cortical mechanisms are not well understood. To identify the brain regions responsible for the tactile stimulation of fabric against human skin, we used the technology of functional magnetic resonance imaging (fMRI), to observe brain activation when the subjects touched silk fabric actively using fingers. Consistent with previous research about brain cognition on sensory stimulation, large activation in the primary somatosensory cortex (SI), the secondary somatosensory cortex (SII) and moto cortex, and little activation in the posterior insula cortex and Broca's Area were observed when the subjects touched silk fabric. The technology of fMRI is a promising tool to observe and characterize the brain cognition on the tactile stimulation of fabric quantitatively. The intensity and extent of activation in the brain regions, especially the primary somatosensory cortex (SI) and the secondary somatosensory cortex (SII), can represent the perception of stimulation of fabric quantitatively. © 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

MRI correlates of general intelligence in neurotypical adults.

PubMed

Malpas, Charles B; Genc, Sila; Saling, Michael M; Velakoulis, Dennis; Desmond, Patricia M; O'Brien, Terence J

2016-02-01

There is growing interest in the neurobiological substrate of general intelligence. Psychometric estimates of general intelligence are reduced in a range of neurological disorders, leading to practical application as sensitive, but non-specific, markers of cerebral disorder. This study examined estimates of general intelligence in neurotypical adults using diffusion tensor imaging and resting-state functional connectivity analysis. General intelligence was related to white matter organisation across multiple brain regions, confirming previous work in older healthy adults. We also found that variation in general intelligence was related to a large functional sub-network involving all cortical lobes of the brain. These findings confirm that individual variance in general intelligence is related to diffusely represented brain networks. Copyright © 2015 Elsevier Ltd. All rights reserved.

A brain-machine interface for control of medically-induced coma.

PubMed

Shanechi, Maryam M; Chemali, Jessica J; Liberman, Max; Solt, Ken; Brown, Emery N

2013-10-01

Medically-induced coma is a drug-induced state of profound brain inactivation and unconsciousness used to treat refractory intracranial hypertension and to manage treatment-resistant epilepsy. The state of coma is achieved by continually monitoring the patient's brain activity with an electroencephalogram (EEG) and manually titrating the anesthetic infusion rate to maintain a specified level of burst suppression, an EEG marker of profound brain inactivation in which bursts of electrical activity alternate with periods of quiescence or suppression. The medical coma is often required for several days. A more rational approach would be to implement a brain-machine interface (BMI) that monitors the EEG and adjusts the anesthetic infusion rate in real time to maintain the specified target level of burst suppression. We used a stochastic control framework to develop a BMI to control medically-induced coma in a rodent model. The BMI controlled an EEG-guided closed-loop infusion of the anesthetic propofol to maintain precisely specified dynamic target levels of burst suppression. We used as the control signal the burst suppression probability (BSP), the brain's instantaneous probability of being in the suppressed state. We characterized the EEG response to propofol using a two-dimensional linear compartment model and estimated the model parameters specific to each animal prior to initiating control. We derived a recursive Bayesian binary filter algorithm to compute the BSP from the EEG and controllers using a linear-quadratic-regulator and a model-predictive control strategy. Both controllers used the estimated BSP as feedback. The BMI accurately controlled burst suppression in individual rodents across dynamic target trajectories, and enabled prompt transitions between target levels while avoiding both undershoot and overshoot. The median performance error for the BMI was 3.6%, the median bias was -1.4% and the overall posterior probability of reliable control was 1 (95% Bayesian credibility interval of [0.87, 1.0]). A BMI can maintain reliable and accurate real-time control of medically-induced coma in a rodent model suggesting this strategy could be applied in patient care.

Association of brain metabolism with sulcation and corpus callosum development assessed by MRI in late-onset small fetuses.

PubMed

Sanz-Cortes, Magdalena; Egaña-Ugrinovic, Gabriela; Simoes, Rui V; Vazquez, Lucia; Bargallo, Nuria; Gratacos, Eduard

2015-06-01

We sought to determine the relationship between fetal brain metabolism and microstructure expressed by brain sulcation, and corpus callosum (CC) development assessed by fetal brain magnetic resonance (MR) imaging and proton MR spectroscopy ((1)H-MRS). A total of 119 fetuses, 64 that were small for gestational age (estimated fetal weight <10th centile and normal umbilical artery Doppler) and 55 controls underwent a 3T MR imaging/(1)H-MRS exam at 37 weeks. Anatomical T2-weighted images were obtained in the 3 orthogonal planes and long echo time (TE) (1)H-MRS acquired from the frontal lobe. Head biometrics, cortical fissure depths (insula, Sylvian, parietooccipital, cingulate, and calcarine), and CC area and biometries were blindly performed by manual and semiautomated delineation using Analyze software and corrected creating ratios for biparietal diameter and frontooccipital diameter, respectively, for group comparison. Spectroscopic data were processed using LCModel software and analyzed as metabolic ratios of N-acetylaspartate (NAA) to choline (Cho), Cho to creatine (Cr), and myo-inositol (Ino) to Cho. Differences between cases and controls were assessed. To test for the association between metabolic ratios and microstructural parameters, bivariate correlation analyses were performed. Spectroscopic findings showed decreased NAA/Cho and increased Cho/Cr ratios in small fetuses. They also presented smaller head biometrics, shorter and smaller CC, and greater insular and cingulate depths. Frontal lobe NAA/Cho significantly correlated with biparietal diameter (r = 0.268; P = .021), head circumference (r = 0.259; P = .026), CC length (r = 0.265; P = .026), CC area (r = 0.317; P = .007), and the area of 6 from the 7 CC subdivisions. It did not correlate with any of the cortical sulcation parameters evaluated. None of the other metabolic ratios presented significant correlations with cortical development or CC parameters. Frontal lobe NAA/Cho levels-which are considered a surrogate marker of neuronal activity-show a strong association with CC development. These results suggest that both metabolic and callosal alterations may be part of the same process of impaired brain development associated with intrauterine growth restriction. Copyright © 2015 Elsevier Inc. All rights reserved.

Image characterization by fractal descriptors in variational mode decomposition domain: Application to brain magnetic resonance

NASA Astrophysics Data System (ADS)

Lahmiri, Salim

2016-08-01

The main purpose of this work is to explore the usefulness of fractal descriptors estimated in multi-resolution domains to characterize biomedical digital image texture. In this regard, three multi-resolution techniques are considered: the well-known discrete wavelet transform (DWT) and the empirical mode decomposition (EMD), and; the newly introduced; variational mode decomposition mode (VMD). The original image is decomposed by the DWT, EMD, and VMD into different scales. Then, Fourier spectrum based fractal descriptors is estimated at specific scales and directions to characterize the image. The support vector machine (SVM) was used to perform supervised classification. The empirical study was applied to the problem of distinguishing between normal and abnormal brain magnetic resonance images (MRI) affected with Alzheimer disease (AD). Our results demonstrate that fractal descriptors estimated in VMD domain outperform those estimated in DWT and EMD domains; and also those directly estimated from the original image.

Comparison of PECARN, CATCH, and CHALICE rules for children with minor head injury: a prospective cohort study.

PubMed

Easter, Joshua S; Bakes, Katherine; Dhaliwal, Jasmeet; Miller, Michael; Caruso, Emily; Haukoos, Jason S

2014-08-01

We evaluate the diagnostic accuracy of clinical decision rules and physician judgment for identifying clinically important traumatic brain injuries in children with minor head injuries presenting to the emergency department. We prospectively enrolled children younger than 18 years and with minor head injury (Glasgow Coma Scale score 13 to 15), presenting within 24 hours of their injuries. We assessed the ability of 3 clinical decision rules (Canadian Assessment of Tomography for Childhood Head Injury [CATCH], Children's Head Injury Algorithm for the Prediction of Important Clinical Events [CHALICE], and Pediatric Emergency Care Applied Research Network [PECARN]) and 2 measures of physician judgment (estimated of <1% risk of traumatic brain injury and actual computed tomography ordering practice) to predict clinically important traumatic brain injury, as defined by death from traumatic brain injury, need for neurosurgery, intubation greater than 24 hours for traumatic brain injury, or hospital admission greater than 2 nights for traumatic brain injury. Among the 1,009 children, 21 (2%; 95% confidence interval [CI] 1% to 3%) had clinically important traumatic brain injuries. Only physician practice and PECARN identified all clinically important traumatic brain injuries, with ranked sensitivities as follows: physician practice and PECARN each 100% (95% CI 84% to 100%), physician estimates 95% (95% CI 76% to 100%), CATCH 91% (95% CI 70% to 99%), and CHALICE 84% (95% CI 60% to 97%). Ranked specificities were as follows: CHALICE 85% (95% CI 82% to 87%), physician estimates 68% (95% CI 65% to 71%), PECARN 62% (95% CI 59% to 66%), physician practice 50% (95% CI 47% to 53%), and CATCH 44% (95% CI 41% to 47%). Of the 5 modalities studied, only physician practice and PECARN identified all clinically important traumatic brain injuries, with PECARN being slightly more specific. CHALICE was incompletely sensitive but the most specific of all rules. CATCH was incompletely sensitive and had the poorest specificity of all modalities. Copyright © 2014 American College of Emergency Physicians. Published by Mosby, Inc. All rights reserved.

Individual differences in GABA content are reliable but are not uniform across the human cortex

PubMed Central

Greenhouse, Ian; Noah, Sean; Maddock, Richard J; Ivry, Richard B

2016-01-01

1H magnetic resonance spectroscopy (MRS) provides a powerful tool to measure gamma-aminobutyric acid (GABA), the principle inhibitory neurotransmitter in the human brain. We asked whether individual differences in MRS estimates of GABA are uniform across the cortex or vary between regions. In two sessions, resting GABA concentrations in the lateral prefrontal, sensorimotor, dorsal premotor, and occipital cortices were measured in twenty-eight healthy individuals. GABA estimates within each region were stable across weeks, with low coefficients of variation. Despite this stability, the GABA estimates were not correlated between regions. In contrast, the percentage of brain tissue per volume, a control measure, was correlated between the three anterior regions. These results provide an interesting dissociation between an anatomical measure of individual differences and a neurochemical measure. The different patterns of anatomy and GABA concentrations have implications for understanding regional variation in the molecular topography of the brain in health and disease. PMID:27288552

Relating resting-state fMRI and EEG whole-brain connectomes across frequency bands.

PubMed

Deligianni, Fani; Centeno, Maria; Carmichael, David W; Clayden, Jonathan D

2014-01-01

Whole brain functional connectomes hold promise for understanding human brain activity across a range of cognitive, developmental and pathological states. So called resting-state (rs) functional MRI studies have contributed to the brain being considered at a macroscopic scale as a set of interacting regions. Interactions are defined as correlation-based signal measurements driven by blood oxygenation level dependent (BOLD) contrast. Understanding the neurophysiological basis of these measurements is important in conveying useful information about brain function. Local coupling between BOLD fMRI and neurophysiological measurements is relatively well defined, with evidence that gamma (range) frequency EEG signals are the closest correlate of BOLD fMRI changes during cognitive processing. However, it is less clear how whole-brain network interactions relate during rest where lower frequency signals have been suggested to play a key role. Simultaneous EEG-fMRI offers the opportunity to observe brain network dynamics with high spatio-temporal resolution. We utilize these measurements to compare the connectomes derived from rs-fMRI and EEG band limited power (BLP). Merging this multi-modal information requires the development of an appropriate statistical framework. We relate the covariance matrices of the Hilbert envelope of the source localized EEG signal across bands to the covariance matrices derived from rs-fMRI with the means of statistical prediction based on sparse Canonical Correlation Analysis (sCCA). Subsequently, we identify the most prominent connections that contribute to this relationship. We compare whole-brain functional connectomes based on their geodesic distance to reliably estimate the performance of the prediction. The performance of predicting fMRI from EEG connectomes is considerably better than predicting EEG from fMRI across all bands, whereas the connectomes derived in low frequency EEG bands resemble best rs-fMRI connectivity.

Relating resting-state fMRI and EEG whole-brain connectomes across frequency bands

PubMed Central

Deligianni, Fani; Centeno, Maria; Carmichael, David W.; Clayden, Jonathan D.

2014-01-01

Whole brain functional connectomes hold promise for understanding human brain activity across a range of cognitive, developmental and pathological states. So called resting-state (rs) functional MRI studies have contributed to the brain being considered at a macroscopic scale as a set of interacting regions. Interactions are defined as correlation-based signal measurements driven by blood oxygenation level dependent (BOLD) contrast. Understanding the neurophysiological basis of these measurements is important in conveying useful information about brain function. Local coupling between BOLD fMRI and neurophysiological measurements is relatively well defined, with evidence that gamma (range) frequency EEG signals are the closest correlate of BOLD fMRI changes during cognitive processing. However, it is less clear how whole-brain network interactions relate during rest where lower frequency signals have been suggested to play a key role. Simultaneous EEG-fMRI offers the opportunity to observe brain network dynamics with high spatio-temporal resolution. We utilize these measurements to compare the connectomes derived from rs-fMRI and EEG band limited power (BLP). Merging this multi-modal information requires the development of an appropriate statistical framework. We relate the covariance matrices of the Hilbert envelope of the source localized EEG signal across bands to the covariance matrices derived from rs-fMRI with the means of statistical prediction based on sparse Canonical Correlation Analysis (sCCA). Subsequently, we identify the most prominent connections that contribute to this relationship. We compare whole-brain functional connectomes based on their geodesic distance to reliably estimate the performance of the prediction. The performance of predicting fMRI from EEG connectomes is considerably better than predicting EEG from fMRI across all bands, whereas the connectomes derived in low frequency EEG bands resemble best rs-fMRI connectivity. PMID:25221467

Are Mental Disorders Brain Diseases, and What Does This Mean? A Clinical-Neuropsychological Perspective.

PubMed

Frisch, Stefan

Neuroscientific research has substantially increased our knowledge about mental disorders in recent years. Along with these benefits, radical postulates have been articulated according to which understanding and treatment of mental disorders should generally be based on biological terms, such as neurons/brain areas, transmitters, genes etc. Proponents of such a 'biological psychiatry' claim that mental disorders are analogous to neurological disorders and refer to neurology and neuropsychology to corroborate their claims. The present article argues that, from a clinical-neuropsychological perspective, 'biological psychiatry' is based on a mechanistic, 'cerebrocentric' framework of brain (dys-)function which has its roots in experimental neuroscience but runs up against narrow limits in clinical neurology and neuropsychology. In fact, understanding and treating neurological disorders generally demands a systems perspective including brain, organism and environment as intrinsically entangled. In this way, 'biological' characterizes a 'holistic', nonreductionist level of explanation, according to which the significance of particular mechanisms can only be estimated in the context of the organism (or person). This is evident in the common observation that local brain damage does not just lead to an isolated loss of function, but to multiple attempts of reorganization and readaptation; it initiates new developments. Furthermore, treating brain disorders necessarily includes aspects of individuality and subjectivity, a conclusion that contradicts the purely 'objectivist', third-person stance put forward by some proponents of biological psychiatry. In sum, understanding and treating brain damage sequelae in the clinical neurosciences demands a biopsychosocial perspective, for both conceptual and historical reasons. The same may hold for psychiatry when adopting a brain-based view on mental disorders. In such a perspective, biological psychiatry seems an interesting project but falls short of its original claims. © 2016 S. Karger AG, Basel.

Integrating robotic action with biologic perception: A brain-machine symbiosis theory

NASA Astrophysics Data System (ADS)

Mahmoudi, Babak

In patients with motor disability the natural cyclic flow of information between the brain and external environment is disrupted by their limb impairment. Brain-Machine Interfaces (BMIs) aim to provide new communication channels between the brain and environment by direct translation of brain's internal states into actions. For enabling the user in a wide range of daily life activities, the challenge is designing neural decoders that autonomously adapt to different tasks, environments, and to changes in the pattern of neural activity. In this dissertation, a novel decoding framework for BMIs is developed in which a computational agent autonomously learns how to translate neural states into action based on maximization of a measure of shared goal between user and the agent. Since the agent and brain share the same goal, a symbiotic relationship between them will evolve therefore this decoding paradigm is called a Brain-Machine Symbiosis (BMS) framework. A decoding agent was implemented within the BMS framework based on the Actor-Critic method of Reinforcement Learning. The rule of the Actor as a neural decoder was to find mapping between the neural representation of motor states in the primary motor cortex (MI) and robot actions in order to solve reaching tasks. The Actor learned the optimal control policy using an evaluative feedback that was estimated by the Critic directly from the user's neural activity of the Nucleus Accumbens (NAcc). Through a series of computational neuroscience studies in a cohort of rats it was demonstrated that NAcc could provide a useful evaluative feedback by predicting the increase or decrease in the probability of earning reward based on the environmental conditions. Using a closed-loop BMI simulator it was demonstrated the Actor-Critic decoding architecture was able to adapt to different tasks as well as changes in the pattern of neural activity. The custom design of a dual micro-wire array enabled simultaneous implantation of MI and NAcc for the development of a full closed-loop system. The Actor-Critic decoding architecture was able to solve the brain-controlled reaching task using a robotic arm by capturing the interdependency between the simultaneous action representation in MI and reward expectation in NAcc.

Semi-automated brain tumor and edema segmentation using MRI.

PubMed

Xie, Kai; Yang, Jie; Zhang, Z G; Zhu, Y M

2005-10-01

Manual segmentation of brain tumors from magnetic resonance images is a challenging and time-consuming task. A semi-automated method has been developed for brain tumor and edema segmentation that will provide objective, reproducible segmentations that are close to the manual results. Additionally, the method segments non-enhancing brain tumor and edema from healthy tissues in magnetic resonance images. In this study, a semi-automated method was developed for brain tumor and edema segmentation and volume measurement using magnetic resonance imaging (MRI). Some novel algorithms for tumor segmentation from MRI were integrated in this medical diagnosis system. We exploit a hybrid level set (HLS) segmentation method driven by region and boundary information simultaneously, region information serves as a propagation force which is robust and boundary information serves as a stopping functional which is accurate. Ten different patients with brain tumors of different size, shape and location were selected, a total of 246 axial tumor-containing slices obtained from 10 patients were used to evaluate the effectiveness of segmentation methods. This method was applied to 10 non-enhancing brain tumors and satisfactory results were achieved. Two quantitative measures for tumor segmentation quality estimation, namely, correspondence ratio (CR) and percent matching (PM), were performed. For the segmentation of brain tumor, the volume total PM varies from 79.12 to 93.25% with the mean of 85.67+/-4.38% while the volume total CR varies from 0.74 to 0.91 with the mean of 0.84+/-0.07. For the segmentation of edema, the volume total PM varies from 72.86 to 87.29% with the mean of 79.54+/-4.18% while the volume total CR varies from 0.69 to 0.85 with the mean of 0.79+/-0.08. The HLS segmentation method perform better than the classical level sets (LS) segmentation method in PM and CR. The results of this research may have potential applications, both as a staging procedure and a method of evaluating tumor response during treatment, this method can be used as a clinical image analysis tool for doctors or radiologists.

