Brain Gym. Simple Activities for Whole Brain Learning.

ERIC Educational Resources Information Center

Dennison, Paul E.; Dennison, Gail E.

This booklet contains simple movements and activities that are used with students in Educational Kinesiology to enhance their experience of whole brain learning. Whole brain learning through movement repatterning and Brain Gym activities enable students to access those parts of the brain previously unavailable to them. These movements of body and…

A simple rapid process for semi-automated brain extraction from magnetic resonance images of the whole mouse head.

PubMed

Delora, Adam; Gonzales, Aaron; Medina, Christopher S; Mitchell, Adam; Mohed, Abdul Faheem; Jacobs, Russell E; Bearer, Elaine L

2016-01-15

Magnetic resonance imaging (MRI) is a well-developed technique in neuroscience. Limitations in applying MRI to rodent models of neuropsychiatric disorders include the large number of animals required to achieve statistical significance, and the paucity of automation tools for the critical early step in processing, brain extraction, which prepares brain images for alignment and voxel-wise statistics. This novel timesaving automation of template-based brain extraction ("skull-stripping") is capable of quickly and reliably extracting the brain from large numbers of whole head images in a single step. The method is simple to install and requires minimal user interaction. This method is equally applicable to different types of MR images. Results were evaluated with Dice and Jacquard similarity indices and compared in 3D surface projections with other stripping approaches. Statistical comparisons demonstrate that individual variation of brain volumes are preserved. A downloadable software package not otherwise available for extraction of brains from whole head images is included here. This software tool increases speed, can be used with an atlas or a template from within the dataset, and produces masks that need little further refinement. Our new automation can be applied to any MR dataset, since the starting point is a template mask generated specifically for that dataset. The method reliably and rapidly extracts brain images from whole head images, rendering them useable for subsequent analytical processing. This software tool will accelerate the exploitation of mouse models for the investigation of human brain disorders by MRI. Copyright © 2015 Elsevier B.V. All rights reserved.

Increased Expression of Simple Ganglioside Species GM2 and GM3 Detected by MALDI Imaging Mass Spectrometry in a Combined Rat Model of Aβ Toxicity and Stroke.

PubMed

Caughlin, Sarah; Hepburn, Jeffrey D; Park, Dae Hee; Jurcic, Kristina; Yeung, Ken K-C; Cechetto, David F; Whitehead, Shawn N

2015-01-01

The aging brain is often characterized by the presence of multiple comorbidities resulting in synergistic damaging effects in the brain as demonstrated through the interaction of Alzheimer's disease (AD) and stroke. Gangliosides, a family of membrane lipids enriched in the central nervous system, may have a mechanistic role in mediating the brain's response to injury as their expression is altered in a number of disease and injury states. Matrix-Assisted Laser Desorption Ionization (MALDI) Imaging Mass Spectrometry (IMS) was used to study the expression of A-series ganglioside species GD1a, GM1, GM2, and GM3 to determine alteration of their expression profiles in the presence of beta-amyloid (Aβ) toxicity in addition to ischemic injury. To model a stroke, rats received a unilateral striatal injection of endothelin-1 (ET-1) (stroke alone group). To model Aβ toxicity, rats received intracerebralventricular (i.c.v.) injections of the toxic 25-35 fragment of the Aβ peptide (Aβ alone group). To model the combination of Aβ toxicity with stroke, rats received both the unilateral ET-1 injection and the bilateral icv injections of Aβ25-35 (combined Aβ/ET-1 group). By 3 d, a significant increase in the simple ganglioside species GM2 was observed in the ischemic brain region of rats who received a stroke (ET-1), with or without Aβ. By 21 d, GM2 levels only remained elevated in the combined Aβ/ET-1 group. GM3 levels however demonstrated a different pattern of expression. By 3 d GM3 was elevated in the ischemic brain region only in the combined Aβ/ET-1 group. By 21 d, GM3 was elevated in the ischemic brain region in both stroke alone and Aβ/ET-1 groups. Overall, results indicate that the accumulation of simple ganglioside species GM2 and GM3 may be indicative of a mechanism of interaction between AD and stroke.

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.

Rotationally invariant clustering of diffusion MRI data using spherical harmonics

NASA Astrophysics Data System (ADS)

Liptrot, Matthew; Lauze, François

2016-03-01

We present a simple approach to the voxelwise classification of brain tissue acquired with diffusion weighted MRI (DWI). The approach leverages the power of spherical harmonics to summarise the diffusion information, sampled at many points over a sphere, using only a handful of coefficients. We use simple features that are invariant to the rotation of the highly orientational diffusion data. This provides a way to directly classify voxels whose diffusion characteristics are similar yet whose primary diffusion orientations differ. Subsequent application of machine-learning to the spherical harmonic coefficients therefore may permit classification of DWI voxels according to their inferred underlying fibre properties, whilst ignoring the specifics of orientation. After smoothing apparent diffusion coefficients volumes, we apply a spherical harmonic transform, which models the multi-directional diffusion data as a collection of spherical basis functions. We use the derived coefficients as voxelwise feature vectors for classification. Using a simple Gaussian mixture model, we examined the classification performance for a range of sub-classes (3-20). The results were compared against existing alternatives for tissue classification e.g. fractional anisotropy (FA) or the standard model used by Camino.1 The approach was implemented on both two publicly-available datasets: an ex-vivo pig brain and in-vivo human brain from the Human Connectome Project (HCP). We have demonstrated how a robust classification of DWI data can be performed without the need for a model reconstruction step. This avoids the potential confounds and uncertainty that such models may impose, and has the benefit of being computable directly from the DWI volumes. As such, the method could prove useful in subsequent pre-processing stages, such as model fitting, where it could inform about individual voxel complexities and improve model parameter choice.

A Bayesian Attractor Model for Perceptual Decision Making

PubMed Central

Bitzer, Sebastian; Bruineberg, Jelle; Kiebel, Stefan J.

2015-01-01

Even for simple perceptual decisions, the mechanisms that the brain employs are still under debate. Although current consensus states that the brain accumulates evidence extracted from noisy sensory information, open questions remain about how this simple model relates to other perceptual phenomena such as flexibility in decisions, decision-dependent modulation of sensory gain, or confidence about a decision. We propose a novel approach of how perceptual decisions are made by combining two influential formalisms into a new model. Specifically, we embed an attractor model of decision making into a probabilistic framework that models decision making as Bayesian inference. We show that the new model can explain decision making behaviour by fitting it to experimental data. In addition, the new model combines for the first time three important features: First, the model can update decisions in response to switches in the underlying stimulus. Second, the probabilistic formulation accounts for top-down effects that may explain recent experimental findings of decision-related gain modulation of sensory neurons. Finally, the model computes an explicit measure of confidence which we relate to recent experimental evidence for confidence computations in perceptual decision tasks. PMID:26267143

A simple physiologically based pharmacokinetic model evaluating the effect of anti-nicotine antibodies on nicotine disposition in the brains of rats and humans

PubMed Central

Saylor, Kyle; Zhang, Chenming

2017-01-01

Physiologically based pharmacokinetic (PBPK) modeling was applied to investigate the effects of anti-nicotine antibodies on nicotine disposition in the brains of rats and humans. Successful construction of both rat and human models was achieved by fitting model outputs to published nicotine concentration time course data in the blood and in the brain. Key parameters presumed to have the most effect on the ability of these antibodies to prevent nicotine from entering the brain were selected for investigation using the human model. These parameters, which included antibody affinity for nicotine, antibody cross-reactivity with cotinine, and antibody concentration, were broken down into different, clinically-derived in silico treatment levels and fed into the human PBPK model. Model predictions suggested that all three parameters, in addition to smoking status, have a sizable impact on anti-nicotine antibodies’ ability to prevent nicotine from entering the brain and that the antibodies elicited by current human vaccines do not have sufficient binding characteristics to reduce brain nicotine concentrations. If the antibody binding characteristics achieved in animal studies can similarly be achieved in human studies, however, nicotine vaccine efficacy in terms of brain nicotine concentration reduction is predicted to meet threshold values for alleviating nicotine dependence. PMID:27473014

Linking brain-wide multivoxel activation patterns to behaviour: Examples from language and math.

PubMed

Raizada, Rajeev D S; Tsao, Feng-Ming; Liu, Huei-Mei; Holloway, Ian D; Ansari, Daniel; Kuhl, Patricia K

2010-05-15

A key goal of cognitive neuroscience is to find simple and direct connections between brain and behaviour. However, fMRI analysis typically involves choices between many possible options, with each choice potentially biasing any brain-behaviour correlations that emerge. Standard methods of fMRI analysis assess each voxel individually, but then face the problem of selection bias when combining those voxels into a region-of-interest, or ROI. Multivariate pattern-based fMRI analysis methods use classifiers to analyse multiple voxels together, but can also introduce selection bias via data-reduction steps as feature selection of voxels, pre-selecting activated regions, or principal components analysis. We show here that strong brain-behaviour links can be revealed without any voxel selection or data reduction, using just plain linear regression as a classifier applied to the whole brain at once, i.e. treating each entire brain volume as a single multi-voxel pattern. The brain-behaviour correlations emerged despite the fact that the classifier was not provided with any information at all about subjects' behaviour, but instead was given only the neural data and its condition-labels. Surprisingly, more powerful classifiers such as a linear SVM and regularised logistic regression produce very similar results. We discuss some possible reasons why the very simple brain-wide linear regression model is able to find correlations with behaviour that are as strong as those obtained on the one hand from a specific ROI and on the other hand from more complex classifiers. In a manner which is unencumbered by arbitrary choices, our approach offers a method for investigating connections between brain and behaviour which is simple, rigorous and direct. Copyright (c) 2010 Elsevier Inc. All rights reserved.

Diversity within a Birdsong

NASA Astrophysics Data System (ADS)

Laje, Rodrigo; Mindlin, Gabriel B.

2002-12-01

We present a model for the activities of neural circuits in a nucleus found in the brains of songbirds: the robust nucleus of the archistriatum (RA). This is a fore brain song control nucleus responsible for the phasic and precise neural signals driving vocal and respiratory motor neurons during singing. Driving a physical model of the avian vocal organ with the signals generated by the neural model, we produce synthetic songs. This allows us to show that certain connectivity architectures in the RA give rise to a wide range of different vocalizations under simple excitatory instructions.

