A Unified Estimation Framework for State-Related Changes in Effective Brain Connectivity.

PubMed

Samdin, S Balqis; Ting, Chee-Ming; Ombao, Hernando; Salleh, Sh-Hussain

2017-04-01

This paper addresses the critical problem of estimating time-evolving effective brain connectivity. Current approaches based on sliding window analysis or time-varying coefficient models do not simultaneously capture both slow and abrupt changes in causal interactions between different brain regions. To overcome these limitations, we develop a unified framework based on a switching vector autoregressive (SVAR) model. Here, the dynamic connectivity regimes are uniquely characterized by distinct vector autoregressive (VAR) processes and allowed to switch between quasi-stationary brain states. The state evolution and the associated directed dependencies are defined by a Markov process and the SVAR parameters. We develop a three-stage estimation algorithm for the SVAR model: 1) feature extraction using time-varying VAR (TV-VAR) coefficients, 2) preliminary regime identification via clustering of the TV-VAR coefficients, 3) refined regime segmentation by Kalman smoothing and parameter estimation via expectation-maximization algorithm under a state-space formulation, using initial estimates from the previous two stages. The proposed framework is adaptive to state-related changes and gives reliable estimates of effective connectivity. Simulation results show that our method provides accurate regime change-point detection and connectivity estimates. In real applications to brain signals, the approach was able to capture directed connectivity state changes in functional magnetic resonance imaging data linked with changes in stimulus conditions, and in epileptic electroencephalograms, differentiating ictal from nonictal periods. The proposed framework accurately identifies state-dependent changes in brain network and provides estimates of connectivity strength and directionality. The proposed approach is useful in neuroscience studies that investigate the dynamics of underlying brain states.

Accelerating Cogent Confabulation: An Exploration in the Architecture Design Space

DTIC Science & Technology

2008-06-01

DATES COVERED (From - To) 1-8 June 2008 4. TITLE AND SUBTITLE ACCELERATING COGENT CONFABULATION: AN EXPLORATION IN THE ARCHITECTURE DESIGN SPACE 5a...spiking neural networks is proposed in reference [8]. Reference [9] investigates the architecture design of a Brain-state-in-a-box model. The...Richard Linderman2, Thomas Renz2, Qing Wu1 Accelerating Cogent Confabulation: an Exploration in the Architecture Design Space POSTPRINT complexity

Promising techniques to illuminate neuromodulatory control of the cerebral cortex in sleeping and waking states.

PubMed

Kanda, Takeshi; Ohyama, Kaoru; Muramoto, Hiroki; Kitajima, Nami; Sekiya, Hiroshi

2017-05-01

Sleep, a common event in daily life, has clear benefits for brain function, but what goes on in the brain when we sleep remains unclear. Sleep was long regarded as a silent state of the brain because the brain seemingly lacks interaction with the surroundings during sleep. Since the discovery of electrical activities in the brain at rest, electrophysiological methods have revealed novel concepts in sleep research. During sleep, the brain generates oscillatory activities that represent characteristic states of sleep. In addition to electrophysiology, opto/chemogenetics and two-photon Ca 2+ imaging methods have clarified that the sleep/wake states organized by neuronal and glial ensembles in the cerebral cortex are transitioned by neuromodulators. Even with these methods, however, it is extremely difficult to elucidate how and when neuromodulators spread, accumulate, and disappear in the extracellular space of the cortex. Thus, real-time monitoring of neuromodulator dynamics at high spatiotemporal resolution is required for further understanding of sleep. Toward direct detection of neuromodulator behavior during sleep and wakefulness, in this review, we discuss developing imaging techniques based on the activation of G-protein-coupled receptors that allow for visualization of neuromodulator dynamics. Copyright © 2017 Elsevier Ireland Ltd and Japan Neuroscience Society. All rights reserved.

Toward a standardized structural-functional group connectome in MNI space.

PubMed

Horn, Andreas; Blankenburg, Felix

2016-01-01

The analysis of the structural architecture of the human brain in terms of connectivity between its subregions has provided profound insights into its underlying functional organization and has coined the concept of the "connectome", a structural description of the elements forming the human brain and the connections among them. Here, as a proof of concept, we introduce a novel group connectome in standard space based on a large sample of 169 subjects from the Enhanced Nathan Kline Institute-Rockland Sample (eNKI-RS). Whole brain structural connectomes of each subject were estimated with a global tracking approach, and the resulting fiber tracts were warped into standard stereotactic (MNI) space using DARTEL. Employing this group connectome, the results of published tracking studies (i.e., the JHU white matter and Oxford thalamic connectivity atlas) could be largely reproduced directly within MNI space. In a second analysis, a study that examined structural connectivity between regions of a functional network, namely the default mode network, was reproduced. Voxel-wise structural centrality was then calculated and compared to others' findings. Furthermore, including additional resting-state fMRI data from the same subjects, structural and functional connectivity matrices between approximately forty thousand nodes of the brain were calculated. This was done to estimate structure-function agreement indices of voxel-wise whole brain connectivity. Taken together, the combination of a novel whole brain fiber tracking approach and an advanced normalization method led to a group connectome that allowed (at least heuristically) performing fiber tracking directly within MNI space. Such an approach may be used for various purposes like the analysis of structural connectivity and modeling experiments that aim at studying the structure-function relationship of the human connectome. Moreover, it may even represent a first step toward a standard DTI template of the human brain in stereotactic space. The standardized group connectome might thus be a promising new resource to better understand and further analyze the anatomical architecture of the human brain on a population level. Copyright © 2015 Elsevier Inc. All rights reserved.

Motor and linguistic linking of space and time in the cerebellum.

PubMed

Oliveri, Massimiliano; Bonnì, Sonia; Turriziani, Patrizia; Koch, Giacomo; Lo Gerfo, Emanuele; Torriero, Sara; Vicario, Carmelo Mario; Petrosini, Laura; Caltagirone, Carlo

2009-11-20

Recent literature documented the presence of spatial-temporal interactions in the human brain. The aim of the present study was to verify whether representation of past and future is also mapped onto spatial representations and whether the cerebellum may be a neural substrate for linking space and time in the linguistic domain. We asked whether processing of the tense of a verb is influenced by the space where response takes place and by the semantics of the verb. Responses to past tense were facilitated in the left space while responses to future tense were facilitated in the right space. Repetitive transcranial magnetic stimulation (rTMS) of the right cerebellum selectively slowed down responses to future tense of action verbs; rTMS of both cerebellar hemispheres decreased accuracy of responses to past tense in the left space and to future tense in the right space for non-verbs, and to future tense in the right space for state verbs. The results suggest that representation of past and future is mapped onto spatial formats and that motor action could represent the link between spatial and temporal dimensions. Right cerebellar, left motor brain networks could be part of the prospective brain, whose primary function is to use past experiences to anticipate future events. Both cerebellar hemispheres could play a role in establishing the grammatical rules for verb conjugation.

An extensive assessment of network alignment algorithms for comparison of brain connectomes.

PubMed

Milano, Marianna; Guzzi, Pietro Hiram; Tymofieva, Olga; Xu, Duan; Hess, Christofer; Veltri, Pierangelo; Cannataro, Mario

2017-06-06

Recently the study of the complex system of connections in neural systems, i.e. the connectome, has gained a central role in neurosciences. The modeling and analysis of connectomes are therefore a growing area. Here we focus on the representation of connectomes by using graph theory formalisms. Macroscopic human brain connectomes are usually derived from neuroimages; the analyzed brains are co-registered in the image domain and brought to a common anatomical space. An atlas is then applied in order to define anatomically meaningful regions that will serve as the nodes of the network - this process is referred to as parcellation. The atlas-based parcellations present some known limitations in cases of early brain development and abnormal anatomy. Consequently, it has been recently proposed to perform atlas-free random brain parcellation into nodes and align brains in the network space instead of the anatomical image space, as a way to deal with the unknown correspondences of the parcels. Such process requires modeling of the brain using graph theory and the subsequent comparison of the structure of graphs. The latter step may be modeled as a network alignment (NA) problem. In this work, we first define the problem formally, then we test six existing state of the art of network aligners on diffusion MRI-derived brain networks. We compare the performances of algorithms by assessing six topological measures. We also evaluated the robustness of algorithms to alterations of the dataset. The results confirm that NA algorithms may be applied in cases of atlas-free parcellation for a fully network-driven comparison of connectomes. The analysis shows MAGNA++ is the best global alignment algorithm. The paper presented a new analysis methodology that uses network alignment for validating atlas-free parcellation brain connectomes. The methodology has been experimented on several brain datasets.

Natural world physical, brain operational, and mind phenomenal space-time

NASA Astrophysics Data System (ADS)

Fingelkurts, Andrew A.; Fingelkurts, Alexander A.; Neves, Carlos F. H.

2010-06-01

Concepts of space and time are widely developed in physics. However, there is a considerable lack of biologically plausible theoretical frameworks that can demonstrate how space and time dimensions are implemented in the activity of the most complex life-system - the brain with a mind. Brain activity is organized both temporally and spatially, thus representing space-time in the brain. Critical analysis of recent research on the space-time organization of the brain's activity pointed to the existence of so-called operational space-time in the brain. This space-time is limited to the execution of brain operations of differing complexity. During each such brain operation a particular short-term spatio-temporal pattern of integrated activity of different brain areas emerges within related operational space-time. At the same time, to have a fully functional human brain one needs to have a subjective mental experience. Current research on the subjective mental experience offers detailed analysis of space-time organization of the mind. According to this research, subjective mental experience (subjective virtual world) has definitive spatial and temporal properties similar to many physical phenomena. Based on systematic review of the propositions and tenets of brain and mind space-time descriptions, our aim in this review essay is to explore the relations between the two. To be precise, we would like to discuss the hypothesis that via the brain operational space-time the mind subjective space-time is connected to otherwise distant physical space-time reality.

Quantification of brain macrostates using dynamical nonstationarity of physiological time series.

PubMed

Latchoumane, Charles-Francois Vincent; Jeong, Jaeseung

2011-04-01

The brain shows complex, nonstationarity temporal dynamics, with abrupt micro- and macrostate transitions during its information processing. Detecting and characterizing these transitions in dynamical states of the brain is a critical issue in the field of neuroscience and psychiatry. In the current study, a novel method is proposed to quantify brain macrostates (e.g., sleep stages or cognitive states) from shifts of dynamical microstates or dynamical nonstationarity. A ``dynamical microstate'' is a temporal unit of the information processing in the brain with fixed dynamical parameters and specific spatial distribution. In this proposed approach, a phase-space-based dynamical dissimilarity map (DDM) is used to detect transitions between dynamically stationary microstates in the time series, and Tsallis time-dependent entropy is applied to quantify dynamical patterns of transitions in the DDM. We demonstrate that the DDM successfully detects transitions between microstates of different temporal dynamics in the simulated physiological time series against high levels of noise. Based on the assumption of nonlinear, deterministic brain dynamics, we also demonstrate that dynamical nonstationarity analysis is useful to quantify brain macrostates (sleep stages I, II, III, IV, and rapid eye movement (REM) sleep) from sleep EEGs with an overall accuracy of 77%. We suggest that dynamical nonstationarity is a useful tool to quantify macroscopic mental states (statistical integration) of the brain using dynamical transitions at the microscopic scale in physiological data.

A Skew-t space-varying regression model for the spectral analysis of resting state brain activity.

PubMed

Ismail, Salimah; Sun, Wenqi; Nathoo, Farouk S; Babul, Arif; Moiseev, Alexader; Beg, Mirza Faisal; Virji-Babul, Naznin

2013-08-01

It is known that in many neurological disorders such as Down syndrome, main brain rhythms shift their frequencies slightly, and characterizing the spatial distribution of these shifts is of interest. This article reports on the development of a Skew-t mixed model for the spatial analysis of resting state brain activity in healthy controls and individuals with Down syndrome. Time series of oscillatory brain activity are recorded using magnetoencephalography, and spectral summaries are examined at multiple sensor locations across the scalp. We focus on the mean frequency of the power spectral density, and use space-varying regression to examine associations with age, gender and Down syndrome across several scalp regions. Spatial smoothing priors are incorporated based on a multivariate Markov random field, and the markedly non-Gaussian nature of the spectral response variable is accommodated by the use of a Skew-t distribution. A range of models representing different assumptions on the association structure and response distribution are examined, and we conduct model selection using the deviance information criterion. (1) Our analysis suggests region-specific differences between healthy controls and individuals with Down syndrome, particularly in the left and right temporal regions, and produces smoothed maps indicating the scalp topography of the estimated differences.

Metabolic changes in rat striatum following convulsive seizures.

PubMed

Darbin, Olivier; Risso, Jean Jacque; Carre, Emily; Lonjon, Michel; Naritoku, Dean K

2005-07-19

Generalized convulsive seizures increase glucose utilization within the brain but their impact on metabolism is not well known. The striatum receives excitatory input from widespread sources in the brain and could potentially reflect energy depletion in the brain resulting from generalized seizures. We utilized multiprobe microdialysis in freely moving rats subjected to maximal electroshock to simultaneously measure glucose, lactate, and pyruvate levels in the interstitial space within striatum and in peripheral subcutaneous tissue. A brief convulsive seizure was associated with marked changes in striatal and peripheral metabolism during the post-ictal state that lasted up to 1 h. There were significant central and peripheral elevations of glucose, pyruvate, and lactate, reflecting increased glucose metabolism. Interestingly, the lactate-to-pyruvate ratio increased significantly in the periphery but remained unchanged in the striatum. Thus, there appears to be brain mechanisms that maintain adequate energy sources and prevent anaerobic shift during the post-ictal state.

Untangling Brain-Wide Dynamics in Consciousness by Cross-Embedding

PubMed Central

Tajima, Satohiro; Yanagawa, Toru; Fujii, Naotaka; Toyoizumi, Taro

2015-01-01

Brain-wide interactions generating complex neural dynamics are considered crucial for emergent cognitive functions. However, the irreducible nature of nonlinear and high-dimensional dynamical interactions challenges conventional reductionist approaches. We introduce a model-free method, based on embedding theorems in nonlinear state-space reconstruction, that permits a simultaneous characterization of complexity in local dynamics, directed interactions between brain areas, and how the complexity is produced by the interactions. We demonstrate this method in large-scale electrophysiological recordings from awake and anesthetized monkeys. The cross-embedding method captures structured interaction underlying cortex-wide dynamics that may be missed by conventional correlation-based analysis, demonstrating a critical role of time-series analysis in characterizing brain state. The method reveals a consciousness-related hierarchy of cortical areas, where dynamical complexity increases along with cross-area information flow. These findings demonstrate the advantages of the cross-embedding method in deciphering large-scale and heterogeneous neuronal systems, suggesting a crucial contribution by sensory-frontoparietal interactions to the emergence of complex brain dynamics during consciousness. PMID:26584045

Spatially Regularized Machine Learning for Task and Resting-state fMRI

PubMed Central

Song, Xiaomu; Panych, Lawrence P.; Chen, Nan-kuei

2015-01-01

Background Reliable mapping of brain function across sessions and/or subjects in task- and resting-state has been a critical challenge for quantitative fMRI studies although it has been intensively addressed in the past decades. New Method A spatially regularized support vector machine (SVM) technique was developed for the reliable brain mapping in task- and resting-state. Unlike most existing SVM-based brain mapping techniques, which implement supervised classifications of specific brain functional states or disorders, the proposed method performs a semi-supervised classification for the general brain function mapping where spatial correlation of fMRI is integrated into the SVM learning. The method can adapt to intra- and inter-subject variations induced by fMRI nonstationarity, and identify a true boundary between active and inactive voxels, or between functionally connected and unconnected voxels in a feature space. Results The method was evaluated using synthetic and experimental data at the individual and group level. Multiple features were evaluated in terms of their contributions to the spatially regularized SVM learning. Reliable mapping results in both task- and resting-state were obtained from individual subjects and at the group level. Comparison with Existing Methods A comparison study was performed with independent component analysis, general linear model, and correlation analysis methods. Experimental results indicate that the proposed method can provide a better or comparable mapping performance at the individual and group level. Conclusions The proposed method can provide accurate and reliable mapping of brain function in task- and resting-state, and is applicable to a variety of quantitative fMRI studies. PMID:26470627

Source space analysis of event-related dynamic reorganization of brain networks.

PubMed

Ioannides, Andreas A; Dimitriadis, Stavros I; Saridis, George A; Voultsidou, Marotesa; Poghosyan, Vahe; Liu, Lichan; Laskaris, Nikolaos A

2012-01-01

How the brain works is nowadays synonymous with how different parts of the brain work together and the derivation of mathematical descriptions for the functional connectivity patterns that can be objectively derived from data of different neuroimaging techniques. In most cases static networks are studied, often relying on resting state recordings. Here, we present a quantitative study of dynamic reconfiguration of connectivity for event-related experiments. Our motivation is the development of a methodology that can be used for personalized monitoring of brain activity. In line with this motivation, we use data with visual stimuli from a typical subject that participated in different experiments that were previously analyzed with traditional methods. The earlier studies identified well-defined changes in specific brain areas at specific latencies related to attention, properties of stimuli, and tasks demands. Using a recently introduced methodology, we track the event-related changes in network organization, at source space level, thus providing a more global and complete view of the stages of processing associated with the regional changes in activity. The results suggest the time evolving modularity as an additional brain code that is accessible with noninvasive means and hence available for personalized monitoring and clinical applications.

Context-aware brain-computer interfaces: exploring the information space of user, technical system and environment

NASA Astrophysics Data System (ADS)

Zander, T. O.; Jatzev, S.

2012-02-01

Brain-computer interface (BCI) systems are usually applied in highly controlled environments such as research laboratories or clinical setups. However, many BCI-based applications are implemented in more complex environments. For example, patients might want to use a BCI system at home, and users without disabilities could benefit from BCI systems in special working environments. In these contexts, it might be more difficult to reliably infer information about brain activity, because many intervening factors add up and disturb the BCI feature space. One solution for this problem would be adding context awareness to the system. We propose to augment the available information space with additional channels carrying information about the user state, the environment and the technical system. In particular, passive BCI systems seem to be capable of adding highly relevant context information—otherwise covert aspects of user state. In this paper, we present a theoretical framework based on general human-machine system research for adding context awareness to a BCI system. Building on that, we present results from a study on a passive BCI, which allows access to the covert aspect of user state related to the perceived loss of control. This study is a proof of concept and demonstrates that context awareness could beneficially be implemented in and combined with a BCI system or a general human-machine system. The EEG data from this experiment are available for public download at www.phypa.org. Parts of this work have already been presented in non-journal publications. This will be indicated specifically by appropriate references in the text.

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

State Space Modeling of Time-Varying Contemporaneous and Lagged Relations in Connectivity Maps

PubMed Central

Molenaar, Peter C. M.; Beltz, Adriene M.; Gates, Kathleen M.; Wilson, Stephen J.

2017-01-01

Most connectivity mapping techniques for neuroimaging data assume stationarity (i.e., network parameters are constant across time), but this assumption does not always hold true. The authors provide a description of a new approach for simultaneously detecting time-varying (or dynamic) contemporaneous and lagged relations in brain connectivity maps. Specifically, they use a novel raw data likelihood estimation technique (involving a second-order extended Kalman filter/smoother embedded in a nonlinear optimizer) to determine the variances of the random walks associated with state space model parameters and their autoregressive components. The authors illustrate their approach with simulated and blood oxygen level-dependent functional magnetic resonance imaging data from 30 daily cigarette smokers performing a verbal working memory task, focusing on seven regions of interest (ROIs). Twelve participants had dynamic directed functional connectivity maps: Eleven had one or more time-varying contemporaneous ROI state loadings, and one had a time-varying autoregressive parameter. Compared to smokers without dynamic maps, smokers with dynamic maps performed the task with greater accuracy. Thus, accurate detection of dynamic brain processes is meaningfully related to behavior in a clinical sample. PMID:26546863

Quantitative dual-probe microdialysis: mathematical model and analysis.

PubMed

Chen, Kevin C; Höistad, Malin; Kehr, Jan; Fuxe, Kjell; Nicholson, Charles

2002-04-01

Steady-state microdialysis is a widely used technique to monitor the concentration changes and distributions of substances in tissues. To obtain more information about brain tissue properties from microdialysis, a dual-probe approach was applied to infuse and sample the radiotracer, [3H]mannitol, simultaneously both in agar gel and in the rat striatum. Because the molecules released by one probe and collected by the other must diffuse through the interstitial space, the concentration profile exhibits dynamic behavior that permits the assessment of the diffusion characteristics in the brain extracellular space and the clearance characteristics. In this paper a mathematical model for dual-probe microdialysis was developed to study brain interstitial diffusion and clearance processes. Theoretical expressions for the spatial distribution of the infused tracer in the brain extracellular space and the temporal concentration at the probe outlet were derived. A fitting program was developed using the simplex algorithm, which finds local minima of the standard deviations between experiments and theory by adjusting the relevant parameters. The theoretical curves accurately fitted the experimental data and generated realistic diffusion parameters, implying that the mathematical model is capable of predicting the interstitial diffusion behavior of [3H]mannitol and that it will be a valuable quantitative tool in dual-probe microdialysis.

Graph-theoretic analysis of discrete-phase-space states for condition change detection and quantification of information

DOEpatents

Hively, Lee M.

2014-09-16

Data collected from devices and human condition may be used to forewarn of critical events such as machine/structural failure or events from brain/heart wave data stroke. By monitoring the data, and determining what values are indicative of a failure forewarning, one can provide adequate notice of the impending failure in order to take preventive measures. This disclosure teaches a computer-based method to convert dynamical numeric data representing physical objects (unstructured data) into discrete-phase-space states, and hence into a graph (structured data) for extraction of condition change.

State-space model with deep learning for functional dynamics estimation in resting-state fMRI.

PubMed

Suk, Heung-Il; Wee, Chong-Yaw; Lee, Seong-Whan; Shen, Dinggang

2016-04-01

Studies on resting-state functional Magnetic Resonance Imaging (rs-fMRI) have shown that different brain regions still actively interact with each other while a subject is at rest, and such functional interaction is not stationary but changes over time. In terms of a large-scale brain network, in this paper, we focus on time-varying patterns of functional networks, i.e., functional dynamics, inherent in rs-fMRI, which is one of the emerging issues along with the network modelling. Specifically, we propose a novel methodological architecture that combines deep learning and state-space modelling, and apply it to rs-fMRI based Mild Cognitive Impairment (MCI) diagnosis. We first devise a Deep Auto-Encoder (DAE) to discover hierarchical non-linear functional relations among regions, by which we transform the regional features into an embedding space, whose bases are complex functional networks. Given the embedded functional features, we then use a Hidden Markov Model (HMM) to estimate dynamic characteristics of functional networks inherent in rs-fMRI via internal states, which are unobservable but can be inferred from observations statistically. By building a generative model with an HMM, we estimate the likelihood of the input features of rs-fMRI as belonging to the corresponding status, i.e., MCI or normal healthy control, based on which we identify the clinical label of a testing subject. In order to validate the effectiveness of the proposed method, we performed experiments on two different datasets and compared with state-of-the-art methods in the literature. We also analyzed the functional networks learned by DAE, estimated the functional connectivities by decoding hidden states in HMM, and investigated the estimated functional connectivities by means of a graph-theoretic approach. Copyright © 2016 Elsevier Inc. All rights reserved.

State-space model with deep learning for functional dynamics estimation in resting-state fMRI

PubMed Central

Suk, Heung-Il; Wee, Chong-Yaw; Lee, Seong-Whan; Shen, Dinggang

2017-01-01

Studies on resting-state functional Magnetic Resonance Imaging (rs-fMRI) have shown that different brain regions still actively interact with each other while a subject is at rest, and such functional interaction is not stationary but changes over time. In terms of a large-scale brain network, in this paper, we focus on time-varying patterns of functional networks, i.e., functional dynamics, inherent in rs-fMRI, which is one of the emerging issues along with the network modelling. Specifically, we propose a novel methodological architecture that combines deep learning and state-space modelling, and apply it to rs-fMRI based Mild Cognitive Impairment (MCI) diagnosis. We first devise a Deep Auto-Encoder (DAE) to discover hierarchical non-linear functional relations among regions, by which we transform the regional features into an embedding space, whose bases are complex functional networks. Given the embedded functional features, we then use a Hidden Markov Model (HMM) to estimate dynamic characteristics of functional networks inherent in rs-fMRI via internal states, which are unobservable but can be inferred from observations statistically. By building a generative model with an HMM, we estimate the likelihood of the input features of rs-fMRI as belonging to the corresponding status, i.e., MCI or normal healthy control, based on which we identify the clinical label of a testing subject. In order to validate the effectiveness of the proposed method, we performed experiments on two different datasets and compared with state-of-the-art methods in the literature. We also analyzed the functional networks learned by DAE, estimated the functional connectivities by decoding hidden states in HMM, and investigated the estimated functional connectivities by means of a graph-theoretic approach. PMID:26774612

Chaos in the brain: imaging via chaoticity of EEG/MEG signals

NASA Astrophysics Data System (ADS)

Kowalik, Zbigniew J.; Elbert, Thomas; Rockstroh, Brigitte; Hoke, Manfried

1995-03-01

Brain electro- (EEG) or magnetoencephalogram (MEG) can be analyzed by using methods of the nonlinear system theory. We show that even for very short and nonstationary time series it is possible to functionally differentiate various brain activities. Usually the analysis assumes that the analyzed signals are both long and stationary, so that the classic spectral methods can be used. Even more convincing results can be obtained under these circumstances when the dimensional analysis or estimation of the Kolmogorov entropy or the Lyapunov exponent are performed. When measuring the spontaneous activity of a human brain the assumption of stationarity is questionable and `static' methods (correlation dimension, entropy, etc.) are then not adequate. In this case `dynamic' methods like pointwise-D2 dimension or chaoticity measures should be applied. Predictability measures in the form of local Lyapunov exponents are capable of revealing directly the chaoticity of a given process, and can practically be applied for functional differentiation of brain activity. We exemplify these in cases of apallic syndrome, tinnitus and schizophrenia. We show that: the average chaoticity in apallic syndrome differentiates brain states both in space and time, chaoticity changes temporally in case of schizophrenia (critical jumps of chaoticity), chaoticity changes locally in space, i.e., in the cortex plane in case of tinnitus.

Probabilistic modeling of anatomical variability using a low dimensional parameterization of diffeomorphisms.

PubMed

Zhang, Miaomiao; Wells, William M; Golland, Polina

2017-10-01

We present an efficient probabilistic model of anatomical variability in a linear space of initial velocities of diffeomorphic transformations and demonstrate its benefits in clinical studies of brain anatomy. To overcome the computational challenges of the high dimensional deformation-based descriptors, we develop a latent variable model for principal geodesic analysis (PGA) based on a low dimensional shape descriptor that effectively captures the intrinsic variability in a population. We define a novel shape prior that explicitly represents principal modes as a multivariate complex Gaussian distribution on the initial velocities in a bandlimited space. We demonstrate the performance of our model on a set of 3D brain MRI scans from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Our model yields a more compact representation of group variation at substantially lower computational cost than the state-of-the-art method such as tangent space PCA (TPCA) and probabilistic principal geodesic analysis (PPGA) that operate in the high dimensional image space. Copyright © 2017 Elsevier B.V. All rights reserved.

Test-retest reliability of resting-state magnetoencephalography power in sensor and source space.

PubMed

Martín-Buro, María Carmen; Garcés, Pilar; Maestú, Fernando

2016-01-01

Several studies have reported changes in spontaneous brain rhythms that could be used as clinical biomarkers or in the evaluation of neuropsychological and drug treatments in longitudinal studies using magnetoencephalography (MEG). There is an increasing necessity to use these measures in early diagnosis and pathology progression; however, there is a lack of studies addressing how reliable they are. Here, we provide the first test-retest reliability estimate of MEG power in resting-state at sensor and source space. In this study, we recorded 3 sessions of resting-state MEG activity from 24 healthy subjects with an interval of a week between each session. Power values were estimated at sensor and source space with beamforming for classical frequency bands: delta (2-4 Hz), theta (4-8 Hz), alpha (8-13 Hz), low beta (13-20 Hz), high beta (20-30 Hz), and gamma (30-45 Hz). Then, test-retest reliability was evaluated using the intraclass correlation coefficient (ICC). We also evaluated the relation between source power and the within-subject variability. In general, ICC of theta, alpha, and low beta power was fairly high (ICC > 0.6) while in delta and gamma power was lower. In source space, fronto-posterior alpha, frontal beta, and medial temporal theta showed the most reliable profiles. Signal-to-noise ratio could be partially responsible for reliability as low signal intensity resulted in high within-subject variability, but also the inherent nature of some brain rhythms in resting-state might be driving these reliability patterns. In conclusion, our results described the reliability of MEG power estimates in each frequency band, which could be considered in disease characterization or clinical trials. © 2015 Wiley Periodicals, Inc.

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.

Dynamics of Entropy in Quantum-like Model of Decision Making

NASA Astrophysics Data System (ADS)

Basieva, Irina; Khrennikov, Andrei; Asano, Masanari; Ohya, Masanori; Tanaka, Yoshiharu

2011-03-01

We present a quantum-like model of decision making in games of the Prisoner's Dilemma type. By this model the brain processes information by using representation of mental states in complex Hilbert space. Driven by the master equation the mental state of a player, say Alice, approaches an equilibrium point in the space of density matrices. By using this equilibrium point Alice determines her mixed (i.e., probabilistic) strategy with respect to Bob. Thus our model is a model of thinking through decoherence of initially pure mental state. Decoherence is induced by interaction with memory and external environment. In this paper we study (numerically) dynamics of quantum entropy of Alice's state in the process of decision making. Our analysis demonstrates that this dynamics depends nontrivially on the initial state of Alice's mind on her own actions and her prediction state (for possible actions of Bob.)

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.

The Self-Paced Graz Brain-Computer Interface: Methods and Applications

PubMed Central

Scherer, Reinhold; Schloegl, Alois; Lee, Felix; Bischof, Horst; Janša, Janez; Pfurtscheller, Gert

2007-01-01

We present the self-paced 3-class Graz brain-computer interface (BCI) which is based on the detection of sensorimotor electroencephalogram (EEG) rhythms induced by motor imagery. Self-paced operation means that the BCI is able to determine whether the ongoing brain activity is intended as control signal (intentional control) or not (non-control state). The presented system is able to automatically reduce electrooculogram (EOG) artifacts, to detect electromyographic (EMG) activity, and uses only three bipolar EEG channels. Two applications are presented: the freeSpace virtual environment (VE) and the Brainloop interface. The freeSpace is a computer-game-like application where subjects have to navigate through the environment and collect coins by autonomously selecting navigation commands. Three subjects participated in these feedback experiments and each learned to navigate through the VE and collect coins. Two out of the three succeeded in collecting all three coins. The Brainloop interface provides an interface between the Graz-BCI and Google Earth. PMID:18350133

Diffeomorphic functional brain surface alignment: Functional demons.

PubMed

Nenning, Karl-Heinz; Liu, Hesheng; Ghosh, Satrajit S; Sabuncu, Mert R; Schwartz, Ernst; Langs, Georg

2017-08-01

Aligning brain structures across individuals is a central prerequisite for comparative neuroimaging studies. Typically, registration approaches assume a strong association between the features used for alignment, such as macro-anatomy, and the variable observed, such as functional activation or connectivity. Here, we propose to use the structure of intrinsic resting state fMRI signal correlation patterns as a basis for alignment of the cortex in functional studies. Rather than assuming the spatial correspondence of functional structures between subjects, we have identified locations with similar connectivity profiles across subjects. We mapped functional connectivity relationships within the brain into an embedding space, and aligned the resulting maps of multiple subjects. We then performed a diffeomorphic alignment of the cortical surfaces, driven by the corresponding features in the joint embedding space. Results show that functional alignment based on resting state fMRI identifies functionally homologous regions across individuals with higher accuracy than alignment based on the spatial correspondence of anatomy. Further, functional alignment enables measurement of the strength of the anatomo-functional link across the cortex, and reveals the uneven distribution of this link. Stronger anatomo-functional dissociation was found in higher association areas compared to primary sensory- and motor areas. Functional alignment based on resting state features improves group analysis of task based functional MRI data, increasing statistical power and improving the delineation of task-specific core regions. Finally, a comparison of the anatomo-functional dissociation between cohorts is demonstrated with a group of left and right handed subjects. Copyright © 2017 Elsevier Inc. All rights reserved.

Nano Goes Magnetic to Attract Big Business

NASA Technical Reports Server (NTRS)

2006-01-01

Glenn Research Center has combined state-of-the-art electrical designs with complex, computer-aided analyses to develop some of today s most advanced power systems, in space and on Earth. The center s Power and On-Board Propulsion Technology Division is the brain behind many of these power systems. For space, this division builds technologies that help power the International Space Station, the Hubble Space Telescope, and Earth-orbiting satellites. For Earth, it has woven advanced aerospace power concepts into commercial energy applications that include solar and nuclear power generation, battery and fuel cell energy storage, communications and telecommunications satellites, cryocoolers, hybrid and electric vehicles, and heating and air-conditioning systems.

State space modeling of time-varying contemporaneous and lagged relations in connectivity maps.

PubMed

Molenaar, Peter C M; Beltz, Adriene M; Gates, Kathleen M; Wilson, Stephen J

2016-01-15

Most connectivity mapping techniques for neuroimaging data assume stationarity (i.e., network parameters are constant across time), but this assumption does not always hold true. The authors provide a description of a new approach for simultaneously detecting time-varying (or dynamic) contemporaneous and lagged relations in brain connectivity maps. Specifically, they use a novel raw data likelihood estimation technique (involving a second-order extended Kalman filter/smoother embedded in a nonlinear optimizer) to determine the variances of the random walks associated with state space model parameters and their autoregressive components. The authors illustrate their approach with simulated and blood oxygen level-dependent functional magnetic resonance imaging data from 30 daily cigarette smokers performing a verbal working memory task, focusing on seven regions of interest (ROIs). Twelve participants had dynamic directed functional connectivity maps: Eleven had one or more time-varying contemporaneous ROI state loadings, and one had a time-varying autoregressive parameter. Compared to smokers without dynamic maps, smokers with dynamic maps performed the task with greater accuracy. Thus, accurate detection of dynamic brain processes is meaningfully related to behavior in a clinical sample. Published by Elsevier Inc.

Feasibility of using diffuse reflectance spectroscopy for the quantification of brain edema

NASA Astrophysics Data System (ADS)

Rodriguez, Juan G.; Sisson, Cynthia; Hendricks, Chad; Pattillo, Chris; McWaters, Megan; Hardjasudarma, Mardjohan; Quarles, Chad; Yaroslavsky, Anna N.; Yaroslavsky, Ilya V.; Battarbee, Harold

2001-05-01

Many diseased states of the brain can result in the displacement of brain tissues and restrict cerebral blood flow, disrupting function in a life-threatening manner. Clinical examples where displacements are observed include venous thromboses, hematomas, strokes, tumors, abscesses, and, particularly, brain edema. For the latter, the brain tissue swells, displacing the cerebral spinal fluid (CSF) layer that surrounds it, eventually pressing itself against the skull. Under such conditions, catheters are often inserted into the brain's ventricles or the subarachnoid space to monitor increased pressure. These are invasive procedures that incur increased risk of infection and consequently are used reluctantly by clinicians. Recent studies in the field of biomedical optics have suggested that the presence or absence of the CSF layer can lead to dramatic changes in NIR signals obtained from diffuse reflectance measurements around the head. In this study, we consider how this sensitivity of NIR signals to CSF might be exploited to non-invasively monitor the onset and resolution of brain edema.

State-Space Analysis of Working Memory in Schizophrenia: An FBIRN Study

ERIC Educational Resources Information Center

Janoos, Firdaus; Brown, Gregory; Morocz, Istvan A.; Wells, William M., III

2013-01-01

The neural correlates of "working memory" (WM) in schizophrenia (SZ) have been extensively studied using the multisite fMRI data acquired by the Functional Biomedical Informatics Research Network (fBIRN) consortium. Although univariate and multivariate analysis methods have been variously employed to localize brain responses under differing task…

Selective Activation of Resting-State Networks following Focal Stimulation in a Connectome-Based Network Model of the Human Brain

PubMed Central

2016-01-01

Abstract When the brain is stimulated, for example, by sensory inputs or goal-oriented tasks, the brain initially responds with activities in specific areas. The subsequent pattern formation of functional networks is constrained by the structural connectivity (SC) of the brain. The extent to which information is processed over short- or long-range SC is unclear. Whole-brain models based on long-range axonal connections, for example, can partly describe measured functional connectivity dynamics at rest. Here, we study the effect of SC on the network response to stimulation. We use a human whole-brain network model comprising long- and short-range connections. We systematically activate each cortical or thalamic area, and investigate the network response as a function of its short- and long-range SC. We show that when the brain is operating at the edge of criticality, stimulation causes a cascade of network recruitments, collapsing onto a smaller space that is partly constrained by SC. We found both short- and long-range SC essential to reproduce experimental results. In particular, the stimulation of specific areas results in the activation of one or more resting-state networks. We suggest that the stimulus-induced brain activity, which may indicate information and cognitive processing, follows specific routes imposed by structural networks explaining the emergence of functional networks. We provide a lookup table linking stimulation targets and functional network activations, which potentially can be useful in diagnostics and treatments with brain stimulation. PMID:27752540

Detecting large-scale networks in the human brain using high-density electroencephalography.

PubMed

Liu, Quanying; Farahibozorg, Seyedehrezvan; Porcaro, Camillo; Wenderoth, Nicole; Mantini, Dante

2017-09-01

High-density electroencephalography (hdEEG) is an emerging brain imaging technique that can be used to investigate fast dynamics of electrical activity in the healthy and the diseased human brain. Its applications are however currently limited by a number of methodological issues, among which the difficulty in obtaining accurate source localizations. In particular, these issues have so far prevented EEG studies from reporting brain networks similar to those previously detected by functional magnetic resonance imaging (fMRI). Here, we report for the first time a robust detection of brain networks from resting state (256-channel) hdEEG recordings. Specifically, we obtained 14 networks previously described in fMRI studies by means of realistic 12-layer head models and exact low-resolution brain electromagnetic tomography (eLORETA) source localization, together with independent component analysis (ICA) for functional connectivity analysis. Our analyses revealed three important methodological aspects. First, brain network reconstruction can be improved by performing source localization using the gray matter as source space, instead of the whole brain. Second, conducting EEG connectivity analyses in individual space rather than on concatenated datasets may be preferable, as it permits to incorporate realistic information on head modeling and electrode positioning. Third, the use of a wide frequency band leads to an unbiased and generally accurate reconstruction of several network maps, whereas filtering data in a narrow frequency band may enhance the detection of specific networks and penalize that of others. We hope that our methodological work will contribute to rise of hdEEG as a powerful tool for brain research. Hum Brain Mapp 38:4631-4643, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Psychoanalysis and the brain - why did freud abandon neuroscience?

PubMed

Northoff, Georg

2012-01-01

Sigmund Freud, the founder of psychoanalysis, was initially a neuroscientist but abandoned neuroscience completely after he made a last attempt to link both in his writing, "Project of a Scientific Psychology," in 1895. The reasons for his subsequent disregard of the brain remain unclear though. I here argue that one central reason may be that the approach to the brain during his time was simply not appealing to Freud. More specifically, Freud was interested in revealing the psychological predispositions of psychodynamic processes. However, he was not so much focused on the actual psychological functions themselves which though were the prime focus of the neuroscience at his time and also in current Cognitive Neuroscience. Instead, he probably would have been more interested in the brain's resting state and its constitution of a spatiotemporal structure. I here assume that the resting state activity constitutes a statistically based virtual structure extending and linking the different discrete points in time and space within the brain. That in turn may serve as template, schemata, or grid for all subsequent neural processing during stimulus-induced activity. As such the resting state' spatiotemporal structure may serve as the neural predisposition of what Freud described as "psychological structure." Hence, Freud and also current neuropsychoanalysis may want to focus more on neural predispositions, the necessary non-sufficient conditions, rather than the neural correlates, i.e., sufficient, conditions of psychodynamic processes.

Field theory of pattern identification

NASA Astrophysics Data System (ADS)

Agu, Masahiro

1988-06-01

Based on the psychological experimental fact that images in mental space are transformed into other images for pattern identification, a field theory of pattern identification of geometrical patterns is developed with the use of gauge field theory in Euclidean space. Here, the ``image'' or state function ψ[χ] of the brain reacting to a geometrical pattern χ is made to correspond to the electron's wave function in Minkowski space. The pattern identification of the pattern χ with the modified pattern χ+Δχ is assumed to be such that their images ψ[χ] and ψ[χ+Δχ] in the brain are transformable with each other through suitable transformation groups such as parallel transformation, dilatation, or rotation. The transformation group is called the ``image potential'' which corresponds to the vector potential of the gauge field. An ``image field'' derived from the image potential is found to be induced in the brain when the two images ψ[χ] and ψ[χ+Δχ] are not transformable through suitable transformation groups or gauge transformations. It is also shown that, when the image field exists, the final state of the image ψ[χ] is expected to be different, depending on the paths of modifications of the pattern χ leading to a final pattern. The above fact is interpreted as a version of the Aharonov and Bohm effect of the electron's wave function [A. Aharonov and D. Bohm, Phys. Rev. 115, 485 (1959)]. An excitation equation of the image field is also derived by postulating that patterns are identified maximally for the purpose of minimizing the number of memorized standard patterns.

A self-organized learning strategy for object recognition by an embedded line of attraction

NASA Astrophysics Data System (ADS)

Seow, Ming-Jung; Alex, Ann T.; Asari, Vijayan K.

2012-04-01

For humans, a picture is worth a thousand words, but to a machine, it is just a seemingly random array of numbers. Although machines are very fast and efficient, they are vastly inferior to humans for everyday information processing. Algorithms that mimic the way the human brain computes and learns may be the solution. In this paper we present a theoretical model based on the observation that images of similar visual perceptions reside in a complex manifold in an image space. The perceived features are often highly structured and hidden in a complex set of relationships or high-dimensional abstractions. To model the pattern manifold, we present a novel learning algorithm using a recurrent neural network. The brain memorizes information using a dynamical system made of interconnected neurons. Retrieval of information is accomplished in an associative sense. It starts from an arbitrary state that might be an encoded representation of a visual image and converges to another state that is stable. The stable state is what the brain remembers. In designing a recurrent neural network, it is usually of prime importance to guarantee the convergence in the dynamics of the network. We propose to modify this picture: if the brain remembers by converging to the state representing familiar patterns, it should also diverge from such states when presented with an unknown encoded representation of a visual image belonging to a different category. That is, the identification of an instability mode is an indication that a presented pattern is far away from any stored pattern and therefore cannot be associated with current memories. These properties can be used to circumvent the plasticity-stability dilemma by using the fluctuating mode as an indicator to create new states. We capture this behavior using a novel neural architecture and learning algorithm, in which the system performs self-organization utilizing a stability mode and an instability mode for the dynamical system. Based on this observation we developed a self- organizing line attractor, which is capable of generating new lines in the feature space to learn unrecognized patterns. Experiments performed on UMIST pose database and CMU face expression variant database for face recognition have shown that the proposed nonlinear line attractor is able to successfully identify the individuals and it provided better recognition rate when compared to the state of the art face recognition techniques. Experiments on FRGC version 2 database has also provided excellent recognition rate in images captured in complex lighting environments. Experiments performed on the Japanese female face expression database and Essex Grimace database using the self organizing line attractor have also shown successful expression invariant face recognition. These results show that the proposed model is able to create nonlinear manifolds in a multidimensional feature space to distinguish complex patterns.

Normalized power transmission between ABP and ICP in TBI.

PubMed

Shahsavari, S; Hallen, T; McKelvey, T; Ritzen, C; Rydenhag, B

2009-01-01

A new approach to study the pulse transmission between the cerebrovascular bed and the intracranial space is presented. In the proposed approach, the normalized power transmission between ABP and ICP has got the main attention rather than the actual power transmission. Evaluating the gain of the proposed transfer function at any single frequency can reveal how the percentage of contribution of that specific frequency component has been changed through the cerebrospinal system. The gain of the new transfer function at the fundamental cardiac frequency was utilized to evaluate the state of the brain in three TBI patients. Results were assessed using the reference evaluations achieved by a novel CT scan-based scoring scheme. In all three study cases, the gain of the transfer function showed a good capability to follow the trend of the CT scores and describe the brain state. Comparing the new transfer function with the traditional one and also the index of compensatory reserve, the proposed transfer function was found more informative about the state of the brain in the patients under study.

Decoding Lifespan Changes of the Human Brain Using Resting-State Functional Connectivity MRI

PubMed Central

Wang, Lubin; Su, Longfei; Shen, Hui; Hu, Dewen

2012-01-01

The development of large-scale functional brain networks is a complex, lifelong process that can be investigated using resting-state functional connectivity MRI (rs-fcMRI). In this study, we aimed to decode the developmental dynamics of the whole-brain functional network in seven decades (8–79 years) of the human lifespan. We first used parametric curve fitting to examine linear and nonlinear age effect on the resting human brain, and then combined manifold learning and support vector machine methods to predict individuals' “brain ages” from rs-fcMRI data. We found that age-related changes in interregional functional connectivity exhibited spatially and temporally specific patterns. During brain development from childhood to senescence, functional connections tended to linearly increase in the emotion system and decrease in the sensorimotor system; while quadratic trajectories were observed in functional connections related to higher-order cognitive functions. The complex patterns of age effect on the whole-brain functional network could be effectively represented by a low-dimensional, nonlinear manifold embedded in the functional connectivity space, which uncovered the inherent structure of brain maturation and aging. Regression of manifold coordinates with age further showed that the manifold representation extracted sufficient information from rs-fcMRI data to make prediction about individual brains' functional development levels. Our study not only gives insights into the neural substrates that underlie behavioral and cognitive changes over age, but also provides a possible way to quantitatively describe the typical and atypical developmental progression of human brain function using rs-fcMRI. PMID:22952990

Decoding lifespan changes of the human brain using resting-state functional connectivity MRI.

PubMed

Wang, Lubin; Su, Longfei; Shen, Hui; Hu, Dewen

2012-01-01

The development of large-scale functional brain networks is a complex, lifelong process that can be investigated using resting-state functional connectivity MRI (rs-fcMRI). In this study, we aimed to decode the developmental dynamics of the whole-brain functional network in seven decades (8-79 years) of the human lifespan. We first used parametric curve fitting to examine linear and nonlinear age effect on the resting human brain, and then combined manifold learning and support vector machine methods to predict individuals' "brain ages" from rs-fcMRI data. We found that age-related changes in interregional functional connectivity exhibited spatially and temporally specific patterns. During brain development from childhood to senescence, functional connections tended to linearly increase in the emotion system and decrease in the sensorimotor system; while quadratic trajectories were observed in functional connections related to higher-order cognitive functions. The complex patterns of age effect on the whole-brain functional network could be effectively represented by a low-dimensional, nonlinear manifold embedded in the functional connectivity space, which uncovered the inherent structure of brain maturation and aging. Regression of manifold coordinates with age further showed that the manifold representation extracted sufficient information from rs-fcMRI data to make prediction about individual brains' functional development levels. Our study not only gives insights into the neural substrates that underlie behavioral and cognitive changes over age, but also provides a possible way to quantitatively describe the typical and atypical developmental progression of human brain function using rs-fcMRI.

A Non-Critical String (Liouville) Approach to Brain Microtubules:. State Vector Reduction, Memory Coding and Capacity

NASA Astrophysics Data System (ADS)

Mavromatos, N. E.; Nanopoulos, D. V.

Microtubule (MT) networks, subneural paracrystalline cytoskeletal structures, seem to play a fundamental role in the neurons. We cast here the complicated MT dynamics in the form of a (1+1)-dimensional noncritical string theory, thus enabling us to provide a consistent quantum treatment of MTs, including enviromental friction effects. We suggest, thus, that the MTs are the microsites, in the brain, for the emergence of stable, macroscopic quantum coherent states, identifiable with the preconscious states. Quantum space-time effects, as described by noncritical string theory, trigger then an organized collapse of the coherent states down to a specific or conscious state. The whole process we estimate to take { O}(1 sec), in excellent agreement with a plethora of experimental/observational findings. The microscopic arrow of time, endemic in noncritical string theory, and apparent here in the self-collapse process, provides a satisfactory and simple resolution to the age-old problem of how the, central to our feelings of awareness, sensation of the progression of time is generated. In addition, the complete integrability of the stringy model for MT we advocate in this work proves sufficient in providing a satisfactory solution to memory coding and capacity. Such features might turn out to be important for a model of the brain as a quantum computer.

Towards a constructionist approach to emotions: verification of the three-dimensional model of affect with EEG-independent component analysis.

PubMed

Wyczesany, Miroslaw; Ligeza, Tomasz S

2015-03-01

The locationist model of affect, which assumes separate brain structures devoted to particular discrete emotions, is currently being questioned as it has not received enough convincing experimental support. An alternative, constructionist approach suggests that our emotional states emerge from the interaction between brain functional networks, which are related to more general, continuous affective categories. In the study, we tested whether the three-dimensional model of affect based on valence, arousal, and dominance (VAD) can reflect brain activity in a more coherent way than the traditional locationist approach. Independent components of brain activity were derived from spontaneous EEG recordings and localized using the DIPFIT method. The correspondence between the spectral power of the revealed brain sources and a mood self-report quantified on the VAD space was analysed. Activation of four (out of nine) clusters of independent brain sources could be successfully explained by the specific combination of three VAD dimensions. The results support the constructionist theory of emotions.

Neural dynamics during repetitive visual stimulation

NASA Astrophysics Data System (ADS)

Tsoneva, Tsvetomira; Garcia-Molina, Gary; Desain, Peter

2015-12-01

Objective. Steady-state visual evoked potentials (SSVEPs), the brain responses to repetitive visual stimulation (RVS), are widely utilized in neuroscience. Their high signal-to-noise ratio and ability to entrain oscillatory brain activity are beneficial for their applications in brain-computer interfaces, investigation of neural processes underlying brain rhythmic activity (steady-state topography) and probing the causal role of brain rhythms in cognition and emotion. This paper aims at analyzing the space and time EEG dynamics in response to RVS at the frequency of stimulation and ongoing rhythms in the delta, theta, alpha, beta, and gamma bands. Approach.We used electroencephalography (EEG) to study the oscillatory brain dynamics during RVS at 10 frequencies in the gamma band (40-60 Hz). We collected an extensive EEG data set from 32 participants and analyzed the RVS evoked and induced responses in the time-frequency domain. Main results. Stable SSVEP over parieto-occipital sites was observed at each of the fundamental frequencies and their harmonics and sub-harmonics. Both the strength and the spatial propagation of the SSVEP response seem sensitive to stimulus frequency. The SSVEP was more localized around the parieto-occipital sites for higher frequencies (>54 Hz) and spread to fronto-central locations for lower frequencies. We observed a strong negative correlation between stimulation frequency and relative power change at that frequency, the first harmonic and the sub-harmonic components over occipital sites. Interestingly, over parietal sites for sub-harmonics a positive correlation of relative power change and stimulation frequency was found. A number of distinct patterns in delta (1-4 Hz), theta (4-8 Hz), alpha (8-12 Hz) and beta (15-30 Hz) bands were also observed. The transient response, from 0 to about 300 ms after stimulation onset, was accompanied by increase in delta and theta power over fronto-central and occipital sites, which returned to baseline after approx. 500 ms. During the steady-state response, we observed alpha band desynchronization over occipital sites and after 500 ms also over frontal sites, while neighboring areas synchronized. The power in beta band over occipital sites increased during the stimulation period, possibly caused by increase in power at sub-harmonic frequencies of stimulation. Gamma power was also enhanced by the stimulation. Significance. These findings have direct implications on the use of RVS and SSVEPs for neural process investigation through steady-state topography, controlled entrainment of brain oscillations and BCIs. A deep understanding of SSVEP propagation in time and space and the link with ongoing brain rhythms is crucial for optimizing the typical SSVEP applications for studying, assisting, or augmenting human cognitive and sensorimotor function.

Glymphatic distribution of CSF-derived apoE into brain is isoform specific and suppressed during sleep deprivation.

PubMed

Achariyar, Thiyagaragan M; Li, Baoman; Peng, Weiguo; Verghese, Philip B; Shi, Yang; McConnell, Evan; Benraiss, Abdellatif; Kasper, Tristan; Song, Wei; Takano, Takahiro; Holtzman, David M; Nedergaard, Maiken; Deane, Rashid

2016-12-08

Apolipoprotein E (apoE) is a major carrier of cholesterol and essential for synaptic plasticity. In brain, it's expressed by many cells but highly expressed by the choroid plexus and the predominant apolipoprotein in cerebrospinal fluid (CSF). The role of apoE in the CSF is unclear. Recently, the glymphatic system was described as a clearance system whereby CSF and ISF (interstitial fluid) is exchanged via the peri-arterial space and convective flow of ISF clearance is mediated by aquaporin 4 (AQP4), a water channel. We reasoned that this system also serves to distribute essential molecules in CSF into brain. The aim was to establish whether apoE in CSF, secreted by the choroid plexus, is distributed into brain, and whether this distribution pattern was altered by sleep deprivation. We used fluorescently labeled lipidated apoE isoforms, lenti-apoE3 delivered to the choroid plexus, immunohistochemistry to map apoE brain distribution, immunolabeled cells and proteins in brain, Western blot analysis and ELISA to determine apoE levels and radiolabeled molecules to quantify CSF inflow into brain and brain clearance in mice. Data were statistically analyzed using ANOVA or Student's t- test. We show that the glymphatic fluid transporting system contributes to the delivery of choroid plexus/CSF-derived human apoE to neurons. CSF-delivered human apoE entered brain via the perivascular space of penetrating arteries and flows radially around arteries, but not veins, in an isoform specific manner (apoE2 > apoE3 > apoE4). Flow of apoE around arteries was facilitated by AQP4, a characteristic feature of the glymphatic system. ApoE3, delivered by lentivirus to the choroid plexus and ependymal layer but not to the parenchymal cells, was present in the CSF, penetrating arteries and neurons. The inflow of CSF, which contains apoE, into brain and its clearance from the interstitium were severely suppressed by sleep deprivation compared to the sleep state. Thus, choroid plexus/CSF provides an additional source of apoE and the glymphatic fluid transporting system delivers it to brain via the periarterial space. By implication, failure in this essential physiological role of the glymphatic fluid flow and ISF clearance may also contribute to apoE isoform-specific disorders in the long term.

Noninvasive Electroencephalogram Based Control of a Robotic Arm for Writing Task Using Hybrid BCI System.

PubMed

Gao, Qiang; Dou, Lixiang; Belkacem, Abdelkader Nasreddine; Chen, Chao

2017-01-01

A novel hybrid brain-computer interface (BCI) based on the electroencephalogram (EEG) signal which consists of a motor imagery- (MI-) based online interactive brain-controlled switch, "teeth clenching" state detector, and a steady-state visual evoked potential- (SSVEP-) based BCI was proposed to provide multidimensional BCI control. MI-based BCI was used as single-pole double throw brain switch (SPDTBS). By combining the SPDTBS with 4-class SSEVP-based BCI, movement of robotic arm was controlled in three-dimensional (3D) space. In addition, muscle artifact (EMG) of "teeth clenching" condition recorded from EEG signal was detected and employed as interrupter, which can initialize the statement of SPDTBS. Real-time writing task was implemented to verify the reliability of the proposed noninvasive hybrid EEG-EMG-BCI. Eight subjects participated in this study and succeeded to manipulate a robotic arm in 3D space to write some English letters. The mean decoding accuracy of writing task was 0.93 ± 0.03. Four subjects achieved the optimal criteria of writing the word "HI" which is the minimum movement of robotic arm directions (15 steps). Other subjects had needed to take from 2 to 4 additional steps to finish the whole process. These results suggested that our proposed hybrid noninvasive EEG-EMG-BCI was robust and efficient for real-time multidimensional robotic arm control.

Noninvasive Electroencephalogram Based Control of a Robotic Arm for Writing Task Using Hybrid BCI System

PubMed Central

Gao, Qiang

2017-01-01

A novel hybrid brain-computer interface (BCI) based on the electroencephalogram (EEG) signal which consists of a motor imagery- (MI-) based online interactive brain-controlled switch, “teeth clenching” state detector, and a steady-state visual evoked potential- (SSVEP-) based BCI was proposed to provide multidimensional BCI control. MI-based BCI was used as single-pole double throw brain switch (SPDTBS). By combining the SPDTBS with 4-class SSEVP-based BCI, movement of robotic arm was controlled in three-dimensional (3D) space. In addition, muscle artifact (EMG) of “teeth clenching” condition recorded from EEG signal was detected and employed as interrupter, which can initialize the statement of SPDTBS. Real-time writing task was implemented to verify the reliability of the proposed noninvasive hybrid EEG-EMG-BCI. Eight subjects participated in this study and succeeded to manipulate a robotic arm in 3D space to write some English letters. The mean decoding accuracy of writing task was 0.93 ± 0.03. Four subjects achieved the optimal criteria of writing the word “HI” which is the minimum movement of robotic arm directions (15 steps). Other subjects had needed to take from 2 to 4 additional steps to finish the whole process. These results suggested that our proposed hybrid noninvasive EEG-EMG-BCI was robust and efficient for real-time multidimensional robotic arm control. PMID:28660211

The brain as a dynamic physical system.

PubMed

McKenna, T M; McMullen, T A; Shlesinger, M F

1994-06-01

The brain is a dynamic system that is non-linear at multiple levels of analysis. Characterization of its non-linear dynamics is fundamental to our understanding of brain function. Identifying families of attractors in phase space analysis, an approach which has proven valuable in describing non-linear mechanical and electrical systems, can prove valuable in describing a range of behaviors and associated neural activity including sensory and motor repertoires. Additionally, transitions between attractors may serve as useful descriptors for analysing state changes in neurons and neural ensembles. Recent observations of synchronous neural activity, and the emerging capability to record the spatiotemporal dynamics of neural activity by voltage-sensitive dyes and electrode arrays, provide opportunities for observing the population dynamics of neural ensembles within a dynamic systems context. New developments in the experimental physics of complex systems, such as the control of chaotic systems, selection of attractors, attractor switching and transient states, can be a source of powerful new analytical tools and insights into the dynamics of neural systems.

Walking through Architectural Spaces: The Impact of Interior Forms on Human Brain Dynamics

PubMed Central

Banaei, Maryam; Hatami, Javad; Yazdanfar, Abbas; Gramann, Klaus

2017-01-01

Neuroarchitecture uses neuroscientific tools to better understand architectural design and its impact on human perception and subjective experience. The form or shape of the built environment is fundamental to architectural design, but not many studies have shown the impact of different forms on the inhabitants’ emotions. This study investigated the neurophysiological correlates of different interior forms on the perceivers’ affective state and the accompanying brain activity. To understand the impact of naturalistic three-dimensional (3D) architectural forms, it is essential to perceive forms from different perspectives. We computed clusters of form features extracted from pictures of residential interiors and constructed exemplary 3D room models based on and representing different formal clusters. To investigate human brain activity during 3D perception of architectural spaces, we used a mobile brain/body imaging (MoBI) approach recording the electroencephalogram (EEG) of participants while they naturally walk through different interior forms in virtual reality (VR). The results revealed a strong impact of curvature geometries on activity in the anterior cingulate cortex (ACC). Theta band activity in ACC correlated with specific feature types (rs (14) = 0.525, p = 0.037) and geometry (rs (14) = −0.579, p = 0.019), providing evidence for a role of this structure in processing architectural features beyond their emotional impact. The posterior cingulate cortex and the occipital lobe were involved in the perception of different room perspectives during the stroll through the rooms. This study sheds new light on the use of mobile EEG and VR in architectural studies and provides the opportunity to study human brain dynamics in participants that actively explore and realistically experience architectural spaces. PMID:29033807

Walking through Architectural Spaces: The Impact of Interior Forms on Human Brain Dynamics.

PubMed

Banaei, Maryam; Hatami, Javad; Yazdanfar, Abbas; Gramann, Klaus

2017-01-01

Neuroarchitecture uses neuroscientific tools to better understand architectural design and its impact on human perception and subjective experience. The form or shape of the built environment is fundamental to architectural design, but not many studies have shown the impact of different forms on the inhabitants' emotions. This study investigated the neurophysiological correlates of different interior forms on the perceivers' affective state and the accompanying brain activity. To understand the impact of naturalistic three-dimensional (3D) architectural forms, it is essential to perceive forms from different perspectives. We computed clusters of form features extracted from pictures of residential interiors and constructed exemplary 3D room models based on and representing different formal clusters. To investigate human brain activity during 3D perception of architectural spaces, we used a mobile brain/body imaging (MoBI) approach recording the electroencephalogram (EEG) of participants while they naturally walk through different interior forms in virtual reality (VR). The results revealed a strong impact of curvature geometries on activity in the anterior cingulate cortex (ACC). Theta band activity in ACC correlated with specific feature types ( r s (14) = 0.525, p = 0.037) and geometry ( r s (14) = -0.579, p = 0.019), providing evidence for a role of this structure in processing architectural features beyond their emotional impact. The posterior cingulate cortex and the occipital lobe were involved in the perception of different room perspectives during the stroll through the rooms. This study sheds new light on the use of mobile EEG and VR in architectural studies and provides the opportunity to study human brain dynamics in participants that actively explore and realistically experience architectural spaces.

Navigating the Neural Space in Search of the Neural Code.

PubMed

Jazayeri, Mehrdad; Afraz, Arash

2017-03-08

The advent of powerful perturbation tools, such as optogenetics, has created new frontiers for probing causal dependencies in neural and behavioral states. These approaches have significantly enhanced the ability to characterize the contribution of different cells and circuits to neural function in health and disease. They have shifted the emphasis of research toward causal interrogations and increased the demand for more precise and powerful tools to control and manipulate neural activity. Here, we clarify the conditions under which measurements and perturbations support causal inferences. We note that the brain functions at multiple scales and that causal dependencies may be best inferred with perturbation tools that interface with the system at the appropriate scale. Finally, we develop a geometric framework to facilitate the interpretation of causal experiments when brain perturbations do or do not respect the intrinsic patterns of brain activity. We describe the challenges and opportunities of applying perturbations in the presence of dynamics, and we close with a general perspective on navigating the activity space of neurons in the search for neural codes. Copyright © 2017 Elsevier Inc. All rights reserved.

The Brain in Space: A Teacher's Guide with Activities for Neuroscience.

ERIC Educational Resources Information Center

MacLeish, Marlene Y.; McLean, Bernice R.

This educators guide discusses the brain and contains activities on neuroscience. Activities include: (1) "The Space Life Sciences"; (2) "Space Neuroscience: A Special Area within the Space Life Sciences"; (3) "Space Life Sciences Research"; (4) "Neurolab: A Special Space Mission to Study the Nervous System"; (5) "The Nervous System"; (6)…

Definitions of state variables and state space for brain-computer interface : Part 2. Extraction and classification of feature vectors.

PubMed

Freeman, Walter J

2007-06-01

The hypothesis is proposed that the central dynamics of the action-perception cycle has five steps: emergence from an existing macroscopic brain state of a pattern that predicts a future goal state; selection of a mesoscopic frame for action control; execution of a limb trajectory by microscopic spike activity; modification of microscopic cortical spike activity by sensory inputs; construction of mesoscopic perceptual patterns; and integration of a new macroscopic brain state. The basis is the circular causality between microscopic entities (neurons) and the mesoscopic and macroscopic entities (populations) self-organized by axosynaptic interactions. Self-organization of neural activity is bidirectional in all cortices. Upwardly the organization of mesoscopic percepts from microscopic spike input predominates in primary sensory areas. Downwardly the organization of spike outputs that direct specific limb movements is by mesoscopic fields constituting plans to achieve predicted goals. The mesoscopic fields in sensory and motor cortices emerge as frames within macroscopic activity. Part 1 describes the action-perception cycle and its derivative reflex arc qualitatively. Part 2 describes the perceptual limb of the arc from microscopic MSA to mesoscopic wave packets, and from these to macroscopic EEG and global ECoG fields that express experience-dependent knowledge in successive states. These macroscopic states are conceived to embed and control mesoscopic frames in premotor and motor cortices that are observed in local ECoG and LFP of frontoparietal areas. The fields sampled by ECoG and LFP are conceived as local patterns of neural activity in which trajectories of multiple spike activities (MSA) emerge that control limb movements. Mesoscopic frames are located by use of the analytic signal from the Hilbert transform after band pass filtering. The state variables in frames are measured to construct feature vectors by which to describe and classify frame patterns. Evidence is cited to justify use of linear analysis. The aim of the review is to enable researchers to conceive and identify goal-oriented states in brain activity for use as commands, in order to relegate the details of execution to adaptive control devices outside the brain.

Physiological Observations and Omics to Develop Personalized Sensormotor Adaptability Countermeasures Using Bed Rest and Space Flight Data

NASA Technical Reports Server (NTRS)

Mulavara, A. P.; Seidler, R. D.; Feiveson, A.; Oddsson, L.; Zanello, S.; Oman, C. M.; Ploutz-Snyder, L.; Peters, B.; Cohen, H. S.; Reschke, M.;

Brain system for mental orientation in space, time, and person.

PubMed

Peer, Michael; Salomon, Roy; Goldberg, Ilan; Blanke, Olaf; Arzy, Shahar

2015-09-01

Orientation is a fundamental mental function that processes the relations between the behaving self to space (places), time (events), and person (people). Behavioral and neuroimaging studies have hinted at interrelations between processing of these three domains. To unravel the neurocognitive basis of orientation, we used high-resolution 7T functional MRI as 16 subjects compared their subjective distance to different places, events, or people. Analysis at the individual-subject level revealed cortical activation related to orientation in space, time, and person in a precisely localized set of structures in the precuneus, inferior parietal, and medial frontal cortex. Comparison of orientation domains revealed a consistent order of cortical activity inside the precuneus and inferior parietal lobes, with space orientation activating posterior regions, followed anteriorly by person and then time. Core regions at the precuneus and inferior parietal lobe were activated for multiple orientation domains, suggesting also common processing for orientation across domains. The medial prefrontal cortex showed a posterior activation for time and anterior for person. Finally, the default-mode network, identified in a separate resting-state scan, was active for all orientation domains and overlapped mostly with person-orientation regions. These findings suggest that mental orientation in space, time, and person is managed by a specific brain system with a highly ordered internal organization, closely related to the default-mode network.

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.

Biotechnology

NASA Image and Video Library

2003-01-12

The short-arm centrifuge subjects an astronaut to conflicting sensory input and study the astronaut's perception of motion. It is one of several instruments used in the Spatial Reorientation Following Space Flight investigation to be conducted after astronauts return to Earth. During space flight, the vestibular organs no longer respond in a familiar way. Instead, inputs from the irner ear do not match those coming from the eyes. While on Earth, you can open your eyes to see if you truly are spinning, but astronauts do not have this luxury. Astronauts can see the floor, but have no sense of down; when they bend their heads forward, the otoliths are not stimulated properly. This state, called sensory conflict, must be resolved by the brain to maintain orientation. When they first return to Earth, astronauts are again disoriented because of sensory conflict. They undergo a period of spatial reorientation, as their brains reconcile what their eyes see and what their vestibular system senses. Recovery can take anywhere from hours to days depending on the length of the mission. Principal Investigator: Dr. William Paloski, Johnson Space Center, Houston, TX.

Microgravity

NASA Image and Video Library

2003-01-12

The short-arm centrifuge subjects an astronaut to conflicting sensory input and study the astronaut's perception of motion. It is one of several instruments used in the Spatial Reorientation Following Space Flight investigation to be conducted on crewmembers. During space flight, the vestibular organs no longer respond in a familiar way. Instead, inputs from the irner ear do not match those coming from the eyes. While on Earth, you can open your eyes to see if you truly are spinning, but astronauts do not have this luxury. Astronauts can see the floor, but have no sense of down; when they bend their heads forward, the otoliths are not stimulated properly. This state, called sensory conflict, must be resolved by the brain to maintain orientation. When they first return to Earth, astronauts are again disoriented because of sensory conflict. They undergo a period of spatial reorientation, as their brains reconcile what their eyes see and what their vestibular system senses. Recovery can take anywhere from hours to days depending on the length of the mission. Principal Investigator: Dr. William Paloski, Johnson Space Center, Houston, TX.

Neural imaginaries and clinical epistemology: Rhetorically mapping the adolescent brain in the clinical encounter.

PubMed

Buchbinder, Mara

2015-10-01

The social work of brain images has taken center stage in recent theorizing of the intersections between neuroscience and society. However, neuroimaging is only one of the discursive modes through which public representations of neurobiology travel. This article adopts an expanded view toward the social implications of neuroscientific thinking to examine how neural imaginaries are constructed in the absence of visual evidence. Drawing on ethnographic fieldwork conducted over 18 months (2008-2009) in a United States multidisciplinary pediatric pain clinic, I examine the pragmatic clinical work undertaken to represent ambiguous symptoms in neurobiological form. Focusing on one physician, I illustrate how, by rhetorically mapping the brain as a therapeutic tool, she engaged in a distinctive form of representation that I call neural imagining. In shifting my focus away from the purely material dimensions of brain images, I juxtapose the cultural work of brain scanning technologies with clinical neural imaginaries in which the teenage brain becomes a space of possibility, not to map things as they are, but rather, things as we hope they might be. These neural imaginaries rely upon a distinctive clinical epistemology that privileges the creative work of the imagination over visualization technologies in revealing the truths of the body. By creating a therapeutic space for adolescents to exercise their imaginative faculties and a discursive template for doing so, neural imagining relocates adolescents' agency with respect to epistemologies of bodily knowledge and the role of visualization practices therein. In doing so, it provides a more hopeful alternative to the dominant popular and scientific representations of the teenage brain that view it primarily through the lens of pathology. Copyright © 2014 Elsevier Ltd. All rights reserved.

Task-Driven Activity Reduces the Cortical Activity Space of the Brain: Experiment and Whole-Brain Modeling

PubMed Central

Hagmann, Patric; Deco, Gustavo

2015-01-01

How a stimulus or a task alters the spontaneous dynamics of the brain remains a fundamental open question in neuroscience. One of the most robust hallmarks of task/stimulus-driven brain dynamics is the decrease of variability with respect to the spontaneous level, an effect seen across multiple experimental conditions and in brain signals observed at different spatiotemporal scales. Recently, it was observed that the trial-to-trial variability and temporal variance of functional magnetic resonance imaging (fMRI) signals decrease in the task-driven activity. Here we examined the dynamics of a large-scale model of the human cortex to provide a mechanistic understanding of these observations. The model allows computing the statistics of synaptic activity in the spontaneous condition and in putative tasks determined by external inputs to a given subset of brain regions. We demonstrated that external inputs decrease the variance, increase the covariances, and decrease the autocovariance of synaptic activity as a consequence of single node and large-scale network dynamics. Altogether, these changes in network statistics imply a reduction of entropy, meaning that the spontaneous synaptic activity outlines a larger multidimensional activity space than does the task-driven activity. We tested this model’s prediction on fMRI signals from healthy humans acquired during rest and task conditions and found a significant decrease of entropy in the stimulus-driven activity. Altogether, our study proposes a mechanism for increasing the information capacity of brain networks by enlarging the volume of possible activity configurations at rest and reliably settling into a confined stimulus-driven state to allow better transmission of stimulus-related information. PMID:26317432

Neural Imaginaries and Clinical Epistemology: Rhetorically Mapping the Adolescent Brain in the Clinical Encounter

PubMed Central

Buchbinder, Mara

2014-01-01

The social work of brain images has taken center stage in recent theorizing of the intersections between neuroscience and society. However, neuroimaging is only one of the discursive modes through which public representations of neurobiology travel. This article adopts an expanded view toward the social implications of neuroscientific thinking to examine how neural imaginaries are constructed in the absence of visual evidence. Drawing on ethnographic fieldwork conducted over 18 months (2008–2009) in a United States multidisciplinary pediatric pain clinic, I examine the pragmatic clinical work undertaken to represent ambiguous symptoms in neurobiological form. Focusing on one physician, I illustrate how, by rhetorically mapping the brain as a therapeutic tool, she engaged in a distinctive form of representation that I call neural imagining. In shifting my focus away from the purely material dimensions of brain images, I juxtapose the cultural work of brain scanning technologies with clinical neural imaginaries in which the teenage brain becomes a space of possibility, not to map things as they are, but rather, things as we hope they might be. These neural imaginaries rely upon a distinctive clinical epistemology that privileges the creative work of the imagination over visualization technologies in revealing the truths of the body. By creating a therapeutic space for adolescents to exercise their imaginative faculties and a discursive template for doing so, neural imagining relocates adolescents’ agency with respect to epistemologies of bodily knowledge and the role of visualization practices therein. In doing so, it provides a more hopeful alternative to the dominant popular and scientific representations of the teenage brain that view it primarily through the lens of pathology. PMID:24780561

Volumetric analysis of cerebrospinal fluid and brain parenchyma in a patient with hydranencephaly and macrocephaly--case report.

PubMed

Radoš, Milan; Klarica, Marijan; Mučić-Pucić, Branka; Nikić, Ines; Raguž, Marina; Galkowski, Valentina; Mandić, Dora; Orešković, Darko

2014-08-28

The aim of this study was to perform for the first time the intracranial volumetric analysis of cerebrospinal fluid (CSF) and brain parenchyma in the supratentorial and infratentorial space in a 30-year-old female patient with hydranencephaly and macrocephaly. A head scan performed using a 3T magnetic resonance was followed by manual segmentation of the brain parenchyma and CSF on T2 coronal brain sections. The volume of CSF and brain parenchyma was measured separately for the supratentorial and infratentorial space. The total volume of the intracranial space was 3645.5 cm3. In the supratentorial space, the volume of CSF was 3375.2 cm3 and the volume of brain parenchyma was 80.3 cm3. In the infratentorial space, the volume of CSF was 101.3 cm3 and the volume of the brain parenchyma was 88.7 cm3. In the supratentorial space, there was severe malacia of almost all brain parenchyma with no visible remnants of the choroid plexuses. Infratentorial structures of the brainstem and cerebellum were hypoplastic but completely developed. Since our patient had no choroid plexuses in the supratentorial space and no obstruction between dural sinuses and CSF, development of hydrocephalus and macrocephaly cannot be explained by the classic hypothesis of CSF physiology with secretion, unidirectional circulation, and absorption as its basic postulates. However, the origin and turnover of the enormous amount of intracranial CSF volume, at least 10-fold larger than normal, and the mechanisms of macroencephaly development could be elucidated by the new hypothesis of CSF physiology recently published by our research team.

Fractals, Coherence and Brain Dynamics

NASA Astrophysics Data System (ADS)

Vitiello, Giuseppe

2010-11-01

I show that the self-similarity property of deterministic fractals provides a direct connection with the space of the entire analytical functions. Fractals are thus described in terms of coherent states in the Fock-Bargmann representation. Conversely, my discussion also provides insights on the geometrical properties of coherent states: it allows to recognize, in some specific sense, fractal properties of coherent states. In particular, the relation is exhibited between fractals and q-deformed coherent states. The connection with the squeezed coherent states is also displayed. In this connection, the non-commutative geometry arising from the fractal relation with squeezed coherent states is discussed and the fractal spectral properties are identified. I also briefly discuss the description of neuro-phenomenological data in terms of squeezed coherent states provided by the dissipative model of brain and consider the fact that laboratory observations have shown evidence that self-similarity characterizes the brain background activity. This suggests that a connection can be established between brain dynamics and the fractal self-similarity properties on the basis of the relation discussed in this report between fractals and squeezed coherent states. Finally, I do not consider in this paper the so-called random fractals, namely those fractals obtained by randomization processes introduced in their iterative generation. Since self-similarity is still a characterizing property in many of such random fractals, my conjecture is that also in such cases there must exist a connection with the coherent state algebraic structure. In condensed matter physics, in many cases the generation by the microscopic dynamics of some kind of coherent states is involved in the process of the emergence of mesoscopic/macroscopic patterns. The discussion presented in this paper suggests that also fractal generation may provide an example of emergence of global features, namely long range correlation at mesoscopic/macroscopic level, from microscopic local deformation processes. In view of the wide spectrum of application of both, fractal studies and coherent state physics, spanning from solid state physics to laser physics, quantum optics, complex dynamical systems and biological systems, the results presented in the present report may lead to interesting practical developments in many research sectors.

Effects of geomagnetic activity variations on the physiological and psychological state of functionally healthy humans: Some results of Azerbaijani studies

NASA Astrophysics Data System (ADS)

Babayev, Elchin S.; Allahverdiyeva, Aysel A.

There are collaborative and cross-disciplinary space weather studies in the Azerbaijan National Academy of Sciences conducted with purposes of revealing possible effects of solar, geomagnetic and cosmic ray variability on certain technological, biological and ecological systems. This paper describes some results of the experimental studies of influence of the periodical and aperiodical changes of geomagnetic activity upon human brain, human health and psycho-emotional state. It also covers the conclusions of studies on influence of violent solar events and severe geomagnetic storms of the solar cycle 23 on the mentioned systems in middle-latitude location. It is experimentally established that weak and moderate geomagnetic storms do not cause significant changes in the brain's bioelectrical activity and exert only stimulating influence while severe disturbances of geomagnetic conditions cause negative influence, seriously disintegrate brain's functionality, activate braking processes and amplify the negative emotional background of an individual. It is concluded that geomagnetic disturbances affect mainly emotional and vegetative spheres of human beings while characteristics reflecting personality properties do not undergo significant changes.

3D Reconstructed Cyto-, Muscarinic M2 Receptor, and Fiber Architecture of the Rat Brain Registered to the Waxholm Space Atlas

PubMed Central

Schubert, Nicole; Axer, Markus; Schober, Martin; Huynh, Anh-Minh; Huysegoms, Marcel; Palomero-Gallagher, Nicola; Bjaalie, Jan G.; Leergaard, Trygve B.; Kirlangic, Mehmet E.; Amunts, Katrin; Zilles, Karl

2016-01-01

High-resolution multiscale and multimodal 3D models of the brain are essential tools to understand its complex structural and functional organization. Neuroimaging techniques addressing different aspects of brain organization should be integrated in a reference space to enable topographically correct alignment and subsequent analysis of the various datasets and their modalities. The Waxholm Space (http://software.incf.org/software/waxholm-space) is a publicly available 3D coordinate-based standard reference space for the mapping and registration of neuroanatomical data in rodent brains. This paper provides a newly developed pipeline combining imaging and reconstruction steps with a novel registration strategy to integrate new neuroimaging modalities into the Waxholm Space atlas. As a proof of principle, we incorporated large scale high-resolution cyto-, muscarinic M2 receptor, and fiber architectonic images of rat brains into the 3D digital MRI based atlas of the Sprague Dawley rat in Waxholm Space. We describe the whole workflow, from image acquisition to reconstruction and registration of these three modalities into the Waxholm Space rat atlas. The registration of the brain sections into the atlas is performed by using both linear and non-linear transformations. The validity of the procedure is qualitatively demonstrated by visual inspection, and a quantitative evaluation is performed by measurement of the concordance between representative atlas-delineated regions and the same regions based on receptor or fiber architectonic data. This novel approach enables for the first time the generation of 3D reconstructed volumes of nerve fibers and fiber tracts, or of muscarinic M2 receptor density distributions, in an entire rat brain. Additionally, our pipeline facilitates the inclusion of further neuroimaging datasets, e.g., 3D reconstructed volumes of histochemical stainings or of the regional distributions of multiple other receptor types, into the Waxholm Space. Thereby, a multiscale and multimodal rat brain model was created in the Waxholm Space atlas of the rat brain. Since the registration of these multimodal high-resolution datasets into the same coordinate system is an indispensable requisite for multi-parameter analyses, this approach enables combined studies on receptor and cell distributions as well as fiber densities in the same anatomical structures at microscopic scales for the first time. PMID:27199682

3D Reconstructed Cyto-, Muscarinic M2 Receptor, and Fiber Architecture of the Rat Brain Registered to the Waxholm Space Atlas.