Optimal-mass-transfer-based estimation of glymphatic transport in living brain

PubMed Central

Zhu, Liangjia; Kolesov, Ivan; Nedergaard, Maiken; Benveniste, Helene; Tannenbaum, Allen

2016-01-01

It was recently shown that the brain-wide cerebrospinal fluid (CSF) and interstitial fluid exchange system designated the ‘glymphatic pathway’ plays a key role in removing waste products from the brain, similarly to the lymphatic system in other body organs1,2. It is therefore important to study the flow patterns of glymphatic transport through the live brain in order to better understand its functionality in normal and pathological states. Unlike blood, the CSF does not flow rapidly through a network of dedicated vessels, but rather through para-vascular channels and brain parenchyma in a slower time-domain, and thus conventional fMRI or other blood-flow sensitive MRI sequences do not provide much useful information about the desired flow patterns. We have accordingly analyzed a series of MRI images, taken at different times, of the brain of a live rat, which was injected with a paramagnetic tracer into the CSF via the lumbar intrathecal space of the spine. Our goal is twofold: (a) find glymphatic (tracer) flow directions in the live rodent brain; and (b) provide a model of a (healthy) brain that will allow the prediction of tracer concentrations given initial conditions. We model the liquid flow through the brain by the diffusion equation. We then use the Optimal Mass Transfer (OMT) approach3 to derive the glymphatic flow vector field, and estimate the diffusion tensors by analyzing the (changes in the) flow. Simulations show that the resulting model successfully reproduces the dominant features of the experimental data. PMID:26877579

BCILAB: a platform for brain-computer interface development

NASA Astrophysics Data System (ADS)

Kothe, Christian Andreas; Makeig, Scott

2013-10-01

Objective. The past two decades have seen dramatic progress in our ability to model brain signals recorded by electroencephalography, functional near-infrared spectroscopy, etc., and to derive real-time estimates of user cognitive state, response, or intent for a variety of purposes: to restore communication by the severely disabled, to effect brain-actuated control and, more recently, to augment human-computer interaction. Continuing these advances, largely achieved through increases in computational power and methods, requires software tools to streamline the creation, testing, evaluation and deployment of new data analysis methods. Approach. Here we present BCILAB, an open-source MATLAB-based toolbox built to address the need for the development and testing of brain-computer interface (BCI) methods by providing an organized collection of over 100 pre-implemented methods and method variants, an easily extensible framework for the rapid prototyping of new methods, and a highly automated framework for systematic testing and evaluation of new implementations. Main results. To validate and illustrate the use of the framework, we present two sample analyses of publicly available data sets from recent BCI competitions and from a rapid serial visual presentation task. We demonstrate the straightforward use of BCILAB to obtain results compatible with the current BCI literature. Significance. The aim of the BCILAB toolbox is to provide the BCI community a powerful toolkit for methods research and evaluation, thereby helping to accelerate the pace of innovation in the field, while complementing the existing spectrum of tools for real-time BCI experimentation, deployment and use.

Troubleshooting in LC-MS/MS method for determining endocannabinoid and endocannabinoid-like molecules in rat brain structures applied to assessing the brain endocannabinoid/endovanilloid system significance.

PubMed

Bystrowska, Beata; Smaga, Irena; Tyszka-Czochara, Małgorzata; Filip, Małgorzata

2014-05-01

In recent years, a potential participation of endocannabinoids (eCBs) and related endocannabinoid-like molecules, including N-acylethanolamines (NAEs), in the physiological and pathophysiological processes has been highlighted, whereas measurement of their levels still remains difficult. The aim of this study was to develop a bioanalytical method that would enable researchers to simultaneously determine quantitatively eCBs (anandamide - AEA and 2-arachidonoylglycerol - 2-AG) and NAEs (oleoylethanolamide or oleoylethanolamine - OEA, palmitoylethanolamide or palmitoylethanolamine - PEA and linoleoylethanolamide or linoleoylethanolamine - LEA) in the rat brain. The analytical problems with analysis and possible solutions have been also shown. The methodology for quantifying eCBs/NAEs by means of a sensitive and selective liquid chromatography tandem mass spectrometry (LC-MS/MS) with electrospray positive ionization and multiple reaction monitoring (MRM) mode was developed and validated. Analytical problems with analyzed compounds were estimated. Reasonably high precision and accuracy of the method were demonstrated in the validation process. The method is linear up to 200 ng/g for AEA, OEA, PEA and LEA and up to 100 μg/g for 2-AG, while the quantification limit reaches 0.2 ng/g and 0.8 μg/g, respectively. Simplicity and rapidity of the assay allows analyzing many samples on a routine basis. This article presents the new procedure applied to the analysis of brain tissues.

Radioligand binding analysis of α 2 adrenoceptors with [11C]yohimbine in brain in vivo: Extended Inhibition Plot correction for plasma protein binding.

PubMed

Phan, Jenny-Ann; Landau, Anne M; Jakobsen, Steen; Wong, Dean F; Gjedde, Albert

2017-11-22

We describe a novel method of kinetic analysis of radioligand binding to neuroreceptors in brain in vivo, here applied to noradrenaline receptors in rat brain. The method uses positron emission tomography (PET) of [ 11 C]yohimbine binding in brain to quantify the density and affinity of α 2 adrenoceptors under condition of changing radioligand binding to plasma proteins. We obtained dynamic PET recordings from brain of Spraque Dawley rats at baseline, followed by pharmacological challenge with unlabeled yohimbine (0.3 mg/kg). The challenge with unlabeled ligand failed to diminish radioligand accumulation in brain tissue, due to the blocking of radioligand binding to plasma proteins that elevated the free fractions of the radioligand in plasma. We devised a method that graphically resolved the masking of unlabeled ligand binding by the increase of radioligand free fractions in plasma. The Extended Inhibition Plot introduced here yielded an estimate of the volume of distribution of non-displaceable ligand in brain tissue that increased with the increase of the free fraction of the radioligand in plasma. The resulting binding potentials of the radioligand declined by 50-60% in the presence of unlabeled ligand. The kinetic unmasking of inhibited binding reflected in the increase of the reference volume of distribution yielded estimates of receptor saturation consistent with the binding of unlabeled ligand.

Longitudinal Brain Development of Numerical Skills in Typically Developing Children and Children with Developmental Dyscalculia.

PubMed

McCaskey, Ursina; von Aster, Michael; Maurer, Urs; Martin, Ernst; O'Gorman Tuura, Ruth; Kucian, Karin

2017-01-01

Developmental dyscalculia (DD) is a learning disability affecting the acquisition of numerical-arithmetical skills. Studies report persistent deficits in number processing and aberrant functional activation of the fronto-parietal numerical network in DD. However, the neural development of numerical abilities has been scarcely investigated. The present paper provides a first attempt to investigate behavioral and neural trajectories of numerical abilities longitudinally in typically developing (TD) and DD children. During a study period of 4 years, 28 children (8-11 years) were evaluated twice by means of neuropsychological tests and a numerical order fMRI paradigm. Over time, TD children improved in numerical abilities and showed a consistent and well-developed fronto-parietal network. In contrast, DD children revealed persistent deficits in number processing and arithmetic. Brain imaging results of the DD group showed an age-related activation increase in parietal regions (intraparietal sulcus), pointing to a delayed development of number processing areas. Besides, an activation increase in frontal areas was observed over time, indicating the use of compensatory mechanisms. In conclusion, results suggest a continuation in neural development of number representation in DD, whereas the neural network for simple ordinal number estimation seems to be stable or show only subtle changes in TD children over time.

Universal characteristics of evolution and development are inherent in fetal autonomic brain maturation.

PubMed

Schmidt, Alexander; Schukat-Talamazzini, Ernst G; Zöllkau, Janine; Pytlik, Adelina; Leibl, Sophia; Kumm, Kathrin; Bode, Franziska; Kynass, Isabelle; Witte, Otto W; Schleussner, Ekkehard; Schneider, Uwe; Hoyer, Dirk

2018-07-01

Adverse prenatal environmental influences to the developing fetus are associated with mental and cardiovascular disease in later life. Universal developmental characteristics such as self-organization, pattern formation, and adaptation in the growing information processing system have not yet been sufficiently analyzed with respect to description of normal fetal development and identification of developmental disturbances. Fetal heart rate patterns are the only non-invasive order parameter of the developing autonomic brain available with respect to the developing complex organ system. The objective of the present study was to investigate whether universal indices, known from evolution and phylogeny, describe the ontogenetic fetal development from 20 weeks of gestation onwards. By means of a 10-fold cross-validated data-driven multivariate regression modeling procedure, relevant indices of heart rate variability (HRV) were explored using 552 fetal heart rate recordings, each lasting over 30 min. We found that models which included HRV indices of increasing fluctuation amplitude, complexity and fractal long-range dependencies largely estimated the maturation age (coefficients of determination 0.61-0.66). Consideration of these characteristics in prenatal care may not only have implications for early identification of developmental disturbances, but also for the development of system-theory-based therapeutic strategies. Copyright © 2018 Elsevier B.V. All rights reserved.

Longitudinal Brain Development of Numerical Skills in Typically Developing Children and Children with Developmental Dyscalculia

PubMed Central

McCaskey, Ursina; von Aster, Michael; Maurer, Urs; Martin, Ernst; O'Gorman Tuura, Ruth; Kucian, Karin

2018-01-01

Developmental dyscalculia (DD) is a learning disability affecting the acquisition of numerical-arithmetical skills. Studies report persistent deficits in number processing and aberrant functional activation of the fronto-parietal numerical network in DD. However, the neural development of numerical abilities has been scarcely investigated. The present paper provides a first attempt to investigate behavioral and neural trajectories of numerical abilities longitudinally in typically developing (TD) and DD children. During a study period of 4 years, 28 children (8–11 years) were evaluated twice by means of neuropsychological tests and a numerical order fMRI paradigm. Over time, TD children improved in numerical abilities and showed a consistent and well-developed fronto-parietal network. In contrast, DD children revealed persistent deficits in number processing and arithmetic. Brain imaging results of the DD group showed an age-related activation increase in parietal regions (intraparietal sulcus), pointing to a delayed development of number processing areas. Besides, an activation increase in frontal areas was observed over time, indicating the use of compensatory mechanisms. In conclusion, results suggest a continuation in neural development of number representation in DD, whereas the neural network for simple ordinal number estimation seems to be stable or show only subtle changes in TD children over time. PMID:29354041

Brain Development and Its Relationship to Early Childhood Education.

ERIC Educational Resources Information Center

Slegers, Brenda

New research on brain development has profound implications in the areas of child development and education. This review of the research describes how the brain develops to shape children's growing intelligence, addressing such questions as: (1) What are the brain's functions? (2) What are the critical or sensitive periods in brain development?…

Brain waves-based index for workload estimation and mental effort engagement recognition

NASA Astrophysics Data System (ADS)

Zammouri, A.; Chraa-Mesbahi, S.; Ait Moussa, A.; Zerouali, S.; Sahnoun, M.; Tairi, H.; Mahraz, A. M.

2017-10-01

The advent of the communication systems and considering the complexity that some impose in their use, it is necessary to incorporate and equip these systems with a certain intelligence which takes into account the cognitive and mental capacities of the human operator. In this work, we address the issue of estimating the mental effort of an operator according to the cognitive tasks difficulty levels. Based on the Electroencephalogram (EEG) measurements, the proposed approach analyzes the user’s brain activity from different brain regions while performing cognitive tasks with several levels of difficulty. At a first time, we propose a variances comparison-based classifier (VCC) that makes use of the Power Spectral Density (PSD) of the EEG signal. The aim of using such a classifier is to highlight the brain regions that enter into interaction according to the cognitive task difficulty. In a second time, we present and describe a new EEG-based index for the estimation of mental efforts. The designed index is based on information recorded from two EEG channels. Results from the VCC demonstrate that powers of the Theta [4-7 Hz] (θ) and Alpha [8-12 Hz] (α) oscillations decrease while increasing the cognitive task difficulty. These decreases are mainly located in parietal and temporal brain regions. Based on the Kappa coefficients, decisions of the introduced index are compared to those obtained from an existing index. This performance assessment method revealed strong agreements. Hence the efficiency of the introduced index.

An information theory framework for dynamic functional domain connectivity.

PubMed

Vergara, Victor M; Miller, Robyn; Calhoun, Vince

2017-06-01

Dynamic functional network connectivity (dFNC) analyzes time evolution of coherent activity in the brain. In this technique dynamic changes are considered for the whole brain. This paper proposes an information theory framework to measure information flowing among subsets of functional networks call functional domains. Our method aims at estimating bits of information contained and shared among domains. The succession of dynamic functional states is estimated at the domain level. Information quantity is based on the probabilities of observing each dynamic state. Mutual information measurement is then obtained from probabilities across domains. Thus, we named this value the cross domain mutual information (CDMI). Strong CDMIs were observed in relation to the subcortical domain. Domains related to sensorial input, motor control and cerebellum form another CDMI cluster. Information flow among other domains was seldom found. Other methods of dynamic connectivity focus on whole brain dFNC matrices. In the current framework, information theory is applied to states estimated from pairs of multi-network functional domains. In this context, we apply information theory to measure information flow across functional domains. Identified CDMI clusters point to known information pathways in the basal ganglia and also among areas of sensorial input, patterns found in static functional connectivity. In contrast, CDMI across brain areas of higher level cognitive processing follow a different pattern that indicates scarce information sharing. These findings show that employing information theory to formally measured information flow through brain domains reveals additional features of functional connectivity. Copyright © 2017 Elsevier B.V. All rights reserved.

Tractography patterns of subthalamic nucleus deep brain stimulation.

PubMed

Vanegas-Arroyave, Nora; Lauro, Peter M; Huang, Ling; Hallett, Mark; Horovitz, Silvina G; Zaghloul, Kareem A; Lungu, Codrin

2016-04-01

Deep brain stimulation therapy is an effective symptomatic treatment for Parkinson's disease, yet the precise mechanisms responsible for its therapeutic effects remain unclear. Although the targets of deep brain stimulation are grey matter structures, axonal modulation is known to play an important role in deep brain stimulation's therapeutic mechanism. Several white matter structures in proximity to the subthalamic nucleus have been implicated in the clinical benefits of deep brain stimulation for Parkinson's disease. We assessed the connectivity patterns that characterize clinically beneficial electrodes in Parkinson's disease patients, after deep brain stimulation of the subthalamic nucleus. We evaluated 22 patients with Parkinson's disease (11 females, age 57 ± 9.1 years, disease duration 13.3 ± 6.3 years) who received bilateral deep brain stimulation of the subthalamic nucleus at the National Institutes of Health. During an initial electrode screening session, one month after deep brain stimulation implantation, the clinical benefits of each contact were determined. The electrode was localized by coregistering preoperative magnetic resonance imaging and postoperative computer tomography images and the volume of tissue activated was estimated from stimulation voltage and impedance. Brain connectivity for the volume of tissue activated of deep brain stimulation contacts was assessed using probabilistic tractography with diffusion-tensor data. Areas most frequently connected to clinically effective contacts included the thalamus, substantia nigra, brainstem and superior frontal gyrus. A series of discriminant analyses demonstrated that the strength of connectivity to the superior frontal gyrus and the thalamus were positively associated with clinical effectiveness. The connectivity patterns observed in our study suggest that the modulation of white matter tracts directed to the superior frontal gyrus and the thalamus is associated with favourable clinical outcomes and may contribute to the therapeutic effects of deep brain stimulation. Our method can be further developed to reliably identify effective deep brain stimulation contacts and aid in the programming process. © The Author (2016). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Brain properties predict proximity to symptom onset in sporadic Alzheimer’s disease

PubMed Central

Vogel, Jacob W; Vachon-Presseau, Etienne; Pichet Binette, Alexa; Tam, Angela; Orban, Pierre; La Joie, Renaud; Savard, Mélissa; Picard, Cynthia; Poirier, Judes; Bellec, Pierre; Breitner, John C S; Villeneuve, Sylvia

2018-01-01

Abstract See Tijms and Visser (doi:10.1093/brain/awy113) for a scientific commentary on this article. Alzheimer’s disease is preceded by a lengthy ‘preclinical’ stage spanning many years, during which subtle brain changes occur in the absence of overt cognitive symptoms. Predicting when the onset of disease symptoms will occur is an unsolved challenge in individuals with sporadic Alzheimer’s disease. In individuals with autosomal dominant genetic Alzheimer’s disease, the age of symptom onset is similar across generations, allowing the prediction of individual onset times with some accuracy. We extend this concept to persons with a parental history of sporadic Alzheimer’s disease to test whether an individual’s symptom onset age can be informed by the onset age of their affected parent, and whether this estimated onset age can be predicted using only MRI. Structural and functional MRIs were acquired from 255 ageing cognitively healthy subjects with a parental history of sporadic Alzheimer’s disease from the PREVENT-AD cohort. Years to estimated symptom onset was calculated as participant age minus age of parental symptom onset. Grey matter volume was extracted from T1-weighted images and whole-brain resting state functional connectivity was evaluated using degree count. Both modalities were summarized using a 444-region cortical-subcortical atlas. The entire sample was divided into training (n = 138) and testing (n = 68) sets. Within the training set, individuals closer to or beyond their parent’s symptom onset demonstrated reduced grey matter volume and altered functional connectivity, specifically in regions known to be vulnerable in Alzheimer’s disease. Machine learning was used to identify a weighted set of imaging features trained to predict years to estimated symptom onset. This feature set alone significantly predicted years to estimated symptom onset in the unseen testing data. This model, using only neuroimaging features, significantly outperformed a similar model instead trained with cognitive, genetic, imaging and demographic features used in a traditional clinical setting. We next tested if these brain properties could be generalized to predict time to clinical progression in a subgroup of 26 individuals from the Alzheimer’s Disease Neuroimaging Initiative, who eventually converted either to mild cognitive impairment or to Alzheimer’s dementia. The feature set trained on years to estimated symptom onset in the PREVENT-AD predicted variance in time to clinical conversion in this separate longitudinal dataset. Adjusting for participant age did not impact any of the results. These findings demonstrate that years to estimated symptom onset or similar measures can be predicted from brain features and may help estimate presymptomatic disease progression in at-risk individuals. PMID:29688388

Occupational exposure to magnetic fields in relation to mortality from brain cancer among electricity generation and transmission workers.

PubMed Central

Harrington, J M; McBride, D I; Sorahan, T; Paddle, G M; van Tongeren, M

1997-01-01

OBJECTIVE: To investigate whether the risks of mortality from brain cancer are related to occupational exposure to magnetic fields. METHODS: A total of 112 cases of primary brain cancer (1972-91) were identified from a cohort of 84,018 male and female employees of the (then) Central Electricity Generating Board and its privatised successor companies. Individual cumulative occupational exposures to magnetic fields were estimated by linking available computerised job history data with magnetic field measurements collected over 675 person-workshifts. Estimated exposure histories of the case workers were compared with those of 654 control workers drawn from the cohort (nested case-control study), by means of conditional logistic regression. RESULTS: For exposure assessments based on arithmetic means, the risk of mortality from brain cancer for subjects with an estimated cumulative exposure to magnetic fields of 5.4-13.4 microT.y v subjects with lower exposures (0.0-5.3 microT.y) was 1.04 (95% confidence interval (95% CI) 0.60 to 1.80). The corresponding relative risk in subjects with higher exposures (> or = 13.5 microT.y) was 0.95 (95% CI 0.54 to 1.69). There was no indication of a positive trend for cumulative exposure and risk of mortality from brain cancer either when the analysis used exposure assessments based on geometric means or when the analysis was restricted to exposures received within five years of the case diagnosis (or corresponding period for controls). CONCLUSIONS: Although the exposure categorisation was based solely on recent observations, the study findings do not support the hypothesis that the risk of brain cancer is associated with occupational exposure to magnetic fields. PMID:9072027

Robust estimation of fractal measures for characterizing the structural complexity of the human brain: optimization and reproducibility

PubMed Central

Goñi, Joaquín; Sporns, Olaf; Cheng, Hu; Aznárez-Sanado, Maite; Wang, Yang; Josa, Santiago; Arrondo, Gonzalo; Mathews, Vincent P; Hummer, Tom A; Kronenberger, William G; Avena-Koenigsberger, Andrea; Saykin, Andrew J.; Pastor, María A.

2013-01-01

High-resolution isotropic three-dimensional reconstructions of human brain gray and white matter structures can be characterized to quantify aspects of their shape, volume and topological complexity. In particular, methods based on fractal analysis have been applied in neuroimaging studies to quantify the structural complexity of the brain in both healthy and impaired conditions. The usefulness of such measures for characterizing individual differences in brain structure critically depends on their within-subject reproducibility in order to allow the robust detection of between-subject differences. This study analyzes key analytic parameters of three fractal-based methods that rely on the box-counting algorithm with the aim to maximize within-subject reproducibility of the fractal characterizations of different brain objects, including the pial surface, the cortical ribbon volume, the white matter volume and the grey matter/white matter boundary. Two separate datasets originating from different imaging centers were analyzed, comprising, 50 subjects with three and 24 subjects with four successive scanning sessions per subject, respectively. The reproducibility of fractal measures was statistically assessed by computing their intra-class correlations. Results reveal differences between different fractal estimators and allow the identification of several parameters that are critical for high reproducibility. Highest reproducibility with intra-class correlations in the range of 0.9–0.95 is achieved with the correlation dimension. Further analyses of the fractal dimensions of parcellated cortical and subcortical gray matter regions suggest robustly estimated and region-specific patterns of individual variability. These results are valuable for defining appropriate parameter configurations when studying changes in fractal descriptors of human brain structure, for instance in studies of neurological diseases that do not allow repeated measurements or for disease-course longitudinal studies. PMID:23831414

Interpretation of the Precision Matrix and Its Application in Estimating Sparse Brain Connectivity during Sleep Spindles from Human Electrocorticography Recordings

PubMed Central

Das, Anup; Sampson, Aaron L.; Lainscsek, Claudia; Muller, Lyle; Lin, Wutu; Doyle, John C.; Cash, Sydney S.; Halgren, Eric; Sejnowski, Terrence J.