Levels of detail analysis of microwave scattering from human head models for brain stroke detection

PubMed Central

2017-01-01

In this paper, we have presented a microwave scattering analysis from multiple human head models. This study incorporates different levels of detail in the human head models and its effect on microwave scattering phenomenon. Two levels of detail are taken into account; (i) Simplified ellipse shaped head model (ii) Anatomically realistic head model, implemented using 2-D geometry. In addition, heterogenic and frequency-dispersive behavior of the brain tissues has also been incorporated in our head models. It is identified during this study that the microwave scattering phenomenon changes significantly once the complexity of head model is increased by incorporating more details using magnetic resonance imaging database. It is also found out that the microwave scattering results match in both types of head model (i.e., geometrically simple and anatomically realistic), once the measurements are made in the structurally simplified regions. However, the results diverge considerably in the complex areas of brain due to the arbitrary shape interface of tissue layers in the anatomically realistic head model. After incorporating various levels of detail, the solution of subject microwave scattering problem and the measurement of transmitted and backscattered signals were obtained using finite element method. Mesh convergence analysis was also performed to achieve error free results with a minimum number of mesh elements and a lesser degree of freedom in the fast computational time. The results were promising and the E-Field values converged for both simple and complex geometrical models. However, the E-Field difference between both types of head model at the same reference point differentiated a lot in terms of magnitude. At complex location, a high difference value of 0.04236 V/m was measured compared to the simple location, where it turned out to be 0.00197 V/m. This study also contributes to provide a comparison analysis between the direct and iterative solvers so as to find out the solution of subject microwave scattering problem in a minimum computational time along with memory resources requirement. It is seen from this study that the microwave imaging may effectively be utilized for the detection, localization and differentiation of different types of brain stroke. The simulation results verified that the microwave imaging can be efficiently exploited to study the significant contrast between electric field values of the normal and abnormal brain tissues for the investigation of brain anomalies. In the end, a specific absorption rate analysis was carried out to compare the ionizing effects of microwave signals to different types of head model using a factor of safety for brain tissues. It is also suggested after careful study of various inversion methods in practice for microwave head imaging, that the contrast source inversion method may be more suitable and computationally efficient for such problems. PMID:29177115

Characteristics of voxel prediction power in full-brain Granger causality analysis of fMRI data

NASA Astrophysics Data System (ADS)

Garg, Rahul; Cecchi, Guillermo A.; Rao, A. Ravishankar

2011-03-01

Functional neuroimaging research is moving from the study of "activations" to the study of "interactions" among brain regions. Granger causality analysis provides a powerful technique to model spatio-temporal interactions among brain regions. We apply this technique to full-brain fMRI data without aggregating any voxel data into regions of interest (ROIs). We circumvent the problem of dimensionality using sparse regression from machine learning. On a simple finger-tapping experiment we found that (1) a small number of voxels in the brain have very high prediction power, explaining the future time course of other voxels in the brain; (2) these voxels occur in small sized clusters (of size 1-4 voxels) distributed throughout the brain; (3) albeit small, these clusters overlap with most of the clusters identified with the non-temporal General Linear Model (GLM); and (4) the method identifies clusters which, while not determined by the task and not detectable by GLM, still influence brain activity.

Chromatographic Behaviour Predicts the Ability of Potential Nootropics to Permeate the Blood-Brain Barrier

PubMed Central

Farsa, Oldřich

2013-01-01

The log BB parameter is the logarithm of the ratio of a compound’s equilibrium concentrations in the brain tissue versus the blood plasma. This parameter is a useful descriptor in assessing the ability of a compound to permeate the blood-brain barrier. The aim of this study was to develop a Hansch-type linear regression QSAR model that correlates the parameter log BB and the retention time of drugs and other organic compounds on a reversed-phase HPLC containing an embedded amide moiety. The retention time was expressed by the capacity factor log k′. The second aim was to estimate the brain’s absorption of 2-(azacycloalkyl)acetamidophenoxyacetic acids, which are analogues of piracetam, nefiracetam, and meclofenoxate. Notably, these acids may be novel nootropics. Two simple regression models that relate log BB and log k′ were developed from an assay performed using a reversed-phase HPLC that contained an embedded amide moiety. Both the quadratic and linear models yielded statistical parameters comparable to previously published models of log BB dependence on various structural characteristics. The models predict that four members of the substituted phenoxyacetic acid series have a strong chance of permeating the barrier and being absorbed in the brain. The results of this study show that a reversed-phase HPLC system containing an embedded amide moiety is a functional in vitro surrogate of the blood-brain barrier. These results suggest that racetam-type nootropic drugs containing a carboxylic moiety could be more poorly absorbed than analogues devoid of the carboxyl group, especially if the compounds penetrate the barrier by a simple diffusion mechanism. PMID:23641330

[The brain in stereotaxic coordinates (a textbook for colleges)].

PubMed

Budantsev, A Iu; Kisliuk, O S; Shul'govskiĭ, V V; Rykunov, D S; Iarkov, A V

1993-01-01

The present textbook is directed forward students of universities and medical colleges, young scientists and practicing doctors dealing with stereotaxic method. The Paxinos and Watson stereotaxic rat brain atlas (1982) is the basis of the textbook. The atlas has been transformed into computer educational program and seven laboratory works: insertion of the electrode into brain, microelectrophoresis, microinjection of drugs into brain, electrolytic destruction in the brain structures, local brain superfusion. The laboratory works are compiled so that they allow not only to study practical use of the stereotaxic method but to model simple problems involving stereotaxic surgery in the deep structures of brain. The textbook is intended for carrying by IBM PC/AT computers. The volume of the textbook is 1.7 Mbytes.

Increased Expression of Simple Ganglioside Species GM2 and GM3 Detected by MALDI Imaging Mass Spectrometry in a Combined Rat Model of Aβ Toxicity and Stroke

PubMed Central

Caughlin, Sarah; Hepburn, Jeffrey D.; Park, Dae Hee; Jurcic, Kristina; Yeung, Ken K.-C.; Cechetto, David F.; Whitehead, Shawn N.

2015-01-01

The aging brain is often characterized by the presence of multiple comorbidities resulting in synergistic damaging effects in the brain as demonstrated through the interaction of Alzheimer’s disease (AD) and stroke. Gangliosides, a family of membrane lipids enriched in the central nervous system, may have a mechanistic role in mediating the brain’s response to injury as their expression is altered in a number of disease and injury states. Matrix-Assisted Laser Desorption Ionization (MALDI) Imaging Mass Spectrometry (IMS) was used to study the expression of A-series ganglioside species GD1a, GM1, GM2, and GM3 to determine alteration of their expression profiles in the presence of beta-amyloid (Aβ) toxicity in addition to ischemic injury. To model a stroke, rats received a unilateral striatal injection of endothelin-1 (ET-1) (stroke alone group). To model Aβ toxicity, rats received intracerebralventricular (icv) injections of the toxic 25-35 fragment of the Aβ peptide (Aβ alone group). To model the combination of Aβ toxicity with stroke, rats received both the unilateral ET-1 injection and the bilateral icv injections of Aβ₂₅₋₃₅ (combined Aβ/ET-1 group). By 3 d, a significant increase in the simple ganglioside species GM2 was observed in the ischemic brain region of rats who received a stroke (ET-1), with or without Aβ. By 21 d, GM2 levels only remained elevated in the combined Aβ/ET-1 group. GM3 levels however demonstrated a different pattern of expression. By 3 d GM3 was elevated in the ischemic brain region only in the combined Aβ/ET-1 group. By 21 d, GM3 was elevated in the ischemic brain region in both stroke alone and Aβ/ET-1 groups. Overall, results indicate that the accumulation of simple ganglioside species GM2 and GM3 may be indicative of a mechanism of interaction between AD and stroke. PMID:26086081

Modeling Spatial and Temporal Aspects of Visual Backward Masking

ERIC Educational Resources Information Center

Hermens, Frouke; Luksys, Gediminas; Gerstner, Wulfram; Herzog, Michael H.; Ernst, Udo

2008-01-01

Visual backward masking is a versatile tool for understanding principles and limitations of visual information processing in the human brain. However, the mechanisms underlying masking are still poorly understood. In the current contribution, the authors show that a structurally simple mathematical model can explain many spatial and temporal…

Predicting outcome in severe traumatic brain injury using a simple prognostic model.

PubMed

Sobuwa, Simpiwe; Hartzenberg, Henry Benjamin; Geduld, Heike; Uys, Corrie

2014-06-17

Several studies have made it possible to predict outcome in severe traumatic brain injury (TBI) making it beneficial as an aid for clinical decision-making in the emergency setting. However, reliable predictive models are lacking for resource-limited prehospital settings such as those in developing countries like South Africa. To develop a simple predictive model for severe TBI using clinical variables in a South African prehospital setting. All consecutive patients admitted at two level-one centres in Cape Town, South Africa, for severe TBI were included. A binary logistic regression model was used, which included three predictor variables: oxygen saturation (SpO₂), Glasgow Coma Scale (GCS) and pupil reactivity. The Glasgow Outcome Scale was used to assess outcome on hospital discharge. A total of 74.4% of the outcomes were correctly predicted by the logistic regression model. The model demonstrated SpO₂ (p=0.019), GCS (p=0.001) and pupil reactivity (p=0.002) as independently significant predictors of outcome in severe TBI. Odds ratios of a good outcome were 3.148 (SpO₂ ≥ 90%), 5.108 (GCS 6 - 8) and 4.405 (pupils bilaterally reactive). This model is potentially useful for effective predictions of outcome in severe TBI.

Dynamical graph theory networks techniques for the analysis of sparse connectivity networks in dementia

NASA Astrophysics Data System (ADS)

Tahmassebi, Amirhessam; Pinker-Domenig, Katja; Wengert, Georg; Lobbes, Marc; Stadlbauer, Andreas; Romero, Francisco J.; Morales, Diego P.; Castillo, Encarnacion; Garcia, Antonio; Botella, Guillermo; Meyer-Bäse, Anke

2017-05-01

Graph network models in dementia have become an important computational technique in neuroscience to study fundamental organizational principles of brain structure and function of neurodegenerative diseases such as dementia. The graph connectivity is reflected in the connectome, the complete set of structural and functional connections of the graph network, which is mostly based on simple Pearson correlation links. In contrast to simple Pearson correlation networks, the partial correlations (PC) only identify direct correlations while indirect associations are eliminated. In addition to this, the state-of-the-art techniques in brain research are based on static graph theory, which is unable to capture the dynamic behavior of the brain connectivity, as it alters with disease evolution. We propose a new research avenue in neuroimaging connectomics based on combining dynamic graph network theory and modeling strategies at different time scales. We present the theoretical framework for area aggregation and time-scale modeling in brain networks as they pertain to disease evolution in dementia. This novel paradigm is extremely powerful, since we can derive both static parameters pertaining to node and area parameters, as well as dynamic parameters, such as system's eigenvalues. By implementing and analyzing dynamically both disease driven PC-networks and regular concentration networks, we reveal differences in the structure of these network that play an important role in the temporal evolution of this disease. The described research is key to advance biomedical research on novel disease prediction trajectories and dementia therapies.

A simple physiologically based pharmacokinetic model evaluating the effect of anti-nicotine antibodies on nicotine disposition in the brains of rats and humans

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

Saylor, Kyle, E-mail: saylor@vt.edu; Zhang, Chenmi

Physiologically based pharmacokinetic (PBPK) modeling was applied to investigate the effects of anti-nicotine antibodies on nicotine disposition in the brains of rats and humans. Successful construction of both rat and human models was achieved by fitting model outputs to published nicotine concentration time course data in the blood and in the brain. Key parameters presumed to have the most effect on the ability of these antibodies to prevent nicotine from entering the brain were selected for investigation using the human model. These parameters, which included antibody affinity for nicotine, antibody cross-reactivity with cotinine, and antibody concentration, were broken down intomore » different, clinically-derived in silico treatment levels and fed into the human PBPK model. Model predictions suggested that all three parameters, in addition to smoking status, have a sizable impact on anti-nicotine antibodies' ability to prevent nicotine from entering the brain and that the antibodies elicited by current human vaccines do not have sufficient binding characteristics to reduce brain nicotine concentrations. If the antibody binding characteristics achieved in animal studies can similarly be achieved in human studies, however, nicotine vaccine efficacy in terms of brain nicotine concentration reduction is predicted to meet threshold values for alleviating nicotine dependence. - Highlights: • Modelling of nicotine disposition in the presence of anti-nicotine antibodies • Key vaccine efficacy factors are evaluated in silico in rats and in humans. • Model predicts insufficient antibody binding in past human nicotine vaccines. • Improving immunogenicity and antibody specificity may lead to vaccine success.« less

Bladder control, urgency, and urge incontinence: evidence from functional brain imaging.