PubMed

Schubert, Nicole; Axer, Markus; Schober, Martin; Huynh, Anh-Minh; Huysegoms, Marcel; Palomero-Gallagher, Nicola; Bjaalie, Jan G; Leergaard, Trygve B; Kirlangic, Mehmet E; Amunts, Katrin; Zilles, Karl

2016-01-01

High-resolution multiscale and multimodal 3D models of the brain are essential tools to understand its complex structural and functional organization. Neuroimaging techniques addressing different aspects of brain organization should be integrated in a reference space to enable topographically correct alignment and subsequent analysis of the various datasets and their modalities. The Waxholm Space (http://software.incf.org/software/waxholm-space) is a publicly available 3D coordinate-based standard reference space for the mapping and registration of neuroanatomical data in rodent brains. This paper provides a newly developed pipeline combining imaging and reconstruction steps with a novel registration strategy to integrate new neuroimaging modalities into the Waxholm Space atlas. As a proof of principle, we incorporated large scale high-resolution cyto-, muscarinic M2 receptor, and fiber architectonic images of rat brains into the 3D digital MRI based atlas of the Sprague Dawley rat in Waxholm Space. We describe the whole workflow, from image acquisition to reconstruction and registration of these three modalities into the Waxholm Space rat atlas. The registration of the brain sections into the atlas is performed by using both linear and non-linear transformations. The validity of the procedure is qualitatively demonstrated by visual inspection, and a quantitative evaluation is performed by measurement of the concordance between representative atlas-delineated regions and the same regions based on receptor or fiber architectonic data. This novel approach enables for the first time the generation of 3D reconstructed volumes of nerve fibers and fiber tracts, or of muscarinic M2 receptor density distributions, in an entire rat brain. Additionally, our pipeline facilitates the inclusion of further neuroimaging datasets, e.g., 3D reconstructed volumes of histochemical stainings or of the regional distributions of multiple other receptor types, into the Waxholm Space. Thereby, a multiscale and multimodal rat brain model was created in the Waxholm Space atlas of the rat brain. Since the registration of these multimodal high-resolution datasets into the same coordinate system is an indispensable requisite for multi-parameter analyses, this approach enables combined studies on receptor and cell distributions as well as fiber densities in the same anatomical structures at microscopic scales for the first time.

Higher Dimensional Meta-State Analysis Reveals Reduced Resting fMRI Connectivity Dynamism in Schizophrenia Patients.

PubMed

Miller, Robyn L; Yaesoubi, Maziar; Turner, Jessica A; Mathalon, Daniel; Preda, Adrian; Pearlson, Godfrey; Adali, Tulay; Calhoun, Vince D

2016-01-01

Resting-state functional brain imaging studies of network connectivity have long assumed that functional connections are stationary on the timescale of a typical scan. Interest in moving beyond this simplifying assumption has emerged only recently. The great hope is that training the right lens on time-varying properties of whole-brain network connectivity will shed additional light on previously concealed brain activation patterns characteristic of serious neurological or psychiatric disorders. We present evidence that multiple explicitly dynamical properties of time-varying whole-brain network connectivity are strongly associated with schizophrenia, a complex mental illness whose symptomatic presentation can vary enormously across subjects. As with so much brain-imaging research, a central challenge for dynamic network connectivity lies in determining transformations of the data that both reduce its dimensionality and expose features that are strongly predictive of important population characteristics. Our paper introduces an elegant, simple method of reducing and organizing data around which a large constellation of mutually informative and intuitive dynamical analyses can be performed. This framework combines a discrete multidimensional data-driven representation of connectivity space with four core dynamism measures computed from large-scale properties of each subject's trajectory, ie., properties not identifiable with any specific moment in time and therefore reasonable to employ in settings lacking inter-subject time-alignment, such as resting-state functional imaging studies. Our analysis exposes pronounced differences between schizophrenia patients (Nsz = 151) and healthy controls (Nhc = 163). Time-varying whole-brain network connectivity patterns are found to be markedly less dynamically active in schizophrenia patients, an effect that is even more pronounced in patients with high levels of hallucinatory behavior. To the best of our knowledge this is the first demonstration that high-level dynamic properties of whole-brain connectivity, generic enough to be commensurable under many decompositions of time-varying connectivity data, exhibit robust and systematic differences between schizophrenia patients and healthy controls.

Higher Dimensional Meta-State Analysis Reveals Reduced Resting fMRI Connectivity Dynamism in Schizophrenia Patients

PubMed Central

Miller, Robyn L.; Yaesoubi, Maziar; Turner, Jessica A.; Mathalon, Daniel; Preda, Adrian; Pearlson, Godfrey; Adali, Tulay; Calhoun, Vince D.

2016-01-01

Resting-state functional brain imaging studies of network connectivity have long assumed that functional connections are stationary on the timescale of a typical scan. Interest in moving beyond this simplifying assumption has emerged only recently. The great hope is that training the right lens on time-varying properties of whole-brain network connectivity will shed additional light on previously concealed brain activation patterns characteristic of serious neurological or psychiatric disorders. We present evidence that multiple explicitly dynamical properties of time-varying whole-brain network connectivity are strongly associated with schizophrenia, a complex mental illness whose symptomatic presentation can vary enormously across subjects. As with so much brain-imaging research, a central challenge for dynamic network connectivity lies in determining transformations of the data that both reduce its dimensionality and expose features that are strongly predictive of important population characteristics. Our paper introduces an elegant, simple method of reducing and organizing data around which a large constellation of mutually informative and intuitive dynamical analyses can be performed. This framework combines a discrete multidimensional data-driven representation of connectivity space with four core dynamism measures computed from large-scale properties of each subject’s trajectory, ie., properties not identifiable with any specific moment in time and therefore reasonable to employ in settings lacking inter-subject time-alignment, such as resting-state functional imaging studies. Our analysis exposes pronounced differences between schizophrenia patients (Nsz = 151) and healthy controls (Nhc = 163). Time-varying whole-brain network connectivity patterns are found to be markedly less dynamically active in schizophrenia patients, an effect that is even more pronounced in patients with high levels of hallucinatory behavior. To the best of our knowledge this is the first demonstration that high-level dynamic properties of whole-brain connectivity, generic enough to be commensurable under many decompositions of time-varying connectivity data, exhibit robust and systematic differences between schizophrenia patients and healthy controls. PMID:26981625

Brain system for mental orientation in space, time, and person

PubMed Central

Peer, Michael; Salomon, Roy; Goldberg, Ilan; Blanke, Olaf; Arzy, Shahar

2015-01-01

Orientation is a fundamental mental function that processes the relations between the behaving self to space (places), time (events), and person (people). Behavioral and neuroimaging studies have hinted at interrelations between processing of these three domains. To unravel the neurocognitive basis of orientation, we used high-resolution 7T functional MRI as 16 subjects compared their subjective distance to different places, events, or people. Analysis at the individual-subject level revealed cortical activation related to orientation in space, time, and person in a precisely localized set of structures in the precuneus, inferior parietal, and medial frontal cortex. Comparison of orientation domains revealed a consistent order of cortical activity inside the precuneus and inferior parietal lobes, with space orientation activating posterior regions, followed anteriorly by person and then time. Core regions at the precuneus and inferior parietal lobe were activated for multiple orientation domains, suggesting also common processing for orientation across domains. The medial prefrontal cortex showed a posterior activation for time and anterior for person. Finally, the default-mode network, identified in a separate resting-state scan, was active for all orientation domains and overlapped mostly with person-orientation regions. These findings suggest that mental orientation in space, time, and person is managed by a specific brain system with a highly ordered internal organization, closely related to the default-mode network. PMID:26283353

Multiband tangent space mapping and feature selection for classification of EEG during motor imagery.

PubMed

Islam, Md Rabiul; Tanaka, Toshihisa; Molla, Md Khademul Islam

2018-05-08

When designing multiclass motor imagery-based brain-computer interface (MI-BCI), a so-called tangent space mapping (TSM) method utilizing the geometric structure of covariance matrices is an effective technique. This paper aims to introduce a method using TSM for finding accurate operational frequency bands related brain activities associated with MI tasks. A multichannel electroencephalogram (EEG) signal is decomposed into multiple subbands, and tangent features are then estimated on each subband. A mutual information analysis-based effective algorithm is implemented to select subbands containing features capable of improving motor imagery classification accuracy. Thus obtained features of selected subbands are combined to get feature space. A principal component analysis-based approach is employed to reduce the features dimension and then the classification is accomplished by a support vector machine (SVM). Offline analysis demonstrates the proposed multiband tangent space mapping with subband selection (MTSMS) approach outperforms state-of-the-art methods. It acheives the highest average classification accuracy for all datasets (BCI competition dataset 2a, IIIa, IIIb, and dataset JK-HH1). The increased classification accuracy of MI tasks with the proposed MTSMS approach can yield effective implementation of BCI. The mutual information-based subband selection method is implemented to tune operation frequency bands to represent actual motor imagery tasks.

Gadolinium-based Contrast Media, Cerebrospinal Fluid and the Glymphatic System: Possible Mechanisms for the Deposition of Gadolinium in the Brain.

PubMed

Taoka, Toshiaki; Naganawa, Shinji

2018-04-10

After Kanda's first report in 2014 on gadolinium (Gd) deposition in brain tissue, a considerable number of studies have investigated the explanation for the observation. Gd deposition in brain tissue after repeated administration of gadolinium-based contrast medium (GBCM) has been histologically proven, and chelate stability has been shown to affect the deposition. However, the mechanism for this deposition has not been fully elucidated. Recently, a hypothesis was introduced that involves the 'glymphatic system', which is a coined word that combines 'gl' for glia cell and 'lymphatic' system. According to this hypothesis, the perivascular space functions as a conduit for cerebrospinal fluid to flow into the brain parenchyma. The perivascular space around the arteries allows cerebrospinal fluid to enter the interstitial space of the brain tissue through water channels controlled by aquaporin 4. The cerebrospinal fluid entering the interstitial space clears waste proteins from the tissue. It then flows into the perivascular space around the vein and is discharged outside the brain. In addition to the hypothesis regarding the glymphatic system, some reports have described that after GBCM administration, some of the GBCM distributes through systemic blood circulation and remains in other compartments including the cerebrospinal fluid. It is thought that the GBCM distributed into the cerebrospinal fluid cavity via the glymphatic system may remain in brain tissue for a longer duration compared to the GBCM in systemic circulation. Glymphatic system may of course act as a clearance system for GBCM from brain tissue. Based on these findings, the mechanism for Gd deposition in the brain will be discussed in this review. The authors speculate that the glymphatic system may be the major contributory factor to the deposition and clearance of gadolinium in brain tissue.

Gadolinium-based Contrast Media, Cerebrospinal Fluid and the Glymphatic System: Possible Mechanisms for the Deposition of Gadolinium in the Brain

PubMed Central

Taoka, Toshiaki; Naganawa, Shinji

2018-01-01

After Kanda’s first report in 2014 on gadolinium (Gd) deposition in brain tissue, a considerable number of studies have investigated the explanation for the observation. Gd deposition in brain tissue after repeated administration of gadolinium-based contrast medium (GBCM) has been histologically proven, and chelate stability has been shown to affect the deposition. However, the mechanism for this deposition has not been fully elucidated. Recently, a hypothesis was introduced that involves the ‘glymphatic system’, which is a coined word that combines ‘gl’ for glia cell and ‘lymphatic’ system. According to this hypothesis, the perivascular space functions as a conduit for cerebrospinal fluid to flow into the brain parenchyma. The perivascular space around the arteries allows cerebrospinal fluid to enter the interstitial space of the brain tissue through water channels controlled by aquaporin 4. The cerebrospinal fluid entering the interstitial space clears waste proteins from the tissue. It then flows into the perivascular space around the vein and is discharged outside the brain. In addition to the hypothesis regarding the glymphatic system, some reports have described that after GBCM administration, some of the GBCM distributes through systemic blood circulation and remains in other compartments including the cerebrospinal fluid. It is thought that the GBCM distributed into the cerebrospinal fluid cavity via the glymphatic system may remain in brain tissue for a longer duration compared to the GBCM in systemic circulation. Glymphatic system may of course act as a clearance system for GBCM from brain tissue. Based on these findings, the mechanism for Gd deposition in the brain will be discussed in this review. The authors speculate that the glymphatic system may be the major contributory factor to the deposition and clearance of gadolinium in brain tissue. PMID:29367513

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.

Segmentation of brain structures in presence of a space-occupying lesion.

PubMed

Pollo, Claudio; Cuadra, Meritxell Bach; Cuisenaire, Olivier; Villemure, Jean-Guy; Thiran, Jean-Philippe

2005-02-15

Brain deformations induced by space-occupying lesions may result in unpredictable position and shape of functionally important brain structures. The aim of this study is to propose a method for segmentation of brain structures by deformation of a segmented brain atlas in presence of a space-occupying lesion. Our approach is based on an a priori model of lesion growth (MLG) that assumes radial expansion from a seeding point and involves three steps: first, an affine registration bringing the atlas and the patient into global correspondence; then, the seeding of a synthetic tumor into the brain atlas providing a template for the lesion; finally, the deformation of the seeded atlas, combining a method derived from optical flow principles and a model of lesion growth. The method was applied on two meningiomas inducing a pure displacement of the underlying brain structures, and segmentation accuracy of ventricles and basal ganglia was assessed. Results show that the segmented structures were consistent with the patient's anatomy and that the deformation accuracy of surrounding brain structures was highly dependent on the accurate placement of the tumor seeding point. Further improvements of the method will optimize the segmentation accuracy. Visualization of brain structures provides useful information for therapeutic consideration of space-occupying lesions, including surgical, radiosurgical, and radiotherapeutic planning, in order to increase treatment efficiency and prevent neurological damage.

Point process modeling and estimation: Advances in the analysis of dynamic neural spiking data

NASA Astrophysics Data System (ADS)

Deng, Xinyi

2016-08-01

A common interest of scientists in many fields is to understand the relationship between the dynamics of a physical system and the occurrences of discrete events within such physical system. Seismologists study the connection between mechanical vibrations of the Earth and the occurrences of earthquakes so that future earthquakes can be better predicted. Astrophysicists study the association between the oscillating energy of celestial regions and the emission of photons to learn the Universe's various objects and their interactions. Neuroscientists study the link between behavior and the millisecond-timescale spike patterns of neurons to understand higher brain functions. Such relationships can often be formulated within the framework of state-space models with point process observations. The basic idea is that the dynamics of the physical systems are driven by the dynamics of some stochastic state variables and the discrete events we observe in an interval are noisy observations with distributions determined by the state variables. This thesis proposes several new methodological developments that advance the framework of state-space models with point process observations at the intersection of statistics and neuroscience. In particular, we develop new methods 1) to characterize the rhythmic spiking activity using history-dependent structure, 2) to model population spike activity using marked point process models, 3) to allow for real-time decision making, and 4) to take into account the need for dimensionality reduction for high-dimensional state and observation processes. We applied these methods to a novel problem of tracking rhythmic dynamics in the spiking of neurons in the subthalamic nucleus of Parkinson's patients with the goal of optimizing placement of deep brain stimulation electrodes. We developed a decoding algorithm that can make decision in real-time (for example, to stimulate the neurons or not) based on various sources of information present in population spiking data. Lastly, we proposed a general three-step paradigm that allows us to relate behavioral outcomes of various tasks to simultaneously recorded neural activity across multiple brain areas, which is a step towards closed-loop therapies for psychological diseases using real-time neural stimulation. These methods are suitable for real-time implementation for content-based feedback experiments.

Information flow dynamics in the brain

NASA Astrophysics Data System (ADS)

Rabinovich, Mikhail I.; Afraimovich, Valentin S.; Bick, Christian; Varona, Pablo

2012-03-01

Timing and dynamics of information in the brain is a hot field in modern neuroscience. The analysis of the temporal evolution of brain information is crucially important for the understanding of higher cognitive mechanisms in normal and pathological states. From the perspective of information dynamics, in this review we discuss working memory capacity, language dynamics, goal-dependent behavior programming and other functions of brain activity. In contrast with the classical description of information theory, which is mostly algebraic, brain flow information dynamics deals with problems such as the stability/instability of information flows, their quality, the timing of sequential processing, the top-down cognitive control of perceptual information, and information creation. In this framework, different types of information flow instabilities correspond to different cognitive disorders. On the other hand, the robustness of cognitive activity is related to the control of the information flow stability. We discuss these problems using both experimental and theoretical approaches, and we argue that brain activity is better understood considering information flows in the phase space of the corresponding dynamical model. In particular, we show how theory helps to understand intriguing experimental results in this matter, and how recent knowledge inspires new theoretical formalisms that can be tested with modern experimental techniques.

A wireless neural recording system with a precision motorized microdrive for freely behaving animals

PubMed Central

Hasegawa, Taku; Fujimoto, Hisataka; Tashiro, Koichiro; Nonomura, Mayu; Tsuchiya, Akira; Watanabe, Dai

2015-01-01

The brain is composed of many different types of neurons. Therefore, analysis of brain activity with single-cell resolution could provide fundamental insights into brain mechanisms. However, the electrical signal of an individual neuron is very small, and precise isolation of single neuronal activity from moving subjects is still challenging. To measure single-unit signals in actively behaving states, establishment of technologies that enable fine control of electrode positioning and strict spike sorting is essential. To further apply such a single-cell recording approach to small brain areas in naturally behaving animals in large spaces or during social interaction, we developed a compact wireless recording system with a motorized microdrive. Wireless control of electrode placement facilitates the exploration of single neuronal activity without affecting animal behaviors. Because the system is equipped with a newly developed data-encoding program, the recorded data are readily compressed almost to theoretical limits and securely transmitted to a host computer. Brain activity can thereby be stably monitored in real time and further analyzed using online or offline spike sorting. Our wireless recording approach using a precision motorized microdrive will become a powerful tool for studying brain mechanisms underlying natural or social behaviors. PMID:25597933

Paravascular channels, cisterns, and the subarachnoid space in the rat brain: A single compartment with preferential pathways

PubMed Central

Bedussi, Beatrice; van der Wel, Nicole N; de Vos, Judith; van Veen, Henk; Siebes, Maria; VanBavel, Ed

2016-01-01

Recent evidence suggests an extensive exchange of fluid and solutes between the subarachnoid space and the brain interstitium, involving preferential pathways along blood vessels. We studied the anatomical relations between brain vasculature, cerebrospinal fluid compartments, and paravascular spaces in male Wistar rats. A fluorescent tracer was infused into the cisterna magna, without affecting intracranial pressure. Tracer distribution was analyzed using a 3D imaging cryomicrotome, confocal microscopy, and correlative light and electron microscopy. We found a strong 3D colocalization of tracer with major arteries and veins in the subarachnoid space and large cisterns, attributed to relatively large subarachnoid space volumes around the vessels. Confocal imaging confirmed this colocalization and also revealed novel cisternal connections between the subarachnoid space and ventricles. Unlike the vessels in the subarachnoid space, penetrating arteries but not veins were surrounded by tracer. Correlative light and electron microscopy images indicated that this paravascular space was located outside of the endothelial layer in capillaries and just outside of the smooth muscle cells in arteries. In conclusion, the cerebrospinal fluid compartment, consisting of the subarachnoid space, cisterns, ventricles, and para-arteriolar spaces, forms a continuous and extensive network that surrounds and penetrates the rat brain, in which mixing may facilitate exchange between interstitial fluid and cerebrospinal fluid. PMID:27306753

Paravascular channels, cisterns, and the subarachnoid space in the rat brain: A single compartment with preferential pathways.

PubMed

Bedussi, Beatrice; van der Wel, Nicole N; de Vos, Judith; van Veen, Henk; Siebes, Maria; VanBavel, Ed; Bakker, Erik Ntp

2017-04-01

Recent evidence suggests an extensive exchange of fluid and solutes between the subarachnoid space and the brain interstitium, involving preferential pathways along blood vessels. We studied the anatomical relations between brain vasculature, cerebrospinal fluid compartments, and paravascular spaces in male Wistar rats. A fluorescent tracer was infused into the cisterna magna, without affecting intracranial pressure. Tracer distribution was analyzed using a 3D imaging cryomicrotome, confocal microscopy, and correlative light and electron microscopy. We found a strong 3D colocalization of tracer with major arteries and veins in the subarachnoid space and large cisterns, attributed to relatively large subarachnoid space volumes around the vessels. Confocal imaging confirmed this colocalization and also revealed novel cisternal connections between the subarachnoid space and ventricles. Unlike the vessels in the subarachnoid space, penetrating arteries but not veins were surrounded by tracer. Correlative light and electron microscopy images indicated that this paravascular space was located outside of the endothelial layer in capillaries and just outside of the smooth muscle cells in arteries. In conclusion, the cerebrospinal fluid compartment, consisting of the subarachnoid space, cisterns, ventricles, and para-arteriolar spaces, forms a continuous and extensive network that surrounds and penetrates the rat brain, in which mixing may facilitate exchange between interstitial fluid and cerebrospinal fluid.

Perivascular Spaces--MRI Marker of Inflammatory Activity in the Brain?

ERIC Educational Resources Information Center

Wuerfel, Jens; Haertle, Mareile; Waiczies, Helmar; Tysiak, Eva; Bechmann, Ingo; Wernecke, Klaus D.; Zipp, Frauke; Paul, Friedemann

2008-01-01

The Virchow-Robin spaces (VRS), perivascular compartments surrounding small blood vessels as they penetrate the brain parenchyma, are increasingly recognized for their role in leucocyte trafficking as well as for their potential to modulate immune responses. In the present study, we investigated VRS numbers and volumes in different brain regions…

Superficial subarachnoid cerebrospinal fluid space expansion after surgical drainage of chronic subdural hematoma.

PubMed

Tosaka, Masahiko; Tsushima, Yoshito; Watanabe, Saiko; Sakamoto, Kazuya; Yodonawa, Masahiko; Kunimine, Hideo; Fujita, Haruyasu; Fujii, Takashi

2015-07-01

The present study examined the computed tomography (CT) findings after surgery and overnight drainage for chronic subdural hematoma (CSDH) to clear the significance of inner superficial subarachnoid CSF space and outer subdural hematoma cavity between the brain surface and the inner skull. A total of 73 sides in 60 patients were evaluated. Head CT was performed on the day after surgery and overnight drainage (1st CT), within 3 weeks of surgery (2nd CT), and more than 3 weeks after surgery (3rd CT). Subdural and subarachnoid spaces were identified to focus on density of fluid, shape of air collection, and location of silicone drainage tube, etc. Cases with subdural space larger than the subarachnoid CSF space were classified as Group SD between the brain and the skull. Cases with subarachnoid CSF space larger than the subdural space were classified as Group SA. Cases with extremely thin (<3 mm) spaces between the brain and the skull were classified as Group NS. Group SA, SD, and NS accounted for 31.9, 55.6 and 12.5% of cases on the 1st CT. No statistical differences were found between Groups SA, SD, and NS in any clinical factors, including recurrence. Group SA were found significantly more on 1st CT than on 2nd and 3rd CT. Subarachnoid CSF space sometimes expands between the brain and skull on CT after surgical overnight drainage. Expansion of the arachnoid space may be a passive phenomenon induced by overnight drainage and delayed re-expansion of the brain parenchyma.

Source Space Estimation of Oscillatory Power and Brain Connectivity in Tinnitus

PubMed Central

Zobay, Oliver; Palmer, Alan R.; Hall, Deborah A.; Sereda, Magdalena; Adjamian, Peyman

2015-01-01

Tinnitus is the perception of an internally generated sound that is postulated to emerge as a result of structural and functional changes in the brain. However, the precise pathophysiology of tinnitus remains unknown. Llinas’ thalamocortical dysrhythmia model suggests that neural deafferentation due to hearing loss causes a dysregulation of coherent activity between thalamus and auditory cortex. This leads to a pathological coupling of theta and gamma oscillatory activity in the resting state, localised to the auditory cortex where normally alpha oscillations should occur. Numerous studies also suggest that tinnitus perception relies on the interplay between auditory and non-auditory brain areas. According to the Global Brain Model, a network of global fronto—parietal—cingulate areas is important in the generation and maintenance of the conscious perception of tinnitus. Thus, the distress experienced by many individuals with tinnitus is related to the top—down influence of this global network on auditory areas. In this magnetoencephalographic study, we compare resting-state oscillatory activity of tinnitus participants and normal-hearing controls to examine effects on spectral power as well as functional and effective connectivity. The analysis is based on beamformer source projection and an atlas-based region-of-interest approach. We find increased functional connectivity within the auditory cortices in the alpha band. A significant increase is also found for the effective connectivity from a global brain network to the auditory cortices in the alpha and beta bands. We do not find evidence of effects on spectral power. Overall, our results provide only limited support for the thalamocortical dysrhythmia and Global Brain models of tinnitus. PMID:25799178

Closed-Loop Deep Brain Stimulation for Refractory Chronic Pain

PubMed Central

Shirvalkar, Prasad; Veuthey, Tess L.; Dawes, Heather E.; Chang, Edward F.

2018-01-01

Pain is a subjective experience that alerts an individual to actual or potential tissue damage. Through mechanisms that are still unclear, normal physiological pain can lose its adaptive value and evolve into pathological chronic neuropathic pain. Chronic pain is a multifaceted experience that can be understood in terms of somatosensory, affective, and cognitive dimensions, each with associated symptoms and neural signals. While there have been many attempts to treat chronic pain, in this article we will argue that feedback-controlled ‘closed-loop’ deep brain stimulation (DBS) offers an urgent and promising route for treatment. Contemporary DBS trials for chronic pain use “open-loop” approaches in which tonic stimulation is delivered with fixed parameters to a single brain region. The impact of key variables such as the target brain region and the stimulation waveform is unclear, and long-term efficacy has mixed results. We hypothesize that chronic pain is due to abnormal synchronization between brain networks encoding the somatosensory, affective and cognitive dimensions of pain, and that multisite, closed-loop DBS provides an intuitive mechanism for disrupting that synchrony. By (1) identifying biomarkers of the subjective pain experience and (2) integrating these signals into a state-space representation of pain, we can create a predictive model of each patient's pain experience. Then, by establishing how stimulation in different brain regions influences individual neural signals, we can design real-time, closed-loop therapies tailored to each patient. While chronic pain is a complex disorder that has eluded modern therapies, rich historical data and state-of-the-art technology can now be used to develop a promising treatment. PMID:29632482

Average is optimal: an inverted-U relationship between trial-to-trial brain activity and behavioral performance.

PubMed

He, Biyu J; Zempel, John M

2013-01-01

It is well known that even under identical task conditions, there is a tremendous amount of trial-to-trial variability in both brain activity and behavioral output. Thus far the vast majority of event-related potential (ERP) studies investigating the relationship between trial-to-trial fluctuations in brain activity and behavioral performance have only tested a monotonic relationship between them. However, it was recently found that across-trial variability can correlate with behavioral performance independent of trial-averaged activity. This finding predicts a U- or inverted-U- shaped relationship between trial-to-trial brain activity and behavioral output, depending on whether larger brain variability is associated with better or worse behavior, respectively. Using a visual stimulus detection task, we provide evidence from human electrocorticography (ECoG) for an inverted-U brain-behavior relationship: When the raw fluctuation in broadband ECoG activity is closer to the across-trial mean, hit rate is higher and reaction times faster. Importantly, we show that this relationship is present not only in the post-stimulus task-evoked brain activity, but also in the pre-stimulus spontaneous brain activity, suggesting anticipatory brain dynamics. Our findings are consistent with the presence of stochastic noise in the brain. They further support attractor network theories, which postulate that the brain settles into a more confined state space under task performance, and proximity to the targeted trajectory is associated with better performance.

Unsupervised classification of operator workload from brain signals.

PubMed

Schultze-Kraft, Matthias; Dähne, Sven; Gugler, Manfred; Curio, Gabriel; Blankertz, Benjamin

2016-06-01

In this study we aimed for the classification of operator workload as it is expected in many real-life workplace environments. We explored brain-signal based workload predictors that differ with respect to the level of label information required for training, including entirely unsupervised approaches. Subjects executed a task on a touch screen that required continuous effort of visual and motor processing with alternating difficulty. We first employed classical approaches for workload state classification that operate on the sensor space of EEG and compared those to the performance of three state-of-the-art spatial filtering methods: common spatial patterns (CSPs) analysis, which requires binary label information; source power co-modulation (SPoC) analysis, which uses the subjects' error rate as a target function; and canonical SPoC (cSPoC) analysis, which solely makes use of cross-frequency power correlations induced by different states of workload and thus represents an unsupervised approach. Finally, we investigated the effects of fusing brain signals and peripheral physiological measures (PPMs) and examined the added value for improving classification performance. Mean classification accuracies of 94%, 92% and 82% were achieved with CSP, SPoC, cSPoC, respectively. These methods outperformed the approaches that did not use spatial filtering and they extracted physiologically plausible components. The performance of the unsupervised cSPoC is significantly increased by augmenting it with PPM features. Our analyses ensured that the signal sources used for classification were of cortical origin and not contaminated with artifacts. Our findings show that workload states can be successfully differentiated from brain signals, even when less and less information from the experimental paradigm is used, thus paving the way for real-world applications in which label information may be noisy or entirely unavailable.

Unsupervised classification of operator workload from brain signals

NASA Astrophysics Data System (ADS)

Schultze-Kraft, Matthias; Dähne, Sven; Gugler, Manfred; Curio, Gabriel; Blankertz, Benjamin

2016-06-01

Objective. In this study we aimed for the classification of operator workload as it is expected in many real-life workplace environments. We explored brain-signal based workload predictors that differ with respect to the level of label information required for training, including entirely unsupervised approaches. Approach. Subjects executed a task on a touch screen that required continuous effort of visual and motor processing with alternating difficulty. We first employed classical approaches for workload state classification that operate on the sensor space of EEG and compared those to the performance of three state-of-the-art spatial filtering methods: common spatial patterns (CSPs) analysis, which requires binary label information; source power co-modulation (SPoC) analysis, which uses the subjects’ error rate as a target function; and canonical SPoC (cSPoC) analysis, which solely makes use of cross-frequency power correlations induced by different states of workload and thus represents an unsupervised approach. Finally, we investigated the effects of fusing brain signals and peripheral physiological measures (PPMs) and examined the added value for improving classification performance. Main results. Mean classification accuracies of 94%, 92% and 82% were achieved with CSP, SPoC, cSPoC, respectively. These methods outperformed the approaches that did not use spatial filtering and they extracted physiologically plausible components. The performance of the unsupervised cSPoC is significantly increased by augmenting it with PPM features. Significance. Our analyses ensured that the signal sources used for classification were of cortical origin and not contaminated with artifacts. Our findings show that workload states can be successfully differentiated from brain signals, even when less and less information from the experimental paradigm is used, thus paving the way for real-world applications in which label information may be noisy or entirely unavailable.

Time rescaling reproduces EEG behavior during transition from propofol anesthesia-induced unconsciousness to consciousness.

PubMed

Boussen, S; Spiegler, A; Benar, C; Carrère, M; Bartolomei, F; Metellus, P; Voituriez, R; Velly, L; Bruder, N; Trébuchon, A

2018-04-16

General anesthesia (GA) is a reversible manipulation of consciousness whose mechanism is mysterious at the level of neural networks leaving space for several competing hypotheses. We recorded electrocorticography (ECoG) signals in patients who underwent intracranial monitoring during awake surgery for the treatment of cerebral tumors in functional areas of the brain. Therefore, we recorded the transition from unconsciousness to consciousness directly on the brain surface. Using frequency resolved interferometry; we studied the intermediate ECoG frequencies (4-40 Hz). In the theoretical study, we used a computational Jansen and Rit neuron model to simulate recovery of consciousness (ROC). During ROC, we found that f increased by a factor equal to 1.62 ± 0.09, and δf varied by the same factor (1.61 ± 0.09) suggesting the existence of a scaling factor. We accelerated the time course of an unconscious EEG trace by an approximate factor 1.6 and we showed that the resulting EEG trace match the conscious state. Using the theoretical model, we successfully reproduced this behavior. We show that the recovery of consciousness corresponds to a transition in the frequency (f, δf) space, which is exactly reproduced by a simple time rescaling. These findings may perhaps be applied to other altered consciousness states.

Neural correlates of establishing, maintaining, and switching brain states

PubMed Central

Tang, Yi-Yuan; Rothbart, Mary K.; Posner, Michael I.

2012-01-01

Although the study of brain states is an old one in neuroscience, there has been growing interest in brain state specification owing to MRI studies tracing brain connectivity at rest. In this review, we summarize recent research on three relatively well-described brain states: the resting, alert, and meditation states. We explore the neural correlates of maintaining a state or switching between states, and argue that the anterior cingulate cortex and striatum play a critical role in state maintenance, whereas the insula has a major role in switching between states. Brain state may serve as a predictor of performance in a variety of perceptual, memory, and problem solving tasks. Thus, understanding brain states is critical for understanding human performance. PMID:22613871

Test of the 'glymphatic' hypothesis demonstrates diffusive and aquaporin-4-independent solute transport in rodent brain parenchyma.

PubMed

Smith, Alex J; Yao, Xiaoming; Dix, James A; Jin, Byung-Ju; Verkman, Alan S

2017-08-21

Transport of solutes through brain involves diffusion and convection. The importance of convective flow in the subarachnoid and paravascular spaces has long been recognized; a recently proposed 'glymphatic' clearance mechanism additionally suggests that aquaporin-4 (AQP4) water channels facilitate convective transport through brain parenchyma. Here, the major experimental underpinnings of the glymphatic mechanism were re-examined by measurements of solute movement in mouse brain following intracisternal or intraparenchymal solute injection. We found that: (i) transport of fluorescent dextrans in brain parenchyma depended on dextran size in a manner consistent with diffusive rather than convective transport; (ii) transport of dextrans in the parenchymal extracellular space, measured by 2-photon fluorescence recovery after photobleaching, was not affected just after cardiorespiratory arrest; and (iii) Aqp4 gene deletion did not impair transport of fluorescent solutes from sub-arachnoid space to brain in mice or rats. Our results do not support the proposed glymphatic mechanism of convective solute transport in brain parenchyma.

Test of the 'glymphatic' hypothesis demonstrates diffusive and aquaporin-4-independent solute transport in rodent brain parenchyma

PubMed Central

Yao, Xiaoming; Dix, James A; Jin, Byung-Ju

2017-01-01

Transport of solutes through brain involves diffusion and convection. The importance of convective flow in the subarachnoid and paravascular spaces has long been recognized; a recently proposed ‘glymphatic’ clearance mechanism additionally suggests that aquaporin-4 (AQP4) water channels facilitate convective transport through brain parenchyma. Here, the major experimental underpinnings of the glymphatic mechanism were re-examined by measurements of solute movement in mouse brain following intracisternal or intraparenchymal solute injection. We found that: (i) transport of fluorescent dextrans in brain parenchyma depended on dextran size in a manner consistent with diffusive rather than convective transport; (ii) transport of dextrans in the parenchymal extracellular space, measured by 2-photon fluorescence recovery after photobleaching, was not affected just after cardiorespiratory arrest; and (iii) Aqp4 gene deletion did not impair transport of fluorescent solutes from sub-arachnoid space to brain in mice or rats. Our results do not support the proposed glymphatic mechanism of convective solute transport in brain parenchyma. PMID:28826498

Determination of Glucose Utilization Rates in Cultured Astrocytes and Neurons with [14C]deoxyglucose: Progress, Pitfalls, and Discovery of Intracellular Glucose Compartmentation.

PubMed

Dienel, Gerald A; Cruz, Nancy F; Sokoloff, Louis; Driscoll, Bernard F

2017-01-01

2-Deoxy-D-[ 14 C]glucose ([ 14 C]DG) is commonly used to determine local glucose utilization rates (CMR glc ) in living brain and to estimate CMR glc in cultured brain cells as rates of [ 14 C]DG phosphorylation. Phosphorylation rates of [ 14 C]DG and its metabolizable fluorescent analog, 2-(N-(7-nitrobenz-2-oxa-1,3-diazol-4-yl)amino)-2-deoxyglucose (2-NBDG), however, do not take into account differences in the kinetics of transport and metabolism of [ 14 C]DG or 2-NBDG and glucose in neuronal and astrocytic cells in cultures or in single cells in brain tissue, and conclusions drawn from these data may, therefore, not be correct. As a first step toward the goal of quantitative determination of CMR glc in astrocytes and neurons in cultures, the steady-state intracellular-to-extracellular concentration ratios (distribution spaces) for glucose and [ 14 C]DG were determined in cultured striatal neurons and astrocytes as functions of extracellular glucose concentration. Unexpectedly, the glucose distribution spaces rose during extreme hypoglycemia, exceeding 1.0 in astrocytes, whereas the [ 14 C]DG distribution space fell at the lowest glucose levels. Calculated CMR glc was greatly overestimated in hypoglycemic and normoglycemic cells because the intracellular glucose concentrations were too high. Determination of the distribution space for [ 14 C]glucose revealed compartmentation of intracellular glucose in astrocytes, and probably, also in neurons. A smaller metabolic pool is readily accessible to hexokinase and communicates with extracellular glucose, whereas the larger pool is sequestered from hexokinase activity. A new experimental approach using double-labeled assays with DG and glucose is suggested to avoid the limitations imposed by glucose compartmentation on metabolic assays.

Sleep: A synchrony of cell activity-driven small network states

PubMed Central

Krueger, James M.; Huang, Yanhua; Rector, David M.; Buysse, Daniel J.

2013-01-01

We posit a bottom-up sleep regulatory paradigm in which state changes are initiated within small networks as a consequence of local cell activity. Bottom-up regulatory mechanisms are prevalent throughout nature, occurring in vastly different systems and levels of organization. Synchronization of state without top-down regulation is a fundamental property of large collections of small semi-autonomous entities. We posit that such synchronization mechanisms are sufficient and necessary for whole organism sleep onset. Within brain we posit that small networks of highly interconnected neurons and glia, e.g. cortical columns, are semi-autonomous units oscillating between sleep-like and wake-like states. We review evidence showing that cells, small networks, and regional areas of brain share sleep-like properties with whole animal sleep. A testable hypothesis focused on how sleep is initiated within local networks is presented. We posit that the release of cell activity-dependent molecules, such as ATP and nitric oxide, into the extracellular space initiates state changes within the local networks where they are produced. We review mechanisms of ATP induction of sleep regulatory substances (SRS) and their actions on receptor trafficking. Finally, we provide an example of how such local metabolic and state changes provide mechanistic explanations for clinical conditions such as insomnia. PMID:23651209

Effects of Spaceflight on Astronaut Brain Structure as Indicated on MRI.

PubMed

Roberts, Donna R; Albrecht, Moritz H; Collins, Heather R; Asemani, Davud; Chatterjee, A Rano; Spampinato, M Vittoria; Zhu, Xun; Chimowitz, Marc I; Antonucci, Michael U

2017-11-02

There is limited information regarding the effects of spaceflight on the anatomical configuration of the brain and on cerebrospinal fluid (CSF) spaces. We used magnetic resonance imaging (MRI) to compare images of 18 astronauts' brains before and after missions of long duration, involving stays on the International Space Station, and of 16 astronauts' brains before and after missions of short duration, involving participation in the Space Shuttle Program. Images were interpreted by readers who were unaware of the flight duration. We also generated paired preflight and postflight MRI cine clips derived from high-resolution, three-dimensional imaging of 12 astronauts after long-duration flights and from 6 astronauts after short-duration flights in order to assess the extent of narrowing of CSF spaces and the displacement of brain structures. We also compared preflight ventricular volumes with postflight ventricular volumes by means of an automated analysis of T 1 -weighted MRIs. The main prespecified analyses focused on the change in the volume of the central sulcus, the change in the volume of CSF spaces at the vertex, and vertical displacement of the brain. Narrowing of the central sulcus occurred in 17 of 18 astronauts after long-duration flights (mean flight time, 164.8 days) and in 3 of 16 astronauts after short-duration flights (mean flight time, 13.6 days) (P<0.001). Cine clips from a subgroup of astronauts showed an upward shift of the brain after all long-duration flights (12 astronauts) but not after short-duration flights (6 astronauts) and narrowing of CSF spaces at the vertex after all long-duration flights (12 astronauts) and in 1 of 6 astronauts after short-duration flights. Three astronauts in the long-duration group had optic-disk edema, and all 3 had narrowing of the central sulcus. A cine clip was available for 1 of these 3 astronauts, and the cine clip showed upward shift of the brain. Narrowing of the central sulcus, upward shift of the brain, and narrowing of CSF spaces at the vertex occurred frequently and predominantly in astronauts after long-duration flights. Further investigation, including repeated postflight imaging conducted after some time on Earth, is required to determine the duration and clinical significance of these changes. (Funded by the National Aeronautics and Space Administration.).

Discriminative analysis of early Alzheimer's disease based on two intrinsically anti-correlated networks with resting-state fMRI.

PubMed

Wang, Kun; Jiang, Tianzi; Liang, Meng; Wang, Liang; Tian, Lixia; Zhang, Xinqing; Li, Kuncheng; Liu, Zhening

2006-01-01

In this work, we proposed a discriminative model of Alzheimer's disease (AD) on the basis of multivariate pattern classification and functional magnetic resonance imaging (fMRI). This model used the correlation/anti-correlation coefficients of two intrinsically anti-correlated networks in resting brains, which have been suggested by two recent studies, as the feature of classification. Pseudo-Fisher Linear Discriminative Analysis (pFLDA) was then performed on the feature space and a linear classifier was generated. Using leave-one-out (LOO) cross validation, our results showed a correct classification rate of 83%. We also compared the proposed model with another one based on the whole brain functional connectivity. Our proposed model outperformed the other one significantly, and this implied that the two intrinsically anti-correlated networks may be a more susceptible part of the whole brain network in the early stage of AD.

VEGF inhibitors in the treatment of cerebral edema in patients with brain cancer

PubMed Central

Gerstner, Elizabeth R.; Duda, Dan G.; di Tomaso, Emmanuelle; Ryg, Peter A.; Loeffler, Jay S.; Sorensen, A. Gregory; Ivy, Percy; Jain, Rakesh K.; Batchelor, Tracy T.

2016-01-01

Most brain tumors oversecrete vascular endothelial growth factor (VEGF), which leads to an abnormally permeable tumor vasculature. This hyperpermeability allows fluid to leak from the intravascular space into the brain parenchyma, which causes vasogenic cerebral edema and increased interstitial fluid pressure. Increased interstitial fluid pressure has an important role in treatment resistance by contributing to tumor hypoxia and preventing adequate tumor penetration of chemotherapy agents. In addition, edema and the corticosteroids needed to control cerebral edema cause significant morbidity and mortality. Agents that block the VEGF pathway are able to decrease vascular permeability and, thus, cerebral edema, by restoring the abnormal tumor vasculature to a more normal state. Decreasing cerebral edema minimizes the adverse effects of corticosteroids and could improve clinical outcomes. Anti-VEGF agents might also be useful in other cancer-related conditions that increase vascular permeability, such as malignant pleural effusions or ascites. PMID:19333229

Spectral properties of the temporal evolution of brain network structure.

PubMed

Wang, Rong; Zhang, Zhen-Zhen; Ma, Jun; Yang, Yong; Lin, Pan; Wu, Ying

2015-12-01

The temporal evolution properties of the brain network are crucial for complex brain processes. In this paper, we investigate the differences in the dynamic brain network during resting and visual stimulation states in a task-positive subnetwork, task-negative subnetwork, and whole-brain network. The dynamic brain network is first constructed from human functional magnetic resonance imaging data based on the sliding window method, and then the eigenvalues corresponding to the network are calculated. We use eigenvalue analysis to analyze the global properties of eigenvalues and the random matrix theory (RMT) method to measure the local properties. For global properties, the shifting of the eigenvalue distribution and the decrease in the largest eigenvalue are linked to visual stimulation in all networks. For local properties, the short-range correlation in eigenvalues as measured by the nearest neighbor spacing distribution is not always sensitive to visual stimulation. However, the long-range correlation in eigenvalues as evaluated by spectral rigidity and number variance not only predicts the universal behavior of the dynamic brain network but also suggests non-consistent changes in different networks. These results demonstrate that the dynamic brain network is more random for the task-positive subnetwork and whole-brain network under visual stimulation but is more regular for the task-negative subnetwork. Our findings provide deeper insight into the importance of spectral properties in the functional brain network, especially the incomparable role of RMT in revealing the intrinsic properties of complex systems.

Spectral properties of the temporal evolution of brain network structure

NASA Astrophysics Data System (ADS)

Wang, Rong; Zhang, Zhen-Zhen; Ma, Jun; Yang, Yong; Lin, Pan; Wu, Ying

2015-12-01

The temporal evolution properties of the brain network are crucial for complex brain processes. In this paper, we investigate the differences in the dynamic brain network during resting and visual stimulation states in a task-positive subnetwork, task-negative subnetwork, and whole-brain network. The dynamic brain network is first constructed from human functional magnetic resonance imaging data based on the sliding window method, and then the eigenvalues corresponding to the network are calculated. We use eigenvalue analysis to analyze the global properties of eigenvalues and the random matrix theory (RMT) method to measure the local properties. For global properties, the shifting of the eigenvalue distribution and the decrease in the largest eigenvalue are linked to visual stimulation in all networks. For local properties, the short-range correlation in eigenvalues as measured by the nearest neighbor spacing distribution is not always sensitive to visual stimulation. However, the long-range correlation in eigenvalues as evaluated by spectral rigidity and number variance not only predicts the universal behavior of the dynamic brain network but also suggests non-consistent changes in different networks. These results demonstrate that the dynamic brain network is more random for the task-positive subnetwork and whole-brain network under visual stimulation but is more regular for the task-negative subnetwork. Our findings provide deeper insight into the importance of spectral properties in the functional brain network, especially the incomparable role of RMT in revealing the intrinsic properties of complex systems.

Optimal feedback control successfully explains changes in neural modulations during experiments with brain-machine interfaces.

PubMed

Benyamini, Miri; Zacksenhouse, Miriam

2015-01-01

Recent experiments with brain-machine-interfaces (BMIs) indicate that the extent of neural modulations increased abruptly upon starting to operate the interface, and especially after the monkey stopped moving its hand. In contrast, neural modulations that are correlated with the kinematics of the movement remained relatively unchanged. Here we demonstrate that similar changes are produced by simulated neurons that encode the relevant signals generated by an optimal feedback controller during simulated BMI experiments. The optimal feedback controller relies on state estimation that integrates both visual and proprioceptive feedback with prior estimations from an internal model. The processing required for optimal state estimation and control were conducted in the state-space, and neural recording was simulated by modeling two populations of neurons that encode either only the estimated state or also the control signal. Spike counts were generated as realizations of doubly stochastic Poisson processes with linear tuning curves. The model successfully reconstructs the main features of the kinematics and neural activity during regular reaching movements. Most importantly, the activity of the simulated neurons successfully reproduces the observed changes in neural modulations upon switching to brain control. Further theoretical analysis and simulations indicate that increasing the process noise during normal reaching movement results in similar changes in neural modulations. Thus, we conclude that the observed changes in neural modulations during BMI experiments can be attributed to increasing process noise associated with the imperfect BMI filter, and, more directly, to the resulting increase in the variance of the encoded signals associated with state estimation and the required control signal.

Optimal feedback control successfully explains changes in neural modulations during experiments with brain-machine interfaces

PubMed Central

Benyamini, Miri; Zacksenhouse, Miriam

2015-01-01

Recent experiments with brain-machine-interfaces (BMIs) indicate that the extent of neural modulations increased abruptly upon starting to operate the interface, and especially after the monkey stopped moving its hand. In contrast, neural modulations that are correlated with the kinematics of the movement remained relatively unchanged. Here we demonstrate that similar changes are produced by simulated neurons that encode the relevant signals generated by an optimal feedback controller during simulated BMI experiments. The optimal feedback controller relies on state estimation that integrates both visual and proprioceptive feedback with prior estimations from an internal model. The processing required for optimal state estimation and control were conducted in the state-space, and neural recording was simulated by modeling two populations of neurons that encode either only the estimated state or also the control signal. Spike counts were generated as realizations of doubly stochastic Poisson processes with linear tuning curves. The model successfully reconstructs the main features of the kinematics and neural activity during regular reaching movements. Most importantly, the activity of the simulated neurons successfully reproduces the observed changes in neural modulations upon switching to brain control. Further theoretical analysis and simulations indicate that increasing the process noise during normal reaching movement results in similar changes in neural modulations. Thus, we conclude that the observed changes in neural modulations during BMI experiments can be attributed to increasing process noise associated with the imperfect BMI filter, and, more directly, to the resulting increase in the variance of the encoded signals associated with state estimation and the required control signal. PMID:26042002

Black Holes as Brains: Neural Networks with Area Law Entropy

NASA Astrophysics Data System (ADS)

Dvali, Gia

2018-04-01

Motivated by the potential similarities between the underlying mechanisms of the enhanced memory storage capacity in black holes and in brain networks, we construct an artificial quantum neural network based on gravity-like synaptic connections and a symmetry structure that allows to describe the network in terms of geometry of a d-dimensional space. We show that the network possesses a critical state in which the gapless neurons emerge that appear to inhabit a (d-1)-dimensional surface, with their number given by the surface area. In the excitations of these neurons, the network can store and retrieve an exponentially large number of patterns within an arbitrarily narrow energy gap. The corresponding micro-state entropy of the brain network exhibits an area law. The neural network can be described in terms of a quantum field, via identifying the different neurons with the different momentum modes of the field, while identifying the synaptic connections among the neurons with the interactions among the corresponding momentum modes. Such a mapping allows to attribute a well-defined sense of geometry to an intrinsically non-local system, such as the neural network, and vice versa, it allows to represent the quantum field model as a neural network.

Enlarged perivascular spaces and cognitive impairment after stroke and transient ischemic attack.

PubMed

Arba, Francesco; Quinn, Terence J; Hankey, Graeme J; Lees, Kennedy R; Wardlaw, Joanna M; Ali, Myzoon; Inzitari, Domenico

2018-01-01

Background Previous studies suggested that enlarged perivascular spaces are neuroimaging markers of cerebral small vessel disease. However, it is not clear whether enlarged perivascular spaces are associated with cognitive impairment. We aimed to determine the cross-sectional relationship between enlarged perivascular spaces and small vessel disease, and to investigate the relationship between enlarged perivascular spaces and subsequent cognitive impairment in patients with recent cerebral ischemic event. Methods Anonymized data were accessed from the virtual international stroke trial archive. We rated number of lacunes, white matter hyperintensities, brain atrophy, and enlarged perivascular spaces with validated scales on magnetic resonance brain images after the index stroke. We defined cognitive impairment as a mini mental state examination score of ≤26, recorded at one year post stroke. We examined the associations between enlarged perivascular spaces and clinical and imaging markers of small vessel disease at presentation and clinical evidence of cognitive impairment at one year using linear and logistic regression models. Results We analyzed data on 430 patients with mean (±SD) age 64.7 (±12.7) years, 276 (64%) males. In linear regression analysis, age (β = 0.24; p < 0.001), hypertension (β = 0.09; p = 0.025), and deep white matter hyperintensities (β = 0.31; p < 0.001) were associated with enlarged perivascular spaces. In logistic regression analysis, basal ganglia enlarged perivascular spaces were independently associated with cognitive impairment at one year after adjusting for clinical confounders (OR = 1.72, 95% CI = 1.22-2.42) and for clinical and imaging confounders (OR = 1.54; 95% CI = 1.03-2.31). Conclusions Our data show that in patients with ischemic cerebral events, enlarged perivascular spaces are cross-sectionally associated with age, hypertension, and white matter hyperintensities and suggest that enlarged perivascular spaces in the basal ganglia are associated with cognitive impairment after one year.

Waveguide Modulator for Interference Tolerant Functional Near Infrared Spectrometer (fNIRS)

NASA Technical Reports Server (NTRS)

Walton, Joanne; Tin, Padetha; Mackey, Jeffrey

2017-01-01

Many crew-related errors in aviation and astronautics are caused by hazardous cognitive states including overstress, disengagement, high fatigue and ineffective crew coordination. Safety can be improved by monitoring and predicting these cognitive states in a non-intrusive manner and designing mitigation strategies. Measuring hemoglobin concentration changes in the brain with functional Near Infrared Spectroscopy is a promising technique for monitoring cognitive state and optimizing human performance during both space and aviation operations. A compact, wearable fNIRS system would provide an innovative early warning system during long duration missions to detect and prevent vigilance decrements in pilots and astronauts. This effort focused on developing a waveguide modulator for use in a fNIRS system.

[The blood-brain barrier in ageing persons].

PubMed

Haaning, Nina; Damsgaard, Else Marie; Moos, Torben

2018-03-26

Brain capillary endothelial cells (BECs) form the ultra-tight blood-brain barrier (BBB). The permeability of the BBB increases with increasing age and neurovascular and neurodegenerative diseases. Major defects of the BBB can be initiated by increased permeability to plasma proteins in small arteriosclerotic arteries and release of proteins from degenerating neurons into the brain extracellular space. These proteins deposit in perivascular spaces, and subsequently negatively influence the BECs leading to decreased expression of barrier proteins. Detection of BBB defects by the use of non-invasive techniques is relevant for clinical use in settings with advanced age and severe brain disorders.

De Sitter Space Without Dynamical Quantum Fluctuations

NASA Astrophysics Data System (ADS)

Boddy, Kimberly K.; Carroll, Sean M.; Pollack, Jason

2016-06-01

We argue that, under certain plausible assumptions, de Sitter space settles into a quiescent vacuum in which there are no dynamical quantum fluctuations. Such fluctuations require either an evolving microstate, or time-dependent histories of out-of-equilibrium recording devices, which we argue are absent in stationary states. For a massive scalar field in a fixed de Sitter background, the cosmic no-hair theorem implies that the state of the patch approaches the vacuum, where there are no fluctuations. We argue that an analogous conclusion holds whenever a patch of de Sitter is embedded in a larger theory with an infinite-dimensional Hilbert space, including semiclassical quantum gravity with false vacua or complementarity in theories with at least one Minkowski vacuum. This reasoning provides an escape from the Boltzmann brain problem in such theories. It also implies that vacuum states do not uptunnel to higher-energy vacua and that perturbations do not decohere while slow-roll inflation occurs, suggesting that eternal inflation is much less common than often supposed. On the other hand, if a de Sitter patch is a closed system with a finite-dimensional Hilbert space, there will be Poincaré recurrences and dynamical Boltzmann fluctuations into lower-entropy states. Our analysis does not alter the conventional understanding of the origin of density fluctuations from primordial inflation, since reheating naturally generates a high-entropy environment and leads to decoherence, nor does it affect the existence of non-dynamical vacuum fluctuations such as those that give rise to the Casimir effect.

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.

Disturbed temporal dynamics of brain synchronization in vision loss.

PubMed

Bola, Michał; Gall, Carolin; Sabel, Bernhard A

2015-06-01

Damage along the visual pathway prevents bottom-up visual input from reaching further processing stages and consequently leads to loss of vision. But perception is not a simple bottom-up process - rather it emerges from activity of widespread cortical networks which coordinate visual processing in space and time. Here we set out to study how vision loss affects activity of brain visual networks and how networks' activity is related to perception. Specifically, we focused on studying temporal patterns of brain activity. To this end, resting-state eyes-closed EEG was recorded from partially blind patients suffering from chronic retina and/or optic-nerve damage (n = 19) and healthy controls (n = 13). Amplitude (power) of oscillatory activity and phase locking value (PLV) were used as measures of local and distant synchronization, respectively. Synchronization time series were created for the low- (7-9 Hz) and high-alpha band (11-13 Hz) and analyzed with three measures of temporal patterns: (i) length of synchronized-/desynchronized-periods, (ii) Higuchi Fractal Dimension (HFD), and (iii) Detrended Fluctuation Analysis (DFA). We revealed that patients exhibit less complex, more random and noise-like temporal dynamics of high-alpha band activity. More random temporal patterns were associated with worse performance in static (r = -.54, p = .017) and kinetic perimetry (r = .47, p = .041). We conclude that disturbed temporal patterns of neural synchronization in vision loss patients indicate disrupted communication within brain visual networks caused by prolonged deafferentation. We propose that because the state of brain networks is essential for normal perception, impaired brain synchronization in patients with vision loss might aggravate the functional consequences of reduced visual input. Copyright © 2015 Elsevier Ltd. All rights reserved.

Scopolamine effects on functional brain connectivity: a pharmacological model of Alzheimer's disease.

PubMed

Bajo, R; Pusil, S; López, M E; Canuet, L; Pereda, E; Osipova, D; Maestú, F; Pekkonen, E

2015-07-01

Scopolamine administration may be considered as a psychopharmacological model of Alzheimer's disease (AD). Here, we studied a group of healthy elderly under scopolamine to test whether it elicits similar changes in brain connectivity as those observed in AD, thereby verifying a possible model of AD impairment. We did it by testing healthy elderly subjects in two experimental conditions: glycopyrrolate (placebo) and scopolamine administration. We then analyzed magnetoencephalographic (MEG) data corresponding to both conditions in resting-state with eyes closed. This analysis was performed in source space by combining a nonlinear frequency band-specific measure of functional connectivity (phase locking value, PLV) with network analysis methods. Under scopolamine, functional connectivity between several brain areas was significantly reduced as compared to placebo, in most frequency bands analyzed. Besides, regarding the two complex network indices studied (clustering and shortest path length), clustering significantly decreased in the alpha band while shortest path length significantly increased also in alpha band both after scopolamine administration. Overall our findings indicate that both PLV and graph analysis are suitable tools to measure brain connectivity changes induced by scopolamine, which causes alterations in brain connectivity apparently similar to those reported in AD.

Distributed affective space represents multiple emotion categories across the human brain

PubMed Central

Saarimäki, Heini; Ejtehadian, Lara Farzaneh; Jääskeläinen, Iiro P; Vuilleumier, Patrik; Sams, Mikko; Nummenmaa, Lauri

2018-01-01

Abstract The functional organization of human emotion systems as well as their neuroanatomical basis and segregation in the brain remains unresolved. Here, we used pattern classification and hierarchical clustering to characterize the organization of a wide array of emotion categories in the human brain. We induced 14 emotions (6 ‘basic’, e.g. fear and anger; and 8 ‘non-basic’, e.g. shame and gratitude) and a neutral state using guided mental imagery while participants' brain activity was measured with functional magnetic resonance imaging (fMRI). Twelve out of 14 emotions could be reliably classified from the haemodynamic signals. All emotions engaged a multitude of brain areas, primarily in midline cortices including anterior and posterior cingulate gyri and precuneus, in subcortical regions, and in motor regions including cerebellum and premotor cortex. Similarity of subjective emotional experiences was associated with similarity of the corresponding neural activation patterns. We conclude that different basic and non-basic emotions have distinguishable neural bases characterized by specific, distributed activation patterns in widespread cortical and subcortical circuits. Regionally differentiated engagement of these circuits defines the unique neural activity pattern and the corresponding subjective feeling associated with each emotion. PMID:29618125

Terminal Transient Phase of Chaotic Transients

NASA Astrophysics Data System (ADS)

Lilienkamp, Thomas; Parlitz, Ulrich

2018-03-01

Transient chaos in spatially extended systems can be characterized by the length of the transient phase, which typically grows quickly with the system size (supertransients). For a large class of these systems, the chaotic phase terminates abruptly, without any obvious precursors in commonly used observables. Here we investigate transient spatiotemporal chaos in two different models of this class. By probing the state space using perturbed trajectories we show the existence of a "terminal transient phase," which occurs prior to the abrupt collapse of chaotic dynamics. During this phase the impact of perturbations is significantly different from the earlier transient and particular patterns of (non)susceptible regions in state space occur close to the chaotic trajectories. We therefore hypothesize that even without perturbations proper precursors for the collapse of chaotic transients exist, which might be highly relevant for coping with spatiotemporal chaos in cardiac arrhythmias or brain functionality, for example.

A Genetic-Based Feature Selection Approach in the Identification of Left/Right Hand Motor Imagery for a Brain-Computer Interface

PubMed Central

Yaacoub, Charles; Mhanna, Georges; Rihana, Sandy

2017-01-01

Electroencephalography is a non-invasive measure of the brain electrical activity generated by millions of neurons. Feature extraction in electroencephalography analysis is a core issue that may lead to accurate brain mental state classification. This paper presents a new feature selection method that improves left/right hand movement identification of a motor imagery brain-computer interface, based on genetic algorithms and artificial neural networks used as classifiers. Raw electroencephalography signals are first preprocessed using appropriate filtering. Feature extraction is carried out afterwards, based on spectral and temporal signal components, and thus a feature vector is constructed. As various features might be inaccurate and mislead the classifier, thus degrading the overall system performance, the proposed approach identifies a subset of features from a large feature space, such that the classifier error rate is reduced. Experimental results show that the proposed method is able to reduce the number of features to as low as 0.5% (i.e., the number of ignored features can reach 99.5%) while improving the accuracy, sensitivity, specificity, and precision of the classifier. PMID:28124985

A Genetic-Based Feature Selection Approach in the Identification of Left/Right Hand Motor Imagery for a Brain-Computer Interface.

PubMed

Yaacoub, Charles; Mhanna, Georges; Rihana, Sandy

2017-01-23

Electroencephalography is a non-invasive measure of the brain electrical activity generated by millions of neurons. Feature extraction in electroencephalography analysis is a core issue that may lead to accurate brain mental state classification. This paper presents a new feature selection method that improves left/right hand movement identification of a motor imagery brain-computer interface, based on genetic algorithms and artificial neural networks used as classifiers. Raw electroencephalography signals are first preprocessed using appropriate filtering. Feature extraction is carried out afterwards, based on spectral and temporal signal components, and thus a feature vector is constructed. As various features might be inaccurate and mislead the classifier, thus degrading the overall system performance, the proposed approach identifies a subset of features from a large feature space, such that the classifier error rate is reduced. Experimental results show that the proposed method is able to reduce the number of features to as low as 0.5% (i.e., the number of ignored features can reach 99.5%) while improving the accuracy, sensitivity, specificity, and precision of the classifier.

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

Brain State Is a Major Factor in Preseizure Hippocampal Network Activity and Influences Success of Seizure Intervention

PubMed Central

Ewell, Laura A.; Liang, Liang; Armstrong, Caren; Soltész, Ivan; Leutgeb, Stefan

2015-01-01

Neural dynamics preceding seizures are of interest because they may shed light on mechanisms of seizure generation and could be predictive. In healthy animals, hippocampal network activity is shaped by behavioral brain state and, in epilepsy, seizures selectively emerge during specific brain states. To determine the degree to which changes in network dynamics before seizure are pathological or reflect ongoing fluctuations in brain state, dorsal hippocampal neurons were recorded during spontaneous seizures in a rat model of temporal lobe epilepsy. Seizures emerged from all brain states, but with a greater likelihood after REM sleep, potentially due to an observed increase in baseline excitability during periods of REM compared with other brains states also characterized by sustained theta oscillations. When comparing the firing patterns of the same neurons across brain states associated with and without seizures, activity dynamics before seizures followed patterns typical of the ongoing brain state, or brain state transitions, and did not differ until the onset of the electrographic seizure. Next, we tested whether disparate activity patterns during distinct brain states would influence the effectiveness of optogenetic curtailment of hippocampal seizures in a mouse model of temporal lobe epilepsy. Optogenetic curtailment was significantly more effective for seizures preceded by non-theta states compared with seizures that emerged from theta states. Our results indicate that consideration of behavioral brain state preceding a seizure is important for the appropriate interpretation of network dynamics leading up to a seizure and for designing effective seizure intervention. SIGNIFICANCE STATEMENT Hippocampal single-unit activity is strongly shaped by behavioral brain state, yet this relationship has been largely ignored when studying activity dynamics before spontaneous seizures in medial temporal lobe epilepsy. In light of the increased attention on using single-unit activity for the prediction of seizure onset and closed-loop seizure intervention, we show a need for monitoring brain state to interpret correctly whether changes in neural activity before seizure onset is pathological or normal. Moreover, we also find that the brain state preceding a seizure determines the success of therapeutic interventions to curtail seizure duration. Together, these findings suggest that seizure prediction and intervention will be more successful if tailored for the specific brain states from which seizures emerge. PMID:26609157

Optimal stimulus scheduling for active estimation of evoked brain networks.

PubMed

Kafashan, MohammadMehdi; Ching, ShiNung

2015-12-01

We consider the problem of optimal probing to learn connections in an evoked dynamic network. Such a network, in which each edge measures an input-output relationship between sites in sensor/actuator-space, is relevant to emerging applications in neural mapping and neural connectivity estimation. We show that the problem of scheduling nodes to a probe (i.e., stimulate) amounts to a problem of optimal sensor scheduling. By formulating the evoked network in state-space, we show that the solution to the greedy probing strategy has a convenient form and, under certain conditions, is optimal over a finite horizon. We adopt an expectation maximization technique to update the state-space parameters in an online fashion and demonstrate the efficacy of the overall approach in a series of detailed numerical examples. The proposed method provides a principled means to actively probe time-varying connections in neuronal networks. The overall method can be implemented in real time and is particularly well-suited to applications in stimulation-based cortical mapping in which the underlying network dynamics are changing over time.

Optimal stimulus scheduling for active estimation of evoked brain networks

NASA Astrophysics Data System (ADS)

Kafashan, MohammadMehdi; Ching, ShiNung

2015-12-01

Objective. We consider the problem of optimal probing to learn connections in an evoked dynamic network. Such a network, in which each edge measures an input-output relationship between sites in sensor/actuator-space, is relevant to emerging applications in neural mapping and neural connectivity estimation. Approach. We show that the problem of scheduling nodes to a probe (i.e., stimulate) amounts to a problem of optimal sensor scheduling. Main results. By formulating the evoked network in state-space, we show that the solution to the greedy probing strategy has a convenient form and, under certain conditions, is optimal over a finite horizon. We adopt an expectation maximization technique to update the state-space parameters in an online fashion and demonstrate the efficacy of the overall approach in a series of detailed numerical examples. Significance. The proposed method provides a principled means to actively probe time-varying connections in neuronal networks. The overall method can be implemented in real time and is particularly well-suited to applications in stimulation-based cortical mapping in which the underlying network dynamics are changing over time.

Drug-induced iatrogenic intraparenchymal hemorrhage.

PubMed

Lapsiwala, Samir; Moftakhar, Roham; Badie, Behnam

2002-07-01

Intracerebral hemorrhage is bleeding into the brain parenchyma with possible extension into the ventricles and subarachnoid space. Each year, approximately 37,000 to 52,400 people suffer from intraparenchymal hemorrhage (IPH) in the United States. This rate is expected to rise dramatically in the next few decades as a result of the increasing age of the population and a change in racial demographics. IPH accounts for 8% to 13% of all stroke cases and is associated with the highest mortality rate.

Comparative study of standard space and real space analysis of quantitative MR brain data.

PubMed

Aribisala, Benjamin S; He, Jiabao; Blamire, Andrew M

2011-06-01

To compare the robustness of region of interest (ROI) analysis of magnetic resonance imaging (MRI) brain data in real space with analysis in standard space and to test the hypothesis that standard space image analysis introduces more partial volume effect errors compared to analysis of the same dataset in real space. Twenty healthy adults with no history or evidence of neurological diseases were recruited; high-resolution T(1)-weighted, quantitative T(1), and B(0) field-map measurements were collected. Algorithms were implemented to perform analysis in real and standard space and used to apply a simple standard ROI template to quantitative T(1) datasets. Regional relaxation values and histograms for both gray and white matter tissues classes were then extracted and compared. Regional mean T(1) values for both gray and white matter were significantly lower using real space compared to standard space analysis. Additionally, regional T(1) histograms were more compact in real space, with smaller right-sided tails indicating lower partial volume errors compared to standard space analysis. Standard space analysis of quantitative MRI brain data introduces more partial volume effect errors biasing the analysis of quantitative data compared to analysis of the same dataset in real space. Copyright © 2011 Wiley-Liss, Inc.

Changes in brain cell shape create residual extracellular space volume and explain tortuosity behavior during osmotic challenge.

PubMed

Chen, K C; Nicholson, C

2000-07-18

Diffusion of molecules in brain extracellular space is constrained by two macroscopic parameters, tortuosity factor lambda and volume fraction alpha. Recent studies in brain slices show that when osmolarity is reduced, lambda increases while alpha decreases. In contrast, with increased osmolarity, alpha increases, but lambda attains a plateau. Using homogenization theory and a variety of lattice models, we found that the plateau behavior of lambda can be explained if the shape of brain cells changes nonuniformly during the shrinking or swelling induced by osmotic challenge. The nonuniform cellular shrinkage creates residual extracellular space that temporarily traps diffusing molecules, thus impeding the macroscopic diffusion. The paper also discusses the definition of tortuosity and its independence of the measurement frame of reference.

The Effect of Body Posture on Brain Glymphatic Transport.

PubMed

Lee, Hedok; Xie, Lulu; Yu, Mei; Kang, Hongyi; Feng, Tian; Deane, Rashid; Logan, Jean; Nedergaard, Maiken; Benveniste, Helene

2015-08-05

The glymphatic pathway expedites clearance of waste, including soluble amyloid β (Aβ) from the brain. Transport through this pathway is controlled by the brain's arousal level because, during sleep or anesthesia, the brain's interstitial space volume expands (compared with wakefulness), resulting in faster waste removal. Humans, as well as animals, exhibit different body postures during sleep, which may also affect waste removal. Therefore, not only the level of consciousness, but also body posture, might affect CSF-interstitial fluid (ISF) exchange efficiency. We used dynamic-contrast-enhanced MRI and kinetic modeling to quantify CSF-ISF exchange rates in anesthetized rodents' brains in supine, prone, or lateral positions. To validate the MRI data and to assess specifically the influence of body posture on clearance of Aβ, we used fluorescence microscopy and radioactive tracers, respectively. The analysis showed that glymphatic transport was most efficient in the lateral position compared with the supine or prone positions. In the prone position, in which the rat's head was in the most upright position (mimicking posture during the awake state), transport was characterized by "retention" of the tracer, slower clearance, and more CSF efflux along larger caliber cervical vessels. The optical imaging and radiotracer studies confirmed that glymphatic transport and Aβ clearance were superior in the lateral and supine positions. We propose that the most popular sleep posture (lateral) has evolved to optimize waste removal during sleep and that posture must be considered in diagnostic imaging procedures developed in the future to assess CSF-ISF transport in humans. The rodent brain removes waste better during sleep or anesthesia compared with the awake state. Animals exhibit different body posture during the awake and sleep states, which might affect the brain's waste removal efficiency. We investigated the influence of body posture on brainwide transport of inert tracers of anesthetized rodents. The major finding of our study was that waste, including Aβ, removal was most efficient in the lateral position (compared with the prone position), which mimics the natural resting/sleeping position of rodents. Although our finding awaits testing in humans, we speculate that the lateral position during sleep has advantage with regard to the removal of waste products including Aβ, because clinical studies have shown that sleep drives Aβ clearance from the brain. Copyright © 2015 the authors 0270-6474/15/3511034-11$15.00/0.

The Effect of Body Posture on Brain Glymphatic Transport

PubMed Central

Lee, Hedok; Xie, Lulu; Yu, Mei; Kang, Hongyi; Feng, Tian; Deane, Rashid; Logan, Jean; Nedergaard, Maiken

2015-01-01

The glymphatic pathway expedites clearance of waste, including soluble amyloid β (Aβ) from the brain. Transport through this pathway is controlled by the brain's arousal level because, during sleep or anesthesia, the brain's interstitial space volume expands (compared with wakefulness), resulting in faster waste removal. Humans, as well as animals, exhibit different body postures during sleep, which may also affect waste removal. Therefore, not only the level of consciousness, but also body posture, might affect CSF–interstitial fluid (ISF) exchange efficiency. We used dynamic-contrast-enhanced MRI and kinetic modeling to quantify CSF-ISF exchange rates in anesthetized rodents' brains in supine, prone, or lateral positions. To validate the MRI data and to assess specifically the influence of body posture on clearance of Aβ, we used fluorescence microscopy and radioactive tracers, respectively. The analysis showed that glymphatic transport was most efficient in the lateral position compared with the supine or prone positions. In the prone position, in which the rat's head was in the most upright position (mimicking posture during the awake state), transport was characterized by “retention” of the tracer, slower clearance, and more CSF efflux along larger caliber cervical vessels. The optical imaging and radiotracer studies confirmed that glymphatic transport and Aβ clearance were superior in the lateral and supine positions. We propose that the most popular sleep posture (lateral) has evolved to optimize waste removal during sleep and that posture must be considered in diagnostic imaging procedures developed in the future to assess CSF-ISF transport in humans. SIGNIFICANCE STATEMENT The rodent brain removes waste better during sleep or anesthesia compared with the awake state. Animals exhibit different body posture during the awake and sleep states, which might affect the brain's waste removal efficiency. We investigated the influence of body posture on brainwide transport of inert tracers of anesthetized rodents. The major finding of our study was that waste, including Aβ, removal was most efficient in the lateral position (compared with the prone position), which mimics the natural resting/sleeping position of rodents. Although our finding awaits testing in humans, we speculate that the lateral position during sleep has advantage with regard to the removal of waste products including Aβ, because clinical studies have shown that sleep drives Aβ clearance from the brain. PMID:26245965

Possible links between extreme levels of space weather changes and human health state in middle latitudes: direct and indirect indicators

NASA Astrophysics Data System (ADS)

Safaraly-Oghlu Babayev, Elchin

The Sun is the main driver of space weather. The possibility that solar activity variations and related changes in the Earth's magnetosphere can affect human life and health has been debated for many decades. This problem is being studied extensively in the late 20th and early 21st centuries and it is still being contradictory in some cases. The relations between space weather changes and the human health have global implications, but they are especially significant for habitants living at high geomagnetic latitudes where the geomagnetic disturbances have larger amplitudes. Nevertheless, the relevant researches are also important for humans living at any geomagnetic latitudes with different levels of geomagnetic activity; recent researches show that weak geomagnetic disturbances can also have adverse effects. Unfortunately, limited comparison of results of investigations on possible effects to humans from geomagnetic activity exists between studies conducted in high, middle and low latitudes. Knowledge about the relationship between solar and geomagnetic activity and the human health would allow to get better prepared beforehand for any future geomagnetic event and its impacts anywhere. For these purposes there are conducted collaborative (jointly with scientists from Israel, Bulgaria, Russia and Belgium) and cross-disciplinary space weather studies in the Azerbaijan National Academy of Sciences for revealing possible effects of solar, geomagnetic and cosmic ray variability on certain technological, biological and ecological systems in different phases of solar cycle 23. This paper describes some recently obtained results of the complex (theoretical, experimental and statistical) studies of influence of the periodical and aperiodical changes of solar, geomagnetic and cosmic ray activities upon human cardio-health state as well as human physiological and psycho-emotional state. It also covers the conclusions of studies on influence of violent solar events and severe geomagnetic storms of the solar cycle 23 on the mentioned systems in middle-latitude location. In these studies, direct and indirect indicators of space weather influence are used: 1) Indirect indicators are essentially epidemiological data showing the temporal and spatial distribution of defined events or health disturbances involving considerable numbers of test subjects over several years. The indirect indicators used in this paper are: temporal distribution of emergency calls and hospital admissions (sudden cardiac deaths, acute myocardial infarction mortality and morbidity, so on), dynamics of traffic accidents, epidemics, etc.; 2) Direct indicators. They are physiological parameters, which can be objectively verified and which are acquired either in vivo, directly on the subject (heart rate and its variability, blood pressure, human brain's functional state, human psycho-emotional state, so on), or in vitro by laboratory diagnostics or tissue investigations. The potential co-factors, e.g. terrestrial (tropospheric) weather, seasons, demographic factor, working environment, etc., were also considered in the interpretation of the indicators. Spectral analyses have revealed certain chronobiological periodicities in the considered data. There are also provided results of daily medical-physiological experiments (acupunctural studies of conductivity of the biologically active points of human body in days with different geomagnetic activity levels) conducted in the Laboratory of Heliobiology, Baku, Azerbaijan, as a part of collaborative studies with Russian institutions such as IZMIRAN and Space Research Institute. They show on the latitudinal and longitudinal dependence of space weather influence. Our complex studies enabled to conclude that not only extremely high, but also very low levels of geomagnetic activity may have signifi- cant influence on human health state, especially, in the cardio-vascular health state and human brain's bioelectrical activity.

Alterations of Intrinsic Brain Connectivity Patterns in Depression and Bipolar Disorders: A Critical Assessment of Magnetoencephalography-Based Evidence

PubMed Central

Alamian, Golnoush; Hincapié, Ana-Sofía; Combrisson, Etienne; Thiery, Thomas; Martel, Véronique; Althukov, Dmitrii; Jerbi, Karim

2017-01-01

Despite being the object of a thriving field of clinical research, the investigation of intrinsic brain network alterations in psychiatric illnesses is still in its early days. Because the pathological alterations are predominantly probed using functional magnetic resonance imaging (fMRI), many questions about the electrophysiological bases of resting-state alterations in psychiatric disorders, particularly among mood disorder patients, remain unanswered. Alongside important research using electroencephalography (EEG), the specific recent contributions and future promise of magnetoencephalography (MEG) in this field are not fully recognized and valued. Here, we provide a critical review of recent findings from MEG resting-state connectivity within major depressive disorder (MDD) and bipolar disorder (BD). The clinical MEG resting-state results are compared with those previously reported with fMRI and EEG. Taken together, MEG appears to be a promising but still critically underexploited technique to unravel the neurophysiological mechanisms that mediate abnormal (both hyper- and hypo-) connectivity patterns involved in MDD and BD. In particular, a major strength of MEG is its ability to provide source-space estimations of neuromagnetic long-range rhythmic synchronization at various frequencies (i.e., oscillatory coupling). The reviewed literature highlights the relevance of probing local and interregional rhythmic synchronization to explore the pathophysiological underpinnings of each disorder. However, before we can fully take advantage of MEG connectivity analyses in psychiatry, several limitations inherent to MEG connectivity analyses need to be understood and taken into account. Thus, we also discuss current methodological challenges and outline paths for future research. MEG resting-state studies provide an important window onto perturbed spontaneous oscillatory brain networks and hence supply an important complement to fMRI-based resting-state measurements in psychiatric populations. PMID:28367127

State-dependencies of learning across brain scales

PubMed Central

Ritter, Petra; Born, Jan; Brecht, Michael; Dinse, Hubert R.; Heinemann, Uwe; Pleger, Burkhard; Schmitz, Dietmar; Schreiber, Susanne; Villringer, Arno; Kempter, Richard

2015-01-01

Learning is a complex brain function operating on different time scales, from milliseconds to years, which induces enduring changes in brain dynamics. The brain also undergoes continuous “spontaneous” shifts in states, which, amongst others, are characterized by rhythmic activity of various frequencies. Besides the most obvious distinct modes of waking and sleep, wake-associated brain states comprise modulations of vigilance and attention. Recent findings show that certain brain states, particularly during sleep, are essential for learning and memory consolidation. Oscillatory activity plays a crucial role on several spatial scales, for example in plasticity at a synaptic level or in communication across brain areas. However, the underlying mechanisms and computational rules linking brain states and rhythms to learning, though relevant for our understanding of brain function and therapeutic approaches in brain disease, have not yet been elucidated. Here we review known mechanisms of how brain states mediate and modulate learning by their characteristic rhythmic signatures. To understand the critical interplay between brain states, brain rhythms, and learning processes, a wide range of experimental and theoretical work in animal models and human subjects from the single synapse to the large-scale cortical level needs to be integrated. By discussing results from experiments and theoretical approaches, we illuminate new avenues for utilizing neuronal learning mechanisms in developing tools and therapies, e.g., for stroke patients and to devise memory enhancement strategies for the elderly. PMID:25767445

Distinct functional states of astrocytes during sleep and wakefulness: Is norepinephrine the master regulator?