2017-01-01

The correlation method from brain imaging has been used to estimate functional connectivity in the human brain. However, brain regions might show very high correlation even when the two regions are not directly connected due to the strong interaction of the two regions with common input from a third region. One previously proposed solution to this problem is to use a sparse regularized inverse covariance matrix or precision matrix (SRPM) assuming that the connectivity structure is sparse. This method yields partial correlations to measure strong direct interactions between pairs of regions while simultaneously removing the influence of the rest of the regions, thus identifying regions that are conditionally independent. To test our methods, we first demonstrated conditions under which the SRPM method could indeed find the true physical connection between a pair of nodes for a spring-mass example and an RC circuit example. The recovery of the connectivity structure using the SRPM method can be explained by energy models using the Boltzmann distribution. We then demonstrated the application of the SRPM method for estimating brain connectivity during stage 2 sleep spindles from human electrocorticography (ECoG) recordings using an 8 × 8 electrode array. The ECoG recordings that we analyzed were from a 32-year-old male patient with long-standing pharmaco-resistant left temporal lobe complex partial epilepsy. Sleep spindles were automatically detected using delay differential analysis and then analyzed with SRPM and the Louvain method for community detection. We found spatially localized brain networks within and between neighboring cortical areas during spindles, in contrast to the case when sleep spindles were not present. PMID:28095202

A high-resolution computational localization method for transcranial magnetic stimulation mapping.

PubMed

Aonuma, Shinta; Gomez-Tames, Jose; Laakso, Ilkka; Hirata, Akimasa; Takakura, Tomokazu; Tamura, Manabu; Muragaki, Yoshihiro

2018-05-15

Transcranial magnetic stimulation (TMS) is used for the mapping of brain motor functions. The complexity of the brain deters determining the exact localization of the stimulation site using simplified methods (e.g., the region below the center of the TMS coil) or conventional computational approaches. This study aimed to present a high-precision localization method for a specific motor area by synthesizing computed non-uniform current distributions in the brain for multiple sessions of TMS. Peritumoral mapping by TMS was conducted on patients who had intra-axial brain neoplasms located within or close to the motor speech area. The electric field induced by TMS was computed using realistic head models constructed from magnetic resonance images of patients. A post-processing method was implemented to determine a TMS hotspot by combining the computed electric fields for the coil orientations and positions that delivered high motor-evoked potentials during peritumoral mapping. The method was compared to the stimulation site localized via intraoperative direct brain stimulation and navigated TMS. Four main results were obtained: 1) the dependence of the computed hotspot area on the number of peritumoral measurements was evaluated; 2) the estimated localization of the hand motor area in eight non-affected hemispheres was in good agreement with the position of a so-called "hand-knob"; 3) the estimated hotspot areas were not sensitive to variations in tissue conductivity; and 4) the hand motor areas estimated by this proposal and direct electric stimulation (DES) were in good agreement in the ipsilateral hemisphere of four glioma patients. The TMS localization method was validated by well-known positions of the "hand-knob" in brains for the non-affected hemisphere, and by a hotspot localized via DES during awake craniotomy for the tumor-containing hemisphere. Copyright © 2018 Elsevier Inc. All rights reserved.

Watershed microinfarct pathology and cognition in older persons.

PubMed

Kapasi, Alifiya; Leurgans, Sue E; James, Bryan D; Boyle, Patricia A; Arvanitakis, Zoe; Nag, Sukriti; Bennett, David A; Buchman, Aron S; Schneider, Julie A

2018-05-30

Brain microinfarcts are common in aging and are associated with cognitive impairment. Anterior and posterior watershed border zones lie at the territories of the anterior, middle, and posterior cerebral arteries, and are more vulnerable to hypoperfusion than brain regions outside the watershed areas. However, little is known about microinfarcts in these regions and how they relate to cognition in aging. Participants from the Rush Memory and Aging Project, a community-based clinical-pathologic study of aging, underwent detailed annual cognitive evaluations. We examined 356 consecutive autopsy cases (mean age-at-death, 91 years [SD = 6.16]; 28% men) for microinfarcts from 3 watershed brain regions (2 anterior and 1 posterior) and 8 brain regions outside the watershed regions. Linear regression models were used to examine the association of cortical watershed microinfarcts with cognition, including global cognition and 5 cognitive domains. Microinfarcts in any region were present in 133 (37%) participants, of which 50 had microinfarcts in watershed regions. Persons with multiple microinfarcts in cortical watershed regions had lower global cognition (estimate = -0.56, standard error (SE) = 0.26, p = 0.03) and lower cognitive function in the specific domains of working memory (estimate = -0.58, SE = 0.27, p = 0.03) and visuospatial abilities (estimate = -0.57, SE = 0.27, p = 0.03), even after controlling for microinfarcts in other brain regions, demographics, and age-related pathologies. Neither the presence nor multiplicity of microinfarcts in brain regions outside the cortical watershed regions were related to global cognition or any of the 5 cognitive domains. These findings suggest that multiple microinfarcts in watershed regions contribute to age-related cognitive impairment. Copyright © 2018 Elsevier Inc. All rights reserved.

Chromatographic Behaviour Predicts the Ability of Potential Nootropics to Permeate the Blood-Brain Barrier

PubMed Central

Farsa, Oldřich

2013-01-01

The log BB parameter is the logarithm of the ratio of a compound’s equilibrium concentrations in the brain tissue versus the blood plasma. This parameter is a useful descriptor in assessing the ability of a compound to permeate the blood-brain barrier. The aim of this study was to develop a Hansch-type linear regression QSAR model that correlates the parameter log BB and the retention time of drugs and other organic compounds on a reversed-phase HPLC containing an embedded amide moiety. The retention time was expressed by the capacity factor log k′. The second aim was to estimate the brain’s absorption of 2-(azacycloalkyl)acetamidophenoxyacetic acids, which are analogues of piracetam, nefiracetam, and meclofenoxate. Notably, these acids may be novel nootropics. Two simple regression models that relate log BB and log k′ were developed from an assay performed using a reversed-phase HPLC that contained an embedded amide moiety. Both the quadratic and linear models yielded statistical parameters comparable to previously published models of log BB dependence on various structural characteristics. The models predict that four members of the substituted phenoxyacetic acid series have a strong chance of permeating the barrier and being absorbed in the brain. The results of this study show that a reversed-phase HPLC system containing an embedded amide moiety is a functional in vitro surrogate of the blood-brain barrier. These results suggest that racetam-type nootropic drugs containing a carboxylic moiety could be more poorly absorbed than analogues devoid of the carboxyl group, especially if the compounds penetrate the barrier by a simple diffusion mechanism. PMID:23641330

Effect of thyroxine on brain microstructure in extremely premature babies: magnetic resonance imaging findings in the TIPIT study.

PubMed

Ng, Sze May; Turner, Mark A; Gamble, Carrol; Didi, Mohammed; Victor, Suresh; Atkinson, Jessica; Sluming, Vanessa; Parkes, Laura M; Tietze, Anna; Abernethy, Laurence J; Weindling, Alan Michael

2014-08-01

In order to assess relationships between thyroid hormone status and findings on brain MRI, a subset of babies was recruited to a multi-centre randomised, placebo-controlled trial of levothyroxine (LT4) supplementation for babies born before 28 weeks' gestation (known as the TIPIT study, for Thyroxine supplementation In Preterm InfanTs). These infants were imaged at term-equivalence. Forty-five TIPIT participants had brain MRI using diffusion tensor imaging (DTI) to estimate white matter development by apparent diffusion coefficient (ADC), fractional anisotropy (FA) and tractography metrics of number and length of streamlines. We made comparisons between babies with the lowest and highest plasma FT4 concentrations during the initial 4 weeks after birth. There were no differences in DTI metrics between babies who had received LT4 supplementation and those who had received a placebo. Among recipients of a placebo, babies in the lowest quartile of plasma-free thyroxine (FT4) concentrations had significantly higher apparent diffusion coefficient measurements in the posterior corpus callosum and streamlines that were shorter and less numerous in the right internal capsule. Among LT4-supplemented babies, those who had plasma FT4 concentrations in the highest quartile had significantly lower apparent diffusion coefficient values in the left occipital lobe, higher fractional anisotropy in the anterior corpus callosum and longer and more numerous streamlines in the anterior corpus callosum. DTI variables were not associated with allocation of placebo or thyroid supplementation. Markers of poorly organised brain microstructure were associated with low plasma FT4 concentrations after birth. The findings suggest that plasma FT4 concentrations affect brain development in very immature infants and that the effect of LT4 supplementation for immature babies with low FT4 plasma concentrations warrants further study.

Development of a brain MRI-based hidden Markov model for dementia recognition

PubMed Central

2013-01-01

Background Dementia is an age-related cognitive decline which is indicated by an early degeneration of cortical and sub-cortical structures. Characterizing those morphological changes can help to understand the disease development and contribute to disease early prediction and prevention. But modeling that can best capture brain structural variability and can be valid in both disease classification and interpretation is extremely challenging. The current study aimed to establish a computational approach for modeling the magnetic resonance imaging (MRI)-based structural complexity of the brain using the framework of hidden Markov models (HMMs) for dementia recognition. Methods Regularity dimension and semi-variogram were used to extract structural features of the brains, and vector quantization method was applied to convert extracted feature vectors to prototype vectors. The output VQ indices were then utilized to estimate parameters for HMMs. To validate its accuracy and robustness, experiments were carried out on individuals who were characterized as non-demented and mild Alzheimer's diseased. Four HMMs were constructed based on the cohort of non-demented young, middle-aged, elder and demented elder subjects separately. Classification was carried out using a data set including both non-demented and demented individuals with a wide age range. Results The proposed HMMs have succeeded in recognition of individual who has mild Alzheimer's disease and achieved a better classification accuracy compared to other related works using different classifiers. Results have shown the ability of the proposed modeling for recognition of early dementia. Conclusion The findings from this research will allow individual classification to support the early diagnosis and prediction of dementia. By using the brain MRI-based HMMs developed in our proposed research, it will be more efficient, robust and can be easily used by clinicians as a computer-aid tool for validating imaging bio-markers for early prediction of dementia. PMID:24564961

Specific binding of [(18)F]fluoroethyl-harmol to monoamine oxidase A in rat brain cryostat sections, and compartmental analysis of binding in living brain.

PubMed

Maschauer, Simone; Haller, Adelina; Riss, Patrick J; Kuwert, Torsten; Prante, Olaf; Cumming, Paul

2015-12-01

We investigated [(18)F]fluoroethyl-harmol ([(18)F]FEH) as a reversible and selective ligand for positron emission tomography (PET) studies of monoamine oxidase A (MAO-A). Binding of [(18)F]FEH in rat brain cryostat sections indicated high affinity (KD = 3 nM), and density (Bmax; 600 pmol/g). The plasma free fraction was 45%, and untransformed parent constituted only 13% of plasma radioactivity at 10 min after injection. Compartmental analysis of PET recordings in pargyline-treated rats showed high permeability to brain (K1; 0.32 mL/g/min) and slow washout (k2; 0.024/min), resulting in a uniformly high equilibrium distribution volume (VD; 20 mL/g). Using this VD to estimate unbound ligand in brain of untreated rats, the binding potential ranged from 4.2 in cerebellum to 7.2 in thalamus. We also calculated maps of rats receiving [(18)F]FEH at a range of specific activities, and then estimated saturation binding parameters in the living brain. In thalamus, striatum and frontal cortex KD was globally close to 300 nM and Bmax was close to 1600 pmol/g; the 100-fold discrepancy in affinity suggests a very low free fraction for [(18)F]FEH in the living brain. Based on a synthesis of findings, we calculate the endogenous dopamine concentration to be 0.4 μM in the striatal compartment containing MAO-A, thus unlikely to exert competition against [(18)F]FEH binding in vivo. In summary, [(18)F]FEH has good properties for the detection of MAO-A in the rat brain by PET, and may present logistic advantages for clinical research at centers lacking a medical cyclotron. We made a compartmental analysis of [(18)F]fluoroethylharmol ([(18)F]FEH) binding to monoamine oxidase A (MAO-A) in living rat brain and estimated the saturation binding parameters from the binding potential (BPND). The Bmax was of comparable magnitude to that in vitro, but with apparent affinity (300 nM), it was 100-fold lower in vivo. PET imaging with [(18) F]FEH is well suited for quantitation of MAO-A in living brain. © 2015 International Society for Neurochemistry.

Quantitative in vivo receptor binding. III. Tracer kinetic modeling of muscarinic cholinergic receptor binding

DOE Office of Scientific and Technical Information (OSTI.GOV)

Frey, K.A.; Hichwa, R.D.; Ehrenkaufer, R.L.

1985-10-01

A tracer kinetic method is developed for the in vivo estimation of high-affinity radioligand binding to central nervous system receptors. Ligand is considered to exist in three brain pools corresponding to free, nonspecifically bound, and specifically bound tracer. These environments, in addition to that of intravascular tracer, are interrelated by a compartmental model of in vivo ligand distribution. A mathematical description of the model is derived, which allows determination of regional blood-brain barrier permeability, nonspecific binding, the rate of receptor-ligand association, and the rate of dissociation of bound ligand, from the time courses of arterial blood and tissue tracer concentrations.more » The term ''free receptor density'' is introduced to describe the receptor population measured by this method. The technique is applied to the in vivo determination of regional muscarinic acetylcholine receptors in the rat, with the use of (TH)scopolamine. Kinetic estimates of free muscarinic receptor density are in general agreement with binding capacities obtained from previous in vivo and in vitro equilibrium binding studies. In the striatum, however, kinetic estimates of free receptor density are less than those in the neocortex--a reversal of the rank ordering of these regions derived from equilibrium determinations. A simplified model is presented that is applicable to tracers that do not readily dissociate from specific binding sites during the experimental period.« less

Recovering fNIRS brain signals: physiological interference suppression with independent component analysis

NASA Astrophysics Data System (ADS)

Zhang, Y.; Shi, M.; Sun, J.; Yang, C.; Zhang, Yajuan; Scopesi, F.; Makobore, P.; Chin, C.; Serra, G.; Wickramasinghe, Y. A. B. D.; Rolfe, P.

2015-02-01

Brain activity can be monitored non-invasively by functional near-infrared spectroscopy (fNIRS), which has several advantages in comparison with other methods, such as flexibility, portability, low cost and fewer physical restrictions. However, in practice fNIRS measurements are often contaminated by physiological interference arising from cardiac contraction, breathing and blood pressure fluctuations, thereby severely limiting the utility of the method. Hence, further improvement is necessary to reduce or eliminate such interference in order that the evoked brain activity information can be extracted reliably from fNIRS data. In the present paper, the multi-distance fNIRS probe configuration has been adopted. The short-distance fNIRS measurement is treated as the virtual channel and the long-distance fNIRS measurement is treated as the measurement channel. Independent component analysis (ICA) is employed for the fNIRS recordings to separate the brain signals and the interference. Least-absolute deviation (LAD) estimator is employed to recover the brain activity signals. We also utilized Monte Carlo simulations based on a five-layer model of the adult human head to evaluate our methodology. The results demonstrate that the ICA algorithm has the potential to separate physiological interference in fNIRS data and the LAD estimator could be a useful criterion to recover the brain activity signals.

Deep Independence Network Analysis of Structural Brain Imaging: Application to Schizophrenia

PubMed Central

Castro, Eduardo; Hjelm, R. Devon; Plis, Sergey M.; Dinh, Laurent; Turner, Jessica A.; Calhoun, Vince D.

2016-01-01

Linear independent component analysis (ICA) is a standard signal processing technique that has been extensively used on neuroimaging data to detect brain networks with coherent brain activity (functional MRI) or covarying structural patterns (structural MRI). However, its formulation assumes that the measured brain signals are generated by a linear mixture of the underlying brain networks and this assumption limits its ability to detect the inherent nonlinear nature of brain interactions. In this paper, we introduce nonlinear independent component estimation (NICE) to structural MRI data to detect abnormal patterns of gray matter concentration in schizophrenia patients. For this biomedical application, we further addressed the issue of model regularization of nonlinear ICA by performing dimensionality reduction prior to NICE, together with an appropriate control of the complexity of the model and the usage of a proper approximation of the probability distribution functions of the estimated components. We show that our results are consistent with previous findings in the literature, but we also demonstrate that the incorporation of nonlinear associations in the data enables the detection of spatial patterns that are not identified by linear ICA. Specifically, we show networks including basal ganglia, cerebellum and thalamus that show significant differences in patients versus controls, some of which show distinct nonlinear patterns. PMID:26891483

Discrete capacity limits and neuroanatomical correlates of visual short-term memory for objects and spatial locations.

PubMed

Konstantinou, Nikos; Constantinidou, Fofi; Kanai, Ryota

2017-02-01

Working memory is responsible for keeping information in mind when it is no longer in view, linking perception with higher cognitive functions. Despite such crucial role, short-term maintenance of visual information is severely limited. Research suggests that capacity limits in visual short-term memory (VSTM) are correlated with sustained activity in distinct brain areas. Here, we investigated whether variability in the structure of the brain is reflected in individual differences of behavioral capacity estimates for spatial and object VSTM. Behavioral capacity estimates were calculated separately for spatial and object information using a novel adaptive staircase procedure and were found to be unrelated, supporting domain-specific VSTM capacity limits. Voxel-based morphometry (VBM) analyses revealed dissociable neuroanatomical correlates of spatial versus object VSTM. Interindividual variability in spatial VSTM was reflected in the gray matter density of the inferior parietal lobule. In contrast, object VSTM was reflected in the gray matter density of the left insula. These dissociable findings highlight the importance of considering domain-specific estimates of VSTM capacity and point to the crucial brain regions that limit VSTM capacity for different types of visual information. Hum Brain Mapp 38:767-778, 2017. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Monitoring of deep brain temperature in infants using multi-frequency microwave radiometry and thermal modelling.

PubMed

Han, J W; Van Leeuwen, G M; Mizushina, S; Van de Kamer, J B; Maruyama, K; Sugiura, T; Azzopardi, D V; Edwards, A D

2001-07-01

In this study we present a design for a multi-frequency microwave radiometer aimed at prolonged monitoring of deep brain temperature in newborn infants and suitable for use during hypothermic neural rescue therapy. We identify appropriate hardware to measure brightness temperature and evaluate the accuracy of the measurements. We describe a method to estimate the tissue temperature distribution from measured brightness temperatures which uses the results of numerical simulations of the tissue temperature as well as the propagation of the microwaves in a realistic detailed three-dimensional infant head model. The temperature retrieval method is then used to evaluate how the statistical fluctuations in the measured brightness temperatures limit the confidence interval for the estimated temperature: for an 18 degrees C temperature differential between cooled surface and deep brain we found a standard error in the estimated central brain temperature of 0.75 degrees C. Evaluation of the systematic errors arising from inaccuracies in model parameters showed that realistic deviations in tissue parameters have little impact compared to uncertainty in the thickness of the bolus between the receiving antenna and the infant's head or in the skull thickness. This highlights the need to pay particular attention to these latter parameters in future practical implementation of the technique.