PubMed

Griffiths, Derek; Tadic, Stasa D

2008-01-01

To review brain imaging studies of bladder control in subjects with normal control and urge incontinence; to define a simple model of supraspinal bladder control; and to propose a neural correlate of urgency and possible origins of urge incontinence. Review of published reports of brain imaging relevant to urine storage, and secondary analyses of our own recent observations. In a simple model of normal urine storage, bladder and urethral afferents received in the periaqueductal gray (PAG) are mapped in the insula, forming the basis of sensation; the anterior cingulate gyrus (ACG) provides monitoring and control; the prefrontal cortex makes voiding decisions. The net result, as the bladder fills, is inhibition of the pontine micturition center (PMC) and of voiding, together with gradual increase in insular response, corresponding to increasing desire to void. In urge-incontinent subjects, brain responses differ. At large bladder volumes and strong sensation, but without detrusor overactivity (DO), most cortical responses become exaggerated, especially in ACG. This may be both a learned reaction to previous incontinence episodes and the neural correlate of urgency. The neural signature of DO itself seems to be prefrontal deactivation. Possible causes of urge incontinence include dysfunction of prefrontal cortex or limbic system, suggested by weak responses and/or deactivation, as well as abnormal afferent signals or re-emergence of infantile reflexes. Bladder control depends on an extensive network of brain regions. Dysfunction in various parts may contribute to urge incontinence, suggesting that there are different phenotypes requiring different treatments. (c) 2007 Wiley-Liss, Inc.

Nature of collective decision-making by simple yes/no decision units.

PubMed

Hasegawa, Eisuke; Mizumoto, Nobuaki; Kobayashi, Kazuya; Dobata, Shigeto; Yoshimura, Jin; Watanabe, Saori; Murakami, Yuuka; Matsuura, Kenji

2017-10-31

The study of collective decision-making spans various fields such as brain and behavioural sciences, economics, management sciences, and artificial intelligence. Despite these interdisciplinary applications, little is known regarding how a group of simple 'yes/no' units, such as neurons in the brain, can select the best option among multiple options. One prerequisite for achieving such correct choices by the brain is correct evaluation of relative option quality, which enables a collective decision maker to efficiently choose the best option. Here, we applied a sensory discrimination mechanism using yes/no units with differential thresholds to a model for making a collective choice among multiple options. The performance corresponding to the correct choice was shown to be affected by various parameters. High performance can be achieved by tuning the threshold distribution with the options' quality distribution. The number of yes/no units allocated to each option and its variability profoundly affects performance. When this variability is large, a quorum decision becomes superior to a majority decision under some conditions. The general features of this collective decision-making by a group of simple yes/no units revealed in this study suggest that this mechanism may be useful in applications across various fields.

Exploratory reconstructability analysis of accident TBI data

NASA Astrophysics Data System (ADS)

Zwick, Martin; Carney, Nancy; Nettleton, Rosemary

2018-02-01

This paper describes the use of reconstructability analysis to perform a secondary study of traumatic brain injury data from automobile accidents. Neutral searches were done and their results displayed with a hypergraph. Directed searches, using both variable-based and state-based models, were applied to predict performance on two cognitive tests and one neurological test. Very simple state-based models gave large uncertainty reductions for all three DVs and sizeable improvements in percent correct for the two cognitive test DVs which were equally sampled. Conditional probability distributions for these models are easily visualized with simple decision trees. Confounding variables and counter-intuitive findings are also reported.

Linking brain-wide multivoxel activation patterns to behaviour: Examples from language and math

PubMed Central

Raizada, Rajeev D.S.; Tsao, Feng-Ming; Liu, Huei-Mei; Holloway, Ian D.; Ansari, Daniel; Kuhl, Patricia K.

2010-01-01

A key goal of cognitive neuroscience is to find simple and direct connections between brain and behaviour. However, fMRI analysis typically involves choices between many possible options, with each choice potentially biasing any brain–behaviour correlations that emerge. Standard methods of fMRI analysis assess each voxel individually, but then face the problem of selection bias when combining those voxels into a region-of-interest, or ROI. Multivariate pattern-based fMRI analysis methods use classifiers to analyse multiple voxels together, but can also introduce selection bias via data-reduction steps as feature selection of voxels, pre-selecting activated regions, or principal components analysis. We show here that strong brain–behaviour links can be revealed without any voxel selection or data reduction, using just plain linear regression as a classifier applied to the whole brain at once, i.e. treating each entire brain volume as a single multi-voxel pattern. The brain–behaviour correlations emerged despite the fact that the classifier was not provided with any information at all about subjects' behaviour, but instead was given only the neural data and its condition-labels. Surprisingly, more powerful classifiers such as a linear SVM and regularised logistic regression produce very similar results. We discuss some possible reasons why the very simple brain-wide linear regression model is able to find correlations with behaviour that are as strong as those obtained on the one hand from a specific ROI and on the other hand from more complex classifiers. In a manner which is unencumbered by arbitrary choices, our approach offers a method for investigating connections between brain and behaviour which is simple, rigorous and direct. PMID:20132896

A simple and accurate rule-based modeling framework for simulation of autocrine/paracrine stimulation of glioblastoma cell motility and proliferation by L1CAM in 2-D culture.

PubMed

Caccavale, Justin; Fiumara, David; Stapf, Michael; Sweitzer, Liedeke; Anderson, Hannah J; Gorky, Jonathan; Dhurjati, Prasad; Galileo, Deni S

2017-12-11

Glioblastoma multiforme (GBM) is a devastating brain cancer for which there is no known cure. Its malignancy is due to rapid cell division along with high motility and invasiveness of cells into the brain tissue. Simple 2-dimensional laboratory assays (e.g., a scratch assay) commonly are used to measure the effects of various experimental perturbations, such as treatment with chemical inhibitors. Several mathematical models have been developed to aid the understanding of the motile behavior and proliferation of GBM cells. However, many are mathematically complicated, look at multiple interdependent phenomena, and/or use modeling software not freely available to the research community. These attributes make the adoption of models and simulations of even simple 2-dimensional cell behavior an uncommon practice by cancer cell biologists. Herein, we developed an accurate, yet simple, rule-based modeling framework to describe the in vitro behavior of GBM cells that are stimulated by the L1CAM protein using freely available NetLogo software. In our model L1CAM is released by cells to act through two cell surface receptors and a point of signaling convergence to increase cell motility and proliferation. A simple graphical interface is provided so that changes can be made easily to several parameters controlling cell behavior, and behavior of the cells is viewed both pictorially and with dedicated graphs. We fully describe the hierarchical rule-based modeling framework, show simulation results under several settings, describe the accuracy compared to experimental data, and discuss the potential usefulness for predicting future experimental outcomes and for use as a teaching tool for cell biology students. It is concluded that this simple modeling framework and its simulations accurately reflect much of the GBM cell motility behavior observed experimentally in vitro in the laboratory. Our framework can be modified easily to suit the needs of investigators interested in other similar intrinsic or extrinsic stimuli that influence cancer or other cell behavior. This modeling framework of a commonly used experimental motility assay (scratch assay) should be useful to both researchers of cell motility and students in a cell biology teaching laboratory.

Hyperpolarized 13C pyruvate mouse brain metabolism with absorptive-mode EPSI at 1 T

NASA Astrophysics Data System (ADS)

Miloushev, Vesselin Z.; Di Gialleonardo, Valentina; Salamanca-Cardona, Lucia; Correa, Fabian; Granlund, Kristin L.; Keshari, Kayvan R.

2017-02-01

The expected signal in echo-planar spectroscopic imaging experiments was explicitly modeled jointly in spatial and spectral dimensions. Using this as a basis, absorptive-mode type detection can be achieved by appropriate choice of spectral delays and post-processing techniques. We discuss the effects of gradient imperfections and demonstrate the implementation of this sequence at low field (1.05 T), with application to hyperpolarized [1-13C] pyruvate imaging of the mouse brain. The sequence achieves sufficient signal-to-noise to monitor the conversion of hyperpolarized [1-13C] pyruvate to lactate in the mouse brain. Hyperpolarized pyruvate imaging of mouse brain metabolism using an absorptive-mode EPSI sequence can be applied to more sophisticated murine disease and treatment models. The simple modifications presented in this work, which permit absorptive-mode detection, are directly translatable to human clinical imaging and generate improved absorptive-mode spectra without the need for refocusing pulses.

Luminance, Colour, Viewpoint and Border Enhanced Disparity Energy Model

PubMed Central

Martins, Jaime A.; Rodrigues, João M. F.; du Buf, Hans

2015-01-01

The visual cortex is able to extract disparity information through the use of binocular cells. This process is reflected by the Disparity Energy Model, which describes the role and functioning of simple and complex binocular neuron populations, and how they are able to extract disparity. This model uses explicit cell parameters to mathematically determine preferred cell disparities, like spatial frequencies, orientations, binocular phases and receptive field positions. However, the brain cannot access such explicit cell parameters; it must rely on cell responses. In this article, we implemented a trained binocular neuronal population, which encodes disparity information implicitly. This allows the population to learn how to decode disparities, in a similar way to how our visual system could have developed this ability during evolution. At the same time, responses of monocular simple and complex cells can also encode line and edge information, which is useful for refining disparities at object borders. The brain should then be able, starting from a low-level disparity draft, to integrate all information, including colour and viewpoint perspective, in order to propagate better estimates to higher cortical areas. PMID:26107954

Electromagnetic field generated in model of human head by simplified telephone transceiver

NASA Astrophysics Data System (ADS)

King, Ronold W. P.

1995-01-01

Possible adverse effects of electromagnetic fields on the human body and especially on the nervous system and the brain are of increasing concern, particularly with reference to cellular telephone transceivers held close to the head. An essential step in the study of this problem is the accurate determination of the complete electromagnetic field penetrating through the skull into the brain. Simple analytical formulas are derived from the theory of the horizontal electric dipole over a layered region. These give the components of the electric and magnetic fields on the air-head surface, in the skin-skull layer, and throughout the brain in terms of a planar model with the dimensions and average electrical properties of the human head. The specific absorption rate (SAR) is also determined.