PubMed Central

O’Donnell, John; Ding, Fengfei; Nedergaard, Maiken

2015-01-01

Astrocytes are the chief supportive cells in the central nervous system, but work over the past 20 years have documented that astrocytes also contribute to complex neural processes, such as working memory. Recent discoveries of norepinephrine-mediated astrocytic Ca2+ responses have raised the possibility that astrocytic activity in the adult brain is driven by global responses to changes in behavioral state. Moreover, analysis of the interstitial space volume suggests that astrocytes may undergo changes in cell volume in response to activation of norepinephrine receptors. This review will focus on what is known about astrocytic functions within the nervous system, and how these functions interrelate with rapid changes in behavioral state mediated by norepinephrine signaling. PMID:26618103

Twenty-first century brain banking. Processing brains for research: the Columbia University methods

PubMed Central

del Amaya, Maria Pilar; Keller, Christian E.

2007-01-01

Carefully categorized postmortem human brains are crucial for research. The lack of generally accepted methods for processing human postmortem brains for research persists. Thus, brain banking is essential; however, it cannot be achieved at the cost of the teaching mission of the academic institution by routing brains away from residency programs, particularly when the autopsy rate is steadily decreasing. A consensus must be reached whereby a brain can be utilizable for diagnosis, research, and teaching. The best diagnostic categorization possible must be secured and the yield of samples for basic investigation maximized. This report focuses on integrated, novel methods currently applied at the New York Brain Bank, Columbia University, New York, which are designed to reach accurate neuropathological diagnosis, optimize the yield of samples, and process fresh-frozen samples suitable for a wide range of modern investigations. The brains donated for research are processed as soon as possible after death. The prosector must have a good command of the neuroanatomy, neuropathology, and the protocol. One half of each brain is immersed in formalin for performing the thorough neuropathologic evaluation, which is combined with the teaching task. The contralateral half is extensively dissected at the fresh state. The anatomical origin of each sample is recorded using the map of Brodmann for the cortical samples. The samples are frozen at −160°C, barcode labeled, and ready for immediate disbursement once categorized diagnostically. A rigorous organization of freezer space, coupled to an electronic tracking system with its attached software, fosters efficient access for retrieval within minutes of any specific frozen samples in storage. This report describes how this achievement is feasible with emphasis on the actual processing of brains donated for research. PMID:17985145

Computational genetic neuroanatomy of the developing mouse brain: dimensionality reduction, visualization, and clustering.

PubMed

Ji, Shuiwang

2013-07-11

The structured organization of cells in the brain plays a key role in its functional efficiency. This delicate organization is the consequence of unique molecular identity of each cell gradually established by precise spatiotemporal gene expression control during development. Currently, studies on the molecular-structural association are beginning to reveal how the spatiotemporal gene expression patterns are related to cellular differentiation and structural development. In this article, we aim at a global, data-driven study of the relationship between gene expressions and neuroanatomy in the developing mouse brain. To enable visual explorations of the high-dimensional data, we map the in situ hybridization gene expression data to a two-dimensional space by preserving both the global and the local structures. Our results show that the developing brain anatomy is largely preserved in the reduced gene expression space. To provide a quantitative analysis, we cluster the reduced data into groups and measure the consistency with neuroanatomy at multiple levels. Our results show that the clusters in the low-dimensional space are more consistent with neuroanatomy than those in the original space. Gene expression patterns and developing brain anatomy are closely related. Dimensionality reduction and visual exploration facilitate the study of this relationship.

Spatial Hyperschematia without Spatial Neglect after Insulo-Thalamic Disconnection

PubMed Central

Saj, Arnaud; Wilcke, Juliane C.; Gschwind, Markus; Emond, Héloïse; Assal, Frédéric

2013-01-01

Different spatial representations are not stored as a single multipurpose map in the brain. Right brain-damaged patients can show a distortion, a compression of peripersonal and extrapersonal space. Here we report the case of a patient with a right insulo-thalamic disconnection without spatial neglect. The patient, compared with 10 healthy control subjects, showed a constant and reliable increase of her peripersonal and extrapersonal egocentric space representations - that we named spatial hyperschematia - yet left her allocentric space representations intact. This striking dissociation shows that our interactions with the surrounding world are represented and processed modularly in the human brain, depending on their frame of reference. PMID:24302992

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

Shear-Induced Amyloid Formation in the Brain: I. Potential Vascular and Parenchymal Processes.

PubMed

Trumbore, Conrad N

2016-09-06

Shear distortion of amyloid-beta (Aβ) solutions accelerates amyloid cascade reactions that may yield different toxic oligomers than those formed in quiescent solutions. Recent experiments indicate that cerebrospinal fluid (CSF) and interstitial fluid (ISF) containing Aβ flow through narrow brain perivascular pathways and brain parenchyma. This paper suggests that such flow causes shear distortion of Aβ molecules involving conformation changes that may be one of the initiating events in the etiology of Alzheimer's disease. Aβ shearing can occur in or around brain arteries and arterioles and is suggested as the origin of cerebral amyloid angiopathy deposits in cerebrovascular walls. Comparatively low flow rates of ISF within the narrow extracellular spaces (ECS) of the brain parenchyma are suggested as a possible initiating factor in both the formation of neurotoxic Aβ42 oligomers and amyloid fibrils. Aβ42 in slow-flowing ISF can gain significant shear energy at or near the walls of tortuous brain ECS flow paths, promoting the formation of a shear-distorted, excited state hydrophobic Aβ42* conformation. This Aβ42* molecule could possibly be involved in one of two paths, one involving rapid adsorption to a brain membrane surface, ultimately forming neurotoxic oligomers on membranes, and the other ultimately forming plaque within the ECS flow pathways. Rising Aβ concentrations combined with shear at or near critical brain membranes are proposed as contributing factors to Alzheimer's disease neurotoxicity. These hypotheses may be applicable in other neurodegenerative diseases, including tauopathies and alpha-synucleinopathies, in which shear-distorted proteins also may form in the brain ECS.

Fuzzy topological digital space and digital fuzzy spline of electroencephalography during epileptic seizures

NASA Astrophysics Data System (ADS)

Shah, Mazlina Muzafar; Wahab, Abdul Fatah

2017-08-01

Epilepsy disease occurs because of there is a temporary electrical disturbance in a group of brain cells (nurons). The recording of electrical signals come from the human brain which can be collected from the scalp of the head is called Electroencephalography (EEG). EEG then considered in digital format and in fuzzy form makes it a fuzzy digital space data form. The purpose of research is to identify the area (curve and surface) in fuzzy digital space affected by inside epilepsy seizure in epileptic patient's brain. The main focus for this research is to generalize fuzzy topological digital space, definition and basic operation also the properties by using digital fuzzy set and the operations. By using fuzzy digital space, the theory of digital fuzzy spline can be introduced to replace grid data that has been use previously to get better result. As a result, the flat of EEG can be fuzzy topological digital space and this type of data can be use to interpolate the digital fuzzy spline.

Neural encoding of large-scale three-dimensional space-properties and constraints.

PubMed

Jeffery, Kate J; Wilson, Jonathan J; Casali, Giulio; Hayman, Robin M

2015-01-01

How the brain represents represent large-scale, navigable space has been the topic of intensive investigation for several decades, resulting in the discovery that neurons in a complex network of cortical and subcortical brain regions co-operatively encode distance, direction, place, movement etc. using a variety of different sensory inputs. However, such studies have mainly been conducted in simple laboratory settings in which animals explore small, two-dimensional (i.e., flat) arenas. The real world, by contrast, is complex and three dimensional with hills, valleys, tunnels, branches, and-for species that can swim or fly-large volumetric spaces. Adding an additional dimension to space adds coding challenges, a primary reason for which is that several basic geometric properties are different in three dimensions. This article will explore the consequences of these challenges for the establishment of a functional three-dimensional metric map of space, one of which is that the brains of some species might have evolved to reduce the dimensionality of the representational space and thus sidestep some of these problems.

Neisseria meningitidis colonization of the brain endothelium and cerebrospinal fluid invasion.

PubMed

Miller, Florence; Lécuyer, Hervé; Join-Lambert, Olivier; Bourdoulous, Sandrine; Marullo, Stefano; Nassif, Xavier; Coureuil, Mathieu

2013-04-01

The brain and meningeal spaces are protected from bacterial invasion by the blood-brain barrier, formed by specialized endothelial cells and tight intercellular junctional complexes. However, once in the bloodstream, Neisseria meningitidis crosses this barrier in about 60% of the cases. This highlights the particular efficacy with which N. meningitidis targets the brain vascular cell wall. The first step of central nervous system invasion is the direct interaction between bacteria and endothelial cells. This step is mediated by the type IV pili, which induce a remodelling of the endothelial monolayer, leading to the opening of the intercellular space. In this review, strategies used by the bacteria to survive in the bloodstream, to colonize the brain vasculature and to cross the blood-brain barrier will be discussed. © 2012 Blackwell Publishing Ltd.

Top-down signal transmission and global hyperconnectivity in auditory-visual synesthesia: Evidence from a functional EEG resting-state study.

PubMed

Brauchli, Christian; Elmer, Stefan; Rogenmoser, Lars; Burkhard, Anja; Jäncke, Lutz

2018-01-01

Auditory-visual (AV) synesthesia is a rare phenomenon in which an auditory stimulus induces a "concurrent" color sensation. Current neurophysiological models of synesthesia mainly hypothesize "hyperconnected" and "hyperactivated" brains, but differ in the directionality of signal transmission. The two-stage model proposes bottom-up signal transmission from inducer- to concurrent- to higher-order brain areas, whereas the disinhibited feedback model postulates top-down signal transmission from inducer- to higher-order- to concurrent brain areas. To test the different models of synesthesia, we estimated local current density, directed and undirected connectivity patterns in the intracranial space during 2 min of resting-state (RS) EEG in 11 AV synesthetes and 11 nonsynesthetes. AV synesthetes demonstrated increased parietal theta, alpha, and lower beta current density compared to nonsynesthetes. Furthermore, AV synesthetes were characterized by increased top-down signal transmission from the superior parietal lobe to the left color processing area V4 in the upper beta frequency band. Analyses of undirected connectivity revealed a global, synesthesia-specific hyperconnectivity in the alpha frequency band. The involvement of the superior parietal lobe even during rest is a strong indicator for its key role in AV synesthesia. By demonstrating top-down signal transmission in AV synesthetes, we provide direct support for the disinhibited feedback model of synesthesia. Finally, we suggest that synesthesia is a consequence of global hyperconnectivity. Hum Brain Mapp 39:522-531, 2018. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Psychoanalysis and the Brain – Why Did Freud Abandon Neuroscience?

PubMed Central

Northoff, Georg

2012-01-01

Sigmund Freud, the founder of psychoanalysis, was initially a neuroscientist but abandoned neuroscience completely after he made a last attempt to link both in his writing, “Project of a Scientific Psychology,” in 1895. The reasons for his subsequent disregard of the brain remain unclear though. I here argue that one central reason may be that the approach to the brain during his time was simply not appealing to Freud. More specifically, Freud was interested in revealing the psychological predispositions of psychodynamic processes. However, he was not so much focused on the actual psychological functions themselves which though were the prime focus of the neuroscience at his time and also in current Cognitive Neuroscience. Instead, he probably would have been more interested in the brain’s resting state and its constitution of a spatiotemporal structure. I here assume that the resting state activity constitutes a statistically based virtual structure extending and linking the different discrete points in time and space within the brain. That in turn may serve as template, schemata, or grid for all subsequent neural processing during stimulus-induced activity. As such the resting state’ spatiotemporal structure may serve as the neural predisposition of what Freud described as “psychological structure.” Hence, Freud and also current neuropsychoanalysis may want to focus more on neural predispositions, the necessary non-sufficient conditions, rather than the neural correlates, i.e., sufficient, conditions of psychodynamic processes. PMID:22485098

Historical epistemology of the body-mind interaction in psychiatry

PubMed Central

Berrios, German E.

2018-01-01

This paper deals with the history of the relationship between the mind-body dualism and the epistemology of madness. Earlier versions of such dualism posed little problem in regard to the manner of their communication. The Cartesian view that mind and body did, in fact, name different substances introduced a problem of incommunicability that is yet to be resolved. Earlier views that madness may be related to changes in the brain began gaining empirical support during the 17th century. Writers on madness chose to resolve the mind-body problem differently Some stated that such communication was not needed; others, that mind was a redundant concept, as madness could be fully explained by structural changes in the brain; and yet others described psychological spaces for madness to inhabit as a symbolic conflict. The epistemology of the neurosciences bypasses the conundrum, as it processes all together the variables representing the brain, subjectivity, and behavior and bridges the “philosophical” gap by means of correlational structures. PMID:29946206

Is the Internet gaming-addicted brain close to be in a pathological state?

PubMed

Park, Chang-Hyun; Chun, Ji-Won; Cho, Huyn; Jung, Young-Chul; Choi, Jihye; Kim, Dai Jin

2017-01-01

Internet gaming addiction (IGA) is becoming a common and widespread mental health concern. Although IGA induces a variety of negative psychosocial consequences, it is yet ambiguous whether the brain addicted to Internet gaming is considered to be in a pathological state. We investigated IGA-induced abnormalities of the brain specifically from the network perspective and qualitatively assessed whether the Internet gaming-addicted brain is in a state similar to the pathological brain. Topological properties of brain functional networks were examined by applying a graph-theoretical approach to analyzing functional magnetic resonance imaging data acquired during a resting state in 19 IGA adolescents and 20 age-matched healthy controls. We compared functional distance-based measures, global and local efficiency of resting state brain functional networks between the two groups to assess how the IGA subjects' brain was topologically altered from the controls' brain. The IGA subjects had severer impulsiveness and their brain functional networks showed higher global efficiency and lower local efficiency relative to the controls. These topological differences suggest that IGA induced brain functional networks to shift toward the random topological architecture, as exhibited in other pathological states. Furthermore, for the IGA subjects, the topological alterations were specifically attributable to interregional connections incident on the frontal region, and the degree of impulsiveness was associated with the topological alterations over the frontolimbic connections. The current findings lend support to the proposition that the Internet gaming-addicted brain could be in the state similar to pathological states in terms of topological characteristics of brain functional networks. © 2015 Society for the Study of Addiction.

Rapid whole-brain resting-state fMRI at 3 T: Efficiency-optimized three-dimensional EPI versus repetition time-matched simultaneous-multi-slice EPI.

PubMed

Stirnberg, Rüdiger; Huijbers, Willem; Brenner, Daniel; Poser, Benedikt A; Breteler, Monique; Stöcker, Tony

2017-12-01

State-of-the-art simultaneous-multi-slice (SMS-)EPI and 3D-EPI share several properties that benefit functional MRI acquisition. Both sequences employ equivalent parallel imaging undersampling with controlled aliasing to achieve high temporal sampling rates. As a volumetric imaging sequence, 3D-EPI offers additional means of acceleration complementary to 2D-CAIPIRINHA sampling, such as fast water excitation and elliptical sampling. We performed an application-oriented comparison between a tailored, six-fold CAIPIRINHA-accelerated 3D-EPI protocol at 530 ms temporal and 2.4 mm isotropic spatial resolution and an SMS-EPI protocol with identical spatial and temporal resolution for whole-brain resting-state fMRI at 3 T. The latter required eight-fold slice acceleration to compensate for the lack of elliptical sampling and fast water excitation. Both sequences used vendor-supplied on-line image reconstruction. We acquired test/retest resting-state fMRI scans in ten volunteers, with simultaneous acquisition of cardiac and respiration data, subsequently used for optional physiological noise removal (nuisance regression). We found that the 3D-EPI protocol has significantly increased temporal signal-to-noise ratio throughout the brain as compared to the SMS-EPI protocol, especially when employing motion and nuisance regression. Both sequence types reliably identified known functional networks with stronger functional connectivity values for the 3D-EPI protocol. We conclude that the more time-efficient 3D-EPI primarily benefits from reduced parallel imaging noise due to a higher, actual k-space sampling density compared to SMS-EPI. The resultant BOLD sensitivity increase makes 3D-EPI a valuable alternative to SMS-EPI for whole-brain fMRI at 3 T, with voxel sizes well below 3 mm isotropic and sampling rates high enough to separate dominant cardiac signals from BOLD signals in the frequency domain. Copyright © 2017 Elsevier Inc. All rights reserved.

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.

On nodes and modes in resting state fMRI

PubMed Central

Friston, Karl J.; Kahan, Joshua; Razi, Adeel; Stephan, Klaas Enno; Sporns, Olaf

2014-01-01

This paper examines intrinsic brain networks in light of recent developments in the characterisation of resting state fMRI timeseries — and simulations of neuronal fluctuations based upon the connectome. Its particular focus is on patterns or modes of distributed activity that underlie functional connectivity. We first demonstrate that the eigenmodes of functional connectivity – or covariance among regions or nodes – are the same as the eigenmodes of the underlying effective connectivity, provided we limit ourselves to symmetrical connections. This symmetry constraint is motivated by appealing to proximity graphs based upon multidimensional scaling. Crucially, the principal modes of functional connectivity correspond to the dynamically unstable modes of effective connectivity that decay slowly and show long term memory. Technically, these modes have small negative Lyapunov exponents that approach zero from below. Interestingly, the superposition of modes – whose exponents are sampled from a power law distribution – produces classical 1/f (scale free) spectra. We conjecture that the emergence of dynamical instability – that underlies intrinsic brain networks – is inevitable in any system that is separated from external states by a Markov blanket. This conjecture appeals to a free energy formulation of nonequilibrium steady-state dynamics. The common theme that emerges from these theoretical considerations is that endogenous fluctuations are dominated by a small number of dynamically unstable modes. We use this as the basis of a dynamic causal model (DCM) of resting state fluctuations — as measured in terms of their complex cross spectra. In this model, effective connectivity is parameterised in terms of eigenmodes and their Lyapunov exponents — that can also be interpreted as locations in a multidimensional scaling space. Model inversion provides not only estimates of edges or connectivity but also the topography and dimensionality of the underlying scaling space. Here, we focus on conceptual issues with simulated fMRI data and provide an illustrative application using an empirical multi-region timeseries. PMID:24862075

A closed-loop anesthetic delivery system for real-time control of burst suppression

NASA Astrophysics Data System (ADS)

Liberman, Max Y.; Ching, ShiNung; Chemali, Jessica; Brown, Emery N.

2013-08-01

Objective. There is growing interest in using closed-loop anesthetic delivery (CLAD) systems to automate control of brain states (sedation, unconsciousness and antinociception) in patients receiving anesthesia care. The accuracy and reliability of these systems can be improved by using as control signals electroencephalogram (EEG) markers for which the neurophysiological links to the anesthetic-induced brain states are well established. Burst suppression, in which bursts of electrical activity alternate with periods of quiescence or suppression, is a well-known, readily discernible EEG marker of profound brain inactivation and unconsciousness. This pattern is commonly maintained when anesthetics are administered to produce a medically-induced coma for cerebral protection in patients suffering from brain injuries or to arrest brain activity in patients having uncontrollable seizures. Although the coma may be required for several hours or days, drug infusion rates are managed inefficiently by manual adjustment. Our objective is to design a CLAD system for burst suppression control to automate management of medically-induced coma. Approach. We establish a CLAD system to control burst suppression consisting of: a two-dimensional linear system model relating the anesthetic brain level to the EEG dynamics; a new control signal, the burst suppression probability (BSP) defining the instantaneous probability of suppression; the BSP filter, a state-space algorithm to estimate the BSP from EEG recordings; a proportional-integral controller; and a system identification procedure to estimate the model and controller parameters. Main results. We demonstrate reliable performance of our system in simulation studies of burst suppression control using both propofol and etomidate in rodent experiments based on Vijn and Sneyd, and in human experiments based on the Schnider pharmacokinetic model for propofol. Using propofol, we further demonstrate that our control system reliably tracks changing target levels of burst suppression in simulated human subjects across different epidemiological profiles. Significance. Our results give new insights into CLAD system design and suggest a control-theory framework to automate second-to-second control of burst suppression for management of medically-induced coma.

Multivariate dynamical modelling of structural change during development.

PubMed

Ziegler, Gabriel; Ridgway, Gerard R; Blakemore, Sarah-Jayne; Ashburner, John; Penny, Will

2017-02-15

Here we introduce a multivariate framework for characterising longitudinal changes in structural MRI using dynamical systems. The general approach enables modelling changes of states in multiple imaging biomarkers typically observed during brain development, plasticity, ageing and degeneration, e.g. regional gray matter volume of multiple regions of interest (ROIs). Structural brain states follow intrinsic dynamics according to a linear system with additional inputs accounting for potential driving forces of brain development. In particular, the inputs to the system are specified to account for known or latent developmental growth/decline factors, e.g. due to effects of growth hormones, puberty, or sudden behavioural changes etc. Because effects of developmental factors might be region-specific, the sensitivity of each ROI to contributions of each factor is explicitly modelled. In addition to the external effects of developmental factors on regional change, the framework enables modelling and inference about directed (potentially reciprocal) interactions between brain regions, due to competition for space, or structural connectivity, and suchlike. This approach accounts for repeated measures in typical MRI studies of development and aging. Model inversion and posterior distributions are obtained using earlier established variational methods enabling Bayesian evidence-based comparisons between various models of structural change. Using this approach we demonstrate dynamic cortical changes during brain maturation between 6 and 22 years of age using a large openly available longitudinal paediatric dataset with 637 scans from 289 individuals. In particular, we model volumetric changes in 26 bilateral ROIs, which cover large portions of cortical and subcortical gray matter. We account for (1) puberty-related effects on gray matter regions; (2) effects of an early transient growth process with additional time-lag parameter; (3) sexual dimorphism by modelling parameter differences between boys and girls. There is evidence that the regional pattern of sensitivity to dynamic hidden growth factors in late childhood is similar across genders and shows a consistent anterior-posterior gradient with strongest impact to prefrontal cortex (PFC) brain changes. Finally, we demonstrate the potential of the framework to explore the coupling of structural changes across a priori defined subnetworks using an example of previously established resting state functional connectivity. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Plasticity of brain wave network interactions and evolution across physiologic states

PubMed Central

Liu, Kang K. L.; Bartsch, Ronny P.; Lin, Aijing; Mantegna, Rosario N.; Ivanov, Plamen Ch.

2015-01-01

Neural plasticity transcends a range of spatio-temporal scales and serves as the basis of various brain activities and physiologic functions. At the microscopic level, it enables the emergence of brain waves with complex temporal dynamics. At the macroscopic level, presence and dominance of specific brain waves is associated with important brain functions. The role of neural plasticity at different levels in generating distinct brain rhythms and how brain rhythms communicate with each other across brain areas to generate physiologic states and functions remains not understood. Here we perform an empirical exploration of neural plasticity at the level of brain wave network interactions representing dynamical communications within and between different brain areas in the frequency domain. We introduce the concept of time delay stability (TDS) to quantify coordinated bursts in the activity of brain waves, and we employ a system-wide Network Physiology integrative approach to probe the network of coordinated brain wave activations and its evolution across physiologic states. We find an association between network structure and physiologic states. We uncover a hierarchical reorganization in the brain wave networks in response to changes in physiologic state, indicating new aspects of neural plasticity at the integrated level. Globally, we find that the entire brain network undergoes a pronounced transition from low connectivity in Deep Sleep and REM to high connectivity in Light Sleep and Wake. In contrast, we find that locally, different brain areas exhibit different network dynamics of brain wave interactions to achieve differentiation in function during different sleep stages. Moreover, our analyses indicate that plasticity also emerges in frequency-specific networks, which represent interactions across brain locations mediated through a specific frequency band. Comparing frequency-specific networks within the same physiologic state we find very different degree of network connectivity and link strength, while at the same time each frequency-specific network is characterized by a different signature pattern of sleep-stage stratification, reflecting a remarkable flexibility in response to change in physiologic state. These new aspects of neural plasticity demonstrate that in addition to dominant brain waves, the network of brain wave interactions is a previously unrecognized hallmark of physiologic state and function. PMID:26578891

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

Detecting Brain State Changes via Fiber-Centered Functional Connectivity Analysis

PubMed Central

Li, Xiang; Lim, Chulwoo; Li, Kaiming; Guo, Lei; Liu, Tianming

2013-01-01

Diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI) have been widely used to study structural and functional brain connectivity in recent years. A common assumption used in many previous functional brain connectivity studies is the temporal stationarity. However, accumulating literature evidence has suggested that functional brain connectivity is under temporal dynamic changes in different time scales. In this paper, a novel and intuitive approach is proposed to model and detect dynamic changes of functional brain states based on multimodal fMRI/DTI data. The basic idea is that functional connectivity patterns of all fiber-connected cortical voxels are concatenated into a descriptive functional feature vector to represent the brain’s state, and the temporal change points of brain states are decided by detecting the abrupt changes of the functional vector patterns via the sliding window approach. Our extensive experimental results have shown that meaningful brain state change points can be detected in task-based fMRI/DTI, resting state fMRI/DTI, and natural stimulus fMRI/DTI data sets. Particularly, the detected change points of functional brain states in task-based fMRI corresponded well to the external stimulus paradigm administered to the participating subjects, thus partially validating the proposed brain state change detection approach. The work in this paper provides novel perspective on the dynamic behaviors of functional brain connectivity and offers a starting point for future elucidation of the complex patterns of functional brain interactions and dynamics. PMID:22941508

Functional characteristics of the brain in college students with internet gaming disorder.

PubMed

Liu, Jun; Li, Weihui; Zhou, Shunke; Zhang, Li; Wang, Zhiyuan; Zhang, Yan; Jiang, Yebin; Li, Lingjiang

2016-03-01

Internet gaming disorder (IGD) is a subtype of internet addiction disorder (IAD), but its pathogenesis remains unclear. This study investigated brain function in IGD individuals using task-state functional magnetic resonance imaging (fMRI). It is a prospective study in 19 IGD individuals and 19 matched healthy controls. They all received internet videogame stimuli while a 3.0 T fMRI was used to assess echo planar imaging. Brain activity was analyzed using the Brain Voyager software package. Functional data were spatially smoothed using Gaussian kernel. The threshold level was positioned at 10 pixels, and the activation range threshold was set to 10 voxels. Activated brain regions were compared between the two groups, as well as the amount of activated voxels. The internet videogame stimuli activated brain regions in both groups. Compared with controls, the IGD group showed increased activation in the right superior parietal lobule, right insular lobe, right precuneus, right cingulated gyrus, right superior temporal gyrus, and left brainstem. There was a significant difference in the number of activated voxels between the two groups. An average of 1078 voxels was activated in the IGD group compared with only 232 in the control group. Internet videogame play activates the vision, space, attention, and execution centers located in the occipital, temporal, parietal, and frontal gyri. Abnormal brain function was noted in IGD subjects, with hypofunction of the frontal cortex. IGD subjects showed laterality activation of the right cerebral hemisphere.

"Application of Box-Behnken design for optimization and development of quetiapine fumarate loaded chitosan nanoparticles for brain delivery via intranasal route* ".

PubMed

Shah, Brijesh; Khunt, Dignesh; Misra, Manju; Padh, Harish

2016-08-01

The objective of the present investigation was to optimize and develop quetiapine fumarate (QF) loaded chitosan nanoparticles (QF-NP) by ionic gelation method using Box-Behnken design. Three independent variables viz., X1-Concentration of chitosan, X2-Concentration of sodium tripolyphosphate and X3-Volume of sodium tripolyphosphate were taken to investigate their effect on dependent variables (Y1-Size, Y2-PDI and Y3-%EE). Optimized formula of QF-NP was selected from the design space which was further evaluated for physicochemical, morphological, solid state characterization, nasal diffusion and in-vivo distribution for brain targeting following non-invasive intranasal administration. The average particle size, PDI, %EE and nasal diffusion were found to be 131.08±7.45nm, 0.252±0.064, 89.93±3.85% and 65.24±5.26% respectively. Neither toxicity nor structural damage on nasal mucosa was observed upon histopathological examination. Significantly higher brain/blood ratio and 2 folds higher nasal bioavailability in brain with QF-NP in comparison to drug solution following intranasal administration revealed preferential nose to brain transport bypassing blood-brain barrier and prolonged retention of QF at site of action suggesting superiority of chitosan as permeability enhancer. Overall, the above finding shows promising results in the area of developing non-invasive intranasal route as an alternative to oral route for brain delivery. Copyright © 2016 Elsevier B.V. All rights reserved.

Rat brain digital stereotaxic white matter atlas with fine tract delineation in Paxinos space and its automated applications in DTI data analysis.

PubMed

Liang, Shengxiang; Wu, Shang; Huang, Qi; Duan, Shaofeng; Liu, Hua; Li, Yuxiao; Zhao, Shujun; Nie, Binbin; Shan, Baoci

2017-11-01

To automatically analyze diffusion tensor images of the rat brain via both voxel-based and ROI-based approaches, we constructed a new white matter atlas of the rat brain with fine tracts delineation in the Paxinos and Watson space. Unlike in previous studies, we constructed a digital atlas image from the latest edition of the Paxinos and Watson. This atlas contains 111 carefully delineated white matter fibers. A white matter network of rat brain based on anatomy was constructed by locating the intersection of all these tracts and recording the nuclei on the pathway of each white matter tract. Moreover, a compatible rat brain template from DTI images was created and standardized into the atlas space. To evaluate the automated application of the atlas in DTI data analysis, a group of rats with right-side middle cerebral artery occlusion (MCAO) and those without were enrolled in this study. The voxel-based analysis result shows that the brain region showing significant declines in signal in the MCAO rats was consistent with the occlusion position. We constructed a stereotaxic white matter atlas of the rat brain with fine tract delineation and a compatible template for the data analysis of DTI images of the rat brain. Copyright © 2017 Elsevier Inc. All rights reserved.

Novel Intrinsic Ignition Method Measuring Local-Global Integration Characterizes Wakefulness and Deep Sleep

PubMed Central

Tagliazucchi, Enzo; Sanjuán, Ana

2017-01-01

Abstract A precise definition of a brain state has proven elusive. Here, we introduce the novel local-global concept of intrinsic ignition characterizing the dynamical complexity of different brain states. Naturally occurring intrinsic ignition events reflect the capability of a given brain area to propagate neuronal activity to other regions, giving rise to different levels of integration. The ignitory capability of brain regions is computed by the elicited level of integration for each intrinsic ignition event in each brain region, averaged over all events. This intrinsic ignition method is shown to clearly distinguish human neuroimaging data of two fundamental brain states (wakefulness and deep sleep). Importantly, whole-brain computational modelling of this data shows that at the optimal working point is found where there is maximal variability of the intrinsic ignition across brain regions. Thus, combining whole brain models with intrinsic ignition can provide novel insights into underlying mechanisms of brain states. PMID:28966977

Novel Intrinsic Ignition Method Measuring Local-Global Integration Characterizes Wakefulness and Deep Sleep.

PubMed

Deco, Gustavo; Tagliazucchi, Enzo; Laufs, Helmut; Sanjuán, Ana; Kringelbach, Morten L

2017-01-01

A precise definition of a brain state has proven elusive. Here, we introduce the novel local-global concept of intrinsic ignition characterizing the dynamical complexity of different brain states. Naturally occurring intrinsic ignition events reflect the capability of a given brain area to propagate neuronal activity to other regions, giving rise to different levels of integration. The ignitory capability of brain regions is computed by the elicited level of integration for each intrinsic ignition event in each brain region, averaged over all events. This intrinsic ignition method is shown to clearly distinguish human neuroimaging data of two fundamental brain states (wakefulness and deep sleep). Importantly, whole-brain computational modelling of this data shows that at the optimal working point is found where there is maximal variability of the intrinsic ignition across brain regions. Thus, combining whole brain models with intrinsic ignition can provide novel insights into underlying mechanisms of brain states.

Functional brain networks in Alzheimer's disease: EEG analysis based on limited penetrable visibility graph and phase space method

NASA Astrophysics Data System (ADS)

Wang, Jiang; Yang, Chen; Wang, Ruofan; Yu, Haitao; Cao, Yibin; Liu, Jing

2016-10-01

In this paper, EEG series are applied to construct functional connections with the correlation between different regions in order to investigate the nonlinear characteristic and the cognitive function of the brain with Alzheimer's disease (AD). First, limited penetrable visibility graph (LPVG) and phase space method map single EEG series into networks, and investigate the underlying chaotic system dynamics of AD brain. Topological properties of the networks are extracted, such as average path length and clustering coefficient. It is found that the network topology of AD in several local brain regions are different from that of the control group with no statistically significant difference existing all over the brain. Furthermore, in order to detect the abnormality of AD brain as a whole, functional connections among different brain regions are reconstructed based on similarity of clustering coefficient sequence (CCSS) of EEG series in the four frequency bands (delta, theta, alpha, and beta), which exhibit obvious small-world properties. Graph analysis demonstrates that for both methodologies, the functional connections between regions of AD brain decrease, particularly in the alpha frequency band. AD causes the graph index complexity of the functional network decreased, the small-world properties weakened, and the vulnerability increased. The obtained results show that the brain functional network constructed by LPVG and phase space method might be more effective to distinguish AD from the normal control than the analysis of single series, which is helpful for revealing the underlying pathological mechanism of the disease.

Pig brain stereotaxic standard space: mapping of cerebral blood flow normative values and effect of MPTP-lesioning.

PubMed

Andersen, Flemming; Watanabe, Hideaki; Bjarkam, Carsten; Danielsen, Erik H; Cumming, Paul

2005-07-15

The analysis of physiological processes in brain by position emission tomography (PET) is facilitated when images are spatially normalized to a standard coordinate system. Thus, PET activation studies of human brain frequently employ the common stereotaxic coordinates of Talairach. We have developed an analogous stereotaxic coordinate system for the brain of the Gottingen miniature pig, based on automatic co-registration of magnetic resonance (MR) images obtained in 22 male pigs. The origin of the pig brain stereotaxic space (0, 0, 0) was arbitrarily placed in the centroid of the pineal gland as identified on the average MRI template. The orthogonal planes were imposed using the line between stereotaxic zero and the optic chiasm. A series of mean MR images in the coronal, sagittal and horizontal planes were generated. To test the utility of the common coordinate system for functional imaging studies of minipig brain, we calculated cerebral blood flow (CBF) maps from normal minipigs and from minipigs with a syndrome of parkisonism induced by 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)-poisoning. These maps were transformed from the native space into the common stereotaxic space. After global normalization of these maps, an undirected search for differences between the groups was then performed using statistical parametric mapping. Using this method, we detected a statistically significant focal increase in CBF in the left cerebellum of the MPTP-lesioned group. We expect the present approach to be of general use in the statistical parametric mapping of CBF and other physiological parameters in living pig brain.

Low-Dimensional Statistics of Anatomical Variability via Compact Representation of Image Deformations.

PubMed

Zhang, Miaomiao; Wells, William M; Golland, Polina

2016-10-01

Using image-based descriptors to investigate clinical hypotheses and therapeutic implications is challenging due to the notorious "curse of dimensionality" coupled with a small sample size. In this paper, we present a low-dimensional analysis of anatomical shape variability in the space of diffeomorphisms and demonstrate its benefits for clinical studies. To combat the high dimensionality of the deformation descriptors, we develop a probabilistic model of principal geodesic analysis in a bandlimited low-dimensional space that still captures the underlying variability of image data. We demonstrate the performance of our model on a set of 3D brain MRI scans from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Our model yields a more compact representation of group variation at substantially lower computational cost than models based on the high-dimensional state-of-the-art approaches such as tangent space PCA (TPCA) and probabilistic principal geodesic analysis (PPGA).

Neural feedback for instantaneous spatiotemporal modulation of afferent pathways in bi-directional brain-machine interfaces.

PubMed

Liu, Jianbo; Khalil, Hassan K; Oweiss, Karim G

2011-10-01

In bi-directional brain-machine interfaces (BMIs), precisely controlling the delivery of microstimulation, both in space and in time, is critical to continuously modulate the neural activity patterns that carry information about the state of the brain-actuated device to sensory areas in the brain. In this paper, we investigate the use of neural feedback to control the spatiotemporal firing patterns of neural ensembles in a model of the thalamocortical pathway. Control of pyramidal (PY) cells in the primary somatosensory cortex (S1) is achieved based on microstimulation of thalamic relay cells through multiple-input multiple-output (MIMO) feedback controllers. This closed loop feedback control mechanism is achieved by simultaneously varying the stimulation parameters across multiple stimulation electrodes in the thalamic circuit based on continuous monitoring of the difference between reference patterns and the evoked responses of the cortical PY cells. We demonstrate that it is feasible to achieve a desired level of performance by controlling the firing activity pattern of a few "key" neural elements in the network. Our results suggest that neural feedback could be an effective method to facilitate the delivery of information to the cortex to substitute lost sensory inputs in cortically controlled BMIs.

Computational genetic neuroanatomy of the developing mouse brain: dimensionality reduction, visualization, and clustering

PubMed Central

2013-01-01

Background The structured organization of cells in the brain plays a key role in its functional efficiency. This delicate organization is the consequence of unique molecular identity of each cell gradually established by precise spatiotemporal gene expression control during development. Currently, studies on the molecular-structural association are beginning to reveal how the spatiotemporal gene expression patterns are related to cellular differentiation and structural development. Results In this article, we aim at a global, data-driven study of the relationship between gene expressions and neuroanatomy in the developing mouse brain. To enable visual explorations of the high-dimensional data, we map the in situ hybridization gene expression data to a two-dimensional space by preserving both the global and the local structures. Our results show that the developing brain anatomy is largely preserved in the reduced gene expression space. To provide a quantitative analysis, we cluster the reduced data into groups and measure the consistency with neuroanatomy at multiple levels. Our results show that the clusters in the low-dimensional space are more consistent with neuroanatomy than those in the original space. Conclusions Gene expression patterns and developing brain anatomy are closely related. Dimensionality reduction and visual exploration facilitate the study of this relationship. PMID:23845024

Three-Dimensional Modeling of the Brain's ECS by Minimum Configurational Energy Packing of Fluid Vesicles

PubMed Central

Nandigam, Ravi K.; Kroll, Daniel M.

2007-01-01

The extracellular space of the brain is the heterogeneous porous medium formed by the spaces between the brain cells. Diffusion in this interstitial space is the mechanism by which glucose and oxygen are delivered to the brain cells from the vascular system. It is also a medium for the transport of certain informational substances between the cells (called volume transmission), and for drug delivery. This work involves three-dimensional modeling of the extracellular space as void space in close-packed arrays of fluid membrane vesicles. These packings are generated by minimizing the configurational energy using a Monte Carlo procedure. Both regular and random packs of vesicles are considered. A random walk algorithm is then used to compute the geometric tortuosities, and the results are compared with published experimental data. For the random packings, it is found that although the absolute values for the tortuosities differ, the dependence of the tortuosity on pore volume fraction is very similar to that observed in experiment. The tortuosities we measure are larger than those computed in previous studies of packings of convex polytopes, and modeling improvements, which require higher resolution studies and an improved modeling of brain cell shapes and mechanical properties, could help resolve remaining discrepancies between model simulations and experiment. It is also shown that the specular reflection scheme is the appropriate technique for implementing zero-flux boundary conditions in random walk simulations commonly encountered in diffusion problems. PMID:17307830

Three-dimensional modeling of the brain's ECS by minimum configurational energy packing of fluid vesicles.

PubMed

Nandigam, Ravi K; Kroll, Daniel M

2007-05-15

The extracellular space of the brain is the heterogeneous porous medium formed by the spaces between the brain cells. Diffusion in this interstitial space is the mechanism by which glucose and oxygen are delivered to the brain cells from the vascular system. It is also a medium for the transport of certain informational substances between the cells (called volume transmission), and for drug delivery. This work involves three-dimensional modeling of the extracellular space as void space in close-packed arrays of fluid membrane vesicles. These packings are generated by minimizing the configurational energy using a Monte Carlo procedure. Both regular and random packs of vesicles are considered. A random walk algorithm is then used to compute the geometric tortuosities, and the results are compared with published experimental data. For the random packings, it is found that although the absolute values for the tortuosities differ, the dependence of the tortuosity on pore volume fraction is very similar to that observed in experiment. The tortuosities we measure are larger than those computed in previous studies of packings of convex polytopes, and modeling improvements, which require higher resolution studies and an improved modeling of brain cell shapes and mechanical properties, could help resolve remaining discrepancies between model simulations and experiment. It is also shown that the specular reflection scheme is the appropriate technique for implementing zero-flux boundary conditions in random walk simulations commonly encountered in diffusion problems.

Classification of fMRI resting-state maps using machine learning techniques: A comparative study

NASA Astrophysics Data System (ADS)

Gallos, Ioannis; Siettos, Constantinos

2017-11-01

We compare the efficiency of Principal Component Analysis (PCA) and nonlinear learning manifold algorithms (ISOMAP and Diffusion maps) for classifying brain maps between groups of schizophrenia patients and healthy from fMRI scans during a resting-state experiment. After a standard pre-processing pipeline, we applied spatial Independent component analysis (ICA) to reduce (a) noise and (b) spatial-temporal dimensionality of fMRI maps. On the cross-correlation matrix of the ICA components, we applied PCA, ISOMAP and Diffusion Maps to find an embedded low-dimensional space. Finally, support-vector-machines (SVM) and k-NN algorithms were used to evaluate the performance of the algorithms in classifying between the two groups.

Long-Term Stability of Motor Cortical Activity: Implications for Brain Machine Interfaces and Optimal Feedback Control.

PubMed

Flint, Robert D; Scheid, Michael R; Wright, Zachary A; Solla, Sara A; Slutzky, Marc W

2016-03-23

The human motor system is capable of remarkably precise control of movements--consider the skill of professional baseball pitchers or surgeons. This precise control relies upon stable representations of movements in the brain. Here, we investigated the stability of cortical activity at multiple spatial and temporal scales by recording local field potentials (LFPs) and action potentials (multiunit spikes, MSPs) while two monkeys controlled a cursor either with their hand or directly from the brain using a brain-machine interface. LFPs and some MSPs were remarkably stable over time periods ranging from 3 d to over 3 years; overall, LFPs were significantly more stable than spikes. We then assessed whether the stability of all neural activity, or just a subset of activity, was necessary to achieve stable behavior. We showed that projections of neural activity into the subspace relevant to the task (the "task-relevant space") were significantly more stable than were projections into the task-irrelevant (or "task-null") space. This provides cortical evidence in support of the minimum intervention principle, which proposes that optimal feedback control (OFC) allows the brain to tightly control only activity in the task-relevant space while allowing activity in the task-irrelevant space to vary substantially from trial to trial. We found that the brain appears capable of maintaining stable movement representations for extremely long periods of time, particularly so for neural activity in the task-relevant space, which agrees with OFC predictions. It is unknown whether cortical signals are stable for more than a few weeks. Here, we demonstrate that motor cortical signals can exhibit high stability over several years. This result is particularly important to brain-machine interfaces because it could enable stable performance with infrequent recalibration. Although we can maintain movement accuracy over time, movement components that are unrelated to the goals of a task (such as elbow position during reaching) often vary from trial to trial. This is consistent with the minimum intervention principle of optimal feedback control. We provide evidence that the motor cortex acts according to this principle: cortical activity is more stable in the task-relevant space and more variable in the task-irrelevant space. Copyright © 2016 the authors 0270-6474/16/363623-10$15.00/0.

Intrinsic Brain Activity in Altered States of Consciousness

PubMed Central

Boly, M.; Phillips, C.; Tshibanda, L.; Vanhaudenhuyse, A.; Schabus, M.; Dang-Vu, T.T.; Moonen, G.; Hustinx, R.; Maquet, P.; Laureys, S.

2010-01-01

Spontaneous brain activity has recently received increasing interest in the neuroimaging community. However, the value of resting-state studies to a better understanding of brain–behavior relationships has been challenged. That altered states of consciousness are a privileged way to study the relationships between spontaneous brain activity and behavior is proposed, and common resting-state brain activity features observed in various states of altered consciousness are reviewed. Early positron emission tomography studies showed that states of extremely low or high brain activity are often associated with unconsciousness. However, this relationship is not absolute, and the precise link between global brain metabolism and awareness remains yet difficult to assert. In contrast, voxel-based analyses identified a systematic impairment of associative frontoparieto–cingulate areas in altered states of consciousness, such as sleep, anesthesia, coma, vegetative state, epileptic loss of consciousness, and somnambulism. In parallel, recent functional magnetic resonance imaging studies have identified structured patterns of slow neuronal oscillations in the resting human brain. Similar coherent blood oxygen level–dependent (BOLD) systemwide patterns can also be found, in particular in the default-mode network, in several states of unconsciousness, such as coma, anesthesia, and slow-wave sleep. The latter results suggest that slow coherent spontaneous BOLD fluctuations cannot be exclusively a reflection of conscious mental activity, but may reflect default brain connectivity shaping brain areas of most likely interactions in a way that transcends levels of consciousness, and whose functional significance remains largely in the dark. PMID:18591474

Late stage infection in sleeping sickness.

PubMed

Wolburg, Hartwig; Mogk, Stefan; Acker, Sven; Frey, Claudia; Meinert, Monika; Schönfeld, Caroline; Lazarus, Michael; Urade, Yoshihiro; Kubata, Bruno Kilunga; Duszenko, Michael

2012-01-01

At the turn of the 19(th) century, trypanosomes were identified as the causative agent of sleeping sickness and their presence within the cerebrospinal fluid of late stage sleeping sickness patients was described. However, no definitive proof of how the parasites reach the brain has been presented so far. Analyzing electron micrographs prepared from rodent brains more than 20 days after infection, we present here conclusive evidence that the parasites first enter the brain via the choroid plexus from where they penetrate the epithelial cell layer to reach the ventricular system. Adversely, no trypanosomes were observed within the parenchyma outside blood vessels. We also show that brain infection depends on the formation of long slender trypanosomes and that the cerebrospinal fluid as well as the stroma of the choroid plexus is a hostile environment for the survival of trypanosomes, which enter the pial space including the Virchow-Robin space via the subarachnoid space to escape degradation. Our data suggest that trypanosomes do not intend to colonize the brain but reside near or within the glia limitans, from where they can re-populate blood vessels and disrupt the sleep wake cycles.

Neural representations of emotion are organized around abstract event features.

PubMed

Skerry, Amy E; Saxe, Rebecca

2015-08-03

Research on emotion attribution has tended to focus on the perception of overt expressions of at most five or six basic emotions. However, our ability to identify others' emotional states is not limited to perception of these canonical expressions. Instead, we make fine-grained inferences about what others feel based on the situations they encounter, relying on knowledge of the eliciting conditions for different emotions. In the present research, we provide convergent behavioral and neural evidence concerning the representations underlying these concepts. First, we find that patterns of activity in mentalizing regions contain information about subtle emotional distinctions conveyed through verbal descriptions of eliciting situations. Second, we identify a space of abstract situation features that well captures the emotion discriminations subjects make behaviorally and show that this feature space outperforms competing models in capturing the similarity space of neural patterns in these regions. Together, the data suggest that our knowledge of others' emotions is abstract and high dimensional, that brain regions selective for mental state reasoning support relatively subtle distinctions between emotion concepts, and that the neural representations in these regions are not reducible to more primitive affective dimensions such as valence and arousal. Copyright © 2015 Elsevier Ltd. All rights reserved.

Neural Representations of Emotion Are Organized around Abstract Event Features

PubMed Central

Skerry, Amy E.; Saxe, Rebecca

2016-01-01

Summary Research on emotion attribution has tended to focus on the perception of overt expressions of at most five or six basic emotions. However, our ability to identify others' emotional states is not limited to perception of these canonical expressions. Instead, we make fine-grained inferences about what others feel based on the situations they encounter, relying on knowledge of the eliciting conditions for different emotions. In the present research, we provide convergent behavioral and neural evidence concerning the representations underlying these concepts. First, we find that patterns of activity in mentalizing regions contain information about subtle emotional distinctions conveyed through verbal descriptions of eliciting situations. Second, we identify a space of abstract situation features that well captures the emotion discriminations subjects make behaviorally and show that this feature space outperforms competing models in capturing the similarity space of neural patterns in these regions. Together, the data suggest that our knowledge of others' emotions is abstract and high dimensional, that brain regions selective for mental state reasoning support relatively subtle distinctions between emotion concepts, and that the neural representations in these regions are not reducible to more primitive affective dimensions such as valence and arousal. PMID:26212878

78 FR 9929 - Current Traumatic Brain Injury State Implementation Partnership Grantees; Non-Competitive One...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-02-12

... Traumatic Brain Injury State Implementation Partnership Grantees; Non-Competitive One-Year Extension Funds...). ACTION: Notice of Non-Competitive One-Year Extension Funds for Current Traumatic Brain Injury (TBI) State... initially authorized by the Traumatic Brain Injury Act of 1996 (Pub. L. 104-166) and was most recently...

Logical Interactions in AN Expanded Space

NASA Astrophysics Data System (ADS)

Tadić, Bosiljka

Understanding the emergent behavior in many complex systems in the physical world and society requires a detailed study of dynamical phenomena occurring and mutually coupled at different scales. The brain processes underlying the social conduct of each, and the emergent social behavior of interacting individuals on a larger scale, represent striking examples of the multiscale complexity. Studies of the human brain, a paradigm of a complex functional system, are enabled by a wealth of brain imaging data that provide clues of how we comprehend space, time, languages, numbers, and differentiate normal from diseased individuals, for example. The social brain, a neural basis for social cognition, represents a dynamically organized part of the brain which is involved in the inference of thoughts, feelings, and intentions going on in the brains of others. Research in this currently unexplored area opens a new perspective on the genesis of the societal organization at different levels and the associated social values...

Resting-state brain activity in the motor cortex reflects task-induced activity: A multi-voxel pattern analysis.

PubMed

Kusano, Toshiki; Kurashige, Hiroki; Nambu, Isao; Moriguchi, Yoshiya; Hanakawa, Takashi; Wada, Yasuhiro; Osu, Rieko

2015-08-01

It has been suggested that resting-state brain activity reflects task-induced brain activity patterns. In this study, we examined whether neural representations of specific movements can be observed in the resting-state brain activity patterns of motor areas. First, we defined two regions of interest (ROIs) to examine brain activity associated with two different behavioral tasks. Using multi-voxel pattern analysis with regularized logistic regression, we designed a decoder to detect voxel-level neural representations corresponding to the tasks in each ROI. Next, we applied the decoder to resting-state brain activity. We found that the decoder discriminated resting-state neural activity with accuracy comparable to that associated with task-induced neural activity. The distribution of learned weighted parameters for each ROI was similar for resting-state and task-induced activities. Large weighted parameters were mainly located on conjunctive areas. Moreover, the accuracy of detection was higher than that for a decoder whose weights were randomly shuffled, indicating that the resting-state brain activity includes multi-voxel patterns similar to the neural representation for the tasks. Therefore, these results suggest that the neural representation of resting-state brain activity is more finely organized and more complex than conventionally considered.

Protecting Neural Structures and Cognitive Function During Prolonged Space Flight by Targeting the Brain Derived Neurotrophic Factor Molecular Network

NASA Technical Reports Server (NTRS)

Schmidt, M. A.; Goodwin, T. J.

2014-01-01

Brain derived neurotrophic factor (BDNF) is the main activity-dependent neurotrophin in the human nervous system. BDNF is implicated in production of new neurons from dentate gyrus stem cells (hippocampal neurogenesis), synapse formation, sprouting of new axons, growth of new axons, sprouting of new dendrites, and neuron survival. Alterations in the amount or activity of BDNF can produce significant detrimental changes to cortical function and synaptic transmission in the human brain. This can result in glial and neuronal dysfunction, which may contribute to a range of clinical conditions, spanning a number of learning, behavioral, and neurological disorders. There is an extensive body of work surrounding the BDNF molecular network, including BDNF gene polymorphisms, methylated BDNF gene promoters, multiple gene transcripts, varied BDNF functional proteins, and different BDNF receptors (whose activation differentially drive the neuron to neurogenesis or apoptosis). BDNF is also closely linked to mitochondrial biogenesis through PGC-1alpha, which can influence brain and muscle metabolic efficiency. BDNF AS A HUMAN SPACE FLIGHT COUNTERMEASURE TARGET Earth-based studies reveal that BDNF is negatively impacted by many of the conditions encountered in the space environment, including oxidative stress, radiation, psychological stressors, sleep deprivation, and many others. A growing body of work suggests that the BDNF network is responsive to a range of diet, nutrition, exercise, drug, and other types of influences. This section explores the BDNF network in the context of 1) protecting the brain and nervous system in the space environment, 2) optimizing neurobehavioral performance in space, and 3) reducing the residual effects of space flight on the nervous system on return to Earth

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.

Spinning, hurting, still, afraid: Living life spaces with Type I Chiari Malformation.

PubMed

Andrews, Gavin J

2018-01-31

Human geography's varied engagement with the brain has involved considerations of the way people know and respond to their environments, and their place-based experiences with emotions, mental illnesses and disorders, intellectual disabilities and particular neurological conditions. This paper argues however that this scholarship could be augmented by, and existing expertise be directed towards, considering physical brain abnormalities and injuries. As a case in point it considers the spatial experience of living with Type 1 Chiari Malformation. Through interviews with four sufferers, the research articulates three domains that they have had to re-negotiate - home space, social space and medical space - emphasizing supportive and challenging aspects of each, as well as meaningful and affective qualities to encounters. The paper concludes with some pointers towards the future study of physical brain abnormalities and injuries and the kinds of knowledge it might create to increase awareness and inform care. Copyright © 2018 Elsevier Ltd. All rights reserved.

Definition, evaluation, and management of brain relaxation during craniotomy.

PubMed

Li, J; Gelb, A W; Flexman, A M; Ji, F; Meng, L

2016-06-01

The term 'brain relaxation' is routinely used to describe the size and firmness of the brain tissue during craniotomy. The status of brain relaxation is an important aspect of neuroanaesthesia practice and is relevant to the operating conditions, retraction injury, and likely patient outcomes. Brain relaxation is determined by the relationship between the volume of the intracranial contents and the capacity of the intracranial space (i.e. a content-space relationship). It is a concept related to, but distinct from, intracranial pressure. The evaluation of brain relaxation should be standardized to facilitate clinical communication and research collaboration. Both advantageous and disadvantageous effects of the various interventions for brain relaxation should be taken into account in patient care. The outcomes that matter the most to patients should be emphasized in defining, evaluating, and managing brain relaxation. To date, brain relaxation has not been reviewed specifically, and the aim of this manuscript is to discuss the current approaches to the definition, evaluation, and management of brain relaxation, knowledge gaps, and targets for future research. © The Author 2016. Published by Oxford University Press on behalf of the British Journal of Anaesthesia. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Social Distance Evaluation in Human Parietal Cortex

PubMed Central

Yamakawa, Yoshinori; Kanai, Ryota; Matsumura, Michikazu; Naito, Eiichi

2009-01-01

Across cultures, social relationships are often thought of, described, and acted out in terms of physical space (e.g. “close friends” “high lord”). Does this cognitive mapping of social concepts arise from shared brain resources for processing social and physical relationships? Using fMRI, we found that the tasks of evaluating social compatibility and of evaluating physical distances engage a common brain substrate in the parietal cortex. The present study shows the possibility of an analytic brain mechanism to process and represent complex networks of social relationships. Given parietal cortex's known role in constructing egocentric maps of physical space, our present findings may help to explain the linguistic, psychological and behavioural links between social and physical space. PMID:19204791

An object-based approach for detecting small brain lesions: application to Virchow-Robin spaces.

PubMed

Descombes, Xavier; Kruggel, Frithjof; Wollny, Gert; Gertz, Hermann Josef

2004-02-01

This paper is concerned with the detection of multiple small brain lesions from magnetic resonance imaging (MRI) data. A model based on the marked point process framework is designed to detect Virchow-Robin spaces (VRSs). These tubular shaped spaces are due to retraction of the brain parenchyma from its supplying arteries. VRS are described by simple geometrical objects that are introduced as small tubular structures. Their radiometric properties are embedded in a data term. A prior model includes interactions describing the clustering property of VRS. A Reversible Jump Markov Chain Monte Carlo algorithm (RJMCMC) optimizes the proposed model, obtained by multiplying the prior and the data model. Example results are shown on T1-weighted MRI datasets of elderly subjects.

Characterizing the glymphatic influx by utilizing intracisternal infusion of fluorescently conjugated cadaverine.

PubMed

Zhang, Cui; Lin, Jun; Wei, Fang; Song, Jian; Chen, Wenyue; Shan, Lidong; Xue, Rong; Wang, Guoqing; Tao, Jin; Zhang, Guoxing; Xu, Guang-Yin; Wang, Linhui

2018-05-15

Accumulating evidence supports that cerebrospinal fluid (CSF) in the subarachnoid space (SAS) could reenter the brain parenchyma via the glymphatic influx. The present study was designed to characterize the detailed pathway of subarachnoid CSF influx by using a novel CSF tracer. Fluorescently conjugated cadaverine (A488-ca), for the first time, was employed to investigate CSF movement in the brain. Following intracisternal infusion of CSF tracers, mice brain was sliced and prepared for fluorescence imaging. Some brain sections were immunostained in order to observe tracer distribution and cellular uptake. A488-ca moved into the brain parenchyma rapidly, and the influx was time and region dependent. A488-ca entered the mice brain more readily and spread more widely than another commonly used CSF tracer-fluorescently conjugated ovalbumin (OA-45). Furthermore, A488-ca could enter the brain parenchyma either along the paravascular space or across the pial surface. Suppression of glymphatic transport by administration with acetazolamide strikingly reduced the influx of A488-ca. More importantly, relative to OA-45 largely remained in the extracellular space, A488-ca exhibited obvious cellular uptake by astrocytes surrounding the blood vessels and neurons in the cerebral cortex. Subarachnoid CSF could flow into the brain parenchyma via the glymphatic influx, in which the transcellular pathway was faithfully traced by intracisternal infusion with fluorescently conjugated cadaverine. These observations extend our comprehension on the glymphatic influx pathway. Copyright © 2018 Elsevier Inc. All rights reserved.

Spatio-temporal models of mental processes from fMRI.

PubMed

Janoos, Firdaus; Machiraju, Raghu; Singh, Shantanu; Morocz, Istvan Ákos

2011-07-15

Understanding the highly complex, spatially distributed and temporally organized phenomena entailed by mental processes using functional MRI is an important research problem in cognitive and clinical neuroscience. Conventional analysis methods focus on the spatial dimension of the data discarding the information about brain function contained in the temporal dimension. This paper presents a fully spatio-temporal multivariate analysis method using a state-space model (SSM) for brain function that yields not only spatial maps of activity but also its temporal structure along with spatially varying estimates of the hemodynamic response. Efficient algorithms for estimating the parameters along with quantitative validations are given. A novel low-dimensional feature-space for representing the data, based on a formal definition of functional similarity, is derived. Quantitative validation of the model and the estimation algorithms is provided with a simulation study. Using a real fMRI study for mental arithmetic, the ability of this neurophysiologically inspired model to represent the spatio-temporal information corresponding to mental processes is demonstrated. Moreover, by comparing the models across multiple subjects, natural patterns in mental processes organized according to different mental abilities are revealed. Copyright © 2011 Elsevier Inc. All rights reserved.

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.

Neonatal brain resting-state functional connectivity imaging modalities.

PubMed

Mohammadi-Nejad, Ali-Reza; Mahmoudzadeh, Mahdi; Hassanpour, Mahlegha S; Wallois, Fabrice; Muzik, Otto; Papadelis, Christos; Hansen, Anne; Soltanian-Zadeh, Hamid; Gelovani, Juri; Nasiriavanaki, Mohammadreza

2018-06-01

Infancy is the most critical period in human brain development. Studies demonstrate that subtle brain abnormalities during this state of life may greatly affect the developmental processes of the newborn infants. One of the rapidly developing methods for early characterization of abnormal brain development is functional connectivity of the brain at rest. While the majority of resting-state studies have been conducted using magnetic resonance imaging (MRI), there is clear evidence that resting-state functional connectivity (rs-FC) can also be evaluated using other imaging modalities. The aim of this review is to compare the advantages and limitations of different modalities used for the mapping of infants' brain functional connectivity at rest. In addition, we introduce photoacoustic tomography, a novel functional neuroimaging modality, as a complementary modality for functional mapping of infants' brain.

Three-dimensional confocal morphometry – a new approach for studying dynamic changes in cell morphology in brain slices

PubMed Central

Chvátal, Alexandr; Anděrová, Miroslava; Kirchhoff, Frank

2007-01-01

Pathological states in the central nervous system lead to dramatic changes in the activity of neuroactive substances in the extracellular space, to changes in ionic homeostasis and often to cell swelling. To quantify changes in cell morphology over a certain period of time, we employed a new technique, three-dimensional confocal morphometry. In our experiments, performed on enhanced green fluorescent protein/glial fibrillary acidic protein astrocytes in brain slices in situ and thus preserving the extracellular microenvironment, confocal morphometry revealed that the application of hypotonic solution evoked two types of volume change. In one population of astrocytes, hypotonic stress evoked small cell volume changes followed by a regulatory volume decrease, while in the second population volume changes were significantly larger without subsequent volume regulation. Three-dimensional cell reconstruction revealed that even though the total astrocyte volume increased during hypotonic stress, the morphological changes in various cell compartments and processes were more complex than have been previously shown, including swelling, shrinking and structural rearrangement. Our data show that astrocytes in brain slices in situ during hypotonic stress display complex behaviour. One population of astrocytes is highly capable of cell volume regulation, while the second population is characterized by prominent cell swelling, accompanied by plastic changes in morphology. It is possible to speculate that these two astrocyte populations play different roles during physiological and pathological states. PMID:17488344

Connectome-harmonic decomposition of human brain activity reveals dynamical repertoire re-organization under LSD.

PubMed

Atasoy, Selen; Roseman, Leor; Kaelen, Mendel; Kringelbach, Morten L; Deco, Gustavo; Carhart-Harris, Robin L

2017-12-15

Recent studies have started to elucidate the effects of lysergic acid diethylamide (LSD) on the human brain but the underlying dynamics are not yet fully understood. Here we used 'connectome-harmonic decomposition', a novel method to investigate the dynamical changes in brain states. We found that LSD alters the energy and the power of individual harmonic brain states in a frequency-selective manner. Remarkably, this leads to an expansion of the repertoire of active brain states, suggestive of a general re-organization of brain dynamics given the non-random increase in co-activation across frequencies. Interestingly, the frequency distribution of the active repertoire of brain states under LSD closely follows power-laws indicating a re-organization of the dynamics at the edge of criticality. Beyond the present findings, these methods open up for a better understanding of the complex brain dynamics in health and disease.

MRI patterns in prolonged low response states following traumatic brain injury in children and adolescents.

PubMed

Patrick, Peter D; Mabry, Jennifer L; Gurka, Matthew J; Buck, Marcia L; Boatwright, Evelyn; Blackman, James A

2007-01-01

To explore the relationship between location and pattern of brain injury identified on MRI and prolonged low response state in children post-traumatic brain injury (TBI). This observational study compared 15 children who spontaneously recovered within 30 days post-TBI to 17 who remained in a prolonged low response state. 92.9% of children with brain stem injury were in the low response group. The predicted probability was 0.81 for brain stem injury alone, increasing to 0.95 with a regional pattern of injury to the brain stem, basal ganglia, and thalamus. Low response state in children post-TBI is strongly correlated with two distinctive regions of injury: the brain stem alone, and an injury pattern to the brain stem, basal ganglia, and thalamus. This study demonstrates the need for large-scale clinical studies using MRI as a tool for outcome assessment in children and adolescents following severe TBI.

Hierarchical probabilistic Gabor and MRF segmentation of brain tumours in MRI volumes.

PubMed

Subbanna, Nagesh K; Precup, Doina; Collins, D Louis; Arbel, Tal

2013-01-01

In this paper, we present a fully automated hierarchical probabilistic framework for segmenting brain tumours from multispectral human brain magnetic resonance images (MRIs) using multiwindow Gabor filters and an adapted Markov Random Field (MRF) framework. In the first stage, a customised Gabor decomposition is developed, based on the combined-space characteristics of the two classes (tumour and non-tumour) in multispectral brain MRIs in order to optimally separate tumour (including edema) from healthy brain tissues. A Bayesian framework then provides a coarse probabilistic texture-based segmentation of tumours (including edema) whose boundaries are then refined at the voxel level through a modified MRF framework that carefully separates the edema from the main tumour. This customised MRF is not only built on the voxel intensities and class labels as in traditional MRFs, but also models the intensity differences between neighbouring voxels in the likelihood model, along with employing a prior based on local tissue class transition probabilities. The second inference stage is shown to resolve local inhomogeneities and impose a smoothing constraint, while also maintaining the appropriate boundaries as supported by the local intensity difference observations. The method was trained and tested on the publicly available MICCAI 2012 Brain Tumour Segmentation Challenge (BRATS) Database [1] on both synthetic and clinical volumes (low grade and high grade tumours). Our method performs well compared to state-of-the-art techniques, outperforming the results of the top methods in cases of clinical high grade and low grade tumour core segmentation by 40% and 45% respectively.

Optimization of SSVEP brain responses with application to eight-command Brain-Computer Interface.

PubMed

Bakardjian, Hovagim; Tanaka, Toshihisa; Cichocki, Andrzej

2010-01-18

This study pursues the optimization of the brain responses to small reversing patterns in a Steady-State Visual Evoked Potentials (SSVEP) paradigm, which could be used to maximize the efficiency of applications such as Brain-Computer Interfaces (BCI). We investigated the SSVEP frequency response for 32 frequencies (5-84 Hz), and the time dynamics of the brain response at 8, 14 and 28 Hz, to aid the definition of the optimal neurophysiological parameters and to outline the onset-delay and other limitations of SSVEP stimuli in applications such as our previously described four-command BCI system. Our results showed that the 5.6-15.3 Hz pattern reversal stimulation evoked the strongest responses, peaking at 12 Hz, and exhibiting weaker local maxima at 28 and 42 Hz. After stimulation onset, the long-term SSVEP response was highly non-stationary and the dynamics, including the first peak, was frequency-dependent. The evaluation of the performance of a frequency-optimized eight-command BCI system with dynamic neurofeedback showed a mean success rate of 98%, and a time delay of 3.4s. Robust BCI performance was achieved by all subjects even when using numerous small patterns clustered very close to each other and moving rapidly in 2D space. These results emphasize the need for SSVEP applications to optimize not only the analysis algorithms but also the stimuli in order to maximize the brain responses they rely on. (c) 2009 Elsevier Ireland Ltd. All rights reserved.

"Where do auditory hallucinations come from?"--a brain morphometry study of schizophrenia patients with inner or outer space hallucinations.

PubMed

Plaze, Marion; Paillère-Martinot, Marie-Laure; Penttilä, Jani; Januel, Dominique; de Beaurepaire, Renaud; Bellivier, Franck; Andoh, Jamila; Galinowski, André; Gallarda, Thierry; Artiges, Eric; Olié, Jean-Pierre; Mangin, Jean-François; Martinot, Jean-Luc; Cachia, Arnaud

2011-01-01

Auditory verbal hallucinations are a cardinal symptom of schizophrenia. Bleuler and Kraepelin distinguished 2 main classes of hallucinations: hallucinations heard outside the head (outer space, or external, hallucinations) and hallucinations heard inside the head (inner space, or internal, hallucinations). This distinction has been confirmed by recent phenomenological studies that identified 3 independent dimensions in auditory hallucinations: language complexity, self-other misattribution, and spatial location. Brain imaging studies in schizophrenia patients with auditory hallucinations have already investigated language complexity and self-other misattribution, but the neural substrate of hallucination spatial location remains unknown. Magnetic resonance images of 45 right-handed patients with schizophrenia and persistent auditory hallucinations and 20 healthy right-handed subjects were acquired. Two homogeneous subgroups of patients were defined based on the hallucination spatial location: patients with only outer space hallucinations (N=12) and patients with only inner space hallucinations (N=15). Between-group differences were then assessed using 2 complementary brain morphometry approaches: voxel-based morphometry and sulcus-based morphometry. Convergent anatomical differences were detected between the patient subgroups in the right temporoparietal junction (rTPJ). In comparison to healthy subjects, opposite deviations in white matter volumes and sulcus displacements were found in patients with inner space hallucination and patients with outer space hallucination. The current results indicate that spatial location of auditory hallucinations is associated with the rTPJ anatomy, a key region of the "where" auditory pathway. The detected tilt in the sulcal junction suggests deviations during early brain maturation, when the superior temporal sulcus and its anterior terminal branch appear and merge.

A new look at cerebrospinal fluid circulation

PubMed Central

2014-01-01

According to the traditional understanding of cerebrospinal fluid (CSF) physiology, the majority of CSF is produced by the choroid plexus, circulates through the ventricles, the cisterns, and the subarachnoid space to be absorbed into the blood by the arachnoid villi. This review surveys key developments leading to the traditional concept. Challenging this concept are novel insights utilizing molecular and cellular biology as well as neuroimaging, which indicate that CSF physiology may be much more complex than previously believed. The CSF circulation comprises not only a directed flow of CSF, but in addition a pulsatile to and fro movement throughout the entire brain with local fluid exchange between blood, interstitial fluid, and CSF. Astrocytes, aquaporins, and other membrane transporters are key elements in brain water and CSF homeostasis. A continuous bidirectional fluid exchange at the blood brain barrier produces flow rates, which exceed the choroidal CSF production rate by far. The CSF circulation around blood vessels penetrating from the subarachnoid space into the Virchow Robin spaces provides both a drainage pathway for the clearance of waste molecules from the brain and a site for the interaction of the systemic immune system with that of the brain. Important physiological functions, for example the regeneration of the brain during sleep, may depend on CSF circulation. PMID:24817998

Anatomical and functional assemblies of brain BOLD oscillations

PubMed Central

Baria, Alexis T.; Baliki, Marwan N.; Parrish, Todd; Apkarian, A. Vania

2011-01-01

Brain oscillatory activity has long been thought to have spatial properties, the details of which are unresolved. Here we examine spatial organizational rules for the human brain oscillatory activity as measured by blood oxygen level-dependent (BOLD). Resting state BOLD signal was transformed into frequency space (Welch’s method), averaged across subjects, and its spatial distribution studied as a function of four frequency bands, spanning the full bandwidth of BOLD. The brain showed anatomically constrained distribution of power for each frequency band. This result was replicated on a repository dataset of 195 subjects. Next, we examined larger-scale organization by parceling the neocortex into regions approximating Brodmann Areas (BAs). This indicated that BAs of simple function/connectivity (unimodal), vs. complex properties (transmodal), are dominated by low frequency BOLD oscillations, and within the visual ventral stream we observe a graded shift of power to higher frequency bands for BAs further removed from the primary visual cortex (increased complexity), linking frequency properties of BOLD to hodology. Additionally, BOLD oscillation properties for the default mode network demonstrated that it is composed of distinct frequency dependent regions. When the same analysis was performed on a visual-motor task, frequency-dependent global and voxel-wise shifts in BOLD oscillations could be detected at brain sites mostly outside those identified with general linear modeling. Thus, analysis of BOLD oscillations in full bandwidth uncovers novel brain organizational rules, linking anatomical structures and functional networks to characteristic BOLD oscillations. The approach also identifies changes in brain intrinsic properties in relation to responses to external inputs. PMID:21613505

A new wrinkle on the brain

NASA Astrophysics Data System (ADS)

Taber, Larry A.

2018-05-01

The folded structure of the human brain is a hallmark of our intelligence — an optimized packing of neurons into a confined space. Similar wrinkling in brain-on-a-chip experiments provides a way of understanding the physics of how this occurs.

Exploring the Virchow–Robin spaces function: A unified theory of brain diseases

PubMed Central

Cherian, Iype; Beltran, Margarita; Kasper, Ekkehard M.; Bhattarai, Binod; Munokami, Sunil; Grasso, Giovanni

2016-01-01

Background: Cerebrospinal fluid (CSF) transport across the central nervous system (CNS) is no longer believed to be on the conventional lines. The Virchow–Robin space (VRS) that facilitates CSF transport from the basal cisterns into the brain interstitial fluid (ISF) has gained interest in a whole new array of studies. Moreover, new line of evidence suggests that VRS may be involved in different pathological mechanisms of brain diseases. Methods: Here, we review emerging studies proving the feasible role of VRS in sleep, Alzheimer's disease, chronic traumatic encephalopathy, and traumatic brain injury (TBI). Results: In this study, we have outlined the possible role of VRS in different pathological conditions. Conclusion: The new insights into the physiology of the CSF circulation may have important clinical relevance for understanding the mechanisms underlying brain pathologies and their cure. PMID:27857861

The Anatomical Distance of Functional Connections Predicts Brain Network Topology in Health and Schizophrenia

PubMed Central

Vértes, Petra E.; Stidd, Reva; Lalonde, François; Clasen, Liv; Rapoport, Judith; Giedd, Jay; Bullmore, Edward T.; Gogtay, Nitin

2013-01-01

The human brain is a topologically complex network embedded in anatomical space. Here, we systematically explored relationships between functional connectivity, complex network topology, and anatomical (Euclidean) distance between connected brain regions, in the resting-state functional magnetic resonance imaging brain networks of 20 healthy volunteers and 19 patients with childhood-onset schizophrenia (COS). Normal between-subject differences in average distance of connected edges in brain graphs were strongly associated with variation in topological properties of functional networks. In addition, a club or subset of connector hubs was identified, in lateral temporal, parietal, dorsal prefrontal, and medial prefrontal/cingulate cortical regions. In COS, there was reduced strength of functional connectivity over short distances especially, and therefore, global mean connection distance of thresholded graphs was significantly greater than normal. As predicted from relationships between spatial and topological properties of normal networks, this disorder-related proportional increase in connection distance was associated with reduced clustering and modularity and increased global efficiency of COS networks. Between-group differences in connection distance were localized specifically to connector hubs of multimodal association cortex. In relation to the neurodevelopmental pathogenesis of schizophrenia, we argue that the data are consistent with the interpretation that spatial and topological disturbances of functional network organization could arise from excessive “pruning” of short-distance functional connections in schizophrenia. PMID:22275481

Kernel-Based Relevance Analysis with Enhanced Interpretability for Detection of Brain Activity Patterns

PubMed Central

Alvarez-Meza, Andres M.; Orozco-Gutierrez, Alvaro; Castellanos-Dominguez, German

2017-01-01

We introduce Enhanced Kernel-based Relevance Analysis (EKRA) that aims to support the automatic identification of brain activity patterns using electroencephalographic recordings. EKRA is a data-driven strategy that incorporates two kernel functions to take advantage of the available joint information, associating neural responses to a given stimulus condition. Regarding this, a Centered Kernel Alignment functional is adjusted to learning the linear projection that best discriminates the input feature set, optimizing the required free parameters automatically. Our approach is carried out in two scenarios: (i) feature selection by computing a relevance vector from extracted neural features to facilitating the physiological interpretation of a given brain activity task, and (ii) enhanced feature selection to perform an additional transformation of relevant features aiming to improve the overall identification accuracy. Accordingly, we provide an alternative feature relevance analysis strategy that allows improving the system performance while favoring the data interpretability. For the validation purpose, EKRA is tested in two well-known tasks of brain activity: motor imagery discrimination and epileptic seizure detection. The obtained results show that the EKRA approach estimates a relevant representation space extracted from the provided supervised information, emphasizing the salient input features. As a result, our proposal outperforms the state-of-the-art methods regarding brain activity discrimination accuracy with the benefit of enhanced physiological interpretation about the task at hand. PMID:29056897

Discriminative Cooperative Networks for Detecting Phase Transitions

NASA Astrophysics Data System (ADS)

Liu, Ye-Hua; van Nieuwenburg, Evert P. L.

2018-04-01

The classification of states of matter and their corresponding phase transitions is a special kind of machine-learning task, where physical data allow for the analysis of new algorithms, which have not been considered in the general computer-science setting so far. Here we introduce an unsupervised machine-learning scheme for detecting phase transitions with a pair of discriminative cooperative networks (DCNs). In this scheme, a guesser network and a learner network cooperate to detect phase transitions from fully unlabeled data. The new scheme is efficient enough for dealing with phase diagrams in two-dimensional parameter spaces, where we can utilize an active contour model—the snake—from computer vision to host the two networks. The snake, with a DCN "brain," moves and learns actively in the parameter space, and locates phase boundaries automatically.

New Perspectives on Spontaneous Brain Activity: Dynamic Networks and Energy Matter.

PubMed

Tozzi, Arturo; Zare, Marzieh; Benasich, April A

2016-01-01

Spontaneous brain activity has received increasing attention as demonstrated by the exponential rise in the number of published article on this topic over the last 30 years. Such "intrinsic" brain activity, generated in the absence of an explicit task, is frequently associated with resting-state or default-mode networks (DMN)s. The focus on characterizing spontaneous brain activity promises to shed new light on questions concerning the structural and functional architecture of the brain and how they are related to "mind". However, many critical questions have yet to be addressed. In this review, we focus on a scarcely explored area, specifically the energetic requirements and constraints of spontaneous activity, taking into account both thermodynamical and informational perspectives. We argue that the "classical" definitions of spontaneous activity do not take into account an important feature, that is, the critical thermodynamic energetic differences between spontaneous and evoked brain activity. Spontaneous brain activity is associated with slower oscillations compared with evoked, task-related activity, hence it exhibits lower levels of enthalpy and "free-energy" (i.e., the energy that can be converted to do work), thus supporting noteworthy thermodynamic energetic differences between spontaneous and evoked brain activity. Increased spike frequency during evoked activity has a significant metabolic cost, consequently, brain functions traditionally associated with spontaneous activity, such as mind wandering, require less energy that other nervous activities. We also review recent empirical observations in neuroscience, in order to capture how spontaneous brain dynamics and mental function can be embedded in a non-linear dynamical framework, which considers nervous activity in terms of phase spaces, particle trajectories, random walks, attractors and/or paths at the edge of the chaos. This takes us from the thermodynamic free-energy, to the realm of "variational free-energy", a theoretical construct pertaining to probability and information theory which allows explanation of unexplored features of spontaneous brain activity.