Computation of acoustic ressure fields produced in feline brain by high-intensity focused ultrasound

NASA Astrophysics Data System (ADS)

Omidi, Nazanin

In 1975, Dunn et al. (JASA 58:512-514) showed that a simple relation describes the ultrasonic threshold for cavitation-induced changes in the mammalian brain. The thresholds for tissue damage were estimated for a variety of acoustic parameters in exposed feline brain. The goal of this study was to improve the estimates for acoustic pressures and intensities present in vivo during those experimental exposures by estimating them using nonlinear rather than linear theory. In our current project, the acoustic pressure waveforms produced in the brains of anesthetized felines were numerically simulated for a spherically focused, nominally f1-transducer (focal length = 13 cm) at increasing values of the source pressure at frequencies of 1, 3, and 9 MHz. The corresponding focal intensities were correlated with the experimental data of Dunn et al. The focal pressure waveforms were also computed at the location of the true maximum. For low source pressures, the computed waveforms were the same as those determined using linear theory, and the focal intensities matched experimentally determined values. For higher source pressures, the focal pressure waveforms became increasingly distorted, with the compressional amplitude of the wave becoming greater, and the rarefactional amplitude becoming lower than the values calculated using linear theory. The implications of these results for clinical exposures are discussed.

Information-geometric measures estimate neural interactions during oscillatory brain states

PubMed Central

Nie, Yimin; Fellous, Jean-Marc; Tatsuno, Masami

2014-01-01

The characterization of functional network structures among multiple neurons is essential to understanding neural information processing. Information geometry (IG), a theory developed for investigating a space of probability distributions has recently been applied to spike-train analysis and has provided robust estimations of neural interactions. Although neural firing in the equilibrium state is often assumed in these studies, in reality, neural activity is non-stationary. The brain exhibits various oscillations depending on cognitive demands or when an animal is asleep. Therefore, the investigation of the IG measures during oscillatory network states is important for testing how the IG method can be applied to real neural data. Using model networks of binary neurons or more realistic spiking neurons, we studied how the single- and pairwise-IG measures were influenced by oscillatory neural activity. Two general oscillatory mechanisms, externally driven oscillations and internally induced oscillations, were considered. In both mechanisms, we found that the single-IG measure was linearly related to the magnitude of the external input, and that the pairwise-IG measure was linearly related to the sum of connection strengths between two neurons. We also observed that the pairwise-IG measure was not dependent on the oscillation frequency. These results are consistent with the previous findings that were obtained under the equilibrium conditions. Therefore, we demonstrate that the IG method provides useful insights into neural interactions under the oscillatory condition that can often be observed in the real brain. PMID:24605089

Information-geometric measures estimate neural interactions during oscillatory brain states.

PubMed

Nie, Yimin; Fellous, Jean-Marc; Tatsuno, Masami

2014-01-01

The characterization of functional network structures among multiple neurons is essential to understanding neural information processing. Information geometry (IG), a theory developed for investigating a space of probability distributions has recently been applied to spike-train analysis and has provided robust estimations of neural interactions. Although neural firing in the equilibrium state is often assumed in these studies, in reality, neural activity is non-stationary. The brain exhibits various oscillations depending on cognitive demands or when an animal is asleep. Therefore, the investigation of the IG measures during oscillatory network states is important for testing how the IG method can be applied to real neural data. Using model networks of binary neurons or more realistic spiking neurons, we studied how the single- and pairwise-IG measures were influenced by oscillatory neural activity. Two general oscillatory mechanisms, externally driven oscillations and internally induced oscillations, were considered. In both mechanisms, we found that the single-IG measure was linearly related to the magnitude of the external input, and that the pairwise-IG measure was linearly related to the sum of connection strengths between two neurons. We also observed that the pairwise-IG measure was not dependent on the oscillation frequency. These results are consistent with the previous findings that were obtained under the equilibrium conditions. Therefore, we demonstrate that the IG method provides useful insights into neural interactions under the oscillatory condition that can often be observed in the real brain.

Brain Regions Related to Impulsivity Mediate the Effects of Early Adversity on Antisocial Behavior.

PubMed

Mackey, Scott; Chaarani, Bader; Kan, Kees-Jan; Spechler, Philip A; Orr, Catherine; Banaschewski, Tobias; Barker, Gareth; Bokde, Arun L W; Bromberg, Uli; Büchel, Christian; Cattrell, Anna; Conrod, Patricia J; Desrivières, Sylvane; Flor, Herta; Frouin, Vincent; Gallinat, Jürgen; Gowland, Penny; Heinz, Andreas; Ittermann, Bernd; Paillère Martinot, Marie-Laure; Artiges, Eric; Nees, Frauke; Papadopoulos-Orfanos, Dimitri; Poustka, Luise; Smolka, Michael N; Jurk, Sarah; Walter, Henrik; Whelan, Robert; Schumann, Gunter; Althoff, Robert R; Garavan, Hugh

2017-08-15

Individual differences in impulsivity and early adversity are known to be strong predictors of adolescent antisocial behavior. However, the neurobiological bases of impulsivity and their relation to antisocial behavior and adversity are poorly understood. Impulsivity was estimated with a temporal discounting task. Voxel-based morphometry was used to determine the brain structural correlates of temporal discounting in a large cohort (n = 1830) of 14- to 15-year-old children. Mediation analysis was then used to determine whether the volumes of brain regions associated with temporal discounting mediate the relation between adverse life events (e.g., family conflict, serious accidents) and antisocial behaviors (e.g., precocious sexual activity, bullying, illicit substance use). Greater temporal discounting (more impulsivity) was associated with 1) lower volume in frontomedial cortex and bilateral insula and 2) greater volume in a subcortical region encompassing the ventral striatum, hypothalamus and anterior thalamus. The volume ratio between these cortical and subcortical regions was found to partially mediate the relation between adverse life events and antisocial behavior. Temporal discounting is related to regions of the brain involved in reward processing and interoception. The results support a developmental imbalance model of impulsivity and are consistent with the idea that negative environmental factors can alter the developing brain in ways that promote antisocial behavior. Copyright © 2016 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

Caffeic acid attenuates lipopolysaccharide-induced sickness behaviour and neuroinflammation in mice.

PubMed

Basu Mallik, Sanchari; Mudgal, Jayesh; Nampoothiri, Madhavan; Hall, Susan; Dukie, Shailendra Anoopkumar-; Grant, Gary; Rao, C Mallikarjuna; Arora, Devinder

2016-10-06

Accumulating data links inflammation, oxidative stress and immune system in the pathophysiology of major depressive disorders. Sickness behaviour is a set of behavioural changes that develop during infection, eventually leading to decrease in mobility and depressed behaviour. Lipopolysaccharide (LPS) induces a depression-like state in animals that mimics sickness behaviour. Caffeic acid, a naturally occurring polyphenol, possesses antioxidant and anti-inflammatory properties. The present study was designed to explore the potential of caffeic acid against LPS-induced sickness behaviour in mice. Caffeic acid (30mg/kg) and imipramine (15mg/kg) were administered orally one hour prior to LPS (1.5mg/kg) challenge. Behavioural assessment was carried out between 1 and 2h and blood samples were collected at 3h post-LPS injection. Additionally, cytokines (brain and serum) and brain oxidative stress markers were estimated. LPS increased the systemic and brain cytokine levels, altered the anti-oxidant defence and produced key signs of sickness behaviour in animals. Caffeic acid treatment significantly reduced the LPS-induced changes, including reduced expression of inflammatory markers in serum and whole brain. Caffeic acid also exerted an anti-oxidant effect, which was evident from the decreased levels of oxidative stress markers in whole brain. Our data suggests that caffeic acid can prevent the neuroinflammation-induced acute and probably the long term neurodegenerative changes. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Beamspace fast fully adaptive brain source localization for limited data sequences

NASA Astrophysics Data System (ADS)

Ravan, Maryam

2017-05-01

In the electroencephalogram (EEG) or magnetoencephalogram (MEG) context, brain source localization methods that rely on estimating second order statistics often fail when the observations are taken over a short time interval, especially when the number of electrodes is large. To address this issue, in previous study, we developed a multistage adaptive processing called fast fully adaptive (FFA) approach that can significantly reduce the required sample support while still processing all available degrees of freedom (DOFs). This approach processes the observed data in stages through a decimation procedure. In this study, we introduce a new form of FFA approach called beamspace FFA. We first divide the brain into smaller regions and transform the measured data from the source space to the beamspace in each region. The FFA approach is then applied to the beamspaced data of each region. The goal of this modification is to benefit the correlation sensitivity reduction between sources in different brain regions. To demonstrate the performance of the beamspace FFA approach in the limited data scenario, simulation results with multiple deep and cortical sources as well as experimental results are compared with regular FFA and widely used FINE approaches. Both simulation and experimental results demonstrate that the beamspace FFA method can localize different types of multiple correlated brain sources in low signal to noise ratios more accurately with limited data.

Information properties of morphologically complex words modulate brain activity during word reading.

PubMed

Hakala, Tero; Hultén, Annika; Lehtonen, Minna; Lagus, Krista; Salmelin, Riitta

2018-06-01

Neuroimaging studies of the reading process point to functionally distinct stages in word recognition. Yet, current understanding of the operations linked to those various stages is mainly descriptive in nature. Approaches developed in the field of computational linguistics may offer a more quantitative approach for understanding brain dynamics. Our aim was to evaluate whether a statistical model of morphology, with well-defined computational principles, can capture the neural dynamics of reading, using the concept of surprisal from information theory as the common measure. The Morfessor model, created for unsupervised discovery of morphemes, is based on the minimum description length principle and attempts to find optimal units of representation for complex words. In a word recognition task, we correlated brain responses to word surprisal values derived from Morfessor and from other psycholinguistic variables that have been linked with various levels of linguistic abstraction. The magnetoencephalography data analysis focused on spatially, temporally and functionally distinct components of cortical activation observed in reading tasks. The early occipital and occipito-temporal responses were correlated with parameters relating to visual complexity and orthographic properties, whereas the later bilateral superior temporal activation was correlated with whole-word based and morphological models. The results show that the word processing costs estimated by the statistical Morfessor model are relevant for brain dynamics of reading during late processing stages. © 2018 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.

Microglia and Beyond: Innate Immune Cells As Regulators of Brain Development and Behavioral Function.

PubMed

Lenz, Kathryn M; Nelson, Lars H

2018-01-01

Innate immune cells play a well-documented role in the etiology and disease course of many brain-based conditions, including multiple sclerosis, Alzheimer's disease, traumatic brain and spinal cord injury, and brain cancers. In contrast, it is only recently becoming clear that innate immune cells, primarily brain resident macrophages called microglia, are also key regulators of brain development. This review summarizes the current state of knowledge regarding microglia in brain development, with particular emphasis on how microglia during development are distinct from microglia later in life. We also summarize the effects of early life perturbations on microglia function in the developing brain, the role that biological sex plays in microglia function, and the potential role that microglia may play in developmental brain disorders. Finally, given how new the field of developmental neuroimmunology is, we highlight what has yet to be learned about how innate immune cells shape the development of brain and behavior.

Brain development and the nature versus nurture debate.

PubMed

Stiles, Joan

2011-01-01

Over the past three decades, developmental neurobiologists have made tremendous progress in defining basic principles of brain development. This work has changed the way we think about how brains develop. Thirty years ago, the dominant model was strongly deterministic. The relationship between brain and behavioral development was viewed as unidirectional; that is, brain maturation enables behavioral development. The advent of modern neurobiological methods has provided overwhelming evidence that it is the interaction of genetic factors and the experience of the individual that guides and supports brain development. Brains do not develop normally in the absence of critical genetic signaling, and they do not develop normally in the absence of essential environmental input. The fundamental facts about brain development should be of critical importance to neuropsychologists trying to understand the relationship between brain and behavioral development. However, the underlying assumptions of most contemporary psychological models reflect largely outdated ideas about how the biological system develops and what it means for something to be innate. Thus, contemporary models of brain development challenge the foundational constructs of the nature versus nurture formulation in psychology. The key to understanding the origins and emergence of both the brain and behavior lies in understanding how inherited and environmental factors are engaged in the dynamic and interactive processes that define and guide development of the neurobehavioral system. Copyright © 2011 Elsevier B.V. All rights reserved.

Influence of O-methylated metabolite penetrating the blood-brain barrier to estimation of dopamine synthesis capacity in human L-[β-(11)C]DOPA PET.

PubMed

Matsubara, Keisuke; Ikoma, Yoko; Okada, Maki; Ibaraki, Masanobu; Suhara, Tetsuya; Kinoshita, Toshibumi; Ito, Hiroshi

2014-02-01

O-methyl metabolite (L-[β-(11)C]OMD) of (11)C-labeled L-3,4-dihydroxyphenylalanine (L-[β-(11)C]DOPA) can penetrate into brain tissue through the blood-brain barrier, and can complicate the estimation of dopamine synthesis capacity by positron emission tomography (PET) study with L-[β-(11)C]DOPA. We evaluated the impact of L-[β-(11)C]OMD on the estimation of the dopamine synthesis capacity in a human L-[β-(11)C]DOPA PET study. The metabolite correction with mathematical modeling of L-[β-(11)C]OMD kinetics in a reference region without decarboxylation and further metabolism, proposed by a previous [(18)F]FDOPA PET study, were implemented to estimate radioactivity of tissue L-[β-(11)C]OMD in 10 normal volunteers. The component of L-[β-(11)C]OMD in tissue time-activity curves (TACs) in 10 regions were subtracted by the estimated radioactivity of L-[β-(11)C]OMD. To evaluate the influence of omitting blood sampling and metabolite correction, relative dopamine synthesis rate (kref) was estimated by Gjedde-Patlak analysis with reference tissue input function, as well as the net dopamine synthesis rate (Ki) by Gjedde-Patlak analysis with the arterial input function and TAC without and with metabolite correction. Overestimation of Ki was observed without metabolite correction. However, the kref and Ki with metabolite correction were significantly correlated. These data suggest that the influence of L-[β-(11)C]OMD is minimal for the estimation of kref as dopamine synthesis capacity.

Development of a practical image-based scatter correction method for brain perfusion SPECT: comparison with the TEW method.

PubMed

Shidahara, Miho; Watabe, Hiroshi; Kim, Kyeong Min; Kato, Takashi; Kawatsu, Shoji; Kato, Rikio; Yoshimura, Kumiko; Iida, Hidehiro; Ito, Kengo

2005-10-01

An image-based scatter correction (IBSC) method was developed to convert scatter-uncorrected into scatter-corrected SPECT images. The purpose of this study was to validate this method by means of phantom simulations and human studies with 99mTc-labeled tracers, based on comparison with the conventional triple energy window (TEW) method. The IBSC method corrects scatter on the reconstructed image I(mub)AC with Chang's attenuation correction factor. The scatter component image is estimated by convolving I(mub)AC with a scatter function followed by multiplication with an image-based scatter fraction function. The IBSC method was evaluated with Monte Carlo simulations and 99mTc-ethyl cysteinate dimer SPECT human brain perfusion studies obtained from five volunteers. The image counts and contrast of the scatter-corrected images obtained by the IBSC and TEW methods were compared. Using data obtained from the simulations, the image counts and contrast of the scatter-corrected images obtained by the IBSC and TEW methods were found to be nearly identical for both gray and white matter. In human brain images, no significant differences in image contrast were observed between the IBSC and TEW methods. The IBSC method is a simple scatter correction technique feasible for use in clinical routine.

Spatiotemporal Neural Dynamics of Word Understanding in 12- to 18-Month-Old-Infants

PubMed Central

Leonard, Matthew K.; Brown, Timothy T.; Hagler, Donald J.; Curran, Megan; Dale, Anders M.; Elman, Jeffrey L.; Halgren, Eric

2011-01-01

Learning words is central in human development. However, lacking clear evidence for how or where language is processed in the developing brain, it is unknown whether these processes are similar in infants and adults. Here, we use magnetoencephalography in combination with high-resolution structural magnetic resonance imaging to noninvasively estimate the spatiotemporal distribution of word-selective brain activity in 12- to 18-month-old infants. Infants watched pictures of common objects and listened to words that they understood. A subset of these infants also listened to familiar words compared with sensory control sounds. In both experiments, words evoked a characteristic event-related brain response peaking ∼400 ms after word onset, which localized to left frontotemporal cortices. In adults, this activity, termed the N400m, is associated with lexico-semantic encoding. Like adults, we find that the amplitude of the infant N400m is also modulated by semantic priming, being reduced to words preceded by a semantically related picture. These findings suggest that similar left frontotemporal areas are used for encoding lexico-semantic information throughout the life span, from the earliest stages of word learning. Furthermore, this ontogenetic consistency implies that the neurophysiological processes underlying the N400m may be important both for understanding already known words and for learning new words. PMID:21209121

The direct analysis of drug distribution of rotigotine-loaded microspheres from tissue sections by LESA coupled with tandem mass spectrometry.

PubMed

Xu, Li-Xiao; Wang, Tian-Tian; Geng, Yin-Yin; Wang, Wen-Yan; Li, Yin; Duan, Xiao-Kun; Xu, Bin; Liu, Charles C; Liu, Wan-Hui

2017-09-01

The direct analysis of drug distribution of rotigotine-loaded microspheres (RoMS) from tissue sections by liquid extraction surface analysis (LESA) coupled with tandem mass spectrometry (MS/MS) was demonstrated. The RoMS distribution in rat tissues assessed by the ambient LESA-MS/MS approach without extensive or tedious sample pretreatment was compared with that obtained by a conventional liquid chromatography tandem mass spectrometry (LC-MS/MS) method in which organ excision and subsequent solvent extraction were commonly employed before analysis. Results obtained from the two were well correlated for a majority of the organs, such as muscle, liver, stomach, and hippocampus. The distribution of RoMS in the brain, however, was found to be mainly focused in the hippocampus and striatum regions as shown by the LESA-imaged profiles. The LESA approach we developed is sensitive enough, with an estimated LLOQ at 0.05 ng/mL of rotigotine in brain tissue, and information-rich with minimal sample preparation, suitable, and promising in assisting the development of new drug delivery systems for controlled drug release and protection. Graphical abstract Workflow for the LESA-MS/MS imaging of brain tissue section after intramuscular RoMS administration.

["Dieu et cerveau, rien que Dieu et cerveau!" Johann Gottfried von Herder (1744-1803) and the neurosciences of this time].

PubMed

Stahnisch, Frank

2007-01-01

The impact of Johann Gottfried von Herder on the broad spectrum of the history of ideas can hardly be estimated by separate categories derived from individual disciplines. It transcends the spheres of philosophy, theology, historiography and even medical anthropology--also because Herder, unlike many of his contemporary philosophers and hommes de lettres, was particularly interested in the neurophysiological and -anatomical investigations of his time. Herder's universal interest in human learning is reflected in numerous personal contacts to contemporary academic scholars and natural scientists, such as the Swiss theologian Johann Caspar Lavater, whose physiognomic doctrine mapped out a comprehensive research programme on character analysis, or the Mainz anatomist Samuel Thomas von Soemmering. Herder tightly received the latter's assumption about the interplay between the human soul and the anatomy of the brain. In this article, it shall be demonstrated that Herder's neurophilosophy was primarily influenced by a "pandynamic assumption of nature" and that it designated the brain centrally as a "working tool of God"--right between the human faculties of rationality, feeling and bodily development. The attractiveness of this concept to both basic brain research and clinical neurology was a result of his anthropological approach which combined latest developments in the natural sciences with a central perspective on the human sciences.

Multivariate Granger causality: an estimation framework based on factorization of the spectral density matrix

PubMed Central

Wen, Xiaotong; Rangarajan, Govindan; Ding, Mingzhou

2013-01-01

Granger causality is increasingly being applied to multi-electrode neurophysiological and functional imaging data to characterize directional interactions between neurons and brain regions. For a multivariate dataset, one might be interested in different subsets of the recorded neurons or brain regions. According to the current estimation framework, for each subset, one conducts a separate autoregressive model fitting process, introducing the potential for unwanted variability and uncertainty. In this paper, we propose a multivariate framework for estimating Granger causality. It is based on spectral density matrix factorization and offers the advantage that the estimation of such a matrix needs to be done only once for the entire multivariate dataset. For any subset of recorded data, Granger causality can be calculated through factorizing the appropriate submatrix of the overall spectral density matrix. PMID:23858479

Long-term valproic acid exposure increases the number of neocortical neurons in the developing rat brain. A possible new animal model of autism.