Brain State Effects on Layer 4 of the Awake Visual Cortex

PubMed Central

Zhuang, Jun; Bereshpolova, Yulia; Stoelzel, Carl R.; Huff, Joseph M.; Hei, Xiaojuan; Alonso, Jose-Manuel

2014-01-01

Awake mammals can switch between alert and nonalert brain states hundreds of times per day. Here, we study the effects of alertness on two cell classes in layer 4 of primary visual cortex of awake rabbits: presumptive excitatory “simple” cells and presumptive fast-spike inhibitory neurons (suspected inhibitory interneurons). We show that in both cell classes, alertness increases the strength and greatly enhances the reliability of visual responses. In simple cells, alertness also increases the temporal frequency bandwidth, but preserves contrast sensitivity, orientation tuning, and selectivity for direction and spatial frequency. Finally, alertness selectively suppresses the simple cell responses to high-contrast stimuli and stimuli moving orthogonal to the preferred direction, effectively enhancing mid-contrast borders. Using a population coding model, we show that these effects of alertness in simple cells—enhanced reliability, higher gain, and increased suppression in orthogonal orientation—could play a major role at increasing the speed of cortical feature detection. PMID:24623767

Time resolved dosimetry of human brain exposed to low frequency pulsed magnetic fields.

PubMed

Paffi, Alessandra; Camera, Francesca; Lucano, Elena; Apollonio, Francesca; Liberti, Micaela

2016-06-21

An accurate dosimetry is a key issue to understanding brain stimulation and related interaction mechanisms with neuronal tissues at the basis of the increasing amount of literature revealing the effects on human brain induced by low-level, low frequency pulsed magnetic fields (PMFs). Most literature on brain dosimetry estimates the maximum E field value reached inside the tissue without considering its time pattern or tissue dispersivity. Nevertheless a time-resolved dosimetry, accounting for dispersive tissues behavior, becomes necessary considering that the threshold for an effect onset may vary depending on the pulse waveform and that tissues may filter the applied stimulatory fields altering the predicted stimulatory waveform's size and shape. In this paper a time-resolved dosimetry has been applied on a realistic brain model exposed to the signal presented in Capone et al (2009 J. Neural Transm. 116 257-65), accounting for the broadband dispersivity of brain tissues up to several kHz, to accurately reconstruct electric field and current density waveforms inside different brain tissues. The results obtained by exposing the Duke's brain model to this PMF signal show that the E peak in the brain is considerably underestimated if a simple monochromatic dosimetry is carried out at the pulse repetition frequency of 75 Hz.

Time resolved dosimetry of human brain exposed to low frequency pulsed magnetic fields

NASA Astrophysics Data System (ADS)

Paffi, Alessandra; Camera, Francesca; Lucano, Elena; Apollonio, Francesca; Liberti, Micaela

2016-06-01

An accurate dosimetry is a key issue to understanding brain stimulation and related interaction mechanisms with neuronal tissues at the basis of the increasing amount of literature revealing the effects on human brain induced by low-level, low frequency pulsed magnetic fields (PMFs). Most literature on brain dosimetry estimates the maximum E field value reached inside the tissue without considering its time pattern or tissue dispersivity. Nevertheless a time-resolved dosimetry, accounting for dispersive tissues behavior, becomes necessary considering that the threshold for an effect onset may vary depending on the pulse waveform and that tissues may filter the applied stimulatory fields altering the predicted stimulatory waveform’s size and shape. In this paper a time-resolved dosimetry has been applied on a realistic brain model exposed to the signal presented in Capone et al (2009 J. Neural Transm. 116 257-65), accounting for the broadband dispersivity of brain tissues up to several kHz, to accurately reconstruct electric field and current density waveforms inside different brain tissues. The results obtained by exposing the Duke’s brain model to this PMF signal show that the E peak in the brain is considerably underestimated if a simple monochromatic dosimetry is carried out at the pulse repetition frequency of 75 Hz.

Computational modelling of the piglet brain to simulate near-infrared spectroscopy and magnetic resonance spectroscopy data collected during oxygen deprivation.

PubMed

Moroz, Tracy; Banaji, Murad; Robertson, Nicola J; Cooper, Chris E; Tachtsidis, Ilias

2012-07-07

We describe a computational model to simulate measurements from near-infrared spectroscopy (NIRS) and magnetic resonance spectroscopy (MRS) in the piglet brain. Piglets are often subjected to anoxic, hypoxic and ischaemic insults, as experimental models for human neonates. The model aims to help interpret measurements and increase understanding of physiological processes occurring during such insults. It is an extension of a previous model of circulation and mitochondrial metabolism. This was developed to predict NIRS measurements in the brains of healthy adults i.e. concentration changes of oxyhaemoglobin and deoxyhaemoglobin and redox state changes of cytochrome c oxidase (CCO). We altered and enhanced the model to apply to the anaesthetized piglet brain. It now includes metabolites measured by (31)P-MRS, namely phosphocreatine, inorganic phosphate and adenosine triphosphate (ATP). It also includes simple descriptions of glycolysis, lactate dynamics and the tricarboxylic acid (TCA) cycle. The model is described, and its simulations compared with existing measurements from piglets during anoxia. The NIRS and MRS measurements are predicted well, although this requires a reduction in blood pressure autoregulation. Predictions of the cerebral metabolic rate of oxygen consumption (CMRO(2)) and lactate concentration, which were not measured, are given. Finally, the model is used to investigate hypotheses regarding changes in CCO redox state during anoxia.

Avian Egg Latebra as Brain Tissue Water Diffusion Model

PubMed Central

Maier, Stephan E.; Mitsouras, Dimitris; Mulkern, Robert V.

2013-01-01

Purpose Simplified models of non-monoexponential diffusion signal decay are of great interest to study the basic constituents of complex diffusion behaviour in tissues. The latebra, a unique structure uniformly present in the yolk of avian eggs, exhibits a non-monoexponential diffusion signal decay. This model is more complex than simple phantoms based on differences between water and lipid diffusion, but is also devoid of microscopic structures with preferential orientation or perfusion effects. Methods Diffusion scans with multiple b-values were performed on a clinical 3 Tesla system in raw and boiled chicken eggs equilibrated to room temperature. Diffusion encoding was applied over the ranges 5–5,000 and 5–50,000 s/mm2. A low read-out bandwidth and chemical shift was used for reliable lipid/water separation. Signal decays were fitted with exponential functions. Results The latebra, when measured over the 5–5,000 s/mm2 range, exhibited independent of preparation clearly biexponential diffusion, with diffusion parameters similar to those typically observed in in-vivo human brain. For the range 5–50,000 s/mm2 there was evidence of a small third, very slow diffusing water component. Conclusion The latebra of the avian egg contains membrane structures, which may explain a deviation from a simple monoexponential diffusion signal decay, which is remarkably similar to the deviation observed in brain tissue. PMID:24105853

Design and Training of Limited-Interconnect Architectures

DTIC Science & Technology

1991-07-16

and signal processing. Neuromorphic (brain like) models, allow an alternative for achieving real-time operation tor such tasks, while having a...compact and robust architecture. Neuromorphic models consist of interconnections of simple computational nodes. In this approach, each node computes a...operational performance. I1. Research Objectives The research objectives were: 1. Development of on- chip local training rules specifically designed for

Neuroimaging explanations of age-related differences in task performance.

PubMed

Steffener, Jason; Barulli, Daniel; Habeck, Christian; Stern, Yaakov

2014-01-01

Advancing age affects both cognitive performance and functional brain activity and interpretation of these effects has led to a variety of conceptual research models without always explicitly linking the two effects. However, to best understand the multifaceted effects of advancing age, age differences in functional brain activity need to be explicitly tied to the cognitive task performance. This work hypothesized that age-related differences in task performance are partially explained by age-related differences in functional brain activity and formally tested these causal relationships. Functional MRI data was from groups of young and old adults engaged in an executive task-switching experiment. Analyses were voxel-wise testing of moderated-mediation and simple mediation statistical path models to determine whether age group, brain activity and their interaction explained task performance in regions demonstrating an effect of age group. Results identified brain regions whose age-related differences in functional brain activity significantly explained age-related differences in task performance. In all identified locations, significant moderated-mediation relationships resulted from increasing brain activity predicting worse (slower) task performance in older but not younger adults. Findings suggest that advancing age links task performance to the level of brain activity. The overall message of this work is that in order to understand the role of functional brain activity on cognitive performance, analysis methods should respect theoretical relationships. Namely, that age affects brain activity and brain activity is related to task performance.

The Human Brain Project and neuromorphic computing

PubMed Central

Calimera, Andrea; Macii, Enrico; Poncino, Massimo

Summary Understanding how the brain manages billions of processing units connected via kilometers of fibers and trillions of synapses, while consuming a few tens of Watts could provide the key to a completely new category of hardware (neuromorphic computing systems). In order to achieve this, a paradigm shift for computing as a whole is needed, which will see it moving away from current “bit precise” computing models and towards new techniques that exploit the stochastic behavior of simple, reliable, very fast, low-power computing devices embedded in intensely recursive architectures. In this paper we summarize how these objectives will be pursued in the Human Brain Project. PMID:24139655

Silicon chip with capacitors and transistors for interfacing organotypic brain slice of rat hippocampus.

PubMed

Hutzler, Michael; Fromherz, Peter

2004-04-01

Probing projections between brain areas and their modulation by synaptic potentiation requires dense arrays of contacts for noninvasive electrical stimulation and recording. Semiconductor technology is able to provide planar arrays with high spatial resolution to be used with planar neuronal structures such as organotypic brain slices. To address basic methodical issues we developed a silicon chip with simple arrays of insulated capacitors and field-effect transistors for stimulation of neuronal activity and recording of evoked field potentials. Brain slices from rat hippocampus were cultured on that substrate. We achieved local stimulation of the CA3 region by applying defined voltage pulses to the chip capacitors. Recording of resulting local field potentials in the CA1 region was accomplished with transistors. The relationship between stimulation and recording was rationalized by a sheet conductor model. By combining a row of capacitors with a row of transistors we determined a simple stimulus-response matrix from CA3 to CA1. Possible contributions of inhomogeneities of synaptic projection, of tissue structure and of neuroelectronic interfacing were considered. The study provides the basis for a development of semiconductor chips with high spatial resolution that are required for long-term studies of topographic mapping.

Prediction of drug transport processes using simple parameters and PLS statistics. The use of ACD/logP and ACD/ChemSketch descriptors.

PubMed

Osterberg, T; Norinder, U

2001-01-01

A method of modelling and predicting biopharmaceutical properties using simple theoretically computed molecular descriptors and multivariate statistics has been investigated for several data sets related to solubility, IAM chromatography, permeability across Caco-2 cell monolayers, human intestinal perfusion, brain-blood partitioning, and P-glycoprotein ATPase activity. The molecular descriptors (e.g. molar refractivity, molar volume, index of refraction, surface tension and density) and logP were computed with ACD/ChemSketch and ACD/logP, respectively. Good statistical models were derived that permit simple computational prediction of biopharmaceutical properties. All final models derived had R(2) values ranging from 0.73 to 0.95 and Q(2) values ranging from 0.69 to 0.86. The RMSEP values for the external test sets ranged from 0.24 to 0.85 (log scale).