A tensorial approach to access cognitive workload related to mental arithmetic from EEG functional connectivity estimates.

PubMed

Dimitriadis, S I; Sun, Yu; Kwok, K; Laskaris, N A; Bezerianos, A

2013-01-01

The association of functional connectivity patterns with particular cognitive tasks has long been a topic of interest in neuroscience, e.g., studies of functional connectivity have demonstrated its potential use for decoding various brain states. However, the high-dimensionality of the pairwise functional connectivity limits its usefulness in some real-time applications. In the present study, the methodology of tensor subspace analysis (TSA) is used to reduce the initial high-dimensionality of the pairwise coupling in the original functional connectivity network to a space of condensed descriptive power, which would significantly decrease the computational cost and facilitate the differentiation of brain states. We assess the feasibility of the proposed method on EEG recordings when the subject was performing mental arithmetic task which differ only in the difficulty level (easy: 1-digit addition v.s. 3-digit additions). Two different cortical connective networks were detected, and by comparing the functional connectivity networks in different work states, it was found that the task-difficulty is best reflected in the connectivity structure of sub-graphs extending over parietooccipital sites. Incorporating this data-driven information within original TSA methodology, we succeeded in predicting the difficulty level from connectivity patterns in an efficient way that can be implemented so as to work in real-time.

Neural Control of a Tracking Task via Attention-Gated Reinforcement Learning for Brain-Machine Interfaces.

PubMed

Wang, Yiwen; Wang, Fang; Xu, Kai; Zhang, Qiaosheng; Zhang, Shaomin; Zheng, Xiaoxiang

2015-05-01

Reinforcement learning (RL)-based brain machine interfaces (BMIs) enable the user to learn from the environment through interactions to complete the task without desired signals, which is promising for clinical applications. Previous studies exploited Q-learning techniques to discriminate neural states into simple directional actions providing the trial initial timing. However, the movements in BMI applications can be quite complicated, and the action timing explicitly shows the intention when to move. The rich actions and the corresponding neural states form a large state-action space, imposing generalization difficulty on Q-learning. In this paper, we propose to adopt attention-gated reinforcement learning (AGREL) as a new learning scheme for BMIs to adaptively decode high-dimensional neural activities into seven distinct movements (directional moves, holdings and resting) due to the efficient weight-updating. We apply AGREL on neural data recorded from M1 of a monkey to directly predict a seven-action set in a time sequence to reconstruct the trajectory of a center-out task. Compared to Q-learning techniques, AGREL could improve the target acquisition rate to 90.16% in average with faster convergence and more stability to follow neural activity over multiple days, indicating the potential to achieve better online decoding performance for more complicated BMI tasks.

Glymphatic MRI in idiopathic normal pressure hydrocephalus.

PubMed

Ringstad, Geir; Vatnehol, Svein Are Sirirud; Eide, Per Kristian

2017-10-01

The glymphatic system has in previous studies been shown as fundamental to clearance of waste metabolites from the brain interstitial space, and is proposed to be instrumental in normal ageing and brain pathology such as Alzheimer's disease and brain trauma. Assessment of glymphatic function using magnetic resonance imaging with intrathecal contrast agent as a cerebrospinal fluid tracer has so far been limited to rodents. We aimed to image cerebrospinal fluid flow characteristics and glymphatic function in humans, and applied the methodology in a prospective study of 15 idiopathic normal pressure hydrocephalus patients (mean age 71.3 ± 8.1 years, three female and 12 male) and eight reference subjects (mean age 41.1 + 13.0 years, six female and two male) with suspected cerebrospinal fluid leakage (seven) and intracranial cyst (one). The imaging protocol included T1-weighted magnetic resonance imaging with equal sequence parameters before and at multiple time points through 24 h after intrathecal injection of the contrast agent gadobutrol at the lumbar level. All study subjects were kept in the supine position between examinations during the first day. Gadobutrol enhancement was measured at all imaging time points from regions of interest placed at predefined locations in brain parenchyma, the subarachnoid and intraventricular space, and inside the sagittal sinus. Parameters demonstrating gadobutrol enhancement and clearance in different locations were compared between idiopathic normal pressure hydrocephalus and reference subjects. A characteristic flow pattern in idiopathic normal hydrocephalus was ventricular reflux of gadobutrol from the subarachnoid space followed by transependymal gadobutrol migration. At the brain surfaces, gadobutrol propagated antegradely along large leptomeningeal arteries in all study subjects, and preceded glymphatic enhancement in adjacent brain tissue, indicating a pivotal role of intracranial pulsations for glymphatic function. In idiopathic normal pressure hydrocephalus, we found delayed enhancement (P < 0.05) and decreased clearance of gadobutrol (P < 0.05) at the Sylvian fissure. Parenchymal (glymphatic) enhancement peaked overnight in both study groups, possibly indicating a crucial role of sleep, and was larger in normal pressure hydrocephalus patients (P < 0.05 at inferior frontal gyrus). We interpret decreased gadobutrol clearance from the subarachnoid space, along with persisting enhancement in brain parenchyma, as signs of reduced glymphatic clearance in idiopathic normal hydrocephalus, and hypothesize that reduced glymphatic function is instrumental for dementia in this disease. The study shows promise for glymphatic magnetic resonance imaging as a method to assess human brain metabolic function and renders a potential for contrast enhanced brain extravascular space imaging. © The Author (2017). Published by Oxford University Press on behalf of the Guarantors of Brain.

Efflux of drugs and solutes from brain: the interactive roles of diffusional transcapillary transport, bulk flow and capillary transporters.

PubMed

Groothuis, Dennis R; Vavra, Michael W; Schlageter, Kurt E; Kang, Eric W-Y; Itskovich, Andrea C; Hertzler, Shannon; Allen, Cathleen V; Lipton, Howard L

2007-01-01

We examined the roles of diffusion, convection and capillary transporters in solute removal from extracellular space (ECS) of the brain. Radiolabeled solutes (eight with passive distribution and four with capillary or cell transporters) were injected into the brains of rats (n=497) and multiple-time point experiments measured the amount remaining in brain as a function of time. For passively distributed compounds, there was a relationship between lipid:water solubility and total brain efflux:diffusional efflux, which dominated when k(p), the transcapillary efflux rate constant, was >10(0) h(-1); when 10(-1)

What insects can tell us about the origins of consciousness

PubMed Central

Barron, Andrew B.; Klein, Colin

2016-01-01

How, why, and when consciousness evolved remain hotly debated topics. Addressing these issues requires considering the distribution of consciousness across the animal phylogenetic tree. Here we propose that at least one invertebrate clade, the insects, has a capacity for the most basic aspect of consciousness: subjective experience. In vertebrates the capacity for subjective experience is supported by integrated structures in the midbrain that create a neural simulation of the state of the mobile animal in space. This integrated and egocentric representation of the world from the animal’s perspective is sufficient for subjective experience. Structures in the insect brain perform analogous functions. Therefore, we argue the insect brain also supports a capacity for subjective experience. In both vertebrates and insects this form of behavioral control system evolved as an efficient solution to basic problems of sensory reafference and true navigation. The brain structures that support subjective experience in vertebrates and insects are very different from each other, but in both cases they are basal to each clade. Hence we propose the origins of subjective experience can be traced to the Cambrian. PMID:27091981

Biodegradable DNA Nanoparticles that Provide Widespread Gene Delivery in the Brain

PubMed Central

Mastorakos, Panagiotis; Song, Eric; Zhang, Clark; Berry, Sneha; Park, Hee Won; Kim, Young Eun; Park, Jong Sung; Lee, Seulki; Suk, Jung Soo; Hanes, Justin

2016-01-01

Successful gene therapy of neurological disorders is predicated on achieving widespread and uniform transgene expression throughout the affected disease area in the brain. However, conventional gene vectors preferentially travel through low-resistance perivascular spaces and/or are confined to the administration site even with the aid of a pressure-driven flow provided by convection-enhanced delivery. Biodegradable DNA nanoparticles offer a safe gene delivery platform devoid of adverse effects associated with virus-based or synthetic non-biodegradable systems. Using a state-of-the-art biodegradable polymer, poly(β-amino ester), we engineered colloidally stable sub-100 nm DNA nanoparticles coated with a non-adhesive polyethylene glycol corona that are able to avoid the adhesive and steric hindrances imposed by the extracellular matrix. Following convection enhanced delivery, these brain-penetrating nanoparticles were able to homogeneously distribute throughout the rodent striatum and mediate widespread and high-level transgene expression. These nanoparticles provide a biodegradable DNA nanoparticle platform enabling uniform transgene expression patterns in vivo and hold promise for the treatment of neurological diseases. PMID:26680637

Direct and accelerated parameter mapping using the unscented Kalman filter.

PubMed

Zhao, Li; Feng, Xue; Meyer, Craig H

2016-05-01

To accelerate parameter mapping using a new paradigm that combines image reconstruction and model regression as a parameter state-tracking problem. In T2 mapping, the T2 map is first encoded in parameter space by multi-TE measurements and then encoded by Fourier transformation with readout/phase encoding gradients. Using a state transition function and a measurement function, the unscented Kalman filter can describe T2 mapping as a dynamic system and directly estimate the T2 map from the k-space data. The proposed method was validated with a numerical brain phantom and volunteer experiments with a multiple-contrast spin echo sequence. Its performance was compared with a conjugate-gradient nonlinear inversion method at undersampling factors of 2 to 8. An accelerated pulse sequence was developed based on this method to achieve prospective undersampling. Compared with the nonlinear inversion reconstruction, the proposed method had higher precision, improved structural similarity and reduced normalized root mean squared error, with acceleration factors up to 8 in numerical phantom and volunteer studies. This work describes a new perspective on parameter mapping by state tracking. The unscented Kalman filter provides a highly accelerated and efficient paradigm for T2 mapping. © 2015 Wiley Periodicals, Inc.

Lateralization of Egocentric and Allocentric Spatial Processing after Parietal Brain Lesions

ERIC Educational Resources Information Center

Iachini, Tina; Ruggiero, Gennaro; Conson, Massimiliano; Trojano, Luigi

2009-01-01

The purpose of this paper was to verify whether left and right parietal brain lesions may selectively impair egocentric and allocentric processing of spatial information in near/far spaces. Two Right-Brain-Damaged (RBD), 2 Left-Brain-Damaged (LBD) patients (not affected by neglect or language disturbances) and eight normal controls were submitted…

Fuzzy topological digital space and their properties of flat electroencephalography in epilepsy disease

NASA Astrophysics Data System (ADS)

Muzafar Shah, Mazlina; Fatah Wahab, Abdul

2017-09-01

There are an abnormal electric activities or irregular interference in brain of epilepsy patient. Then a sensor will be put in patient’s scalp to measure and records all electric activities in brain. The result of the records known as Electroencephalography (EEG). The EEG has been transfer to flat EEG because it’s easier to analyze. In this study, the uncertainty in flat EEG data will be considered as fuzzy digital space. The purpose of this research is to show that the flat EEG is fuzzy topological digital space. Therefore, the main focus for this research is to introduce fuzzy topological digital space concepts with their properties such as neighbourhood, interior and closure by using fuzzy set digital concept and Chang’s fuzzy topology approach. The product fuzzy topology digital also will be shown. By introduce this concept, the data in flat EEG can considering having fuzzy topology digital properties and can identify the area in fuzzy digital space that has been affected by epilepsy seizure in epileptic patient’s brain.

Resting state fMRI: A review on methods in resting state connectivity analysis and resting state networks.

PubMed

Smitha, K A; Akhil Raja, K; Arun, K M; Rajesh, P G; Thomas, Bejoy; Kapilamoorthy, T R; Kesavadas, Chandrasekharan

2017-08-01

The inquisitiveness about what happens in the brain has been there since the beginning of humankind. Functional magnetic resonance imaging is a prominent tool which helps in the non-invasive examination, localisation as well as lateralisation of brain functions such as language, memory, etc. In recent years, there is an apparent shift in the focus of neuroscience research to studies dealing with a brain at 'resting state'. Here the spotlight is on the intrinsic activity within the brain, in the absence of any sensory or cognitive stimulus. The analyses of functional brain connectivity in the state of rest have revealed different resting state networks, which depict specific functions and varied spatial topology. However, different statistical methods have been introduced to study resting state functional magnetic resonance imaging connectivity, yet producing consistent results. In this article, we introduce the concept of resting state functional magnetic resonance imaging in detail, then discuss three most widely used methods for analysis, describe a few of the resting state networks featuring the brain regions, associated cognitive functions and clinical applications of resting state functional magnetic resonance imaging. This review aims to highlight the utility and importance of studying resting state functional magnetic resonance imaging connectivity, underlining its complementary nature to the task-based functional magnetic resonance imaging.

Effective connectivity: Influence, causality and biophysical modeling

PubMed Central

Valdes-Sosa, Pedro A.; Roebroeck, Alard; Daunizeau, Jean; Friston, Karl

2011-01-01

This is the final paper in a Comments and Controversies series dedicated to “The identification of interacting networks in the brain using fMRI: Model selection, causality and deconvolution”. We argue that discovering effective connectivity depends critically on state-space models with biophysically informed observation and state equations. These models have to be endowed with priors on unknown parameters and afford checks for model Identifiability. We consider the similarities and differences among Dynamic Causal Modeling, Granger Causal Modeling and other approaches. We establish links between past and current statistical causal modeling, in terms of Bayesian dependency graphs and Wiener–Akaike–Granger–Schweder influence measures. We show that some of the challenges faced in this field have promising solutions and speculate on future developments. PMID:21477655

Recent Pharmacology Studies on the International Space Station

NASA Technical Reports Server (NTRS)

Wotring, Virginia

2014-01-01

The environment on the International Space Station (ISS) includes a variety of potential stressors including the absence of Earth's gravity, elevated exposure to radiation, confined living and working quarters, a heavy workload, and high public visibility. The effects of this extreme environment on pharmacokinetics, pharmacodynamics, and even on stored medication doses, are not yet understood. Dr. Wotring will discuss recent analyses of medication doses that experienced long duration storage on the ISS and a recent retrospective examination of medication use during long-duration spaceflights. She will also describe new pharmacology experiments that are scheduled for upcoming ISS missions. Dr. Virginia E. Wotring is a Senior Scientist in the Division of Space Life Sciences in the Universities Space Research Association, and Pharmacology Discipline Lead at NASA's Johnson Space Center, Human Heath and Countermeasures Division. She received her doctorate in Pharmacological and Physiological Science from Saint Louis University after earning a B.S. in Chemistry at Florida State University. She has published multiple studies on ligand gated ion channels in the brain and spinal cord. Her research experience includes drug mechanisms of action, drug receptor structure/function relationships and gene & protein expression. She joined USRA (and spaceflight research) in 2009. In 2012, her book reviewing pharmacology in spaceflight was published by Springer: Space Pharmacology, Space Development Series.

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

A pairwise maximum entropy model accurately describes resting-state human brain networks

PubMed Central

Watanabe, Takamitsu; Hirose, Satoshi; Wada, Hiroyuki; Imai, Yoshio; Machida, Toru; Shirouzu, Ichiro; Konishi, Seiki; Miyashita, Yasushi; Masuda, Naoki

2013-01-01

The resting-state human brain networks underlie fundamental cognitive functions and consist of complex interactions among brain regions. However, the level of complexity of the resting-state networks has not been quantified, which has prevented comprehensive descriptions of the brain activity as an integrative system. Here, we address this issue by demonstrating that a pairwise maximum entropy model, which takes into account region-specific activity rates and pairwise interactions, can be robustly and accurately fitted to resting-state human brain activities obtained by functional magnetic resonance imaging. Furthermore, to validate the approximation of the resting-state networks by the pairwise maximum entropy model, we show that the functional interactions estimated by the pairwise maximum entropy model reflect anatomical connexions more accurately than the conventional functional connectivity method. These findings indicate that a relatively simple statistical model not only captures the structure of the resting-state networks but also provides a possible method to derive physiological information about various large-scale brain networks. PMID:23340410

Investigating the Intersession Reliability of Dynamic Brain-State Properties.

PubMed

Smith, Derek M; Zhao, Yrian; Keilholz, Shella D; Schumacher, Eric H

2018-06-01

Dynamic functional connectivity metrics have much to offer to the neuroscience of individual differences of cognition. Yet, despite the recent expansion in dynamic connectivity research, limited resources have been devoted to the study of the reliability of these connectivity measures. To address this, resting-state functional magnetic resonance imaging data from 100 Human Connectome Project subjects were compared across 2 scan days. Brain states (i.e., patterns of coactivity across regions) were identified by classifying each time frame using k means clustering. This was done with and without global signal regression (GSR). Multiple gauges of reliability indicated consistency in the brain-state properties across days and GSR attenuated the reliability of the brain states. Changes in the brain-state properties across the course of the scan were investigated as well. The results demonstrate that summary metrics describing the clustering of individual time frames have adequate test/retest reliability, and thus, these patterns of brain activation may hold promise for individual-difference research.

Quality of Life Following Brain Injury: Perspectives from Brain Injury Association of America State Affiliates

ERIC Educational Resources Information Center

Degeneffe, Charles Edmund; Tucker, Mark

2012-01-01

Objective: to examine the perspectives of brain injury professionals concerning family members' feelings about the quality of life experienced by individuals with brain injuries. Participants: participating in the study were 28 individuals in leadership positions with the state affiliates of the Brain Injury Association of America (BIAA). Methods:…

Dynamic Connectivity Patterns in Conscious and Unconscious Brain

PubMed Central

Ma, Yuncong; Hamilton, Christina

2017-01-01

Abstract Brain functional connectivity undergoes dynamic changes from the awake to unconscious states. However, how the dynamics of functional connectivity patterns are linked to consciousness at the behavioral level remains elusive. In this study, we acquired resting-state functional magnetic resonance imaging data during wakefulness and graded levels of consciousness in rats. Data were analyzed using a dynamic approach combining the sliding window method and k-means clustering. Our results demonstrate that whole-brain networks contained several quasi-stable patterns that dynamically recurred from the awake state into anesthetized states. Remarkably, two brain connectivity states with distinct spatial similarity to the structure of anatomical connectivity were strongly biased toward high and low consciousness levels, respectively. These results provide compelling neuroimaging evidence linking the dynamics of whole-brain functional connectivity patterns and states of consciousness at the behavioral level. PMID:27846731

Circadian rhythms in Macaca mulatta monkeys during Bion 11 flight

NASA Technical Reports Server (NTRS)

Alpatov, A. M.; Hoban-Higgins, T. M.; Klimovitsky, V. Y.; Tumurova, E. G.; Fuller, C. A.

2000-01-01

Circadian rhythms of primate brain temperature, head and ankle skin temperature, motor activity, and heart rate were studied during spaceflight and on the ground. In space, the circadian rhythms of all the parameters were synchronized with diurnal Zeitgebers. However, in space the brain temperature rhythm showed a significantly more delayed phase angle, which may be ascribed to an increase of the endogenous circadian period.

Assessing the mean strength and variations of the time-to-time fluctuations of resting-state brain activity.

PubMed

Li, Zhengjun; Zang, Yu-Feng; Ding, Jianping; Wang, Ze

2017-04-01

The time-to-time fluctuations (TTFs) of resting-state brain activity as captured by resting-state fMRI (rsfMRI) have been repeatedly shown to be informative of functional brain structures and disease-related alterations. TTFs can be characterized by the mean and the range of successive difference. The former can be measured with the mean squared successive difference (MSSD), which is mathematically similar to standard deviation; the latter can be calculated by the variability of the successive difference (VSD). The purpose of this study was to evaluate both the resting state-MSSD and VSD of rsfMRI regarding their test-retest stability, sensitivity to brain state change, as well as their biological meanings. We hypothesized that MSSD and VSD are reliable in resting brain; both measures are sensitive to brain state changes such as eyes-open compared to eyes-closed condition; both are predictive of age. These hypotheses were tested with three rsfMRI datasets and proven true, suggesting both MSSD and VSD as reliable and useful tools for resting-state studies.

Independent component analysis of EEG dipole source localization in resting and action state of brain

NASA Astrophysics Data System (ADS)

Almurshedi, Ahmed; Ismail, Abd Khamim

2015-04-01

EEG source localization was studied in order to determine the location of the brain sources that are responsible for the measured potentials at the scalp electrodes using EEGLAB with Independent Component Analysis (ICA) algorithm. Neuron source locations are responsible in generating current dipoles in different states of brain through the measured potentials. The current dipole sources localization are measured by fitting an equivalent current dipole model using a non-linear optimization technique with the implementation of standardized boundary element head model. To fit dipole models to ICA components in an EEGLAB dataset, ICA decomposition is performed and appropriate components to be fitted are selected. The topographical scalp distributions of delta, theta, alpha, and beta power spectrum and cross coherence of EEG signals are observed. In close eyes condition it shows that during resting and action states of brain, alpha band was activated from occipital (O1, O2) and partial (P3, P4) area. Therefore, parieto-occipital area of brain are active in both resting and action state of brain. However cross coherence tells that there is more coherence between right and left hemisphere in action state of brain than that in the resting state. The preliminary result indicates that these potentials arise from the same generators in the brain.

Understanding Epileptiform After-Discharges as Rhythmic Oscillatory Transients.

PubMed

Baier, Gerold; Taylor, Peter N; Wang, Yujiang

2017-01-01

Electro-cortical activity in patients with epilepsy may show abnormal rhythmic transients in response to stimulation. Even when using the same stimulation parameters in the same patient, wide variability in the duration of transient response has been reported. These transients have long been considered important for the mapping of the excitability levels in the epileptic brain but their dynamic mechanism is still not well understood. To investigate the occurrence of abnormal transients dynamically, we use a thalamo-cortical neural population model of epileptic spike-wave activity and study the interaction between slow and fast subsystems. In a reduced version of the thalamo-cortical model, slow wave oscillations arise from a fold of cycles (FoC) bifurcation. This marks the onset of a region of bistability between a high amplitude oscillatory rhythm and the background state. In vicinity of the bistability in parameter space, the model has excitable dynamics, showing prolonged rhythmic transients in response to suprathreshold pulse stimulation. We analyse the state space geometry of the bistable and excitable states, and find that the rhythmic transient arises when the impending FoC bifurcation deforms the state space and creates an area of locally reduced attraction to the fixed point. This area essentially allows trajectories to dwell there before escaping to the stable steady state, thus creating rhythmic transients. In the full thalamo-cortical model, we find a similar FoC bifurcation structure. Based on the analysis, we propose an explanation of why stimulation induced epileptiform activity may vary between trials, and predict how the variability could be related to ongoing oscillatory background activity. We compare our dynamic mechanism with other mechanisms (such as a slow parameter change) to generate excitable transients, and we discuss the proposed excitability mechanism in the context of stimulation responses in the epileptic cortex.

Microfiberoptic fluorescence photobleaching reveals size-dependent macromolecule diffusion in extracellular space deep in brain.

PubMed

Zador, Zsolt; Magzoub, Mazin; Jin, Songwan; Manley, Geoffrey T; Papadopoulos, Marios C; Verkman, A S

2008-03-01

Diffusion in brain extracellular space (ECS) is important for nonsynaptic intercellular communication, extracellular ionic buffering, and delivery of drugs and metabolites. We measured macromolecular diffusion in normally light-inaccessible regions of mouse brain by microfiberoptic epifluorescence photobleaching, in which a fiberoptic with a micron-size tip is introduced deep in brain tissue. In brain cortex, the diffusion of a noninteracting molecule [fluorescein isothiocyanate (FITC)-dextran, 70 kDa] was slowed 4.5 +/- 0.5-fold compared with its diffusion in water (D(o)/D), and was depth-independent down to 800 microm from the brain surface. Diffusion was significantly accelerated (D(o)/D of 2.9+/-0.3) in mice lacking the glial water channel aquaporin-4. FITC-dextran diffusion varied greatly in different regions of brain, with D(o)/D of 3.5 +/- 0.3 in hippocampus and 7.4 +/- 0.3 in thalamus. Remarkably, D(o)/D in deep brain was strongly dependent on solute size, whereas diffusion in cortex changed little with solute size. Mathematical modeling of ECS diffusion required nonuniform ECS dimensions in deep brain, which we call "heterometricity," to account for the size-dependent diffusion. Our results provide the first data on molecular diffusion in ECS deep in brain in vivo and demonstrate previously unrecognized hindrance and heterometricity for diffusion of large macromolecules in deep brain.

Venous or arterial blood components trigger more brain swelling, tissue death after acute subdural hematoma compared to elderly atrophic brain with subdural effusion (SDE) model rats.

PubMed

Wajima, Daisuke; Sato, Fumiya; Kawamura, Kenya; Sugiura, Keisuke; Nakagawa, Ichiro; Motoyama, Yasushi; Park, Young-Soo; Nakase, Hiroyuki

2017-09-01

Acute subdural hematoma (ASDH) is a frequent complication of severe head injury, whose secondary ischemic lesions are often responsible for the severity of the disease. We focused on the differences of secondary ischemic lesions caused by the components, 0.4ml venous- or arterial-blood, or saline, infused in the subdural space, evaluating the differences in vivo model, using rats. The saline infused rats are made for elderly atrophic brain with subdural effusion (SDE) model. Our data showed that subdural blood, both venous- and arterial-blood, aggravate brain edema and lesion development more than SDE. This study is the first study, in which different fluids in rats' subdural space, ASDH or SDE are compared with the extension of early and delayed brain damage by measuring brain edema and histological lesion volume. Blood constituents started to affect the degree of ischemia underneath the subdural hemorrhage, leading to more pronounced breakdown of the blood-brain barrier and brain damage. This indicates that further strategies to treat blood-dependent effects more efficiently are in view for patients with ASDH. Copyright © 2017 Elsevier B.V. All rights reserved.

Task vs. rest-different network configurations between the coactivation and the resting-state brain networks.

PubMed

Di, Xin; Gohel, Suril; Kim, Eun H; Biswal, Bharat B

2013-01-01

There is a growing interest in studies of human brain networks using resting-state functional magnetic resonance imaging (fMRI). However, it is unclear whether and how brain networks measured during the resting-state exhibit comparable properties to brain networks during task performance. In the present study, we investigated meta-analytic coactivation patterns among brain regions based upon published neuroimaging studies, and compared the coactivation network configurations with those in the resting-state network. The strength of resting-state functional connectivity between two regions were strongly correlated with the coactivation strength. However, the coactivation network showed greater global efficiency, smaller mean clustering coefficient, and lower modularity compared with the resting-state network, which suggest a more efficient global information transmission and between system integrations during task performing. Hub shifts were also observed within the thalamus and the left inferior temporal cortex. The thalamus and the left inferior temporal cortex exhibited higher and lower degrees, respectively in the coactivation network compared with the resting-state network. These results shed light regarding the reconfiguration of the brain networks between task and resting-state conditions, and highlight the role of the thalamus in change of network configurations in task vs. rest.

Task vs. rest—different network configurations between the coactivation and the resting-state brain networks

PubMed Central

Di, Xin; Gohel, Suril; Kim, Eun H.; Biswal, Bharat B.

2013-01-01

There is a growing interest in studies of human brain networks using resting-state functional magnetic resonance imaging (fMRI). However, it is unclear whether and how brain networks measured during the resting-state exhibit comparable properties to brain networks during task performance. In the present study, we investigated meta-analytic coactivation patterns among brain regions based upon published neuroimaging studies, and compared the coactivation network configurations with those in the resting-state network. The strength of resting-state functional connectivity between two regions were strongly correlated with the coactivation strength. However, the coactivation network showed greater global efficiency, smaller mean clustering coefficient, and lower modularity compared with the resting-state network, which suggest a more efficient global information transmission and between system integrations during task performing. Hub shifts were also observed within the thalamus and the left inferior temporal cortex. The thalamus and the left inferior temporal cortex exhibited higher and lower degrees, respectively in the coactivation network compared with the resting-state network. These results shed light regarding the reconfiguration of the brain networks between task and resting-state conditions, and highlight the role of the thalamus in change of network configurations in task vs. rest. PMID:24062654

Comparision between Brain Atrophy and Subdural Volume to Predict Chronic Subdural Hematoma: Volumetric CT Imaging Analysis

PubMed Central

Ju, Min-Wook; Kwon, Hyon-Jo; Choi, Seung-Won; Koh, Hyeon-Song; Youm, Jin-Young; Song, Shi-Hun

2015-01-01

Objective Brain atrophy and subdural hygroma were well known factors that enlarge the subdural space, which induced formation of chronic subdural hematoma (CSDH). Thus, we identified the subdural volume that could be used to predict the rate of future CSDH after head trauma using a computed tomography (CT) volumetric analysis. Methods A single institution case-control study was conducted involving 1,186 patients who visited our hospital after head trauma from January 1, 2010 to December 31, 2014. Fifty-one patients with delayed CSDH were identified, and 50 patients with age and sex matched for control. Intracranial volume (ICV), the brain parenchyme, and the subdural space were segmented using CT image-based software. To adjust for variations in head size, volume ratios were assessed as a percentage of ICV [brain volume index (BVI), subdural volume index (SVI)]. The maximum depth of the subdural space on both sides was used to estimate the SVI. Results Before adjusting for cranium size, brain volume tended to be smaller, and subdural space volume was significantly larger in the CSDH group (p=0.138, p=0.021, respectively). The BVI and SVI were significantly different (p=0.003, p=0.001, respectively). SVI [area under the curve (AUC), 77.3%; p=0.008] was a more reliable technique for predicting CSDH than BVI (AUC, 68.1%; p=0.001). Bilateral subdural depth (sum of subdural depth on both sides) increased linearly with SVI (p<0.0001). Conclusion Subdural space volume was significantly larger in CSDH groups. SVI was a more reliable technique for predicting CSDH. Bilateral subdural depth was useful to measure SVI. PMID:27169071

Comparision between Brain Atrophy and Subdural Volume to Predict Chronic Subdural Hematoma: Volumetric CT Imaging Analysis.

PubMed

Ju, Min-Wook; Kim, Seon-Hwan; Kwon, Hyon-Jo; Choi, Seung-Won; Koh, Hyeon-Song; Youm, Jin-Young; Song, Shi-Hun

2015-10-01

Brain atrophy and subdural hygroma were well known factors that enlarge the subdural space, which induced formation of chronic subdural hematoma (CSDH). Thus, we identified the subdural volume that could be used to predict the rate of future CSDH after head trauma using a computed tomography (CT) volumetric analysis. A single institution case-control study was conducted involving 1,186 patients who visited our hospital after head trauma from January 1, 2010 to December 31, 2014. Fifty-one patients with delayed CSDH were identified, and 50 patients with age and sex matched for control. Intracranial volume (ICV), the brain parenchyme, and the subdural space were segmented using CT image-based software. To adjust for variations in head size, volume ratios were assessed as a percentage of ICV [brain volume index (BVI), subdural volume index (SVI)]. The maximum depth of the subdural space on both sides was used to estimate the SVI. Before adjusting for cranium size, brain volume tended to be smaller, and subdural space volume was significantly larger in the CSDH group (p=0.138, p=0.021, respectively). The BVI and SVI were significantly different (p=0.003, p=0.001, respectively). SVI [area under the curve (AUC), 77.3%; p=0.008] was a more reliable technique for predicting CSDH than BVI (AUC, 68.1%; p=0.001). Bilateral subdural depth (sum of subdural depth on both sides) increased linearly with SVI (p<0.0001). Subdural space volume was significantly larger in CSDH groups. SVI was a more reliable technique for predicting CSDH. Bilateral subdural depth was useful to measure SVI.

Adaptive Instrument Module: Space Instrument Controller "Brain" through Programmable Logic Devices

NASA Technical Reports Server (NTRS)

Darrin, Ann Garrison; Conde, Richard; Chern, Bobbie; Luers, Phil; Jurczyk, Steve; Mills, Carl; Day, John H. (Technical Monitor)

2001-01-01

The Adaptive Instrument Module (AIM) will be the first true demonstration of reconfigurable computing with field-programmable gate arrays (FPGAs) in space, enabling the 'brain' of the system to evolve or adapt to changing requirements. In partnership with NASA Goddard Space Flight Center and the Australian Cooperative Research Centre for Satellite Systems (CRC-SS), APL has built the flight version to be flown on the Australian university-class satellite FEDSAT. The AIM provides satellites the flexibility to adapt to changing mission requirements by reconfiguring standardized processing hardware rather than incurring the large costs associated with new builds. This ability to reconfigure the processing in response to changing mission needs leads to true evolveable computing, wherein the instrument 'brain' can learn from new science data in order to perform state-of-the-art data processing. The development of the AIM is significant in its enormous potential to reduce total life-cycle costs for future space exploration missions. The advent of RAM-based FPGAs whose configuration can be changed at any time has enabled the development of the AIM for processing tasks that could not be performed in software. The use of the AIM enables reconfiguration of the FPGA circuitry while the spacecraft is in flight, with many accompanying advantages. The AIM demonstrates the practicalities of using reconfigurable computing hardware devices by conducting a series of designed experiments. These include the demonstration of implementing data compression, data filtering, and communication message processing and inter-experiment data computation. The second generation is the Adaptive Processing Template (ADAPT) which is further described in this paper. The next step forward is to make the hardware itself adaptable and the ADAPT pursues this challenge by developing a reconfigurable module that will be capable of functioning efficiently in various applications. ADAPT will take advantage of radiation tolerant RAM-based field programmable gate array (FPGA) technology to develop a reconfigurable processor that combines the flexibility of a general purpose processor running software with the performance of application specific processing hardware for a variety of high performance computing applications.

Diffusion and related transport mechanisms in brain tissue

NASA Astrophysics Data System (ADS)

Nicholson, Charles

2001-07-01

Diffusion plays a crucial role in brain function. The spaces between cells can be likened to the water phase of a foam and many substances move within this complicated region. Diffusion in this interstitial space can be accurately modelled with appropriate modifications of classical equations and quantified from measurements based on novel micro-techniques. Besides delivering glucose and oxygen from the vascular system to brain cells, diffusion also moves informational substances between cells, a process known as volume transmission. Deviations from expected results reveal how local uptake, degradation or bulk flow may modify the transport of molecules. Diffusion is also essential to many therapies that deliver drugs to the brain. The diffusion-generated concentration distributions of well-chosen molecules also reveal the structure of brain tissue. This structure is represented by the volume fraction (void space) and the tortuosity (hindrance to diffusion imposed by local boundaries or local viscosity). Analysis of these parameters also reveals how the local geometry of the brain changes with time or under pathological conditions. Theoretical and experimental approaches borrow from classical diffusion theory and from porous media concepts. Earlier studies were based on radiotracers but the recent methods use a point-source paradigm coupled with micro-sensors or optical imaging of macromolecules labelled with fluorescent tags. These concepts and methods are likely to be applicable elsewhere to measure diffusion properties in very small volumes of highly structured but delicate material.

Brain structural plasticity with spaceflight.

PubMed

Koppelmans, Vincent; Bloomberg, Jacob J; Mulavara, Ajitkumar P; Seidler, Rachael D

2016-01-01

Humans undergo extensive sensorimotor adaptation during spaceflight due to altered vestibular inputs and body unloading. No studies have yet evaluated the effects of spaceflight on human brain structure despite the fact that recently reported optic nerve structural changes are hypothesized to occur due to increased intracranial pressure occurring with microgravity. This is the first report on human brain structural changes with spaceflight. We evaluated retrospective longitudinal T2-weighted MRI scans and balance data from 27 astronauts (thirteen ~2-week shuttle crew members and fourteen ~6-month International Space Station crew members) to determine spaceflight effects on brain structure, and whether any pre to postflight brain changes are associated with balance changes. Data were obtained from the NASA Lifetime Surveillance of Astronaut Health. Brain scans were segmented into gray matter maps and normalized into MNI space using a stepwise approach through subject specific templates. Non-parametric permutation testing was used to analyze pre to postflight volumetric gray matter changes. We found extensive volumetric gray matter decreases, including large areas covering the temporal and frontal poles and around the orbits. This effect was larger in International Space Station versus shuttle crew members in some regions. There were bilateral focal gray matter increases within the medial primary somatosensory and motor cortex; i.e., the cerebral areas where the lower limbs are represented. These intriguing findings are observed in a retrospective data set; future prospective studies should probe the underlying mechanisms and behavioral consequences.

Decoding Spontaneous Emotional States in the Human Brain

PubMed Central

Kragel, Philip A.; Knodt, Annchen R.; Hariri, Ahmad R.; LaBar, Kevin S.

2016-01-01

Pattern classification of human brain activity provides unique insight into the neural underpinnings of diverse mental states. These multivariate tools have recently been used within the field of affective neuroscience to classify distributed patterns of brain activation evoked during emotion induction procedures. Here we assess whether neural models developed to discriminate among distinct emotion categories exhibit predictive validity in the absence of exteroceptive emotional stimulation. In two experiments, we show that spontaneous fluctuations in human resting-state brain activity can be decoded into categories of experience delineating unique emotional states that exhibit spatiotemporal coherence, covary with individual differences in mood and personality traits, and predict on-line, self-reported feelings. These findings validate objective, brain-based models of emotion and show how emotional states dynamically emerge from the activity of separable neural systems. PMID:27627738

Brain State Differentiation and Behavioral Inflexibility in Autism†

PubMed Central

Uddin, Lucina Q.; Supekar, Kaustubh; Lynch, Charles J.; Cheng, Katherine M.; Odriozola, Paola; Barth, Maria E.; Phillips, Jennifer; Feinstein, Carl; Abrams, Daniel A.; Menon, Vinod

2015-01-01

Autism spectrum disorders (ASDs) are characterized by social impairments alongside cognitive and behavioral inflexibility. While social deficits in ASDs have extensively been characterized, the neurobiological basis of inflexibility and its relation to core clinical symptoms of the disorder are unknown. We acquired functional neuroimaging data from 2 cohorts, each consisting of 17 children with ASDs and 17 age- and IQ-matched typically developing (TD) children, during stimulus-evoked brain states involving performance of social attention and numerical problem solving tasks, as well as during intrinsic, resting brain states. Effective connectivity between key nodes of the salience network, default mode network, and central executive network was used to obtain indices of functional organization across evoked and intrinsic brain states. In both cohorts examined, a machine learning algorithm was able to discriminate intrinsic (resting) and evoked (task) functional brain network configurations more accurately in TD children than in children with ASD. Brain state discriminability was related to severity of restricted and repetitive behaviors, indicating that weak modulation of brain states may contribute to behavioral inflexibility in ASD. These findings provide novel evidence for a potential link between neurophysiological inflexibility and core symptoms of this complex neurodevelopmental disorder. PMID:25073720

Feasibility of approaches combining sensor and source features in brain-computer interface.

PubMed

Ahn, Minkyu; Hong, Jun Hee; Jun, Sung Chan

2012-02-15

Brain-computer interface (BCI) provides a new channel for communication between brain and computers through brain signals. Cost-effective EEG provides good temporal resolution, but its spatial resolution is poor and sensor information is blurred by inherent noise. To overcome these issues, spatial filtering and feature extraction techniques have been developed. Source imaging, transformation of sensor signals into the source space through source localizer, has gained attention as a new approach for BCI. It has been reported that the source imaging yields some improvement of BCI performance. However, there exists no thorough investigation on how source imaging information overlaps with, and is complementary to, sensor information. Information (visible information) from the source space may overlap as well as be exclusive to information from the sensor space is hypothesized. Therefore, we can extract more information from the sensor and source spaces if our hypothesis is true, thereby contributing to more accurate BCI systems. In this work, features from each space (sensor or source), and two strategies combining sensor and source features are assessed. The information distribution among the sensor, source, and combined spaces is discussed through a Venn diagram for 18 motor imagery datasets. Additional 5 motor imagery datasets from the BCI Competition III site were examined. The results showed that the addition of source information yielded about 3.8% classification improvement for 18 motor imagery datasets and showed an average accuracy of 75.56% for BCI Competition data. Our proposed approach is promising, and improved performance may be possible with better head model. Copyright © 2011 Elsevier B.V. All rights reserved.

Astronauts Need Their Rest Too: Sleep-Wake Actigraphy and Light Exposure During Space Flight

NASA Technical Reports Server (NTRS)

Czeisler, Charles; Bloomberg, Jacob; Lee, Angie (Technical Monitor)

2002-01-01

The success and effectiveness of human space flight depends on astronauts' ability to maintain a high level of cognitive performance and vigilance. This alert state ensures the proper operation of sophisticated instrumentation. An important way for humans to remedy fatigue and maintain alertness is to get plenty of rest. Astronauts, however, commonly experience difficulty sleeping while in space. During flight, they may also experience disruption of the body's circadian rhythm - the natural phases the body goes through every day as we oscillate between states of high activity during the waking day and recuperation, rest, and repair during nighttime sleep. Both of these factors are associated with impairment of alertness and performance, which could have important consequences during a mission in space. The human body was designed to sleep at night and be alert and active during the day. We receive these cues from the time of day or amount of light, such as the rising or setting of the sun. However, in the environment of the Space Shuttle or the International Space Station where light levels are highly variable, the characteristics of a 24-hour light/dark cycle are not present to cue the astronauts' bodies about what time of the day it is. Astronauts orbiting Earth see a sunset and sunrise every 90 minutes, sending potentially disruptive signals to the area of the brain that regulates sleep. On STS-107, researchers will measure sleep-wake activity with state-of-the-art technology to quantify how much sleep astronauts obtain in space. Because light is the most powerful time cue to the body's circadian system, individual light exposure patterns of the astronauts will also be monitored to determine if light exposure is associated with sleep disruption. The results of this research could lead to the development of a new treatment for sleep disturbances, enabling crewmembers to avoid the decrements in alertness and performance due to sleep deprivation. What we learn about sleep in space informs treatment for earthbound populations, such as the elderly and insomniacs, who experience frequent sleep disturbances or altered sleep patterns.

Glymphatic MRI in idiopathic normal pressure hydrocephalus

PubMed Central

Ringstad, Geir; Vatnehol, Svein Are Sirirud; Eide, Per Kristian

2017-01-01

Abstract The glymphatic system has in previous studies been shown as fundamental to clearance of waste metabolites from the brain interstitial space, and is proposed to be instrumental in normal ageing and brain pathology such as Alzheimer’s disease and brain trauma. Assessment of glymphatic function using magnetic resonance imaging with intrathecal contrast agent as a cerebrospinal fluid tracer has so far been limited to rodents. We aimed to image cerebrospinal fluid flow characteristics and glymphatic function in humans, and applied the methodology in a prospective study of 15 idiopathic normal pressure hydrocephalus patients (mean age 71.3 ± 8.1 years, three female and 12 male) and eight reference subjects (mean age 41.1 + 13.0 years, six female and two male) with suspected cerebrospinal fluid leakage (seven) and intracranial cyst (one). The imaging protocol included T1-weighted magnetic resonance imaging with equal sequence parameters before and at multiple time points through 24 h after intrathecal injection of the contrast agent gadobutrol at the lumbar level. All study subjects were kept in the supine position between examinations during the first day. Gadobutrol enhancement was measured at all imaging time points from regions of interest placed at predefined locations in brain parenchyma, the subarachnoid and intraventricular space, and inside the sagittal sinus. Parameters demonstrating gadobutrol enhancement and clearance in different locations were compared between idiopathic normal pressure hydrocephalus and reference subjects. A characteristic flow pattern in idiopathic normal hydrocephalus was ventricular reflux of gadobutrol from the subarachnoid space followed by transependymal gadobutrol migration. At the brain surfaces, gadobutrol propagated antegradely along large leptomeningeal arteries in all study subjects, and preceded glymphatic enhancement in adjacent brain tissue, indicating a pivotal role of intracranial pulsations for glymphatic function. In idiopathic normal pressure hydrocephalus, we found delayed enhancement (P < 0.05) and decreased clearance of gadobutrol (P < 0.05) at the Sylvian fissure. Parenchymal (glymphatic) enhancement peaked overnight in both study groups, possibly indicating a crucial role of sleep, and was larger in normal pressure hydrocephalus patients (P < 0.05 at inferior frontal gyrus). We interpret decreased gadobutrol clearance from the subarachnoid space, along with persisting enhancement in brain parenchyma, as signs of reduced glymphatic clearance in idiopathic normal hydrocephalus, and hypothesize that reduced glymphatic function is instrumental for dementia in this disease. The study shows promise for glymphatic magnetic resonance imaging as a method to assess human brain metabolic function and renders a potential for contrast enhanced brain extravascular space imaging. PMID:28969373

Stuck in a State of Inattention? Functional Hyperconnectivity as an Indicator of Disturbed Intrinsic Brain Dynamics in Adolescents With Concussion: A Pilot Study

PubMed Central

Virji-Babul, Naznin

2018-01-01

Sports-related concussion in youth is a major public health issue. Evaluating the diffuse and often subtle changes in structure and function that occur in the brain, particularly in this population, remains a significant challenge. The goal of this pilot study was to evaluate the relationship between the intrinsic dynamics of the brain using resting-state functional magnetic resonance imaging (rs-fMRI) and relate these findings to structural brain correlates from diffusion tensor imaging in a group of adolescents with sports-related concussions (n = 6) and a group of healthy adolescent athletes (n = 6). We analyzed rs-fMRI data using a sliding windows approach and related the functional findings to structural brain correlates by applying graph theory analysis to the diffusion tensor imaging data. Within the resting-state condition, we extracted three separate brain states in both groups. Our analysis revealed that the brain dynamics in healthy adolescents was characterized by a dynamic pattern, shifting equally between three brain states; however, in adolescents with concussion, the pattern was more static with a longer time spent in one brain state. Importantly, this lack of dynamic flexibility in the concussed group was associated with increased nodal strength in the left middle frontal gyrus, suggesting reorganization in a region related to attention. This preliminary report shows that both the intrinsic brain dynamics and structural organization are altered in networks related to attention in adolescents with concussion. This first report in adolescents will be used to inform future studies in a larger cohort. PMID:29357675

Stuck in a State of Inattention? Functional Hyperconnectivity as an Indicator of Disturbed Intrinsic Brain Dynamics in Adolescents With Concussion: A Pilot Study.

PubMed

Muller, Angela M; Virji-Babul, Naznin

2018-01-01

Sports-related concussion in youth is a major public health issue. Evaluating the diffuse and often subtle changes in structure and function that occur in the brain, particularly in this population, remains a significant challenge. The goal of this pilot study was to evaluate the relationship between the intrinsic dynamics of the brain using resting-state functional magnetic resonance imaging (rs-fMRI) and relate these findings to structural brain correlates from diffusion tensor imaging in a group of adolescents with sports-related concussions ( n = 6) and a group of healthy adolescent athletes ( n = 6). We analyzed rs-fMRI data using a sliding windows approach and related the functional findings to structural brain correlates by applying graph theory analysis to the diffusion tensor imaging data. Within the resting-state condition, we extracted three separate brain states in both groups. Our analysis revealed that the brain dynamics in healthy adolescents was characterized by a dynamic pattern, shifting equally between three brain states; however, in adolescents with concussion, the pattern was more static with a longer time spent in one brain state. Importantly, this lack of dynamic flexibility in the concussed group was associated with increased nodal strength in the left middle frontal gyrus, suggesting reorganization in a region related to attention. This preliminary report shows that both the intrinsic brain dynamics and structural organization are altered in networks related to attention in adolescents with concussion. This first report in adolescents will be used to inform future studies in a larger cohort.

The choice of the source space and the Laplacian matrix in LORETA and the spatio-temporal Kalman filter EEG inverse methods.

PubMed

Habboush, Nawar; Hamid, Laith; Japaridze, Natia; Wiegand, Gert; Heute, Ulrich; Stephani, Ulrich; Galka, Andreas; Siniatchkin, Michael

2015-08-01

The discretization of the brain and the definition of the Laplacian matrix influence the results of methods based on spatial and spatio-temporal smoothness, since the Laplacian operator is used to define the smoothness based on the neighborhood of each grid point. In this paper, the results of low resolution electromagnetic tomography (LORETA) and the spatiotemporal Kalman filter (STKF) are computed using, first, a greymatter source space with the standard definition of the Laplacian matrix and, second, using a whole-brain source space and a modified definition of the Laplacian matrix. Electroencephalographic (EEG) source imaging results of five inter-ictal spikes from a pre-surgical patient with epilepsy are used to validate the two aforementioned approaches. The results using the whole-brain source space and the modified definition of the Laplacian matrix were concentrated in a single source activation, stable, and concordant with the location of the focal cortical dysplasia (FCD) in the patient's brain compared with the results which use a grey-matter grid and the classical definition of the Laplacian matrix. This proof-of-concept study demonstrates a substantial improvement of source localization with both LORETA and STKF and constitutes a basis for further research in a large population of patients with epilepsy.

A priori collaboration in population imaging: The Uniform Neuro-Imaging of Virchow-Robin Spaces Enlargement consortium.

PubMed

Adams, Hieab H H; Hilal, Saima; Schwingenschuh, Petra; Wittfeld, Katharina; van der Lee, Sven J; DeCarli, Charles; Vernooij, Meike W; Katschnig-Winter, Petra; Habes, Mohamad; Chen, Christopher; Seshadri, Sudha; van Duijn, Cornelia M; Ikram, M Kamran; Grabe, Hans J; Schmidt, Reinhold; Ikram, M Arfan

2015-12-01

Virchow-Robin spaces (VRS), or perivascular spaces, are compartments of interstitial fluid enclosing cerebral blood vessels and are potential imaging markers of various underlying brain pathologies. Despite a growing interest in the study of enlarged VRS, the heterogeneity in rating and quantification methods combined with small sample sizes have so far hampered advancement in the field. The Uniform Neuro-Imaging of Virchow-Robin Spaces Enlargement (UNIVRSE) consortium was established with primary aims to harmonize rating and analysis (www.uconsortium.org). The UNIVRSE consortium brings together 13 (sub)cohorts from five countries, totaling 16,000 subjects and over 25,000 scans. Eight different magnetic resonance imaging protocols were used in the consortium. VRS rating was harmonized using a validated protocol that was developed by the two founding members, with high reliability independent of scanner type, rater experience, or concomitant brain pathology. Initial analyses revealed risk factors for enlarged VRS including increased age, sex, high blood pressure, brain infarcts, and white matter lesions, but this varied by brain region. Early collaborative efforts between cohort studies with respect to data harmonization and joint analyses can advance the field of population (neuro)imaging. The UNIVRSE consortium will focus efforts on other potential correlates of enlarged VRS, including genetics, cognition, stroke, and dementia.

Biosocial Spaces and Neurocomputational Governance: Brain-Based and Brain-Targeted Technologies in Education

ERIC Educational Resources Information Center

Williamson, Ben; Pykett, Jessica; Nemorin, Selena

2018-01-01

Recently, technologies based on neuroscientific insights into brain function and structure have been promoted for application in education. The novel practices and environments produced by these technologies require new forms of "biosocial" analysis to unpack their implications for education, learning and governance. This article…

Enhanced ICBM Diffusion Tensor Template of the Human Brain

PubMed Central

Zhang, Shengwei; Peng, Huiling; Dawe, Robert J.; Arfanakis, Konstantinos

2010-01-01

Development of a diffusion tensor (DT) template that is representative of the micro-architecture of the human brain is crucial for comparisons of neuronal structural integrity and brain connectivity across populations, as well as for the generation of a detailed white matter atlas. Furthermore, a DT template in ICBM space may simplify consolidation of information from DT, anatomical and functional MRI studies. The previously developed “IIT DT brain template” was produced in ICBM-152 space, based on a large number of subjects from a limited age-range, using data with minimal image artifacts, and non-linear registration. That template was characterized by higher image sharpness, provided the ability to distinguish smaller white matter fiber structures, and contained fewer image artifacts, than several previously published DT templates. However, low-dimensional registration was used in the development of that template, which led to a mismatch of DT information across subjects, eventually manifested as loss of local diffusion information and errors in the final tensors. Also, low-dimensional registration led to a mismatch of the anatomy in the IIT and ICBM-152 templates. In this work, a significantly improved DT brain template in ICBM-152 space was developed, using high-dimensional non-linear registration and the raw data collected for the purposes of the IIT template. The accuracy of inter-subject DT matching was significantly increased compared to that achieved for the development of the IIT template. Consequently, the new template contained DT information that was more representative of single-subject human brain data, and was characterized by higher image sharpness than the IIT template. Furthermore, a bootstrap approach demonstrated that the variance of tensor characteristics was lower in the new template. Additionally, compared to the IIT template, brain anatomy in the new template more accurately matched ICBM-152 space. Finally, spatial normalization of a number of DT datasets through registration to the new and existing IIT templates was improved when using the new template. PMID:20851772

Principal States of Dynamic Functional Connectivity Reveal the Link Between Resting-State and Task-State Brain: An fMRI Study.

PubMed

Cheng, Lin; Zhu, Yang; Sun, Junfeng; Deng, Lifu; He, Naying; Yang, Yang; Ling, Huawei; Ayaz, Hasan; Fu, Yi; Tong, Shanbao

2018-01-25

Task-related reorganization of functional connectivity (FC) has been widely investigated. Under classic static FC analysis, brain networks under task and rest have been demonstrated a general similarity. However, brain activity and cognitive process are believed to be dynamic and adaptive. Since static FC inherently ignores the distinct temporal patterns between rest and task, dynamic FC may be more a suitable technique to characterize the brain's dynamic and adaptive activities. In this study, we adopted [Formula: see text]-means clustering to investigate task-related spatiotemporal reorganization of dynamic brain networks and hypothesized that dynamic FC would be able to reveal the link between resting-state and task-state brain organization, including broadly similar spatial patterns but distinct temporal patterns. In order to test this hypothesis, this study examined the dynamic FC in default-mode network (DMN) and motor-related network (MN) using Blood-Oxygenation-Level-Dependent (BOLD)-fMRI data from 26 healthy subjects during rest (REST) and a hand closing-and-opening (HCO) task. Two principal FC states in REST and one principal FC state in HCO were identified. The first principal FC state in REST was found similar to that in HCO, which appeared to represent intrinsic network architecture and validated the broadly similar spatial patterns between REST and HCO. However, the second FC principal state in REST with much shorter "dwell time" implied the transient functional relationship between DMN and MN during REST. In addition, a more frequent shifting between two principal FC states indicated that brain network dynamically maintained a "default mode" in the motor system during REST, whereas the presence of a single principal FC state and reduced FC variability implied a more temporally stable connectivity during HCO, validating the distinct temporal patterns between REST and HCO. Our results further demonstrated that dynamic FC analysis could offer unique insights in understanding how the brain reorganizes itself during rest and task states, and the ways in which the brain adaptively responds to the cognitive requirements of tasks.

Brain entropy and human intelligence: A resting-state fMRI study

PubMed Central

Calderone, Daniel; Morales, Leah J.

2018-01-01

Human intelligence comprises comprehension of and reasoning about an infinitely variable external environment. A brain capable of large variability in neural configurations, or states, will more easily understand and predict variable external events. Entropy measures the variety of configurations possible within a system, and recently the concept of brain entropy has been defined as the number of neural states a given brain can access. This study investigates the relationship between human intelligence and brain entropy, to determine whether neural variability as reflected in neuroimaging signals carries information about intellectual ability. We hypothesize that intelligence will be positively associated with entropy in a sample of 892 healthy adults, using resting-state fMRI. Intelligence is measured with the Shipley Vocabulary and WASI Matrix Reasoning tests. Brain entropy was positively associated with intelligence. This relation was most strongly observed in the prefrontal cortex, inferior temporal lobes, and cerebellum. This relationship between high brain entropy and high intelligence indicates an essential role for entropy in brain functioning. It demonstrates that access to variable neural states predicts complex behavioral performance, and specifically shows that entropy derived from neuroimaging signals at rest carries information about intellectual capacity. Future work in this area may elucidate the links between brain entropy in both resting and active states and various forms of intelligence. This insight has the potential to provide predictive information about adaptive behavior and to delineate the subdivisions and nature of intelligence based on entropic patterns. PMID:29432427

Brain entropy and human intelligence: A resting-state fMRI study.

PubMed

Saxe, Glenn N; Calderone, Daniel; Morales, Leah J

2018-01-01

Human intelligence comprises comprehension of and reasoning about an infinitely variable external environment. A brain capable of large variability in neural configurations, or states, will more easily understand and predict variable external events. Entropy measures the variety of configurations possible within a system, and recently the concept of brain entropy has been defined as the number of neural states a given brain can access. This study investigates the relationship between human intelligence and brain entropy, to determine whether neural variability as reflected in neuroimaging signals carries information about intellectual ability. We hypothesize that intelligence will be positively associated with entropy in a sample of 892 healthy adults, using resting-state fMRI. Intelligence is measured with the Shipley Vocabulary and WASI Matrix Reasoning tests. Brain entropy was positively associated with intelligence. This relation was most strongly observed in the prefrontal cortex, inferior temporal lobes, and cerebellum. This relationship between high brain entropy and high intelligence indicates an essential role for entropy in brain functioning. It demonstrates that access to variable neural states predicts complex behavioral performance, and specifically shows that entropy derived from neuroimaging signals at rest carries information about intellectual capacity. Future work in this area may elucidate the links between brain entropy in both resting and active states and various forms of intelligence. This insight has the potential to provide predictive information about adaptive behavior and to delineate the subdivisions and nature of intelligence based on entropic patterns.

Measuring the representational space of music with fMRI: a case study with Sting.

PubMed

Levitin, Daniel J; Grafton, Scott T

2016-12-01

Functional brain imaging has revealed much about the neuroanatomical substrates of higher cognition, including music, language, learning, and memory. The technique lends itself to studying of groups of individuals. In contrast, the nature of expert performance is typically studied through the examination of exceptional individuals using behavioral case studies and retrospective biography. Here, we combined fMRI and the study of an individual who is a world-class expert musician and composer in order to better understand the neural underpinnings of his music perception and cognition, in particular, his mental representations for music. We used state of the art multivoxel pattern analysis (MVPA) and representational dissimilarity analysis (RDA) in a fixed set of brain regions to test three exploratory hypotheses with the musician Sting: (1) Composing would recruit neutral structures that are both unique and distinguishable from other creative acts, such as composing prose or visual art; (2) listening and imagining music would recruit similar neural regions, indicating that musical memory shares anatomical substrates with music listening; (3) the MVPA and RDA results would help us to map the representational space for music, revealing which musical pieces and genres are perceived to be similar in the musician's mental models for music. Our hypotheses were confirmed. The act of composing, and even of imagining elements of the composed piece separately, such as melody and rhythm, activated a similar cluster of brain regions, and were distinct from prose and visual art. Listened and imagined music showed high similarity, and in addition, notable similarity/dissimilarity patterns emerged among the various pieces used as stimuli: Muzak and Top 100/Pop songs were far from all other musical styles in Mahalanobis distance (Euclidean representational space), whereas jazz, R&B, tango and rock were comparatively close. Closer inspection revealed principaled explanations for the similarity clusters found, based on key, tempo, motif, and orchestration.

Frequency specific brain networks in Parkinson's disease and comorbid depression.

PubMed

Qian, Long; Zhang, Yi; Zheng, Li; Fu, Xuemei; Liu, Weiguo; Shang, Yuqing; Zhang, Yaoyu; Xu, Yuanyuan; Liu, Yijun; Zhu, Huaiqiu; Gao, Jia-Hong

2017-02-01

The topological organization underlying the human brain was extensively investigated using resting-state functional magnetic resonance imaging, focusing on a low frequency of signal oscillation from 0.01 to 0.1 Hz. However, the frequency specificities with regard to the topological properties of the brain networks have not been fully revealed. In this study, a novel complementary ensemble empirical mode decomposition (CEEMD) method was used to separate the fMRI time series into five characteristic oscillations with distinct frequencies. Then, the small world properties of brain networks were analyzed for each of these five oscillations in patients (n = 67) with depressed Parkinson's disease (DPD, n = 20) , non-depressed Parkinson's disease (NDPD, n = 47) and healthy controls (HC, n = 46). Compared with HC, the results showed decreased network efficiency in characteristic oscillations from 0.05 to 0.12 Hz and from 0.02 to 0.05 Hz for the DPD and NDPD patients, respectively. Furthermore, compared with HC, the most significant inter-group difference across five brain oscillations was found in the basal ganglia (0.01 to 0.05 Hz) and paralimbic-limbic network (0.02 to 0.22 Hz) for the DPD patients, and in the visual cortex (0.02 to 0.05 Hz) for the NDPD patients. Compared with NDPD, the DPD patients showed reduced efficiency of nodes in the basal ganglia network (0.01 to 0.05 Hz). Our results demonstrated that DPD is characterized by a disrupted topological organization in large-scale brain functional networks. Moreover, the CEEMD analysis suggested a prominent dissociation in the topological organization of brain networks between DPD and NDPD in both space and frequency domains. Our findings indicated that these characteristic oscillatory activities in different functional circuits may contribute to distinct motor and non-motor components of clinical impairments in Parkinson's disease.

Chaotic itinerancy within the coupled dynamics between a physical body and neural oscillator networks

PubMed Central

Mori, Hiroki; Okuyama, Yuji; Asada, Minoru

2017-01-01

Chaotic itinerancy is a phenomenon in which the state of a nonlinear dynamical system spontaneously explores and attracts certain states in a state space. From this perspective, the diverse behavior of animals and its spontaneous transitions lead to a complex coupled dynamical system, including a physical body and a brain. Herein, a series of simulations using different types of non-linear oscillator networks (i.e., regular, small-world, scale-free, random) with a musculoskeletal model (i.e., a snake-like robot) as a physical body are conducted to understand how the chaotic itinerancy of bodily behavior emerges from the coupled dynamics between the body and the brain. A behavior analysis (behavior clustering) and network analysis for the classified behavior are then applied. The former consists of feature vector extraction from the motions and classification of the movement patterns that emerged from the coupled dynamics. The network structures behind the classified movement patterns are revealed by estimating the “information networks” different from the given non-linear oscillator networks based on the transfer entropy which finds the information flow among neurons. The experimental results show that: (1) the number of movement patterns and their duration depend on the sensor ratio to control the balance of strength between the body and the brain dynamics and on the type of the given non-linear oscillator networks; and (2) two kinds of information networks are found behind two kinds movement patterns with different durations by utilizing the complex network measures, clustering coefficient and the shortest path length with a negative and a positive relationship with the duration periods of movement patterns. The current results seem promising for a future extension of the method to a more complicated body and environment. Several requirements are also discussed. PMID:28796797

Chaotic itinerancy within the coupled dynamics between a physical body and neural oscillator networks.

PubMed

Park, Jihoon; Mori, Hiroki; Okuyama, Yuji; Asada, Minoru

2017-01-01

Chaotic itinerancy is a phenomenon in which the state of a nonlinear dynamical system spontaneously explores and attracts certain states in a state space. From this perspective, the diverse behavior of animals and its spontaneous transitions lead to a complex coupled dynamical system, including a physical body and a brain. Herein, a series of simulations using different types of non-linear oscillator networks (i.e., regular, small-world, scale-free, random) with a musculoskeletal model (i.e., a snake-like robot) as a physical body are conducted to understand how the chaotic itinerancy of bodily behavior emerges from the coupled dynamics between the body and the brain. A behavior analysis (behavior clustering) and network analysis for the classified behavior are then applied. The former consists of feature vector extraction from the motions and classification of the movement patterns that emerged from the coupled dynamics. The network structures behind the classified movement patterns are revealed by estimating the "information networks" different from the given non-linear oscillator networks based on the transfer entropy which finds the information flow among neurons. The experimental results show that: (1) the number of movement patterns and their duration depend on the sensor ratio to control the balance of strength between the body and the brain dynamics and on the type of the given non-linear oscillator networks; and (2) two kinds of information networks are found behind two kinds movement patterns with different durations by utilizing the complex network measures, clustering coefficient and the shortest path length with a negative and a positive relationship with the duration periods of movement patterns. The current results seem promising for a future extension of the method to a more complicated body and environment. Several requirements are also discussed.

Using Reinforcement Learning to Provide Stable Brain-Machine Interface Control Despite Neural Input Reorganization

PubMed Central

Pohlmeyer, Eric A.; Mahmoudi, Babak; Geng, Shijia; Prins, Noeline W.; Sanchez, Justin C.

2014-01-01

Brain-machine interface (BMI) systems give users direct neural control of robotic, communication, or functional electrical stimulation systems. As BMI systems begin transitioning from laboratory settings into activities of daily living, an important goal is to develop neural decoding algorithms that can be calibrated with a minimal burden on the user, provide stable control for long periods of time, and can be responsive to fluctuations in the decoder’s neural input space (e.g. neurons appearing or being lost amongst electrode recordings). These are significant challenges for static neural decoding algorithms that assume stationary input/output relationships. Here we use an actor-critic reinforcement learning architecture to provide an adaptive BMI controller that can successfully adapt to dramatic neural reorganizations, can maintain its performance over long time periods, and which does not require the user to produce specific kinetic or kinematic activities to calibrate the BMI. Two marmoset monkeys used the Reinforcement Learning BMI (RLBMI) to successfully control a robotic arm during a two-target reaching task. The RLBMI was initialized using random initial conditions, and it quickly learned to control the robot from brain states using only a binary evaluative feedback regarding whether previously chosen robot actions were good or bad. The RLBMI was able to maintain control over the system throughout sessions spanning multiple weeks. Furthermore, the RLBMI was able to quickly adapt and maintain control of the robot despite dramatic perturbations to the neural inputs, including a series of tests in which the neuron input space was deliberately halved or doubled. PMID:24498055

NASA Robotic Neurosurgery Testbed

NASA Technical Reports Server (NTRS)

Mah, Robert

1997-01-01

The detection of tissue interface (e.g., normal tissue, cancer, tumor) has been limited clinically to tactile feedback, temperature monitoring, and the use of a miniature ultrasound probe for tissue differentiation during surgical operations, In neurosurgery, the needle used in the standard stereotactic CT or MRI guided brain biopsy provides no information about the tissue being sampled. The tissue sampled depends entirely upon the accuracy with which the localization provided by the preoperative CT or MRI scan is translated to the intracranial biopsy site. In addition, no information about the tissue being traversed by the needle (e.g., a blood vessel) is provided. Hemorrhage due to the biopsy needle tearing a blood vessel within the brain is the most devastating complication of stereotactic CT/MRI guided brain biopsy. A robotic neurosurgery testbed has been developed at NASA Ames Research Center as a spin-off of technologies from space, aeronautics and medical programs. The invention entitled "Robotic Neurosurgery Leading to Multimodality Devices for Tissue Identification" is nearing a state ready for commercialization. The devices will: 1) improve diagnostic accuracy and precision of general surgery, with near term emphasis on stereotactic brain biopsy, 2) automate tissue identification, with near term emphasis on stereotactic brain biopsy, to permit remote control of the procedure, and 3) reduce morbidity for stereotactic brain biopsy. The commercial impact from this work is the potential development of a whole new generation of smart surgical tools to increase the safety, accuracy and efficiency of surgical procedures. Other potential markets include smart surgical tools for tumor ablation in neurosurgery, general exploratory surgery, prostate cancer surgery, and breast cancer surgery.

NASA Robotic Neurosurgery Testbed

NASA Technical Reports Server (NTRS)

Mah, Robert

1997-01-01

The detection of tissue interface (e.g., normal tissue, cancer, tumor) has been limited clinically to tactile feedback, temperature monitoring, and the use of a miniature ultrasound probe for tissue differentiation during surgical operations. In neurosurgery, the needle used in the standard stereotactic CT (Computational Tomography) or MRI (Magnetic Resonance Imaging) guided brain biopsy provides no information about the tissue being sampled. The tissue sampled depends entirely upon the accuracy with which the localization provided by the preoperative CT or MRI scan is translated to the intracranial biopsy site. In addition, no information about the tissue being traversed by the needle (e.g., a blood vessel) is provided. Hemorrhage due to the biopsy needle tearing a blood vessel within the brain is the most devastating complication of stereotactic CT/MRI guided brain biopsy. A robotic neurosurgery testbed has been developed at NASA Ames Research Center as a spin-off of technologies from space, aeronautics and medical programs. The invention entitled 'Robotic Neurosurgery Leading to Multimodality Devices for Tissue Identification' is nearing a state ready for commercialization. The devices will: 1) improve diagnostic accuracy and precision of general surgery, with near term emphasis on stereotactic brain biopsy, 2) automate tissue identification, with near term emphasis on stereotactic brain biopsy, to permit remote control of the procedure, and 3) reduce morbidity for stereotactic brain biopsy. The commercial impact from this work is the potential development of a whole new generation of smart surgical tools to increase the safety, accuracy and efficiency of surgical procedures. Other potential markets include smart surgical tools for tumor ablation in neurosurgery, general exploratory surgery, prostate cancer surgery, and breast cancer surgery.

State-Space Analysis of Granger-Geweke Causality Measures with Application to fMRI.

PubMed

Solo, Victor

2016-05-01

The recent interest in the dynamics of networks and the advent, across a range of applications, of measuring modalities that operate on different temporal scales have put the spotlight on some significant gaps in the theory of multivariate time series. Fundamental to the description of network dynamics is the direction of interaction between nodes, accompanied by a measure of the strength of such interactions. Granger causality and its associated frequency domain strength measures (GEMs) (due to Geweke) provide a framework for the formulation and analysis of these issues. In pursuing this setup, three significant unresolved issues emerge. First, computing GEMs involves computing submodels of vector time series models, for which reliable methods do not exist. Second, the impact of filtering on GEMs has never been definitively established. Third, the impact of downsampling on GEMs has never been established. In this work, using state-space methods, we resolve all these issues and illustrate the results with some simulations. Our analysis is motivated by some problems in (fMRI) brain imaging, to which we apply it, but it is of general applicability.

State-Space Analysis of Granger-Geweke Causality Measures with Application to fMRI

PubMed Central

Solo, Victor

2017-01-01

The recent interest in the dynamics of networks and the advent, across a range of applications, of measuring modalities that operate on different temporal scales have put the spotlight on some significant gaps in the theory of multivariate time series. Fundamental to the description of network dynamics is the direction of interaction between nodes, accompanied by a measure of the strength of such interactions. Granger causality and its associated frequency domain strength measures (GEMs) (due to Geweke) provide a framework for the formulation and analysis of these issues. In pursuing this setup, three significant unresolved issues emerge. First, computing GEMs involves computing submodels of vector time series models, for which reliable methods do not exist. Second, the impact of filtering on GEMs has never been definitively established. Third, the impact of downsampling on GEMs has never been established. In this work, using state-space methods, we resolve all these issues and illustrate the results with some simulations. Our analysis is motivated by some problems in (fMRI) brain imaging, to which we apply it, but it is of general applicability. PMID:26942749

Neural coordination can be enhanced by occasional interruption of normal firing patterns: a self-optimizing spiking neural network model.

PubMed

Woodward, Alexander; Froese, Tom; Ikegami, Takashi

2015-02-01

The state space of a conventional Hopfield network typically exhibits many different attractors of which only a small subset satisfies constraints between neurons in a globally optimal fashion. It has recently been demonstrated that combining Hebbian learning with occasional alterations of normal neural states avoids this problem by means of self-organized enlargement of the best basins of attraction. However, so far it is not clear to what extent this process of self-optimization is also operative in real brains. Here we demonstrate that it can be transferred to more biologically plausible neural networks by implementing a self-optimizing spiking neural network model. In addition, by using this spiking neural network to emulate a Hopfield network with Hebbian learning, we attempt to make a connection between rate-based and temporal coding based neural systems. Although further work is required to make this model more realistic, it already suggests that the efficacy of the self-optimizing process is independent from the simplifying assumptions of a conventional Hopfield network. We also discuss natural and cultural processes that could be responsible for occasional alteration of neural firing patterns in actual brains. Copyright © 2014 Elsevier Ltd. All rights reserved.

Dynamic Organization of Hierarchical Memories

PubMed Central

Kurikawa, Tomoki; Kaneko, Kunihiko

2016-01-01

In the brain, external objects are categorized in a hierarchical way. Although it is widely accepted that objects are represented as static attractors in neural state space, this view does not take account interaction between intrinsic neural dynamics and external input, which is essential to understand how neural system responds to inputs. Indeed, structured spontaneous neural activity without external inputs is known to exist, and its relationship with evoked activities is discussed. Then, how categorical representation is embedded into the spontaneous and evoked activities has to be uncovered. To address this question, we studied bifurcation process with increasing input after hierarchically clustered associative memories are learned. We found a “dynamic categorization”; neural activity without input wanders globally over the state space including all memories. Then with the increase of input strength, diffuse representation of higher category exhibits transitions to focused ones specific to each object. The hierarchy of memories is embedded in the transition probability from one memory to another during the spontaneous dynamics. With increased input strength, neural activity wanders over a narrower state space including a smaller set of memories, showing more specific category or memory corresponding to the applied input. Moreover, such coarse-to-fine transitions are also observed temporally during transient process under constant input, which agrees with experimental findings in the temporal cortex. These results suggest the hierarchy emerging through interaction with an external input underlies hierarchy during transient process, as well as in the spontaneous activity. PMID:27618549

Quantifying causal emergence shows that macro can beat micro.

PubMed

Hoel, Erik P; Albantakis, Larissa; Tononi, Giulio

2013-12-03

Causal interactions within complex systems can be analyzed at multiple spatial and temporal scales. For example, the brain can be analyzed at the level of neurons, neuronal groups, and areas, over tens, hundreds, or thousands of milliseconds. It is widely assumed that, once a micro level is fixed, macro levels are fixed too, a relation called supervenience. It is also assumed that, although macro descriptions may be convenient, only the micro level is causally complete, because it includes every detail, thus leaving no room for causation at the macro level. However, this assumption can only be evaluated under a proper measure of causation. Here, we use a measure [effective information (EI)] that depends on both the effectiveness of a system's mechanisms and the size of its state space: EI is higher the more the mechanisms constrain the system's possible past and future states. By measuring EI at micro and macro levels in simple systems whose micro mechanisms are fixed, we show that for certain causal architectures EI can peak at a macro level in space and/or time. This happens when coarse-grained macro mechanisms are more effective (more deterministic and/or less degenerate) than the underlying micro mechanisms, to an extent that overcomes the smaller state space. Thus, although the macro level supervenes upon the micro, it can supersede it causally, leading to genuine causal emergence--the gain in EI when moving from a micro to a macro level of analysis.

Wavelet-space correlation imaging for high-speed MRI without motion monitoring or data segmentation.

PubMed

Li, Yu; Wang, Hui; Tkach, Jean; Roach, David; Woods, Jason; Dumoulin, Charles

2015-12-01

This study aims to (i) develop a new high-speed MRI approach by implementing correlation imaging in wavelet-space, and (ii) demonstrate the ability of wavelet-space correlation imaging to image human anatomy with involuntary or physiological motion. Correlation imaging is a high-speed MRI framework in which image reconstruction relies on quantification of data correlation. The presented work integrates correlation imaging with a wavelet transform technique developed originally in the field of signal and image processing. This provides a new high-speed MRI approach to motion-free data collection without motion monitoring or data segmentation. The new approach, called "wavelet-space correlation imaging", is investigated in brain imaging with involuntary motion and chest imaging with free-breathing. Wavelet-space correlation imaging can exceed the speed limit of conventional parallel imaging methods. Using this approach with high acceleration factors (6 for brain MRI, 16 for cardiac MRI, and 8 for lung MRI), motion-free images can be generated in static brain MRI with involuntary motion and nonsegmented dynamic cardiac/lung MRI with free-breathing. Wavelet-space correlation imaging enables high-speed MRI in the presence of involuntary motion or physiological dynamics without motion monitoring or data segmentation. © 2014 Wiley Periodicals, Inc.

Wavelet-space Correlation Imaging for High-speed MRI without Motion Monitoring or Data Segmentation

PubMed Central

Li, Yu; Wang, Hui; Tkach, Jean; Roach, David; Woods, Jason; Dumoulin, Charles

2014-01-01

Purpose This study aims to 1) develop a new high-speed MRI approach by implementing correlation imaging in wavelet-space, and 2) demonstrate the ability of wavelet-space correlation imaging to image human anatomy with involuntary or physiological motion. Methods Correlation imaging is a high-speed MRI framework in which image reconstruction relies on quantification of data correlation. The presented work integrates correlation imaging with a wavelet transform technique developed originally in the field of signal and image processing. This provides a new high-speed MRI approach to motion-free data collection without motion monitoring or data segmentation. The new approach, called “wavelet-space correlation imaging”, is investigated in brain imaging with involuntary motion and chest imaging with free-breathing. Results Wavelet-space correlation imaging can exceed the speed limit of conventional parallel imaging methods. Using this approach with high acceleration factors (6 for brain MRI, 16 for cardiac MRI and 8 for lung MRI), motion-free images can be generated in static brain MRI with involuntary motion and nonsegmented dynamic cardiac/lung MRI with free-breathing. Conclusion Wavelet-space correlation imaging enables high-speed MRI in the presence of involuntary motion or physiological dynamics without motion monitoring or data segmentation. PMID:25470230

Microgravity

NASA Image and Video Library

1997-09-25

What started out as an attempt to develop a light which would allow for the growth of plants in space led to a remarkable discovery: The Light Emitting Diode (LED). This device through extensive study and experimentation has developed into a tool used by surgeons in the fight against brain cancer in children. Pictured is a mock-up of brain surgery being performed. By encapsulating the end of the LED with a balloon, light is diffused over a larger area of the brain allowing the surgeon a better view. This is one of many programs that begin as research for the space program, and through extensive study end up benefitting all of mankind.

Reconstruction of human brain spontaneous activity based on frequency-pattern analysis of magnetoencephalography data

PubMed Central

Llinás, Rodolfo R.; Ustinin, Mikhail N.; Rykunov, Stanislav D.; Boyko, Anna I.; Sychev, Vyacheslav V.; Walton, Kerry D.; Rabello, Guilherme M.; Garcia, John

2015-01-01

A new method for the analysis and localization of brain activity has been developed, based on multichannel magnetic field recordings, over minutes, superimposed on the MRI of the individual. Here, a high resolution Fourier Transform is obtained over the entire recording period, leading to a detailed multi-frequency spectrum. Further analysis implements a total decomposition of the frequency components into functionally invariant entities, each having an invariant field pattern localizable in recording space. The method, addressed as functional tomography, makes it possible to find the distribution of magnetic field sources in space. Here, the method is applied to the analysis of simulated data, to oscillating signals activating a physical current dipoles phantom, and to recordings of spontaneous brain activity in 10 healthy adults. In the analysis of simulated data, 61 dipoles are localized with 0.7 mm precision. Concerning the physical phantom the method is able to localize three simultaneously activated current dipoles with 1 mm precision. Spatial resolution 3 mm was attained when localizing spontaneous alpha rhythm activity in 10 healthy adults, where the alpha peak was specified for each subject individually. Co-registration of the functional tomograms with each subject's head MRI localized alpha range activity to the occipital and/or posterior parietal brain region. This is the first application of this new functional tomography to human brain activity. The method successfully provides an overall view of brain electrical activity, a detailed spectral description and, combined with MRI, the localization of sources in anatomical brain space. PMID:26528119

Describing the Neuron Axons Network of the Human Brain by Continuous Flow Models

NASA Astrophysics Data System (ADS)

Hizanidis, J.; Katsaloulis, P.; Verganelakis, D. A.; Provata, A.