PubMed

Sabers, Anne; Bertelsen, Freja C B; Scheel-Krüger, Jørgen; Nyengaard, Jens R; Møller, Arne

2014-09-19

The aim of this study was to test the hypothesis that long-term fetal valproic acid (VPA) exposure at doses relevant to the human clinic interferes with normal brain development. Pregnant rats were given intraperitoneal injections of VPA (20mg/kg or 100mg/kg) continuously during the last 9-12 days of pregnancy and during the lactation period until sacrifice on the 23rd postnatal day. Total number of neocortical neurons was estimated using the optical fractionator and frontal cortical thicknesses were sampled in VPA exposed pups compared with an unexposed control group. We found that pups exposed to 20mg/kg and 100mg/kg doses of VPA had statistically significant higher total number of neurons in neocortex by 15.8% and 12.3%, respectively (p<0.05) compared to controls amounting to 15.5×10(6) neocortical neurons (p<0.01). There was no statistical difference between the two VPA groups. Pups exposed to100mg/kg, but not to 20mg/kg VPA displayed a significant (p<0.05) broader (7.5%) of frontal cortical thickness compared to controls. Our results support the hypothesis that fetal exposure of VPA may interfere with normal brain development by disturbing neocortical organization, resulting in overgrowth of frontal lobes and increased neuronal cell numbers. The results indirectly suggest that prenatal VPA may contribute as a causative factor in the brain developmental disturbances equivalent to those seen in human autism spectrum disorders. We therefore suggest that this version of the VPA model may provide a translational model of autism. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Leveraging Clinical Imaging Archives for Radiomics: Reliability of Automated Methods for Brain Volume Measurement.

PubMed

Adduru, Viraj R; Michael, Andrew M; Helguera, Maria; Baum, Stefi A; Moore, Gregory J

2017-09-01

Purpose To validate the use of thick-section clinically acquired magnetic resonance (MR) imaging data for estimating total brain volume (TBV), gray matter (GM) volume (GMV), and white matter (WM) volume (WMV) by using three widely used automated toolboxes: SPM ( www.fil.ion.ucl.ac.uk/spm/ ), FreeSurfer ( surfer.nmr.mgh.harvard.edu ), and FSL (FMRIB software library; Oxford Centre for Functional MR Imaging of the Brain, Oxford, England, https://fsl.fmrib.ox.ac.uk/fsl ). Materials and Methods MR images from a clinical archive were used and data were deidentified. The three methods were applied to estimate brain volumes from thin-section research-quality brain MR images and routine thick-section clinical MR images acquired from the same 38 patients (age range, 1-71 years; mean age, 22 years; 11 women). By using these automated methods, TBV, GMV, and WMV were estimated. Thin- versus thick-section volume comparisons were made for each method by using intraclass correlation coefficients (ICCs). Results SPM exhibited excellent ICCs (0.97, 0.85, and 0.83 for TBV, GMV, and WMV, respectively). FSL exhibited ICCs of 0.69, 0.51, and 0.60 for TBV, GMV, and WMV, respectively, but they were lower than with SPM. FreeSurfer exhibited excellent ICC of 0.63 only for TBV. Application of SPM's voxel-based morphometry on the modulated images of thin-section images and interpolated thick-section images showed fair to excellent ICCs (0.37-0.98) for the majority of brain regions (88.47% [306924 of 346916 voxels] of WM and 80.35% [377 282 of 469 502 voxels] of GM). Conclusion Thick-section clinical-quality MR images can be reliably used for computing quantitative brain metrics such as TBV, GMV, and WMV by using SPM. © RSNA, 2017 Online supplemental material is available for this article.

Coding “What” and “When” in the Archer Fish Retina

PubMed Central

Vasserman, Genadiy; Shamir, Maoz; Ben Simon, Avi; Segev, Ronen

2010-01-01

Traditionally, the information content of the neural response is quantified using statistics of the responses relative to stimulus onset time with the assumption that the brain uses onset time to infer stimulus identity. However, stimulus onset time must also be estimated by the brain, making the utility of such an approach questionable. How can stimulus onset be estimated from the neural responses with sufficient accuracy to ensure reliable stimulus identification? We address this question using the framework of colour coding by the archer fish retinal ganglion cell. We found that stimulus identity, “what”, can be estimated from the responses of best single cells with an accuracy comparable to that of the animal's psychophysical estimation. However, to extract this information, an accurate estimation of stimulus onset is essential. We show that stimulus onset time, “when”, can be estimated using a linear-nonlinear readout mechanism that requires the response of a population of 100 cells. Thus, stimulus onset time can be estimated using a relatively simple readout. However, large nerve cell populations are required to achieve sufficient accuracy. PMID:21079682

Influence of the partial volume correction method on 18F-fluorodeoxyglucose brain kinetic modelling from dynamic PET images reconstructed with resolution model based OSEM

PubMed Central

Bowen, Spencer L.; Byars, Larry G.; Michel, Christian J.; Chonde, Daniel B.; Catana, Ciprian

2014-01-01

Kinetic parameters estimated from dynamic 18F-fluorodeoxyglucose PET acquisitions have been used frequently to assess brain function in humans. Neglecting partial volume correction (PVC) for a dynamic series has been shown to produce significant bias in model estimates. Accurate PVC requires a space-variant model describing the reconstructed image spatial point spread function (PSF) that accounts for resolution limitations, including non-uniformities across the field of view due to the parallax effect. For OSEM, image resolution convergence is local and influenced significantly by the number of iterations, the count density, and background-to-target ratio. As both count density and background-to-target values for a brain structure can change during a dynamic scan, the local image resolution may also concurrently vary. When PVC is applied post-reconstruction the kinetic parameter estimates may be biased when neglecting the frame-dependent resolution. We explored the influence of the PVC method and implementation on kinetic parameters estimated by fitting 18F-fluorodeoxyglucose dynamic data acquired on a dedicated brain PET scanner and reconstructed with and without PSF modelling in the OSEM algorithm. The performance of several PVC algorithms was quantified with a phantom experiment, an anthropomorphic Monte Carlo simulation, and a patient scan. Using the last frame reconstructed image only for regional spread function (RSF) generation, as opposed to computing RSFs for each frame independently, and applying perturbation GTM PVC with PSF based OSEM produced the lowest magnitude bias kinetic parameter estimates in most instances, although at the cost of increased noise compared to the PVC methods utilizing conventional OSEM. Use of the last frame RSFs for PVC with no PSF modelling in the OSEM algorithm produced the lowest bias in CMRGlc estimates, although by less than 5% in most cases compared to the other PVC methods. The results indicate that the PVC implementation and choice of PSF modelling in the reconstruction can significantly impact model parameters. PMID:24052021

Rey's Auditory Verbal Learning Test scores can be predicted from whole brain MRI in Alzheimer's disease.

PubMed

Moradi, Elaheh; Hallikainen, Ilona; Hänninen, Tuomo; Tohka, Jussi

2017-01-01

Rey's Auditory Verbal Learning Test (RAVLT) is a powerful neuropsychological tool for testing episodic memory, which is widely used for the cognitive assessment in dementia and pre-dementia conditions. Several studies have shown that an impairment in RAVLT scores reflect well the underlying pathology caused by Alzheimer's disease (AD), thus making RAVLT an effective early marker to detect AD in persons with memory complaints. We investigated the association between RAVLT scores (RAVLT Immediate and RAVLT Percent Forgetting) and the structural brain atrophy caused by AD. The aim was to comprehensively study to what extent the RAVLT scores are predictable based on structural magnetic resonance imaging (MRI) data using machine learning approaches as well as to find the most important brain regions for the estimation of RAVLT scores. For this, we built a predictive model to estimate RAVLT scores from gray matter density via elastic net penalized linear regression model. The proposed approach provided highly significant cross-validated correlation between the estimated and observed RAVLT Immediate (R = 0.50) and RAVLT Percent Forgetting (R = 0.43) in a dataset consisting of 806 AD, mild cognitive impairment (MCI) or healthy subjects. In addition, the selected machine learning method provided more accurate estimates of RAVLT scores than the relevance vector regression used earlier for the estimation of RAVLT based on MRI data. The top predictors were medial temporal lobe structures and amygdala for the estimation of RAVLT Immediate and angular gyrus, hippocampus and amygdala for the estimation of RAVLT Percent Forgetting. Further, the conversion of MCI subjects to AD in 3-years could be predicted based on either observed or estimated RAVLT scores with an accuracy comparable to MRI-based biomarkers.

Epigenetic Age Acceleration Assessed with Human White-Matter Images.

PubMed

Hodgson, Karen; Carless, Melanie A; Kulkarni, Hemant; Curran, Joanne E; Sprooten, Emma; Knowles, Emma E; Mathias, Samuel; Göring, Harald H H; Yao, Nailin; Olvera, Rene L; Fox, Peter T; Almasy, Laura; Duggirala, Ravi; Blangero, John; Glahn, David C

2017-05-03

The accurate estimation of age using methylation data has proved a useful and heritable biomarker, with acceleration in epigenetic age predicting a number of age-related phenotypes. Measures of white matter integrity in the brain are also heritable and highly sensitive to both normal and pathological aging processes across adulthood. We consider the phenotypic and genetic interrelationships between epigenetic age acceleration and white matter integrity in humans. Our goal was to investigate processes that underlie interindividual variability in age-related changes in the brain. Using blood taken from a Mexican-American extended pedigree sample ( n = 628; age = 23.28-93.11 years), epigenetic age was estimated using the method developed by Horvath (2013). For n = 376 individuals, diffusion tensor imaging scans were also available. The interrelationship between epigenetic age acceleration and global white matter integrity was investigated with variance decomposition methods. To test for neuroanatomical specificity, 16 specific tracts were additionally considered. We observed negative phenotypic correlations between epigenetic age acceleration and global white matter tract integrity (ρ pheno = -0.119, p = 0.028), with evidence of shared genetic (ρ gene = -0.463, p = 0.013) but not environmental influences. Negative phenotypic and genetic correlations with age acceleration were also seen for a number of specific white matter tracts, along with additional negative phenotypic correlations between granulocyte abundance and white matter integrity. These findings (i.e., increased acceleration in epigenetic age in peripheral blood correlates with reduced white matter integrity in the brain and shares common genetic influences) provide a window into the neurobiology of aging processes within the brain and a potential biomarker of normal and pathological brain aging. SIGNIFICANCE STATEMENT Epigenetic measures can be used to predict age with a high degree of accuracy and so capture acceleration in biological age, relative to chronological age. The white matter tracts within the brain are also highly sensitive to aging processes. We show that increased biological aging (measured using epigenetic data from blood samples) is correlated with reduced integrity of white matter tracts within the human brain (measured using diffusion tensor imaging) with data from a large sample of Mexican-American families. Given the family design of the sample, we are also able to demonstrate that epigenetic aging and white matter tract integrity also share common genetic influences. Therefore, epigenetic age may be a potential, and accessible, biomarker of brain aging. Copyright © 2017 the authors 0270-6474/17/374735-09$15.00/0.

Genomic connectivity networks based on the BrainSpan atlas of the developing human brain

NASA Astrophysics Data System (ADS)

Mahfouz, Ahmed; Ziats, Mark N.; Rennert, Owen M.; Lelieveldt, Boudewijn P. F.; Reinders, Marcel J. T.

2014-03-01

The human brain comprises systems of networks that span the molecular, cellular, anatomic and functional levels. Molecular studies of the developing brain have focused on elucidating networks among gene products that may drive cellular brain development by functioning together in biological pathways. On the other hand, studies of the brain connectome attempt to determine how anatomically distinct brain regions are connected to each other, either anatomically (diffusion tensor imaging) or functionally (functional MRI and EEG), and how they change over development. A global examination of the relationship between gene expression and connectivity in the developing human brain is necessary to understand how the genetic signature of different brain regions instructs connections to other regions. Furthermore, analyzing the development of connectivity networks based on the spatio-temporal dynamics of gene expression provides a new insight into the effect of neurodevelopmental disease genes on brain networks. In this work, we construct connectivity networks between brain regions based on the similarity of their gene expression signature, termed "Genomic Connectivity Networks" (GCNs). Genomic connectivity networks were constructed using data from the BrainSpan Transcriptional Atlas of the Developing Human Brain. Our goal was to understand how the genetic signatures of anatomically distinct brain regions relate to each other across development. We assessed the neurodevelopmental changes in connectivity patterns of brain regions when networks were constructed with genes implicated in the neurodevelopmental disorder autism (autism spectrum disorder; ASD). Using graph theory metrics to characterize the GCNs, we show that ASD-GCNs are relatively less connected later in development with the cerebellum showing a very distinct expression of ASD-associated genes compared to other brain regions.

Consensus document on European brain research.

PubMed

Olesen, Jes; Baker, Mary G; Freund, Tamas; di Luca, Monica; Mendlewicz, Julien; Ragan, Ian; Westphal, Manfred

2006-08-01

Brain disease psychiatric and neurologic disease combined represents a considerable social and economic burden in Europe. Data collected by the World Health Organization (WHO) suggest that brain diseases are responsible for 35% of Europe's total disease burden. An analysis of all health economic studies of brain diseases in Europe, published by the European Brain Council (EBC) in June 2005, estimated the total cost of brain disease in Europe in 2004 to be Euro 386 billion. That burden is set to grow, mainly due to the fact that the European population is ageing. Investment in brain sciences does not match that burden now, let alone in the future. Brain research received only 8% of the life science budget in the European Commission's Fifth Framework Programme, which represents less than 0.01% of the annual cost of brain disorders for that period. Over the last decade, Europe has been losing ground to the USA and Japan in terms of both basic and clinical research. Many of Europe's young researchers are taking up posts in the USA and staying there. Big pharmaceutical companies are fleeing Europe for the USA, taking their drug development programmes with them. Research in the brain sciences now holds the promise of therapies that halt and even reverse neurodegeneration, of better diagnostic tools, neural prostheses for the paralysed and drugs for depression and anxiety that are tailored to the individual, thereby eliminating or reducing side effects. Our growing understanding of the normal brain could lead to better prevention of brain disease and to more effective teaching methods. The need for innovative treatments has never been greater, and Europe boasts clusters of excellent researchers in biotechnology who could collaborate with brain scientists and the pharmaceutical industry to realise this promise. But if Europe is to seize these opportunities and meet the challenge of brain disease, it needs to go forward on the basis of greater collaboration between countries, greater collaboration between industry, academia and patient organisations, and increased investment in the brain sciences. The EBC was formed in 2002 to bring together scientists, clinicians, the pharmaceutical industry, charities and patient organisations from all over Europe to campaign for these goals. It takes a novel, bottom-up approach to research policy, and in developing this consensus document, it aims to promote a greater and more focused effort in this area, to improve public understanding of the brain sciences and above all, to support brain research as a priority under the European Commission's Seventh Framework Programme (FP7, 2007-2013). The research programme outlined here was first conceived by the EBC board. An outline was sent to all member organisations and a number of individual experts for comments. Following that, a table of contents was developed. The 45 research themes were written by groups of experts from across Europe who represent a wide range of disciplines. Each one contains a proposal for future research on a specific brain-related theme which the EBC believes could form the basis of one or more integrated projects or strategic targeted research projects (STREP) funded under FP7. The EBC has deliberately focused on the major diseases and then described the basic research needed to understand and treat or perhaps even cure those diseases. The programme is therefore constructed "from man to molecule" and not the other way round, with equal importance attached to basic and clinical research. The EBC suggests that each of the proposed integrated projects or STREP should be awarded a budget in the order of Euro 10 to 15 million. In addition, brain research should be treated as an important element of many other parts of FP7, such as the European Research Council and research programmes on information technology and the causes of violence. Any research programme that concerns human behaviour should, by definition, take account of brain research. The EBC envisages that the priority for brain research it proposes at the European level will translate into higher priority for brain research at the national level, and this document may also serve as a starting point for the development of national consensus programmes. It seems likely that consensus conferences on brain research in Europe may further develop the themes and ideas discussed here. An EBC task force may also be established to further the consensus process. In general, increasing funding in the brain sciences would bring enormous economic returns by lightening the burden on healthcare systems and increasing the productivity of affected individuals-and might easily pay for itself. The human and social returns of such an investment are inestimable. And the time to act is now.

Choline supply of preterm infants: assessment of dietary intake and pathophysiological considerations.

PubMed

Bernhard, Wolfgang; Full, Anna; Arand, Jörg; Maas, Christoph; Poets, Christian F; Franz, Axel R

2013-04-01

Choline forms the head group of phosphatidylcholines, comprising 40-50 % of cellular membranes and 70-95 % of phospholipids in surfactant, bile, and lipoproteins. Moreover, choline serves as the precursor of acetylcholine and is important for brain differentiation and function. While accepted as essential for fetal and neonatal development, its role in preterm infant nutrition has not yet gained much attention. The adequate intake of choline of preterm infants was estimated from international recommendations for infants, children, and adults. Choline intake relative to other nutrients was determined retrospectively in all inborn infants below 1,000 g (extremely low birth weight) or below 28 weeks gestational age, admitted to our department in 2006 and 2007 (N = 93). Estimation of adequate intake showed that children with 290 g body weight need more choline than those with 1,200 g (31.4 and 25.2 mg/kg/day, respectively). Day-by-day variability was high for all nutrient intakes including choline. In contrast to the continuous intrauterine choline delivery, median supply reached a plateau at d11 (21.7 mg/kg/day; 25th/75th percentile: 19.6; 23.9). Individual choline supply at d0-d1 and d2-d3 was <10 mg/kg/day in 100 and 69 % of infants, respectively. Furthermore, intakes <10 mg/kg/day were frequently observed beyond day 11. Median adequate intakes (27.4 mg/kg/day at 735 g body weight) were achieved in <2 %. Nutritional intake of choline in this cohort of preterm infants was frequently less than the estimated adequate intake, with particular shortage until postnatal d10. Because choline is important for brain development, future studies are needed to investigate the effects of adequate nutritional choline intake on long-term neurodevelopment in VLBW infants.

Risk of Leptomeningeal Disease in Patients Treated With Stereotactic Radiosurgery Targeting the Postoperative Resection Cavity for Brain Metastases

DOE Office of Scientific and Technical Information (OSTI.GOV)

Atalar, Banu; Modlin, Leslie A.; Choi, Clara Y.H.

Purpose: We sought to determine the risk of leptomeningeal disease (LMD) in patients treated with stereotactic radiosurgery (SRS) targeting the postsurgical resection cavity of a brain metastasis, deferring whole-brain radiation therapy (WBRT) in all patients. Methods and Materials: We retrospectively reviewed 175 brain metastasis resection cavities in 165 patients treated from 1998 to 2011 with postoperative SRS. The cumulative incidence rates, with death as a competing risk, of LMD, local failure (LF), and distant brain parenchymal failure (DF) were estimated. Variables associated with LMD were evaluated, including LF, DF, posterior fossa location, resection type (en-bloc vs piecemeal or unknown), andmore » histology (lung, colon, breast, melanoma, gynecologic, other). Results: With a median follow-up of 12 months (range, 1-157 months), median overall survival was 17 months. Twenty-one of 165 patients (13%) developed LMD at a median of 5 months (range, 2-33 months) following SRS. The 1-year cumulative incidence rates, with death as a competing risk, were 10% (95% confidence interval [CI], 6%-15%) for developing LF, 54% (95% CI, 46%-61%) for DF, and 11% (95% CI, 7%-17%) for LMD. On univariate analysis, only breast cancer histology (hazard ratio, 2.96) was associated with an increased risk of LMD. The 1-year cumulative incidence of LMD was 24% (95% CI, 9%-41%) for breast cancer compared to 9% (95% CI, 5%-14%) for non-breast histology (P=.004). Conclusions: In patients treated with SRS targeting the postoperative cavity following resection, those with breast cancer histology were at higher risk of LMD. It is unknown whether the inclusion of whole-brain irradiation or novel strategies such as preresection SRS would improve this risk or if the rate of LMD is inherently higher with breast histology.« less

Real-time state estimation in a flight simulator using fNIRS.