Modeling of light distribution in the brain for topographical imaging

NASA Astrophysics Data System (ADS)

Okada, Eiji; Hayashi, Toshiyuki; Kawaguchi, Hiroshi

2004-07-01

Multi-channel optical imaging system can obtain a topographical distribution of the activated region in the brain cortex by a simple mapping algorithm. Near-infrared light is strongly scattered in the head and the volume of tissue that contributes to the change in the optical signal detected with source-detector pair on the head surface is broadly distributed in the brain. This scattering effect results in poor resolution and contrast in the topographic image of the brain activity. We report theoretical investigations on the spatial resolution of the topographic imaging of the brain activity. The head model for the theoretical study consists of five layers that imitate the scalp, skull, subarachnoid space, gray matter and white matter. The light propagation in the head model is predicted by Monte Carlo simulation to obtain the spatial sensitivity profile for a source-detector pair. The source-detector pairs are one dimensionally arranged on the surface of the model and the distance between the adjoining source-detector pairs are varied from 4 mm to 32 mm. The change in detected intensity caused by the absorption change is obtained by Monte Carlo simulation. The position of absorption change is reconstructed by the conventional mapping algorithm and the reconstruction algorithm using the spatial sensitivity profiles. We discuss the effective interval between the source-detector pairs and the choice of reconstruction algorithms to improve the topographic images of brain activity.

Imaging approach to mechanistic study of nanoparticle interactions with the blood-brain barrier.

PubMed

Bramini, Mattia; Ye, Dong; Hallerbach, Anna; Nic Raghnaill, Michelle; Salvati, Anna; Aberg, Christoffer; Dawson, Kenneth A

2014-05-27

Understanding nanoparticle interactions with the central nervous system, in particular the blood-brain barrier, is key to advances in therapeutics, as well as assessing the safety of nanoparticles. Challenges in achieving insights have been significant, even for relatively simple models. Here we use a combination of live cell imaging and computational analysis to directly study nanoparticle translocation across a human in vitro blood-brain barrier model. This approach allows us to identify and avoid problems in more conventional inferential in vitro measurements by identifying the catalogue of events of barrier internalization and translocation as they occur. Potentially this approach opens up the window of applicability of in vitro models, thereby enabling in depth mechanistic studies in the future. Model nanoparticles are used to illustrate the method. For those, we find that translocation, though rare, appears to take place. On the other hand, barrier uptake is efficient, and since barrier export is small, there is significant accumulation within the barrier.

Resolving Structural Variability in Network Models and the Brain

PubMed Central

Klimm, Florian; Bassett, Danielle S.; Carlson, Jean M.; Mucha, Peter J.

2014-01-01

Large-scale white matter pathways crisscrossing the cortex create a complex pattern of connectivity that underlies human cognitive function. Generative mechanisms for this architecture have been difficult to identify in part because little is known in general about mechanistic drivers of structured networks. Here we contrast network properties derived from diffusion spectrum imaging data of the human brain with 13 synthetic network models chosen to probe the roles of physical network embedding and temporal network growth. We characterize both the empirical and synthetic networks using familiar graph metrics, but presented here in a more complete statistical form, as scatter plots and distributions, to reveal the full range of variability of each measure across scales in the network. We focus specifically on the degree distribution, degree assortativity, hierarchy, topological Rentian scaling, and topological fractal scaling—in addition to several summary statistics, including the mean clustering coefficient, the shortest path-length, and the network diameter. The models are investigated in a progressive, branching sequence, aimed at capturing different elements thought to be important in the brain, and range from simple random and regular networks, to models that incorporate specific growth rules and constraints. We find that synthetic models that constrain the network nodes to be physically embedded in anatomical brain regions tend to produce distributions that are most similar to the corresponding measurements for the brain. We also find that network models hardcoded to display one network property (e.g., assortativity) do not in general simultaneously display a second (e.g., hierarchy). This relative independence of network properties suggests that multiple neurobiological mechanisms might be at play in the development of human brain network architecture. Together, the network models that we develop and employ provide a potentially useful starting point for the statistical inference of brain network structure from neuroimaging data. PMID:24675546

Neural Decoding and "Inner" Psychophysics: A Distance-to-Bound Approach for Linking Mind, Brain, and Behavior.

PubMed

Ritchie, J Brendan; Carlson, Thomas A

2016-01-01

A fundamental challenge for cognitive neuroscience is characterizing how the primitives of psychological theory are neurally implemented. Attempts to meet this challenge are a manifestation of what Fechner called "inner" psychophysics: the theory of the precise mapping between mental quantities and the brain. In his own time, inner psychophysics remained an unrealized ambition for Fechner. We suggest that, today, multivariate pattern analysis (MVPA), or neural "decoding," methods provide a promising starting point for developing an inner psychophysics. A cornerstone of these methods are simple linear classifiers applied to neural activity in high-dimensional activation spaces. We describe an approach to inner psychophysics based on the shared architecture of linear classifiers and observers under decision boundary models such as signal detection theory. Under this approach, distance from a decision boundary through activation space, as estimated by linear classifiers, can be used to predict reaction time in accordance with signal detection theory, and distance-to-bound models of reaction time. Our "neural distance-to-bound" approach is potentially quite general, and simple to implement. Furthermore, our recent work on visual object recognition suggests it is empirically viable. We believe the approach constitutes an important step along the path to an inner psychophysics that links mind, brain, and behavior.

Animal models for studying transport across the blood-brain barrier.

PubMed

Bonate, P L

1995-01-01

There are many reasons for wishing to determine the rate of uptake of a drug from blood into brain parenchyma. However, when faced with doing so for the first time, choosing a method can be a formidable task. There are at least 7 methods from which to choose: indicator dilution, brain uptake index, microdialysis, external registration, PET scanning, in situ perfusion, and compartmental modeling. Each method has advantages and disadvantages. Some methods require very little equipment while others require equipment that can cost millions of dollars. Some methods require very little technical experience whereas others require complex surgical manipulation. The mathematics alone for the various methods range from simple algebra to complex integral calculus and differential equations. Like most things in science, as the complexity of the technique increases, so does the quantity of information it provides. This review is meant to serve as a starting point for the researcher who wishes to study transport and uptake across the blood-brain barrier in animal models. An overview of the mathematical theory, as well as an introduction to the techniques, is presented.

fMRI activation patterns in an analytic reasoning task: consistency with EEG source localization

NASA Astrophysics Data System (ADS)

Li, Bian; Vasanta, Kalyana C.; O'Boyle, Michael; Baker, Mary C.; Nutter, Brian; Mitra, Sunanda

2010-03-01

Functional magnetic resonance imaging (fMRI) is used to model brain activation patterns associated with various perceptual and cognitive processes as reflected by the hemodynamic (BOLD) response. While many sensory and motor tasks are associated with relatively simple activation patterns in localized regions, higher-order cognitive tasks may produce activity in many different brain areas involving complex neural circuitry. We applied a recently proposed probabilistic independent component analysis technique (PICA) to determine the true dimensionality of the fMRI data and used EEG localization to identify the common activated patterns (mapped as Brodmann areas) associated with a complex cognitive task like analytic reasoning. Our preliminary study suggests that a hybrid GLM/PICA analysis may reveal additional regions of activation (beyond simple GLM) that are consistent with electroencephalography (EEG) source localization patterns.

Bioreactivity: Studies on a Simple Brain Stem Reflex in Behaving Animals

DTIC Science & Technology

1988-07-22

neuromodulation , or complex behavioral processes, such as arousal, is finding a simple system that will permit such analyses. The brain stem...systems important in neuromodulation and arousal. Initial pharmacologic studies showed that locally applied norepinephrine facilitated the reflex

The Zebrafish Brain in Research and Teaching: A Simple in Vivo and in Vitro Model for the Study of Spontaneous Neural Activity

ERIC Educational Resources Information Center

Vargas, R.; Johannesdottir, I. P.; Sigurgeirsson, B.; Porsteinsson, H.; Karlsson, K. AE.

2011-01-01

Recently, the zebrafish ("Danio rerio") has been established as a key animal model in neuroscience. Behavioral, genetic, and immunohistochemical techniques have been used to describe the connectivity of diverse neural circuits. However, few studies have used zebrafish to understand the function of cerebral structures or to study neural circuits.…

Multi-sensory integration in a small brain

NASA Astrophysics Data System (ADS)

Gepner, Ruben; Wolk, Jason; Gershow, Marc

Understanding how fluctuating multi-sensory stimuli are integrated and transformed in neural circuits has proved a difficult task. To address this question, we study the sensori-motor transformations happening in the brain of the Drosophila larva, a tractable model system with about 10,000 neurons. Using genetic tools that allow us to manipulate the activity of individual brain cells through their transparent body, we observe the stochastic decisions made by freely-behaving animals as their visual and olfactory environments fluctuate independently. We then use simple linear-nonlinear models to correlate outputs with relevant features in the inputs, and adaptive filtering processes to track changes in these relevant parameters used by the larva's brain to make decisions. We show how these techniques allow us to probe how statistics of stimuli from different sensory modalities combine to affect behavior, and can potentially guide our understanding of how neural circuits are anatomically and functionally integrated. Supported by NIH Grant 1DP2EB022359 and NSF Grant PHY-1455015.

Human development XIII: the connection between the structure of the overtone system and the tone language of music. Some implications for our understanding of the human brain.

PubMed

Ventegodt, Søren; Hermansen, Tyge Dahl; Kandel, Isack; Merrick, Joav

2008-07-13

The functioning brain behaves like one highly-structured, coherent, informational field. It can be popularly described as a "coherent ball of energy", making the idea of a local highly-structured quantum field that carries the consciousness very appealing. If that is so, the structure of the experience of music might be a quite unique window into a hidden quantum reality of the brain, and even of life itself. The structure of music is then a mirror of a much more complex, but similar, structure of the energetic field of the working brain. This paper discusses how the perception of music is organized in the human brain with respect to the known tone scales of major and minor. The patterns used by the brain seem to be similar to the overtones of vibrating matter, giving a positive experience of harmonies in major. However, we also like the minor scale, which can explain brain patterns as fractal-like, giving a symmetric "downward reflection" of the major scale into the minor scale. We analyze the implication of beautiful and ugly tones and harmonies for the model. We conclude that when it comes to simple perception of harmonies, the most simple is the most beautiful and the most complex is the most ugly, but in music, even the most disharmonic harmony can be beautiful, if experienced as a part of a dynamic release of musical tension. This can be taken as a general metaphor of painful, yet meaningful, and developing experiences in human life.

Decoding natural images from evoked brain activities using encoding models with invertible mapping.

PubMed

Li, Chao; Xu, Junhai; Liu, Baolin

2018-05-21

Recent studies have built encoding models in the early visual cortex, and reliable mappings have been made between the low-level visual features of stimuli and brain activities. However, these mappings are irreversible, so that the features cannot be directly decoded. To solve this problem, we designed a sparse framework-based encoding model that predicted brain activities from a complete feature representation. Moreover, according to the distribution and activation rules of neurons in the primary visual cortex (V1), three key transformations were introduced into the basic feature to improve the model performance. In this setting, the mapping was simple enough that it could be inverted using a closed-form formula. Using this mapping, we designed a hybrid identification method based on the support vector machine (SVM), and tested it on a published functional magnetic resonance imaging (fMRI) dataset. The experiments confirmed the rationality of our encoding model, and the identification accuracies for 2 subjects increased from 92% and 72% to 98% and 92% with the chance level only 0.8%. Copyright © 2018 Elsevier Ltd. All rights reserved.