2014-12-01

The multifractal spectrum Dq (Rényi dimensions) is used for the analysis and comparison between the Neuron Axons Network (NAN) of healthy and pathological human brains because it conveys information about the statistics in many scales, from the very rare to the most frequent network configurations. Comparison of the Fractional Anisotropy Magnetic Resonance Images between healthy and pathological brains is performed with and without noise reduction. Modelling the complex structure of the NAN in the human brain is undertaken using the dynamics of the Lorenz model in the chaotic regime. The Lorenz multifractal spectra capture well the human brain characteristics in the large negative q's which represent the rare network configurations. In order to achieve a closer approximation in the positive part of the spectrum (q > 0) two independent modifications are considered: a) redistribution of the dense parts of the Lorenz model's phase space into their neighbouring areas and b) inclusion of additive uniform noise in the Lorenz model. Both modifications, independently, drive the Lorenz spectrum closer to the human NAN one in the positive q region without destroying the already good correspondence of the negative spectra. The modelling process shows that the unmodified Lorenz model in its full chaotic regime has a phase space distribution with high fluctuations in its dense parts, while the fluctuations in the human brain NAN are smoother. The induced modifications (phase space redistribution or additive noise) moderate the fluctuations only in the positive part of the Lorenz spectrum leading to a faithful representation of the human brain axons network in all scales.

Geometry Processing of Conventionally Produced Mouse Brain Slice Images.

PubMed

Agarwal, Nitin; Xu, Xiangmin; Gopi, M

2018-04-21

Brain mapping research in most neuroanatomical laboratories relies on conventional processing techniques, which often introduce histological artifacts such as tissue tears and tissue loss. In this paper we present techniques and algorithms for automatic registration and 3D reconstruction of conventionally produced mouse brain slices in a standardized atlas space. This is achieved first by constructing a virtual 3D mouse brain model from annotated slices of Allen Reference Atlas (ARA). Virtual re-slicing of the reconstructed model generates ARA-based slice images corresponding to the microscopic images of histological brain sections. These image pairs are aligned using a geometric approach through contour images. Histological artifacts in the microscopic images are detected and removed using Constrained Delaunay Triangulation before performing global alignment. Finally, non-linear registration is performed by solving Laplace's equation with Dirichlet boundary conditions. Our methods provide significant improvements over previously reported registration techniques for the tested slices in 3D space, especially on slices with significant histological artifacts. Further, as one of the application we count the number of neurons in various anatomical regions using a dataset of 51 microscopic slices from a single mouse brain. To the best of our knowledge the presented work is the first that automatically registers both clean as well as highly damaged high-resolutions histological slices of mouse brain to a 3D annotated reference atlas space. This work represents a significant contribution to this subfield of neuroscience as it provides tools to neuroanatomist for analyzing and processing histological data. Copyright © 2018 Elsevier B.V. All rights reserved.

Vascular basement membranes as pathways for the passage of fluid into and out of the brain.

PubMed

Morris, Alan W J; Sharp, Matthew MacGregor; Albargothy, Nazira J; Fernandes, Rute; Hawkes, Cheryl A; Verma, Ajay; Weller, Roy O; Carare, Roxana O

2016-05-01

In the absence of conventional lymphatics, drainage of interstitial fluid and solutes from the brain parenchyma to cervical lymph nodes is along basement membranes in the walls of cerebral capillaries and tunica media of arteries. Perivascular pathways are also involved in the entry of CSF into the brain by the convective influx/glymphatic system. The objective of this study is to differentiate the cerebral vascular basement membrane pathways by which fluid passes out of the brain from the pathway by which CSF enters the brain. Experiment 1: 0.5 µl of soluble biotinylated or fluorescent Aβ, or 1 µl 15 nm gold nanoparticles was injected into the mouse hippocampus and their distributions determined at 5 min by transmission electron microscopy. Aβ was distributed within the extracellular spaces of the hippocampus and within basement membranes of capillaries and tunica media of arteries. Nanoparticles did not enter capillary basement membranes from the extracellular spaces. Experiment 2: 2 µl of 15 nm nanoparticles were injected into mouse CSF. Within 5 min, groups of nanoparticles were present in the pial-glial basement membrane on the outer aspect of cortical arteries between the investing layer of pia mater and the glia limitans. The results of this study and previous research suggest that cerebral vascular basement membranes form the pathways by which fluid passes into and out of the brain but that different basement membrane layers are involved. The significance of these findings for neuroimmunology, Alzheimer's disease, drug delivery to the brain and the concept of the Virchow-Robin space are discussed.

Diffusion in Brain Extracellular Space

PubMed Central

Syková, Eva; Nicholson, Charles

2009-01-01

Diffusion in the extracellular space (ECS) of the brain is constrained by the volume fraction and the tortuosity and a modified diffusion equation represents the transport behavior of many molecules in the brain. Deviations from the equation reveal loss of molecules across the blood-brain barrier, through cellular uptake, binding or other mechanisms. Early diffusion measurements used radiolabeled sucrose and other tracers. Presently, the real-time iontophoresis (RTI) method is employed for small ions and the integrative optical imaging (IOI) method for fluorescent macromolecules, including dextrans or proteins. Theoretical models and simulations of the ECS have explored the influence of ECS geometry, effects of dead-space microdomains, extracellular matrix and interaction of macromolecules with ECS channels. Extensive experimental studies with the RTI method employing the cation tetramethylammonium (TMA) in normal brain tissue show that the volume fraction of the ECS typically is about 20% and the tortuosity about 1.6 (i.e. free diffusion coefficient of TMA is reduced by 2.6), although there are regional variations. These parameters change during development and aging. Diffusion properties have been characterized in several interventions, including brain stimulation, osmotic challenge and knockout of extracellular matrix components. Measurements have also been made during ischemia, in models of Alzheimer's and Parkinson's diseases and in human gliomas. Overall, these studies improve our conception of ECS structure and the roles of glia and extracellular matrix in modulating the ECS microenvironment. Knowledge of ECS diffusion properties are valuable in contexts ranging from understanding extrasynaptic volume transmission to the development of paradigms for drug delivery to the brain. PMID:18923183

[Are subcortical signs in the EEG a reliable indication of brain stem displacement and impaction processes by intracranial space-occupying processes? A comparative computer tomography-electroencephalography study].

PubMed

Zettler, H; Järisch, M; Leonhard, T

1985-01-01

Within the scope of an elektroencephalographic-computertomographic comperative study carried out in 430 patients, the concurrence of secondary brain stem damage due to mass displacement and herniation processes and parroxysmal generalised slow activity in the EEG ("intermittant frontal delta rhythms", "projected discharges", "subcortical signs") in intracranial space-occupying processes were studied among others. The occurrence of the EEG pattern was independent of the presence of brain stem displacements in about 20 and 25 per cent, respectively, of the 152 patients with supratentorial space occupations. The absence of the characteristics on 80 per cent of the patients with clear CT criteria for a secondary brain stem impairment shows that it is not suitable as a warning sign of an imminent intracranial decompensation and that in particular from the non-occurrence in the EEG no contribution to the operative risk and to the choice of the time of the operation can be derived. A relation between the occurrence of paroxysmal slow activity and the acuity of the course of the disease or the degree of malignity of cerebral tumours could not be verified. Possible causes of the inconstant occurrence of this EEG pattern in brain stem alterations are discussed.

The ALTEA/ALTEINO projects: studying functional effects of microgravity and cosmic radiation

NASA Technical Reports Server (NTRS)

Narici, L.; Belli, F.; Bidoli, V.; Casolino, M.; De Pascale, M. P.; Di Fino, L.; Furano, G.; Modena, I.; Morselli, A.; Picozza, P.;

The ALTEA/ALTEINO projects: studying functional effects of microgravity and cosmic radiation.

PubMed

Narici, L; Belli, F; Bidoli, V; Casolino, M; De Pascale, M P; Di Fino, L; Furano, G; Modena, I; Morselli, A; Picozza, P; Reali, E; Rinaldi, A; Ruggieri, D; Sparvoli, R; Zaconte, V; Sannita, W G; Carozzo, S; Licoccia, S; Romagnoli, P; Traversa, E; Cotronei, V; Vazquez, M; Miller, J; Salnitskii, V P; Shevchenko, O I; Petrov, V P; Trukhanov, K A; Galper, A; Khodarovich, A; Korotkov, M G; Popov, A; Vavilov, N; Avdeev, S; Boezio, M; Bonvicini, W; Vacchi, A; Zampa, N; Mazzenga, G; Ricci, M; Spillantini, P; Castellini, G; Vittori, R; Carlson, P; Fuglesang, C; Schardt, D

2004-01-01

The ALTEA project investigates the risks of functional brain damage induced by particle radiation in space. A modular facility (the ALTEA facility) is being implemented and will be operated in the International Space Station (ISS) to record electrophysiological and behavioral descriptors of brain function and to monitor their time dynamics and correlation with particles and space environment. The focus of the program will be on abnormal visual perceptions (often reported as "light flashes" by astronauts) and the impact on retinal and brain visual structures of particle in microgravity conditions. The facility will be made available to the international scientific community for human neurophysiological, electrophysiological and psychophysics experiments, studies on particle fluxes, and dosimetry. A precursor of ALTEA (the 'Alteino' project) helps set the experimental baseline for the ALTEA experiments, while providing novel information on the radiation environment onboard the ISS and on the brain electrophysiology of the astronauts during orbital flights. Alteino was flown to the ISS on the Soyuz TM34 as part of mission Marco Polo. Controlled ground experiments using mice and accelerator beams complete the experimental strategy of ALTEA. We present here the status of progress of the ALTEA project and preliminary results of the Alteino study on brain dynamics, particle fluxes and abnormal visual perceptions. c2004 COSPAR. Published by Elsevier Ltd. All rights reserved.

Characterization of task-free and task-performance brain states via functional connectome patterns.

PubMed

Zhang, Xin; Guo, Lei; Li, Xiang; Zhang, Tuo; Zhu, Dajiang; Li, Kaiming; Chen, Hanbo; Lv, Jinglei; Jin, Changfeng; Zhao, Qun; Li, Lingjiang; Liu, Tianming

2013-12-01

Both resting state fMRI (R-fMRI) and task-based fMRI (T-fMRI) have been widely used to study the functional activities of the human brain during task-free and task-performance periods, respectively. However, due to the difficulty in strictly controlling the participating subject's mental status and their cognitive behaviors during R-fMRI/T-fMRI scans, it has been challenging to ascertain whether or not an R-fMRI/T-fMRI scan truly reflects the participant's functional brain states during task-free/task-performance periods. This paper presents a novel computational approach to characterizing and differentiating the brain's functional status into task-free or task-performance states, by which the functional brain activities can be effectively understood and differentiated. Briefly, the brain's functional state is represented by a whole-brain quasi-stable connectome pattern (WQCP) of R-fMRI or T-fMRI data based on 358 consistent cortical landmarks across individuals, and then an effective sparse representation method was applied to learn the atomic connectome patterns (ACPs) of both task-free and task-performance states. Experimental results demonstrated that the learned ACPs for R-fMRI and T-fMRI datasets are substantially different, as expected. A certain portion of ACPs from R-fMRI and T-fMRI data were overlapped, suggesting some subjects with overlapping ACPs were not in the expected task-free/task-performance brain states. Besides, potential outliers in the T-fMRI dataset were further investigated via functional activation detections in different groups, and our results revealed unexpected task-performances of some subjects. This work offers novel insights into the functional architectures of the brain. Copyright © 2013 Elsevier B.V. All rights reserved.

Characterization of Task-free and Task-performance Brain States via Functional Connectome Patterns

PubMed Central

Zhang, Xin; Guo, Lei; Li, Xiang; Zhang, Tuo; Zhu, Dajiang; Li, Kaiming; Chen, Hanbo; Lv, Jinglei; Jin, Changfeng; Zhao, Qun; Li, Lingjiang; Liu, Tianming

2014-01-01

Both resting state fMRI (R-fMRI) and task-based fMRI (T-fMRI) have been widely used to study the functional activities of the human brain during task-free and task-performance periods, respectively. However, due to the difficulty in strictly controlling the participating subject's mental status and their cognitive behaviors during R-fMRI/T-fMRI scans, it has been challenging to ascertain whether or not an R-fMRI/T-fMRI scan truly reflects the participant's functional brain states during task-free/task-performance periods. This paper presents a novel computational approach to characterizing and differentiating the brain's functional status into task-free or task-performance states, by which the functional brain activities can be effectively understood and differentiated. Briefly, the brain's functional state is represented by a whole-brain quasi-stable connectome pattern (WQCP) of R-fMRI or T-fMRI data based on 358 consistent cortical landmarks across individuals, and then an effective sparse representation method was applied to learn the atomic connectome patterns (ACP) of both task-free and task-performance states. Experimental results demonstrated that the learned ACPs for R-fMRI and T-fMRI datasets are substantially different, as expected. A certain portion of ACPs from R-fMRI and T-fMRI data were overlapped, suggesting some subjects with overlapping ACPs were not in the expected task-free/task-performance brain states. Besides, potential outliers in the T-fMRI dataset were further investigated via functional activation detections in different groups, and our results revealed unexpected task-performances of some subjects. This work offers novel insights into the functional architectures of the brain. PMID:23938590

Characterizing and Differentiating Brain State Dynamics via Hidden Markov Models

PubMed Central

Ou, Jinli; Xie, Li; Jin, Changfeng; Li, Xiang; Zhu, Dajiang; Jiang, Rongxin; Chen, Yaowu

2014-01-01

Functional connectivity measured from resting state fMRI (R-fMRI) data has been widely used to examine the brain’s functional activities and has been recently used to characterize and differentiate brain conditions. However, the dynamical transition patterns of the brain’s functional states have been less explored. In this work, we propose a novel computational framework to quantitatively characterize the brain state dynamics via hidden Markov models (HMMs) learned from the observations of temporally dynamic functional connectomics, denoted as functional connectome states. The framework has been applied to the R-fMRI dataset including 44 post-traumatic stress disorder (PTSD) patients and 51 normal control (NC) subjects. Experimental results show that both PTSD and NC brains were undergoing remarkable changes in resting state and mainly transiting amongst a few brain states. Interestingly, further prediction with the best-matched HMM demonstrates that PTSD would enter into, but could not disengage from, a negative mood state. Importantly, 84 % of PTSD patients and 86 % of NC subjects are successfully classified via multiple HMMs using majority voting. PMID:25331991

Towards a fourth spatial dimension of brain activity.

PubMed

Tozzi, Arturo; Peters, James F

2016-06-01

Current advances in neurosciences deal with the functional architecture of the central nervous system, paving the way for general theories that improve our understanding of brain activity. From topology, a strong concept comes into play in understanding brain functions, namely, the 4D space of a "hypersphere's torus", undetectable by observers living in a 3D world. The torus may be compared with a video game with biplanes in aerial combat: when a biplane flies off one edge of gaming display, it does not crash but rather it comes back from the opposite edge of the screen. Our thoughts exhibit similar behaviour, i.e. the unique ability to connect past, present and future events in a single, coherent picture as if we were allowed to watch the three screens of past-present-future "glued" together in a mental kaleidoscope. Here we hypothesize that brain functions are embedded in a imperceptible fourth spatial dimension and propose a method to empirically assess its presence. Neuroimaging fMRI series can be evaluated, looking for the topological hallmark of the presence of a fourth dimension. Indeed, there is a typical feature which reveal the existence of a functional hypersphere: the simultaneous activation of areas opposite each other on the 3D cortical surface. Our suggestion-substantiated by recent findings-that brain activity takes place on a closed, donut-like trajectory helps to solve long-standing mysteries concerning our psychological activities, such as mind-wandering, memory retrieval, consciousness and dreaming state.

Invariant measures in brain dynamics

NASA Astrophysics Data System (ADS)

Boyarsky, Abraham; Góra, Paweł

2006-10-01

This note concerns brain activity at the level of neural ensembles and uses ideas from ergodic dynamical systems to model and characterize chaotic patterns among these ensembles during conscious mental activity. Central to our model is the definition of a space of neural ensembles and the assumption of discrete time ensemble dynamics. We argue that continuous invariant measures draw the attention of deeper brain processes, engendering emergent properties such as consciousness. Invariant measures supported on a finite set of ensembles reflect periodic behavior, whereas the existence of continuous invariant measures reflect the dynamics of nonrepeating ensemble patterns that elicit the interest of deeper mental processes. We shall consider two different ways to achieve continuous invariant measures on the space of neural ensembles: (1) via quantum jitters, and (2) via sensory input accompanied by inner thought processes which engender a “folding” property on the space of ensembles.

State-Dependent Changes of Connectivity Patterns and Functional Brain Network Topology in Autism Spectrum Disorder

ERIC Educational Resources Information Center

Barttfeld, Pablo; Wicker, Bruno; Cukier, Sebastian; Navarta, Silvana; Lew, Sergio; Leiguarda, Ramon; Sigman, Mariano

2012-01-01

Anatomical and functional brain studies have converged to the hypothesis that autism spectrum disorders (ASD) are associated with atypical connectivity. Using a modified resting-state paradigm to drive subjects' attention, we provide evidence of a very marked interaction between ASD brain functional connectivity and cognitive state. We show that…

Differential brain network activity across mood states in bipolar disorder.

PubMed

Brady, Roscoe O; Tandon, Neeraj; Masters, Grace A; Margolis, Allison; Cohen, Bruce M; Keshavan, Matcheri; Öngür, Dost

2017-01-01

This study aimed to identify how the activity of large-scale brain networks differs between mood states in bipolar disorder. The authors measured spontaneous brain activity in subjects with bipolar disorder in mania and euthymia and compared these states to a healthy comparison population. 23 subjects with bipolar disorder type I in a manic episode, 24 euthymic bipolar I subjects, and 23 matched healthy comparison (HC) subjects underwent resting state fMRI scans. Using an existing parcellation of the whole brain, we measured functional connectivity between brain regions and identified significant differences between groups. In unbiased whole-brain analyses, functional connectivity between parietal, occipital, and frontal nodes within the dorsal attention network (DAN) were significantly greater in mania than euthymia or HC subjects. In the default mode network (DMN), connectivity between dorsal frontal nodes and the rest of the DMN differentiated both mood state and diagnosis. The bipolar groups were separate cohorts rather than subjects imaged longitudinally across mood states. Bipolar mood states are associated with highly significant alterations in connectivity in two large-scale brain networks. These same networks also differentiate bipolar mania and euthymia from a HC population. State related changes in DAN and DMN connectivity suggest a circuit based pathology underlying cognitive dysfunction as well as activity/reactivity in bipolar mania. Altered activities in neural networks may be biomarkers of bipolar disorder diagnosis and mood state that are accessible to neuromodulation and are promising novel targets for scientific investigation and possible clinical intervention. Copyright © 2016 Elsevier B.V. All rights reserved.

The Central Control of Energy Expenditure: Exploiting Torpor for Medical Applications.

PubMed

Cerri, Matteo

2017-02-10

Autonomic thermoregulation is a recently acquired function, as it appears for the first time in mammals and provides the brain with the ability to control energy expenditure. The importance of such control can easily be highlighted by the ability of a heterogeneous group of mammals to actively reduce metabolic rate and enter a condition of regulated hypometabolism known as torpor. The central neural circuits of thermoregulatory cold defense have been recently unraveled and could in theory be exploited to reduce energy expenditure in species that do not normally use torpor, inducing a state called synthetic torpor. This approach may represent the first steps toward the development of a technology to induce a safe and reversible state of hypometabolism in humans, unlocking many applications ranging from new medical procedures to deep space travel.

Toward determining the lifetime occurrence of metastatic brain tumors estimated from 2007 United States cancer incidence data

PubMed Central

Davis, Faith G.; Dolecek, Therese A.; McCarthy, Bridget J.; Villano, John L.

2012-01-01

Few population estimates of brain metastasis in the United States are available, prompting this study. Our objective was to estimate the expected number of metastatic brain tumors that would subsequently develop among incident cancer cases for 1 diagnosis year in the United States. Incidence proportions for primary cancer sites known to develop brain metastasis were applied to United States cancer incidence data for 2007 that were retrieved from accessible data sets through Centers for Disease Control and Prevention (CDC Wonder) and Surveillance, Epidemiology, and End Results (SEER) Program Web sites. Incidence proportions were identified for cancer sites, reflecting 80% of all cancers. It was conservatively estimated that almost 70 000 new brain metastases would occur over the remaining lifetime of individuals who received a diagnosis in 2007 of primary invasive cancer in the United States. That is, 6% of newly diagnosed cases of cancer during 2007 would be expected to develop brain metastasis as a progression of their original cancer diagnosis; the most frequent sites for metastases being lung and bronchus and breast cancers. The estimated numbers of brain metastasis will be expected to be higher among white individuals, female individuals, and older age groups. Changing patterns in the occurrence of primary cancers, trends in populations at risk, effectiveness of treatments on survival, and access to those treatments will influence the extent of brain tumor metastasis at the population level. These findings provide insight on the patterns of brain tumor metastasis and the future burden of this condition in the United States. PMID:22898372

Using Brain–Computer Interfaces and Brain-State Dependent Stimulation as Tools in Cognitive Neuroscience

PubMed Central

Jensen, Ole; Bahramisharif, Ali; Oostenveld, Robert; Klanke, Stefan; Hadjipapas, Avgis; Okazaki, Yuka O.; van Gerven, Marcel A. J.

2011-01-01

Large efforts are currently being made to develop and improve online analysis of brain activity which can be used, e.g., for brain–computer interfacing (BCI). A BCI allows a subject to control a device by willfully changing his/her own brain activity. BCI therefore holds the promise as a tool for aiding the disabled and for augmenting human performance. While technical developments obviously are important, we will here argue that new insight gained from cognitive neuroscience can be used to identify signatures of neural activation which reliably can be modulated by the subject at will. This review will focus mainly on oscillatory activity in the alpha band which is strongly modulated by changes in covert attention. Besides developing BCIs for their traditional purpose, they might also be used as a research tool for cognitive neuroscience. There is currently a strong interest in how brain-state fluctuations impact cognition. These state fluctuations are partly reflected by ongoing oscillatory activity. The functional role of the brain state can be investigated by introducing stimuli in real-time to subjects depending on the actual state of the brain. This principle of brain-state dependent stimulation may also be used as a practical tool for augmenting human behavior. In conclusion, new approaches based on online analysis of ongoing brain activity are currently in rapid development. These approaches are amongst others informed by new insight gained from electroencephalography/magnetoencephalography studies in cognitive neuroscience and hold the promise of providing new ways for investigating the brain at work. PMID:21687463

Toward determining the lifetime occurrence of metastatic brain tumors estimated from 2007 United States cancer incidence data.

PubMed

Davis, Faith G; Dolecek, Therese A; McCarthy, Bridget J; Villano, John L

2012-09-01

Few population estimates of brain metastasis in the United States are available, prompting this study. Our objective was to estimate the expected number of metastatic brain tumors that would subsequently develop among incident cancer cases for 1 diagnosis year in the United States. Incidence proportions for primary cancer sites known to develop brain metastasis were applied to United States cancer incidence data for 2007 that were retrieved from accessible data sets through Centers for Disease Control and Prevention (CDC Wonder) and Surveillance, Epidemiology, and End Results (SEER) Program Web sites. Incidence proportions were identified for cancer sites, reflecting 80% of all cancers. It was conservatively estimated that almost 70 000 new brain metastases would occur over the remaining lifetime of individuals who received a diagnosis in 2007 of primary invasive cancer in the United States. That is, 6% of newly diagnosed cases of cancer during 2007 would be expected to develop brain metastasis as a progression of their original cancer diagnosis; the most frequent sites for metastases being lung and bronchus and breast cancers. The estimated numbers of brain metastasis will be expected to be higher among white individuals, female individuals, and older age groups. Changing patterns in the occurrence of primary cancers, trends in populations at risk, effectiveness of treatments on survival, and access to those treatments will influence the extent of brain tumor metastasis at the population level. These findings provide insight on the patterns of brain tumor metastasis and the future burden of this condition in the United States.

Split-brain patients neglect left personal space during right-handed gestures.

PubMed

Lausberg, Hedda; Kita, Sotaro; Zaidel, Eran; Ptito, Alain

2003-01-01

Since some patients with right hemisphere damage or with spontaneous callosal disconnection neglect the left half of space, it has been suggested that the left cerebral hemisphere predominantly attends to the right half of space. However, clinical investigations of patients having undergone surgical callosal section have not shown neglect when the hemispheres are tested separately. These observations question the validity of theoretical models that propose a left hemispheric specialisation for attending to the right half of space. The present study aims to investigate neglect and the use of space by either hand in gestural demonstrations in three split-brain patients as compared to five patients with partial callosotomy and 11 healthy subjects. Subjects were asked to demonstrate with precise gestures and without speaking the content of animated scenes with two moving objects. The results show that in the absence of primary perceptual or representational neglect, split-brain patients neglect left personal space in right-handed gestural demonstrations. Since this neglect of left personal space cannot be explained by directional or spatial akinesia, it is suggested that it originates at the conceptual level, where the spatial coordinates for right-hand gestures are planned. The present findings are at odds with the position that the separate left hemisphere possesses adequate mechanisms for acting in both halves of space and neglect results from right hemisphere suppression of this potential. Rather, the results provide support for theoretical models that consider the left hemisphere as specialised for processing the right half of space during the execution of descriptive gestures.

A combined MR and CT study for precise quantitative analysis of the avian brain

NASA Astrophysics Data System (ADS)

Jirak, Daniel; Janacek, Jiri; Kear, Benjamin P.

2015-10-01

Brain size is widely used as a measure of behavioural complexity and sensory-locomotive capacity in avians but has largely relied upon laborious dissections, endoneurocranial tissue displacement, and physical measurement to derive comparative volumes. As an alternative, we present a new precise calculation method based upon coupled magnetic resonance (MR) imaging and computed tomography (CT). Our approach utilizes a novel interactive Fakir probe cross-referenced with an automated CT protocol to efficiently generate total volumes and surface areas of the brain tissue and endoneurocranial space, as well as the discrete cephalic compartments. We also complemented our procedures by using sodium polytungstate (SPT) as a contrast agent. This greatly enhanced CT applications but did not degrade MR quality and is therefore practical for virtual brain tissue reconstructions employing multiple imaging modalities. To demonstrate our technique, we visualized sex-based brain size differentiation in a sample set of Ring-necked pheasants (Phasianus colchicus). This revealed no significant variance in relative volume or surface areas of the primary brain regions. Rather, a trend towards isometric enlargement of the total brain and endoneurocranial space was evidenced in males versus females, thus advocating a non-differential sexually dimorphic pattern of brain size increase amongst these facultatively flying birds.

The Ghosts of Brain States Past: Remembering Reactivates the Brain Regions Engaged during Encoding

ERIC Educational Resources Information Center

Danker, Jared F.; Anderson, John R.

2010-01-01

There is growing evidence that the brain regions involved in encoding an episode are partially reactivated when that episode is later remembered. That is, the process of remembering an episode involves literally returning to the brain state that was present during that episode. This article reviews studies of episodic and associative memory that…

An Energy Model of Place Cell Network in Three Dimensional Space.

PubMed

Wang, Yihong; Xu, Xuying; Wang, Rubin

2018-01-01

Place cells are important elements in the spatial representation system of the brain. A considerable amount of experimental data and classical models are achieved in this area. However, an important question has not been addressed, which is how the three dimensional space is represented by the place cells. This question is preliminarily surveyed by energy coding method in this research. Energy coding method argues that neural information can be expressed by neural energy and it is convenient to model and compute for neural systems due to the global and linearly addable properties of neural energy. Nevertheless, the models of functional neural networks based on energy coding method have not been established. In this work, we construct a place cell network model to represent three dimensional space on an energy level. Then we define the place field and place field center and test the locating performance in three dimensional space. The results imply that the model successfully simulates the basic properties of place cells. The individual place cell obtains unique spatial selectivity. The place fields in three dimensional space vary in size and energy consumption. Furthermore, the locating error is limited to a certain level and the simulated place field agrees to the experimental results. In conclusion, this is an effective model to represent three dimensional space by energy method. The research verifies the energy efficiency principle of the brain during the neural coding for three dimensional spatial information. It is the first step to complete the three dimensional spatial representing system of the brain, and helps us further understand how the energy efficiency principle directs the locating, navigating, and path planning function of the brain.

Resting State fMRI in the moving fetus: a robust framework for motion, bias field and spin history correction.

PubMed

Ferrazzi, Giulio; Kuklisova Murgasova, Maria; Arichi, Tomoki; Malamateniou, Christina; Fox, Matthew J; Makropoulos, Antonios; Allsop, Joanna; Rutherford, Mary; Malik, Shaihan; Aljabar, Paul; Hajnal, Joseph V

2014-11-01

There is growing interest in exploring fetal functional brain development, particularly with Resting State fMRI. However, during a typical fMRI acquisition, the womb moves due to maternal respiration and the fetus may perform large-scale and unpredictable movements. Conventional fMRI processing pipelines, which assume that brain movements are infrequent or at least small, are not suitable. Previous published studies have tackled this problem by adopting conventional methods and discarding as much as 40% or more of the acquired data. In this work, we developed and tested a processing framework for fetal Resting State fMRI, capable of correcting gross motion. The method comprises bias field and spin history corrections in the scanner frame of reference, combined with slice to volume registration and scattered data interpolation to place all data into a consistent anatomical space. The aim is to recover an ordered set of samples suitable for further analysis using standard tools such as Group Independent Component Analysis (Group ICA). We have tested the approach using simulations and in vivo data acquired at 1.5 T. After full motion correction, Group ICA performed on a population of 8 fetuses extracted 20 networks, 6 of which were identified as matching those previously observed in preterm babies. Copyright © 2014 Elsevier Inc. All rights reserved.

CSF smear (image)

MedlinePlus

... is a clear fluid that circulates in the space surrounding the spinal cord and brain. CSF protects the brain and spinal cord from injury by acting like a liquid cushion. CSF is usually obtained through a lumbar ...

Lumbar puncture (image)

MedlinePlus

... is a clear fluid that circulates in the space surrounding the spinal cord and brain. CSF protects the brain and spinal cord from injury by acting like a liquid cushion. CSF is usually obtained through a lumbar ...

CSF chemistry (image)

MedlinePlus

... is a clear fluid that circulates in the space surrounding the spinal cord and brain. CSF protects the brain and spinal cord from injury by acting like a liquid cushion. CSF is usually obtained through a lumbar ...

Optimal trajectories of brain state transitions

PubMed Central

Gu, Shi; Betzel, Richard F.; Mattar, Marcelo G.; Cieslak, Matthew; Delio, Philip R.; Grafton, Scott T.; Pasqualetti, Fabio; Bassett, Danielle S.

2017-01-01

The complexity of neural dynamics stems in part from the complexity of the underlying anatomy. Yet how white matter structure constrains how the brain transitions from one cognitive state to another remains unknown. Here we address this question by drawing on recent advances in network control theory to model the underlying mechanisms of brain state transitions as elicited by the collective control of region sets. We find that previously identified attention and executive control systems are poised to affect a broad array of state transitions that cannot easily be classified by traditional engineering-based notions of control. This theoretical versatility comes with a vulnerability to injury. In patients with mild traumatic brain injury, we observe a loss of specificity in putative control processes, suggesting greater susceptibility to neurophysiological noise. These results offer fundamental insights into the mechanisms driving brain state transitions in healthy cognition and their alteration following injury. PMID:28088484

Spatial model of convective solute transport in brain extracellular space does not support a “glymphatic” mechanism

PubMed Central

Jin, Byung-Ju; Smith, Alex J.

2016-01-01

A “glymphatic system,” which involves convective fluid transport from para-arterial to paravenous cerebrospinal fluid through brain extracellular space (ECS), has been proposed to account for solute clearance in brain, and aquaporin-4 water channels in astrocyte endfeet may have a role in this process. Here, we investigate the major predictions of the glymphatic mechanism by modeling diffusive and convective transport in brain ECS and by solving the Navier–Stokes and convection–diffusion equations, using realistic ECS geometry for short-range transport between para-arterial and paravenous spaces. Major model parameters include para-arterial and paravenous pressures, ECS volume fraction, solute diffusion coefficient, and astrocyte foot-process water permeability. The model predicts solute accumulation and clearance from the ECS after a step change in solute concentration in para-arterial fluid. The principal and robust conclusions of the model are as follows: (a) significant convective transport requires a sustained pressure difference of several mmHg between the para-arterial and paravenous fluid and is not affected by pulsatile pressure fluctuations; (b) astrocyte endfoot water permeability does not substantially alter the rate of convective transport in ECS as the resistance to flow across endfeet is far greater than in the gaps surrounding them; and (c) diffusion (without convection) in the ECS is adequate to account for experimental transport studies in brain parenchyma. Therefore, our modeling results do not support a physiologically important role for local parenchymal convective flow in solute transport through brain ECS. PMID:27836940

Spatial model of convective solute transport in brain extracellular space does not support a "glymphatic" mechanism.

PubMed

Jin, Byung-Ju; Smith, Alex J; Verkman, Alan S

2016-12-01

A "glymphatic system," which involves convective fluid transport from para-arterial to paravenous cerebrospinal fluid through brain extracellular space (ECS), has been proposed to account for solute clearance in brain, and aquaporin-4 water channels in astrocyte endfeet may have a role in this process. Here, we investigate the major predictions of the glymphatic mechanism by modeling diffusive and convective transport in brain ECS and by solving the Navier-Stokes and convection-diffusion equations, using realistic ECS geometry for short-range transport between para-arterial and paravenous spaces. Major model parameters include para-arterial and paravenous pressures, ECS volume fraction, solute diffusion coefficient, and astrocyte foot-process water permeability. The model predicts solute accumulation and clearance from the ECS after a step change in solute concentration in para-arterial fluid. The principal and robust conclusions of the model are as follows: (a) significant convective transport requires a sustained pressure difference of several mmHg between the para-arterial and paravenous fluid and is not affected by pulsatile pressure fluctuations; (b) astrocyte endfoot water permeability does not substantially alter the rate of convective transport in ECS as the resistance to flow across endfeet is far greater than in the gaps surrounding them; and (c) diffusion (without convection) in the ECS is adequate to account for experimental transport studies in brain parenchyma. Therefore, our modeling results do not support a physiologically important role for local parenchymal convective flow in solute transport through brain ECS. © 2016 Jin et al.

An Integrated Neuroscience and Engineering Approach to Classifying Human Brain-States

DTIC Science & Technology

2015-12-22

AFRL-AFOSR-VA-TR-2016-0037 An Integrated Neuroscience and Engineering Approach to Classifying Human Brain-States Adrian Lee UNIVERSITY OF WASHINGTON...to 14-09-2015 4. TITLE AND SUBTITLE An Integrated Neuroscience and Engineering Approach to Classifying Human Brain- States 5a.  CONTRACT NUMBER 5b...specific cognitive states remains elusive, owing perhaps to limited crosstalk between the fields of neuroscience and engineering. Here, we report a

Diminished neural network dynamics after moderate and severe traumatic brain injury.

PubMed

Gilbert, Nicholas; Bernier, Rachel A; Calhoun, Vincent D; Brenner, Einat; Grossner, Emily; Rajtmajer, Sarah M; Hillary, Frank G

2018-01-01

Over the past decade there has been increasing enthusiasm in the cognitive neurosciences around using network science to understand the system-level changes associated with brain disorders. A growing literature has used whole-brain fMRI analysis to examine changes in the brain's subnetworks following traumatic brain injury (TBI). Much of network modeling in this literature has focused on static network mapping, which provides a window into gross inter-nodal relationships, but is insensitive to more subtle fluctuations in network dynamics, which may be an important predictor of neural network plasticity. In this study, we examine the dynamic connectivity with focus on state-level connectivity (state) and evaluate the reliability of dynamic network states over the course of two runs of intermittent task and resting data. The goal was to examine the dynamic properties of neural networks engaged periodically with task stimulation in order to determine: 1) the reliability of inter-nodal and network-level characteristics over time and 2) the transitions between distinct network states after traumatic brain injury. To do so, we enrolled 23 individuals with moderate and severe TBI at least 1-year post injury and 19 age- and education-matched healthy adults using functional MRI methods, dynamic connectivity modeling, and graph theory. The results reveal several distinct network "states" that were reliably evident when comparing runs; the overall frequency of dynamic network states are highly reproducible (r-values>0.8) for both samples. Analysis of movement between states resulted in fewer state transitions in the TBI sample and, in a few cases, brain injury resulted in the appearance of states not exhibited by the healthy control (HC) sample. Overall, the findings presented here demonstrate the reliability of observable dynamic mental states during periods of on-task performance and support emerging evidence that brain injury may result in diminished network dynamics.

Noninvasive, localized, and transient brain drug delivery using focused ultrasound and microbubbles

NASA Astrophysics Data System (ADS)

Choi, James J.

In the United States, Alzheimer's disease (AD), Parkinson's disease (PD), and brain cancer caused 72,432, 19,566 and 12,886 deaths in 2006, respectively. Whereas the number of deaths due to major disorders such as heart disease, stroke, and prostate cancer have decreased since 2006, deaths attributed to AD, PD, and brain cancer have not. Treatment options for patients with CNS disorders remain limited despite significant advances in knowledge of CNS disease pathways and development of neurologically potent agents. One of the major obstacles is that the cerebral microvasculature is lined by a specialized and highly regulated blood-brain barrier (BBB) that prevents large agents from entering the brain extracellular space. The purpose of this dissertation is to design a noninvasive, localized, and transient BBB opening system using focused ultrasound (FUS) and determine ultrasound and microbubble conditions that can effectively and safely deliver large pharmacologically-relevant-sized agents to the brain. To meet this end, an in vivo mouse brain drug delivery system using a stereotactic-based targeting method was developed. FUS was applied noninvasively through the intact skin and skull, which allowed for long-term and high-throughput studies. With this system, more than 150 mice were exposed to one of 31 distinct acoustic and microbubble conditions. The feasibility of delivering a large MRI contrast agent was first demonstrated in vivo in both wild-type and transgenic Alzheimer's disease model (APP/PS1) mice. A wide range of acoustic and microbubble conditions were then evaluated for their ability to deliver agents to a target region. Interestingly, the possible design space of parameters was found to be vast and different conditions resulted in distinct spatial distributions and doses delivered. In particular, BBB opening was shown to be dependent on the microbubble diameter, acoustic pressure, pulse repetition frequency (PRF), and pulse length (PL). Each set of conditions determined both the size of agents that can traverse the BBB, and also the level of safety of the technique. In one set of conditions (peak-rarefactional pressure: 0.61 MPa, PRF: 10 Hz, PL: 20 ms), large 70-kDa dextran was delivered to a target region, but were associated with detectable damaged sites as indicated by dark neurons, microvacuolations, and erythrocyte extravasations. Another set of conditions (peak-rarefactional pressure: 0.46 MPa, PRF: 5 Hz, PL: 0.2 ms) delivered 3-kDa dextran homogeneously and diffusely to a target region in the brain without any detectable dark neurons, microvacuolations, or erythrocyte extravasations. Each distinct set of conditions may thus be used for different clinical application, i.e., treatment of brain cancer and AD. In conclusion, an effective method to noninvasively, locally, and transiently deliver large therapeutic agents through the BBB was developed.

Influence of the optimization methods on neural state estimation quality of the drive system with elasticity.

PubMed

Orlowska-Kowalska, Teresa; Kaminski, Marcin

2014-01-01

The paper deals with the implementation of optimized neural networks (NNs) for state variable estimation of the drive system with an elastic joint. The signals estimated by NNs are used in the control structure with a state-space controller and additional feedbacks from the shaft torque and the load speed. High estimation quality is very important for the correct operation of a closed-loop system. The precision of state variables estimation depends on the generalization properties of NNs. A short review of optimization methods of the NN is presented. Two techniques typical for regularization and pruning methods are described and tested in detail: the Bayesian regularization and the Optimal Brain Damage methods. Simulation results show good precision of both optimized neural estimators for a wide range of changes of the load speed and the load torque, not only for nominal but also changed parameters of the drive system. The simulation results are verified in a laboratory setup.

Brain templates and atlases.

PubMed

Evans, Alan C; Janke, Andrew L; Collins, D Louis; Baillet, Sylvain

2012-08-15

The core concept within the field of brain mapping is the use of a standardized, or "stereotaxic", 3D coordinate frame for data analysis and reporting of findings from neuroimaging experiments. This simple construct allows brain researchers to combine data from many subjects such that group-averaged signals, be they structural or functional, can be detected above the background noise that would swamp subtle signals from any single subject. Where the signal is robust enough to be detected in individuals, it allows for the exploration of inter-individual variance in the location of that signal. From a larger perspective, it provides a powerful medium for comparison and/or combination of brain mapping findings from different imaging modalities and laboratories around the world. Finally, it provides a framework for the creation of large-scale neuroimaging databases or "atlases" that capture the population mean and variance in anatomical or physiological metrics as a function of age or disease. However, while the above benefits are not in question at first order, there are a number of conceptual and practical challenges that introduce second-order incompatibilities among experimental data. Stereotaxic mapping requires two basic components: (i) the specification of the 3D stereotaxic coordinate space, and (ii) a mapping function that transforms a 3D brain image from "native" space, i.e. the coordinate frame of the scanner at data acquisition, to that stereotaxic space. The first component is usually expressed by the choice of a representative 3D MR image that serves as target "template" or atlas. The native image is re-sampled from native to stereotaxic space under the mapping function that may have few or many degrees of freedom, depending upon the experimental design. The optimal choice of atlas template and mapping function depend upon considerations of age, gender, hemispheric asymmetry, anatomical correspondence, spatial normalization methodology and disease-specificity. Accounting, or not, for these various factors in defining stereotaxic space has created the specter of an ever-expanding set of atlases, customized for a particular experiment, that are mutually incompatible. These difficulties continue to plague the brain mapping field. This review article summarizes the evolution of stereotaxic space in term of the basic principles and associated conceptual challenges, the creation of population atlases and the future trends that can be expected in atlas evolution. Copyright © 2012 Elsevier Inc. All rights reserved.

Shape Classification Using Wasserstein Distance for Brain Morphometry Analysis.

PubMed

Su, Zhengyu; Zeng, Wei; Wang, Yalin; Lu, Zhong-Lin; Gu, Xianfeng

2015-01-01

Brain morphometry study plays a fundamental role in medical imaging analysis and diagnosis. This work proposes a novel framework for brain cortical surface classification using Wasserstein distance, based on uniformization theory and Riemannian optimal mass transport theory. By Poincare uniformization theorem, all shapes can be conformally deformed to one of the three canonical spaces: the unit sphere, the Euclidean plane or the hyperbolic plane. The uniformization map will distort the surface area elements. The area-distortion factor gives a probability measure on the canonical uniformization space. All the probability measures on a Riemannian manifold form the Wasserstein space. Given any 2 probability measures, there is a unique optimal mass transport map between them, the transportation cost defines the Wasserstein distance between them. Wasserstein distance gives a Riemannian metric for the Wasserstein space. It intrinsically measures the dissimilarities between shapes and thus has the potential for shape classification. To the best of our knowledge, this is the first. work to introduce the optimal mass transport map to general Riemannian manifolds. The method is based on geodesic power Voronoi diagram. Comparing to the conventional methods, our approach solely depends on Riemannian metrics and is invariant under rigid motions and scalings, thus it intrinsically measures shape distance. Experimental results on classifying brain cortical surfaces with different intelligence quotients demonstrated the efficiency and efficacy of our method.

Shape Classification Using Wasserstein Distance for Brain Morphometry Analysis

PubMed Central

Su, Zhengyu; Zeng, Wei; Wang, Yalin; Lu, Zhong-Lin; Gu, Xianfeng

2015-01-01

Brain morphometry study plays a fundamental role in medical imaging analysis and diagnosis. This work proposes a novel framework for brain cortical surface classification using Wasserstein distance, based on uniformization theory and Riemannian optimal mass transport theory. By Poincare uniformization theorem, all shapes can be conformally deformed to one of the three canonical spaces: the unit sphere, the Euclidean plane or the hyperbolic plane. The uniformization map will distort the surface area elements. The area-distortion factor gives a probability measure on the canonical uniformization space. All the probability measures on a Riemannian manifold form the Wasserstein space. Given any 2 probability measures, there is a unique optimal mass transport map between them, the transportation cost defines the Wasserstein distance between them. Wasserstein distance gives a Riemannian metric for the Wasserstein space. It intrinsically measures the dissimilarities between shapes and thus has the potential for shape classification. To the best of our knowledge, this is the first work to introduce the optimal mass transport map to general Riemannian manifolds. The method is based on geodesic power Voronoi diagram. Comparing to the conventional methods, our approach solely depends on Riemannian metrics and is invariant under rigid motions and scalings, thus it intrinsically measures shape distance. Experimental results on classifying brain cortical surfaces with different intelligence quotients demonstrated the efficiency and efficacy of our method. PMID:26221691

NASA sponsored Light Emitting Diode (LED) development helps in cancer treatment

NASA Technical Reports Server (NTRS)

1997-01-01

What started out as an attempt to develop a light which would allow for the growth of plants in space led to a remarkable discovery: The Light Emitting Diode (LED). This device through extensive study and experimentation has developed into a tool used by surgeons in the fight against brain cancer in children. Pictured is a mock-up of brain surgery being performed. By encapsulating the end of the LED with a balloon, light is diffused over a larger area of the brain allowing the surgeon a better view. This is one of many programs that begin as research for the space program, and through extensive study end up benefitting all of mankind.

High-resolution in vivo Wistar rodent brain atlas based on T1 weighted image

NASA Astrophysics Data System (ADS)

Huang, Su; Lu, Zhongkang; Huang, Weimin; Seramani, Sankar; Ramasamy, Boominathan; Sekar, Sakthivel; Guan, Cuntai; Bhakoo, Kishore

2016-03-01

Image based atlases for rats brain have a significant impact on pre-clinical research. In this project we acquired T1-weighted images from Wistar rodent brains with fine 59μm isotropical resolution for generation of the atlas template image. By applying post-process procedures using a semi-automatic brain extraction method, we delineated the brain tissues from source data. Furthermore, we applied a symmetric group-wise normalization method to generate an optimized template of T1 image of rodent brain, then aligned our template to the Waxholm Space. In addition, we defined several simple and explicit landmarks to corresponding our template with the well known Paxinos stereotaxic reference system. Anchoring at the origin of the Waxholm Space, we applied piece-wise linear transformation method to map the voxels of the template into the coordinates system in Paxinos' stereotoxic coordinates to facilitate the labelling task. We also cross-referenced our data with both published rodent brain atlas and image atlases available online, methodologically labelling the template to produce a Wistar brain atlas identifying more than 130 structures. Particular attention was paid to the cortex and cerebellum, as these areas encompass the most researched aspects of brain functions. Moreover, we adopted the structure hierarchy and naming nomenclature common to various atlases, so that the names and hierarchy structure presented in the atlas are readily recognised for easy use. It is believed the atlas will present a useful tool in rodent brain functional and pharmaceutical studies.

Quantitative estimation of a ratio of intracranial cerebrospinal fluid volume to brain volume based on segmentation of CT images in patients with extra-axial hematoma.

PubMed

Nguyen, Ha Son; Patel, Mohit; Li, Luyuan; Kurpad, Shekar; Mueller, Wade

2017-02-01

Background Diminishing volume of intracranial cerebrospinal fluid (CSF) in patients with space-occupying masses have been attributed to unfavorable outcome associated with reduction of cerebral perfusion pressure and subsequent brain ischemia. Objective The objective of this article is to employ a ratio of CSF volume to brain volume for longitudinal assessment of space-volume relationships in patients with extra-axial hematoma and to determine variability of the ratio among patients with different types and stages of hematoma. Patients and methods In our retrospective study, we reviewed 113 patients with surgical extra-axial hematomas. We included 28 patients (age 61.7 +/- 17.7 years; 19 males, nine females) with an acute epidural hematoma (EDH) ( n = 5) and subacute/chronic subdural hematoma (SDH) ( n = 23). We excluded 85 patients, in order, due to acute SDH ( n = 76), concurrent intraparenchymal pathology ( n = 6), and bilateral pathology ( n = 3). Noncontrast CT images of the head were obtained using a CT scanner (2004 GE LightSpeed VCT CT system, tube voltage 140 kVp, tube current 310 mA, 5 mm section thickness) preoperatively, postoperatively (3.8 ± 5.8 hours from surgery), and at follow-up clinic visit (48.2 ± 27.7 days after surgery). Each CT scan was loaded into an OsiriX (Pixmeo, Switzerland) workstation to segment pixels based on radiodensity properties measured in Hounsfield units (HU). Based on HU values from -30 to 100, brain, CSF spaces, vascular structures, hematoma, and/or postsurgical fluid were segregated from bony structures, and subsequently hematoma and/or postsurgical fluid were manually selected and removed from the images. The remaining images represented overall brain volume-containing only CSF spaces, vascular structures, and brain parenchyma. Thereafter, the ratio between the total number of voxels representing CSF volume (based on values between 0 and 15 HU) to the total number of voxels representing overall brain volume was calculated. Results CSF/brain volume ratio varied significantly during the course of the disease, being the lowest preoperatively, 0.051 ± 0.032; higher after surgical evacuation of hematoma, 0.067 ± 0.040; and highest at follow-up visit, 0.083 ± 0.040 ( p < 0.01). Using a repeated regression analysis, we found a significant association ( p < 0.01) of the ratio with age (odds ratio, 1.019; 95% CI, 1.009-1.029) and type of hematoma (odds ratio, 0.405; 95% CI, 0.303-0.540). Conclusion CSF/brain volume ratio calculated from CT images has potential to reflect dynamics of intracranial volume changes in patients with space-occupying mass.

A resting state functional magnetic resonance imaging study of concussion in collegiate athletes.

PubMed

Czerniak, Suzanne M; Sikoglu, Elif M; Liso Navarro, Ana A; McCafferty, Joseph; Eisenstock, Jordan; Stevenson, J Herbert; King, Jean A; Moore, Constance M

2015-06-01

Sports-related concussions are currently diagnosed through multi-domain assessment by a medical professional and may utilize neurocognitive testing as an aid. However, these tests have only been able to detect differences in the days to week post-concussion. Here, we investigate a measure of brain function, namely resting state functional connectivity, which may detect residual brain differences in the weeks to months after concussion. Twenty-one student athletes (9 concussed within 6 months of enrollment; 12 non-concussed; between ages 18 and 22 years) were recruited for this study. All participants completed the Wisconsin Card Sorting Task and the Color-Word Interference Test. Neuroimaging data, specifically resting state functional Magnetic Resonance Imaging data, were acquired to examine resting state functional connectivity. Two sample t-tests were used to compare the neurocognitive scores and resting state functional connectivity patterns among concussed and non-concussed participants. Correlations between neurocognitive scores and resting state functional connectivity measures were also determined across all subjects. There were no significant differences in neurocognitive performance between concussed and non-concussed groups. Concussed subjects had significantly increased connections between areas of the brain that underlie executive function. Across all subjects, better neurocognitive performance corresponded to stronger brain connectivity. Even at rest, brains of concussed athletes may have to 'work harder' than their healthy peers to achieve similar neurocognitive results. Resting state brain connectivity may be able to detect prolonged brain differences in concussed athletes in a more quantitative manner than neurocognitive test scores.

Resting State Network Topology of the Ferret Brain

PubMed Central

Zhou, Zhe Charles; Salzwedel, Andrew P.; Radtke-Schuller, Susanne; Li, Yuhui; Sellers, Kristin K.; Gilmore, John H.; Shih, Yen-Yu Ian; Fröhlich, Flavio; Gao, Wei

2016-01-01

Resting state functional magnetic resonance imaging (rsfMRI) has emerged as a versatile tool for non-invasive measurement of functional connectivity patterns in the brain. RsfMRI brain dynamics in rodents, non-human primates, and humans share similar properties; however, little is known about the resting state functional connectivity patterns in the ferret, an animal model with high potential for developmental and cognitive translational study. To address this knowledge-gap, we performed rsfMRI on anesthetized ferrets using a 9.4 tesla MRI scanner, and subsequently performed group-level independent component analysis (gICA) to identify functionally connected brain networks. Group-level ICA analysis revealed distributed sensory, motor, and higher-order networks in the ferret brain. Subsequent connectivity analysis showed interconnected higher-order networks that constituted a putative default mode network (DMN), a network that exhibits altered connectivity in neuropsychiatric disorders. Finally, we assessed ferret brain topological efficiency using graph theory analysis and found that the ferret brain exhibits small-world properties. Overall, these results provide additional evidence for pan-species resting-state networks, further supporting ferret-based studies of sensory and cognitive function. PMID:27596024

Non-traumatic subdural hematoma secondary to septic brain embolism: A rare cause of unexpected death in a drug addict suffering from undiagnosed bacterial endocarditis.

PubMed

Geisenberger, D; Huppertz, L M; Büchsel, M; Kramer, L; Pollak, S; Große Perdekamp, M

2015-12-01

Acute subdural hematomas are mostly due to blunt traumatization of the head. In rare instances, subdural bleeding occurs without evidence of a previous trauma following spontaneous hemorrhage, e.g. from a ruptured aneurysm or an intracerebral hematoma perforating the brain surface and the arachnoid. The paper presents the morphological, microbiological and toxicological findings in a 38-year-old drug addict who was found by his partner in a dazed state. When brought to a hospital, he underwent trepanation to empty a right-sided subdural hematoma, but he died already 4h after admission. Autopsy revealed previously undiagnosed infective endocarditis of the aortic valve as well as multiple infarctions of brain, spleen and kidneys obviously caused by septic emboli. The subdural hematoma originated from a subcortical brain hemorrhage which had perforated into the subdural space. Microbiological investigation of the polypous vegetations adhering to the aortic valve revealed colonization by Streptococcus mitis and Klebsiella oxytoca. According to the toxicological analysis, no psychotropic substances had contributed to the lethal outcome. The case reported underlines that all deaths of drug addicts should be subjected to complete forensic autopsy, as apart from intoxications also natural and traumatic causes of death have to be taken into consideration. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

A meta-analysis of neurofunctional imaging studies of emotion and cognition in major depression.

PubMed

Diener, Carsten; Kuehner, Christine; Brusniak, Wencke; Ubl, Bettina; Wessa, Michèle; Flor, Herta

2012-07-02

Major depressive disorder (MDD) is characterized by altered emotional and cognitive functioning. We performed a voxel-based whole-brain meta-analysis of functional neuroimaging data on altered emotion and cognition in MDD. Forty peer-reviewed studies in English-language published between 1998 and 2010 were included, which used functional neuroimaging during cognitive-emotional challenge in adult individuals with MDD and healthy controls. All studies reported between-groups differences for whole-brain analyses in standardized neuroanatomical space and were subjected to Activation Likelihood Estimation (ALE) of brain cluster showing altered responsivity in MDD. ALE resulted in thresholded and false discovery rate corrected hypo- and hyperactive brain regions. Against the background of a complex neural activation pattern, studies converged in predominantly hypoactive cluster in the anterior insular and rostral anterior cingulate cortex linked to affectively biased information processing and poor cognitive control. Frontal areas showed not only similar under- but also over-activation during cognitive-emotional challenge. On the subcortical level, we identified activation alterations in the thalamus and striatum which were involved in biased valence processing of emotional stimuli in MDD. These results for active conditions extend findings from ALE meta-analyses of resting state and antidepressant treatment studies and emphasize the key role of the anterior insular and rostral anterior cingulate cortex for altered emotion and cognition in MDD. Copyright © 2012 Elsevier Inc. All rights reserved.

Association between number of cell phone contracts and brain tumor incidence in nineteen U.S. States.

PubMed

Lehrer, Steven; Green, Sheryl; Stock, Richard G

2011-02-01

Some concern has arisen about adverse health effects of cell phones, especially the possibility that the low power microwave-frequency signal transmitted by the antennas on handsets might cause brain tumors or accelerate the growth of subclinical tumors. We analyzed data from the Statistical Report: Primary Brain Tumors in the United States, 2000-2004 and 2007 cell phone subscription data from the Governing State and Local Sourcebook. There was a significant correlation between number of cell phone subscriptions and brain tumors in nineteen US states (r = 0.950, P < 0.001). Because increased numbers of both cell phone subscriptions and brain tumors could be due solely to the fact that some states, such as New York, have much larger populations than other states, such as North Dakota, multiple linear regression was performed with number of brain tumors as the dependent variable, cell phone subscriptions, population, mean family income and mean age as independent variables. The effect of cell phone subscriptions was significant (P = 0.017), and independent of the effect of mean family income (P = 0.894), population (P = 0.003) and age (0.499). The very linear relationship between cell phone usage and brain tumor incidence is disturbing and certainly needs further epidemiological evaluation. In the meantime, it would be prudent to limit exposure to all sources of electro-magnetic radiation.

A freely-moving monkey treadmill model.

PubMed

Foster, Justin D; Nuyujukian, Paul; Freifeld, Oren; Gao, Hua; Walker, Ross; I Ryu, Stephen; H Meng, Teresa; Murmann, Boris; J Black, Michael; Shenoy, Krishna V

2014-08-01

Motor neuroscience and brain-machine interface (BMI) design is based on examining how the brain controls voluntary movement, typically by recording neural activity and behavior from animal models. Recording technologies used with these animal models have traditionally limited the range of behaviors that can be studied, and thus the generality of science and engineering research. We aim to design a freely-moving animal model using neural and behavioral recording technologies that do not constrain movement. We have established a freely-moving rhesus monkey model employing technology that transmits neural activity from an intracortical array using a head-mounted device and records behavior through computer vision using markerless motion capture. We demonstrate the flexibility and utility of this new monkey model, including the first recordings from motor cortex while rhesus monkeys walk quadrupedally on a treadmill. Using this monkey model, we show that multi-unit threshold-crossing neural activity encodes the phase of walking and that the average firing rate of the threshold crossings covaries with the speed of individual steps. On a population level, we find that neural state-space trajectories of walking at different speeds have similar rotational dynamics in some dimensions that evolve at the step rate of walking, yet robustly separate by speed in other state-space dimensions. Freely-moving animal models may allow neuroscientists to examine a wider range of behaviors and can provide a flexible experimental paradigm for examining the neural mechanisms that underlie movement generation across behaviors and environments. For BMIs, freely-moving animal models have the potential to aid prosthetic design by examining how neural encoding changes with posture, environment and other real-world context changes. Understanding this new realm of behavior in more naturalistic settings is essential for overall progress of basic motor neuroscience and for the successful translation of BMIs to people with paralysis.

Role of mitochondrial calcium uptake homeostasis in resting state fMRI brain networks.

PubMed

Kannurpatti, Sridhar S; Sanganahalli, Basavaraju G; Herman, Peter; Hyder, Fahmeed

2015-11-01

Mitochondrial Ca(2+) uptake influences both brain energy metabolism and neural signaling. Given that brain mitochondrial organelles are distributed in relation to vascular density, which varies considerably across brain regions, we hypothesized different physiological impacts of mitochondrial Ca(2+) uptake across brain regions. We tested the hypothesis by monitoring brain "intrinsic activity" derived from the resting state functional MRI (fMRI) blood oxygen level dependent (BOLD) fluctuations in different functional networks spanning the somatosensory cortex, caudate putamen, hippocampus and thalamus, in normal and perturbed mitochondrial Ca(2+) uptake states. In anesthetized rats at 11.7 T, mitochondrial Ca(2+) uptake was inhibited or enhanced respectively by treatments with Ru360 or kaempferol. Surprisingly, mitochondrial Ca(2+) uptake inhibition by Ru360 and enhancement by kaempferol led to similar dose-dependent decreases in brain-wide intrinsic activities in both the frequency domain (spectral amplitude) and temporal domain (resting state functional connectivity; RSFC). The fact that there were similar dose-dependent decreases in the frequency and temporal domains of the resting state fMRI-BOLD fluctuations during mitochondrial Ca(2+) uptake inhibition or enhancement indicated that mitochondrial Ca(2+) uptake and its homeostasis may strongly influence the brain's functional organization at rest. Interestingly, the resting state fMRI-derived intrinsic activities in the caudate putamen and thalamic regions saturated much faster with increasing dosage of either drug treatment than the drug-induced trends observed in cortical and hippocampal regions. Regional differences in how the spectral amplitude and RSFC changed with treatment indicate distinct mitochondrion-mediated spontaneous neuronal activity coupling within the various RSFC networks determined by resting state fMRI. Copyright © 2015 John Wiley & Sons, Ltd.

Brain Extracellular Space: The Final Frontier of Neuroscience.

PubMed

Nicholson, Charles; Hrabětová, Sabina

2017-11-21

Brain extracellular space is the narrow microenvironment that surrounds every cell of the central nervous system. It contains a solution that closely resembles cerebrospinal fluid with the addition of extracellular matrix molecules. The space provides a reservoir for ions essential to the electrical activity of neurons and forms an intercellular chemical communication channel. Attempts to reveal the size and structure of the extracellular space using electron microscopy have had limited success; however, a biophysical approach based on diffusion of selected probe molecules has proved useful. A point-source paradigm, realized in the real-time iontophoresis method using tetramethylammonium, as well as earlier radiotracer methods, have shown that the extracellular space occupies ∼20% of brain tissue and small molecules have an effective diffusion coefficient that is two-fifths that in a free solution. Monte Carlo modeling indicates that geometrical constraints, including dead-space microdomains, contribute to the hindrance to diffusion. Imaging the spread of macromolecules shows them increasingly hindered as a function of size and suggests that the gaps between cells are predominantly ∼40 nm with wider local expansions that may represent dead-spaces. Diffusion measurements also characterize interactions of ions and proteins with the chondroitin and heparan sulfate components of the extracellular matrix; however, the many roles of the matrix are only starting to become apparent. The existence and magnitude of bulk flow and the so-called glymphatic system are topics of current interest and controversy. The extracellular space is an exciting area for research that will be propelled by emerging technologies. Copyright © 2017 Biophysical Society. Published by Elsevier Inc. All rights reserved.

NASA Astrophysics Data System (ADS)

In life and work, Claudio Bunster prefers extreme challenges. Bunster, a physicist who contemplates brain-warping theories of space and time, returned to his native Chile from the United States precisely when most intellectuals would have stayed clear - during the middle of the Pinochet dictatorship. Shut out of the universities by the military government, he broke the conventional mold by founding a research institute that he then moved from Santiago, the capital, to Valdivia in southern Chile, against the flow of minds and money. He led the presidential science advisory committee during the administration of Eduardo Frei, and served on the Dialogue Board of Human Rights to reconcile Chilean military and civil society. For his achievements, Bunster was elected to the National Academy of Sciences in 2005. In his Inaugural Article, which appeared in the July 24, 2007 issue of PNAS [1], he showed that after a black hole swallows a magnetic monopole, the space-time singularity starts rotating.

Stability of whole brain and regional network topology within and between resting and cognitive states.

PubMed

Rzucidlo, Justyna K; Roseman, Paige L; Laurienti, Paul J; Dagenbach, Dale

2013-01-01

Graph-theory based analyses of resting state functional Magnetic Resonance Imaging (fMRI) data have been used to map the network organization of the brain. While numerous analyses of resting state brain organization exist, many questions remain unexplored. The present study examines the stability of findings based on this approach over repeated resting state and working memory state sessions within the same individuals. This allows assessment of stability of network topology within the same state for both rest and working memory, and between rest and working memory as well. fMRI scans were performed on five participants while at rest and while performing the 2-back working memory task five times each, with task state alternating while they were in the scanner. Voxel-based whole brain network analyses were performed on the resulting data along with analyses of functional connectivity in regions associated with resting state and working memory. Network topology was fairly stable across repeated sessions of the same task, but varied significantly between rest and working memory. In the whole brain analysis, local efficiency, Eloc, differed significantly between rest and working memory. Analyses of network statistics for the precuneus and dorsolateral prefrontal cortex revealed significant differences in degree as a function of task state for both regions and in local efficiency for the precuneus. Conversely, no significant differences were observed across repeated sessions of the same state. These findings suggest that network topology is fairly stable within individuals across time for the same state, but also fluid between states. Whole brain voxel-based network analyses may prove to be a valuable tool for exploring how functional connectivity changes in response to task demands.

Individual diversity of functional brain network economy.

PubMed

Hahn, Andreas; Kranz, Georg S; Sladky, Ronald; Ganger, Sebastian; Windischberger, Christian; Kasper, Siegfried; Lanzenberger, Rupert

2015-04-01

On average, brain network economy represents a trade-off between communication efficiency, robustness, and connection cost, although an analogous understanding on an individual level is largely missing. Evaluating resting-state networks of 42 healthy participants with seven Tesla functional magnetic resonance imaging and graph theory revealed that not even half of all possible connections were common across subjects. The strongest similarities among individuals were observed for interhemispheric and/or short-range connections, which may relate to the essential feature of the human brain to develop specialized systems within each hemisphere. Despite this marked variability in individual network architecture, all subjects exhibited equal small-world properties. Furthermore, interdependency between four major network economy metrics was observed across healthy individuals. The characteristic path length was associated with the clustering coefficient (peak correlation r=0.93), the response to network attacks (r=-0.97), and the physical connection cost in three-dimensional space (r=-0.62). On the other hand, clustering was negatively related to attack response (r=-0.75) and connection cost (r=-0.59). Finally, increased connection cost was associated with better response to attacks (r=0.65). This indicates that functional brain networks with high global information transfer also exhibit strong network resilience. However, it seems that these advantages come at the cost of decreased local communication efficiency and increased physical connection cost. Except for wiring length, the results were replicated on a subsample at three Tesla (n=20). These findings highlight the finely tuned interrelationships between different parameters of brain network economy. Moreover, the understanding of the individual diversity of functional brain network economy may provide further insights in the vulnerability to mental and neurological disorders.

Interstitial solute transport in 3D reconstructed neuropil occurs by diffusion rather than bulk flow.

PubMed

Holter, Karl Erik; Kehlet, Benjamin; Devor, Anna; Sejnowski, Terrence J; Dale, Anders M; Omholt, Stig W; Ottersen, Ole Petter; Nagelhus, Erlend Arnulf; Mardal, Kent-André; Pettersen, Klas H

2017-09-12

The brain lacks lymph vessels and must rely on other mechanisms for clearance of waste products, including amyloid [Formula: see text] that may form pathological aggregates if not effectively cleared. It has been proposed that flow of interstitial fluid through the brain's interstitial space provides a mechanism for waste clearance. Here we compute the permeability and simulate pressure-mediated bulk flow through 3D electron microscope (EM) reconstructions of interstitial space. The space was divided into sheets (i.e., space between two parallel membranes) and tunnels (where three or more membranes meet). Simulation results indicate that even for larger extracellular volume fractions than what is reported for sleep and for geometries with a high tunnel volume fraction, the permeability was too low to allow for any substantial bulk flow at physiological hydrostatic pressure gradients. For two different geometries with the same extracellular volume fraction the geometry with the most tunnel volume had [Formula: see text] higher permeability, but the bulk flow was still insignificant. These simulation results suggest that even large molecule solutes would be more easily cleared from the brain interstitium by diffusion than by bulk flow. Thus, diffusion within the interstitial space combined with advection along vessels is likely to substitute for the lymphatic drainage system in other organs.

Space physiology II: adaptation of the central nervous system to space flight--past, current, and future studies.

PubMed

Clément, Gilles; Ngo-Anh, Jennifer Thu

2013-07-01

Experiments performed in orbit on the central nervous system have focused on the control of posture, eye movements, spatial orientation, as well as cognitive processes, such as three-dimensional visual perception and mental representation of space. Brain activity has also been recorded during and immediately after space flight for evaluating the changes in brain structure activation during tasks involving perception, attention, memory, decision, and action. Recent ground-based studies brought evidence that the inputs from the neurovestibular system also participate in orthostatic intolerance. It is, therefore, important to revisit the flight data of neuroscience studies in the light of new models of integrative physiology. The outcomes of this exercise will increase our knowledge on the adaptation of body functions to changing gravitational environment, vestibular disorders, aging, and our approach towards more effective countermeasures during human space flight and planetary exploration.

Optimal spatiotemporal representation of multichannel EEG for recognition of brain states associated with distinct visual stimulus

NASA Astrophysics Data System (ADS)

Hramov, Alexander; Musatov, Vyacheslav Yu.; Runnova, Anastasija E.; Efremova, Tatiana Yu.; Koronovskii, Alexey A.; Pisarchik, Alexander N.

2018-04-01

In the paper we propose an approach based on artificial neural networks for recognition of different human brain states associated with distinct visual stimulus. Based on the developed numerical technique and the analysis of obtained experimental multichannel EEG data, we optimize the spatiotemporal representation of multichannel EEG to provide close to 97% accuracy in recognition of the EEG brain states during visual perception. Different interpretations of an ambiguous image produce different oscillatory patterns in the human EEG with similar features for every interpretation. Since these features are inherent to all subjects, a single artificial network can classify with high quality the associated brain states of other subjects.

Monitoring alert and drowsy states by modeling EEG source nonstationarity

NASA Astrophysics Data System (ADS)

Hsu, Sheng-Hsiou; Jung, Tzyy-Ping

2017-10-01

Objective. As a human brain performs various cognitive functions within ever-changing environments, states of the brain characterized by recorded brain activities such as electroencephalogram (EEG) are inevitably nonstationary. The challenges of analyzing the nonstationary EEG signals include finding neurocognitive sources that underlie different brain states and using EEG data to quantitatively assess the state changes. Approach. This study hypothesizes that brain activities under different states, e.g. levels of alertness, can be modeled as distinct compositions of statistically independent sources using independent component analysis (ICA). This study presents a framework to quantitatively assess the EEG source nonstationarity and estimate levels of alertness. The framework was tested against EEG data collected from 10 subjects performing a sustained-attention task in a driving simulator. Main results. Empirical results illustrate that EEG signals under alert versus drowsy states, indexed by reaction speeds to driving challenges, can be characterized by distinct ICA models. By quantifying the goodness-of-fit of each ICA model to the EEG data using the model deviation index (MDI), we found that MDIs were significantly correlated with the reaction speeds (r  =  -0.390 with alertness models and r  =  0.449 with drowsiness models) and the opposite correlations indicated that the two models accounted for sources in the alert and drowsy states, respectively. Based on the observed source nonstationarity, this study also proposes an online framework using a subject-specific ICA model trained with an initial (alert) state to track the level of alertness. For classification of alert against drowsy states, the proposed online framework achieved an averaged area-under-curve of 0.745 and compared favorably with a classic power-based approach. Significance. This ICA-based framework provides a new way to study changes of brain states and can be applied to monitoring cognitive or mental states of human operators in attention-critical settings or in passive brain-computer interfaces.

Simultaneous measurement of glucose transport and utilization in the human brain

PubMed Central

Shestov, Alexander A.; Emir, Uzay E.; Kumar, Anjali; Henry, Pierre-Gilles; Seaquist, Elizabeth R.

2011-01-01

Glucose is the primary fuel for brain function, and determining the kinetics of cerebral glucose transport and utilization is critical for quantifying cerebral energy metabolism. The kinetic parameters of cerebral glucose transport, KMt and Vmaxt, in humans have so far been obtained by measuring steady-state brain glucose levels by proton (1H) NMR as a function of plasma glucose levels and fitting steady-state models to these data. Extraction of the kinetic parameters for cerebral glucose transport necessitated assuming a constant cerebral metabolic rate of glucose (CMRglc) obtained from other tracer studies, such as 13C NMR. Here we present new methodology to simultaneously obtain kinetic parameters for glucose transport and utilization in the human brain by fitting both dynamic and steady-state 1H NMR data with a reversible, non-steady-state Michaelis-Menten model. Dynamic data were obtained by measuring brain and plasma glucose time courses during glucose infusions to raise and maintain plasma concentration at ∼17 mmol/l for ∼2 h in five healthy volunteers. Steady-state brain vs. plasma glucose concentrations were taken from literature and the steady-state portions of data from the five volunteers. In addition to providing simultaneous measurements of glucose transport and utilization and obviating assumptions for constant CMRglc, this methodology does not necessitate infusions of expensive or radioactive tracers. Using this new methodology, we found that the maximum transport capacity for glucose through the blood-brain barrier was nearly twofold higher than maximum cerebral glucose utilization. The glucose transport and utilization parameters were consistent with previously published values for human brain. PMID:21791622

Functional Brain Networks Develop from a “Local to Distributed” Organization

PubMed Central

Power, Jonathan D.; Dosenbach, Nico U. F.; Church, Jessica A.; Miezin, Francis M.; Schlaggar, Bradley L.; Petersen, Steven E.

2009-01-01

The mature human brain is organized into a collection of specialized functional networks that flexibly interact to support various cognitive functions. Studies of development often attempt to identify the organizing principles that guide the maturation of these functional networks. In this report, we combine resting state functional connectivity MRI (rs-fcMRI), graph analysis, community detection, and spring-embedding visualization techniques to analyze four separate networks defined in earlier studies. As we have previously reported, we find, across development, a trend toward ‘segregation’ (a general decrease in correlation strength) between regions close in anatomical space and ‘integration’ (an increased correlation strength) between selected regions distant in space. The generalization of these earlier trends across multiple networks suggests that this is a general developmental principle for changes in functional connectivity that would extend to large-scale graph theoretic analyses of large-scale brain networks. Communities in children are predominantly arranged by anatomical proximity, while communities in adults predominantly reflect functional relationships, as defined from adult fMRI studies. In sum, over development, the organization of multiple functional networks shifts from a local anatomical emphasis in children to a more “distributed” architecture in young adults. We argue that this “local to distributed” developmental characterization has important implications for understanding the development of neural systems underlying cognition. Further, graph metrics (e.g., clustering coefficients and average path lengths) are similar in child and adult graphs, with both showing “small-world”-like properties, while community detection by modularity optimization reveals stable communities within the graphs that are clearly different between young children and young adults. These observations suggest that early school age children and adults both have relatively efficient systems that may solve similar information processing problems in divergent ways. PMID:19412534

Functional brain networks develop from a "local to distributed" organization.

PubMed

Fair, Damien A; Cohen, Alexander L; Power, Jonathan D; Dosenbach, Nico U F; Church, Jessica A; Miezin, Francis M; Schlaggar, Bradley L; Petersen, Steven E

2009-05-01

The mature human brain is organized into a collection of specialized functional networks that flexibly interact to support various cognitive functions. Studies of development often attempt to identify the organizing principles that guide the maturation of these functional networks. In this report, we combine resting state functional connectivity MRI (rs-fcMRI), graph analysis, community detection, and spring-embedding visualization techniques to analyze four separate networks defined in earlier studies. As we have previously reported, we find, across development, a trend toward 'segregation' (a general decrease in correlation strength) between regions close in anatomical space and 'integration' (an increased correlation strength) between selected regions distant in space. The generalization of these earlier trends across multiple networks suggests that this is a general developmental principle for changes in functional connectivity that would extend to large-scale graph theoretic analyses of large-scale brain networks. Communities in children are predominantly arranged by anatomical proximity, while communities in adults predominantly reflect functional relationships, as defined from adult fMRI studies. In sum, over development, the organization of multiple functional networks shifts from a local anatomical emphasis in children to a more "distributed" architecture in young adults. We argue that this "local to distributed" developmental characterization has important implications for understanding the development of neural systems underlying cognition. Further, graph metrics (e.g., clustering coefficients and average path lengths) are similar in child and adult graphs, with both showing "small-world"-like properties, while community detection by modularity optimization reveals stable communities within the graphs that are clearly different between young children and young adults. These observations suggest that early school age children and adults both have relatively efficient systems that may solve similar information processing problems in divergent ways.

Exploring Deep Space - Uncovering the Anatomy of Periventricular Structures to Reveal the Lateral Ventricles of the Human Brain.

PubMed

Colibaba, Alexandru S; Calma, Aicee Dawn B; Webb, Alexandra L; Valter, Krisztina

2017-10-22

Anatomy students are typically provided with two-dimensional (2D) sections and images when studying cerebral ventricular anatomy and students find this challenging. Because the ventricles are negative spaces located deep within the brain, the only way to understand their anatomy is by appreciating their boundaries formed by related structures. Looking at a 2D representation of these spaces, in any of the cardinal planes, will not enable visualisation of all of the structures that form the boundaries of the ventricles. Thus, using 2D sections alone requires students to compute their own mental image of the 3D ventricular spaces. The aim of this study was to develop a reproducible method for dissecting the human brain to create an educational resource to enhance student understanding of the intricate relationships between the ventricles and periventricular structures. To achieve this, we created a video resource that features a step-by-step guide using a fiber dissection method to reveal the lateral and third ventricles together with the closely related limbic system and basal ganglia structures. One of the advantages of this method is that it enables delineation of the white matter tracts that are difficult to distinguish using other dissection techniques. This video is accompanied by a written protocol that provides a systematic description of the process to aid in the reproduction of the brain dissection. This package offers a valuable anatomy teaching resource for educators and students alike. By following these instructions educators can create teaching resources and students can be guided to produce their own brain dissection as a hands-on practical activity. We recommend that this video guide be incorporated into neuroanatomy teaching to enhance student understanding of the morphology and clinical relevance of the ventricles.

MR-guided transcranial focused ultrasound safely enhances interstitial dispersion of large polymeric nanoparticles in the living brain

PubMed Central

Mohammadabadi, Ali; Nguyen, Ben A.; Guo, Sijia; Winkles, Jeffrey A.; Kim, Anthony J.; Gullapalli, Rao; Keller, Asaf; Frenkel, Victor

2018-01-01

Generating spatially controlled, non-destructive changes in the interstitial spaces of the brain has a host of potential clinical applications, including enhancing the delivery of therapeutics, modulating biological features within the tissue microenvironment, altering fluid and pressure dynamics, and increasing the clearance of toxins, such as plaques found in Alzheimer’s disease. Recently we demonstrated that ultrasound can non-destructively enlarge the interstitial spaces of the brain ex vivo. The goal of the current study was to determine whether these effects could be reproduced in the living brain using non-invasive, transcranial MRI-guided focused ultrasound (MRgFUS). The left striatum of healthy rats was treated using MRgFUS. Computer simulations facilitated treatment planning, and targeting was validated using MRI acoustic radiation force impulse imaging. Following MRgFUS treatments, Evans blue dye or nanoparticle probes were infused to assess changes in the interstitial space. In MRgFUS-treated animals, enhanced dispersion was observed compared to controls for 70 nm (12.8 ± 0.9 mm3 vs. 10.6 ± 1.0 mm3, p = 0.01), 200 nm (10.9 ± 1.4 mm3 vs. 7.4 ± 0.7 mm3, p = 0.01) and 700 nm (7.5 ± 0.4 mm3 vs. 5.4 ± 1.2 mm3, p = 0.02) nanoparticles, indicating enlargement of the interstitial spaces. No evidence of significant histological or electrophysiological injury was identified. These findings suggest that transcranial ultrasound can safely and effectively modulate the brain interstitium and increase the dispersion of large therapeutic entities such as particulate drug carriers or modified viruses. This has the potential to expand the therapeutic uses of MRgFUS. PMID:29415084

Emerging Object Representations in the Visual System Predict Reaction Times for Categorization

PubMed Central

Ritchie, J. Brendan; Tovar, David A.; Carlson, Thomas A.