PubMed

Gateau, Thibault; Durantin, Gautier; Lancelot, Francois; Scannella, Sebastien; Dehais, Frederic

2015-01-01

Working memory is a key executive function for flying an aircraft. This function is particularly critical when pilots have to recall series of air traffic control instructions. However, working memory limitations may jeopardize flight safety. Since the functional near-infrared spectroscopy (fNIRS) method seems promising for assessing working memory load, our objective is to implement an on-line fNIRS-based inference system that integrates two complementary estimators. The first estimator is a real-time state estimation MACD-based algorithm dedicated to identifying the pilot's instantaneous mental state (not-on-task vs. on-task). It does not require a calibration process to perform its estimation. The second estimator is an on-line SVM-based classifier that is able to discriminate task difficulty (low working memory load vs. high working memory load). These two estimators were tested with 19 pilots who were placed in a realistic flight simulator and were asked to recall air traffic control instructions. We found that the estimated pilot's mental state matched significantly better than chance with the pilot's real state (62% global accuracy, 58% specificity, and 72% sensitivity). The second estimator, dedicated to assessing single trial working memory loads, led to 80% classification accuracy, 72% specificity, and 89% sensitivity. These two estimators establish reusable blocks for further fNIRS-based passive brain computer interface development.

Response of avian embryonic brain to spatially segmented x-ray microbeams.

PubMed

Dilmanian, F A; Morris, G M; Le Duc, G; Huang, X; Ren, B; Bacarian, T; Allen, J C; Kalef-Ezra, J; Orion, I; Rosen, E M; Sandhu, T; Sathé, P; Wu, X Y; Zhong, Z; Shivaprasad, H L

2001-05-01

Duck embryo was studied as a model for assessing the effects of microbeam radiation therapy (MRT) on the human infant brain. Because of the high risk of radiation-induced disruption of the developmental process in the immature brain, conventional wide-beam radiotherapy of brain tumors is seldom carried out in infants under the age of three. Other types of treatment for pediatric brain tumors are frequently ineffective. Recent findings from studies in Grenoble on the brain of suckling rats indicate that MRT could be of benefit for the treatment of early childhood tumors. In our studies, duck embryos were irradiated at 3-4 days prior to hatching. Irradiation was carried out using a single exposure of synchrotron-generated X-rays, either in the form of parallel microplanar beams (microbeams), or as non-segmented broad beam. The individual microplanar beams had a width of 27 microm and height of 11 mm, and a center-to-center spacing of 100 microm. Doses to the exposed areas of embryo brain were 40, 80, 160 and 450 Gy (in-slice dose) for the microbeam, and 6, 12 and 18 Gy for the broad beam. The biological end point employed in the study was ataxia. This neurological symptom of radiation damage to the brain developed within 75 days of hatching. Histopathological analysis of brain tissue did not reveal any radiation induced lesions for microbeam doses of 40-160 Gy (in-slice), although some incidences of ataxia were observed in that dose group. However, severe brain lesions did occur in animals in the 450 Gy microbeam dose groups, and mild lesions in the 18 Gy broad beam dose group. These results indicate that embryonic duck brain has an appreciably higher tolerance to the microbeam modality, as compared to the broad beam modality. When the microbeam dose was normalized to the full volume of the irradiated tissue. i.e., the dose averaged over microbeams and the space between the microbeams, brain tolerance was estimated to be about three times higher to microbeam irradiation as compared with broad beam irradiation.

A New Life, A New Brain.

ERIC Educational Resources Information Center

Eliot, Lise

2001-01-01

Discusses the connection between brain development and human educational needs based on neuroscience research. Considers brain development from conception, including cell structure, myelination, and regional development of the brain, stressing the importance of a child's early environment and the prenatal vulnerability of the brain. (JPB)

Variation in orbitofrontal cortex volume: relation to sex, emotion regulation and affect.

PubMed

Welborn, B Locke; Papademetris, Xenophon; Reis, Deidre L; Rajeevan, Nallakkandi; Bloise, Suzanne M; Gray, Jeremy R

2009-12-01

Sex differences in brain structure have been examined extensively but are not completely understood, especially in relation to possible functional correlates. Our two aims in this study were to investigate sex differences in brain structure, and to investigate a possible relation between orbitofrontal cortex subregions and affective individual differences. We used tensor-based morphometry to estimate local brain volume from MPRAGE images in 117 healthy right-handed adults (58 female), age 18-40 years. We entered estimates of local brain volume as the dependent variable in a GLM, controlling for age, intelligence and whole-brain volume. Men had larger left planum temporale. Women had larger ventromedial prefrontal cortex (vmPFC), right lateral orbitofrontal (rlOFC), cerebellum, and bilateral basal ganglia and nearby white matter. vmPFC but not rlOFC volume covaried with self-reported emotion regulation strategies (reappraisal, suppression), expressivity of positive emotions (but not of negative), strength of emotional impulses, and cognitive but not somatic anxiety. vmPFC volume statistically mediated sex differences in emotion suppression. The results confirm prior reports of sex differences in orbitofrontal cortex structure, and are the first to show that normal variation in vmPFC volume is systematically related to emotion regulation and affective individual differences.

CEREBRA: a 3-D visualization tool for brain network extracted from fMRI data.

PubMed

Nasir, Baris; Yarman Vural, Fatos T

2016-08-01

In this paper, we introduce a new tool, CEREBRA, to visualize the 3D network of human brain, extracted from the fMRI data. The tool aims to analyze the brain connectivity by representing the selected voxels as the nodes of the network. The edge weights among the voxels are estimated by considering the relationships among the voxel time series. The tool enables the researchers to observe the active brain regions and the interactions among them by using graph theoretic measures, such as, the edge weight and node degree distributions. CEREBRA provides an interactive interface with basic display and editing options for the researchers to study their hypotheses about the connectivity of the brain network. CEREBRA interactively simplifies the network by selecting the active voxels and the most correlated edge weights. The researchers may remove the voxels and edges by using local and global thresholds selected on the window. The built-in graph reduction algorithms are then eliminate the irrelevant regions, voxels and edges and display various properties of the network. The toolbox is capable of space-time representation of the voxel time series and estimated arc weights by using the animated heat maps.

Python Executable Script for Estimating Two Effective Parameters to Individualize Brain-Computer Interfaces: Individual Alpha Frequency and Neurophysiological Predictor.

PubMed

Alonso-Valerdi, Luz María

2016-01-01

A brain-computer interface (BCI) aims to establish communication between the human brain and a computing system so as to enable the interaction between an individual and his environment without using the brain output pathways. Individuals control a BCI system by modulating their brain signals through mental tasks (e.g., motor imagery or mental calculation) or sensory stimulation (e.g., auditory, visual, or tactile). As users modulate their brain signals at different frequencies and at different levels, the appropriate characterization of those signals is necessary. The modulation of brain signals through mental tasks is furthermore a skill that requires training. Unfortunately, not all the users acquire such skill. A practical solution to this problem is to assess the user probability of controlling a BCI system. Another possible solution is to set the bandwidth of the brain oscillations, which is highly sensitive to the users' age, sex and anatomy. With this in mind, NeuroIndex, a Python executable script, estimates a neurophysiological prediction index and the individual alpha frequency (IAF) of the user in question. These two parameters are useful to characterize the user EEG signals, and decide how to go through the complex process of adapting the human brain and the computing system on the basis of previously proposed methods. NeuroIndeX is not only the implementation of those methods, but it also complements the methods each other and provides an alternative way to obtain the prediction parameter. However, an important limitation of this application is its dependency on the IAF value, and some results should be interpreted with caution. The script along with some electroencephalographic datasets are available on a GitHub repository in order to corroborate the functionality and usability of this application.

Python Executable Script for Estimating Two Effective Parameters to Individualize Brain-Computer Interfaces: Individual Alpha Frequency and Neurophysiological Predictor

PubMed Central

Alonso-Valerdi, Luz María

2016-01-01

A brain-computer interface (BCI) aims to establish communication between the human brain and a computing system so as to enable the interaction between an individual and his environment without using the brain output pathways. Individuals control a BCI system by modulating their brain signals through mental tasks (e.g., motor imagery or mental calculation) or sensory stimulation (e.g., auditory, visual, or tactile). As users modulate their brain signals at different frequencies and at different levels, the appropriate characterization of those signals is necessary. The modulation of brain signals through mental tasks is furthermore a skill that requires training. Unfortunately, not all the users acquire such skill. A practical solution to this problem is to assess the user probability of controlling a BCI system. Another possible solution is to set the bandwidth of the brain oscillations, which is highly sensitive to the users' age, sex and anatomy. With this in mind, NeuroIndex, a Python executable script, estimates a neurophysiological prediction index and the individual alpha frequency (IAF) of the user in question. These two parameters are useful to characterize the user EEG signals, and decide how to go through the complex process of adapting the human brain and the computing system on the basis of previously proposed methods. NeuroIndeX is not only the implementation of those methods, but it also complements the methods each other and provides an alternative way to obtain the prediction parameter. However, an important limitation of this application is its dependency on the IAF value, and some results should be interpreted with caution. The script along with some electroencephalographic datasets are available on a GitHub repository in order to corroborate the functionality and usability of this application. PMID:27445783

Safety evaluation of mercury based Ayurvedic formulation (Sidh Makardhwaj) on brain cerebrum, liver & kidney in rats

PubMed Central

Kumar, Gajendra; Srivastava, Amita; Sharma, Surinder Kumar; Gupta, Yogendra Kumar

2014-01-01

Background & objectives: Sidh Makardhwaj (SM) is a mercury based Ayurvedic formulation used in rheumatoid arthritis and neurological disorders. However, toxicity concerns due to mercury content are often raised. Therefore, the present study was carried out to evaluate the effect of SM on brain cerebrum, liver and kidney in rats. Methods: Graded doses of SM (10, 50, 100 mg/kg), mercuric chloride (1 mg/kg) and normal saline were administered orally to male Wistar rats for 28 days. Behavioural parameters were assessed on days 1, 7, 14 and 28 using Morris water maze, passive avoidance, elevated plus maze and rota rod. Liver and kidney function tests were done on day 28. Animals were sacrificed and brain cerebrum acetylcholinesterase activity, levels of malondialdehyde (MDA), reduced glutathione (GSH) in brain cerebrum, liver, kidney were estimated. The levels of mercury in brain cerebrum, liver and kidney were estimated and histopathology of these tissues was also performed. Results: SM in the doses used did not cause significant change in neurobehavioural parameters, brain cerebrum AChE activity, liver (ALT, AST, ALP bilirubin) and kidney (serum urea and creatinine) function tests as compared to control. The levels of mercury in brain cerebrum, liver, and kidney were found to be raised in dose dependent manner. However, the levels of MDA and GSH in these tissues did not show significant changes at doses of 10 and 50 mg/kg. Also, there was no histopathological change in cytoarchitecture of brain cerebrum, liver, and kidney tissues at doses of 10 and 50 mg/kg. Interpretation & conclusions: The findings of the present study suggest that Sidh Makardhwaj upto five times the equivalent human dose administered for 28 days did not show any toxicological effects on rat brain cerebrum, liver and kidney. PMID:24927349

Safety evaluation of mercury based Ayurvedic formulation (Sidh Makardhwaj) on brain cerebrum, liver & kidney in rats.

PubMed

Kumar, Gajendra; Srivastava, Amita; Sharma, Surinder Kumar; Gupta, Yogendra Kumar

2014-04-01

Sidh Makardhwaj (SM) is a mercury based Ayurvedic formulation used in rheumatoid arthritis and neurological disorders. However, toxicity concerns due to mercury content are often raised. Therefore, the present study was carried out to evaluate the effect of SM on brain cerebrum, liver and kidney in rats. Graded doses of SM (10, 50, 100 mg/kg), mercuric chloride (1 mg/kg) and normal saline were administered orally to male Wistar rats for 28 days. Behavioural parameters were assessed on days 1, 7, 14 and 28 using Morris water maze, passive avoidance, elevated plus maze and rota rod. Liver and kidney function tests were done on day 28. Animals were sacrificed and brain cerebrum acetylcholinesterase activity, levels of malondialdehyde (MDA), reduced glutathione (GSH) in brain cerebrum, liver, kidney were estimated. The levels of mercury in brain cerebrum, liver and kidney were estimated and histopathology of these tissues was also performed. SM in the doses used did not cause significant change in neurobehavioural parameters, brain cerebrum AChE activity, liver (ALT, AST, ALP bilirubin) and kidney (serum urea and creatinine) function tests as compared to control. The levels of mercury in brain cerebrum, liver, and kidney were found to be raised in dose dependent manner. However, the levels of MDA and GSH in these tissues did not show significant changes at doses of 10 and 50 mg/kg. Also, there was no histopathological change in cytoarchitecture of brain cerebrum, liver, and kidney tissues at doses of 10 and 50 mg/kg. The findings of the present study suggest that Sidh Makardhwaj upto five times the equivalent human dose administered for 28 days did not show any toxicological effects on rat brain cerebrum, liver and kidney.

Developmental implications of children's brain networks and learning.

PubMed

Chan, John S Y; Wang, Yifeng; Yan, Jin H; Chen, Huafu

2016-10-01

The human brain works as a synergistic system where information exchanges between functional neuronal networks. Rudimentary networks are observed in the brain during infancy. In recent years, the question of how functional networks develop and mature in children has been a hotly discussed topic. In this review, we examined the developmental characteristics of functional networks and the impacts of skill training on children's brains. We first focused on the general rules of brain network development and on the typical and atypical development of children's brain networks. After that, we highlighted the essentials of neural plasticity and the effects of learning on brain network development. We also discussed two important theoretical and practical concerns in brain network training. Finally, we concluded by presenting the significance of network training in typically and atypically developed brains.

Automatic detection and quantitative analysis of cells in the mouse primary motor cortex

NASA Astrophysics Data System (ADS)

Meng, Yunlong; He, Yong; Wu, Jingpeng; Chen, Shangbin; Li, Anan; Gong, Hui

2014-09-01

Neuronal cells play very important role on metabolism regulation and mechanism control, so cell number is a fundamental determinant of brain function. Combined suitable cell-labeling approaches with recently proposed three-dimensional optical imaging techniques, whole mouse brain coronal sections can be acquired with 1-μm voxel resolution. We have developed a completely automatic pipeline to perform cell centroids detection, and provided three-dimensional quantitative information of cells in the primary motor cortex of C57BL/6 mouse. It involves four principal steps: i) preprocessing; ii) image binarization; iii) cell centroids extraction and contour segmentation; iv) laminar density estimation. Investigations on the presented method reveal promising detection accuracy in terms of recall and precision, with average recall rate 92.1% and average precision rate 86.2%. We also analyze laminar density distribution of cells from pial surface to corpus callosum from the output vectorizations of detected cell centroids in mouse primary motor cortex, and find significant cellular density distribution variations in different layers. This automatic cell centroids detection approach will be beneficial for fast cell-counting and accurate density estimation, as time-consuming and error-prone manual identification is avoided.

Measuring specific receptor binding of a PET radioligand in human brain without pharmacological blockade: The genomic plot.

PubMed

Veronese, Mattia; Zanotti-Fregonara, Paolo; Rizzo, Gaia; Bertoldo, Alessandra; Innis, Robert B; Turkheimer, Federico E

2016-04-15

PET studies allow in vivo imaging of the density of brain receptor species. The PET signal, however, is the sum of the fraction of radioligand that is specifically bound to the target receptor and the non-displaceable fraction (i.e. the non-specifically bound radioligand plus the free ligand in tissue). Therefore, measuring the non-displaceable fraction, which is generally assumed to be constant across the brain, is a necessary step to obtain regional estimates of the specific fractions. The nondisplaceable binding can be directly measured if a reference region, i.e. a region devoid of any specific binding, is available. Many receptors are however widely expressed across the brain, and a true reference region is rarely available. In these cases, the nonspecific binding can be obtained after competitive pharmacological blockade, which is often contraindicated in humans. In this work we introduce the genomic plot for estimating the nondisplaceable fraction using baseline scans only. The genomic plot is a transformation of the Lassen graphical method in which the brain maps of mRNA transcripts of the target receptor obtained from the Allen brain atlas are used as a surrogate measure of the specific binding. Thus, the genomic plot allows the calculation of the specific and nondisplaceable components of radioligand uptake without the need of pharmacological blockade. We first assessed the statistical properties of the method with computer simulations. Then we sought ground-truth validation using human PET datasets of seven different neuroreceptor radioligands, where nonspecific fractions were either obtained separately using drug displacement or available from a true reference region. The population nondisplaceable fractions estimated by the genomic plot were very close to those measured by actual human blocking studies (mean relative difference between 2% and 7%). However, these estimates were valid only when mRNA expressions were predictive of protein levels (i.e. there were no significant post-transcriptional changes). This condition can be readily established a priori by assessing the correlation between PET and mRNA expression. Copyright © 2016 Elsevier Inc. All rights reserved.

Measuring specific receptor binding of a PET radioligand in human brain without pharmacological blockade: The genomic plot

PubMed Central

Veronese, Mattia; Zanotti-Fregonara, Paolo; Rizzo, Gaia; Bertoldo, Alessandra; Innis, Robert B.; Turkheimer, Federico E.

2016-01-01

PET studies allow in vivo imaging of the density of brain receptor species. The PET signal, however, is the sum of the fraction of radioligand that is specifically bound to the target receptor and the non-displaceable fraction (i.e. the non-specifically bound radioligand plus the free ligand in tissue). Therefore, measuring the non-displaceable fraction, which is generally assumed to be constant across the brain, is a necessary step to obtain regional estimates of the specific fractions. The nondisplaceable binding can be directly measured if a reference region, i.e. a region devoid of any specific binding, is available. Many receptors are however widely expressed across the brain, and a true reference region is rarely available. In these cases, the nonspecific binding can be obtained after competitive pharmacological blockade, which is often contraindicated in humans. In this work we introduce the genomic plot for estimating the nondisplaceable fraction using baseline scans only. The genomic plot is a transformation of the Lassen graphical method in which the brain maps of mRNA transcripts of the target receptor obtained from the Allen brain atlas are used as a surrogate measure of the specific binding. Thus, the genomic plot allows the calculation of the specific and nondisplaceable components of radioligand uptake without the need of pharmacological blockade. We first assessed the statistical properties of the method with computer simulations. Then we sought ground-truth validation using human PET datasets of seven different neuroreceptor radioligands, where nonspecific fractions were either obtained separately using drug displacement or available from a true reference region. The population nondisplaceable fractions estimated by the genomic plot were very close to those measured by actual human blocking studies (mean relative difference between 2% and 7%). However, these estimates were valid only when mRNA expressions were predictive of protein levels (i.e. there were no significant post-transcriptional changes). This condition can be readily established a priori by assessing the correlation between PET and mRNA expression. PMID:26850512

Mapping social behavior-induced brain activation at cellular resolution in the mouse

PubMed Central

Kim, Yongsoo; Venkataraju, Kannan Umadevi; Pradhan, Kith; Mende, Carolin; Taranda, Julian; Turaga, Srinivas C.; Arganda-Carreras, Ignacio; Ng, Lydia; Hawrylycz, Michael J.; Rockland, Kathleen; Seung, H. Sebastian; Osten, Pavel

2014-01-01

Understanding how brain activation mediates behaviors is a central goal of systems neuroscience. Here we apply an automated method for mapping brain activation in the mouse in order to probe how sex-specific social behaviors are represented in the male brain. Our method uses the immediate early gene c-fos, a marker of neuronal activation, visualized by serial two-photon tomography: the c-fos-GFP-positive neurons are computationally detected, their distribution is registered to a reference brain and a brain atlas, and their numbers are analyzed by statistical tests. Our results reveal distinct and shared female and male interaction-evoked patterns of male brain activation representing sex discrimination and social recognition. We also identify brain regions whose degree of activity correlates to specific features of social behaviors and estimate the total numbers and the densities of activated neurons per brain areas. Our study opens the door to automated screening of behavior-evoked brain activation in the mouse. PMID:25558063

Brain Metastasis: Unique Challenges and Open Opportunities

PubMed Central

Lowery, Frank J.; Yu, Dihua

2016-01-01

The metastasis of cancer to the central nervous system (CNS) remains a devastating clinical reality, carrying an estimated survival time of less than one year in spite of recent therapeutic breakthroughs for other disease contexts. Advances in brain metastasis research are hindered by a number of reasons, including its complicated nature and the difficulty of modeling metastatic cancer growth in the unique brain microenvironment. In this review, we will discuss the clinical challenge, and compare the values and limitations of the available models for brain metastasis research. Additionally, we will specifically address current knowledge on how brain metastases take advantage of the unique brain environment to benefit their own growth. Finally, we will explore the distinctive metabolic and nutrient characteristics of the brain; how these paradoxically represent barriers to establishment of brain metastasis, but also provide ample supplies for metastatic cells’ growth in the brain. We envision that multi-disciplinary innovative approaches will open opportunities for the field to make breakthroughs in tackling unique challenges of brain metastasis. PMID:27939792

Kinetic study of benzyl [1-14C]acetate as a potential probe for astrocytic energy metabolism in the rat brain: Comparison with benzyl [2-14C]acetate.