Ising model with conserved magnetization on the human connectome: Implications on the relation structure-function in wakefulness and anesthesia

NASA Astrophysics Data System (ADS)

Stramaglia, S.; Pellicoro, M.; Angelini, L.; Amico, E.; Aerts, H.; Cortés, J. M.; Laureys, S.; Marinazzo, D.

2017-04-01

Dynamical models implemented on the large scale architecture of the human brain may shed light on how a function arises from the underlying structure. This is the case notably for simple abstract models, such as the Ising model. We compare the spin correlations of the Ising model and the empirical functional brain correlations, both at the single link level and at the modular level, and show that their match increases at the modular level in anesthesia, in line with recent results and theories. Moreover, we show that at the peak of the specific heat (the critical state), the spin correlations are minimally shaped by the underlying structural network, explaining how the best match between the structure and function is obtained at the onset of criticality, as previously observed. These findings confirm that brain dynamics under anesthesia shows a departure from criticality and could open the way to novel perspectives when the conserved magnetization is interpreted in terms of a homeostatic principle imposed to neural activity.

The frequently used intraperitoneal hyponatraemia model induces hypovolaemic hyponatraemia with possible model-dependent brain sodium loss.

PubMed

Overgaard-Steensen, Christian; Stødkilde-Jørgensen, Hans; Larsson, Anders; Tønnesen, Else; Frøkiaer, Jørgen; Ring, Troels

2016-07-01

What is the central question of this study? The brain response to acute hyponatraemia is usually studied in rodents by intraperitoneal instillation of hypotonic fluids (i.p. model). The i.p. model is described as 'dilutional' and 'syndrome of inappropriate ADH (SIADH)', but the mechanism has not been explored systematically and might affect the brain response. Therefore, in vivo brain and muscle response were studied in pigs. What is the main finding and its importance? The i.p. model induces hypovolaemic hyponatraemia attributable to sodium redistribution, not dilution. A large reduction in brain sodium is observed, probably because of the specific mechanism causing the hyponatraemia. This is not accounted for in current understanding of the brain response to acute hyponatraemia. Hyponatraemia is common clinically, and if it develops rapidly, brain oedema evolves, and severe morbidity and even death may occur. Experimentally, acute hyponatraemia is most frequently studied in small animal models, in which the hyponatraemia is produced by intraperitoneal instillation of hypotonic fluids (i.p. model). This hyponatraemia model is described as 'dilutional' or 'syndrome of inappropriate ADH (SIADH)', but seminal studies contradict this interpretation. To confront this issue, we developed an i.p. model in a large animal (the pig) and studied water and electrolyte responses in brain, muscle, plasma and urine. We hypothesized that hyponatraemia was induced by simple water dilution, with no change in organ sodium content. Moderate hypotonic hyponatraemia was induced by a single i.v. dose of desmopressin and intraperitoneal instillation of 2.5% glucose. All animals were anaesthetized and intensively monitored. In vivo brain and muscle water was determined by magnetic resonance imaging and related to the plasma sodium concentration. Muscle water content increased less than expected as a result of pure dilution, and muscle sodium content decreased significantly (by 28%). Sodium was redistributed to the peritoneal fluid, resulting in a significantly reduced plasma volume. This shows that the i.p. model induces hypovolaemic hyponatraemia and not dilutional/SIADH hyponatraemia. Brain oedema evolved, but brain sodium content decreased significantly (by 21%). To conclude, the i.p. model induces hypovolaemic hyponatraemia attributable to sodium redistribution and not water dilution. The large reduction in brain sodium is probably attributable to the specific mechanism that causes the hyponatraemia. This is not accounted for in the current understanding of the brain response to acute hyponatraemia. © 2016 The Authors. Experimental Physiology © 2016 The Physiological Society.

Computational modelling of the piglet brain to simulate near-infrared spectroscopy and magnetic resonance spectroscopy data collected during oxygen deprivation

PubMed Central

Moroz, Tracy; Banaji, Murad; Robertson, Nicola J.; Cooper, Chris E.; Tachtsidis, Ilias

2012-01-01

We describe a computational model to simulate measurements from near-infrared spectroscopy (NIRS) and magnetic resonance spectroscopy (MRS) in the piglet brain. Piglets are often subjected to anoxic, hypoxic and ischaemic insults, as experimental models for human neonates. The model aims to help interpret measurements and increase understanding of physiological processes occurring during such insults. It is an extension of a previous model of circulation and mitochondrial metabolism. This was developed to predict NIRS measurements in the brains of healthy adults i.e. concentration changes of oxyhaemoglobin and deoxyhaemoglobin and redox state changes of cytochrome c oxidase (CCO). We altered and enhanced the model to apply to the anaesthetized piglet brain. It now includes metabolites measured by 31P-MRS, namely phosphocreatine, inorganic phosphate and adenosine triphosphate (ATP). It also includes simple descriptions of glycolysis, lactate dynamics and the tricarboxylic acid (TCA) cycle. The model is described, and its simulations compared with existing measurements from piglets during anoxia. The NIRS and MRS measurements are predicted well, although this requires a reduction in blood pressure autoregulation. Predictions of the cerebral metabolic rate of oxygen consumption (CMRO2) and lactate concentration, which were not measured, are given. Finally, the model is used to investigate hypotheses regarding changes in CCO redox state during anoxia. PMID:22279158

Optimizing Experimental Design for Comparing Models of Brain Function

PubMed Central

Daunizeau, Jean; Preuschoff, Kerstin; Friston, Karl; Stephan, Klaas

2011-01-01

This article presents the first attempt to formalize the optimization of experimental design with the aim of comparing models of brain function based on neuroimaging data. We demonstrate our approach in the context of Dynamic Causal Modelling (DCM), which relates experimental manipulations to observed network dynamics (via hidden neuronal states) and provides an inference framework for selecting among candidate models. Here, we show how to optimize the sensitivity of model selection by choosing among experimental designs according to their respective model selection accuracy. Using Bayesian decision theory, we (i) derive the Laplace-Chernoff risk for model selection, (ii) disclose its relationship with classical design optimality criteria and (iii) assess its sensitivity to basic modelling assumptions. We then evaluate the approach when identifying brain networks using DCM. Monte-Carlo simulations and empirical analyses of fMRI data from a simple bimanual motor task in humans serve to demonstrate the relationship between network identification and the optimal experimental design. For example, we show that deciding whether there is a feedback connection requires shorter epoch durations, relative to asking whether there is experimentally induced change in a connection that is known to be present. Finally, we discuss limitations and potential extensions of this work. PMID:22125485

Toward Model Building for Visual Aesthetic Perception

PubMed Central

Lughofer, Edwin; Zeng, Xianyi

2017-01-01

Several models of visual aesthetic perception have been proposed in recent years. Such models have drawn on investigations into the neural underpinnings of visual aesthetics, utilizing neurophysiological techniques and brain imaging techniques including functional magnetic resonance imaging, magnetoencephalography, and electroencephalography. The neural mechanisms underlying the aesthetic perception of the visual arts have been explained from the perspectives of neuropsychology, brain and cognitive science, informatics, and statistics. Although corresponding models have been constructed, the majority of these models contain elements that are difficult to be simulated or quantified using simple mathematical functions. In this review, we discuss the hypotheses, conceptions, and structures of six typical models for human aesthetic appreciation in the visual domain: the neuropsychological, information processing, mirror, quartet, and two hierarchical feed-forward layered models. Additionally, the neural foundation of aesthetic perception, appreciation, or judgement for each model is summarized. The development of a unified framework for the neurobiological mechanisms underlying the aesthetic perception of visual art and the validation of this framework via mathematical simulation is an interesting challenge in neuroaesthetics research. This review aims to provide information regarding the most promising proposals for bridging the gap between visual information processing and brain activity involved in aesthetic appreciation. PMID:29270194

Developing guinea pig brain as a model for cortical folding.

PubMed

Hatakeyama, Jun; Sato, Haruka; Shimamura, Kenji

2017-05-01

The cerebral cortex in mammals, the neocortex specifically, is highly diverse among species with respect to its size and morphology, likely reflecting the immense adaptiveness of this lineage. In particular, the pattern and number of convoluted ridges and fissures, called gyri and sulci, respectively, on the surface of the cortex are variable among species and even individuals. However, little is known about the mechanism of cortical folding, although there have been several hypotheses proposed. Recent studies on embryonic neurogenesis revealed the differences in cortical progenitors as a critical factor of the process of gyrification. Here, we investigated the gyrification processes using developing guinea pig brains that form a simple but fundamental pattern of gyri. In addition, we established an electroporation-mediated gene transfer method for guinea pig embryos. We introduce the guinea pig brain as a useful model system to understand the mechanisms and basic principle of cortical folding. © 2017 Japanese Society of Developmental Biologists.

A novel method for fast imaging of brain function, non-invasively, with light

NASA Astrophysics Data System (ADS)

Chance, Britton; Anday, Endla; Nioka, Shoko; Zhou, Shuoming; Hong, Long; Worden, Katherine; Li, C.; Murray, T.; Ovetsky, Y.; Pidikiti, D.; Thomas, R.

1998-05-01

Imaging of the human body by any non-invasive technique has been an appropriate goal of physics and medicine, and great success has been obtained with both Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) in brain imaging. Non-imaging responses to functional activation using near infrared spectroscopy of brain (fNIR) obtained in 1993 (Chance, et al. [1]) and in 1994 (Tamura, et al. [2]) are now complemented with images of pre-frontal and parietal stimulation in adults and pre-term neonates in this communication (see also [3]). Prior studies used continuous [4], pulsed [3] or modulated [5] light. The amplitude and phase cancellation of optical patterns as demonstrated for single source detector pairs affords remarkable sensitivity of small object detection in model systems [6]. The methods have now been elaborated with multiple source detector combinations (nine sources, four detectors). Using simple back projection algorithms it is now possible to image sensorimotor and cognitive activation of adult and pre- and full-term neonate human brain function in times < 30 sec and with two dimensional resolutions of < 1 cm in two dimensional displays. The method can be used in evaluation of adult and neonatal cerebral dysfunction in a simple, portable and affordable method that does not require immobilization, as contrasted to MRI and PET.

Paclitaxel loaded phospholipid-based gel as a drug delivery system for local treatment of glioma.