2015-01-01

Recognizing an object takes just a fraction of a second, less than the blink of an eye. Applying multivariate pattern analysis, or “brain decoding”, methods to magnetoencephalography (MEG) data has allowed researchers to characterize, in high temporal resolution, the emerging representation of object categories that underlie our capacity for rapid recognition. Shortly after stimulus onset, object exemplars cluster by category in a high-dimensional activation space in the brain. In this emerging activation space, the decodability of exemplar category varies over time, reflecting the brain’s transformation of visual inputs into coherent category representations. How do these emerging representations relate to categorization behavior? Recently it has been proposed that the distance of an exemplar representation from a categorical boundary in an activation space is critical for perceptual decision-making, and that reaction times should therefore correlate with distance from the boundary. The predictions of this distance hypothesis have been born out in human inferior temporal cortex (IT), an area of the brain crucial for the representation of object categories. When viewed in the context of a time varying neural signal, the optimal time to “read out” category information is when category representations in the brain are most decodable. Here, we show that the distance from a decision boundary through activation space, as measured using MEG decoding methods, correlates with reaction times for visual categorization during the period of peak decodability. Our results suggest that the brain begins to read out information about exemplar category at the optimal time for use in choice behaviour, and support the hypothesis that the structure of the representation for objects in the visual system is partially constitutive of the decision process in recognition. PMID:26107634

The structure of the perivascular compartment in the old canine brain: a case study.

PubMed

Criswell, Theodore P; Sharp, Matthew MacGregor; Dobson, Howard; Finucane, Ciara; Weller, Roy O; Verma, Ajay; Carare, Roxana O

2017-11-15

Dilatation of periarteriolar spaces in MRI of the ageing human brains occurs in white matter (WM), basal ganglia and midbrain but not in cerebral cortex. Perivenous collagenous occurs in periventricular but not in subcortical WM.Here we test the hypotheses that (a) the capacity for dilatation of periarteriolar spaces correlates with the anatomical distribution of leptomeningeal cells coating intracerebral arteries and (b) the regional development of perivenous collagenous in the WM correlates with the population of intramural cells in the walls of veins.The anatomical distribution of leptomeningeal and intramural cells related to cerebral blood vessels is best documented by electron microscopy, requiring perfusion-fixed tissue not available in human material. We therefore analysed perfusion-fixed brain from a 12-year-old Beagle dog as the canine brain represents the anatomical arrangement in the human brain. Results showed regional variation in the arrangement of leptomeningeal cells around blood vessels. Arterioles are enveloped by one complete layer of leptomeninges often with a second incomplete layer in the WM. Venules showed incomplete layers of leptomeningeal cells. Intramural cell expression was higher in the post-capillary venules of the subcortical WM when compared with periventricular WM, suggesting that periventricular collagenosis around venules may be due to a lower resistance in the venular walls. It appears that the regional variation in the capacity for dilatation of arteriolar perivascular spaces in the white WM may be related to the number of perivascular leptomeningeal cells surrounding vessels in different areas of the brain. © 2017 The Author(s). Published by Portland Press Limited on behalf of the Biochemical Society.

Early metabolic/cellular-level resuscitation following terminal brain stem herniation: implications for organ transplantation.

PubMed

Arbour, Richard B

2013-01-01

Patients with terminal brain stem herniation experience global physiological consequences and represent a challenging population in critical care practice as a result of multiple factors. The first factor is severe depression of consciousness, with resulting compromise in airway stability and lung ventilation. Second, with increasing severity of brain trauma, progressive brain edema, mass effect, herniation syndromes, and subsequent distortion/displacement of the brain stem follow. Third, with progression of intracranial pathophysiology to terminal brain stem herniation, multisystem consequences occur, including dysfunction of the hypothalamic-pituitary axis, depletion of stress hormones, and decreased thyroid hormone bioavailability as well as biphasic cardiovascular state. Cardiovascular dysfunction in phase 1 is a hyperdynamic and hypertensive state characterized by elevated systemic vascular resistance and cardiac contractility. Cardiovascular dysfunction in phase 2 is a hypotensive state characterized by decreased systemic vascular resistance and tissue perfusion. Rapid changes along the continuum of hyperperfusion versus hypoperfusion increase risk of end-organ damage, specifically pulmonary dysfunction from hemodynamic stress and high-flow states as well as ischemic changes consequent to low-flow states. A pronounced inflammatory state occurs, affecting pulmonary function and gas exchange and contributing to hemodynamic instability as a result of additional vasodilatation. Coagulopathy also occurs as a result of consumption of clotting factors as well as dilution of clotting factors and platelets consequent to aggressive crystalloid administration. Each consequence of terminal brain stem injury complicates clinical management within this patient demographic. In general, these multisystem consequences are managed with mechanism-based interventions within the context of caring for the donor's organs (liver, kidneys, heart, etc.) after death by neurological criteria. These processes begin far earlier in the continuum of injury, at the moment of terminal brain stem herniation. As such, aggressive, mechanism-based care, including hormonal replacement therapy, becomes clinically appropriate before formal brain death declaration to support cardiopulmonary stability following terminal brain stem herniation.

Social representations and contextual adjustments as two distinct components of the Theory of Mind brain network: Evidence from the REMICS task.

PubMed

Lavoie, Marie-Audrey; Vistoli, Damien; Sutliff, Stephanie; Jackson, Philip L; Achim, Amélie M

2016-08-01

Theory of mind (ToM) refers to the ability to infer the mental states of others. Behavioral measures of ToM usually present information about both a character and the context in which this character is placed, and these different pieces of information can be used to infer the character's mental states. A set of brain regions designated as the ToM brain network is recognized to support (ToM) inferences. Different brain regions within that network could however support different ToM processes. This functional magnetic resonance imaging (fMRI) study aimed to distinguish the brain regions supporting two aspects inherent to many ToM tasks, i.e., the ability to infer or represent mental states and the ability to use the context to adjust these inferences. Nineteen healthy subjects were scanned during the REMICS task, a novel task designed to orthogonally manipulate mental state inferences (as opposed to physical inferences) and contextual adjustments of inferences (as opposed to inferences that do not require contextual adjustments). We observed that mental state inferences and contextual adjustments, which are important aspects of most behavioral ToM tasks, rely on distinct brain regions or subregions within the classical brain network activated in previous ToM research. Notably, an interesting dissociation emerged within the medial prefrontal cortex (mPFC) and temporo-parietal junctions (TPJ) such that the inferior part of these brain regions responded to mental state inferences while the superior part of these brain regions responded to the requirement for contextual adjustments. This study provides evidence that the overall set of brain regions activated during ToM tasks supports different processes, and highlights that cognitive processes related to contextual adjustments have an important role in ToM and should be further studied. Copyright © 2016 Elsevier Ltd. All rights reserved.

Decreased integration and information capacity in stroke measured by whole brain models of resting state activity.

PubMed

Adhikari, Mohit H; Hacker, Carl D; Siegel, Josh S; Griffa, Alessandra; Hagmann, Patric; Deco, Gustavo; Corbetta, Maurizio

2017-04-01

While several studies have shown that focal lesions affect the communication between structurally normal regions of the brain, and that these changes may correlate with behavioural deficits, their impact on brain's information processing capacity is currently unknown. Here we test the hypothesis that focal lesions decrease the brain's information processing capacity, of which changes in functional connectivity may be a measurable correlate. To measure processing capacity, we turned to whole brain computational modelling to estimate the integration and segregation of information in brain networks. First, we measured functional connectivity between different brain areas with resting state functional magnetic resonance imaging in healthy subjects (n = 26), and subjects who had suffered a cortical stroke (n = 36). We then used a whole-brain network model that coupled average excitatory activities of local regions via anatomical connectivity. Model parameters were optimized in each healthy or stroke participant to maximize correlation between model and empirical functional connectivity, so that the model's effective connectivity was a veridical representation of healthy or lesioned brain networks. Subsequently, we calculated two model-based measures: 'integration', a graph theoretical measure obtained from functional connectivity, which measures the connectedness of brain networks, and 'information capacity', an information theoretical measure that cannot be obtained empirically, representative of the segregative ability of brain networks to encode distinct stimuli. We found that both measures were decreased in stroke patients, as compared to healthy controls, particularly at the level of resting-state networks. Furthermore, we found that these measures, especially information capacity, correlate with measures of behavioural impairment and the segregation of resting-state networks empirically measured. This study shows that focal lesions affect the brain's ability to represent stimuli and task states, and that information capacity measured through whole brain models is a theory-driven measure of processing capacity that could be used as a biomarker of injury for outcome prediction or target for rehabilitation intervention. © The Author (2017). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

The modulation of EEG variability between internally- and externally-driven cognitive states varies with maturation and task performance

PubMed Central

Willatt, Stephanie E.; Cortese, Filomeno; Protzner, Andrea B.

2017-01-01

Increasing evidence suggests that brain signal variability is an important measure of brain function reflecting information processing capacity and functional integrity. In this study, we examined how maturation from childhood to adulthood affects the magnitude and spatial extent of state-to-state transitions in brain signal variability, and how this relates to cognitive performance. We looked at variability changes between resting-state and task (a symbol-matching task with three levels of difficulty), and within trial (fixation, post-stimulus, and post-response). We calculated variability with multiscale entropy (MSE), and additionally examined spectral power density (SPD) from electroencephalography (EEG) in children aged 8–14, and in adults aged 18–33. Our results suggest that maturation is characterized by increased local information processing (higher MSE at fine temporal scales) and decreased long-range interactions with other neural populations (lower MSE at coarse temporal scales). Children show MSE changes that are similar in magnitude, but greater in spatial extent when transitioning between internally- and externally-driven brain states. Additionally, we found that in children, greater changes in task difficulty were associated with greater magnitude of modulation in MSE. Our results suggest that the interplay between maturational and state-to-state changes in brain signal variability manifest across different spatial and temporal scales, and influence information processing capacity in the brain. PMID:28750035

Resting state cerebral blood flow with arterial spin labeling MRI in developing human brains.

PubMed

Liu, Feng; Duan, Yunsuo; Peterson, Bradley S; Asllani, Iris; Zelaya, Fernando; Lythgoe, David; Kangarlu, Alayar

2018-07-01

The development of brain circuits is coupled with changes in neurovascular coupling, which refers to the close relationship between neural activity and cerebral blood flow (CBF). Studying the characteristics of CBF during resting state in developing brain can be a complementary way to understand the functional connectivity of the developing brain. Arterial spin labeling (ASL), as a noninvasive MR technique, is particularly attractive for studying cerebral perfusion in children and even newborns. We have collected pulsed ASL data in resting state for 47 healthy subjects from young children to adolescence (aged from 6 to 20 years old). In addition to studying the developmental change of static CBF maps during resting state, we also analyzed the CBF time series to reveal the dynamic characteristics of CBF in differing age groups. We used the seed-based correlation analysis to examine the temporal relationship of CBF time series between the selected ROIs and other brain regions. We have shown the developmental patterns in both static CBF maps and dynamic characteristics of CBF. While higher CBF of default mode network (DMN) in all age groups supports that DMN is the prominent active network during the resting state, the CBF connectivity patterns of some typical resting state networks show distinct patterns of metabolic activity during the resting state in the developing brains. Copyright © 2018 European Paediatric Neurology Society. All rights reserved.

Defined types of cortical interneurone structure space and spike timing in the hippocampus

PubMed Central

Somogyi, Peter; Klausberger, Thomas

2005-01-01

The cerebral cortex encodes, stores and combines information about the internal and external environment in rhythmic activity of multiple frequency ranges. Neurones of the cortex can be defined, recognized and compared on the comprehensive application of the following measures: (i) brain area- and cell domain-specific distribution of input and output synapses, (ii) expression of molecules involved in cell signalling, (iii) membrane and synaptic properties reflecting the expression of membrane proteins, (iv) temporal structure of firing in vivo, resulting from (i)–(iii). Spatial and temporal measures of neurones in the network reflect an indivisible unity of evolutionary design, i.e. neurones do not have separate structure or function. The blueprint of this design is most easily accessible in the CA1 area of the hippocampus, where a relatively uniform population of pyramidal cells and their inputs follow an instantly recognizable laminated pattern and act within stereotyped network activity patterns. Reviewing the cell types and their spatio-temporal interactions, we suggest that CA1 pyramidal cells are supported by at least 16 distinct types of GABAergic neurone. During a given behaviour-contingent network oscillation, interneurones of a given type exhibit similar firing patterns. During different network oscillations representing two distinct brain states, interneurones of the same class show different firing patterns modulating their postsynaptic target-domain in a brain-state-dependent manner. These results suggest roles for specific interneurone types in structuring the activity of pyramidal cells via their respective target domains, and accurately timing and synchronizing pyramidal cell discharge, rather than providing generalized inhibition. Finally, interneurones belonging to different classes may fire preferentially at distinct time points during a given oscillation. As different interneurones innervate distinct domains of the pyramidal cells, the different compartments will receive GABAergic input differentiated in time. Such a dynamic, spatio-temporal, GABAergic control, which evolves distinct patterns during different brain states, is ideally suited to regulating the input integration of individual pyramidal cells contributing to the formation of cell assemblies and representations in the hippocampus and, probably, throughout the cerebral cortex. PMID:15539390

Deep Learning for Brain MRI Segmentation: State of the Art and Future Directions.

PubMed

Akkus, Zeynettin; Galimzianova, Alfiia; Hoogi, Assaf; Rubin, Daniel L; Erickson, Bradley J

2017-08-01

Quantitative analysis of brain MRI is routine for many neurological diseases and conditions and relies on accurate segmentation of structures of interest. Deep learning-based segmentation approaches for brain MRI are gaining interest due to their self-learning and generalization ability over large amounts of data. As the deep learning architectures are becoming more mature, they gradually outperform previous state-of-the-art classical machine learning algorithms. This review aims to provide an overview of current deep learning-based segmentation approaches for quantitative brain MRI. First we review the current deep learning architectures used for segmentation of anatomical brain structures and brain lesions. Next, the performance, speed, and properties of deep learning approaches are summarized and discussed. Finally, we provide a critical assessment of the current state and identify likely future developments and trends.

A dural lymphatic vascular system that drains brain interstitial fluid and macromolecules

PubMed Central

Aspelund, Aleksanteri; Antila, Salli; Proulx, Steven T.; Karlsen, Tine Veronica; Karaman, Sinem; Detmar, Michael; Wiig, Helge

2015-01-01

The central nervous system (CNS) is considered an organ devoid of lymphatic vasculature. Yet, part of the cerebrospinal fluid (CSF) drains into the cervical lymph nodes (LNs). The mechanism of CSF entry into the LNs has been unclear. Here we report the surprising finding of a lymphatic vessel network in the dura mater of the mouse brain. We show that dural lymphatic vessels absorb CSF from the adjacent subarachnoid space and brain interstitial fluid (ISF) via the glymphatic system. Dural lymphatic vessels transport fluid into deep cervical LNs (dcLNs) via foramina at the base of the skull. In a transgenic mouse model expressing a VEGF-C/D trap and displaying complete aplasia of the dural lymphatic vessels, macromolecule clearance from the brain was attenuated and transport from the subarachnoid space into dcLNs was abrogated. Surprisingly, brain ISF pressure and water content were unaffected. Overall, these findings indicate that the mechanism of CSF flow into the dcLNs is directly via an adjacent dural lymphatic network, which may be important for the clearance of macromolecules from the brain. Importantly, these results call for a reexamination of the role of the lymphatic system in CNS physiology and disease. PMID:26077718

A dural lymphatic vascular system that drains brain interstitial fluid and macromolecules.

PubMed

Aspelund, Aleksanteri; Antila, Salli; Proulx, Steven T; Karlsen, Tine Veronica; Karaman, Sinem; Detmar, Michael; Wiig, Helge; Alitalo, Kari

2015-06-29

The central nervous system (CNS) is considered an organ devoid of lymphatic vasculature. Yet, part of the cerebrospinal fluid (CSF) drains into the cervical lymph nodes (LNs). The mechanism of CSF entry into the LNs has been unclear. Here we report the surprising finding of a lymphatic vessel network in the dura mater of the mouse brain. We show that dural lymphatic vessels absorb CSF from the adjacent subarachnoid space and brain interstitial fluid (ISF) via the glymphatic system. Dural lymphatic vessels transport fluid into deep cervical LNs (dcLNs) via foramina at the base of the skull. In a transgenic mouse model expressing a VEGF-C/D trap and displaying complete aplasia of the dural lymphatic vessels, macromolecule clearance from the brain was attenuated and transport from the subarachnoid space into dcLNs was abrogated. Surprisingly, brain ISF pressure and water content were unaffected. Overall, these findings indicate that the mechanism of CSF flow into the dcLNs is directly via an adjacent dural lymphatic network, which may be important for the clearance of macromolecules from the brain. Importantly, these results call for a reexamination of the role of the lymphatic system in CNS physiology and disease. © 2015 Aspelund et al.

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.

Memory Loss and the Onset of Alzheimer's Disease Could Be Under the Control of Extracellular Heat Shock Proteins.

PubMed

Arispe, Nelson; De Maio, Antonio

2018-01-01

Alzheimer's disease (AD) is a major contemporary and escalating malady in which amyloid-β (Aβ) peptides are the most likely causative agent. Aβ peptides spontaneously tend to aggregate in extracellular fluids following a progression from a monomeric state, through intermediate forms, ending in amyloid fibers and plaques. It is generally accepted now that the neurotoxic agents leading to cellular death, memory loss, and other AD characteristics are the Aβ intermediate aggregated states. However, Aβ peptides are continuously produced, released into the extracellular space, and rapidly cleared from healthy brains. Coincidentally, members of the heat shock proteins (hsp) family are present in the extracellular medium of healthy cells and body fluids, opening the possibility that hsps and Aβ could meet and interact in the extracellular milieu of the brain. In this perspective and reflection article, we place our investigation showing that the presence of Hsp70s mitigate the formation of low molecular weight Aβ peptide oligomers resulting in a reduction of cellular toxicity, in context of the current understanding of the disease. We propose that it may be an inverse relationship between the presence of Hsp70, the stage of Aβ oligomers, neurotoxicity, and the incidence of AD, particularly since the expression and circulating levels of hsp decrease with aging. Combining these observations, we propose that changes in the dynamics of Hsp70s and Aβ concentrations in the circulating brain fluids during aging defines the control of the formation of Aβ toxic aggregates, thus determining the conditions for neuron degeneration and the incidence of AD.

Exploiting neurovascular coupling: a Bayesian sequential Monte Carlo approach applied to simulated EEG fNIRS data

NASA Astrophysics Data System (ADS)

Croce, Pierpaolo; Zappasodi, Filippo; Merla, Arcangelo; Chiarelli, Antonio Maria

2017-08-01

Objective. Electrical and hemodynamic brain activity are linked through the neurovascular coupling process and they can be simultaneously measured through integration of electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS). Thanks to the lack of electro-optical interference, the two procedures can be easily combined and, whereas EEG provides electrophysiological information, fNIRS can provide measurements of two hemodynamic variables, such as oxygenated and deoxygenated hemoglobin. A Bayesian sequential Monte Carlo approach (particle filter, PF) was applied to simulated recordings of electrical and neurovascular mediated hemodynamic activity, and the advantages of a unified framework were shown. Approach. Multiple neural activities and hemodynamic responses were simulated in the primary motor cortex of a subject brain. EEG and fNIRS recordings were obtained by means of forward models of volume conduction and light propagation through the head. A state space model of combined EEG and fNIRS data was built and its dynamic evolution was estimated through a Bayesian sequential Monte Carlo approach (PF). Main results. We showed the feasibility of the procedure and the improvements in both electrical and hemodynamic brain activity reconstruction when using the PF on combined EEG and fNIRS measurements. Significance. The investigated procedure allows one to combine the information provided by the two methodologies, and, by taking advantage of a physical model of the coupling between electrical and hemodynamic response, to obtain a better estimate of brain activity evolution. Despite the high computational demand, application of such an approach to in vivo recordings could fully exploit the advantages of this combined brain imaging technology.

A digital 3D atlas of the marmoset brain based on multi-modal MRI.

PubMed

Liu, Cirong; Ye, Frank Q; Yen, Cecil Chern-Chyi; Newman, John D; Glen, Daniel; Leopold, David A; Silva, Afonso C

2018-04-01

The common marmoset (Callithrix jacchus) is a New-World monkey of growing interest in neuroscience. Magnetic resonance imaging (MRI) is an essential tool to unveil the anatomical and functional organization of the marmoset brain. To facilitate identification of regions of interest, it is desirable to register MR images to an atlas of the brain. However, currently available atlases of the marmoset brain are mainly based on 2D histological data, which are difficult to apply to 3D imaging techniques. Here, we constructed a 3D digital atlas based on high-resolution ex-vivo MRI images, including magnetization transfer ratio (a T1-like contrast), T2w images, and multi-shell diffusion MRI. Based on the multi-modal MRI images, we manually delineated 54 cortical areas and 16 subcortical regions on one hemisphere of the brain (the core version). The 54 cortical areas were merged into 13 larger cortical regions according to their locations to yield a coarse version of the atlas, and also parcellated into 106 sub-regions using a connectivity-based parcellation method to produce a refined atlas. Finally, we compared the new atlas set with existing histology atlases and demonstrated its applications in connectome studies, and in resting state and stimulus-based fMRI. The atlas set has been integrated into the widely-distributed neuroimaging data analysis software AFNI and SUMA, providing a readily usable multi-modal template space with multi-level anatomical labels (including labels from the Paxinos atlas) that can facilitate various neuroimaging studies of marmosets. Published by Elsevier Inc.

Toward reliable characterization of functional homogeneity in the human brain: Preprocessing, scan duration, imaging resolution and computational space

PubMed Central

Zuo, Xi-Nian; Xu, Ting; Jiang, Lili; Yang, Zhi; Cao, Xiao-Yan; He, Yong; Zang, Yu-Feng; Castellanos, F. Xavier; Milham, Michael P.

2013-01-01

While researchers have extensively characterized functional connectivity between brain regions, the characterization of functional homogeneity within a region of the brain connectome is in early stages of development. Several functional homogeneity measures were proposed previously, among which regional homogeneity (ReHo) was most widely used as a measure to characterize functional homogeneity of resting state fMRI (R-fMRI) signals within a small region (Zang et al., 2004). Despite a burgeoning literature on ReHo in the field of neuroimaging brain disorders, its test–retest (TRT) reliability remains unestablished. Using two sets of public R-fMRI TRT data, we systematically evaluated the ReHo’s TRT reliability and further investigated the various factors influencing its reliability and found: 1) nuisance (head motion, white matter, and cerebrospinal fluid) correction of R-fMRI time series can significantly improve the TRT reliability of ReHo while additional removal of global brain signal reduces its reliability, 2) spatial smoothing of R-fMRI time series artificially enhances ReHo intensity and influences its reliability, 3) surface-based R-fMRI computation largely improves the TRT reliability of ReHo, 4) a scan duration of 5 min can achieve reliable estimates of ReHo, and 5) fast sampling rates of R-fMRI dramatically increase the reliability of ReHo. Inspired by these findings and seeking a highly reliable approach to exploratory analysis of the human functional connectome, we established an R-fMRI pipeline to conduct ReHo computations in both 3-dimensions (volume) and 2-dimensions (surface). PMID:23085497

Relationships between the resting-state network and the P3: Evidence from a scalp EEG study

NASA Astrophysics Data System (ADS)

Li, Fali; Liu, Tiejun; Wang, Fei; Li, He; Gong, Diankun; Zhang, Rui; Jiang, Yi; Tian, Yin; Guo, Daqing; Yao, Dezhong; Xu, Peng

2015-10-01

The P3 is an important event-related potential that can be used to identify neural activity related to the cognitive processes of the human brain. However, the relationships, especially the functional correlations, between resting-state brain activity and the P3 have not been well established. In this study, we investigated the relationships between P3 properties (i.e., amplitude and latency) and resting-state brain networks. The results indicated that P3 amplitude was significantly correlated with resting-state network topology, and in general, larger P3 amplitudes could be evoked when the resting-state brain network was more efficient. However, no significant relationships were found for the corresponding P3 latency. Additionally, the long-range connections between the prefrontal/frontal and parietal/occipital brain regions, which represent the synchronous activity of these areas, were functionally related to the P3 parameters, especially P3 amplitude. The findings of the current study may help us better understand inter-subject variation in the P3, which may be instructive for clinical diagnosis, cognitive neuroscience studies, and potential subject selection for brain-computer interface applications.

A new brain-computer interface design using fuzzy ARTMAP.

PubMed

Palaniappan, Ramaswamy; Paramesran, Raveendran; Nishida, Shogo; Saiwaki, Naoki

2002-09-01

This paper proposes a new brain-computer interface (BCI) design using fuzzy ARTMAP (FA) neural network, as well as an application of the design. The objective of this BCI-FA design is to classify the best three of the five available mental tasks for each subject using power spectral density (PSD) values of electroencephalogram (EEG) signals. These PSD values are extracted using the Wiener-Khinchine and autoregressive methods. Ten experiments employing different triplets of mental tasks are studied for each subject. The findings show that the average BCI-FA outputs for four subjects gave less than 6% of error using the best triplets of mental tasks identified from the classification performances of FA. This implies that the BCI-FA can be successfully used with a tri-state switching device. As an application, a proposed tri-state Morse code scheme could be utilized to translate the outputs of this BCI-FA design into English letters. In this scheme, the three BCI-FA outputs correspond to a dot and a dash, which are the two basic Morse code alphabets and a space to denote the end (or beginning) of a dot or a dash. The construction of English letters using this tri-state Morse code scheme is determined only by the sequence of mental tasks and is independent of the time duration of each mental task. This is especially useful for constructing letters that are represented as multiple dots or dashes. This combination of BCI-FA design and the tri-state Morse code scheme could be developed as a communication system for paralyzed patients.

Fast mental states decoding in mixed reality.

PubMed

De Massari, Daniele; Pacheco, Daniel; Malekshahi, Rahim; Betella, Alberto; Verschure, Paul F M J; Birbaumer, Niels; Caria, Andrea

2014-01-01

The combination of Brain-Computer Interface (BCI) technology, allowing online monitoring and decoding of brain activity, with virtual and mixed reality (MR) systems may help to shape and guide implicit and explicit learning using ecological scenarios. Real-time information of ongoing brain states acquired through BCI might be exploited for controlling data presentation in virtual environments. Brain states discrimination during mixed reality experience is thus critical for adapting specific data features to contingent brain activity. In this study we recorded electroencephalographic (EEG) data while participants experienced MR scenarios implemented through the eXperience Induction Machine (XIM). The XIM is a novel framework modeling the integration of a sensing system that evaluates and measures physiological and psychological states with a number of actuators and effectors that coherently reacts to the user's actions. We then assessed continuous EEG-based discrimination of spatial navigation, reading and calculation performed in MR, using linear discriminant analysis (LDA) and support vector machine (SVM) classifiers. Dynamic single trial classification showed high accuracy of LDA and SVM classifiers in detecting multiple brain states as well as in differentiating between high and low mental workload, using a 5 s time-window shifting every 200 ms. Our results indicate overall better performance of LDA with respect to SVM and suggest applicability of our approach in a BCI-controlled MR scenario. Ultimately, successful prediction of brain states might be used to drive adaptation of data representation in order to boost information processing in MR.

Fast mental states decoding in mixed reality

PubMed Central

De Massari, Daniele; Pacheco, Daniel; Malekshahi, Rahim; Betella, Alberto; Verschure, Paul F. M. J.; Birbaumer, Niels; Caria, Andrea

2014-01-01

The combination of Brain-Computer Interface (BCI) technology, allowing online monitoring and decoding of brain activity, with virtual and mixed reality (MR) systems may help to shape and guide implicit and explicit learning using ecological scenarios. Real-time information of ongoing brain states acquired through BCI might be exploited for controlling data presentation in virtual environments. Brain states discrimination during mixed reality experience is thus critical for adapting specific data features to contingent brain activity. In this study we recorded electroencephalographic (EEG) data while participants experienced MR scenarios implemented through the eXperience Induction Machine (XIM). The XIM is a novel framework modeling the integration of a sensing system that evaluates and measures physiological and psychological states with a number of actuators and effectors that coherently reacts to the user's actions. We then assessed continuous EEG-based discrimination of spatial navigation, reading and calculation performed in MR, using linear discriminant analysis (LDA) and support vector machine (SVM) classifiers. Dynamic single trial classification showed high accuracy of LDA and SVM classifiers in detecting multiple brain states as well as in differentiating between high and low mental workload, using a 5 s time-window shifting every 200 ms. Our results indicate overall better performance of LDA with respect to SVM and suggest applicability of our approach in a BCI-controlled MR scenario. Ultimately, successful prediction of brain states might be used to drive adaptation of data representation in order to boost information processing in MR. PMID:25505878

Optimization of Brain T2 Mapping Using Standard CPMG Sequence In A Clinical Scanner

NASA Astrophysics Data System (ADS)

Hnilicová, P.; Bittšanský, M.; Dobrota, D.

2014-04-01

In magnetic resonance imaging, transverse relaxation time (T2) mapping is a useful quantitative tool enabling enhanced diagnostics of many brain pathologies. The aim of our study was to test the influence of different sequence parameters on calculated T2 values, including multi-slice measurements, slice position, interslice gap, echo spacing, and pulse duration. Measurements were performed using standard multi-slice multi-echo CPMG imaging sequence on a 1.5 Tesla routine whole body MR scanner. We used multiple phantoms with different agarose concentrations (0 % to 4 %) and verified the results on a healthy volunteer. It appeared that neither the pulse duration, the size of interslice gap nor the slice shift had any impact on the T2. The measurement accuracy was increased with shorter echo spacing. Standard multi-slice multi-echo CPMG protocol with the shortest echo spacing, also the smallest available interslice gap (100 % of slice thickness) and shorter pulse duration was found to be optimal and reliable for calculating T2 maps in the human brain.

Baseline brain energy supports the state of consciousness.

PubMed

Shulman, Robert G; Hyder, Fahmeed; Rothman, Douglas L

2009-07-07

An individual, human or animal, is defined to be in a conscious state empirically by the behavioral ability to respond meaningfully to stimuli, whereas the loss of consciousness is defined by unresponsiveness. PET measurements of glucose or oxygen consumption show a widespread approximately 45% reduction in cerebral energy consumption with anesthesia-induced loss of consciousness. Because baseline brain energy consumption has been shown by (13)C magnetic resonance spectroscopy to be almost exclusively dedicated to neuronal signaling, we propose that the high level of brain energy is a necessary property of the conscious state. Two additional neuronal properties of the conscious state change with anesthesia. The delocalized fMRI activity patterns in rat brain during sensory stimulation at a higher energy state (close to the awake) collapse to a contralateral somatosensory response at lower energy state (deep anesthesia). Firing rates of an ensemble of neurons in the rat somatosensory cortex shift from the gamma-band range (20-40 Hz) at higher energy state to <10 Hz at lower energy state. With the conscious state defined by the individual's behavior and maintained by high cerebral energy, measurable properties of that state are the widespread fMRI patterns and high frequency neuronal activity, both of which support the extensive interregional communication characteristic of consciousness. This usage of high brain energies when the person is in the "state" of consciousness differs from most studies, which attend the smaller energy increments observed during the stimulations that form the "contents" of that state.

Detecting brain dynamics during resting state: a tensor based evolutionary clustering approach

NASA Astrophysics Data System (ADS)

Al-sharoa, Esraa; Al-khassaweneh, Mahmood; Aviyente, Selin

2017-08-01

Human brain is a complex network with connections across different regions. Understanding the functional connectivity (FC) of the brain is important both during resting state and task; as disruptions in connectivity patterns are indicators of different psychopathological and neurological diseases. In this work, we study the resting state functional connectivity networks (FCNs) of the brain from fMRI BOLD signals. Recent studies have shown that FCNs are dynamic even during resting state and understanding the temporal dynamics of FCNs is important for differentiating between different conditions. Therefore, it is important to develop algorithms to track the dynamic formation and dissociation of FCNs of the brain during resting state. In this paper, we propose a two step tensor based community detection algorithm to identify and track the brain network community structure across time. First, we introduce an information-theoretic function to reduce the dynamic FCN and identify the time points that are similar topologically to combine them into a tensor. These time points will be used to identify the different FC states. Second, a tensor based spectral clustering approach is developed to identify the community structure of the constructed tensors. The proposed algorithm applies Tucker decomposition to the constructed tensors and extract the orthogonal factor matrices along the connectivity mode to determine the common subspace within each FC state. The detected community structure is summarized and described as FC states. The results illustrate the dynamic structure of resting state networks (RSNs), including the default mode network, somatomotor network, subcortical network and visual network.

A Simulation Model of Periarterial Clearance of Amyloid-β from the Brain

PubMed Central

Diem, Alexandra K.; Tan, Mingyi; Bressloff, Neil W.; Hawkes, Cheryl; Morris, Alan W. J.; Weller, Roy O.; Carare, Roxana O.

2016-01-01

The accumulation of soluble and insoluble amyloid-β (Aβ) in the brain indicates failure of elimination of Aβ from the brain with age and Alzheimer's disease (AD). There is a variety of mechanisms for elimination of Aβ from the brain. They include the action of microglia and enzymes together with receptor-mediated absorption of Aβ into the blood and periarterial lymphatic drainage of Aβ. Although the brain possesses no conventional lymphatics, experimental studies have shown that fluid and solutes, such as Aβ, are eliminated from the brain along 100 nm wide basement membranes in the walls of cerebral capillaries and arteries. This lymphatic drainage pathway is reflected in the deposition of Aβ in the walls of human arteries with age and AD as cerebral amyloid angiopathy (CAA). Initially, Aβ diffuses through the extracellular spaces of gray matter in the brain and then enters basement membranes in capillaries and arteries to flow out of the brain. Although diffusion through the extracellular spaces of the brain has been well characterized, the exact mechanism whereby perivascular elimination of Aβ occurs has not been resolved. Here we use a computational model to describe the process of periarterial drainage in the context of diffusion in the brain, demonstrating that periarterial drainage along basement membranes is very rapid compared with diffusion. Our results are a validation of experimental data and are significant in the context of failure of periarterial drainage as a mechanism underlying the pathogenesis of AD as well as complications associated with its immunotherapy. PMID:26903861

Resting state network topology of the ferret brain.

PubMed

Zhou, Zhe Charles; Salzwedel, Andrew P; Radtke-Schuller, Susanne; Li, Yuhui; Sellers, Kristin K; Gilmore, John H; Shih, Yen-Yu Ian; Fröhlich, Flavio; Gao, Wei

2016-12-01

Resting state functional magnetic resonance imaging (rsfMRI) has emerged as a versatile tool for non-invasive measurement of functional connectivity patterns in the brain. RsfMRI brain dynamics in rodents, non-human primates, and humans share similar properties; however, little is known about the resting state functional connectivity patterns in the ferret, an animal model with high potential for developmental and cognitive translational study. To address this knowledge-gap, we performed rsfMRI on anesthetized ferrets using a 9.4T MRI scanner, and subsequently performed group-level independent component analysis (gICA) to identify functionally connected brain networks. Group-level ICA analysis revealed distributed sensory, motor, and higher-order networks in the ferret brain. Subsequent connectivity analysis showed interconnected higher-order networks that constituted a putative default mode network (DMN), a network that exhibits altered connectivity in neuropsychiatric disorders. Finally, we assessed ferret brain topological efficiency using graph theory analysis and found that the ferret brain exhibits small-world properties. Overall, these results provide additional evidence for pan-species resting-state networks, further supporting ferret-based studies of sensory and cognitive function. Copyright © 2016 Elsevier Inc. All rights reserved.

Stereotactically Standard Areas: Applied Mathematics in the Service of Brain Targeting in Deep Brain Stimulation.

PubMed

Mavridis, Ioannis N

2017-12-11

The concept of stereotactically standard areas (SSAs) within human brain nuclei belongs to the knowledge of the modern field of stereotactic brain microanatomy. These are areas resisting the individual variability of the nuclear location in stereotactic space. This paper summarizes the current knowledge regarding SSAs. A mathematical formula of SSAs was recently invented, allowing for their robust, reproducible, and accurate application to laboratory studies and clinical practice. Thus, SSAs open new doors for the application of stereotactic microanatomy to highly accurate brain targeting, which is mainly useful for minimally invasive neurosurgical procedures, such as deep brain stimulation.

Focal stimulation of the brain by entirely extracranial means. An example of radiation controlled focal pharmacology

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

Remler, M.P.

A method for focal stimulation of the brain by entirely extracranial means is presented. A focal x ray lesion of cortex was made that reduces the blood-brain barrier in that area. Then parenteral penicillin was administered. Penicillin is primarily confined to the vascular space by the blood-brain barrier in all parts of the brain except for some leakage into the brain at higher doses. An increased concentration of penicillin is created in the irradiated cortex. The penicillin creates a focal epileptic lesion in the irradiated area. This is an example of radiation-controlled focal pharmacology in the central nervous system. (auth)

A comparative quantitative analysis of cytoarchitecture and minicolumnar organization in Broca's area in humans and great apes.

PubMed

Schenker, Natalie M; Buxhoeveden, Daniel P; Blackmon, William L; Amunts, Katrin; Zilles, Karl; Semendeferi, Katerina

2008-09-01

Broca's area was identified in the inferior frontal gyrus of chimpanzee, bonobo, gorilla, and orangutan brains through direct cytoarchitectonic comparison with human brains. Across species, Broca's area comprises Brodmann's areas 44 and 45. We found that these areas exhibited similar cytoarchitectonic characteristics in all species examined. We analyzed the minicolumnar organization of cells in layer III of Broca's area in 11 human and 9 great ape specimens. A semiautomated method was used to analyze digitized images of histological sections stained for Nissl substance. Horizontal spacing distance and gray level index (GLI; or the area fraction occupied by cells) were quantified in all images. In contrast to area Tpt, the only cortical area for which comparative minicolumnar data have been published previously for humans and one of the great apes, we found no population-level asymmetry, for either horizontal spacing distance or GLI. Only human females exhibited a leftward asymmetry in GLI. GLI was lower in humans than in great apes (P < 0.001), allowing more space for connectivity in layer III. In humans, horizontal spacing distance was greater than in great apes but smaller relative to brain size. (c) 2008 Wiley-Liss, Inc.

Brain Perivascular Spaces as Biomarkers of Vascular Risk: Results from the Northern Manhattan Study.

PubMed

Gutierrez, J; Elkind, M S V; Dong, C; Di Tullio, M; Rundek, T; Sacco, R L; Wright, C B

2017-05-01

Dilated perivascular spaces in the brain are associated with greater arterial pulsatility. We hypothesized that perivascular spaces identify individuals at higher risk for systemic and cerebral vascular events. Stroke-free participants in the population-based Northern Manhattan Study had brain MR imaging performed and were followed for myocardial infarction, any stroke, and death. Imaging analyses distinguished perivascular spaces from lesions presumably ischemic. Perivascular spaces were further subdivided into lesions with diameters of ≤3 mm (small perivascular spaces) and >3 mm (large perivascular spaces). We calculated relative rates of events with Poisson models and hazard ratios with Cox proportional models. The Northern Manhattan Study participants who had MR imaging data available for review ( n = 1228; 59% women, 65% Hispanic; mean age, 71 ± 9 years) were followed for an average of 9 ± 2 years. Participants in the highest tertile of the small perivascular space score had a higher relative rate of all deaths (relative rate, 1.38; 95% CI, 1.01-1.91), vascular death (relative rate, 1.87; 95% CI, 1.12-3.14), myocardial infarction (relative rate, 2.08; 95% CI, 1.01-4.31), any stroke (relative rate, 1.79; 95% CI, 1.03-3.11), and any vascular event (relative rate, 1.74; 95% CI, 1.18-2.56). After we adjusted for confounders, there was a higher risk of vascular death (hazard ratio, 1.06; 95% CI, 1.01-1.11), myocardial infarction (hazard ratio, 2.22; 95% CI, 1.12-4.42), and any vascular event (hazard ratio, 1.04; 95% CI, 1.01-1.08) with higher small perivascular space scores. In this multiethnic, population-based study, participants with a high burden of small perivascular spaces had increased risk of vascular events. By gaining pathophysiologic insight into the mechanism of perivascular space dilation, we may be able to propose novel therapies to better prevent vascular disorders in the population. © 2017 by American Journal of Neuroradiology.

An efficient rhythmic component expression and weighting synthesis strategy for classifying motor imagery EEG in a brain computer interface

NASA Astrophysics Data System (ADS)

Wang, Tao; He, Bin

2004-03-01

The recognition of mental states during motor imagery tasks is crucial for EEG-based brain computer interface research. We have developed a new algorithm by means of frequency decomposition and weighting synthesis strategy for recognizing imagined right- and left-hand movements. A frequency range from 5 to 25 Hz was divided into 20 band bins for each trial, and the corresponding envelopes of filtered EEG signals for each trial were extracted as a measure of instantaneous power at each frequency band. The dimensionality of the feature space was reduced from 200 (corresponding to 2 s) to 3 by down-sampling of envelopes of the feature signals, and subsequently applying principal component analysis. The linear discriminate analysis algorithm was then used to classify the features, due to its generalization capability. Each frequency band bin was weighted by a function determined according to the classification accuracy during the training process. The present classification algorithm was applied to a dataset of nine human subjects, and achieved a success rate of classification of 90% in training and 77% in testing. The present promising results suggest that the present classification algorithm can be used in initiating a general-purpose mental state recognition based on motor imagery tasks.

The "glymphatic" mechanism for solute clearance in Alzheimer's disease: game changer or unproven speculation?

PubMed

Smith, Alex J; Verkman, Alan S

2018-02-01

How solutes and macromolecules are removed from brain tissue is of central importance in normal brain physiology and in how toxic protein aggregates are cleared in neurodegenerative conditions, including Alzheimer's disease (AD). Conventionally, solute transport in the narrow and tortuous extracellular space in brain parenchyma has been thought to be primarily diffusive and nondirectional. The recently proposed "glymphatic" (glial-lymphatic) hypothesis posits that solute clearance is convective and driven by active fluid transport from para-arterial to paravenous spaces though aquaporin-4 water channels in astrocyte endfeet. Glymphatic, convective solute clearance has received much attention because of its broad implications for AD and other brain pathologies and even the function of sleep. However, the theoretical plausibility of glymphatic transport has been questioned, and recent data have challenged its experimental underpinnings. A substantiated mechanism of solute clearance in the brain is of considerable importance because of its implications for pathogenic mechanisms of neurologic diseases and delivery of therapeutics.-Smith, A. J., Verkman, A. S. The "glymphatic" mechanism for solute clearance in Alzheimer's disease: game changer or unproven speculation?

The brain’s resting state activity is shaped by synchronized cross frequency coupling of neural oscillations (Author’s Manuscript)

DTIC Science & Technology

2015-02-11

uncovered. Using magnetoencephalography ( MEG ) imaging during rest in 12 healthy subjects we analyse the resting state networks and their underlying...across the whole brain of the resting state is generated. Human magnetoencephalography ( MEG ) of the whole brain emphasized the contribution of...frequency oscillations coordinate long-range communication (Stein, Chiang, and König, 2000). However, these MEG findings do not align entirely with

Effects of One Year of Spaceflight on Neurocognitive Function

NASA Technical Reports Server (NTRS)

Seidler, R. D.; Mulavara, A. P.; Koppelmans, V.; Kofman, I. S.; Cassady, K.; Yuan , P.; De Dios, Y. E.; Gadd, N.; Riascos, R. F.; Wood, S. J.;

Riemannian Metric Optimization on Surfaces (RMOS) for Intrinsic Brain Mapping in the Laplace-Beltrami Embedding Space

PubMed Central

Gahm, Jin Kyu; Shi, Yonggang

2018-01-01

Surface mapping methods play an important role in various brain imaging studies from tracking the maturation of adolescent brains to mapping gray matter atrophy patterns in Alzheimer’s disease. Popular surface mapping approaches based on spherical registration, however, have inherent numerical limitations when severe metric distortions are present during the spherical parameterization step. In this paper, we propose a novel computational framework for intrinsic surface mapping in the Laplace-Beltrami (LB) embedding space based on Riemannian metric optimization on surfaces (RMOS). Given a diffeomorphism between two surfaces, an isometry can be defined using the pullback metric, which in turn results in identical LB embeddings from the two surfaces. The proposed RMOS approach builds upon this mathematical foundation and achieves general feature-driven surface mapping in the LB embedding space by iteratively optimizing the Riemannian metric defined on the edges of triangular meshes. At the core of our framework is an optimization engine that converts an energy function for surface mapping into a distance measure in the LB embedding space, which can be effectively optimized using gradients of the LB eigen-system with respect to the Riemannian metrics. In the experimental results, we compare the RMOS algorithm with spherical registration using large-scale brain imaging data, and show that RMOS achieves superior performance in the prediction of hippocampal subfields and cortical gyral labels, and the holistic mapping of striatal surfaces for the construction of a striatal connectivity atlas from substantia nigra. PMID:29574399

Schizotypal Perceptual Aberrations of Time: Correlation between Score, Behavior and Brain Activity

PubMed Central

Arzy, Shahar; Mohr, Christine; Molnar-Szakacs, Istvan; Blanke, Olaf

2011-01-01

A fundamental trait of the human self is its continuum experience of space and time. Perceptual aberrations of this spatial and temporal continuity is a major characteristic of schizophrenia spectrum disturbances – including schizophrenia, schizotypal personality disorder and schizotypy. We have previously found the classical Perceptual Aberration Scale (PAS) scores, related to body and space, to be positively correlated with both behavior and temporo-parietal activation in healthy participants performing a task involving self-projection in space. However, not much is known about the relationship between temporal perceptual aberration, behavior and brain activity. To this aim, we composed a temporal Perceptual Aberration Scale (tPAS) similar to the traditional PAS. Testing on 170 participants suggested similar performance for PAS and tPAS. We then correlated tPAS and PAS scores to participants' performance and neural activity in a task of self-projection in time. tPAS scores correlated positively with reaction times across task conditions, as did PAS scores. Evoked potential mapping and electrical neuroimaging showed self-projection in time to recruit a network of brain regions at the left anterior temporal cortex, right temporo-parietal junction, and occipito-temporal cortex, and duration of activation in this network positively correlated with tPAS and PAS scores. These data demonstrate that schizotypal perceptual aberrations of both time and space, as reflected by tPAS and PAS scores, are positively correlated with performance and brain activation during self-projection in time in healthy individuals along the schizophrenia spectrum. PMID:21267456

Chronic Subdural Hematoma in the Aged, Trauma or Degeneration?

PubMed

Lee, Kyeong-Seok

2016-01-01

Chronic subdural hematomas (CSHs) are generally regarded to be a traumatic lesion. It was regarded as a stroke in 17th century, an inflammatory disease in 19th century. From 20th century, it became a traumatic lesion. CSH frequently occur after a trauma, however, it cannot occur when there is no enough subdural space even after a severe head injury. CSH may occur without trauma, when there is sufficient subdural space. The author tried to investigate trends in the causation of CSH. By a review of literature, the author suggested a different view on the causation of CSH. CSH usually originated from either a subdural hygroma or an acute subdural hematoma. Development of CSH starts from the separation of the dural border cell (DBC) layer, which induces proliferation of DBCs with production of neomembrane. Capillaries will follow along the neomembrane. Hemorrhage would occur into the subdural fluid either by tearing of bridge veins or repeated microhemorrhage from the neomembrane. That is the mechanism of hematoma enlargement. Trauma or bleeding tendency may precipitate development of CSH, however, it cannot lead CSH, if there is no sufficient subdural space. The key determinant for development of CSH is a sufficient subdural space, in other words, brain atrophy. The most common and universal cause of brain atrophy is the aging. Modifying Virchow's description, CSH is sometimes traumatic, but most often caused by degeneration of the brain. Now, it is reasonable that degeneration of brain might play pivotal role in development of CSH in the aged persons.

Deep two-photon microscopic imaging through brain tissue using the second singlet state from fluorescent agent chlorophyll α in spinach leaf

NASA Astrophysics Data System (ADS)

Shi, Lingyan; Rodríguez-Contreras, Adrián; Budansky, Yury; Pu, Yang; An Nguyen, Thien; Alfano, Robert R.

2014-06-01

Two-photon (2P) excitation of the second singlet (S) state was studied to achieve deep optical microscopic imaging in brain tissue when both the excitation (800 nm) and emission (685 nm) wavelengths lie in the "tissue optical window" (650 to 950 nm). S2 state technique was used to investigate chlorophyll α (Chl α) fluorescence inside a spinach leaf under a thick layer of freshly sliced rat brain tissue in combination with 2P microscopic imaging. Strong emission at the peak wavelength of 685 nm under the 2P S state of Chl α enabled the imaging depth up to 450 μm through rat brain tissue.

Deep two-photon microscopic imaging through brain tissue using the second singlet state from fluorescent agent chlorophyll α in spinach leaf.

PubMed

Shi, Lingyan; Rodríguez-Contreras, Adrián; Budansky, Yury; Pu, Yang; Nguyen, Thien An; Alfano, Robert R

2014-06-01

Two-photon (2P) excitation of the second singlet (S₂) state was studied to achieve deep optical microscopic imaging in brain tissue when both the excitation (800 nm) and emission (685 nm) wavelengths lie in the "tissue optical window" (650 to 950 nm). S₂ state technique was used to investigate chlorophyll α (Chl α) fluorescence inside a spinach leaf under a thick layer of freshly sliced rat brain tissue in combination with 2P microscopic imaging. Strong emission at the peak wavelength of 685 nm under the 2P S₂ state of Chl α enabled the imaging depth up to 450 μm through rat brain tissue.

Can brain scans prove criminals unaccountable?

PubMed Central

Roache, Rebecca

2014-01-01

Leonard Berlin reports that neuroscientific data play an increasing role in court. They have been used to argue that criminals are not morally responsible for their behaviour because their brains are ‘faulty’, and there is evidence that such data lead judges to pass more lenient sentences. I raise two concerns about the view that neuroscience can show criminals not to be morally responsible: That the brains of (say) violent criminals differ from most people’s brains does not straightforwardly show that violent criminals are less morally responsible. Behavioral states arise inter alia from brain states, and since violent criminals’ behavioral states differ from those of most people, it is unsurprising that violent criminals’ brains should differ from most people’s brains. This no more shows violent criminals to have diminished moral responsibility than differences between the brains of cheerful and uncheerful people show either group to have diminished moral responsibility.Those who view brain abnormalities as evidence of reduced moral responsibility rely on the assumptions that people with normal brains have free will and that we know what sorts of brain activity undermine free will. However, both of these assumptions are highly controversial. As a result, neuroscience is not a reliable source of information about moral responsibility. I conclude that, until we settle whether and under what circumstances brain activity is incompatible with free will, neuroscience cannot tell us anything useful about criminal accountability. PMID:25009758

Quadcopter control in three-dimensional space using a noninvasive motor imagery based brain-computer interface

PubMed Central

LaFleur, Karl; Cassady, Kaitlin; Doud, Alexander; Shades, Kaleb; Rogin, Eitan; He, Bin

2013-01-01

Objective At the balanced intersection of human and machine adaptation is found the optimally functioning brain-computer interface (BCI). In this study, we report a novel experiment of BCI controlling a robotic quadcopter in three-dimensional physical space using noninvasive scalp EEG in human subjects. We then quantify the performance of this system using metrics suitable for asynchronous BCI. Lastly, we examine the impact that operation of a real world device has on subjects’ control with comparison to a two-dimensional virtual cursor task. Approach Five human subjects were trained to modulate their sensorimotor rhythms to control an AR Drone navigating a three-dimensional physical space. Visual feedback was provided via a forward facing camera on the hull of the drone. Individual subjects were able to accurately acquire up to 90.5% of all valid targets presented while travelling at an average straight-line speed of 0.69 m/s. Significance Freely exploring and interacting with the world around us is a crucial element of autonomy that is lost in the context of neurodegenerative disease. Brain-computer interfaces are systems that aim to restore or enhance a user’s ability to interact with the environment via a computer and through the use of only thought. We demonstrate for the first time the ability to control a flying robot in the three-dimensional physical space using noninvasive scalp recorded EEG in humans. Our work indicates the potential of noninvasive EEG based BCI systems to accomplish complex control in three-dimensional physical space. The present study may serve as a framework for the investigation of multidimensional non-invasive brain-computer interface control in a physical environment using telepresence robotics. PMID:23735712

Simultaneous measurement of glucose transport and utilization in the human brain.

PubMed

Shestov, Alexander A; Emir, Uzay E; Kumar, Anjali; Henry, Pierre-Gilles; Seaquist, Elizabeth R; Öz, Gülin

2011-11-01

Glucose is the primary fuel for brain function, and determining the kinetics of cerebral glucose transport and utilization is critical for quantifying cerebral energy metabolism. The kinetic parameters of cerebral glucose transport, K(M)(t) and V(max)(t), in humans have so far been obtained by measuring steady-state brain glucose levels by proton ((1)H) NMR as a function of plasma glucose levels and fitting steady-state models to these data. Extraction of the kinetic parameters for cerebral glucose transport necessitated assuming a constant cerebral metabolic rate of glucose (CMR(glc)) obtained from other tracer studies, such as (13)C NMR. Here we present new methodology to simultaneously obtain kinetic parameters for glucose transport and utilization in the human brain by fitting both dynamic and steady-state (1)H NMR data with a reversible, non-steady-state Michaelis-Menten model. Dynamic data were obtained by measuring brain and plasma glucose time courses during glucose infusions to raise and maintain plasma concentration at ∼17 mmol/l for ∼2 h in five healthy volunteers. Steady-state brain vs. plasma glucose concentrations were taken from literature and the steady-state portions of data from the five volunteers. In addition to providing simultaneous measurements of glucose transport and utilization and obviating assumptions for constant CMR(glc), this methodology does not necessitate infusions of expensive or radioactive tracers. Using this new methodology, we found that the maximum transport capacity for glucose through the blood-brain barrier was nearly twofold higher than maximum cerebral glucose utilization. The glucose transport and utilization parameters were consistent with previously published values for human brain.

Random-Walk Model of Diffusion in Three Dimensions in Brain Extracellular Space: Comparison with Microfiberoptic Photobleaching Measurements

PubMed Central

Jin, Songwan; Zador, Zsolt; Verkman, A. S.

2008-01-01

Diffusion through the extracellular space (ECS) in brain is important in drug delivery, intercellular communication, and extracellular ionic buffering. The ECS comprises ∼20% of brain parenchymal volume and contains cell-cell gaps ∼50 nm. We developed a random-walk model to simulate macromolecule diffusion in brain ECS in three dimensions using realistic ECS dimensions. Model inputs included ECS volume fraction (α), cell size, cell-cell gap geometry, intercellular lake (expanded regions of brain ECS) dimensions, and molecular size of the diffusing solute. Model output was relative solute diffusion in water versus brain ECS (Do/D). Experimental Do/D for comparison with model predictions was measured using a microfiberoptic fluorescence photobleaching method involving stereotaxic insertion of a micron-size optical fiber into mouse brain. Do/D for the small solute calcein in different regions of brain was in the range 3.0–4.1, and increased with brain cell swelling after water intoxication. Do/D also increased with increasing size of the diffusing solute, particularly in deep brain nuclei. Simulations of measured Do/D using realistic α, cell size and cell-cell gap required the presence of intercellular lakes at multicell contact points, and the contact length of cell-cell gaps to be least 50-fold smaller than cell size. The model accurately predicted Do/D for different solute sizes. Also, the modeling showed unanticipated effects on Do/D of changing ECS and cell dimensions that implicated solute trapping by lakes. Our model establishes the geometric constraints to account quantitatively for the relatively modest slowing of solute and macromolecule diffusion in brain ECS. PMID:18469079

Random-walk model of diffusion in three dimensions in brain extracellular space: comparison with microfiberoptic photobleaching measurements.

PubMed

Jin, Songwan; Zador, Zsolt; Verkman, A S

2008-08-01

Diffusion through the extracellular space (ECS) in brain is important in drug delivery, intercellular communication, and extracellular ionic buffering. The ECS comprises approximately 20% of brain parenchymal volume and contains cell-cell gaps approximately 50 nm. We developed a random-walk model to simulate macromolecule diffusion in brain ECS in three dimensions using realistic ECS dimensions. Model inputs included ECS volume fraction (alpha), cell size, cell-cell gap geometry, intercellular lake (expanded regions of brain ECS) dimensions, and molecular size of the diffusing solute. Model output was relative solute diffusion in water versus brain ECS (D(o)/D). Experimental D(o)/D for comparison with model predictions was measured using a microfiberoptic fluorescence photobleaching method involving stereotaxic insertion of a micron-size optical fiber into mouse brain. D(o)/D for the small solute calcein in different regions of brain was in the range 3.0-4.1, and increased with brain cell swelling after water intoxication. D(o)/D also increased with increasing size of the diffusing solute, particularly in deep brain nuclei. Simulations of measured D(o)/D using realistic alpha, cell size and cell-cell gap required the presence of intercellular lakes at multicell contact points, and the contact length of cell-cell gaps to be least 50-fold smaller than cell size. The model accurately predicted D(o)/D for different solute sizes. Also, the modeling showed unanticipated effects on D(o)/D of changing ECS and cell dimensions that implicated solute trapping by lakes. Our model establishes the geometric constraints to account quantitatively for the relatively modest slowing of solute and macromolecule diffusion in brain ECS.

Multi-scale integration and predictability in resting state brain activity

PubMed Central

Kolchinsky, Artemy; van den Heuvel, Martijn P.; Griffa, Alessandra; Hagmann, Patric; Rocha, Luis M.; Sporns, Olaf; Goñi, Joaquín

2014-01-01

The human brain displays heterogeneous organization in both structure and function. Here we develop a method to characterize brain regions and networks in terms of information-theoretic measures. We look at how these measures scale when larger spatial regions as well as larger connectome sub-networks are considered. This framework is applied to human brain fMRI recordings of resting-state activity and DSI-inferred structural connectivity. We find that strong functional coupling across large spatial distances distinguishes functional hubs from unimodal low-level areas, and that this long-range functional coupling correlates with structural long-range efficiency on the connectome. We also find a set of connectome regions that are both internally integrated and coupled to the rest of the brain, and which resemble previously reported resting-state networks. Finally, we argue that information-theoretic measures are useful for characterizing the functional organization of the brain at multiple scales. PMID:25104933

BRAIN NETWORKS. Correlated gene expression supports synchronous activity in brain networks.

PubMed

Richiardi, Jonas; Altmann, Andre; Milazzo, Anna-Clare; Chang, Catie; Chakravarty, M Mallar; Banaschewski, Tobias; Barker, Gareth J; Bokde, Arun L W; Bromberg, Uli; Büchel, Christian; Conrod, Patricia; Fauth-Bühler, Mira; Flor, Herta; Frouin, Vincent; Gallinat, Jürgen; Garavan, Hugh; Gowland, Penny; Heinz, Andreas; Lemaître, Hervé; Mann, Karl F; Martinot, Jean-Luc; Nees, Frauke; Paus, Tomáš; Pausova, Zdenka; Rietschel, Marcella; Robbins, Trevor W; Smolka, Michael N; Spanagel, Rainer; Ströhle, Andreas; Schumann, Gunter; Hawrylycz, Mike; Poline, Jean-Baptiste; Greicius, Michael D

2015-06-12

During rest, brain activity is synchronized between different regions widely distributed throughout the brain, forming functional networks. However, the molecular mechanisms supporting functional connectivity remain undefined. We show that functional brain networks defined with resting-state functional magnetic resonance imaging can be recapitulated by using measures of correlated gene expression in a post mortem brain tissue data set. The set of 136 genes we identify is significantly enriched for ion channels. Polymorphisms in this set of genes significantly affect resting-state functional connectivity in a large sample of healthy adolescents. Expression levels of these genes are also significantly associated with axonal connectivity in the mouse. The results provide convergent, multimodal evidence that resting-state functional networks correlate with the orchestrated activity of dozens of genes linked to ion channel activity and synaptic function. Copyright © 2015, American Association for the Advancement of Science.

Characterizing dynamic amplitude of low-frequency fluctuation and its relationship with dynamic functional connectivity: An application to schizophrenia.

PubMed

Fu, Zening; Tu, Yiheng; Di, Xin; Du, Yuhui; Pearlson, G D; Turner, J A; Biswal, Bharat B; Zhang, Zhiguo; Calhoun, V D

2017-09-20

The human brain is a highly dynamic system with non-stationary neural activity and rapidly-changing neural interaction. Resting-state dynamic functional connectivity (dFC) has been widely studied during recent years, and the emerging aberrant dFC patterns have been identified as important features of many mental disorders such as schizophrenia (SZ). However, only focusing on the time-varying patterns in FC is not enough, since the local neural activity itself (in contrast to the inter-connectivity) is also found to be highly fluctuating from research using high-temporal-resolution imaging techniques. Exploring the time-varying patterns in brain activity and their relationships with time-varying brain connectivity is important for advancing our understanding of the co-evolutionary property of brain network and the underlying mechanism of brain dynamics. In this study, we introduced a framework for characterizing time-varying brain activity and exploring its associations with time-varying brain connectivity, and applied this framework to a resting-state fMRI dataset including 151 SZ patients and 163 age- and gender matched healthy controls (HCs). In this framework, 48 brain regions were first identified as intrinsic connectivity networks (ICNs) using group independent component analysis (GICA). A sliding window approach was then adopted for the estimation of dynamic amplitude of low-frequency fluctuation (dALFF) and dFC, which were used to measure time-varying brain activity and time-varying brain connectivity respectively. The dALFF was further clustered into six reoccurring states by the k-means clustering method and the group difference in occurrences of dALFF states was explored. Lastly, correlation coefficients between dALFF and dFC were calculated and the group difference in these dALFF-dFC correlations was explored. Our results suggested that 1) ALFF of brain regions was highly fluctuating during the resting-state and such dynamic patterns are altered in SZ, 2) dALFF and dFC were correlated in time and their correlations are altered in SZ. The overall results support and expand prior work on abnormalities of brain activity, static FC (sFC) and dFC in SZ, and provide new evidence on aberrant time-varying brain activity and its associations with brain connectivity in SZ, which might underscore the disrupted brain cognitive functions in this mental disorder. Copyright © 2017 Elsevier Inc. All rights reserved.

Hematopoietic Stem Cell Therapy to Countermeasure Cancer in Astronauts during Exploration of Deep Space

NASA Technical Reports Server (NTRS)

Ohi, S.; Kindred, R. P.; Roach, A-N.; Edossa, A.; Kim, B. C.; Gonda, S. R.; Emami, K.

2004-01-01

Exposure to cosmic radiation can cause chromosomal mutations, which may lead to cancer in astronauts engaged in space exploration. Therefore, our goals are to develop countermeasures to prevent space-induced cancer using hematopoietic stem cell therapy (HSCT) and gene therapy. This presentation focuses on HSCT for cancer. Our previous experiments on a simulated, space-induced immuno-deficiency model (mouse hind limb unloading ) indicated that transplanted hematopoietic stem cells (HSCs) could enhance the host's immunity by effectively eliminating bacterial infection (Ohi S, et. al. J Grav Physiol 10, P63-64, 2003; Ohi S, et. al. Proceedings of the Space Technology and Applications International Forum (STAIF) . American Institute of Physics, New York, pp. 938-950, 2004). Hence, we hypothesized that the HSCs might be effective in combating cancer as well. Studies of cocultured mouse HSCs with beta-galactosidase marked rat gliosarcoma spheroids (9L/lacZ), a cancer model, indicated antagonistic interactions , resulting in destruction of the spheroids by HSCs. Trypan Blue dye-exclusion assays were consistent with the conclusion. These results show potential usehlness of HSCT for cancer. Currently, the NASA Hydrodynamic Focusing Bioreactor (HFB), a space analog tissue/cell culture system, is being used to study invasion of the gliosarcoma (GS) spheroids into mouse brain with or without co-cultured HSCs. This may simulate the metastasis of gliosarcoma to brain. There is a tendency for the HSCs to inhibit invasion of GS spheroids into brain, as evidenced by the X-gal staining.

Specificity, Size, and Frequency of Spaces That Characterize the Mechanism of Bulk Transepithelial Transport of Prions in the Nasal Cavities of Hamsters and Mice

PubMed Central

Ayers, J. I.; Bartz, J. C.

2016-01-01

ABSTRACT Inhalation of infected brain homogenate results in transepithelial transport of prions across the nasal mucosa of hamsters, some of which occurs rapidly in relatively large amounts between cells (A. E. Kincaid, K. F. Hudson, M. W. Richey, and J. C. Bartz, J. Virol 86:12731–12740, 2012, doi:http://dx.doi.org/10.1128/JVI.01930-12). Bulk transepithelial transport in the nasal cavity has not been studied to date. In the present study, we characterized the frequency, size, and specificity of the intercellular spaces that mediate the bulk transport of inhaled prions between cells of mice or hamsters following extranasal inoculation with mock-infected brain homogenate, different strains of prion-infected brain homogenate, or brain homogenate mixed with India ink. Infected or mock-infected inoculum was identified within lymphatic vessels of the lamina propria and in spaces of >5 μm between a small number of cells of the nasal mucosa in >90% of animals from 5 to 60 min after inhalation. The width of the spaces between cells, the amount of the inoculum within the lumen of lymphatic vessels, and the timing of the transport indicate that this type of transport was taking place through preexisting spaces in the nasal cavity that were orders of magnitude wider than what is normally reported for paracellular transport. The indiscriminate rapid bulk transport of brain homogenate in the nasal cavity results in immediate entry into nasal cavity lymphatics following inhalation. This novel mechanism may underlie the recent report of the early detection of prions in blood following inhalation and has implications for horizontal prion transmission. IMPORTANCE The results of these studies demonstrate that the nasal mucosa of mice and hamsters is not an absolute anatomical barrier to inhaled prion-infected or uninfected brain homogenate. Relatively large amounts of infected and uninfected brain homogenate rapidly cross the nasal mucosa and enter the lumen of lymphatic vessels following inhalation. These bulk transepithelial transport events were relatively rare but present in >90% of animals 5 to 60 min following inhalation. This novel mechanism of bulk transepithelial transport was seen in experimental and control hamsters and mice, indicating that it was not species specific or in response to prion exposure. The indiscriminate bulk intercellular transport of inhaled pathogens across the nasal mucosa followed by entry into the lymphatic system may be a mechanism that underlies the entry and spread of other toxins and pathogens in olfactory system-driven animals. PMID:27384659

Dorsal raphe nucleus of brain in the rats flown in space inflight and postflight alteration of structure

NASA Astrophysics Data System (ADS)

Krasnov, I.

The structure of brain dorsal raphe nucleus (DRN) was studied in the rats flown in space aboard Space Shuttle "Columbia" (STS-58, SLS-2 program) and dissected on day 13 of the mission ("inflight" rats) and in 5-6 hours after finishing 14-day flight ("postflight" rats). The brain of "inflight" rats were excised after decapitation, sectioned sagitally halves of brain were fixed by immersion in 2,5 % glutaraldehyde in 0.1 M cacodylate buffer pH 7.3 at 4°C and kept in the flight at 4°C. After landing the brain frontal 0.5 mm sections from DRN area were osmificated and embedded in araldite at NASA ARC. The brains of "postflight": and control rats were underwent to the same procedure. Electronmicroscopical analysis, computer morphometry and glial cell count were performed at Moscow. In DRN neuropil of "inflight" rats the most part of axo-dendritic synapses were surrounded by glia cell processes and had decreased electron density of pre- and postsynaptic membrane and pronounced diminution of synaptic vesicle amount while dendrites were characterized by decrease in matrix electron density and microtubule quantity that in total indicates the decline of afferent flow reaching DRN neurons in microgravity. In DRN neurons of "inflight" rats all mitochondria were characterized by evenly increased dimensions, decreased matrix electron density, small amount of short and far- between located cristae and enlarged intermembrane and intercristae spaces, that in total points out low level of coupling of oxidation to phosphorilation, decrease in energy supply of neuron. Amount of ribosome in cytoplasm was significantly decreased indicating lower lever of biosynthetic processes. The last is supported by diminished dimensions of neuronal body, nucleus and nucleolus (place of r RNA synthesis), cross section area of that were reduced in DRN neurons of "inflight" rats by 18.8 % (p < 0.01), 11.1 % and 26.6 % (p <0,005) correspondingly. Ultrastructure and dimensions of intracellular structures in DRN of "postflight" rats were not differ significantly fo rm analogous parameters of "inflight" rats. The results of study point out the decrease in mircrogravity in functional activity of DRN - main serotoniner gic center of brain and in combination with the data (Krasnov et. A.; 1998; Krasnov, Dyachkova, 2000) about inflight alteration in locus coeruleus - main noradrenergic center allow to propose the mechanism of decline of growth hormone secretion in mammals during space flight.

Statistical Analyses of Brain Surfaces Using Gaussian Random Fields on 2-D Manifolds

PubMed Central

Staib, Lawrence H.; Xu, Dongrong; Zhu, Hongtu; Peterson, Bradley S.

2008-01-01

Interest in the morphometric analysis of the brain and its subregions has recently intensified because growth or degeneration of the brain in health or illness affects not only the volume but also the shape of cortical and subcortical brain regions, and new image processing techniques permit detection of small and highly localized perturbations in shape or localized volume, with remarkable precision. An appropriate statistical representation of the shape of a brain region is essential, however, for detecting, localizing, and interpreting variability in its surface contour and for identifying differences in volume of the underlying tissue that produce that variability across individuals and groups of individuals. Our statistical representation of the shape of a brain region is defined by a reference region for that region and by a Gaussian random field (GRF) that is defined across the entire surface of the region. We first select a reference region from a set of segmented brain images of healthy individuals. The GRF is then estimated as the signed Euclidean distances between points on the surface of the reference region and the corresponding points on the corresponding region in images of brains that have been coregistered to the reference. Correspondences between points on these surfaces are defined through deformations of each region of a brain into the coordinate space of the reference region using the principles of fluid dynamics. The warped, coregistered region of each subject is then unwarped into its native space, simultaneously bringing into that space the map of corresponding points that was established when the surfaces of the subject and reference regions were tightly coregistered. The proposed statistical description of the shape of surface contours makes no assumptions, other than smoothness, about the shape of the region or its GRF. The description also allows for the detection and localization of statistically significant differences in the shapes of the surfaces across groups of subjects at both a fine and coarse scale. We demonstrate the effectiveness of these statistical methods by applying them to study differences in shape of the amygdala and hippocampus in a large sample of normal subjects and in subjects with attention deficit/hyperactivity disorder (ADHD). PMID:17243583

Enlarging the scope: grasping brain complexity

PubMed Central

Tognoli, Emmanuelle; Kelso, J. A. Scott

2014-01-01

To further advance our understanding of the brain, new concepts and theories are needed. In particular, the ability of the brain to create information flows must be reconciled with its propensity for synchronization and mass action. The theoretical and empirical framework of Coordination Dynamics, a key aspect of which is metastability, are presented as a starting point to study the interplay of integrative and segregative tendencies that are expressed in space and time during the normal course of brain and behavioral function. Some recent shifts in perspective are emphasized, that may ultimately lead to a better understanding of brain complexity. PMID:25009476

Reliability of resting-state microstate features in electroencephalography.

PubMed

Khanna, Arjun; Pascual-Leone, Alvaro; Farzan, Faranak

2014-01-01

Electroencephalographic (EEG) microstate analysis is a method of identifying quasi-stable functional brain states ("microstates") that are altered in a number of neuropsychiatric disorders, suggesting their potential use as biomarkers of neurophysiological health and disease. However, use of EEG microstates as neurophysiological biomarkers requires assessment of the test-retest reliability of microstate analysis. We analyzed resting-state, eyes-closed, 30-channel EEG from 10 healthy subjects over 3 sessions spaced approximately 48 hours apart. We identified four microstate classes and calculated the average duration, frequency, and coverage fraction of these microstates. Using Cronbach's α and the standard error of measurement (SEM) as indicators of reliability, we examined: (1) the test-retest reliability of microstate features using a variety of different approaches; (2) the consistency between TAAHC and k-means clustering algorithms; and (3) whether microstate analysis can be reliably conducted with 19 and 8 electrodes. The approach of identifying a single set of "global" microstate maps showed the highest reliability (mean Cronbach's α > 0.8, SEM ≈ 10% of mean values) compared to microstates derived by each session or each recording. There was notably low reliability in features calculated from maps extracted individually for each recording, suggesting that the analysis is most reliable when maps are held constant. Features were highly consistent across clustering methods (Cronbach's α > 0.9). All features had high test-retest reliability with 19 and 8 electrodes. High test-retest reliability and cross-method consistency of microstate features suggests their potential as biomarkers for assessment of the brain's neurophysiological health.

Diminished neural network dynamics after moderate and severe traumatic brain injury

PubMed Central

Gilbert, Nicholas; Bernier, Rachel A.; Calhoun, Vincent D.; Brenner, Einat; Grossner, Emily; Rajtmajer, Sarah M.

2018-01-01

Over the past decade there has been increasing enthusiasm in the cognitive neurosciences around using network science to understand the system-level changes associated with brain disorders. A growing literature has used whole-brain fMRI analysis to examine changes in the brain’s subnetworks following traumatic brain injury (TBI). Much of network modeling in this literature has focused on static network mapping, which provides a window into gross inter-nodal relationships, but is insensitive to more subtle fluctuations in network dynamics, which may be an important predictor of neural network plasticity. In this study, we examine the dynamic connectivity with focus on state-level connectivity (state) and evaluate the reliability of dynamic network states over the course of two runs of intermittent task and resting data. The goal was to examine the dynamic properties of neural networks engaged periodically with task stimulation in order to determine: 1) the reliability of inter-nodal and network-level characteristics over time and 2) the transitions between distinct network states after traumatic brain injury. To do so, we enrolled 23 individuals with moderate and severe TBI at least 1-year post injury and 19 age- and education-matched healthy adults using functional MRI methods, dynamic connectivity modeling, and graph theory. The results reveal several distinct network “states” that were reliably evident when comparing runs; the overall frequency of dynamic network states are highly reproducible (r-values>0.8) for both samples. Analysis of movement between states resulted in fewer state transitions in the TBI sample and, in a few cases, brain injury resulted in the appearance of states not exhibited by the healthy control (HC) sample. Overall, the findings presented here demonstrate the reliability of observable dynamic mental states during periods of on-task performance and support emerging evidence that brain injury may result in diminished network dynamics. PMID:29883447

Dynamic Repertoire of Intrinsic Brain States Is Reduced in Propofol-Induced Unconsciousness

PubMed Central

Liu, Xiping; Pillay, Siveshigan

2015-01-01

Abstract The richness of conscious experience is thought to scale with the size of the repertoire of causal brain states, and it may be diminished in anesthesia. We estimated the state repertoire from dynamic analysis of intrinsic functional brain networks in conscious sedated and unconscious anesthetized rats. Functional resonance images were obtained from 30-min whole-brain resting-state blood oxygen level-dependent (BOLD) signals at propofol infusion rates of 20 and 40 mg/kg/h, intravenously. Dynamic brain networks were defined at the voxel level by sliding window analysis of regional homogeneity (ReHo) or coincident threshold crossings (CTC) of the BOLD signal acquired in nine sagittal slices. The state repertoire was characterized by the temporal variance of the number of voxels with significant ReHo or positive CTC. From low to high propofol dose, the temporal variances of ReHo and CTC were reduced by 78%±20% and 76%±20%, respectively. Both baseline and propofol-induced reduction of CTC temporal variance increased from lateral to medial position. Group analysis showed a 20% reduction in the number of unique states at the higher propofol dose. Analysis of temporal variance in 12 anatomically defined regions of interest predicted that the largest changes occurred in visual cortex, parietal cortex, and caudate-putamen. The results suggest that the repertoire of large-scale brain states derived from the spatiotemporal dynamics of intrinsic networks is substantially reduced at an anesthetic dose associated with loss of consciousness. PMID:24702200

Recruitment of Additional Corticospinal Pathways in the Human Brain with State-Dependent Paired Associative Stimulation.

PubMed

Kraus, Dominic; Naros, Georgios; Guggenberger, Robert; Leão, Maria Teresa; Ziemann, Ulf; Gharabaghi, Alireza

2018-02-07

Standard brain stimulation protocols modify human motor cortex excitability by modulating the gain of the activated corticospinal pathways. However, the restoration of motor function following lesions of the corticospinal tract requires also the recruitment of additional neurons to increase the net corticospinal output. For this purpose, we investigated a novel protocol based on brain state-dependent paired associative stimulation.Motor imagery (MI)-related electroencephalography was recorded in healthy males and females for brain state-dependent control of both cortical and peripheral stimulation in a brain-machine interface environment. State-dependency was investigated with concurrent, delayed, and independent stimulation relative to the MI task. Specifically, sensorimotor event-related desynchronization (ERD) in the β-band (16-22 Hz) triggered peripheral stimulation through passive hand opening by a robotic orthosis and transcranial magnetic stimulation to the respective cortical motor representation, either synchronously or subsequently. These MI-related paradigms were compared with paired cortical and peripheral input applied independent of the brain state. Cortical stimulation resulted in a significant increase in corticospinal excitability only when applied brain state-dependently and synchronously to peripheral input. These gains were resistant to a depotentiation task, revealed a nonlinear evolution of plasticity, and were mediated via the recruitment of additional corticospinal neurons rather than via synchronization of neuronal firing. Recruitment of additional corticospinal pathways may be achieved when cortical and peripheral inputs are applied concurrently, and during β-ERD. These findings resemble a gating mechanism and are potentially important for developing closed-loop brain stimulation for the treatment of hand paralysis following lesions of the corticospinal tract. SIGNIFICANCE STATEMENT The activity state of the motor system influences the excitability of corticospinal pathways to external input. State-dependent interventions harness this property to increase the connectivity between motor cortex and muscles. These stimulation protocols modulate the gain of the activated pathways, but not the overall corticospinal recruitment. In this study, a brain-machine interface paired peripheral stimulation through passive hand opening with transcranial magnetic stimulation to the respective cortical motor representation during volitional β-band desynchronization. Cortical stimulation resulted in the recruitment of additional corticospinal pathways, but only when applied brain state-dependently and synchronously to peripheral input. These effects resemble a gating mechanism and may be important for the restoration of motor function following lesions of the corticospinal tract. Copyright © 2018 the authors 0270-6474/18/381397-12$15.00/0.

High Slew-Rate Head-Only Gradient for Improving Distortion in Echo Planar Imaging: Preliminary Experience

PubMed Central

Tan, Ek T.; Lee, Seung-Kyun; Weavers, Paul T.; Graziani, Dominic; Piel, Joseph E.; Shu, Yunhong; Huston, John; Bernstein, Matt A.; Foo, Thomas K.F.