PubMed

Okada, Maki; Yanamoto, Kazuhiko; Kagawa, Tomohiko; Yoshino, Keiko; Hosoi, Rie; Abe, Kohji; Zhang, Ming-Rong; Inoue, Osamu

2016-02-01

Brain uptake of [(14)C]acetate has been reported to be a useful marker of astrocytic energy metabolism. In addition to uptake values, the rate of radiolabeled acetate washout from the brain appears to reflect CO2 exhaustion and oxygen consumption in astrocytes. We measured the time-radioactivity curves of benzyl [1-(14)C]acetate ([1-(14)C]BA), a lipophilic probe of [1-(14)C]acetate, and compared it with that of benzyl [2-(14)C]acetate ([2-(14)C]BA) in rat brains. The highest brain uptake was observed immediately after injecting either [1-(14)C]BA or [2-(14)C]BA, and both subsequently disappeared from the brain in a single-exponential manner. Estimated [1-(14)C]BA washout rates in the cerebral cortex and cerebellum were higher than those of [2-(14)C]BA. These results suggested that [1-(14)C]BA could be a useful probe for estimating the astrocytic oxidative metabolism. The [1-(14)C]BA washout rate in the cerebral cortex of immature rats was lower than that of mature rats. An autoradiographic study showed that the washout rates of [1-(14)C]BA from the rat brains of a lithium-pilocarpine-induced status epilepticus model were not significantly different from the values in control rat brains except for the medial septal nucleus. These results implied that the enhancement of amino acid turnover rate rather than astrocytic oxidative metabolism was increased in status epilepticus. © The Author(s) 2015.

Scaling of Brain Metabolism with a Fixed Energy Budget per Neuron: Implications for Neuronal Activity, Plasticity and Evolution

PubMed Central

Herculano-Houzel, Suzana

2011-01-01

It is usually considered that larger brains have larger neurons, which consume more energy individually, and are therefore accompanied by a larger number of glial cells per neuron. These notions, however, have never been tested. Based on glucose and oxygen metabolic rates in awake animals and their recently determined numbers of neurons, here I show that, contrary to the expected, the estimated glucose use per neuron is remarkably constant, varying only by 40% across the six species of rodents and primates (including humans). The estimated average glucose use per neuron does not correlate with neuronal density in any structure. This suggests that the energy budget of the whole brain per neuron is fixed across species and brain sizes, such that total glucose use by the brain as a whole, by the cerebral cortex and also by the cerebellum alone are linear functions of the number of neurons in the structures across the species (although the average glucose consumption per neuron is at least 10× higher in the cerebral cortex than in the cerebellum). These results indicate that the apparently remarkable use in humans of 20% of the whole body energy budget by a brain that represents only 2% of body mass is explained simply by its large number of neurons. Because synaptic activity is considered the major determinant of metabolic cost, a conserved energy budget per neuron has several profound implications for synaptic homeostasis and the regulation of firing rates, synaptic plasticity, brain imaging, pathologies, and for brain scaling in evolution. PMID:21390261

Analysis of the influence of handset phone position on RF exposure of brain tissue.

PubMed

Ghanmi, Amal; Varsier, Nadège; Hadjem, Abdelhamid; Conil, Emmanuelle; Picon, Odile; Wiart, Joe

2014-12-01

Exposure to mobile phone radio frequency (RF) electromagnetic fields depends on many different parameters. For epidemiological studies investigating the risk of brain cancer linked to RF exposure from mobile phones, it is of great interest to characterize brain tissue exposure and to know which parameters this exposure is sensitive to. One such parameter is the position of the phone during communication. In this article, we analyze the influence of the phone position on the brain exposure by comparing the specific absorption rate (SAR) induced in the head by two different mobile phone models operating in Global System for Mobile Communications (GSM) frequency bands. To achieve this objective, 80 different phone positions were chosen using an experiment based on the Latin hypercube sampling (LHS) to select a representative set of positions. The averaged SAR over 10 g (SAR10 g) in the head, the averaged SAR over 1 g (SAR1 g ) in the brain, and the averaged SAR in different anatomical brain structures were estimated at 900 and 1800 MHz for the 80 positions. The results illustrate that SAR distributions inside the brain area are sensitive to the position of the mobile phone relative to the head. The results also show that for 5-10% of the studied positions the SAR10 g in the head and the SAR1 g in the brain can be 20% higher than the SAR estimated for the standard cheek position and that the Specific Anthropomorphic Mannequin (SAM) model is conservative for 95% of all the studied positions. © 2014 Wiley Periodicals, Inc.

Scaling of brain metabolism with a fixed energy budget per neuron: implications for neuronal activity, plasticity and evolution.

PubMed

Herculano-Houzel, Suzana

2011-03-01

It is usually considered that larger brains have larger neurons, which consume more energy individually, and are therefore accompanied by a larger number of glial cells per neuron. These notions, however, have never been tested. Based on glucose and oxygen metabolic rates in awake animals and their recently determined numbers of neurons, here I show that, contrary to the expected, the estimated glucose use per neuron is remarkably constant, varying only by 40% across the six species of rodents and primates (including humans). The estimated average glucose use per neuron does not correlate with neuronal density in any structure. This suggests that the energy budget of the whole brain per neuron is fixed across species and brain sizes, such that total glucose use by the brain as a whole, by the cerebral cortex and also by the cerebellum alone are linear functions of the number of neurons in the structures across the species (although the average glucose consumption per neuron is at least 10× higher in the cerebral cortex than in the cerebellum). These results indicate that the apparently remarkable use in humans of 20% of the whole body energy budget by a brain that represents only 2% of body mass is explained simply by its large number of neurons. Because synaptic activity is considered the major determinant of metabolic cost, a conserved energy budget per neuron has several profound implications for synaptic homeostasis and the regulation of firing rates, synaptic plasticity, brain imaging, pathologies, and for brain scaling in evolution.

Unifying framework for multimodal brain MRI segmentation based on Hidden Markov Chains.

PubMed

Bricq, S; Collet, Ch; Armspach, J P

2008-12-01

In the frame of 3D medical imaging, accurate segmentation of multimodal brain MR images is of interest for many brain disorders. However, due to several factors such as noise, imaging artifacts, intrinsic tissue variation and partial volume effects, tissue classification remains a challenging task. In this paper, we present a unifying framework for unsupervised segmentation of multimodal brain MR images including partial volume effect, bias field correction, and information given by a probabilistic atlas. Here-proposed method takes into account neighborhood information using a Hidden Markov Chain (HMC) model. Due to the limited resolution of imaging devices, voxels may be composed of a mixture of different tissue types, this partial volume effect is included to achieve an accurate segmentation of brain tissues. Instead of assigning each voxel to a single tissue class (i.e., hard classification), we compute the relative amount of each pure tissue class in each voxel (mixture estimation). Further, a bias field estimation step is added to the proposed algorithm to correct intensity inhomogeneities. Furthermore, atlas priors were incorporated using probabilistic brain atlas containing prior expectations about the spatial localization of different tissue classes. This atlas is considered as a complementary sensor and the proposed method is extended to multimodal brain MRI without any user-tunable parameter (unsupervised algorithm). To validate this new unifying framework, we present experimental results on both synthetic and real brain images, for which the ground truth is available. Comparison with other often used techniques demonstrates the accuracy and the robustness of this new Markovian segmentation scheme.

Estimation of sensitivity and specificity of brain magnetic resonance imaging and single photon emission computed tomography in the diagnosis of olfactory dysfunction after head traumas.

PubMed

Atighechi, Saeid; Zolfaghari, Aliasghar; Baradaranfar, Mohammadhossein; Dadgarnia, Mohammadhossein

2013-01-01

Olfactory dysfunction has an incidence of 5-10% after head injury. Several objective and subjective tests had been proposed. Recent studies showed that brain single photon emission computed tomography (SPECT) and brain magnetic resonance imaging (MRI) have good diagnostic value in this era in which the most common sites of involvement were olfactory bulb and olfactory nerve in MRI and frontal lobe in SPECT. This study aimed to estimate the sensitivity and specificity of brain MRI and brain SPECT in the diagnosis of traumatic hyposmia and anosmia. From February 2009 to March 2011, 63 patients with head injury and smell complaint were selected for this study. Using an identification test and a threshold smell test, 28 were anosmic and 27 had hyposmia and the remaining 8 were normosmic. All of them underwent brain MRI and SPECT. The sensitivity of SPECT was 81.5 and 85.7% in hyposmia and anosmia, respectively. Its specificity was 87.5% in anosmia and 87.7% in anosmia. MRI sensitivity was 66.7% in hyposmia but 82.1% in anosmia. Its specificity was 85.7% in anosmia and 87.7% in anosmia. If MRI and SPECT were considered together, the sensitivity was 92.3% in hyposmia and 92% in anosmia, but the specificity was 87% in both cases. According to our study, both brain MRI and SPECT have high sensitivity and specificity in the diagnosis of traumatic anosmia, although brain SPECT is slightly superior to MRI. If the two techniques are applied together, the accuracy will be increased.

Imaging brain development: the adolescent brain.

PubMed

Blakemore, Sarah-Jayne

2012-06-01

The past 15 years have seen a rapid expansion in the number of studies using neuroimaging techniques to investigate maturational changes in the human brain. In this paper, I review MRI studies on structural changes in the developing brain, and fMRI studies on functional changes in the social brain during adolescence. Both MRI and fMRI studies point to adolescence as a period of continued neural development. In the final section, I discuss a number of areas of research that are just beginning and may be the subject of developmental neuroimaging in the next twenty years. Future studies might focus on complex questions including the development of functional connectivity; how gender and puberty influence adolescent brain development; the effects of genes, environment and culture on the adolescent brain; development of the atypical adolescent brain; and implications for policy of the study of the adolescent brain. Copyright © 2011 Elsevier Inc. All rights reserved.

Clinical validation of the General Ability Index--Estimate (GAI-E): estimating premorbid GAI.

PubMed

Schoenberg, Mike R; Lange, Rael T; Iverson, Grant L; Chelune, Gordon J; Scott, James G; Adams, Russell L

2006-09-01

The clinical utility of the General Ability Index--Estimate (GAI-E; Lange, Schoenberg, Chelune, Scott, & Adams, 2005) for estimating premorbid GAI scores was investigated using the WAIS-III standardization clinical trials sample (The Psychological Corporation, 1997). The GAI-E algorithms combine Vocabulary, Information, Matrix Reasoning, and Picture Completion subtest raw scores with demographic variables to predict GAI. Ten GAI-E algorithms were developed combining demographic variables with single subtest scaled scores and with two subtests. Estimated GAI are presented for participants diagnosed with dementia (n = 50), traumatic brain injury (n = 20), Huntington's disease (n = 15), Korsakoff's disease (n = 12), chronic alcohol abuse (n = 32), temporal lobectomy (n = 17), and schizophrenia (n = 44). In addition, a small sample of participants without dementia and diagnosed with depression (n = 32) was used as a clinical comparison group. The GAI-E algorithms provided estimates of GAI that closely approximated scores expected for a healthy adult population. The greatest differences between estimated GAI and obtained GAI were observed for the single subtest GAI-E algorithms using the Vocabulary, Information, and Matrix Reasoning subtests. Based on these data, recommendations for the use of the GAI-E algorithms are presented.

Equilibrium-point control hypothesis examined by measured arm stiffness during multijoint movement.

PubMed

Gomi, H; Kawato

1996-04-05

For the last 20 years, it has been hypothesized that well-coordinated, multijoint movements are executed without complex computation by the brain, with the use of springlike muscle properties and peripheral neural feedback loops. However, it has been technically and conceptually difficult to examine this "equilibrium-point control" hypothesis directly in physiological or behavioral experiments. A high-performance manipulandum was developed and used here to measure human arm stiffness, the magnitude of which during multijoint movement is important for this hypothesis. Here, the equilibrium-point trajectory was estimated from the measured stiffness, the actual trajectory, and the generated torque. Its velocity profile differed from that of the actual trajectory. These results argue against the hypothesis that the brain sends as a motor command only an equilibrium-point trajectory similar to the actual trajectory.

Development of Open Brain Simulator for Human Biomechatronics

NASA Astrophysics Data System (ADS)

Otake, Mihoko; Takagi, Toshihisa; Asama, Hajime

Modeling and simulation based on mechanisms is important in order to design and control mechatronic systems. In particular, in-depth understanding and realistic modeling of biological systems is indispensable for biomechatronics. This paper presents open brain simulator, which estimates the neural state of human through external measurement for the purpose of improving motor and social skills. Macroscopic anatomical nervous systems model was built which can be connected to the musculoskeletal model. Microscopic anatomical and physiological neural models were interfaced to the macroscopic model. Neural activities of somatosensory area and Purkinje cell were calculated from motion capture data. The simulator provides technical infrastructure for human biomechatronics, which is promising for the novel diagnosis of neurological disorders and their treatments through medication and movement therapy, and for motor learning support system supporting acquisition of motor skill considering neural mechanism.

Sparse and Adaptive Diffusion Dictionary (SADD) for recovering intra-voxel white matter structure.

PubMed

Aranda, Ramon; Ramirez-Manzanares, Alonso; Rivera, Mariano

2015-12-01

On the analysis of the Diffusion-Weighted Magnetic Resonance Images, multi-compartment models overcome the limitations of the well-known Diffusion Tensor model for fitting in vivo brain axonal orientations at voxels with fiber crossings, branching, kissing or bifurcations. Some successful multi-compartment methods are based on diffusion dictionaries. The diffusion dictionary-based methods assume that the observed Magnetic Resonance signal at each voxel is a linear combination of the fixed dictionary elements (dictionary atoms). The atoms are fixed along different orientations and diffusivity profiles. In this work, we present a sparse and adaptive diffusion dictionary method based on the Diffusion Basis Functions Model to estimate in vivo brain axonal fiber populations. Our proposal overcomes the following limitations of the diffusion dictionary-based methods: the limited angular resolution and the fixed shapes for the atom set. We propose to iteratively re-estimate the orientations and the diffusivity profile of the atoms independently at each voxel by using a simplified and easier-to-solve mathematical approach. As a result, we improve the fitting of the Diffusion-Weighted Magnetic Resonance signal. The advantages with respect to the former Diffusion Basis Functions method are demonstrated on the synthetic data-set used on the 2012 HARDI Reconstruction Challenge and in vivo human data. We demonstrate that improvements obtained in the intra-voxel fiber structure estimations benefit brain research allowing to obtain better tractography estimations. Hence, these improvements result in an accurate computation of the brain connectivity patterns. Copyright © 2015 Elsevier B.V. All rights reserved.

Functional localization of a "Time Keeper" function separate from attentional resources and task strategy.

PubMed

Tracy, J I; Faro, S H; Mohamed, F B; Pinsk, M; Pinus, A

2000-03-01

The functional neuroanatomy of time estimation has not been well-documented. This research investigated the fMRI measured brain response to an explicit, prospective time interval production (TIP) task. The study tested for the presence of brain activity reflecting a primary time keeper function, distinct from the brain systems involved either in conscious strategies to monitor time or attentional resource and other cognitive processes to accomplish the task. In the TIP task participants were given a time interval and asked to indicate when it elapsed. Two control tasks (counting forwards, backwards) were administered, in addition to a dual task format of the TIP task. Whole brain images were collected at 1.5 Tesla. Analyses (n = 6) yielded a statistical parametric map (SPM ¿z¿) reflecting time keeping and not strategy (counting, number manipulation) or attention resource utilization. Additional SPM ¿z¿s involving activation associated with the accuracy and magnitude the of time estimation response are presented. Results revealed lateral cerebellar and inferior temporal lobe activation were associated with primary time keeping. Behavioral data provided evidence that the procedures for the explicit time judgements did not occur automatically and utilized controlled processes. Activation sites associated with accuracy, magnitude, and the dual task provided indications of the other structures involved in time estimation that implemented task components related to controlled processing. The data are consistent with prior proposals that the cerebellum is a repository of codes for time processing, but also implicate temporal lobe structures for this type of time estimation task. Copyright 2000 Academic Press.

Adjusting head circumference for covariates in autism: clinical correlates of a highly heritable continuous trait.

PubMed

Chaste, Pauline; Klei, Lambertus; Sanders, Stephan J; Murtha, Michael T; Hus, Vanessa; Lowe, Jennifer K; Willsey, A Jeremy; Moreno-De-Luca, Daniel; Yu, Timothy W; Fombonne, Eric; Geschwind, Daniel; Grice, Dorothy E; Ledbetter, David H; Lord, Catherine; Mane, Shrikant M; Lese Martin, Christa; Martin, Donna M; Morrow, Eric M; Walsh, Christopher A; Sutcliffe, James S; State, Matthew W; Devlin, Bernie; Cook, Edwin H; Kim, Soo-Jeong

2013-10-15

Brain development follows a different trajectory in children with autism spectrum disorders (ASD) than in typically developing children. A proxy for neurodevelopment could be head circumference (HC), but studies assessing HC and its clinical correlates in ASD have been inconsistent. This study investigates HC and clinical correlates in the Simons Simplex Collection cohort. We used a mixed linear model to estimate effects of covariates and the deviation from the expected HC given parental HC (genetic deviation). After excluding individuals with incomplete data, 7225 individuals in 1891 families remained for analysis. We examined the relationship between HC/genetic deviation of HC and clinical parameters. Gender, age, height, weight, genetic ancestry, and ASD status were significant predictors of HC (estimate of the ASD effect = .2 cm). HC was approximately normally distributed in probands and unaffected relatives, with only a few outliers. Genetic deviation of HC was also normally distributed, consistent with a random sampling of parental genes. Whereas larger HC than expected was associated with ASD symptom severity and regression, IQ decreased with the absolute value of the genetic deviation of HC. Measured against expected values derived from covariates of ASD subjects, statistical outliers for HC were uncommon. HC is a strongly heritable trait, and population norms for HC would be far more accurate if covariates including genetic ancestry, height, and age were taken into account. The association of diminishing IQ with absolute deviation from predicted HC values suggests HC could reflect subtle underlying brain development and warrants further investigation. © 2013 Society of Biological Psychiatry.

Adjusting head circumference for covariates in autism: clinical correlates of a highly heritable continuous trait

PubMed Central

Chaste, Pauline; Klei, Lambertus; Sanders, Stephan J.; Murtha, Michael T.; Hus, Vanessa; Lowe, Jennifer K.; Willsey, A. Jeremy; Moreno-De-Luca, Daniel; Yu, Timothy W.; Fombonne, Eric; Geschwind, Daniel; Grice, Dorothy E.; Ledbetter, David H.; Lord, Catherine; Mane, Shrikant M.; Martin, Christa Lese; Martin, Donna M.; Morrow, Eric M.; Walsh, Christopher A.; Sutcliffe, James S.; State, Matthew W.; Devlin, Bernie; Cook, Edwin H.; Kim, Soo-Jeong

2013-01-01

BACKGROUND Brain development follows a different trajectory in children with Autism Spectrum Disorders (ASD) than in typically developing children. A proxy for neurodevelopment could be head circumference (HC), but studies assessing HC and its clinical correlates in ASD have been inconsistent. This study investigates HC and clinical correlates in the Simons Simplex Collection cohort. METHODS We used a mixed linear model to estimate effects of covariates and the deviation from the expected HC given parental HC (genetic deviation). After excluding individuals with incomplete data, 7225 individuals in 1891 families remained for analysis. We examined the relationship between HC/genetic deviation of HC and clinical parameters. RESULTS Gender, age, height, weight, genetic ancestry and ASD status were significant predictors of HC (estimate of the ASD effect=0.2cm). HC was approximately normally distributed in probands and unaffected relatives, with only a few outliers. Genetic deviation of HC was also normally distributed, consistent with a random sampling of parental genes. Whereas larger HC than expected was associated with ASD symptom severity and regression, IQ decreased with the absolute value of the genetic deviation of HC. CONCLUSIONS Measured against expected values derived from covariates of ASD subjects, statistical outliers for HC were uncommon. HC is a strongly heritable trait and population norms for HC would be far more accurate if covariates including genetic ancestry, height and age were taken into account. The association of diminishing IQ with absolute deviation from predicted HC values suggests HC could reflect subtle underlying brain development and warrants further investigation. PMID:23746936

Characterization in humans of 18F-MNI-444, a PET radiotracer for brain adenosine 2A receptors.