PubMed

Chen, Tijia; Gong, Ting; Zhao, Ting; Liu, Xing; Fu, Yao; Zhang, Zhirong; Gong, Tao

2017-08-07

Paclitaxel (PTX) is a chemotherapeutic agent and has been widely used in clinic against human cancer. However, it has limited application in brain tumor treatment due to the poor penetration of blood brain barrier. Local delivery system is a promising carrier of PTX in the treatment of glioma. A biodegradable phospholipid-based gel (PG) system was developed for intratumoral injection and evaluated in brain glioma-bearing mice model. PTX loaded PG was composed of phospholipid, ethanol, medium chain triglyceride, triacetin and PTX. It was prepared by a very simple method. The system was a transparent solution with good fluidity, while turned into a gel after phase-transition when ethanol diffused. Both in vitro dissolution and in vivo imaging study proved the sustained release effect of PG system. In vivo tolerability study showed a better tolerability after mice treated with PTX PG compared with free PTX. The survival time of brain glioma-bearing mice after treatment with PTX PG was significantly prolonged compared with mice treated by free PTX (P<0.05). In conclusion, this study developed a novel PG based local PTX delivery system with simple preparation method, good tolerability and high therapeutic efficacy. It has a great potential to improve the clinical management of glioma. Copyright © 2017 Elsevier B.V. All rights reserved.

A Novel Closed-Head Model of Mild Traumatic Brain Injury Using Focal Primary Overpressure Blast to the Cranium in Mice

PubMed Central

Guley, Natalie H.; Rogers, Joshua T.; Del Mar, Nobel A.; Deng, Yunping; Islam, Rafiqul M.; D'Surney, Lauren; Ferrell, Jessica; Deng, Bowei; Hines-Beard, Jessica; Bu, Wei; Ren, Huiling; Elberger, Andrea J.; Marchetta, Jeffrey G.; Rex, Tonia S.; Honig, Marcia G.

2016-01-01

Abstract Mild traumatic brain injury (TBI) from focal head impact is the most common form of TBI in humans. Animal models, however, typically use direct impact to the exposed dura or skull, or blast to the entire head. We present a detailed characterization of a novel overpressure blast system to create focal closed-head mild TBI in mice. A high-pressure air pulse limited to a 7.5 mm diameter area on the left side of the head overlying the forebrain is delivered to anesthetized mice. The mouse eyes and ears are shielded, and its head and body are cushioned to minimize movement. This approach creates mild TBI by a pressure wave that acts on the brain, with minimal accompanying head acceleration-deceleration. A single 20-psi blast yields no functional deficits or brain injury, while a single 25–40 psi blast yields only slight motor deficits and brain damage. By contrast, a single 50–60 psi blast produces significant visual, motor, and neuropsychiatric impairments and axonal damage and microglial activation in major fiber tracts, but no contusive brain injury. This model thus reproduces the widespread axonal injury and functional impairments characteristic of closed-head mild TBI, without the complications of systemic or ocular blast effects or head acceleration that typically occur in other blast or impact models of closed-skull mild TBI. Accordingly, our model provides a simple way to examine the biomechanics, pathophysiology, and functional deficits that result from TBI and can serve as a reliable platform for testing therapies that reduce brain pathology and deficits. PMID:26414413

The detection of brain oedema with frequency-dependent phase shift electromagnetic induction.

PubMed

González, César A; Rubinsky, Boris

2006-06-01

The spectroscopic distribution of inductive phase shift in the brain as a function of the relative volume of oedema was evaluated with theoretical and experimental methods in the frequency range 1 to 8 MHz. The theoretical study employed a simple mathematical model of electromagnetic induction in tissue and brain tissue data available from the literature to calculate the phase shift as a function of oedema in the bulk of the brain. Experimental data were generated from bulk measurements of ex vivo homogenized pig brain tissue mixed with various volumes of physiological saline in a volume sample typical of the human brain. There is good agreement between the analytical and the experimental results. Detectable changes in phase shift begin from a frequency of about 3 MHz to 4 MHz in the tested compositions and volume. The phase shift increases with frequency and fluid content. The results suggest that measuring phase shift in the bulk of the brain has the potential for becoming a robust means for non-contact detection of oedema in the brain.

A combined solenoid-surface RF coil for high-resolution whole-brain rat imaging on a 3.0 Tesla clinical MR scanner.

PubMed

Underhill, Hunter R; Yuan, Chun; Hayes, Cecil E

2010-09-01

Rat brain models effectively simulate a multitude of human neurological disorders. Improvements in coil design have facilitated the wider utilization of rat brain models by enabling the utilization of clinical MR scanners for image acquisition. In this study, a novel coil design, subsequently referred to as the rat brain coil, is described that exploits and combines the strengths of both solenoids and surface coils into a simple, multichannel, receive-only coil dedicated to whole-brain rat imaging on a 3.0 T clinical MR scanner. Compared with a multiturn solenoid mouse body coil, a 3-cm surface coil, a modified Helmholtz coil, and a phased-array surface coil, the rat brain coil improved signal-to-noise ratio by approximately 72, 61, 78, and 242%, respectively. Effects of the rat brain coil on amplitudes of static field and radiofrequency field uniformity were similar to each of the other coils. In vivo, whole-brain images of an adult male rat were acquired with a T(2)-weighted spin-echo sequence using an isotropic acquisition resolution of 0.25 x 0.25 x 0.25 mm(3) in 60.6 min. Multiplanar images of the in vivo rat brain with identification of anatomic structures are presented. Improvement in signal-to-noise ratio afforded by the rat brain coil may broaden experiments that utilize clinical MR scanners for in vivo image acquisition. 2010 Wiley-Liss, Inc.

Feature extraction inspired by V1 in visual cortex

NASA Astrophysics Data System (ADS)

Lv, Chao; Xu, Yuelei; Zhang, Xulei; Ma, Shiping; Li, Shuai; Xin, Peng; Zhu, Mingning; Ma, Hongqiang

2018-04-01

Target feature extraction plays an important role in pattern recognition. It is the most complicated activity in the brain mechanism of biological vision. Inspired by high properties of primary visual cortex (V1) in extracting dynamic and static features, a visual perception model was raised. Firstly, 28 spatial-temporal filters with different orientations, half-squaring operation and divisive normalization were adopted to obtain the responses of V1 simple cells; then, an adjustable parameter was added to the output weight so that the response of complex cells was got. Experimental results indicate that the proposed V1 model can perceive motion information well. Besides, it has a good edge detection capability. The model inspired by V1 has good performance in feature extraction and effectively combines brain-inspired intelligence with computer vision.

Understanding mental retardation in Down's syndrome using trisomy 16 mouse models.

PubMed

Galdzicki, Z; Siarey, R J

2003-06-01

Mental retardation in Down's syndrome, human trisomy 21, is characterized by developmental delays, language and memory deficits and other cognitive abnormalities. Neurophysiological and functional information is needed to understand the mechanisms of mental retardation in Down's syndrome. The trisomy mouse models provide windows into the molecular and developmental effects associated with abnormal chromosome numbers. The distal segment of mouse chromosome 16 is homologous to nearly the entire long arm of human chromosome 21. Therefore, mice with full or segmental trisomy 16 (Ts65Dn) are considered reliable animal models of Down's syndrome. Ts65Dn mice demonstrate impaired learning in spatial tests and abnormalities in hippocampal synaptic plasticity. We hypothesize that the physiological impairments in the Ts65Dn mouse hippocampus can model the suboptimal brain function occuring at various levels of Down's syndrome brain hierarchy, starting at a single neuron, and then affecting simple and complex neuronal networks. Once these elements create the gross brain structure, their dysfunctional activity cannot be overcome by extensive plasticity and redundancy, and therefore, at the end of the maturation period the mind inside this brain remains deficient and delayed in its capabilities. The complicated interactions that govern this aberrant developmental process cannot be rescued through existing compensatory mechanisms. In summary, overexpression of genes from chromosome 21 shifts biological homeostasis in the Down's syndrome brain to a new less functional state.

Cancer-associated fibroblast promote transmigration through endothelial brain cells in three-dimensional in vitro models.

PubMed

Choi, Yoon Pyo; Lee, Joo Hyun; Gao, Ming-Qing; Kim, Baek Gil; Kang, Suki; Kim, Se Hoon; Cho, Nam Hoon

2014-11-01

Brain metastases are associated with high morbidity as well as with poor prognosis and survival in breast cancer patients. Despite its clinical importance, metastasis of breast cancer cells through the blood-brain barrier (BBB) is poorly understood. The objective of our study was to investigate whether cancer-associated fibroblasts (CAFs) play crucial roles in breast cancer brain metastasis. Using a cell adhesion assays, in vitro BBB permeability and transmigration assays and soft agar colony formation assays, we investigated the physical roles of CAFs in breast cancer brain metastasis. We also performed immunofluorescence, flow cytometric analysis, Droplet Digital PCR and Simon™ Simple Western System to confirm changes in expression levels. We established two novel three-dimensional (3D) culture systems using a perpendicular slide chamber and applying 3D embedded culture method to reflect brain metastasis conditions. With a newly developed device, CAFs was proven to promote cell adhesion to human brain microvascular endothelial cells, in vitro BBB permeability and transmigration and colony formation of breast cancer cells. Furthermore, CAFs enhanced the invasive migration of breast cancer cells in two kinds of 3D cultures. These 3D models also reliably recapitulate the initial steps of BBB transmigration, micro-metastasis and colonization. Expression of integrin α5β1 and αvβ3, c-MET and α2,6-siayltransferase was increased in breast cancer cells that migrated through the BBB. In conclusion, based on our in vitro BBB and co-culture models, our data suggest that CAFs may play a role in breast cancer brain metastasis. © 2014 UICC.

Does preliminary optimisation of an anatomically correct skull-brain model using simple simulants produce clinically realistic ballistic injury fracture patterns?

PubMed

Mahoney, P F; Carr, D J; Delaney, R J; Hunt, N; Harrison, S; Breeze, J; Gibb, I

2017-07-01

Ballistic head injury remains a significant threat to military personnel. Studying such injuries requires a model that can be used with a military helmet. This paper describes further work on a skull-brain model using skulls made from three different polyurethane plastics and a series of skull 'fills' to simulate brain (3, 5, 7 and 10% gelatine by mass and PermaGel™). The models were subjected to ballistic impact from 7.62 × 39 mm mild steel core bullets. The first part of the work compares the different polyurethanes (mean bullet muzzle velocity of 708 m/s), and the second part compares the different fills (mean bullet muzzle velocity of 680 m/s). The impact events were filmed using high speed cameras. The resulting fracture patterns in the skulls were reviewed and scored by five clinicians experienced in assessing penetrating head injury. In over half of the models, one or more assessors felt aspects of the fracture pattern were close to real injury. Limitations of the model include the skull being manufactured in two parts and the lack of a realistic skin layer. Further work is ongoing to address these.

Integrating Retraction Modeling Into an Atlas-Based Framework for Brain Shift Prediction

PubMed Central

Chen, Ishita; Ong, Rowena E.; Simpson, Amber L.; Sun, Kay; Thompson, Reid C.