2016-01-01

Purpose To investigate the effects on echo planar imaging (EPI) distortion of using high gradient slew rates (SR) of up to 700 T/m/s for in-vivo human brain imaging, with a dedicated, head-only gradient coil. Materials and Methods Simulation studies were first performed to determine the expected echo spacing and distortion reduction in EPI. A head gradient of 42-cm inner diameter and with asymmetric transverse coils was then installed in a whole-body, conventional 3T MRI system. Human subject imaging was performed on five subjects to determine the effects of EPI on echo spacing and signal dropout at various gradient slew rates. The feasibility of whole-brain imaging at 1.5 mm-isotropic spatial resolution was demonstrated with gradient-echo and spin-echo diffusion-weighted EPI. Results As compared to a whole-body gradient coil, the EPI echo spacing in the head-only gradient coil was reduced by 48%. Simulation and in vivo results, respectively, showed up to 25-26% and 19% improvement in signal dropout. Whole-brain imaging with EPI at 1.5 mm spatial resolution provided good whole-brain coverage, spatial linearity, and low spatial distortion effects. Conclusion Our results of human brain imaging with EPI using the compact head gradient coil at slew rates higher than in conventional whole-body MR systems demonstrate substantially improved image distortion, and point to a potential for benefits to non-EPI pulse sequences. PMID:26921117

A challenge to chaotic itinerancy from brain dynamics

NASA Astrophysics Data System (ADS)

Kay, Leslie M.

2003-09-01

Brain hermeneutics and chaotic itinerancy proposed by Tsuda are attractive characterizations of perceptual dynamics in the mammalian olfactory system. This theory proposes that perception occurs at the interface between itinerant neural representation and interaction with the environment. Quantifiable application of these dynamics has been hampered by the lack of definable history and action processes which characterize the changes induced by behavioral state, attention, and learning. Local field potentials measured from several brain areas were used to characterize dynamic activity patterns for their use as representations of history and action processes. The signals were recorded from olfactory areas (olfactory bulb, OB, and pyriform cortex) and hippocampal areas (entorhinal cortex and dentate gyrus, DG) in the brains of rats. During odor-guided behavior the system shows dynamics at three temporal scales. Short time-scale changes are system-wide and can occur in the space of a single sniff. They are predictable, associated with learned shifts in behavioral state and occur periodically on the scale of the intertrial interval. These changes occupy the theta (2-12 Hz), beta (15-30 Hz), and gamma (40-100 Hz) frequency bands within and between all areas. Medium time-scale changes occur relatively unpredictably, manifesting in these data as alterations in connection strength between the OB and DG. These changes are strongly correlated with performance in associated trial blocks (5-10 min) and may be due to fluctuations in attention, mood, or amount of reward received. Long time-scale changes are likely related to learning or decline due to aging or disease. These may be modeled as slow monotonic processes that occur within or across days or even weeks or years. The folding of different time scales is proposed as a mechanism for chaotic itinerancy, represented by dynamic processes instead of static connection strengths. Thus, the individual maintains continuity of experience within the stability of fast periodic and slow monotonic processes, while medium scale events alter experience and performance dramatically but temporarily. These processes together with as yet to be determined action effects from motor system feedback are proposed as an instantiation of brain hermeneutics and chaotic itinerancy.

Automatic labeling of MR brain images through extensible learning and atlas forests.

PubMed

Xu, Lijun; Liu, Hong; Song, Enmin; Yan, Meng; Jin, Renchao; Hung, Chih-Cheng

2017-12-01

Multiatlas-based method is extensively used in MR brain images segmentation because of its simplicity and robustness. This method provides excellent accuracy although it is time consuming and limited in terms of obtaining information about new atlases. In this study, an automatic labeling of MR brain images through extensible learning and atlas forest is presented to address these limitations. We propose an extensible learning model which allows the multiatlas-based framework capable of managing the datasets with numerous atlases or dynamic atlas datasets and simultaneously ensure the accuracy of automatic labeling. Two new strategies are used to reduce the time and space complexity and improve the efficiency of the automatic labeling of brain MR images. First, atlases are encoded to atlas forests through random forest technology to reduce the time consumed for cross-registration between atlases and target image, and a scatter spatial vector is designed to eliminate errors caused by inaccurate registration. Second, an atlas selection method based on the extensible learning model is used to select atlases for target image without traversing the entire dataset and then obtain the accurate labeling. The labeling results of the proposed method were evaluated in three public datasets, namely, IBSR, LONI LPBA40, and ADNI. With the proposed method, the dice coefficient metric values on the three datasets were 84.17 ± 4.61%, 83.25 ± 4.29%, and 81.88 ± 4.53% which were 5% higher than those of the conventional method, respectively. The efficiency of the extensible learning model was evaluated by state-of-the-art methods for labeling of MR brain images. Experimental results showed that the proposed method could achieve accurate labeling for MR brain images without traversing the entire datasets. In the proposed multiatlas-based method, extensible learning and atlas forests were applied to control the automatic labeling of brain anatomies on large atlas datasets or dynamic atlas datasets and obtain accurate results. © 2017 American Association of Physicists in Medicine.

Determination of Vascular Dementia Brain in Distinct Frequency Bands with Whole Brain Functional Connectivity Patterns

PubMed Central

Zhang, Delong; Liu, Bo; Chen, Jun; Peng, Xiaoling; Liu, Xian; Fan, Yuanyuan; Liu, Ming; Huang, Ruiwang

2013-01-01

Recent studies have shown that multivariate pattern analysis (MVPA) can be useful for distinguishing brain disorders into categories. Such analyses can substantially enrich and facilitate clinical diagnoses. Using MPVA methods, whole brain functional networks, especially those derived using different frequency windows, can be applied to detect brain states. We constructed whole brain functional networks for groups of vascular dementia (VaD) patients and controls using resting state BOLD-fMRI (rsfMRI) data from three frequency bands - slow-5 (0.01∼0.027 Hz), slow-4 (0.027∼0.073 Hz), and whole-band (0.01∼0.073 Hz). Then we used the support vector machine (SVM), a type of MVPA classifier, to determine the patterns of functional connectivity. Our results showed that the brain functional networks derived from rsfMRI data (19 VaD patients and 20 controls) in these three frequency bands appear to reflect neurobiological changes in VaD patients. Such differences could be used to differentiate the brain states of VaD patients from those of healthy individuals. We also found that the functional connectivity patterns of the human brain in the three frequency bands differed, as did their ability to differentiate brain states. Specifically, the ability of the functional connectivity pattern to differentiate VaD brains from healthy ones was more efficient in the slow-5 (0.01∼0.027 Hz) band than in the other two frequency bands. Our findings suggest that the MVPA approach could be used to detect abnormalities in the functional connectivity of VaD patients in distinct frequency bands. Identifying such abnormalities may contribute to our understanding of the pathogenesis of VaD. PMID:23359801

A common brain network among state, trait, and pathological anxiety from whole-brain functional connectivity.

PubMed

Takagi, Yu; Sakai, Yuki; Abe, Yoshinari; Nishida, Seiji; Harrison, Ben J; Martínez-Zalacaín, Ignacio; Soriano-Mas, Carles; Narumoto, Jin; Tanaka, Saori C

2018-05-15

Anxiety is one of the most common mental states of humans. Although it drives us to avoid frightening situations and to achieve our goals, it may also impose significant suffering and burden if it becomes extreme. Because we experience anxiety in a variety of forms, previous studies investigated neural substrates of anxiety in a variety of ways. These studies revealed that individuals with high state, trait, or pathological anxiety showed altered neural substrates. However, no studies have directly investigated whether the different dimensions of anxiety share a common neural substrate, despite its theoretical and practical importance. Here, we investigated a brain network of anxiety shared by different dimensions of anxiety in a unified analytical framework using functional magnetic resonance imaging (fMRI). We analyzed different datasets in a single scale, which was defined by an anxiety-related brain network derived from whole brain. We first conducted the anxiety provocation task with healthy participants who tended to feel anxiety related to obsessive-compulsive disorder (OCD) in their daily life. We found a common state anxiety brain network across participants (1585 trials obtained from 10 participants). Then, using the resting-state fMRI in combination with the participants' behavioral trait anxiety scale scores (879 participants from the Human Connectome Project), we demonstrated that trait anxiety shared the same brain network as state anxiety. Furthermore, the brain network between common to state and trait anxiety could detect patients with OCD, which is characterized by pathological anxiety-driven behaviors (174 participants from multi-site datasets). Our findings provide direct evidence that different dimensions of anxiety have a substantial biological inter-relationship. Our results also provide a biologically defined dimension of anxiety, which may promote further investigation of various human characteristics, including psychiatric disorders, from the perspective of anxiety. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

Hearing Scenes: A Neuromagnetic Signature of Auditory Source and Reverberant Space Separation

PubMed Central

Oliva, Aude

2017-01-01

Abstract Perceiving the geometry of surrounding space is a multisensory process, crucial to contextualizing object perception and guiding navigation behavior. Humans can make judgments about surrounding spaces from reverberation cues, caused by sounds reflecting off multiple interior surfaces. However, it remains unclear how the brain represents reverberant spaces separately from sound sources. Here, we report separable neural signatures of auditory space and source perception during magnetoencephalography (MEG) recording as subjects listened to brief sounds convolved with monaural room impulse responses (RIRs). The decoding signature of sound sources began at 57 ms after stimulus onset and peaked at 130 ms, while space decoding started at 138 ms and peaked at 386 ms. Importantly, these neuromagnetic responses were readily dissociable in form and time: while sound source decoding exhibited an early and transient response, the neural signature of space was sustained and independent of the original source that produced it. The reverberant space response was robust to variations in sound source, and vice versa, indicating a generalized response not tied to specific source-space combinations. These results provide the first neuromagnetic evidence for robust, dissociable auditory source and reverberant space representations in the human brain and reveal the temporal dynamics of how auditory scene analysis extracts percepts from complex naturalistic auditory signals. PMID:28451630

Near, yet so Far: The Effect of Pictorial Cues on Spatial Attention

ERIC Educational Resources Information Center

Nicholls, Michael E. R.; Forte, Jason D.; Loetscher, Tobias; Orr, Catherine A.; Yates, Mark J.; Bradshaw, John L.

2011-01-01

Distinct cognitive and neural mechanisms underlie perception and action in near (within-reach) and far (outside-reach) space. Objects in far space can be brought into the brain's near-space through tool-use. We determined whether a near object can be pushed into far space by changing the pictorial context in which it occurs. Participants (n = 372)…

Tyurin with TRAC experiment in Destiny laboratory

NASA Image and Video Library

2007-01-02

ISS014-E-11047 (2 Jan. 2007) --- Cosmonaut Mikhail Tyurin, Expedition 14 flight engineer representing Russia's Federal Space Agency, works with the Test of Reaction and Adaptation Capabilities (TRAC) experiment in the Destiny laboratory of the International Space Station. The TRAC investigation will test the theory of brain adaptation during space flight by testing hand-eye coordination before, during and after the space flight.

Inferring pathological states in cortical neuron microcircuits.

PubMed

Rydzewski, Jakub; Nowak, Wieslaw; Nicosia, Giuseppe

2015-12-07

The brain activity is to a large extent determined by states of neural cortex microcircuits. Unfortunately, accuracy of results from neural circuits׳ mathematical models is often biased by the presence of uncertainties in underlying experimental data. Moreover, due to problems with uncertainties identification in a multidimensional parameters space, it is almost impossible to classify states of the neural cortex, which correspond to a particular set of the parameters. Here, we develop a complete methodology for determining uncertainties and the novel protocol for classifying all states in any neuroinformatic model. Further, we test this protocol on the mathematical, nonlinear model of such a microcircuit developed by Giugliano et al. (2008) and applied in the experimental data analysis of Huntington׳s disease. Up to now, the link between parameter domains in the mathematical model of Huntington׳s disease and the pathological states in cortical microcircuits has remained unclear. In this paper we precisely identify all the uncertainties, the most crucial input parameters and domains that drive the system into an unhealthy state. The scheme proposed here is general and can be easily applied to other mathematical models of biological phenomena. Copyright © 2015 Elsevier Ltd. All rights reserved.

Radial q-space sampling for DSI

PubMed Central

Baete, Steven H.; Yutzy, Stephen; Boada, Fernando, E.

2015-01-01

Purpose Diffusion Spectrum Imaging (DSI) has been shown to be an effective tool for non-invasively depicting the anatomical details of brain microstructure. Existing implementations of DSI sample the diffusion encoding space using a rectangular grid. Here we present a different implementation of DSI whereby a radially symmetric q-space sampling scheme for DSI (RDSI) is used to improve the angular resolution and accuracy of the reconstructed Orientation Distribution Functions (ODF). Methods Q-space is sampled by acquiring several q-space samples along a number of radial lines. Each of these radial lines in q-space is analytically connected to a value of the ODF at the same angular location by the Fourier slice theorem. Results Computer simulations and in vivo brain results demonstrate that RDSI correctly estimates the ODF when moderately high b-values (4000 s/mm2) and number of q-space samples (236) are used. Conclusion The nominal angular resolution of RDSI depends on the number of radial lines used in the sampling scheme, and only weakly on the maximum b-value. In addition, the radial analytical reconstruction reduces truncation artifacts which affect Cartesian reconstructions. Hence, a radial acquisition of q-space can be favorable for DSI. PMID:26363002

Lateral orbitofrontal cortex anticipates choices and integrates prior with current information

PubMed Central

Nogueira, Ramon; Abolafia, Juan M.; Drugowitsch, Jan; Balaguer-Ballester, Emili; Sanchez-Vives, Maria V.; Moreno-Bote, Rubén

2017-01-01

Adaptive behavior requires integrating prior with current information to anticipate upcoming events. Brain structures related to this computation should bring relevant signals from the recent past into the present. Here we report that rats can integrate the most recent prior information with sensory information, thereby improving behavior on a perceptual decision-making task with outcome-dependent past trial history. We find that anticipatory signals in the orbitofrontal cortex about upcoming choice increase over time and are even present before stimulus onset. These neuronal signals also represent the stimulus and relevant second-order combinations of past state variables. The encoding of choice, stimulus and second-order past state variables resides, up to movement onset, in overlapping populations. The neuronal representation of choice before stimulus onset and its build-up once the stimulus is presented suggest that orbitofrontal cortex plays a role in transforming immediate prior and stimulus information into choices using a compact state-space representation. PMID:28337990

Brain regions involved in subprocesses of small-space episodic object-location memory: a systematic review of lesion and functional neuroimaging studies.

PubMed

Zimmermann, Kathrin; Eschen, Anne

2017-04-01

Object-location memory (OLM) enables us to keep track of the locations of objects in our environment. The neurocognitive model of OLM (Postma, A., Kessels, R. P. C., & Van Asselen, M. (2004). The neuropsychology of object-location memory. In G. L. Allen (Ed.), Human spatial memory: Remembering where (pp. 143-160). Mahwah, NJ: Lawrence Erlbaum, Postma, A., Kessels, R. P. C., & Van Asselen, M. (2008). How the brain remembers and forgets where things are: The neurocognition of object-location memory. Neuroscience & Biobehavioral Reviews, 32, 1339-1345. doi: 10.1016/j.neubiorev.2008.05.001 ) proposes that distinct brain regions are specialised for different subprocesses of OLM (object processing, location processing, and object-location binding; categorical and coordinate OLM; egocentric and allocentric OLM). It was based mainly on findings from lesion studies. However, recent episodic memory studies point to a contribution of additional or different brain regions to object and location processing within episodic OLM. To evaluate and update the neurocognitive model of OLM, we therefore conducted a systematic literature search for lesion as well as functional neuroimaging studies contrasting small-space episodic OLM with object memory or location memory. We identified 10 relevant lesion studies and 8 relevant functional neuroimaging studies. We could confirm some of the proposals of the neurocognitive model of OLM, but also differing hypotheses from episodic memory research, about which brain regions are involved in the different subprocesses of small-space episodic OLM. In addition, we were able to identify new brain regions as well as important research gaps.

Reproducibility assessment of brain responses to visual food stimuli in adults with overweight and obesity.

PubMed

Drew Sayer, R; Tamer, Gregory G; Chen, Ningning; Tregellas, Jason R; Cornier, Marc-Andre; Kareken, David A; Talavage, Thomas M; McCrory, Megan A; Campbell, Wayne W

2016-10-01

The brain's reward system influences ingestive behavior and subsequently obesity risk. Functional magnetic resonance imaging (fMRI) is a common method for investigating brain reward function. This study sought to assess the reproducibility of fasting-state brain responses to visual food stimuli using BOLD fMRI. A priori brain regions of interest included bilateral insula, amygdala, orbitofrontal cortex, caudate, and putamen. Fasting-state fMRI and appetite assessments were completed by 28 women (n = 16) and men (n = 12) with overweight or obesity on 2 days. Reproducibility was assessed by comparing mean fasting-state brain responses and measuring test-retest reliability of these responses on the two testing days. Mean fasting-state brain responses on day 2 were reduced compared with day 1 in the left insula and right amygdala, but mean day 1 and day 2 responses were not different in the other regions of interest. With the exception of the left orbitofrontal cortex response (fair reliability), test-retest reliabilities of brain responses were poor or unreliable. fMRI-measured responses to visual food cues in adults with overweight or obesity show relatively good mean-level reproducibility but considerable within-subject variability. Poor test-retest reliability reduces the likelihood of observing true correlations and increases the necessary sample sizes for studies. © 2016 The Obesity Society.

Altered Whole-Brain and Network-Based Functional Connectivity in Parkinson's Disease.

PubMed

de Schipper, Laura J; Hafkemeijer, Anne; van der Grond, Jeroen; Marinus, Johan; Henselmans, Johanna M L; van Hilten, Jacobus J

2018-01-01

Background: Functional imaging methods, such as resting-state functional magnetic resonance imaging, reflect changes in neural connectivity and may help to assess the widespread consequences of disease-specific network changes in Parkinson's disease. In this study we used a relatively new graph analysis approach in functional imaging: eigenvector centrality mapping. This model-free method, applied to all voxels in the brain, identifies prominent regions in the brain network hierarchy and detects localized differences between patient populations. In other neurological disorders, eigenvector centrality mapping has been linked to changes in functional connectivity in certain nodes of brain networks. Objectives: Examining changes in functional brain connectivity architecture on a whole brain and network level in patients with Parkinson's disease. Methods: Whole brain resting-state functional architecture was studied with a recently introduced graph analysis approach (eigenvector centrality mapping). Functional connectivity was further investigated in relation to eight known resting-state networks. Cross-sectional analyses included group comparison of functional connectivity measures of Parkinson's disease patients ( n = 107) with control subjects ( n = 58) and correlations with clinical data, including motor and cognitive impairment and a composite measure of predominantly non-dopaminergic symptoms. Results: Eigenvector centrality mapping revealed that frontoparietal regions were more prominent in the whole-brain network function in patients compared to control subjects, while frontal and occipital brain areas were less prominent in patients. Using standard resting-state networks, we found predominantly increased functional connectivity, namely within sensorimotor system and visual networks in patients. Regional group differences in functional connectivity of both techniques between patients and control subjects partly overlapped for highly connected posterior brain regions, in particular in the posterior cingulate cortex and precuneus. Clinico-functional imaging relations were not found. Conclusions: Changes on the level of functional brain connectivity architecture might provide a different perspective of pathological consequences of Parkinson's disease. The involvement of specific, highly connected (hub) brain regions may influence whole brain functional network architecture in Parkinson's disease.

Connectivity dynamics in typical development and its relationship to autistic traits and autism spectrum disorder.

PubMed

Rashid, Barnaly; Blanken, Laura M E; Muetzel, Ryan L; Miller, Robyn; Damaraju, Eswar; Arbabshirani, Mohammad R; Erhardt, Erik B; Verhulst, Frank C; van der Lugt, Aad; Jaddoe, Vincent W V; Tiemeier, Henning; White, Tonya; Calhoun, Vince

2018-03-30

Recent advances in neuroimaging techniques have provided significant insights into developmental trajectories of human brain function. Characterizations of typical neurodevelopment provide a framework for understanding altered neurodevelopment, including differences in brain function related to developmental disorders and psychopathology. Historically, most functional connectivity studies of typical and atypical development operate under the assumption that connectivity remains static over time. We hypothesized that relaxing stationarity assumptions would reveal novel features of both typical brain development related to children on the autism spectrum. We employed a "chronnectomic" (recurring, time-varying patterns of connectivity) approach to evaluate transient states of connectivity using resting-state functional MRI in a population-based sample of 774 6- to 10-year-old children. Dynamic connectivity was evaluated using a sliding-window approach, and revealed four transient states. Internetwork connectivity increased with age in modularized dynamic states, illustrating an important pattern of connectivity in the developing brain. Furthermore, we demonstrated that higher levels of autistic traits and ASD diagnosis were associated with longer dwell times in a globally disconnected state. These results provide a roadmap to the chronnectomic organization of the developing brain and suggest that characteristics of functional brain connectivity are related to children on the autism spectrum. © 2018 Wiley Periodicals, Inc.

A special form of cerebral lacunae: expanding lacunae.

PubMed Central

Homeyer, P; Cornu, P; Lacomblez, L; Chiras, J; Derouesné, C

1996-01-01

The case of a 42 year old man with headache, blurred vision, and diplopia allowed the description of a particular form of cerebral lacunae-that is, expanding lacunae. Brain MRI showed hydrocephalus and multiple lesions in the thalamomesencephalic region. The radiological features of these lesions were similar to the histological brain coronal section of a case reported in 1983 in which expanding lacunae were related to a dilatation of the perivascular spaces and a focal segmental necrotising angiitis. The role of the lymphatic drainage of the brain is discussed to explain the dilatation of the perivascular spaces. The hypothesis of a hydrodynamic factor being responsible for the expanding character of the lacunae was suggested by the location of the lesions and the influence of various clinical events on the symptomatology. Images PMID:8708692

A State Space Model for Spatial Updating of Remembered Visual Targets during Eye Movements

PubMed Central

Mohsenzadeh, Yalda; Dash, Suryadeep; Crawford, J. Douglas

2016-01-01

In the oculomotor system, spatial updating is the ability to aim a saccade toward a remembered visual target position despite intervening eye movements. Although this has been the subject of extensive experimental investigation, there is still no unifying theoretical framework to explain the neural mechanism for this phenomenon, and how it influences visual signals in the brain. Here, we propose a unified state-space model (SSM) to account for the dynamics of spatial updating during two types of eye movement; saccades and smooth pursuit. Our proposed model is a non-linear SSM and implemented through a recurrent radial-basis-function neural network in a dual Extended Kalman filter (EKF) structure. The model parameters and internal states (remembered target position) are estimated sequentially using the EKF method. The proposed model replicates two fundamental experimental observations: continuous gaze-centered updating of visual memory-related activity during smooth pursuit, and predictive remapping of visual memory activity before and during saccades. Moreover, our model makes the new prediction that, when uncertainty of input signals is incorporated in the model, neural population activity and receptive fields expand just before and during saccades. These results suggest that visual remapping and motor updating are part of a common visuomotor mechanism, and that subjective perceptual constancy arises in part from training the visual system on motor tasks. PMID:27242452

Biosensor Technologies for Augmented Brain-Computer Interfaces in the Next Decades

DTIC Science & Technology

2012-05-13

Research Triangle Park, NC 27709-2211 Augmented brain–computer interface (ABCI);biosensor; cognitive-state monitoring; electroencephalogram( EEG ); human...biosensor; cognitive-state monitoring; electroencephalogram ( EEG ); human brain imaging Manuscript received November 28, 2011; accepted December 20...magnetic reso- nance imaging (fMRI) [1], positron emission tomography (PET) [2], electroencephalograms ( EEGs ) and optical brain imaging techniques (i.e

77 FR 37909 - Meeting: Board of Scientific Counselors, National Center for Injury Prevention and Control...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-06-25

... Traumatic Brain Injury (TBI) among Children in the United States (U01); CE12-005: Field Triage of Traumatic Brain Injury (TBI) in Older Adults Taking Anticoagulants or Platelet Inhibitors (U01); CE12-006: Alcohol... Short and Long Term Consequences of Traumatic Brain Injury (TBI) among Children in the United States...

Attributed graph distance measure for automatic detection of attention deficit hyperactive disordered subjects.

PubMed

Dey, Soumyabrata; Rao, A Ravishankar; Shah, Mubarak

2014-01-01

Attention Deficit Hyperactive Disorder (ADHD) is getting a lot of attention recently for two reasons. First, it is one of the most commonly found childhood disorders and second, the root cause of the problem is still unknown. Functional Magnetic Resonance Imaging (fMRI) data has become a popular tool for the analysis of ADHD, which is the focus of our current research. In this paper we propose a novel framework for the automatic classification of the ADHD subjects using their resting state fMRI (rs-fMRI) data of the brain. We construct brain functional connectivity networks for all the subjects. The nodes of the network are constructed with clusters of highly active voxels and edges between any pair of nodes represent the correlations between their average fMRI time series. The activity level of the voxels are measured based on the average power of their corresponding fMRI time-series. For each node of the networks, a local descriptor comprising of a set of attributes of the node is computed. Next, the Multi-Dimensional Scaling (MDS) technique is used to project all the subjects from the unknown graph-space to a low dimensional space based on their inter-graph distance measures. Finally, the Support Vector Machine (SVM) classifier is used on the low dimensional projected space for automatic classification of the ADHD subjects. Exhaustive experimental validation of the proposed method is performed using the data set released for the ADHD-200 competition. Our method shows promise as we achieve impressive classification accuracies on the training (70.49%) and test data sets (73.55%). Our results reveal that the detection rates are higher when classification is performed separately on the male and female groups of subjects.

Brain Oscillations in Sport: Toward EEG Biomarkers of Performance.

PubMed

Cheron, Guy; Petit, Géraldine; Cheron, Julian; Leroy, Axelle; Cebolla, Anita; Cevallos, Carlos; Petieau, Mathieu; Hoellinger, Thomas; Zarka, David; Clarinval, Anne-Marie; Dan, Bernard

2016-01-01

Brain dynamics is at the basis of top performance accomplishment in sports. The search for neural biomarkers of performance remains a challenge in movement science and sport psychology. The non-invasive nature of high-density electroencephalography (EEG) recording has made it a most promising avenue for providing quantitative feedback to practitioners and coaches. Here, we review the current relevance of the main types of EEG oscillations in order to trace a perspective for future practical applications of EEG and event-related potentials (ERP) in sport. In this context, the hypotheses of unified brain rhythms and continuity between wake and sleep states should provide a functional template for EEG biomarkers in sport. The oscillations in the thalamo-cortical and hippocampal circuitry including the physiology of the place cells and the grid cells provide a frame of reference for the analysis of delta, theta, beta, alpha (incl.mu), and gamma oscillations recorded in the space field of human performance. Based on recent neuronal models facilitating the distinction between the different dynamic regimes (selective gating and binding) in these different oscillations we suggest an integrated approach articulating together the classical biomechanical factors (3D movements and EMG) and the high-density EEG and ERP signals to allow finer mathematical analysis to optimize sport performance, such as microstates, coherency/directionality analysis and neural generators.

Consciousness in the universe: a review of the 'Orch OR' theory.

PubMed

Hameroff, Stuart; Penrose, Roger

2014-03-01

The nature of consciousness, the mechanism by which it occurs in the brain, and its ultimate place in the universe are unknown. We proposed in the mid 1990's that consciousness depends on biologically 'orchestrated' coherent quantum processes in collections of microtubules within brain neurons, that these quantum processes correlate with, and regulate, neuronal synaptic and membrane activity, and that the continuous Schrödinger evolution of each such process terminates in accordance with the specific Diósi-Penrose (DP) scheme of 'objective reduction' ('OR') of the quantum state. This orchestrated OR activity ('Orch OR') is taken to result in moments of conscious awareness and/or choice. The DP form of OR is related to the fundamentals of quantum mechanics and space-time geometry, so Orch OR suggests that there is a connection between the brain's biomolecular processes and the basic structure of the universe. Here we review Orch OR in light of criticisms and developments in quantum biology, neuroscience, physics and cosmology. We also introduce a novel suggestion of 'beat frequencies' of faster microtubule vibrations as a possible source of the observed electro-encephalographic ('EEG') correlates of consciousness. We conclude that consciousness plays an intrinsic role in the universe. Copyright © 2013 Elsevier B.V. All rights reserved.

Application of State-Space Smoothing to fMRI Data for Calculation of Lagged Transinformation between Human Brain Activations

NASA Astrophysics Data System (ADS)

Watanabe, Jobu

2009-09-01

Mutual information can be given a directional sense by introducing a time lag in one of the variables. In an author's previous study, to investigate the network dynamics of human brain regions, lagged transinformation (LTI) was introduced using time delayed mutual information. The LTI makes it possible to quantify the time course of dynamic information transfer between regions in the temporal domain. The LTI was applied to functional magnetic resonance imaging (fMRI) data involved in neural processing of the transformation and comparison from three-dimensional (3D) visual information to a two-dimensional (2D) location to calculate directed information flows between the activated brain regions. In the present study, for more precise estimation of LTI, Kalman filter smoothing was applied to the same fMRI data. Because the smoothing method exploits the full length of the time series data for the estimation, its application increases the precision. Large information flows were found from the bilateral prefrontal cortices to the parietal cortices. The results suggest that information of the 3D images stored as working memory was retrieved and transferred from the prefrontal cortices to the parietal cortices for comparison with information of the 2D images.

Brain Oscillations in Sport: Toward EEG Biomarkers of Performance

PubMed Central

Cheron, Guy; Petit, Géraldine; Cheron, Julian; Leroy, Axelle; Cebolla, Anita; Cevallos, Carlos; Petieau, Mathieu; Hoellinger, Thomas; Zarka, David; Clarinval, Anne-Marie; Dan, Bernard

2016-01-01

Brain dynamics is at the basis of top performance accomplishment in sports. The search for neural biomarkers of performance remains a challenge in movement science and sport psychology. The non-invasive nature of high-density electroencephalography (EEG) recording has made it a most promising avenue for providing quantitative feedback to practitioners and coaches. Here, we review the current relevance of the main types of EEG oscillations in order to trace a perspective for future practical applications of EEG and event-related potentials (ERP) in sport. In this context, the hypotheses of unified brain rhythms and continuity between wake and sleep states should provide a functional template for EEG biomarkers in sport. The oscillations in the thalamo-cortical and hippocampal circuitry including the physiology of the place cells and the grid cells provide a frame of reference for the analysis of delta, theta, beta, alpha (incl.mu), and gamma oscillations recorded in the space field of human performance. Based on recent neuronal models facilitating the distinction between the different dynamic regimes (selective gating and binding) in these different oscillations we suggest an integrated approach articulating together the classical biomechanical factors (3D movements and EMG) and the high-density EEG and ERP signals to allow finer mathematical analysis to optimize sport performance, such as microstates, coherency/directionality analysis and neural generators. PMID:26955362

The contribution of small vessel disease to subtypes of Alzheimer's disease: a study on cerebrospinal fluid and imaging biomarkers.

PubMed

Ferreira, Daniel; Shams, Sara; Cavallin, Lena; Viitanen, Matti; Martola, Juha; Granberg, Tobias; Shams, Mana; Aspelin, Peter; Kristoffersen-Wiberg, Maria; Nordberg, Agneta; Wahlund, Lars-Olof; Westman, Eric

2018-05-30

We investigated whether subtypes of Alzheimer's disease (AD), that is, typical, limbic-predominant, hippocampal-sparing, and minimal atrophy AD, had a specific signature of small vessel disease and neurodegeneration. Four hundred twenty-three clinically diagnosed AD patients were included (161 typical, 121 limbic-predominant, 70 hippocampal-sparing, 71 minimal atrophy). One hundred fifty-six fulfilled a biomarkers-based AD diagnosis. White matter hyperintensities and cerebral microbleeds (CMB) had the highest prevalence in limbic-predominant AD, and the lowest prevalence in minimal atrophy AD. CMB existed evenly in lobar and deep brain areas in limbic-predominant, typical, and hippocampal-sparing AD. In minimal atrophy AD, CMB were mainly located in brain lobar areas. Perivascular spaces in the centrum semiovale were more prevalent in typical AD. Small vessel disease contributed to the prediction of Mini-Mental State Examination. Minimal atrophy AD showed highly pathological levels of cerebrospinal fluid Aß 1-42 , total tau, and phosphorylated tau, in the absence of overt brain atrophy. Cerebral amyloid angiopathy seems to have a stronger contribution to hippocampal-sparing and minimal atrophy AD, whereas hypertensive arteriopathy may have a stronger contribution to typical and limbic-predominant AD. Copyright © 2018 Elsevier Inc. All rights reserved.

The Exosome Secretory Pathway Transports Amyloid Precursor Protein Carboxyl-terminal Fragments from the Cell into the Brain Extracellular Space*

PubMed Central

Perez-Gonzalez, Rocio; Gauthier, Sebastien A.; Kumar, Asok; Levy, Efrat

2012-01-01

In vitro studies have shown that neuronal cell cultures secrete exosomes containing amyloid-β precursor protein (APP) and the APP-processing products, C-terminal fragments (CTFs) and amyloid-β (Aβ). We investigated the secretion of full-length APP (flAPP) and APP CTFs via the exosome secretory pathway in vivo. To this end, we developed a novel protocol designed to isolate exosomes secreted into mouse brain extracellular space. Exosomes with typical morphology were isolated from freshly removed mouse brains and from frozen mouse and human brain tissues, demonstrating that exosomes can be isolated from post-mortem tissue frozen for long periods of time. flAPP, APP CTFs, and enzymes that cleave both flAPP and APP CTFs were identified in brain exosomes. Although higher levels of both flAPP and APP CTFs were observed in exosomes isolated from the brains of transgenic mice overexpressing human APP (Tg2576) compared with wild-type control mice, there was no difference in the number of secreted brain exosomes. These data indicate that the levels of flAPP and APP CTFs associated with exosomes mirror the cellular levels of flAPP and APP CTFs. Interestingly, exosomes isolated from the brains of both Tg2576 and wild-type mice are enriched with APP CTFs relative to flAPP. Thus, we hypothesize that the exosome secretory pathway plays a pleiotropic role in the brain: exosome secretion is beneficial to the cell, acting as a specific releasing system of neurotoxic APP CTFs and Aβ, but the secretion of exosomes enriched with APP CTFs, neurotoxic proteins that are also a source of secreted Aβ, is harmful to the brain. PMID:23129776

How Does Evolution Design a Brain Capable of Learning Language?

ERIC Educational Resources Information Center

Savage-Rumbaugh, E. Sue

1993-01-01

Discusses methods of assessing language comprehension in apes. Considers the possible effect of brain physiology on the differences between productive and receptive language skills. Examines the possibility that differences between synaptic transmission and volume transmission, or transmission across extracellular spaces, of neurological impulses…

Variability of human brain and muscle optical pathlength in different experimental conditions

NASA Astrophysics Data System (ADS)

Ferrari, Marco; Wei, Qingnong; De Blasi, Roberto A.; Quaresima, Valentina; Zaccanti, Giovanni

1993-09-01

Pathlength can be evaluated by measuring the time taken from a picosecond (psec) near infrared (IR) laser pulse to cross tissue. Differential pathlength factor (DPF) is calculated by dividing the mean pathlength by the inter-fiber distance. Data on DPF variability on humans are scarce. We investigated the forehead and forearm DPF in resting conditions and dynamically during brain hypoxic hypoxia, muscle ischemia and voluntary isometric exercise. At 3 cm inter optode spacing DPF at 800 nm was 4.3 +/- 0.2 (n equals 14, mean +/- SD) on the forearm, and 6.5 +/- 0.5 (n equals 8) on the forehead. Brain, muscle, and breast DPF values were almost constant over the inter optode spacing 2.5 - 4 cm. DPF was roughly constant in the central region of forehead. DPF drastically decreased under the fronto- temporal junction for the presence of muscle in the optical field. DPF decreased 5 - 10% during forearm ischemia with and without maximal voluntary contraction and during brain hypoxic hypoxia.

Harvey Cushing's ghosts: death and hauntings in modern medicine.

PubMed

Shin, Paul

2011-06-01

The passing of Yale School of Medicine's 2010 Bicentennial occasions a moment of reflecting on the past, present, and future of medical education and research at Yale and beyond. Last June, a ribbon-cutting ceremony inaugurated the opening of the Cushing Center in the Cushing-Whitney Medical Library. Named after Harvey Cushing, an early 20th-century neurosurgeon and former Yale College alum, the dual education/exhibition space now houses hundreds of gross brain specimens constituting the Cushing Tumor Registry. Originally a personal collection, Cushing donated his numerous medical specimens, photographs, and other medical relics from his deathbed, relinquishing the brains to Yale only under the condition that a suitable space be erected to preserve the many specimens. Some 70 years later and after nearly being destroyed, Cushing's wish is fully realized: The once desiccated, hidden brains have been painstakingly restored and are now on view in the Cushing Center. The brains express Cushing's singular and spectral worldview as a surgeon, artist, athlete, soldier, book collector, and historian.

Morphology, morphometry and probability mapping of the pars opercularis of the inferior frontal gyrus: an in vivo MRI analysis.

PubMed

Tomaiuolo, F; MacDonald, J D; Caramanos, Z; Posner, G; Chiavaras, M; Evans, A C; Petrides, M

1999-09-01

The pars opercularis occupies the posterior part of the inferior frontal gyrus. Electrical stimulation or damage of this region interferes with language production. The present study investigated the morphology and morphometry of the pars opercularis in 108 normal adult human cerebral hemispheres by means of magnetic resonance imaging. The brain images were transformed into a standardized proportional steoreotaxic space (i.e. that of Talairach and Tournoux) in order to minimize interindividual brain size variability. There was considerable variability in the shape and location of the pars opercularis across brains and between cerebral hemispheres. There was no significant difference or correlation between left and right hemisphere grey matter volumes. There was also no significant difference between sex and side of asymmetry of the pars opercularis. A probability map of the pars opercularis was constructed by averaging its location and extent in each individual normalized brain into Talairach space to aid in localization of activity changes in functional neuroimaging studies.

Disruption of astrocyte-vascular coupling and the blood-brain barrier by invading glioma cells

PubMed Central

Watkins, Stacey; Robel, Stefanie; Kimbrough, Ian F.; Robert, Stephanie M.; Ellis-Davies, Graham; Sontheimer, Harald

2014-01-01

Astrocytic endfeet cover the entire cerebral vasculature and serve as exchange sites for ions, metabolites, and energy substrates from the blood to the brain. They maintain endothelial tight junctions that form the blood-brain barrier (BBB) and release vasoactive molecules that regulate vascular tone. Malignant gliomas are highly invasive tumors that use the perivascular space for invasion and co-opt existing vessels as satellite tumors form. Here we use a clinically relevant mouse model of glioma and find that glioma cells, as they populate the perivascular space of pre-existing vessels, displace astrocytic endfeet from endothelial or vascular smooth muscle cells. This causes a focal breach in the BBB. Furthermore, astrocyte-mediated gliovascular coupling is lost, and glioma cells seize control over regulation of vascular tone through Ca2+-dependent release of K+. These findings have important clinical implications regarding blood flow in the tumor-associated brain and the ability to locally deliver chemotherapeutic drugs in disease. PMID:24943270

Harvey Cushing’s Ghosts: Death and Hauntings in Modern Medicine

PubMed Central

Shin, Paul

2011-01-01

The passing of Yale School of Medicine’s 2010 Bicentennial occasions a moment of reflecting on the past, present, and future of medical education and research at Yale and beyond. Last June, a ribbon-cutting ceremony inaugurated the opening of the Cushing Center in the Cushing-Whitney Medical Library. Named after Harvey Cushing, an early 20th-century neurosurgeon and former Yale College alum, the dual education/exhibition space now houses hundreds of gross brain specimens constituting the Cushing Tumor Registry. Originally a personal collection, Cushing donated his numerous medical specimens, photographs, and other medical relics from his deathbed, relinquishing the brains to Yale only under the condition that a suitable space be erected to preserve the many specimens. Some 70 years later and after nearly being destroyed, Cushing’s wish is fully realized: The once desiccated, hidden brains have been painstakingly restored and are now on view in the Cushing Center. The brains express Cushing’s singular and spectral worldview as a surgeon, artist, athlete, soldier, book collector, and historian. PMID:21698039

How lateral inhibition and fast retinogeniculo-cortical oscillations create vision: A new hypothesis.

PubMed

Jerath, Ravinder; Cearley, Shannon M; Barnes, Vernon A; Nixon-Shapiro, Elizabeth

2016-11-01

The role of the physiological processes involved in human vision escapes clarification in current literature. Many unanswered questions about vision include: 1) whether there is more to lateral inhibition than previously proposed, 2) the role of the discs in rods and cones, 3) how inverted images on the retina are converted to erect images for visual perception, 4) what portion of the image formed on the retina is actually processed in the brain, 5) the reason we have an after-image with antagonistic colors, and 6) how we remember space. This theoretical article attempts to clarify some of the physiological processes involved with human vision. The global integration of visual information is conceptual; therefore, we include illustrations to present our theory. Universally, the eyeball is 2.4cm and works together with membrane potential, correspondingly representing the retinal layers, photoreceptors, and cortex. Images formed within the photoreceptors must first be converted into chemical signals on the photoreceptors' individual discs and the signals at each disc are transduced from light photons into electrical signals. We contend that the discs code the electrical signals into accurate distances and are shown in our figures. The pre-existing oscillations among the various cortices including the striate and parietal cortex, and the retina work in unison to create an infrastructure of visual space that functionally "places" the objects within this "neural" space. The horizontal layers integrate all discs accurately to create a retina that is pre-coded for distance. Our theory suggests image inversion never takes place on the retina, but rather images fall onto the retina as compressed and coiled, then amplified through lateral inhibition through intensification and amplification on the OFF-center cones. The intensified and amplified images are decompressed and expanded in the brain, which become the images we perceive as external vision. This is a theoretical article presenting a novel hypothesis about the physiological processes in vision, and expounds upon the visual aspect of two of our previously published articles, "A unified 3D default space consciousness model combining neurological and physiological processes that underlie conscious experience", and "Functional representation of vision within the mind: A visual consciousness model based in 3D default space." Currently, neuroscience teaches that visual images are initially inverted on the retina, processed in the brain, and then conscious perception of vision happens in the visual cortex. Here, we propose that inversion of visual images never takes place because images enter the retina as coiled and compressed graded potentials that are intensified and amplified in OFF-center photoreceptors. Once they reach the brain, they are decompressed and expanded to the original size of the image, which is perceived by the brain as the external image. We adduce that pre-existing oscillations (alpha, beta, and gamma) among the various cortices in the brain (including the striate and parietal cortex) and the retina, work together in unison to create an infrastructure of visual space thatfunctionally "places" the objects within a "neural" space. These fast oscillations "bring" the faculties of the cortical activity to the retina, creating the infrastructure of the space within the eye where visual information can be immediately recognized by the brain. By this we mean that the visual (striate) cortex synchronizes the information with the photoreceptors in the retina, and the brain instantaneously receives the already processed visual image, thereby relinquishing the eye from being required to send the information to the brain to be interpreted before it can rise to consciousness. The visual system is a heavily studied area of neuroscience yet very little is known about how vision occurs. We believe that our novel hypothesis provides new insights into how vision becomes part of consciousness, helps to reconcile various previously proposed models, and further elucidates current questions in vision based on our unified 3D default space model. Illustrations are provided to aid in explaining our theory. Copyright © 2016. Published by Elsevier Ltd.

The Social Brain Is Not Enough: On the Importance of the Ecological Brain for the Origin of Language.

PubMed

Ferretti, Francesco

2016-01-01

In this paper, I assume that the study of the origin of language is strictly connected to the analysis of the traits that distinguish human language from animal communication. Usually, human language is said to be unique in the animal kingdom because it enables and/or requires intentionality or mindreading. By emphasizing the importance of mindreading, the social brain hypothesis has provided major insights within the origin of language debate. However, as studies on non-human primates have demonstrated that intentional forms of communication are already present in these species to a greater or lesser extent, I maintain that the social brain is a necessary but not a sufficient condition to explain the uniqueness of language. In this paper, I suggest that the distinctive feature of human communication resides in the ability to tell stories, and that the origin of language should be traced with respect to the capacity to produce discourses, rather than phrases or words. As narrative requires the ability to link events distant from one another in space and time, my proposal is that in order to explain the origin of language, we need to appeal to both the social brain and the ecological brain - that is, the cognitive devices which allow us to mentally travel in space and time.

Interacting Turing-Hopf Instabilities Drive Symmetry-Breaking Transitions in a Mean-Field Model of the Cortex: A Mechanism for the Slow Oscillation

NASA Astrophysics Data System (ADS)

Steyn-Ross, Moira L.; Steyn-Ross, D. A.; Sleigh, J. W.

2013-04-01

Electrical recordings of brain activity during the transition from wake to anesthetic coma show temporal and spectral alterations that are correlated with gross changes in the underlying brain state. Entry into anesthetic unconsciousness is signposted by the emergence of large, slow oscillations of electrical activity (≲1Hz) similar to the slow waves observed in natural sleep. Here we present a two-dimensional mean-field model of the cortex in which slow spatiotemporal oscillations arise spontaneously through a Turing (spatial) symmetry-breaking bifurcation that is modulated by a Hopf (temporal) instability. In our model, populations of neurons are densely interlinked by chemical synapses, and by interneuronal gap junctions represented as an inhibitory diffusive coupling. To demonstrate cortical behavior over a wide range of distinct brain states, we explore model dynamics in the vicinity of a general-anesthetic-induced transition from “wake” to “coma.” In this region, the system is poised at a codimension-2 point where competing Turing and Hopf instabilities coexist. We model anesthesia as a moderate reduction in inhibitory diffusion, paired with an increase in inhibitory postsynaptic response, producing a coma state that is characterized by emergent low-frequency oscillations whose dynamics is chaotic in time and space. The effect of long-range axonal white-matter connectivity is probed with the inclusion of a single idealized point-to-point connection. We find that the additional excitation from the long-range connection can provoke seizurelike bursts of cortical activity when inhibitory diffusion is weak, but has little impact on an active cortex. Our proposed dynamic mechanism for the origin of anesthetic slow waves complements—and contrasts with—conventional explanations that require cyclic modulation of ion-channel conductances. We postulate that a similar bifurcation mechanism might underpin the slow waves of natural sleep and comment on the possible consequences of chaotic dynamics for memory processing and learning.

How the brain assigns a neural tag to arbitrary points in a high-dimensional space

NASA Astrophysics Data System (ADS)

Stevens, Charles

Brains in almost all organisms need to deal with very complex stimuli. For example, most mammals are very good at face recognition, and faces are very complex objects indeed. For example, modern face recognition software represents a face as a point in a 10,000 dimensional space. Every human must be able to learn to recognize any of the 7 billion faces in the world, and can recognize familiar faces after a display of the face is viewed for only a few hundred milliseconds. Because we do not understand how faces are assigned locations in a high-dimensional space by the brain, attacking the problem of how face recognition is accomplished is very difficult. But a much easier problem of the same sort can be studied for odor recognition. For the mouse, each odor is assigned a point in a 1000 dimensional space, and the fruit fly assigns any odor a location in only a 50 dimensional space. A fly has about 50 distinct types of odorant receptor neurons (ORNs), each of which produce nerve impulses at a specific rate for each different odor. This pattern of firing produced across 50 ORNs is called `a combinatorial odor code', and this code assigns every odor a point in a 50 dimensional space that is used to identify the odor. In order to learn the odor, the brain must alter the strength of synapses. The combinatorial code cannot itself by used to change synaptic strength because all odors use same neurons to form the code, and so all synapses would be changed for any odor and the odors could not be distinguished. In order to learn an odor, the brain must assign a set of neurons - the odor tag - that have the property that these neurons (1) should make use of all of the information available about the odor, and (2) insure that any two tags overlap as little as possible (so one odor does not modify synapses used by other odors). In the talk, I will explain how the olfactory system of both the fruit fly and the mouse produce a tag for each odor that has these two properties. Supported by NSF.

Computer Vision Evidence Supporting Craniometric Alignment of Rat Brain Atlases to Streamline Expert-Guided, First-Order Migration of Hypothalamic Spatial Datasets Related to Behavioral Control

PubMed Central

Khan, Arshad M.; Perez, Jose G.; Wells, Claire E.; Fuentes, Olac

2018-01-01

The rat has arguably the most widely studied brain among all animals, with numerous reference atlases for rat brain having been published since 1946. For example, many neuroscientists have used the atlases of Paxinos and Watson (PW, first published in 1982) or Swanson (S, first published in 1992) as guides to probe or map specific rat brain structures and their connections. Despite nearly three decades of contemporaneous publication, no independent attempt has been made to establish a basic framework that allows data mapped in PW to be placed in register with S, or vice versa. Such data migration would allow scientists to accurately contextualize neuroanatomical data mapped exclusively in only one atlas with data mapped in the other. Here, we provide a tool that allows levels from any of the seven published editions of atlases comprising three distinct PW reference spaces to be aligned to atlas levels from any of the four published editions representing S reference space. This alignment is based on registration of the anteroposterior stereotaxic coordinate (z) measured from the skull landmark, Bregma (β). Atlas level alignments performed along the z axis using one-dimensional Cleveland dot plots were in general agreement with alignments obtained independently using a custom-made computer vision application that utilized the scale-invariant feature transform (SIFT) and Random Sample Consensus (RANSAC) operation to compare regions of interest in photomicrographs of Nissl-stained tissue sections from the PW and S reference spaces. We show that z-aligned point source data (unpublished hypothalamic microinjection sites) can be migrated from PW to S space to a first-order approximation in the mediolateral and dorsoventral dimensions using anisotropic scaling of the vector-formatted atlas templates, together with expert-guided relocation of obvious outliers in the migrated datasets. The migrated data can be contextualized with other datasets mapped in S space, including neuronal cell bodies, axons, and chemoarchitecture; to generate data-constrained hypotheses difficult to formulate otherwise. The alignment strategies provided in this study constitute a basic starting point for first-order, user-guided data migration between PW and S reference spaces along three dimensions that is potentially extensible to other spatial reference systems for the rat brain. PMID:29765309

Hubs of Anticorrelation in High-Resolution Resting-State Functional Connectivity Network Architecture.

PubMed

Gopinath, Kaundinya; Krishnamurthy, Venkatagiri; Cabanban, Romeo; Crosson, Bruce A

2015-06-01

A major focus of brain research recently has been to map the resting-state functional connectivity (rsFC) network architecture of the normal brain and pathology through functional magnetic resonance imaging. However, the phenomenon of anticorrelations in resting-state signals between different brain regions has not been adequately examined. The preponderance of studies on resting-state fMRI (rsFMRI) have either ignored anticorrelations in rsFC networks or adopted methods in data analysis, which have rendered anticorrelations in rsFC networks uninterpretable. The few studies that have examined anticorrelations in rsFC networks using conventional methods have found anticorrelations to be weak in strength and not very reproducible across subjects. Anticorrelations in rsFC network architecture could reflect mechanisms that subserve a number of important brain processes. In this preliminary study, we examined the properties of anticorrelated rsFC networks by systematically focusing on negative cross-correlation coefficients (CCs) among rsFMRI voxel time series across the brain with graph theory-based network analysis. A number of methods were implemented to enhance the neuronal specificity of resting-state functional connections that yield negative CCs, although at the cost of decreased sensitivity. Hubs of anticorrelation were seen in a number of cortical and subcortical brain regions. Examination of the anticorrelation maps of these hubs indicated that negative CCs in rsFC network architecture highlight a number of regulatory interactions between brain networks and regions, including reciprocal modulations, suppression, inhibition, and neurofeedback.

Traumatic Brain Injury in the United States: An Epidemiologic Overview

DTIC Science & Technology

2009-01-01

discussed. Mt Sinai J Med 76:105–110, 2009.  2009 Mount Sinai School of Medicine Key Words: epidemiology, head injury, traumatic brain injury. A...traumatic brain injury in the civilian population of the United States. J Head Trauma Rehabil 2008; 23: 394–400. 3. Sosin DM, Sniezek JE, Thurman DJ...consciousness, a practical scale. Lancet 1974; 2: 81–84. 5. Kay T, Harrington DE, Adams R, et al. Definition of mild traumatic brain injury. J Head

Reconfiguration of Brain Network Architectures between Resting-State and Complexity-Dependent Cognitive Reasoning.

PubMed

Hearne, Luke J; Cocchi, Luca; Zalesky, Andrew; Mattingley, Jason B

2017-08-30

Our capacity for higher cognitive reasoning has a measurable limit. This limit is thought to arise from the brain's capacity to flexibly reconfigure interactions between spatially distributed networks. Recent work, however, has suggested that reconfigurations of task-related networks are modest when compared with intrinsic "resting-state" network architecture. Here we combined resting-state and task-driven functional magnetic resonance imaging to examine how flexible, task-specific reconfigurations associated with increasing reasoning demands are integrated within a stable intrinsic brain topology. Human participants (21 males and 28 females) underwent an initial resting-state scan, followed by a cognitive reasoning task involving different levels of complexity, followed by a second resting-state scan. The reasoning task required participants to deduce the identity of a missing element in a 4 × 4 matrix, and item difficulty was scaled parametrically as determined by relational complexity theory. Analyses revealed that external task engagement was characterized by a significant change in functional brain modules. Specifically, resting-state and null-task demand conditions were associated with more segregated brain-network topology, whereas increases in reasoning complexity resulted in merging of resting-state modules. Further increments in task complexity did not change the established modular architecture, but affected selective patterns of connectivity between frontoparietal, subcortical, cingulo-opercular, and default-mode networks. Larger increases in network efficiency within the newly established task modules were associated with higher reasoning accuracy. Our results shed light on the network architectures that underlie external task engagement, and highlight selective changes in brain connectivity supporting increases in task complexity. SIGNIFICANCE STATEMENT Humans have clear limits in their ability to solve complex reasoning problems. It is thought that such limitations arise from flexible, moment-to-moment reconfigurations of functional brain networks. It is less clear how such task-driven adaptive changes in connectivity relate to stable, intrinsic networks of the brain and behavioral performance. We found that increased reasoning demands rely on selective patterns of connectivity within cortical networks that emerged in addition to a more general, task-induced modular architecture. This task-driven architecture reverted to a more segregated resting-state architecture both immediately before and after the task. These findings reveal how flexibility in human brain networks is integral to achieving successful reasoning performance across different levels of cognitive demand. Copyright © 2017 the authors 0270-6474/17/378399-13$15.00/0.

Taking care of one's brain: how manipulating the brain changes people's selves.

PubMed

Brenninkmeijer, Jonna

2010-01-01

The increasing attention to the brain in science and the media, and people's continuing quest for a better life, have resulted in a successful self-help industry for brain enhancement. Apart from brain books, foods and games, there are several devices on the market that people can use to stimulate their brains and become happier, healthier or more successful. People can, for example, switch their brain state into relaxation or concentration with a light-and-sound machine, they can train their brain waves to cure their Attention Deficit Hyperactivity Disorder (ADHD) or solve their sleeping problems with a neurofeedback device, or they can influence the firing of their neurons with electric or magnetic stimulation to overcome their depression and anxieties. Working on your self with a brain device can be seen as a contemporary form of Michel Foucault's "technologies of the self." Foucault described how since antiquity people had used techniques such as reading manuscripts, listening to teachers, or saying prayers to "act on their selves" and control their own thoughts and behaviours. Different techniques, Foucault stated, are based on different precepts and constitute different selves. I follow Foucault by stating that using a brain device for self-improvement indeed constitutes a new self. Drawing on interviews with users of brain devices and observations of the practices in brain clinics, I analyse how a new self takes shape in the use of brain devices; not a monistic (neuroscientific) self, but a "layered" self of all kinds of entities that exchange and control each other continuously.

Resting state brain dynamics and its transients: a combined TMS-EEG study.

PubMed

Bonnard, Mireille; Chen, Sophie; Gaychet, Jérôme; Carrere, Marcel; Woodman, Marmaduke; Giusiano, Bernard; Jirsa, Viktor

2016-08-04

The brain at rest exhibits a spatio-temporally rich dynamics which adheres to systematic behaviours that persist in task paradigms but appear altered in disease. Despite this hypothesis, many rest state paradigms do not act directly upon the rest state and therefore cannot confirm hypotheses about its mechanisms. To address this challenge, we combined transcranial magnetic stimulation (TMS) and electroencephalography (EEG) to study brain's relaxation toward rest following a transient perturbation. Specifically, TMS targeted either the medial prefrontal cortex (MPFC), i.e. part of the Default Mode Network (DMN) or the superior parietal lobule (SPL), involved in the Dorsal Attention Network. TMS was triggered by a given brain state, namely an increase in occipital alpha rhythm power. Following the initial TMS-Evoked Potential, TMS at MPFC enhances the induced occipital alpha rhythm, called Event Related Synchronisation, with a longer transient lifetime than TMS at SPL, and a higher amplitude. Our findings show a strong coupling between MPFC and the occipital alpha power. Although the rest state is organized around a core of resting state networks, the DMN functionally takes a special role among these resting state networks.

Metronidazole and hydroxymetronidazole central nervous system distribution: 1. microdialysis assessment of brain extracellular fluid concentrations in patients with acute brain injury.

PubMed

Frasca, Denis; Dahyot-Fizelier, Claire; Adier, Christophe; Mimoz, Olivier; Debaene, Bertrand; Couet, William; Marchand, Sandrine

2014-01-01

The distribution of metronidazole in the central nervous system has only been described based on cerebrospinal fluid data. However, extracellular fluid (ECF) concentrations may better predict its antimicrobial effect and/or side effects. We sought to explore by microdialysis brain ECF metronidazole distribution in patients with acute brain injury. Four brain-injured patients monitored by cerebral microdialysis received 500 mg of metronidazole over 0.5 h every 8 h. Brain dialysates and blood samples were collected at steady state over 8 h. Probe recoveries were evaluated by in vivo retrodialysis in each patient for metronidazole. Metronidazole and OH-metronidazole were assayed by high-pressure liquid chromatography, and a noncompartmental pharmacokinetic analysis was performed. Probe recovery was equal to 78.8% ± 1.3% for metronidazole in patients. Unbound brain metronidazole concentration-time curves were delayed compared to unbound plasma concentration-time curves but with a mean metronidazole unbound brain/plasma AUC0-τ ratio equal to 102% ± 19% (ranging from 87 to 124%). The unbound plasma concentration-time profiles for OH-metronidazole were flat, with mean average steady-state concentrations equal to 4.0 ± 0.7 μg ml(-1). This microdialysis study describes the steady-state brain distribution of metronidazole in patients and confirms its extensive distribution.

Hierarchical functional modularity in the resting-state human brain.

PubMed

Ferrarini, Luca; Veer, Ilya M; Baerends, Evelinda; van Tol, Marie-José; Renken, Remco J; van der Wee, Nic J A; Veltman, Dirk J; Aleman, André; Zitman, Frans G; Penninx, Brenda W J H; van Buchem, Mark A; Reiber, Johan H C; Rombouts, Serge A R B; Milles, Julien

2009-07-01

Functional magnetic resonance imaging (fMRI) studies have shown that anatomically distinct brain regions are functionally connected during the resting state. Basic topological properties in the brain functional connectivity (BFC) map have highlighted the BFC's small-world topology. Modularity, a more advanced topological property, has been hypothesized to be evolutionary advantageous, contributing to adaptive aspects of anatomical and functional brain connectivity. However, current definitions of modularity for complex networks focus on nonoverlapping clusters, and are seriously limited by disregarding inclusive relationships. Therefore, BFC's modularity has been mainly qualitatively investigated. Here, we introduce a new definition of modularity, based on a recently improved clustering measurement, which overcomes limitations of previous definitions, and apply it to the study of BFC in resting state fMRI of 53 healthy subjects. Results show hierarchical functional modularity in the brain. Copyright 2009 Wiley-Liss, Inc

Controllability of structural brain networks

NASA Astrophysics Data System (ADS)

Gu, Shi; Pasqualetti, Fabio; Cieslak, Matthew; Telesford, Qawi K.; Yu, Alfred B.; Kahn, Ari E.; Medaglia, John D.; Vettel, Jean M.; Miller, Michael B.; Grafton, Scott T.; Bassett, Danielle S.

2015-10-01

Cognitive function is driven by dynamic interactions between large-scale neural circuits or networks, enabling behaviour. However, fundamental principles constraining these dynamic network processes have remained elusive. Here we use tools from control and network theories to offer a mechanistic explanation for how the brain moves between cognitive states drawn from the network organization of white matter microstructure. Our results suggest that densely connected areas, particularly in the default mode system, facilitate the movement of the brain to many easily reachable states. Weakly connected areas, particularly in cognitive control systems, facilitate the movement of the brain to difficult-to-reach states. Areas located on the boundary between network communities, particularly in attentional control systems, facilitate the integration or segregation of diverse cognitive systems. Our results suggest that structural network differences between cognitive circuits dictate their distinct roles in controlling trajectories of brain network function.

The iodide space in rabbit brain

PubMed Central

Ahmed, Nawal; Van Harreveld, A.

1969-01-01

1. The iodide space in rabbit brain varies greatly depending on the conditions under which it is determined. 2. When 131I- only is used the iodide space 4 hr after administration of the marker is of the order of 2%. The iodide content of the cerebrospinal fluid (c.s.f.) is about 1% of that of the serum. 3. Depression of the active iodide transport by perchlorate increases the space to 8·2% and the iodide content of the c.s.f. to 26% of that of the serum. 4. The active iodide transport can also be depressed by saturation with unlabelled iodide. Up to a serum iodide concentration of 5 mM the space determined after 5 hr remained constant at 2·7%. The iodide space grew when the serum iodide content was enhanced from 5 to 20 mM, to become constant at a value of 10·6% on further increase of the serum iodide (up to 50 mM). The iodide content of the c.s.f. increased in a similar manner as the space with the iodide concentration of the serum to about 1/3 of the serum concentration. The iodide space of the muscle was independent of the plasma iodide content. 5. From 4 to 8 hr after administration of 131I- alone or with unlabelled iodide (to a serum concentration of 15 mM) the iodide space remained relatively constant. 6. When 131I- was administered in the fluid with which the ventricles were perfused an iodide space of about 7% was attained after about 5 hr. 7. In experiments in which 131I- was administered intravenously and the sink action of the c.s.f. was eliminated by perfusion of the ventricles with a perfusate containing as much 131I- as the plasma, the iodide space was 10·2%. When in addition active iodide transport was depressed by perchlorate the space increased to 16·8%. 8. Intravenous administration of labelled and unlabelled iodide (to a serum concentration of 20-40 mM) and ventricle perfusion with the same concentration of 131I- and unlabelled iodide as in the plasma yielded an iodide space of 20·8%. In similar experiments the iodide concentration of the perfusate was so adjusted that after 5 hr perfusion its iodide content hardly changed during the passage through the ventricles. Under these conditions the iodide concentration of the extracellular and perfusion fluids can be considered to be near equal. The iodide space computed on the basis of the iodide content of the outflowing fluid was 22·5%. 9. The large iodide space could be equated with the extracellular space if the iodide remained extracellular. This seems to be the case in the muscle where the iodide space is similar to the inulin space. 10. The large effects on the iodide space of perchlorate and saturation with unlabelled iodide in experiments in which the marker was administered intravenously and in the perfusate (7 and 8) suggests the presence of an active iodide transport from the brain extracellular fluid into the blood over the blood—brain barrier. PMID:4310942

Viral nerve necrosis in hatchery-produced fry of Asian seabass Lates calcarifer: sequential microscopic analysis of histopathology.

PubMed

Azad, I S; Shekhar, M S; Thirunavukkarasu, A R; Jithendran, K P

2006-12-14

We studied the natural progression of viral nerve necrosis (VNN) in larvae of Asian seabass Lates calcarifer Bloch from 0 to 40 days post-hatch (dph). The hatchlings were reared in the vicinity of a confirmed nodavirus-affected older batch. Using light and electron microscopy (EM), we made a sequential analysis of histopathological manifestations in nerve tissue and other organs. There were no changes from the day of hatching until 4 dph. Larvae at 4 dph had viral particles in the intramuscular spaces underlying the skin, but the nerve cells of the brain were normal. The first signs of necrosis of the brain cells were observed at 6 dph. EM observations revealed characteristic membrane-bound viral particles measuring 30 nm in the cytoplasm of nerve cells of the brain, spinal cord and retina. Histological samples of fry examined when group mortalities reached 20 to 35% revealed highly vacuolated brains, empty nerve cell cytoplasm and viral particles in the intercellular spaces. Viral particles occurred extensively in the intramuscular spaces and the epidermal layers. These observations were corroborated by positive immunostaining of the virus-rich intramuscular spaces. EM studies also revealed progressive necrotic changes in the cells harboring the virus. Results emphasize the need to maintain hygiene in the hatchery environment and to develop strategies for prevention of disease spread among cohabiting seabass and other susceptible fish larvae. Intramuscular localization of the nodavirus in both preclinical healthy-looking and post-clinical moribund larvae suggests that virus neutralization strategies during larval development could be effective in controlling VNN-associated mortalities.

Riemannian metric optimization on surfaces (RMOS) for intrinsic brain mapping in the Laplace-Beltrami embedding space.

PubMed

Gahm, Jin Kyu; Shi, Yonggang

2018-05-01

Surface mapping methods play an important role in various brain imaging studies from tracking the maturation of adolescent brains to mapping gray matter atrophy patterns in Alzheimer's disease. Popular surface mapping approaches based on spherical registration, however, have inherent numerical limitations when severe metric distortions are present during the spherical parameterization step. In this paper, we propose a novel computational framework for intrinsic surface mapping in the Laplace-Beltrami (LB) embedding space based on Riemannian metric optimization on surfaces (RMOS). Given a diffeomorphism between two surfaces, an isometry can be defined using the pullback metric, which in turn results in identical LB embeddings from the two surfaces. The proposed RMOS approach builds upon this mathematical foundation and achieves general feature-driven surface mapping in the LB embedding space by iteratively optimizing the Riemannian metric defined on the edges of triangular meshes. At the core of our framework is an optimization engine that converts an energy function for surface mapping into a distance measure in the LB embedding space, which can be effectively optimized using gradients of the LB eigen-system with respect to the Riemannian metrics. In the experimental results, we compare the RMOS algorithm with spherical registration using large-scale brain imaging data, and show that RMOS achieves superior performance in the prediction of hippocampal subfields and cortical gyral labels, and the holistic mapping of striatal surfaces for the construction of a striatal connectivity atlas from substantia nigra. Copyright © 2018 Elsevier B.V. All rights reserved.

Chronic Subdural Hematoma in the Aged, Trauma or Degeneration?

PubMed Central

2016-01-01

Chronic subdural hematomas (CSHs) are generally regarded to be a traumatic lesion. It was regarded as a stroke in 17th century, an inflammatory disease in 19th century. From 20th century, it became a traumatic lesion. CSH frequently occur after a trauma, however, it cannot occur when there is no enough subdural space even after a severe head injury. CSH may occur without trauma, when there is sufficient subdural space. The author tried to investigate trends in the causation of CSH. By a review of literature, the author suggested a different view on the causation of CSH. CSH usually originated from either a subdural hygroma or an acute subdural hematoma. Development of CSH starts from the separation of the dural border cell (DBC) layer, which induces proliferation of DBCs with production of neomembrane. Capillaries will follow along the neomembrane. Hemorrhage would occur into the subdural fluid either by tearing of bridge veins or repeated microhemorrhage from the neomembrane. That is the mechanism of hematoma enlargement. Trauma or bleeding tendency may precipitate development of CSH, however, it cannot lead CSH, if there is no sufficient subdural space. The key determinant for development of CSH is a sufficient subdural space, in other words, brain atrophy. The most common and universal cause of brain atrophy is the aging. Modifying Virchow's description, CSH is sometimes traumatic, but most often caused by degeneration of the brain. Now, it is reasonable that degeneration of brain might play pivotal role in development of CSH in the aged persons. PMID:26885279

Abundant extracellular myelin in the meninges of patients with multiple sclerosis.

PubMed

Kooi, E-J; van Horssen, J; Witte, M E; Amor, S; Bø, L; Dijkstra, C D; van der Valk, P; Geurts, J J G

2009-06-01

In multiple sclerosis (MS) myelin debris has been observed within MS lesions, in cerebrospinal fluid and cervical lymph nodes, but the route of myelin transport out of the brain is unknown. Drainage of interstitial fluid from the brain parenchyma involves the perivascular spaces and leptomeninges, but the presence of myelin debris in these compartments has not been described. To determine whether myelin products are present in the meninges and perivascular spaces of MS patients. Formalin-fixed brain tissue containing meninges from 29 MS patients, 9 non-neurological controls, 6 Alzheimer's disease, 5 stroke, 5 meningitis and 7 leucodystrophy patients was investigated, and immunohistochemically stained for several myelin proteins [proteolipid protein (PLP), myelin basic protein (MBP), myelin oligodendrocyte glycoprotein (MOG) and 2',3'-cyclic nucleotide 3'-phosphodiesterase (CNPase)]. On brain material from MS patients and (non)neurological controls, PLP immunostaining was used to systematically investigate the presence of myelin debris in the meninges, using a semiquantitative scale. Extensive extracellular presence of myelin particles, positive for PLP, MBP, MOG and CNPase in the leptomeninges of MS patients, was observed. Myelin particles were also observed in perivascular spaces of MS patients. Immunohistochemical double-labelling for macrophage and dendritic cell markers and PLP confirmed that the vast majority of myelin particles were located extracellularly. Extracellular myelin particles were virtually absent in meningeal tissue of non-neurological controls, Alzheimer's disease, stroke, meningitis and leucodystrophy cases. In MS leptomeninges and perivascular spaces, abundant extracellular myelin can be found, whereas this is not the case for controls and other neurological disease. This may be relevant for understanding sustained immunogenicity or, alternatively, tolerogenicity in MS.

Spatiotemporal psychopathology I: No rest for the brain's resting state activity in depression? Spatiotemporal psychopathology of depressive symptoms.

PubMed

Northoff, Georg

2016-01-15

Despite intense neurobiological investigation in psychiatric disorders like major depressive disorder (MDD), the basic disturbance that underlies the psychopathological symptoms of MDD remains, nevertheless, unclear. Neuroimaging has focused mainly on the brain's extrinsic activity, specifically task-evoked or stimulus-induced activity, as related to the various sensorimotor, affective, cognitive, and social functions. Recently, the focus has shifted to the brain's intrinsic activity, otherwise known as its resting state activity. While various abnormalities have been observed during this activity, their meaning and significance for depression, along with its various psychopathological symptoms, are yet to be defined. Based on findings in healthy brain resting state activity and its particular spatial and temporal structure - defined in a functional and physiological sense rather than anatomical and structural - I claim that the various depressive symptoms are spatiotemporal disturbances of the resting state activity and its spatiotemporal structure. This is supported by recent findings that link ruminations and increased self-focus in depression to abnormal spatial organization of resting state activity. Analogously, affective and cognitive symptoms like anhedonia, suicidal ideation, and thought disorder can be traced to an increased focus on the past, increased past-focus as basic temporal disturbance o the resting state. Based on these findings, I conclude that the various depressive symptoms must be conceived as spatiotemporal disturbances of the brain's resting state's activity and its spatiotemporal structure. Importantly, this entails a new form of psychopathology, "Spatiotemporal Psychopathology" that directly links the brain and psyche, therefore having major diagnostic and therapeutic implications for clinical practice. Copyright © 2015 Elsevier B.V. All rights reserved.

Intrinsic resting-state activity predicts working memory brain activation and behavioral performance.

PubMed

Zou, Qihong; Ross, Thomas J; Gu, Hong; Geng, Xiujuan; Zuo, Xi-Nian; Hong, L Elliot; Gao, Jia-Hong; Stein, Elliot A; Zang, Yu-Feng; Yang, Yihong

2013-12-01

Although resting-state brain activity has been demonstrated to correspond with task-evoked brain activation, the relationship between intrinsic and evoked brain activity has not been fully characterized. For example, it is unclear whether intrinsic activity can also predict task-evoked deactivation and whether the rest-task relationship is dependent on task load. In this study, we addressed these issues on 40 healthy control subjects using resting-state and task-driven [N-back working memory (WM) task] functional magnetic resonance imaging data collected in the same session. Using amplitude of low-frequency fluctuation (ALFF) as an index of intrinsic resting-state activity, we found that ALFF in the middle frontal gyrus and inferior/superior parietal lobules was positively correlated with WM task-evoked activation, while ALFF in the medial prefrontal cortex, posterior cingulate cortex, superior frontal gyrus, superior temporal gyrus, and fusiform gyrus was negatively correlated with WM task-evoked deactivation. Further, the relationship between the intrinsic resting-state activity and task-evoked activation in lateral/superior frontal gyri, inferior/superior parietal lobules, superior temporal gyrus, and midline regions was stronger at higher WM task loads. In addition, both resting-state activity and the task-evoked activation in the superior parietal lobule/precuneus were significantly correlated with the WM task behavioral performance, explaining similar portions of intersubject performance variance. Together, these findings suggest that intrinsic resting-state activity facilitates or is permissive of specific brain circuit engagement to perform a cognitive task, and that resting activity can predict subsequent task-evoked brain responses and behavioral performance. Copyright © 2012 Wiley Periodicals, Inc.

Plasma Concentration of Prolactin, Testosterone Might Be Associated with Brain Response to Visual Erotic Stimuli in Healthy Heterosexual Males

PubMed Central

Seo, Younghee; Kim, Ji-Woong; Choi, Jeewook

2009-01-01

Objective Many studies have showed that excess or lack of sexual hormones, such as prolactin and testosterone, induced the sexual dysfunction in humans. Little, however, is known about the role of sexual hormones showing normal range in, especially, the basal state unexposed to any sexual stimulation. We hypothesized sexual hormones in the basal state may affect sexual behavior. Methods We investigated the association of the sexual hormones level in the basal hormonal state before visual sexual stimulation with the sexual response-related brain activity during the stimulation. Twelve heterosexual men were recorded the functional MRI signals of their brain activation elicited by passive viewing erotic (ERO), happy-faced (HA) couple, food and nature pictures. Both plasma prolacitn and testosterone concentrations were measured before functional MR scanning. A voxel wise regression analyses were performed to investigate the relationship between the concentration of sexual hormones in basal state and brain activity elicited by ERO minus HA, not food minus nature, contrast. Results The plasma concentration of prolactin in basal state showed positive association with the activity of the brain involving cognitive component of sexual behavior including the left middle frontal gyrus, paracingulate/superior frontal/anterior cingulate gyri, bilateral parietal lobule, right angular, bilateral precuneus and right cerebellum. Testosterone in basal state was positively associated with the brain activity of the bilateral supplementary motor area which related with motivational component of sexual behavior. Conclusion Our results suggested sexual hormones in basal state may have their specific target regions or network associated with sexual response. PMID:20046395

Plasma concentration of prolactin, testosterone might be associated with brain response to visual erotic stimuli in healthy heterosexual males.

PubMed

Seo, Younghee; Jeong, Bumseok; Kim, Ji-Woong; Choi, Jeewook

2009-09-01

Many studies have showed that excess or lack of sexual hormones, such as prolactin and testosterone, induced the sexual dysfunction in humans. Little, however, is known about the role of sexual hormones showing normal range in, especially, the basal state unexposed to any sexual stimulation. We hypothesized sexual hormones in the basal state may affect sexual behavior. We investigated the association of the sexual hormones level in the basal hormonal state before visual sexual stimulation with the sexual response-related brain activity during the stimulation. Twelve heterosexual men were recorded the functional MRI signals of their brain activation elicited by passive viewing erotic (ERO), happy-faced (HA) couple, food and nature pictures. Both plasma prolacitn and testosterone concentrations were measured before functional MR scanning. A voxel wise regression analyses were performed to investigate the relationship between the concentration of sexual hormones in basal state and brain activity elicited by ERO minus HA, not food minus nature, contrast. The plasma concentration of prolactin in basal state showed positive association with the activity of the brain involving cognitive component of sexual behavior including the left middle frontal gyrus, paracingulate/superior frontal/anterior cingulate gyri, bilateral parietal lobule, right angular, bilateral precuneus and right cerebellum. Testosterone in basal state was positively associated with the brain activity of the bilateral supplementary motor area which related with motivational component of sexual behavior. Our results suggested sexual hormones in basal state may have their specific target regions or network associated with sexual response.

Lopez-Alegria with TRAC experiment in Destiny laboratory

NASA Image and Video Library

2007-01-02

ISS014-E-11061 (2 Jan. 2007) --- Astronaut Michael E. Lopez-Alegria, Expedition 14 commander and NASA space station science officer, works with the Test of Reaction and Adaptation Capabilities (TRAC) experiment in the Destiny laboratory of the International Space Station. The TRAC investigation will test the theory of brain adaptation during space flight by testing hand-eye coordination before, during and after the space flight.

Neural correlates of consciousness in patients who have emerged from a minimally conscious state: a cross-sectional multimodal imaging study.

PubMed

Di Perri, Carol; Bahri, Mohamed Ali; Amico, Enrico; Thibaut, Aurore; Heine, Lizette; Antonopoulos, Georgios; Charland-Verville, Vanessa; Wannez, Sarah; Gomez, Francisco; Hustinx, Roland; Tshibanda, Luaba; Demertzi, Athena; Soddu, Andrea; Laureys, Steven

2016-07-01

Between pathologically impaired consciousness and normal consciousness exists a scarcely researched transition zone, referred to as emergence from minimally conscious state, in which patients regain the capacity for functional communication, object use, or both. We investigated neural correlates of consciousness in these patients compared with patients with disorders of consciousness and healthy controls, by multimodal imaging. In this cross-sectional, multimodal imaging study, patients with unresponsive wakefulness syndrome, patients in a minimally conscious state, and patients who had emerged from a minimally conscious state, diagnosed with the Coma Recovery Scale-Revised, were recruited from the neurology department of the Centre Hospitalier Universitaire de Liège, Belgium. Key exclusion criteria were neuroimaging examination in an acute state, sedation or anaesthesia during scanning, large focal brain damage, motion parameters of more than 3 mm in translation and 3° in rotation, and suboptimal segmentation and normalisation. We acquired resting state functional and structural MRI data and (18)F-fluorodeoxyglucose (FDG) PET data; we used seed-based functional MRI (fMRI) analysis to investigate positive default mode network connectivity (within-network correlations) and negative default mode network connectivity (between-network anticorrelations). We correlated FDG-PET brain metabolism with fMRI connectivity. We used voxel-based morphometry to test the effect of anatomical deformations on functional connectivity. We recruited a convenience sample of 58 patients (21 [36%] with unresponsive wakefulness syndrome, 24 [41%] in a minimally conscious state, and 13 [22%] who had emerged from a minimally conscious state) and 35 healthy controls between Oct 1, 2009, and Oct 31, 2014. We detected consciousness-level-dependent increases (from unresponsive wakefulness syndrome, minimally conscious state, emergence from minimally conscious state, to healthy controls) for positive and negative default mode network connectivity, brain metabolism, and grey matter volume (p<0·05 false discovery rate corrected for multiple comparisons). Positive default mode network connectivity differed between patients and controls but not among patient groups (F test p<0·0001). Negative default mode network connectivity was only detected in healthy controls and in those who had emerged from a minimally conscious state; patients with unresponsive wakefulness syndrome or in a minimally conscious state showed pathological between-network positive connectivity (hyperconnectivity; F test p<0·0001). Brain metabolism correlated with positive default mode network connectivity (Spearman's r=0·50 [95% CI 0·26 to 0·61]; p<0·0001) and negative default mode network connectivity (Spearman's r=-0·52 [-0·35 to -0·67); p<0·0001). Grey matter volume did not differ between the studied groups (F test p=0·06). Partial preservation of between-network anticorrelations, which are seemingly of neuronal origin and cannot be solely explained by morphological deformations, characterise patients who have emerged from a minimally conscious state. Conversely, patients with disorders of consciousness show pathological between-network correlations. Apart from a deeper understanding of the neural correlates of consciousness, these findings have clinical implications and might be particularly relevant for outcome prediction and could inspire new therapeutic options. Belgian National Funds for Scientific Research (FNRS), European Commission, Natural Sciences and Engineering Research Council of Canada, James McDonnell Foundation, European Space Agency, Mind Science Foundation, French Speaking Community Concerted Research Action, Fondazione Europea di Ricerca Biomedica, University and University Hospital of Liège (Liège, Belgium), and University of Western Ontario (London, ON, Canada). Copyright © 2016 Elsevier Ltd. All rights reserved.