PubMed

Barret, Olivier; Hannestad, Jonas; Vala, Christine; Alagille, David; Tavares, Adriana; Laruelle, Marc; Jennings, Danna; Marek, Ken; Russell, David; Seibyl, John; Tamagnan, Gilles

2015-04-01

PET with selective adenosine 2A receptor (A2A) radiotracers can be used to study a variety of neurodegenerative and neuropsychiatric disorders in vivo and to support drug-discovery studies targeting A2A. The aim of this study was to describe the first in vivo evaluation of (18)F-MNI-444, a novel PET radiotracer for imaging A2A, in healthy human subjects. Ten healthy human volunteers were enrolled in this study; 6 completed the brain PET studies and 4 participated in the whole-body PET studies. Arterial blood was collected for invasive kinetic modeling of the brain PET data. Noninvasive methods of data quantification were also explored. Test-retest reproducibility was evaluated in 5 subjects. Radiotracer distribution and dosimetry was determined using serial whole-body PET images acquired over 6 h post-radiotracer injection. Urine samples were collected to calculate urinary excretion. After intravenous bolus injection, (18)F-MNI-444 rapidly entered the brain and displayed a distribution consistent with known A2A densities in the brain. Binding potentials ranging from 2.6 to 4.9 were measured in A2A-rich regions, with an average test-retest variability of less than 10%. The estimated whole-body radiation effective dose was approximately 0.023 mSv/MBq. (18)F-MNI-444 is a useful PET radiotracer for imaging A2A in the human brain. The superior in vivo brain kinetic properties of (18)F-MNI-444, compared with previously developed A2A radiotracers, provide the opportunity to foster global use of in vivo A2A PET imaging in neuroscience research. © 2015 by the Society of Nuclear Medicine and Molecular Imaging, Inc.

Understanding the role of the perivascular space in cerebral small vessel disease.

PubMed

Brown, Rosalind; Benveniste, Helene; Black, Sandra E; Charpak, Serge; Dichgans, Martin; Joutel, Anne; Nedergaard, Maiken; Smith, Kenneth J; Zlokovic, Berislav V; Wardlaw, Joanna M

2018-05-02

Small vessel diseases are a group of disorders that result from pathological alteration of the small blood vessels in the brain, including the small arteries, capillaries and veins. Of the 35-36 million people that are estimated to suffer from dementia worldwide, up to 65% have an SVD component. Furthermore, SVD causes 20-25% of strokes, worsens outcome after stroke and is a leading cause of disability, cognitive impairment and poor mobility. Yet the underlying cause(s) of SVD are not fully understood.Magnetic resonance imaging (MRI) has confirmed enlarged perivascular spaces (PVS) as a hallmark feature of SVD. In healthy tissue, these spaces are proposed to form part of a complex brain fluid drainage system which supports interstitial fluid exchange and may also facilitate clearance of waste products from the brain. The pathophysiological signature of PVS, and what this infers about their function and interaction with cerebral microcirculation, plus subsequent downstream effects on lesion development in the brain has not been established. Here we discuss the potential of enlarged PVS to be a unique biomarker for SVD and related brain disorders with a vascular component. We propose that widening of PVS suggests presence of peri-vascular cell debris and other waste products that forms part of a vicious cycle involving impaired cerebrovascular reactivity (CVR), blood-brain barrier (BBB) dysfunction, perivascular inflammation and ultimately impaired clearance of waste proteins from the interstitial fluid (ISF) space, leading to accumulation of toxins, hypoxia and tissue damage.Here, we outline current knowledge, questions and hypotheses regarding understanding the brain fluid dynamics underpinning dementia and stroke through the common denominator of SVD.

Genome-wide scan of healthy human connectome discovers SPON1 gene variant influencing dementia severity

PubMed Central

Jahanshad, Neda; Rajagopalan, Priya; Hua, Xue; Hibar, Derrek P.; Nir, Talia M.; Toga, Arthur W.; Jack, Clifford R.; Saykin, Andrew J.; Green, Robert C.; Weiner, Michael W.; Medland, Sarah E.; Montgomery, Grant W.; Hansell, Narelle K.; McMahon, Katie L.; de Zubicaray, Greig I.; Martin, Nicholas G.; Wright, Margaret J.; Thompson, Paul M.; Weiner, Michael; Aisen, Paul; Weiner, Michael; Aisen, Paul; Petersen, Ronald; Jack, Clifford R.; Jagust, William; Trojanowski, John Q.; Toga, Arthur W.; Beckett, Laurel; Green, Robert C.; Saykin, Andrew J.; Morris, John; Liu, Enchi; Green, Robert C.; Montine, Tom; Petersen, Ronald; Aisen, Paul; Gamst, Anthony; Thomas, Ronald G.; Donohue, Michael; Walter, Sarah; Gessert, Devon; Sather, Tamie; Beckett, Laurel; Harvey, Danielle; Gamst, Anthony; Donohue, Michael; Kornak, John; Jack, Clifford R.; Dale, Anders; Bernstein, Matthew; Felmlee, Joel; Fox, Nick; Thompson, Paul; Schuff, Norbert; Alexander, Gene; DeCarli, Charles; Jagust, William; Bandy, Dan; Koeppe, Robert A.; Foster, Norm; Reiman, Eric M.; Chen, Kewei; Mathis, Chet; Morris, John; Cairns, Nigel J.; Taylor-Reinwald, Lisa; Trojanowki, J.Q.; Shaw, Les; Lee, Virginia M.Y.; Korecka, Magdalena; Toga, Arthur W.; Crawford, Karen; Neu, Scott; Saykin, Andrew J.; Foroud, Tatiana M.; Potkin, Steven; Shen, Li; Khachaturian, Zaven; Frank, Richard; Snyder, Peter J.; Molchan, Susan; Kaye, Jeffrey; Quinn, Joseph; Lind, Betty; Dolen, Sara; Schneider, Lon S.; Pawluczyk, Sonia; Spann, Bryan M.; Brewer, James; Vanderswag, Helen; Heidebrink, Judith L.; Lord, Joanne L.; Petersen, Ronald; Johnson, Kris; Doody, Rachelle S.; Villanueva-Meyer, Javier; Chowdhury, Munir; Stern, Yaakov; Honig, Lawrence S.; Bell, Karen L.; Morris, John C.; Ances, Beau; Carroll, Maria; Leon, Sue; Mintun, Mark A.; Schneider, Stacy; Marson, Daniel; Griffith, Randall; Clark, David; Grossman, Hillel; Mitsis, Effie; Romirowsky, Aliza; deToledo-Morrell, Leyla; Shah, Raj C.; Duara, Ranjan; Varon, Daniel; Roberts, Peggy; Albert, Marilyn; Onyike, Chiadi; Kielb, Stephanie; Rusinek, Henry; de Leon, Mony J.; Glodzik, Lidia; De Santi, Susan; Doraiswamy, P. Murali; Petrella, Jeffrey R.; Coleman, R. Edward; Arnold, Steven E.; Karlawish, Jason H.; Wolk, David; Smith, Charles D.; Jicha, Greg; Hardy, Peter; Lopez, Oscar L.; Oakley, MaryAnn; Simpson, Donna M.; Porsteinsson, Anton P.; Goldstein, Bonnie S.; Martin, Kim; Makino, Kelly M.; Ismail, M. Saleem; Brand, Connie; Mulnard, Ruth A.; Thai, Gaby; Mc-Adams-Ortiz, Catherine; Womack, Kyle; Mathews, Dana; Quiceno, Mary; Diaz-Arrastia, Ramon; King, Richard; Weiner, Myron; Martin-Cook, Kristen; DeVous, Michael; Levey, Allan I.; Lah, James J.; Cellar, Janet S.; Burns, Jeffrey M.; Anderson, Heather S.; Swerdlow, Russell H.; Apostolova, Liana; Lu, Po H.; Bartzokis, George; Silverman, Daniel H.S.; Graff-Radford, Neill R.; Parfitt, Francine; Johnson, Heather; Farlow, Martin R.; Hake, Ann Marie; Matthews, Brandy R.; Herring, Scott; van Dyck, Christopher H.; Carson, Richard E.; MacAvoy, Martha G.; Chertkow, Howard; Bergman, Howard; Hosein, Chris; Black, Sandra; Stefanovic, Bojana; Caldwell, Curtis; Hsiung, Ging-Yuek Robin; Feldman, Howard; Mudge, Benita; Assaly, Michele; Kertesz, Andrew; Rogers, John; Trost, Dick; Bernick, Charles; Munic, Donna; Kerwin, Diana; Mesulam, Marek-Marsel; Lipowski, Kristina; Wu, Chuang-Kuo; Johnson, Nancy; Sadowsky, Carl; Martinez, Walter; Villena, Teresa; Turner, Raymond Scott; Johnson, Kathleen; Reynolds, Brigid; Sperling, Reisa A.; Johnson, Keith A.; Marshall, Gad; Frey, Meghan; Yesavage, Jerome; Taylor, Joy L.; Lane, Barton; Rosen, Allyson; Tinklenberg, Jared; Sabbagh, Marwan; Belden, Christine; Jacobson, Sandra; Kowall, Neil; Killiany, Ronald; Budson, Andrew E.; Norbash, Alexander; Johnson, Patricia Lynn; Obisesan, Thomas O.; Wolday, Saba; Bwayo, Salome K.; Lerner, Alan; Hudson, Leon; Ogrocki, Paula; Fletcher, Evan; Carmichael, Owen; Olichney, John; DeCarli, Charles; Kittur, Smita; Borrie, Michael; Lee, T.-Y.; Bartha, Rob; Johnson, Sterling; Asthana, Sanjay; Carlsson, Cynthia M.; Potkin, Steven G.; Preda, Adrian; Nguyen, Dana; Tariot, Pierre; Fleisher, Adam; Reeder, Stephanie; Bates, Vernice; Capote, Horacio; Rainka, Michelle; Scharre, Douglas W.; Kataki, Maria; Zimmerman, Earl A.; Celmins, Dzintra; Brown, Alice D.; Pearlson, Godfrey D.; Blank, Karen; Anderson, Karen; Saykin, Andrew J.; Santulli, Robert B.; Schwartz, Eben S.; Sink, Kaycee M.; Williamson, Jeff D.; Garg, Pradeep; Watkins, Franklin; Ott, Brian R.; Querfurth, Henry; Tremont, Geoffrey; Salloway, Stephen; Malloy, Paul; Correia, Stephen; Rosen, Howard J.; Miller, Bruce L.; Mintzer, Jacobo; Longmire, Crystal Flynn; Spicer, Kenneth; Finger, Elizabeth; Rachinsky, Irina; Rogers, John; Kertesz, Andrew; Drost, Dick

2013-01-01

Aberrant connectivity is implicated in many neurological and psychiatric disorders, including Alzheimer’s disease and schizophrenia. However, other than a few disease-associated candidate genes, we know little about the degree to which genetics play a role in the brain networks; we know even less about specific genes that influence brain connections. Twin and family-based studies can generate estimates of overall genetic influences on a trait, but genome-wide association scans (GWASs) can screen the genome for specific variants influencing the brain or risk for disease. To identify the heritability of various brain connections, we scanned healthy young adult twins with high-field, high-angular resolution diffusion MRI. We adapted GWASs to screen the brain’s connectivity pattern, allowing us to discover genetic variants that affect the human brain’s wiring. The association of connectivity with the SPON1 variant at rs2618516 on chromosome 11 (11p15.2) reached connectome-wide, genome-wide significance after stringent statistical corrections were enforced, and it was replicated in an independent subsample. rs2618516 was shown to affect brain structure in an elderly population with varying degrees of dementia. Older people who carried the connectivity variant had significantly milder clinical dementia scores and lower risk of Alzheimer’s disease. As a posthoc analysis, we conducted GWASs on several organizational and topological network measures derived from the matrices to discover variants in and around genes associated with autism (MACROD2), development (NEDD4), and mental retardation (UBE2A) significantly associated with connectivity. Connectome-wide, genome-wide screening offers substantial promise to discover genes affecting brain connectivity and risk for brain diseases. PMID:23471985

Regional differences in actomyosin contraction shape the primary vesicles in the embryonic chicken brain

NASA Astrophysics Data System (ADS)

Filas, Benjamen A.; Oltean, Alina; Majidi, Shabnam; Bayly, Philip V.; Beebe, David C.; Taber, Larry A.

2012-12-01

In the early embryo, the brain initially forms as a relatively straight, cylindrical epithelial tube composed of neural stem cells. The brain tube then divides into three primary vesicles (forebrain, midbrain, hindbrain), as well as a series of bulges (rhombomeres) in the hindbrain. The boundaries between these subdivisions have been well studied as regions of differential gene expression, but the morphogenetic mechanisms that generate these constrictions are not well understood. Here, we show that regional variations in actomyosin-based contractility play a major role in vesicle formation in the embryonic chicken brain. In particular, boundaries did not form in brains exposed to the nonmuscle myosin II inhibitor blebbistatin, whereas increasing contractile force using calyculin or ATP deepened boundaries considerably. Tissue staining showed that contraction likely occurs at the inner part of the wall, as F-actin and phosphorylated myosin are concentrated at the apical side. However, relatively little actin and myosin was found in rhombomere boundaries. To determine the specific physical mechanisms that drive vesicle formation, we developed a finite-element model for the brain tube. Regional apical contraction was simulated in the model, with contractile anisotropy and strength estimated from contractile protein distributions and measurements of cell shapes. The model shows that a combination of circumferential contraction in the boundary regions and relatively isotropic contraction between boundaries can generate realistic morphologies for the primary vesicles. In contrast, rhombomere formation likely involves longitudinal contraction between boundaries. Further simulations suggest that these different mechanisms are dictated by regional differences in initial morphology and the need to withstand cerebrospinal fluid pressure. This study provides a new understanding of early brain morphogenesis.

Visual function at 11 years of age in preterm-born children with and without fetal brain sparing.

PubMed

Kok, Joke H; Prick, Liesbeth; Merckel, Elly; Everhard, Yolande; Verkerk, Gijs J Q; Scherjon, Sicco A

2007-06-01

We have demonstrated earlier an accelerated maturation of the visual evoked potential in the first year of life in preterm infants with antenatal brain sparing. We have now assessed visual functioning at 11 years of age in the same cohort and compared the groups with and without brain sparing. One hundred sixteen survivors included in a study on the outcome of preterm infants born at <33 weeks' gestation with and without fetal brain sparing and admitted to the NICU were followed extensively. Ninety-eight infants (85%) were again assessed at 11 years of age. Data were available for fetal Doppler measurements indicating brain sparing, neonatal cerebral ultrasound scanning, and developmental outcome in the first 5 years. Mean birth weight was 1303 g; mean gestational age was 29.8 weeks. The infants were divided into 2 groups with and without brain sparing. Visual functioning was estimated by measuring visual acuity, visual fields, eye position, and binocular function and by visual motor tests. Six percent of the children were found to have a visual acuity of <0.8, 12% had strabismus, and 14% to 46% showed abnormal results on the visual motor tests. No statistical differences were found between the 2 groups. However, children with severe cerebral ultrasound diagnoses in the neonatal period were found to have significantly more abnormalities on visual functioning and lower scores on visual motor tests than children without these morbidities. Children with fetal brain sparing do not demonstrate a different development of their visual functioning at late school age. However, an abnormal cerebral ultrasound in the neonatal period is associated with impaired visual function in later life.

Effects of omega-3 polyunsaturated fatty acids on human brain morphology and function: What is the evidence?

PubMed

Bos, Dienke J; van Montfort, Simone J T; Oranje, Bob; Durston, Sarah; Smeets, Paul A M

2016-03-01

Public opinion and media coverage suggest that there are benefits of long-chain ω-3 polyunsaturated fatty acid (LC-PUFA) intake on brain functioning. However, it is an open question whether this is indeed the case. Therefore, we reviewed the evidence for effects of ω-3 LC-PUFA on human brain morphology and function. We included studies on (1) naturalistic long-term ω-3 LC-PUFA intake during life (2) the effects of short-term ω-3 LC-PUFA supplementation in healthy subjects and (3) the effects of ω-3 LC-PUFA supplementation as alternative or add-on treatment for psychiatric or neurological disorders. To date, 24 studies have been published on the effect of ω-3 LC-PUFA on brain function and structure. Findings from naturalistic studies and clinical trials in healthy individuals indicate that ω-3 LC-PUFA intake may be associated with increased functional activation of the prefrontal cortex in children, and greater gray matter volume and white matter integrity during aging. However, most naturalistic studies were cross-sectional or did not find any effect on cognition. As such, it is hard to estimate the magnitude of any beneficial effects. Furthermore, there is only limited evidence to support that ω-3 LC-PUFA supplementation is beneficial in brain disorders, such as Alzheimer's Disease, Attention Deficit/Hyperactivity Disorder, Major Depressive Disorder and schizophrenia. Overall, the literature suggests that sensitivity to supplementation may vary over development, and as a consequence of brain disorders. The biological mechanisms underlying any (beneficial) effects ω-3 LC-PUFAs on the brain are currently unknown and need to be investigated. Copyright © 2016 Elsevier B.V. and ECNP. All rights reserved.

Ongoing behavior predicts perceptual report of interval duration

PubMed Central

Gouvêa, Thiago S.; Monteiro, Tiago; Soares, Sofia; Atallah, Bassam V.; Paton, Joseph J.

2014-01-01

The ability to estimate the passage of time is essential for adaptive behavior in complex environments. Yet, it is not known how the brain encodes time over the durations necessary to explain animal behavior. Under temporally structured reinforcement schedules, animals tend to develop temporally structured behavior, and interval timing has been suggested to be accomplished by learning sequences of behavioral states. If this is true, trial to trial fluctuations in behavioral sequences should be predictive of fluctuations in time estimation. We trained rodents in an duration categorization task while continuously monitoring their behavior with a high speed camera. Animals developed highly reproducible behavioral sequences during the interval being timed. Moreover, those sequences were often predictive of perceptual report from early in the trial, providing support to the idea that animals may use learned behavioral patterns to estimate the duration of time intervals. To better resolve the issue, we propose that continuous and simultaneous behavioral and neural monitoring will enable identification of neural activity related to time perception that is not explained by ongoing behavior. PMID:24672473

Transcranial photoacoustic tomography of the monkey brain

NASA Astrophysics Data System (ADS)

Nie, Liming; Huang, Chao; Guo, Zijian; Anastasio, Mark; Wang, Lihong V.

2012-02-01

A photoacoustic tomography (PAT) system using a virtual point ultrasonic transducer was developed for transcranial imaging of monkey brains. The virtual point transducer provided a 10 times greater field-of-view (FOV) than finiteaperture unfocused transducers, which enables large primate imaging. The cerebral cortex of a monkey brain was accurately mapped transcranially, through up to two skulls ranging from 4 to 8 mm in thickness. The mass density and speed of sound distributions of the skull were estimated from adjunct X-ray CT image data and utilized with a timereversal algorithm to mitigate artifacts in the reconstructed image due to acoustic aberration. The oxygenation saturation (sO2) in blood phantoms through a monkey skull was also imaged and quantified, with results consistent with measurements by a gas analyzer. The oxygenation saturation (sO2) in blood phantoms through a monkey skull was also imaged and quantified, with results consistent with measurements by a gas analyzer. Our experimental results demonstrate that PAT can overcome the optical and ultrasound attenuation of a relatively thick skull, and the imaging aberration caused by skull can be corrected to a great extent.