Voxel-based statistical analysis of cerebral glucose metabolism in patients with permanent vegetative state after acquired brain injury.

PubMed

Kim, Yong Wook; Kim, Hyoung Seop; An, Young-Sil; Im, Sang Hee

2010-10-01

Permanent vegetative state is defined as the impaired level of consciousness longer than 12 months after traumatic causes and 3 months after non-traumatic causes of brain injury. Although many studies assessed the cerebral metabolism in patients with acute and persistent vegetative state after brain injury, few studies investigated the cerebral metabolism in patients with permanent vegetative state. In this study, we performed the voxel-based analysis of cerebral glucose metabolism and investigated the relationship between regional cerebral glucose metabolism and the severity of impaired consciousness in patients with permanent vegetative state after acquired brain injury. We compared the regional cerebral glucose metabolism as demonstrated by F-18 fluorodeoxyglucose positron emission tomography from 12 patients with permanent vegetative state after acquired brain injury with those from 12 control subjects. Additionally, covariance analysis was performed to identify regions where decreased changes in regional cerebral glucose metabolism significantly correlated with a decrease of level of consciousness measured by JFK-coma recovery scale. Statistical analysis was performed using statistical parametric mapping. Compared with controls, patients with permanent vegetative state demonstrated decreased cerebral glucose metabolism in the left precuneus, both posterior cingulate cortices, the left superior parietal lobule (P(corrected) < 0.001), and increased cerebral glucose metabolism in the both cerebellum and the right supramarginal cortices (P(corrected) < 0.001). In the covariance analysis, a decrease in the level of consciousness was significantly correlated with decreased cerebral glucose metabolism in the both posterior cingulate cortices (P(uncorrected) < 0.005). Our findings suggest that the posteromedial parietal cortex, which are part of neural network for consciousness, may be relevant structure for pathophysiological mechanism in patients with permanent vegetative state after acquired brain injury.

The Whole-Brain “Global” Signal from Resting State fMRI as a Potential Biomarker of Quantitative State Changes in Glucose Metabolism

PubMed Central

Thompson, Garth J.; Grimmer, Timo; Drzezga, Alexander; Herman, Peter

2016-01-01

Abstract The evolution of functional magnetic resonance imaging to resting state (R-fMRI) allows measurement of changes in brain networks attributed to state changes, such as in neuropsychiatric diseases versus healthy controls. Since these networks are observed by comparing normalized R-fMRI signals, it is difficult to determine the metabolic basis of such group differences. To investigate the metabolic basis of R-fMRI network differences within a normal range, eyes open versus eyes closed in healthy human subjects was used. R-fMRI was recorded simultaneously with fluoro-deoxyglucose positron emission tomography (FDG-PET). Higher baseline FDG was observed in the eyes open state. Variance-based metrics calculated from R-fMRI did not match the baseline shift in FDG. Functional connectivity density (FCD)-based metrics showed a shift similar to the baseline shift of FDG, however, this was lost if R-fMRI “nuisance signals” were regressed before FCD calculation. Average correlation with the mean R-fMRI signal across the whole brain, generally regarded as a “nuisance signal,” also showed a shift similar to the baseline of FDG. Thus, despite lacking a baseline itself, changes in whole-brain correlation may reflect changes in baseline brain metabolism. Conversely, variance-based metrics may remain similar between states due to inherent region-to-region differences overwhelming the differences between normal physiological states. As most previous studies have excluded the spatial means of R-fMRI metrics from their analysis, this work presents the first evidence of a potential R-fMRI biomarker for baseline shifts in quantifiable metabolism between brain states. PMID:27029438

The Whole-Brain "Global" Signal from Resting State fMRI as a Potential Biomarker of Quantitative State Changes in Glucose Metabolism.

PubMed

Thompson, Garth J; Riedl, Valentin; Grimmer, Timo; Drzezga, Alexander; Herman, Peter; Hyder, Fahmeed

2016-07-01

The evolution of functional magnetic resonance imaging to resting state (R-fMRI) allows measurement of changes in brain networks attributed to state changes, such as in neuropsychiatric diseases versus healthy controls. Since these networks are observed by comparing normalized R-fMRI signals, it is difficult to determine the metabolic basis of such group differences. To investigate the metabolic basis of R-fMRI network differences within a normal range, eyes open versus eyes closed in healthy human subjects was used. R-fMRI was recorded simultaneously with fluoro-deoxyglucose positron emission tomography (FDG-PET). Higher baseline FDG was observed in the eyes open state. Variance-based metrics calculated from R-fMRI did not match the baseline shift in FDG. Functional connectivity density (FCD)-based metrics showed a shift similar to the baseline shift of FDG, however, this was lost if R-fMRI "nuisance signals" were regressed before FCD calculation. Average correlation with the mean R-fMRI signal across the whole brain, generally regarded as a "nuisance signal," also showed a shift similar to the baseline of FDG. Thus, despite lacking a baseline itself, changes in whole-brain correlation may reflect changes in baseline brain metabolism. Conversely, variance-based metrics may remain similar between states due to inherent region-to-region differences overwhelming the differences between normal physiological states. As most previous studies have excluded the spatial means of R-fMRI metrics from their analysis, this work presents the first evidence of a potential R-fMRI biomarker for baseline shifts in quantifiable metabolism between brain states.

Your Brain Outdoors

ERIC Educational Resources Information Center

MacEachren, Zabe

2012-01-01

The way technology influences a person's cognition is seldom recognized, but is of increasing interest among brain researchers. Outdoor educators tend to pay attention to the way different activities offer different perceptions of an environment. When natural spaces can no longer be accessed, they adapt and simulate natural activities in available…

Personalizing E-Learning.

ERIC Educational Resources Information Center

Bollet, Robert M.; Fallon, Santiago

2002-01-01

Discussion of the concepts of learning and training focuses on how to incorporate the whole brain in the learning process when personalizing electronic learning. Suggests that trainers will need to learn teaching strategies that embrace the right brain's need for time and space to examine, contemplate, integrate, and utilize information.…

In vivo NAD assay reveals the intracellular NAD contents and redox state in healthy human brain and their age dependences

PubMed Central

Zhu, Xiao-Hong; Lu, Ming; Lee, Byeong-Yeul; Ugurbil, Kamil; Chen, Wei

2015-01-01

NAD is an essential metabolite that exists in NAD+ or NADH form in all living cells. Despite its critical roles in regulating mitochondrial energy production through the NAD+/NADH redox state and modulating cellular signaling processes through the activity of the NAD+-dependent enzymes, the method for quantifying intracellular NAD contents and redox state is limited to a few in vitro or ex vivo assays, which are not suitable for studying a living brain or organ. Here, we present a magnetic resonance (MR) -based in vivo NAD assay that uses the high-field MR scanner and is capable of noninvasively assessing NAD+ and NADH contents and the NAD+/NADH redox state in intact human brain. The results of this study provide the first insight, to our knowledge, into the cellular NAD concentrations and redox state in the brains of healthy volunteers. Furthermore, an age-dependent increase of intracellular NADH and age-dependent reductions in NAD+, total NAD contents, and NAD+/NADH redox potential of the healthy human brain were revealed in this study. The overall findings not only provide direct evidence of declined mitochondrial functions and altered NAD homeostasis that accompany the normal aging process but also, elucidate the merits and potentials of this new NAD assay for noninvasively studying the intracellular NAD metabolism and redox state in normal and diseased human brain or other organs in situ. PMID:25730862

Spiral Imaging in fMRI

PubMed Central

Glover, Gary H.

2011-01-01

T2*-weighted Blood Oxygen Level Dependent (BOLD) functional magnetic resonance imaging (fMRI) requires efficient acquisition methods in order to fully sample the brain in a several second time period. The most widely used approach is Echo Planar Imaging (EPI), which utilizes a Cartesian trajectory to cover k-space. This trajectory is subject to ghosts from off-resonance and gradient imperfections and is intrinsically sensitive to cardiac-induced pulsatile motion from substantial first- and higher order moments of the gradient waveform near the k-space origin. In addition, only the readout direction gradient contributes significant energy to the trajectory. By contrast, the Spiral method samples k-space with an Archimedean or similar trajectory that begins at the k-space center and spirals to the edge (Spiral-out), or its reverse, ending at the origin (Spiral-in). Spiral methods have reduced sensitivity to motion, shorter readout times, improved signal recovery in most frontal and parietal brain regions, and exhibit blurring artifacts instead of ghosts or geometric distortion. Methods combining Spiral-in and Spiral-out trajectories have further advantages in terms of diminished susceptibility-induced signal dropout and increased BOLD signal. In measurements of temporal signal to noise ratio measured in 8 subjects, Spiral-in/out exhibited significant increases over EPI in voxel volumes recovered in frontal and whole brain regions (18% and 10%, respectively). PMID:22036995

Parallel updating and weighting of multiple spatial maps for visual stability during whole body motion

PubMed Central

Medendorp, W. P.

2015-01-01

It is known that the brain uses multiple reference frames to code spatial information, including eye-centered and body-centered frames. When we move our body in space, these internal representations are no longer in register with external space, unless they are actively updated. Whether the brain updates multiple spatial representations in parallel, or whether it restricts its updating mechanisms to a single reference frame from which other representations are constructed, remains an open question. We developed an optimal integration model to simulate the updating of visual space across body motion in multiple or single reference frames. To test this model, we designed an experiment in which participants had to remember the location of a briefly presented target while being translated sideways. The behavioral responses were in agreement with a model that uses a combination of eye- and body-centered representations, weighted according to the reliability in which the target location is stored and updated in each reference frame. Our findings suggest that the brain simultaneously updates multiple spatial representations across body motion. Because both representations are kept in sync, they can be optimally combined to provide a more precise estimate of visual locations in space than based on single-frame updating mechanisms. PMID:26490289

Representational Distance Learning for Deep Neural Networks

PubMed Central

McClure, Patrick; Kriegeskorte, Nikolaus

2016-01-01

Deep neural networks (DNNs) provide useful models of visual representational transformations. We present a method that enables a DNN (student) to learn from the internal representational spaces of a reference model (teacher), which could be another DNN or, in the future, a biological brain. Representational spaces of the student and the teacher are characterized by representational distance matrices (RDMs). We propose representational distance learning (RDL), a stochastic gradient descent method that drives the RDMs of the student to approximate the RDMs of the teacher. We demonstrate that RDL is competitive with other transfer learning techniques for two publicly available benchmark computer vision datasets (MNIST and CIFAR-100), while allowing for architectural differences between student and teacher. By pulling the student's RDMs toward those of the teacher, RDL significantly improved visual classification performance when compared to baseline networks that did not use transfer learning. In the future, RDL may enable combined supervised training of deep neural networks using task constraints (e.g., images and category labels) and constraints from brain-activity measurements, so as to build models that replicate the internal representational spaces of biological brains. PMID:28082889

Representational Distance Learning for Deep Neural Networks.

PubMed

McClure, Patrick; Kriegeskorte, Nikolaus

2016-01-01

Deep neural networks (DNNs) provide useful models of visual representational transformations. We present a method that enables a DNN (student) to learn from the internal representational spaces of a reference model (teacher), which could be another DNN or, in the future, a biological brain. Representational spaces of the student and the teacher are characterized by representational distance matrices (RDMs). We propose representational distance learning (RDL), a stochastic gradient descent method that drives the RDMs of the student to approximate the RDMs of the teacher. We demonstrate that RDL is competitive with other transfer learning techniques for two publicly available benchmark computer vision datasets (MNIST and CIFAR-100), while allowing for architectural differences between student and teacher. By pulling the student's RDMs toward those of the teacher, RDL significantly improved visual classification performance when compared to baseline networks that did not use transfer learning. In the future, RDL may enable combined supervised training of deep neural networks using task constraints (e.g., images and category labels) and constraints from brain-activity measurements, so as to build models that replicate the internal representational spaces of biological brains.

Is functional integration of resting state brain networks an unspecific biomarker for working memory performance?

PubMed

Alavash, Mohsen; Doebler, Philipp; Holling, Heinz; Thiel, Christiane M; Gießing, Carsten

2015-03-01

Is there one optimal topology of functional brain networks at rest from which our cognitive performance would profit? Previous studies suggest that functional integration of resting state brain networks is an important biomarker for cognitive performance. However, it is still unknown whether higher network integration is an unspecific predictor for good cognitive performance or, alternatively, whether specific network organization during rest predicts only specific cognitive abilities. Here, we investigated the relationship between network integration at rest and cognitive performance using two tasks that measured different aspects of working memory; one task assessed visual-spatial and the other numerical working memory. Network clustering, modularity and efficiency were computed to capture network integration on different levels of network organization, and to statistically compare their correlations with the performance in each working memory test. The results revealed that each working memory aspect profits from a different resting state topology, and the tests showed significantly different correlations with each of the measures of network integration. While higher global network integration and modularity predicted significantly better performance in visual-spatial working memory, both measures showed no significant correlation with numerical working memory performance. In contrast, numerical working memory was superior in subjects with highly clustered brain networks, predominantly in the intraparietal sulcus, a core brain region of the working memory network. Our findings suggest that a specific balance between local and global functional integration of resting state brain networks facilitates special aspects of cognitive performance. In the context of working memory, while visual-spatial performance is facilitated by globally integrated functional resting state brain networks, numerical working memory profits from increased capacities for local processing, especially in brain regions involved in working memory performance. Copyright © 2014 Elsevier Inc. All rights reserved.

Increased power spectral density in resting-state pain-related brain networks in fibromyalgia.

PubMed

Kim, Ji-Young; Kim, Seong-Ho; Seo, Jeehye; Kim, Sang-Hyon; Han, Seung Woo; Nam, Eon Jeong; Kim, Seong-Kyu; Lee, Hui Joong; Lee, Seung-Jae; Kim, Yang-Tae; Chang, Yongmin

2013-09-01

Fibromyalgia (FM), characterized by chronic widespread pain, is known to be associated with heightened responses to painful stimuli and atypical resting-state functional connectivity among pain-related regions of the brain. Previous studies of FM using resting-state functional magnetic resonance imaging (rs-fMRI) have focused on intrinsic functional connectivity, which maps the spatial distribution of temporal correlations among spontaneous low-frequency fluctuation in functional MRI (fMRI) resting-state data. In the current study, using rs-fMRI data in the frequency domain, we investigated the possible alteration of power spectral density (PSD) of low-frequency fluctuation in brain regions associated with central pain processing in patients with FM. rsfMRI data were obtained from 19 patients with FM and 20 age-matched healthy female control subjects. For each subject, the PSDs for each brain region identified from functional connectivity maps were computed for the frequency band of 0.01 to 0.25 Hz. For each group, the average PSD was determined for each brain region and a 2-sample t test was performed to determine the difference in power between the 2 groups. According to the results, patients with FM exhibited significantly increased frequency power in the primary somatosensory cortex (S1), supplementary motor area (SMA), dorsolateral prefrontal cortex, and amygdala. In patients with FM, the increase in PSD did not show an association with depression or anxiety. Therefore, our findings of atypical increased frequency power during the resting state in pain-related brain regions may implicate the enhanced resting-state baseline neural activity in several brain regions associated with pain processing in FM. Copyright © 2013 International Association for the Study of Pain. Published by Elsevier B.V. All rights reserved.

MR elastography of hydrocephalus

NASA Astrophysics Data System (ADS)

Pattison, Adam J.; Lollis, S. Scott; Perrinez, Phillip R.; Weaver, John B.; Paulsen, Keith D.

2009-02-01

Hydrocephalus occurs due to a blockage in the transmission of cerebrospinal fluid (CSF) in either the ventricles or subarachnoid space. Characteristics of this condition include increased intracranial pressure, which can result in neurologic deterioration [1]. Magnetic resonance elastography (MRE) is an imaging technique that estimates the mechanical properties of tissue in vivo. While some investigations of brain tissue have been performed using MRE [2,3,4,5], the effects due to changes in interstitial pressure and fluid content on the mechanical properties of the brain remain unknown. The purpose of this work is to assess the potential of MRE to differentiate between the reconstructed properties of normal and hydrocephalic brains. MRE data was acquired in 18 female feline subjects, 12 of which received kaolin injections resulting in an acute form of hydrocephalus. In each animal, four MRE scans were performed during the process including one pre-injection and three post-injection scans. The elastic parameters were obtained using a subzone-based reconstruction algorithm that solves Navier's equations for linearly elastic materials [6]. The remaining cats were used as controls, injected with saline instead of kaolin. To determine the state of hydrocephalus, ventricular volume was estimated from segmenting anatomical images. The mean ventricular volume of hydrocephalic cats significantly increased (P <~ 0.0001) between the first and second scans. The mean volume was not observed to increase (P >~ 0.5) for the control cats. Also, there was an observable increase in the recorded elastic shear modulus of brain tissue in the normal and hydrocephalic acquisitions. Results suggest that MRE is able to detect changes in the mechanical properties of brain tissue resulting from kaolin-induced hydrocephalus, indicating the need for further study.

Toward reliable characterization of functional homogeneity in the human brain: preprocessing, scan duration, imaging resolution and computational space.

PubMed

Zuo, Xi-Nian; Xu, Ting; Jiang, Lili; Yang, Zhi; Cao, Xiao-Yan; He, Yong; Zang, Yu-Feng; Castellanos, F Xavier; Milham, Michael P

2013-01-15

While researchers have extensively characterized functional connectivity between brain regions, the characterization of functional homogeneity within a region of the brain connectome is in early stages of development. Several functional homogeneity measures were proposed previously, among which regional homogeneity (ReHo) was most widely used as a measure to characterize functional homogeneity of resting state fMRI (R-fMRI) signals within a small region (Zang et al., 2004). Despite a burgeoning literature on ReHo in the field of neuroimaging brain disorders, its test-retest (TRT) reliability remains unestablished. Using two sets of public R-fMRI TRT data, we systematically evaluated the ReHo's TRT reliability and further investigated the various factors influencing its reliability and found: 1) nuisance (head motion, white matter, and cerebrospinal fluid) correction of R-fMRI time series can significantly improve the TRT reliability of ReHo while additional removal of global brain signal reduces its reliability, 2) spatial smoothing of R-fMRI time series artificially enhances ReHo intensity and influences its reliability, 3) surface-based R-fMRI computation largely improves the TRT reliability of ReHo, 4) a scan duration of 5 min can achieve reliable estimates of ReHo, and 5) fast sampling rates of R-fMRI dramatically increase the reliability of ReHo. Inspired by these findings and seeking a highly reliable approach to exploratory analysis of the human functional connectome, we established an R-fMRI pipeline to conduct ReHo computations in both 3-dimensions (volume) and 2-dimensions (surface). Copyright © 2012 Elsevier Inc. All rights reserved.

Construction of brain atlases based on a multi-center MRI dataset of 2020 Chinese adults

PubMed Central

Liang, Peipeng; Shi, Lin; Chen, Nan; Luo, Yishan; Wang, Xing; Liu, Kai; Mok, Vincent CT; Chu, Winnie CW; Wang, Defeng; Li, Kuncheng

2015-01-01

Despite the known morphological differences (e.g., brain shape and size) in the brains of populations of different origins (e.g., age and race), the Chinese brain atlas is less studied. In the current study, we developed a statistical brain atlas based on a multi-center high quality magnetic resonance imaging (MRI) dataset of 2020 Chinese adults (18–76 years old). We constructed 12 Chinese brain atlas from the age 20 year to the age 75 at a 5 years interval. New Chinese brain standard space, coordinates, and brain area labels were further defined. The new Chinese brain atlas was validated in brain registration and segmentation. It was found that, as contrast to the MNI152 template, the proposed Chinese atlas showed higher accuracy in hippocampus segmentation and relatively smaller shape deformations during registration. These results indicate that a population-specific time varying brain atlas may be more appropriate for studies involving Chinese populations. PMID:26678304

A novel content-based active contour model for brain tumor segmentation.

PubMed

Sachdeva, Jainy; Kumar, Vinod; Gupta, Indra; Khandelwal, Niranjan; Ahuja, Chirag Kamal

2012-06-01

Brain tumor segmentation is a crucial step in surgical and treatment planning. Intensity-based active contour models such as gradient vector flow (GVF), magneto static active contour (MAC) and fluid vector flow (FVF) have been proposed to segment homogeneous objects/tumors in medical images. In this study, extensive experiments are done to analyze the performance of intensity-based techniques for homogeneous tumors on brain magnetic resonance (MR) images. The analysis shows that the state-of-art methods fail to segment homogeneous tumors against similar background or when these tumors show partial diversity toward the background. They also have preconvergence problem in case of false edges/saddle points. However, the presence of weak edges and diffused edges (due to edema around the tumor) leads to oversegmentation by intensity-based techniques. Therefore, the proposed method content-based active contour (CBAC) uses both intensity and texture information present within the active contour to overcome above-stated problems capturing large range in an image. It also proposes a novel use of Gray-Level Co-occurrence Matrix to define texture space for tumor segmentation. The effectiveness of this method is tested on two different real data sets (55 patients - more than 600 images) containing five different types of homogeneous, heterogeneous, diffused tumors and synthetic images (non-MR benchmark images). Remarkable results are obtained in segmenting homogeneous tumors of uniform intensity, complex content heterogeneous, diffused tumors on MR images (T1-weighted, postcontrast T1-weighted and T2-weighted) and synthetic images (non-MR benchmark images of varying intensity, texture, noise content and false edges). Further, tumor volume is efficiently extracted from 2-dimensional slices and is named as 2.5-dimensional segmentation. Copyright © 2012 Elsevier Inc. All rights reserved.

Transformational electronics: a powerful way to revolutionize our information world

NASA Astrophysics Data System (ADS)

Rojas, Jhonathan P.; Torres Sevilla, Galo A.; Ghoneim, Mohamed T.; Hussain, Aftab M.; Ahmed, Sally M.; Nassar, Joanna M.; Bahabry, Rabab R.; Nour, Maha; Kutbee, Arwa T.; Byas, Ernesto; Al-Saif, Bidoor; Alamri, Amal M.; Hussain, Muhammad M.

2014-06-01

With the emergence of cloud computation, we are facing the rising waves of big data. It is our time to leverage such opportunity by increasing data usage both by man and machine. We need ultra-mobile computation with high data processing speed, ultra-large memory, energy efficiency and multi-functionality. Additionally, we have to deploy energy-efficient multi-functional 3D ICs for robust cyber-physical system establishment. To achieve such lofty goals we have to mimic human brain, which is inarguably the world's most powerful and energy efficient computer. Brain's cortex has folded architecture to increase surface area in an ultra-compact space to contain its neuron and synapses. Therefore, it is imperative to overcome two integration challenges: (i) finding out a low-cost 3D IC fabrication process and (ii) foldable substrates creation with ultra-large-scale-integration of high performance energy efficient electronics. Hence, we show a low-cost generic batch process based on trench-protect-peel-recycle to fabricate rigid and flexible 3D ICs as well as high performance flexible electronics. As of today we have made every single component to make a fully flexible computer including non-planar state-of-the-art FinFETs. Additionally we have demonstrated various solid-state memory, movable MEMS devices, energy harvesting and storage components. To show the versatility of our process, we have extended our process towards other inorganic semiconductor substrates such as silicon germanium and III-V materials. Finally, we report first ever fully flexible programmable silicon based microprocessor towards foldable brain computation and wirelessly programmable stretchable and flexible thermal patch for pain management for smart bionics.

Emotion regulation in children with behavior problems: linking behavioral and brain processes.

PubMed

Granic, Isabela; Meusel, Liesel-Ann; Lamm, Connie; Woltering, Steven; Lewis, Marc D

2012-08-01

Past studies have shown that aggressive children exhibit rigid (rather than flexible) parent-child interactions; these rigid repertoires may provide the context through which children fail to acquire emotion-regulation skills. Difficulties in regulating emotion are associated with minimal activity in dorsal systems in the cerebral cortex, for example, the anterior cingulate cortex. The current study aimed to integrate parent-child and neurocognitive indices of emotion regulation and examine their associations for the first time. Sixty children (8-12 years old) referred for treatment for aggression underwent two assessments. Brain processes related to emotion regulation were assessed using dense-array EEG with a computerized go/no-go task. The N2 amplitudes thought to tap inhibitory control were recorded, and a source analysis was conducted. In the second assessment, parents and children were videotaped while trying to solve a conflict topic. State space grids were used to derive two dynamic flexibility parameters from the coded videotapes: (a) the number of transitions between emotional states and (b) the dispersion of emotional states, based on proportional durations in each state. The regression results showed that flexibility measures were not related to N2 amplitudes. However, flexibility measures were significantly associated with the ratio of dorsal to ventral source activation: for transitions, ΔR 2 = .27, F (1, 34) = 13.13, p = .001; for dispersion, ΔR 2 = .29, F (1, 35) = 14.76, p < .001. Thus, in support of our main hypothesis, greater dyadic flexibility was associated with a higher ratio of dorsomedial to ventral activation, suggesting that children with more flexible parent-child interactions are able to recruit relatively more dorsomedial activity in challenging situations.

Brain Entropy Mapping Using fMRI

PubMed Central

Wang, Ze; Li, Yin; Childress, Anna Rose; Detre, John A.

2014-01-01

Entropy is an important trait for life as well as the human brain. Characterizing brain entropy (BEN) may provide an informative tool to assess brain states and brain functions. Yet little is known about the distribution and regional organization of BEN in normal brain. The purpose of this study was to examine the whole brain entropy patterns using a large cohort of normal subjects. A series of experiments were first performed to validate an approximate entropy measure regarding its sensitivity, specificity, and reliability using synthetic data and fMRI data. Resting state fMRI data from a large cohort of normal subjects (n = 1049) from multi-sites were then used to derive a 3-dimensional BEN map, showing a sharp low-high entropy contrast between the neocortex and the rest of brain. The spatial heterogeneity of resting BEN was further studied using a data-driven clustering method, and the entire brain was found to be organized into 7 hierarchical regional BEN networks that are consistent with known structural and functional brain parcellations. These findings suggest BEN mapping as a physiologically and functionally meaningful measure for studying brain functions. PMID:24657999

Handedness- and brain size-related efficiency differences in small-world brain networks: a resting-state functional magnetic resonance imaging study.

PubMed

Li, Meiling; Wang, Junping; Liu, Feng; Chen, Heng; Lu, Fengmei; Wu, Guorong; Yu, Chunshui; Chen, Huafu

2015-05-01

The human brain has been described as a complex network, which integrates information with high efficiency. However, the relationships between the efficiency of human brain functional networks and handedness and brain size remain unclear. Twenty-one left-handed and 32 right-handed healthy subjects underwent a resting-state functional magnetic resonance imaging scan. The whole brain functional networks were constructed by thresholding Pearson correlation matrices of 90 cortical and subcortical regions. Graph theory-based methods were employed to further analyze their topological properties. As expected, all participants demonstrated small-world topology, suggesting a highly efficient topological structure. Furthermore, we found that smaller brains showed higher local efficiency, whereas larger brains showed higher global efficiency, reflecting a suitable efficiency balance between local specialization and global integration of brain functional activity. Compared with right-handers, significant alterations in nodal efficiency were revealed in left-handers, involving the anterior and median cingulate gyrus, middle temporal gyrus, angular gyrus, and amygdala. Our findings indicated that the functional network organization in the human brain was associated with handedness and brain size.

Brain Tumor Image Segmentation in MRI Image

NASA Astrophysics Data System (ADS)

Peni Agustin Tjahyaningtijas, Hapsari

2018-04-01

Brain tumor segmentation plays an important role in medical image processing. Treatment of patients with brain tumors is highly dependent on early detection of these tumors. Early detection of brain tumors will improve the patient’s life chances. Diagnosis of brain tumors by experts usually use a manual segmentation that is difficult and time consuming because of the necessary automatic segmentation. Nowadays automatic segmentation is very populer and can be a solution to the problem of tumor brain segmentation with better performance. The purpose of this paper is to provide a review of MRI-based brain tumor segmentation methods. There are number of existing review papers, focusing on traditional methods for MRI-based brain tumor image segmentation. this paper, we focus on the recent trend of automatic segmentation in this field. First, an introduction to brain tumors and methods for brain tumor segmentation is given. Then, the state-of-the-art algorithms with a focus on recent trend of full automatic segmentaion are discussed. Finally, an assessment of the current state is presented and future developments to standardize MRI-based brain tumor segmentation methods into daily clinical routine are addressed.

Resting state brain networks and their implications in neurodegenerative disease

NASA Astrophysics Data System (ADS)

Sohn, William S.; Yoo, Kwangsun; Kim, Jinho; Jeong, Yong

2012-10-01

Neurons are the basic units of the brain, and form network by connecting via synapses. So far, there have been limited ways to measure the brain networks. Recently, various imaging modalities are widely used for this purpose. In this paper, brain network mapping using resting state fMRI will be introduced with several applications including neurodegenerative disease such as Alzheimer's disease, frontotemporal lobar degeneration and Parkinson's disease. The resting functional connectivity using intrinsic functional connectivity in mouse is useful since we can take advantage of perturbation or stimulation of certain nodes of the network. The study of brain connectivity will open a new era in understanding of brain and diseases thus will be an essential foundation for future research.

Resting-state fMRI and social cognition: An opportunity to connect.

PubMed

Doruyter, Alex; Groenewold, Nynke A; Dupont, Patrick; Stein, Dan J; Warwick, James M

2017-09-01

Many psychiatric disorders are characterized by altered social cognition. The importance of social cognition has previously been recognized by the National Institute of Mental Health Research Domain Criteria project, in which it features as a core domain. Social task-based functional magnetic resonance imaging (fMRI) currently offers the most direct insight into how the brain processes social information; however, resting-state fMRI may be just as important in understanding the biology and network nature of social processing. Resting-state fMRI allows researchers to investigate the functional relationships between brain regions in a neutral state: so-called resting functional connectivity (RFC). There is evidence that RFC is predictive of how the brain processes information during social tasks. This is important because it shifts the focus from possibly context-dependent aberrations to context-independent aberrations in functional network architecture. Rather than being analysed in isolation, the study of resting-state brain networks shows promise in linking results of task-based fMRI results, structural connectivity, molecular imaging findings, and performance measures of social cognition-which may prove crucial in furthering our understanding of the social brain. Copyright © 2017 John Wiley & Sons, Ltd.

Structural development of human brain white matter from mid-fetal to perinatal stage

NASA Astrophysics Data System (ADS)

Ouyang, Austin; Yu, Qiaowen; Mishra, Virendra; Chalak, Lina; Jeon, Tina; Sivarajan, Muraleedharan; Jackson, Greg; Rollins, Nancy; Liu, Shuwei; Huang, Hao

2015-03-01

The structures of developing human brain white matter (WM) tracts can be effectively quantified by DTI-derived metrics, including fractional anisotropy (FA), mean, axial and radial diffusivity (MD, AD and RD). However, dynamics of WM microstructure during very early developmental period from mid-fetal to perinatal stage is unknown. It is difficult to accurately measure microstructural properties of these WM tracts due to severe contamination from cerebrospinal fluid (CSF). In this study, high resolution DTI of fetal brains at mid-fetal stage (20 weeks of gestation or 20wg), 19 brains in the middle of 3rd trimester (35wg) and 17 brains around term (40wg) were acquired. We established first population-averaged DTI templates at these three time points and extracted WM skeleton. 16 major WM tracts in limbic, projection, commissural and association tract groups were traced with DTI tractography in native space. The WM skeleton in the template space was inversely transformed back to the native space for measuring core WM microstructures of each individual tract. Continuous microstructural enhancement and volumetric increase of WM tracts were found from 20wg to 40wg. The microstructural enhancement from FA measurement is decelerated in late 3rd trimester compared to mid-fetal to middle 3rd trimester, while volumetric increase of prefrontal WM tracts is accelerated. The microstructural enhancement from 35wg to 40wg is heterogeneous among different tract groups with microstructures of association tracts undergoing most dramatic change. Besides decreases of RD indicating active myelination, the decrease of AD for most WM tracts during late 3rd trimester suggests axonal packing process.

High slew-rate head-only gradient for improving distortion in echo planar imaging: Preliminary experience.

PubMed

Tan, Ek T; Lee, Seung-Kyun; Weavers, Paul T; Graziani, Dominic; Piel, Joseph E; Shu, Yunhong; Huston, John; Bernstein, Matt A; Foo, Thomas K F

2016-09-01

To investigate the effects on echo planar imaging (EPI) distortion of using high gradient slew rates (SR) of up to 700 T/m/s for in vivo human brain imaging, with a dedicated, head-only gradient coil. Simulation studies were first performed to determine the expected echo spacing and distortion reduction in EPI. A head gradient of 42-cm inner diameter and with asymmetric transverse coils was then installed in a whole-body, conventional 3T magnetic resonance imaging (MRI) system. Human subject imaging was performed on five subjects to determine the effects of EPI on echo spacing and signal dropout at various gradient slew rates. The feasibility of whole-brain imaging at 1.5 mm-isotropic spatial resolution was demonstrated with gradient-echo and spin-echo diffusion-weighted EPI. As compared to a whole-body gradient coil, the EPI echo spacing in the head-only gradient coil was reduced by 48%. Simulation and in vivo results, respectively, showed up to 25-26% and 19% improvement in signal dropout. Whole-brain imaging with EPI at 1.5 mm spatial resolution provided good whole-brain coverage, spatial linearity, and low spatial distortion effects. Our results of human brain imaging with EPI using the compact head gradient coil at slew rates higher than in conventional whole-body MR systems demonstrate substantially improved image distortion, and point to a potential for benefits to non-EPI pulse sequences. J. Magn. Reson. Imaging 2016;44:653-664. © 2016 International Society for Magnetic Resonance in Medicine.

Mechanical characterization of human brain tumors from patients and comparison to potential surgical phantoms.

PubMed

Stewart, Daniel C; Rubiano, Andrés; Dyson, Kyle; Simmons, Chelsey S

2017-01-01

While mechanical properties of the brain have been investigated thoroughly, the mechanical properties of human brain tumors rarely have been directly quantified due to the complexities of acquiring human tissue. Quantifying the mechanical properties of brain tumors is a necessary prerequisite, though, to identify appropriate materials for surgical tool testing and to define target parameters for cell biology and tissue engineering applications. Since characterization methods vary widely for soft biological and synthetic materials, here, we have developed a characterization method compatible with abnormally shaped human brain tumors, mouse tumors, animal tissue and common hydrogels, which enables direct comparison among samples. Samples were tested using a custom-built millimeter-scale indenter, and resulting force-displacement data is analyzed to quantify the steady-state modulus of each sample. We have directly quantified the quasi-static mechanical properties of human brain tumors with effective moduli ranging from 0.17-16.06 kPa for various pathologies. Of the readily available and inexpensive animal tissues tested, chicken liver (steady-state modulus 0.44 ± 0.13 kPa) has similar mechanical properties to normal human brain tissue while chicken crassus gizzard muscle (steady-state modulus 3.00 ± 0.65 kPa) has similar mechanical properties to human brain tumors. Other materials frequently used to mimic brain tissue in mechanical tests, like ballistic gel and chicken breast, were found to be significantly stiffer than both normal and diseased brain tissue. We have directly compared quasi-static properties of brain tissue, brain tumors, and common mechanical surrogates, though additional tests would be required to determine more complex constitutive models.

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.

Multiple intracranial space-occupying lesions in a renal transplant recipient from an area endemic for tuberculosis (TB): TB vs. toxoplasmosis.

PubMed

Bagchi, S; Sachdev, S S; Nalwa, A; Das, C J; Sinha, S; Suri, V; Mahajan, S; Bhowmik, D; Agarwal, S

2014-10-01

Renal transplant recipients may present with intracranial space-occupying lesions (SOLs) due to infections as well as a post-transplant lymphoproliferative disorder (PTLD). Here, we discuss a renal transplant recipient who presented with neurologic symptoms and magnetic resonance imaging (MRI) of the brain showed multiple focal SOLs. Tuberculosis (TB), toxoplasmosis, nocardiosis, fungal infections, and PTLD were considered in the differential diagnosis. MRI spectroscopy was suggestive of an infectious cause, such as toxoplasmosis or TB. Serologic tests using Toxoplasma were negative. A brain biopsy followed by immunohistochemical staining using Toxoplasma antibody demonstrated multiple intravascular cysts of toxoplasma. This case highlights the diagnostic dilemma in an immunocompromised patient with multiple focal brain lesions, especially in areas where TB is endemic. © 2014 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

A transition in brain state during propofol-induced unconsciousness.

PubMed

Mukamel, Eran A; Pirondini, Elvira; Babadi, Behtash; Wong, Kin Foon Kevin; Pierce, Eric T; Harrell, P Grace; Walsh, John L; Salazar-Gomez, Andres F; Cash, Sydney S; Eskandar, Emad N; Weiner, Veronica S; Brown, Emery N; Purdon, Patrick L

2014-01-15

Rhythmic oscillations shape cortical dynamics during active behavior, sleep, and general anesthesia. Cross-frequency phase-amplitude coupling is a prominent feature of cortical oscillations, but its role in organizing conscious and unconscious brain states is poorly understood. Using high-density EEG and intracranial electrocorticography during gradual induction of propofol general anesthesia in humans, we discovered a rapid drug-induced transition between distinct states with opposite phase-amplitude coupling and different cortical source distributions. One state occurs during unconsciousness and may be similar to sleep slow oscillations. A second state occurs at the loss or recovery of consciousness and resembles an enhanced slow cortical potential. These results provide objective electrophysiological landmarks of distinct unconscious brain states, and could be used to help improve EEG-based monitoring for general anesthesia.

Classification of Multiple Seizure-Like States in Three Different Rodent Models of Epileptogenesis.

PubMed

Guirgis, Mirna; Serletis, Demitre; Zhang, Jane; Florez, Carlos; Dian, Joshua A; Carlen, Peter L; Bardakjian, Berj L

2014-01-01

Epilepsy is a dynamical disease and its effects are evident in over fifty million people worldwide. This study focused on objective classification of the multiple states involved in the brain's epileptiform activity. Four datasets from three different rodent hippocampal preparations were explored, wherein seizure-like-events (SLE) were induced by the perfusion of a low - Mg(2+) /high-K(+) solution or 4-Aminopyridine. Local field potentials were recorded from CA3 pyramidal neurons and interneurons and modeled as Markov processes. Specifically, hidden Markov models (HMM) were used to determine the nature of the states present. Properties of the Hilbert transform were used to construct the feature spaces for HMM training. By sequentially applying the HMM training algorithm, multiple states were identified both in episodes of SLE and nonSLE activity. Specifically, preSLE and postSLE states were differentiated and multiple inner SLE states were identified. This was accomplished using features extracted from the lower frequencies (1-4 Hz, 4-8 Hz) alongside those of both the low- (40-100 Hz) and high-gamma (100-200 Hz) of the recorded electrical activity. The learning paradigm of this HMM-based system eliminates the inherent bias associated with other learning algorithms that depend on predetermined state segmentation and renders it an appropriate candidate for SLE classification.

Functional connectivity analysis of resting-state fMRI networks in nicotine dependent patients

NASA Astrophysics Data System (ADS)

Smith, Aria; Ehtemami, Anahid; Fratte, Daniel; Meyer-Baese, Anke; Zavala-Romero, Olmo; Goudriaan, Anna E.; Schmaal, Lianne; Schulte, Mieke H. J.

2016-03-01

Brain imaging studies identified brain networks that play a key role in nicotine dependence-related behavior. Functional connectivity of the brain is dynamic; it changes over time due to different causes such as learning, or quitting a habit. Functional connectivity analysis is useful in discovering and comparing patterns between functional magnetic resonance imaging (fMRI) scans of patients' brains. In the resting state, the patient is asked to remain calm and not do any task to minimize the contribution of external stimuli. The study of resting-state fMRI networks have shown functionally connected brain regions that have a high level of activity during this state. In this project, we are interested in the relationship between these functionally connected brain regions to identify nicotine dependent patients, who underwent a smoking cessation treatment. Our approach is on the comparison of the set of connections between the fMRI scans before and after treatment. We applied support vector machines, a machine learning technique, to classify patients based on receiving the treatment or the placebo. Using the functional connectivity (CONN) toolbox, we were able to form a correlation matrix based on the functional connectivity between different regions of the brain. The experimental results show that there is inadequate predictive information to classify nicotine dependent patients using the SVM classifier. We propose other classification methods be explored to better classify the nicotine dependent patients.

Effect of Resting-State fNIRS Scanning Duration on Functional Brain Connectivity and Graph Theory Metrics of Brain Network.

PubMed

Geng, Shujie; Liu, Xiangyu; Biswal, Bharat B; Niu, Haijing

2017-01-01

As an emerging brain imaging technique, functional near infrared spectroscopy (fNIRS) has attracted widespread attention for advancing resting-state functional connectivity (FC) and graph theoretical analyses of brain networks. However, it remains largely unknown how the duration of the fNIRS signal scanning is related to stable and reproducible functional brain network features. To answer this question, we collected resting-state fNIRS signals (10-min duration, two runs) from 18 participants and then truncated the hemodynamic time series into 30-s time bins that ranged from 1 to 10 min. Measures of nodal efficiency, nodal betweenness, network local efficiency, global efficiency, and clustering coefficient were computed for each subject at each fNIRS signal acquisition duration. Analyses of the stability and between-run reproducibility were performed to identify optimal time length for each measure. We found that the FC, nodal efficiency and nodal betweenness stabilized and were reproducible after 1 min of fNIRS signal acquisition, whereas network clustering coefficient, local and global efficiencies stabilized after 1 min and were reproducible after 5 min of fNIRS signal acquisition for only local and global efficiencies. These quantitative results provide direct evidence regarding the choice of the resting-state fNIRS scanning duration for functional brain connectivity and topological metric stability of brain network connectivity.

Reproducibility assessment of brain responses to visual food stimuli in adults with overweight and obesity

PubMed Central

Sayer, R Drew; Tamer, Gregory G; Chen, Ningning; Tregellas, Jason R; Cornier, Marc-Andre; Kareken, David A; Talavage, Thomas M; McCrory, Megan A; Campbell, Wayne W

2016-01-01

Objective The brain’s reward system influences ingestive behavior and subsequently, obesity risk. Functional magnetic resonance imaging (fMRI) is a common method for investigating brain reward function. We sought to assess the reproducibility of fasting-state brain responses to visual food stimuli using BOLD fMRI. Methods A priori brain regions of interest included bilateral insula, amygdala, orbitofrontal cortex, caudate, and putamen. Fasting-state fMRI and appetite assessments were completed by 28 women (n=16) and men (n=12) with overweight or obesity on 2 days. Reproducibility was assessed by comparing mean fasting-state brain responses and measuring test-retest reliability of these responses on the 2 testing days. Results Mean fasting-state brain responses on Day 2 were reduced compared to Day 1 in the left insula and right amygdala, but mean Day 1 and Day 2 responses were not different in the other regions of interest. With the exception of the left orbitofrontal cortex response (fair reliability), test-retest reliabilities of brain responses were poor or unreliable. Conclusion fMRI-measured responses to visual food cues in adults with overweight or obesity show relatively good mean-level reproducibility, but considerable within-subject variability. Poor test-retest reliability reduces the likelihood of observing true correlations and increases the necessary sample sizes for studies. PMID:27542906

Resting-state functional brain connectivity: lessons from functional near-infrared spectroscopy.

PubMed

Niu, Haijing; He, Yong

2014-04-01

Resting-state functional near-infrared spectroscopy (R-fNIRS) is an active area of interest and is currently attracting considerable attention as a new imaging tool for the study of resting-state brain function. Using variations in hemodynamic concentration signals, R-fNIRS measures the brain's low-frequency spontaneous neural activity, combining the advantages of portability, low-cost, high temporal sampling rate and less physical burden to participants. The temporal synchronization of spontaneous neuronal activity in anatomically separated regions is referred to as resting-state functional connectivity (RSFC). In the past several years, an increasing body of R-fNIRS RSFC studies has led to many important findings about functional integration among local or whole-brain regions by measuring inter-regional temporal synchronization. Here, we summarize recent advances made in the R-fNIRS RSFC methodologies, from the detection of RSFC (e.g., seed-based correlation analysis, independent component analysis, whole-brain correlation analysis, and graph-theoretical topological analysis), to the assessment of RSFC performance (e.g., reliability, repeatability, and validity), to the application of RSFC in studying normal development and brain disorders. The literature reviewed here suggests that RSFC analyses based on R-fNIRS data are valid and reliable for the study of brain function in healthy and diseased populations, thus providing a promising imaging tool for cognitive science and clinics.

Williams with TRAC experiment in Destiny

NASA Image and Video Library

2007-03-08

ISS014-E-16215 (8 March 2007) --- Astronaut Sunita L. Williams, Expedition 14 flight engineer, works with the Test of Reaction and Adaptation Capabilities (TRAC) experiment in the Destiny laboratory of the International Space Station. The TRAC investigation will test the theory of brain adaptation during space flight by testing hand-eye coordination before, during and after the space flight.

Williams with TRAC experiment in Destiny

NASA Image and Video Library

2007-03-08

ISS014-E-16210 (8 March 2007) --- Astronaut Sunita L. Williams, Expedition 14 flight engineer, works with the Test of Reaction and Adaptation Capabilities (TRAC) experiment in the Destiny laboratory of the International Space Station. The TRAC investigation will test the theory of brain adaptation during space flight by testing hand-eye coordination before, during and after the space flight.

Williams with TRAC experiment in Destiny

NASA Image and Video Library

2007-03-08

ISS014-E-16214 (8 March 2007) --- Astronaut Sunita L. Williams, Expedition 14 flight engineer, works with the Test of Reaction and Adaptation Capabilities (TRAC) experiment in the Destiny laboratory of the International Space Station. The TRAC investigation will test the theory of brain adaptation during space flight by testing hand-eye coordination before, during and after the space flight.

Intrinsic and task-evoked network architectures of the human brain

PubMed Central

Cole, Michael W.; Bassett, Danielle S.; Power, Jonathan D.; Braver, Todd S.; Petersen, Steven E.

2014-01-01

Summary Many functional network properties of the human brain have been identified during rest and task states, yet it remains unclear how the two relate. We identified a whole-brain network architecture present across dozens of task states that was highly similar to the resting-state network architecture. The most frequent functional connectivity strengths across tasks closely matched the strengths observed at rest, suggesting this is an “intrinsic”, standard architecture of functional brain organization. Further, a set of small but consistent changes common across tasks suggests the existence of a task-general network architecture distinguishing task states from rest. These results indicate the brain’s functional network architecture during task performance is shaped primarily by an intrinsic network architecture that is also present during rest, and secondarily by evoked task-general and task-specific network changes. This establishes a strong relationship between resting-state functional connectivity and task-evoked functional connectivity – areas of neuroscientific inquiry typically considered separately. PMID:24991964

Sculpting the Intrinsic Modular Organization of Spontaneous Brain Activity by Art.

PubMed

Lin, Chia-Shu; Liu, Yong; Huang, Wei-Yuan; Lu, Chia-Feng; Teng, Shin; Ju, Tzong-Ching; He, Yong; Wu, Yu-Te; Jiang, Tianzi; Hsieh, Jen-Chuen

2013-01-01

Artistic training is a complex learning that requires the meticulous orchestration of sophisticated polysensory, motor, cognitive, and emotional elements of mental capacity to harvest an aesthetic creation. In this study, we investigated the architecture of the resting-state functional connectivity networks from professional painters, dancers and pianists. Using a graph-based network analysis, we focused on the art-related changes of modular organization and functional hubs in the resting-state functional connectivity network. We report that the brain architecture of artists consists of a hierarchical modular organization where art-unique and artistic form-specific brain states collectively mirror the mind states of virtuosos. We show that even in the resting state, this type of extraordinary and long-lasting training can macroscopically imprint a neural network system of spontaneous activity in which the related brain regions become functionally and topologically modularized in both domain-general and domain-specific manners. The attuned modularity reflects a resilient plasticity nurtured by long-term experience.

Sculpting the Intrinsic Modular Organization of Spontaneous Brain Activity by Art

PubMed Central

Lin, Chia-Shu; Liu, Yong; Huang, Wei-Yuan; Lu, Chia-Feng; Teng, Shin; Ju, Tzong-Ching; He, Yong; Wu, Yu-Te; Jiang, Tianzi; Hsieh, Jen-Chuen

2013-01-01

Artistic training is a complex learning that requires the meticulous orchestration of sophisticated polysensory, motor, cognitive, and emotional elements of mental capacity to harvest an aesthetic creation. In this study, we investigated the architecture of the resting-state functional connectivity networks from professional painters, dancers and pianists. Using a graph-based network analysis, we focused on the art-related changes of modular organization and functional hubs in the resting-state functional connectivity network. We report that the brain architecture of artists consists of a hierarchical modular organization where art-unique and artistic form-specific brain states collectively mirror the mind states of virtuosos. We show that even in the resting state, this type of extraordinary and long-lasting training can macroscopically imprint a neural network system of spontaneous activity in which the related brain regions become functionally and topologically modularized in both domain-general and domain-specific manners. The attuned modularity reflects a resilient plasticity nurtured by long-term experience. PMID:23840527

Resting-state brain networks revealed by granger causal connectivity in frogs.

PubMed

Xue, Fei; Fang, Guangzhan; Yue, Xizi; Zhao, Ermi; Brauth, Steven E; Tang, Yezhong

2016-10-15

Resting-state networks (RSNs) refer to the spontaneous brain activity generated under resting conditions, which maintain the dynamic connectivity of functional brain networks for automatic perception or higher order cognitive functions. Here, Granger causal connectivity analysis (GCCA) was used to explore brain RSNs in the music frog (Babina daunchina) during different behavioral activity phases. The results reveal that a causal network in the frog brain can be identified during the resting state which reflects both brain lateralization and sexual dimorphism. Specifically (1) ascending causal connections from the left mesencephalon to both sides of the telencephalon are significantly higher than those from the right mesencephalon, while the right telencephalon gives rise to the strongest efferent projections among all brain regions; (2) causal connections from the left mesencephalon in females are significantly higher than those in males and (3) these connections are similar during both the high and low behavioral activity phases in this species although almost all electroencephalograph (EEG) spectral bands showed higher power in the high activity phase for all nodes. The functional features of this network match important characteristics of auditory perception in this species. Thus we propose that this causal network maintains auditory perception during the resting state for unexpected auditory inputs as resting-state networks do in other species. These results are also consistent with the idea that females are more sensitive to auditory stimuli than males during the reproductive season. In addition, these results imply that even when not behaviorally active, the frogs remain vigilant for detecting external stimuli. Copyright © 2016 IBRO. Published by Elsevier Ltd. All rights reserved.

Radial q-space sampling for DSI.

PubMed

Baete, Steven H; Yutzy, Stephen; Boada, Fernando E

2016-09-01

Diffusion spectrum imaging (DSI) has been shown to be an effective tool for noninvasively depicting the anatomical details of brain microstructure. Existing implementations of DSI sample the diffusion encoding space using a rectangular grid. Here we present a different implementation of DSI whereby a radially symmetric q-space sampling scheme for DSI is used to improve the angular resolution and accuracy of the reconstructed orientation distribution functions. Q-space is sampled by acquiring several q-space samples along a number of radial lines. Each of these radial lines in q-space is analytically connected to a value of the orientation distribution functions at the same angular location by the Fourier slice theorem. Computer simulations and in vivo brain results demonstrate that radial diffusion spectrum imaging correctly estimates the orientation distribution functions when moderately high b-values (4000 s/mm2) and number of q-space samples (236) are used. The nominal angular resolution of radial diffusion spectrum imaging depends on the number of radial lines used in the sampling scheme, and only weakly on the maximum b-value. In addition, the radial analytical reconstruction reduces truncation artifacts which affect Cartesian reconstructions. Hence, a radial acquisition of q-space can be favorable for DSI. Magn Reson Med 76:769-780, 2016. © 2015 Wiley Periodicals, Inc. © 2015 Wiley Periodicals, Inc.

Indestructible plastic: the neuroscience of the new aging brain.

PubMed

Holman, Constance; de Villers-Sidani, Etienne

2014-01-01

In recent years, research on experience-dependent plasticity has provided valuable insight on adaptation to environmental input across the lifespan, and advances in understanding the minute cellular changes underlying the brain's capacity for self-reorganization have opened exciting new possibilities for treating illness and injury. Ongoing work in this line of inquiry has also come to deeply influence another field: cognitive neuroscience of the normal aging. This complex process, once considered inevitable or beyond the reach of treatment, has been transformed into an arena of intense investigation and strategic intervention. However, important questions remain about this characterization of the aging brain, and the assumptions it makes about the social, cultural, and biological space occupied by cognition in the older individual and body. The following paper will provide a critical examination of the move from basic experiments on the neurophysiology of experience-dependent plasticity to the growing market for (and public conception of) cognitive aging as a medicalized space for intervention by neuroscience-backed technologies. Entangled with changing concepts of normality, pathology, and self-preservation, we will argue that this new understanding, led by personalized cognitive training strategies, is approaching a point where interdisciplinary research is crucial to provide a holistic and nuanced understanding of the aging process. This new outlook will allow us to move forward in a space where our knowledge, like our new conception of the brain, is never static.

Unified model of brain tissue microstructure dynamically binds diffusion and osmosis with extracellular space geometry

NASA Astrophysics Data System (ADS)

Yousefnezhad, Mohsen; Fotouhi, Morteza; Vejdani, Kaveh; Kamali-Zare, Padideh

2016-09-01

We present a universal model of brain tissue microstructure that dynamically links osmosis and diffusion with geometrical parameters of brain extracellular space (ECS). Our model robustly describes and predicts the nonlinear time dependency of tortuosity (λ =√{D /D* } ) changes with very high precision in various media with uniform and nonuniform osmolarity distribution, as demonstrated by previously published experimental data (D = free diffusion coefficient, D* = effective diffusion coefficient). To construct this model, we first developed a multiscale technique for computationally effective modeling of osmolarity in the brain tissue. Osmolarity differences across cell membranes lead to changes in the ECS dynamics. The evolution of the underlying dynamics is then captured by a level set method. Subsequently, using a homogenization technique, we derived a coarse-grained model with parameters that are explicitly related to the geometry of cells and their associated ECS. Our modeling results in very accurate analytical approximation of tortuosity based on time, space, osmolarity differences across cell membranes, and water permeability of cell membranes. Our model provides a unique platform for studying ECS dynamics not only in physiologic conditions such as sleep-wake cycles and aging but also in pathologic conditions such as stroke, seizure, and neoplasia, as well as in predictive pharmacokinetic modeling such as predicting medication biodistribution and efficacy and novel biomolecule development and testing.

Cannula Implantation into the Cisterna Magna of Rodents.

PubMed

Xavier, Anna L R; Hauglund, Natalie Linea; von Holstein-Rathlou, Stephanie; Li, Qianliang; Sanggaard, Simon; Lou, Nanhong; Lundgaard, Iben; Nedergaard, Maiken

2018-05-23

Cisterna magna cannulation (CMc) is a straightforward procedure that enables direct access to the cerebrospinal fluid (CSF) without operative damage to the skull or the brain parenchyma. In anesthetized rodents, the exposure of the dura mater by blunt dissection of the neck muscles allows the insertion of a cannula into the cisterna magna (CM). The cannula, composed either by a fine beveled needle or borosilicate capillary, is attached via a polyethylene (PE) tube to a syringe. Using a syringe pump, molecules can then be injected at controlled rates directly into the CM, which is continuous with the subarachnoid space. From the subarachnoid space, we can trace CSF fluxes by convective flow into the perivascular space around penetrating arterioles, where solute exchange with the interstitial fluid (ISF) occurs. CMc can be performed for acute injections immediately following the surgery, or for chronic implantation, with later injection in anesthetized or awake, freely moving rodents. Quantitation of tracer distribution in the brain parenchyma can be performed by epifluorescence, 2-photon microscopy, and magnetic resonance imaging (MRI), depending on the physico-chemical properties of the injected molecules. Thus, CMc in conjunction with various imaging techniques offers a powerful tool for assessment of the glymphatic system and CSF dynamics and function. Furthermore, CMc can be utilized as a conduit for fast, brain-wide delivery of signaling molecules and metabolic substrates that could not otherwise cross the blood brain barrier (BBB).

Aquaporin-4 facilitator TGN-073 promotes interstitial fluid circulation within the blood-brain barrier: [17O]H2O JJVCPE MRI study.

PubMed

Huber, Vincent J; Igarashi, Hironaka; Ueki, Satoshi; Kwee, Ingrid L; Nakada, Tsutomu

2018-06-13

The blood-brain barrier (BBB), which imposes significant water permeability restriction, effectively isolates the brain from the systemic circulation. Seemingly paradoxical, the abundance of aquaporin-4 (AQP-4) on the inside of the BBB strongly indicates the presence of unique water dynamics essential for brain function. On the basis of the highly specific localization of AQP-4, namely, astrocyte end feet at the glia limitans externa and pericapillary Virchow-Robin space, we hypothesized that the AQP-4 system serves as an interstitial fluid circulator, moving interstitial fluid from the glia limitans externa to pericapillary Virchow-Robin space to ensure proper glymphatic flow draining into the cerebrospinal fluid. The hypothesis was tested directly using the AQP-4 facilitator TGN-073 developed in our laboratory, and [O]H2O JJ vicinal coupling proton exchange MRI, a method capable of tracing water molecules delivered into the blood circulation. The results unambiguously showed that facilitation of AQP-4 by TGN-073 increased turnover of interstitial fluid through the system, resulting in a significant reduction in [O]H2O contents of cortex with normal flux into the cerebrospinal fluid. The study further suggested that in addition to providing the necessary water for proper glymphatic flow, the AQP-4 system produces a water gradient within the interstitial space promoting circulation of interstitial fluid within the BBB.

Abnormal Functional MRI BOLD Contrast in the Vegetative State after Severe Traumatic Brain Injury

ERIC Educational Resources Information Center

Heelmann, Volker

2010-01-01

For the rehabilitation process, the treatment of patients surviving brain injury in a vegetative state is still a serious challenge. The aim of this study was to investigate patients exhibiting severely disturbed consciousness using functional magnetic resonance imaging. Five cases of posttraumatic vegetative state and one with minimal…

Decoding brain state transitions in the pedunculopontine nucleus: cooperative phasic and tonic mechanisms

PubMed Central

Petzold, Anne; Valencia, Miguel; Pál, Balázs; Mena-Segovia, Juan

2015-01-01

Cholinergic neurons of the pedunculopontine nucleus (PPN) are most active during the waking state. Their activation is deemed to cause a switch in the global brain activity from sleep to wakefulness, while their sustained discharge may contribute to upholding the waking state and enhancing arousal. Similarly, non-cholinergic PPN neurons are responsive to brain state transitions and their activation may influence some of the same targets of cholinergic neurons, suggesting that they operate in coordination. Yet, it is not clear how the discharge of distinct classes of PPN neurons organize during brain states. Here, we monitored the in vivo network activity of PPN neurons in the anesthetized rat across two distinct levels of cortical dynamics and their transitions. We identified a highly structured configuration in PPN network activity during slow-wave activity that was replaced by decorrelated activity during the activated state (AS). During the transition, neurons were predominantly excited (phasically or tonically), but some were inhibited. Identified cholinergic neurons displayed phasic and short latency responses to sensory stimulation, whereas the majority of non-cholinergic showed tonic responses and remained at high discharge rates beyond the state transition. In vitro recordings demonstrate that cholinergic neurons exhibit fast adaptation that prevents them from discharging at high rates over prolonged time periods. Our data shows that PPN neurons have distinct but complementary roles during brain state transitions, where cholinergic neurons provide a fast and transient response to sensory events that drive state transitions, whereas non-cholinergic neurons maintain an elevated firing rate during global activation. PMID:26582977

Evaluation of sliding window correlation performance for characterizing dynamic functional connectivity and brain states

PubMed Central

Shakil, Sadia; Lee, Chin-Hui; Keilholz, Shella Dawn

2016-01-01

A promising recent development in the study of brain function is the dynamic analysis of resting-state functional MRI scans, which can enhance understanding of normal cognition and alterations that result from brain disorders. One widely used method of capturing the dynamics of functional connectivity is sliding window correlation (SWC). However, in the absence of a “gold standard” for comparison, evaluating the performance of the SWC in typical resting-state data is challenging. This study uses simulated networks (SNs) with known transitions to examine the effects of parameters such as window length, window offset, window type, noise, filtering, and sampling rate on the SWC performance. The SWC time course was calculated for all node pairs of each SN and then clustered using the k-means algorithm to determine how resulting brain states match known configurations and transitions in the SNs. The outcomes show that the detection of state transitions and durations in the SWC is most strongly influenced by the window length and offset, followed by noise and filtering parameters. The effect of the image sampling rate was relatively insignificant. Tapered windows provide less sensitivity to state transitions than rectangular windows, which could be the result of the sharp transitions in the SNs. Overall, the SWC gave poor estimates of correlation for each brain state. Clustering based on the SWC time course did not reliably reflect the underlying state transitions unless the window length was comparable to the state duration, highlighting the need for new adaptive window analysis techniques. PMID:26952197

Robust Brain Hyperglycemia during General Anesthesia: Relationships with Metabolic Brain Inhibition and Vasodilation

PubMed Central

Bola, R. Aaron; Kiyatkin, Eugene A.

2016-01-01

Glucose is the main energetic substrate for the metabolic activity of brain cells and its proper delivery into the extracellular space is essential for maintaining normal neural functions. Under physiological conditions, glucose continuously enters the extracellular space from arterial blood via gradient-dependent facilitated diffusion governed by the GLUT-1 transporters. Due to this gradient-dependent mechanism, glucose levels rise in the brain after consumption of glucose-containing foods and drinks. Glucose entry is also accelerated due to local neuronal activation and neuro-vascular coupling, resulting in transient hyperglycemia to prevent any metabolic deficit. Here, we explored another mechanism that is activated during general anesthesia and results in significant brain hyperglycemia. By using enzyme-based glucose biosensors we demonstrate that glucose levels in the nucleus accumbens (NAc) strongly increase after iv injection of Equthesin, a mixture of chloral hydrate and sodium pentobarbital, which is often used for general anesthesia in rats. By combining electrochemical recordings with brain, muscle, and skin temperature monitoring, we show that the gradual increase in brain glucose occurring during the development of general anesthesia tightly correlate with decreases in brain-muscle temperature differentials, suggesting that this rise in glucose is related to metabolic inhibition. While the decreased consumption of glucose by brain cells could contribute to the development of hyperglycemia, an exceptionally strong positive correlation (r = 0.99) between glucose rise and increases in skin-muscle temperature differentials was also found, suggesting the strong vasodilation of cerebral vessels as the primary mechanism for accelerated entry of glucose into brain tissue. Our present data could explain drastic differences in basal glucose levels found in awake and anesthetized animal preparations. They also suggest that glucose entry into brain tissue could be strongly modulated by pharmacological drugs via drug-induced changes in metabolic activity and the tone of cerebral vessels. PMID:26913008

The role of brain barriers in fluid movement in the CNS: is there a 'glymphatic' system?

PubMed

Abbott, N Joan; Pizzo, Michelle E; Preston, Jane E; Janigro, Damir; Thorne, Robert G

2018-03-01

Brain fluids are rigidly regulated to provide stable environments for neuronal function, e.g., low K + , Ca 2+ , and protein to optimise signalling and minimise neurotoxicity. At the same time, neuronal and astroglial waste must be promptly removed. The interstitial fluid (ISF) of the brain tissue and the cerebrospinal fluid (CSF) bathing the CNS are integral to this homeostasis and the idea of a glia-lymph or 'glymphatic' system for waste clearance from brain has developed over the last 5 years. This links bulk (convective) flow of CSF into brain along the outside of penetrating arteries, glia-mediated convective transport of fluid and solutes through the brain extracellular space (ECS) involving the aquaporin-4 (AQP4) water channel, and finally delivery of fluid to venules for clearance along peri-venous spaces. However, recent evidence favours important amendments to the 'glymphatic' hypothesis, particularly concerning the role of glia and transfer of solutes within the ECS. This review discusses studies which question the role of AQP4 in ISF flow and the lack of evidence for its ability to transport solutes; summarizes attributes of brain ECS that strongly favour the diffusion of small and large molecules without ISF flow; discusses work on hydraulic conductivity and the nature of the extracellular matrix which may impede fluid movement; and reconsiders the roles of the perivascular space (PVS) in CSF-ISF exchange and drainage. We also consider the extent to which CSF-ISF exchange is possible and desirable, the impact of neuropathology on fluid drainage, and why using CSF as a proxy measure of brain components or drug delivery is problematic. We propose that new work and key historical studies both support the concept of a perivascular fluid system, whereby CSF enters the brain via PVS convective flow or dispersion along larger caliber arteries/arterioles, diffusion predominantly regulates CSF/ISF exchange at the level of the neurovascular unit associated with CNS microvessels, and, finally, a mixture of CSF/ISF/waste products is normally cleared along the PVS of venules/veins as well as other pathways; such a system may or may not constitute a true 'circulation', but, at the least, suggests a comprehensive re-evaluation of the previously proposed 'glymphatic' concepts in favour of a new system better taking into account basic cerebrovascular physiology and fluid transport considerations.

Building a neuroscience of pleasure and well-being

PubMed Central

Berridge, Kent C; Kringelbach, Morten L

2012-01-01

Background How is happiness generated via brain function in lucky individuals who have the good fortune to be happy? Conceptually, well-being or happiness has long been viewed as requiring at least two crucial ingredients: positive affect or pleasure (hedonia) and a sense of meaningfulness or engagement in life (eudaimonia). Science has recently made progress in relating hedonic pleasure to brain function, and so here we survey new insights into how brains generate the hedonic ingredient of sustained or frequent pleasure. We also briefly discuss how brains might connect hedonia states of pleasure to eudaimonia assessments of meaningfulness, and so create balanced states of positive well-being. Results Notable progress has been made in understanding brain bases of hedonic processing, producing insights into that brain systems that cause and/or code sensory pleasures. Progress has been facilitated by the recognition that hedonic brain mechanisms are largely shared between humans and other mammals, allowing application of conclusions from animal studies to a better understanding of human pleasures. In the past few years, evidence has also grown to indicate that for humans, brain mechanisms of higher abstract pleasures strongly overlap with more basic sensory pleasures. This overlap may provide a window into underlying brain circuitry that generates all pleasures, including even the hedonic quality of pervasive well-being that detaches from any particular sensation to apply to daily life in a more sustained or frequent fashion. Conclusions Hedonic insights are applied to understanding human well-being here. Our strategy combines new findings on brain mediators that generate the pleasure of sensations with evidence that human brains use many of the same hedonic circuits from sensory pleasures to create the higher pleasures. This in turn may be linked to how hedonic systems interact with other brain systems relevant to self-understanding and the meaning components of eudaimonic happiness. Finally, we speculate a bit about how brains that generate hedonia states might link to eudaimonia assessments to create properly balanced states of positive well-being that approach true happiness. PMID:22328976

Soft drink effects on sensorimotor rhythm brain computer interface performance and resting-state spectral power.

PubMed

Mundahl, John; Jianjun Meng; He, Jeffrey; Bin He

2016-08-01

Brain-computer interface (BCI) systems allow users to directly control computers and other machines by modulating their brain waves. In the present study, we investigated the effect of soft drinks on resting state (RS) EEG signals and BCI control. Eight healthy human volunteers each participated in three sessions of BCI cursor tasks and resting state EEG. During each session, the subjects drank an unlabeled soft drink with either sugar, caffeine, or neither ingredient. A comparison of resting state spectral power shows a substantial decrease in alpha and beta power after caffeine consumption relative to control. Despite attenuation of the frequency range used for the control signal, caffeine average BCI performance was the same as control. Our work provides a useful characterization of caffeine, the world's most popular stimulant, on brain signal frequencies and their effect on BCI performance.

EEG source space analysis of the supervised factor analytic approach for the classification of multi-directional arm movement

NASA Astrophysics Data System (ADS)

Shenoy Handiru, Vikram; Vinod, A. P.; Guan, Cuntai

2017-08-01

Objective. In electroencephalography (EEG)-based brain-computer interface (BCI) systems for motor control tasks the conventional practice is to decode motor intentions by using scalp EEG. However, scalp EEG only reveals certain limited information about the complex tasks of movement with a higher degree of freedom. Therefore, our objective is to investigate the effectiveness of source-space EEG in extracting relevant features that discriminate arm movement in multiple directions. Approach. We have proposed a novel feature extraction algorithm based on supervised factor analysis that models the data from source-space EEG. To this end, we computed the features from the source dipoles confined to Brodmann areas of interest (BA4a, BA4p and BA6). Further, we embedded class-wise labels of multi-direction (multi-class) source-space EEG to an unsupervised factor analysis to make it into a supervised learning method. Main Results. Our approach provided an average decoding accuracy of 71% for the classification of hand movement in four orthogonal directions, that is significantly higher (>10%) than the classification accuracy obtained using state-of-the-art spatial pattern features in sensor space. Also, the group analysis on the spectral characteristics of source-space EEG indicates that the slow cortical potentials from a set of cortical source dipoles reveal discriminative information regarding the movement parameter, direction. Significance. This study presents evidence that low-frequency components in the source space play an important role in movement kinematics, and thus it may lead to new strategies for BCI-based neurorehabilitation.

Metronidazole and Hydroxymetronidazole Central Nervous System Distribution: 1. Microdialysis Assessment of Brain Extracellular Fluid Concentrations in Patients with Acute Brain Injury

PubMed Central

Frasca, Denis; Dahyot-Fizelier, Claire; Adier, Christophe; Mimoz, Olivier; Debaene, Bertrand; Couet, William

2014-01-01

The distribution of metronidazole in the central nervous system has only been described based on cerebrospinal fluid data. However, extracellular fluid (ECF) concentrations may better predict its antimicrobial effect and/or side effects. We sought to explore by microdialysis brain ECF metronidazole distribution in patients with acute brain injury. Four brain-injured patients monitored by cerebral microdialysis received 500 mg of metronidazole over 0.5 h every 8 h. Brain dialysates and blood samples were collected at steady state over 8 h. Probe recoveries were evaluated by in vivo retrodialysis in each patient for metronidazole. Metronidazole and OH-metronidazole were assayed by high-pressure liquid chromatography, and a noncompartmental pharmacokinetic analysis was performed. Probe recovery was equal to 78.8% ± 1.3% for metronidazole in patients. Unbound brain metronidazole concentration-time curves were delayed compared to unbound plasma concentration-time curves but with a mean metronidazole unbound brain/plasma AUC0–τ ratio equal to 102% ± 19% (ranging from 87 to 124%). The unbound plasma concentration-time profiles for OH-metronidazole were flat, with mean average steady-state concentrations equal to 4.0 ± 0.7 μg ml−1. This microdialysis study describes the steady-state brain distribution of metronidazole in patients and confirms its extensive distribution. PMID:24277041

Brain functional network connectivity based on a visual task: visual information processing-related brain regions are significantly activated in the task state.

PubMed

Yang, Yan-Li; Deng, Hong-Xia; Xing, Gui-Yang; Xia, Xiao-Luan; Li, Hai-Fang

2015-02-01

It is not clear whether the method used in functional brain-network related research can be applied to explore the feature binding mechanism of visual perception. In this study, we investigated feature binding of color and shape in visual perception. Functional magnetic resonance imaging data were collected from 38 healthy volunteers at rest and while performing a visual perception task to construct brain networks active during resting and task states. Results showed that brain regions involved in visual information processing were obviously activated during the task. The components were partitioned using a greedy algorithm, indicating the visual network existed during the resting state. Z-values in the vision-related brain regions were calculated, confirming the dynamic balance of the brain network. Connectivity between brain regions was determined, and the result showed that occipital and lingual gyri were stable brain regions in the visual system network, the parietal lobe played a very important role in the binding process of color features and shape features, and the fusiform and inferior temporal gyri were crucial for processing color and shape information. Experimental findings indicate that understanding visual feature binding and cognitive processes will help establish computational models of vision, improve image recognition technology, and provide a new theoretical mechanism for feature binding in visual perception.

Functional Characterization of Resting and Adenovirus-Induced Reactive Astrocytes in Three-Dimensional Culture

PubMed Central

Woo, Junsung; Im, Sun-Kyoung; Chun, Heejung; Jung, Soon-Young; Oh, Soo-Jin; Choi, Nakwon

2017-01-01

Brain is a rich environment where neurons and glia interact with neighboring cells as well as extracellular matrix in three-dimensional (3D) space. Astrocytes, which are the most abundant cells in the mammalian brain, reside in 3D space and extend highly branched processes that form microdomains and contact synapses. It has been suggested that astrocytes cultured in 3D might be maintained in a less reactive state as compared to those growing in a traditional, two-dimensional (2D) monolayer culture. However, the functional characterization of the astrocytes in 3D culture has been lacking. Here we cocultured neurons and astrocytes in 3D and examined the morphological, molecular biological, and electrophysiological properties of the 3D-cultured hippocampal astrocytes. In our 3D neuron-astrocyte coculture, astrocytes showed a typical morphology of a small soma with many branches and exhibited a unique membrane property of passive conductance, more closely resembling their native in vivo counterparts. Moreover, we also induced reactive astrocytosis in culture by infecting with high-titer adenovirus to mimic pathophysiological conditions in vivo. Adenoviral infection induced morphological changes in astrocytes, increased passive conductance, and increased GABA content as well as tonic GABA release, which are characteristics of reactive gliosis. Together, our study presents a powerful in vitro model resembling both physiological and pathophysiological conditions in vivo, and thereby provides a versatile experimental tool for studying various neurological diseases that accompany reactive astrocytes. PMID:28680301

Decoding Trajectories from Posterior Parietal Cortex Ensembles

PubMed Central

Mulliken, Grant H.; Musallam, Sam; Andersen, Richard A.

2009-01-01

High-level cognitive signals in the posterior parietal cortex (PPC) have previously been used to decode the intended endpoint of a reach, providing the first evidence that PPC can be used for direct control of a neural prosthesis (Musallam et al., 2004). Here we expand on this work by showing that PPC neural activity can be harnessed to estimate not only the endpoint but also to continuously control the trajectory of an end effector. Specifically, we trained two monkeys to use a joystick to guide a cursor on a computer screen to peripheral target locations while maintaining central ocular fixation. We found that we could accurately reconstruct the trajectory of the cursor using a relatively small ensemble of simultaneously recorded PPC neurons. Using a goal-based Kalman filter that incorporates target information into the state-space, we showed that the decoded estimate of cursor position could be significantly improved. Finally, we tested whether we could decode trajectories during closed-loop brain control sessions, in which the real-time position of the cursor was determined solely by a monkey’s neural activity in PPC. The monkey learned to perform brain control trajectories at 80% success rate(for 8 targets) after just 4–5 sessions. This improvement in behavioral performance was accompanied by a corresponding enhancement in neural tuning properties (i.e., increased tuning depth and coverage of encoding parameter space) as well as an increase in off-line decoding performance of the PPC ensemble. PMID:19036985

Relationship of Topology, Multiscale Phase Synchronization, and State Transitions in Human Brain Networks

PubMed Central

Kim, Minkyung; Kim, Seunghwan; Mashour, George A.; Lee, UnCheol

2017-01-01

How the brain reconstitutes consciousness and cognition after a major perturbation like general anesthesia is an important question with significant neuroscientific and clinical implications. Recent empirical studies in animals and humans suggest that the recovery of consciousness after anesthesia is not random but ordered. Emergence patterns have been classified as progressive and abrupt transitions from anesthesia to consciousness, with associated differences in duration and electroencephalogram (EEG) properties. We hypothesized that the progressive and abrupt emergence patterns from the unconscious state are associated with, respectively, continuous and discontinuous synchronization transitions in functional brain networks. The discontinuous transition is explainable with the concept of explosive synchronization, which has been studied almost exclusively in network science. We used the Kuramato model, a simple oscillatory network model, to simulate progressive and abrupt transitions in anatomical human brain networks acquired from diffusion tensor imaging (DTI) of 82 brain regions. To facilitate explosive synchronization, distinct frequencies for hub nodes with a large frequency disassortativity (i.e., higher frequency nodes linking with lower frequency nodes, or vice versa) were applied to the brain network. In this simulation study, we demonstrated that both progressive and abrupt transitions follow distinct synchronization processes at the individual node, cluster, and global network levels. The characteristic synchronization patterns of brain regions that are “progressive and earlier” or “abrupt but delayed” account for previously reported behavioral responses of gradual and abrupt emergence from the unconscious state. The characteristic network synchronization processes observed at different scales provide new insights into how regional brain functions are reconstituted during progressive and abrupt emergence from the unconscious state. This theoretical approach also offers a principled explanation of how the brain reconstitutes consciousness and cognitive functions after physiologic (sleep), pharmacologic (anesthesia), and pathologic (coma) perturbations. PMID:28713258

Relationship of Topology, Multiscale Phase Synchronization, and State Transitions in Human Brain Networks.

PubMed

Kim, Minkyung; Kim, Seunghwan; Mashour, George A; Lee, UnCheol

2017-01-01

How the brain reconstitutes consciousness and cognition after a major perturbation like general anesthesia is an important question with significant neuroscientific and clinical implications. Recent empirical studies in animals and humans suggest that the recovery of consciousness after anesthesia is not random but ordered. Emergence patterns have been classified as progressive and abrupt transitions from anesthesia to consciousness, with associated differences in duration and electroencephalogram (EEG) properties. We hypothesized that the progressive and abrupt emergence patterns from the unconscious state are associated with, respectively, continuous and discontinuous synchronization transitions in functional brain networks. The discontinuous transition is explainable with the concept of explosive synchronization, which has been studied almost exclusively in network science. We used the Kuramato model, a simple oscillatory network model, to simulate progressive and abrupt transitions in anatomical human brain networks acquired from diffusion tensor imaging (DTI) of 82 brain regions. To facilitate explosive synchronization, distinct frequencies for hub nodes with a large frequency disassortativity (i.e., higher frequency nodes linking with lower frequency nodes, or vice versa) were applied to the brain network. In this simulation study, we demonstrated that both progressive and abrupt transitions follow distinct synchronization processes at the individual node, cluster, and global network levels. The characteristic synchronization patterns of brain regions that are "progressive and earlier" or "abrupt but delayed" account for previously reported behavioral responses of gradual and abrupt emergence from the unconscious state. The characteristic network synchronization processes observed at different scales provide new insights into how regional brain functions are reconstituted during progressive and abrupt emergence from the unconscious state. This theoretical approach also offers a principled explanation of how the brain reconstitutes consciousness and cognitive functions after physiologic (sleep), pharmacologic (anesthesia), and pathologic (coma) perturbations.