2015-01-01

In recent work, an atlas-based statistical model for brain shift prediction, which accounts for uncertainty in the intraoperative environment, has been proposed. Previous work reported in the literature using this technique did not account for local deformation caused by surgical retraction. It is challenging to precisely localize the retractor location prior to surgery and the retractor is often moved in the course of the procedure. This paper proposes a technique that involves computing the retractor-induced brain deformation in the operating room through an active model solve and linearly superposing the solution with the precomputed deformation atlas. As a result, the new method takes advantage of the atlas-based framework’s accounting for uncertainties while also incorporating the effects of retraction with minimal intraoperative computing. This new approach was tested using simulation and phantom experiments. The results showed an improvement in average shift correction from 50% (ranging from 14 to 81%) for gravity atlas alone to 80% using the active solve retraction component (ranging from 73 to 85%). This paper presents a novel yet simple way to integrate retraction into the atlas-based brain shift computation framework. PMID:23864146

Human Development XIII: The Connection Between the Structure of the Overtone System and the Tone Language of Music. Some Implications for Our Understanding of the Human Brain

PubMed Central

Ventegodt, Søren; Hermansen, Tyge Dahl; Kandel, Isack; Merrick, Joav

2008-01-01

The functioning brain behaves like one highly-structured, coherent, informational field. It can be popularly described as a “coherent ball of energy”, making the idea of a local highly-structured quantum field that carries the consciousness very appealing. If that is so, the structure of the experience of music might be a quite unique window into a hidden quantum reality of the brain, and even of life itself. The structure of music is then a mirror of a much more complex, but similar, structure of the energetic field of the working brain. This paper discusses how the perception of music is organized in the human brain with respect to the known tone scales of major and minor. The patterns used by the brain seem to be similar to the overtones of vibrating matter, giving a positive experience of harmonies in major. However, we also like the minor scale, which can explain brain patterns as fractal-like, giving a symmetric “downward reflection” of the major scale into the minor scale. We analyze the implication of beautiful and ugly tones and harmonies for the model. We conclude that when it comes to simple perception of harmonies, the most simple is the most beautiful and the most complex is the most ugly, but in music, even the most disharmonic harmony can be beautiful, if experienced as a part of a dynamic release of musical tension. This can be taken as a general metaphor of painful, yet meaningful, and developing experiences in human life. PMID:18661052

Comparative survival study of glial cells and cells composing walls of blood vessels in crustacean ventral nerve cord after photodynamic treatment

NASA Astrophysics Data System (ADS)

Kolosov, Mikhail S.; Shubina, Elena

2015-03-01

Photodynamic therapy is a prospective treatment modality of brain cancers. It is of importance to have information about relative survival rate of different cell types in nerve tissue during photodynamic treatment. Particularly, for development of sparing strategy of the photodynamic therapy of brain tumors, which pursuits both total elimination of malignant cells, which are usually of glial origin, and, at the same time, preservation of normal blood circulation as well as normal glial cells in the brain. The aim of this work was to carry out comparative survival study of glial cells and cells composing walls of blood vessels after photodynamic treatment, using simple model object - ventral nerve cord of crustacean.

A simple method for EEG guided transcranial electrical stimulation without models.

PubMed

Cancelli, Andrea; Cottone, Carlo; Tecchio, Franca; Truong, Dennis Q; Dmochowski, Jacek; Bikson, Marom

2016-06-01

There is longstanding interest in using EEG measurements to inform transcranial Electrical Stimulation (tES) but adoption is lacking because users need a simple and adaptable recipe. The conventional approach is to use anatomical head-models for both source localization (the EEG inverse problem) and current flow modeling (the tES forward model), but this approach is computationally demanding, requires an anatomical MRI, and strict assumptions about the target brain regions. We evaluate techniques whereby tES dose is derived from EEG without the need for an anatomical head model, target assumptions, difficult case-by-case conjecture, or many stimulation electrodes. We developed a simple two-step approach to EEG-guided tES that based on the topography of the EEG: (1) selects locations to be used for stimulation; (2) determines current applied to each electrode. Each step is performed based solely on the EEG with no need for head models or source localization. Cortical dipoles represent idealized brain targets. EEG-guided tES strategies are verified using a finite element method simulation of the EEG generated by a dipole, oriented either tangential or radial to the scalp surface, and then simulating the tES-generated electric field produced by each model-free technique. These model-free approaches are compared to a 'gold standard' numerically optimized dose of tES that assumes perfect understanding of the dipole location and head anatomy. We vary the number of electrodes from a few to over three hundred, with focality or intensity as optimization criterion. Model-free approaches evaluated include (1) voltage-to-voltage, (2) voltage-to-current; (3) Laplacian; and two Ad-Hoc techniques (4) dipole sink-to-sink; and (5) sink to concentric. Our results demonstrate that simple ad hoc approaches can achieve reasonable targeting for the case of a cortical dipole, remarkably with only 2-8 electrodes and no need for a model of the head. Our approach is verified directly only for a theoretically localized source, but may be potentially applied to an arbitrary EEG topography. For its simplicity and linearity, our recipe for model-free EEG guided tES lends itself to broad adoption and can be applied to static (tDCS), time-variant (e.g., tACS, tRNS, tPCS), or closed-loop tES.

A simple method for EEG guided transcranial electrical stimulation without models

NASA Astrophysics Data System (ADS)

Cancelli, Andrea; Cottone, Carlo; Tecchio, Franca; Truong, Dennis Q.; Dmochowski, Jacek; Bikson, Marom

2016-06-01

Objective. There is longstanding interest in using EEG measurements to inform transcranial Electrical Stimulation (tES) but adoption is lacking because users need a simple and adaptable recipe. The conventional approach is to use anatomical head-models for both source localization (the EEG inverse problem) and current flow modeling (the tES forward model), but this approach is computationally demanding, requires an anatomical MRI, and strict assumptions about the target brain regions. We evaluate techniques whereby tES dose is derived from EEG without the need for an anatomical head model, target assumptions, difficult case-by-case conjecture, or many stimulation electrodes. Approach. We developed a simple two-step approach to EEG-guided tES that based on the topography of the EEG: (1) selects locations to be used for stimulation; (2) determines current applied to each electrode. Each step is performed based solely on the EEG with no need for head models or source localization. Cortical dipoles represent idealized brain targets. EEG-guided tES strategies are verified using a finite element method simulation of the EEG generated by a dipole, oriented either tangential or radial to the scalp surface, and then simulating the tES-generated electric field produced by each model-free technique. These model-free approaches are compared to a ‘gold standard’ numerically optimized dose of tES that assumes perfect understanding of the dipole location and head anatomy. We vary the number of electrodes from a few to over three hundred, with focality or intensity as optimization criterion. Main results. Model-free approaches evaluated include (1) voltage-to-voltage, (2) voltage-to-current; (3) Laplacian; and two Ad-Hoc techniques (4) dipole sink-to-sink; and (5) sink to concentric. Our results demonstrate that simple ad hoc approaches can achieve reasonable targeting for the case of a cortical dipole, remarkably with only 2-8 electrodes and no need for a model of the head. Significance. Our approach is verified directly only for a theoretically localized source, but may be potentially applied to an arbitrary EEG topography. For its simplicity and linearity, our recipe for model-free EEG guided tES lends itself to broad adoption and can be applied to static (tDCS), time-variant (e.g., tACS, tRNS, tPCS), or closed-loop tES.

A Behavior-Based Circuit Model of How Outcome Expectations Organize Learned Behavior in Larval "Drosophila"

ERIC Educational Resources Information Center

Schleyer, Michael; Saumweber, Timo; Nahrendorf, Wiebke; Fischer, Benjamin; von Alpen, Desiree; Pauls, Dennis; Thum, Andreas; Gerber, Bertram

2011-01-01

Drosophila larvae combine a numerically simple brain, a correspondingly moderate behavioral complexity, and the availability of a rich toolbox for transgenic manipulation. This makes them attractive as a study case when trying to achieve a circuit-level understanding of behavior organization. From a series of behavioral experiments, we suggest a…

The Library and Human Memory Simulation Studies. Reports on File Organization Studies.

ERIC Educational Resources Information Center

Reilly, Kevin D.

This report describes digital computer simulation efforts in a study of memory systems for two important cases: that of the individual the brain; and that of society, the library. A neural system model is presented in which a complex system is produced by connecting simple hypothetical neurons whose states change under application of a…

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

NASA Astrophysics Data System (ADS)

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

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

Neural representations of the concepts in simple sentences: Concept activation prediction and context effects.

PubMed

Just, Marcel Adam; Wang, Jing; Cherkassky, Vladimir L

2017-08-15

Although it has been possible to identify individual concepts from a concept's brain activation pattern, there have been significant obstacles to identifying a proposition from its fMRI signature. Here we demonstrate the ability to decode individual prototype sentences from readers' brain activation patterns, by using theory-driven regions of interest and semantic properties. It is possible to predict the fMRI brain activation patterns evoked by propositions and words which are entirely new to the model with reliably above-chance rank accuracy. The two core components implemented in the model that reflect the theory were the choice of intermediate semantic features and the brain regions associated with the neurosemantic dimensions. This approach also predicts the neural representation of object nouns across participants, studies, and sentence contexts. Moreover, we find that the neural representation of an agent-verb-object proto-sentence is more accurately characterized by the neural signatures of its components as they occur in a similar context than by the neural signatures of these components as they occur in isolation. Copyright © 2017 Elsevier Ltd. All rights reserved.

The ontogeny of great ape gesture - not a simple story. Comment on "Towards a Computational Comparative Neuroprimatology: Framing the language-ready brain" by Michael A. Arbib

NASA Astrophysics Data System (ADS)

Liebal, Katja

2016-03-01

Although there is an increasing number of studies investigating gestural communication in primates other than humans in both natural and captive settings [1], very little is known about how they acquire their gestures. Different mechanisms have been proposed, including genetic transmission [2], social learning [3], or ontogenetic ritualization [4]. This latter mechanism is central to Arbib's paper [5], because he uses dyadic brain modeling - that is ;modeling the brains of two creatures as they interact with each other, so that the action of one affects the perception of the other and so the cycle of interactions continues, with both brains changing in the process; - to explain how gestures might emerge in ontogeny from previously non-communicative behaviors over the course of repeated and increasingly abbreviated and thus ritualized interactions. The aim of my comment is to discuss the current evidence from primate gesture research with regard the different mechanisms proposed for gesture acquisition and how this might confirm or challenge Arbib's approach.

The roots of empathy: through the lens of rodent models

PubMed Central

Meyza, K.Z.; Bartal, I. Ben-Ami; Monfils, M.H.; Panksepp, J.B.; Knapska, E.

2016-01-01

Empathy is a phenomenon often considered dependent on higher-order emotional control and an ability to relate to the emotional state of others. It is, by many, attributed only to species having well-developed cortical circuits capable of performing such complex tasks. However, over the years, a wealth of data has been accumulated showing that rodents are capable not only of sharing emotional states of their conspecifics, but also of prosocial behavior driven by such shared experiences. The study of rodent empathic behaviors is only now becoming an independent research field. Relevant animal models allow precise manipulation of neural networks, thereby offering insight into the foundations of empathy in the mammalian brains. Here we review the data on empathic behaviors in rat and mouse models, their neurobiological and neurophysiological correlates, and the factors influencing these behaviors. We discuss how simple rodent models of empathy enhance our understanding of how brain controls empathic behaviors. PMID:27825924

Development of an experimental model of brain tissue heterotopia in the lung

PubMed Central

Quemelo, Paulo Roberto Veiga; Sbragia, Lourenço; Peres, Luiz Cesar

2007-01-01

Summary The presence of heterotopic brain tissue in the lung is a rare abnormality. The cases reported thus far are usually associated with neural tube defects (NTD). As there are no reports of experimental models of NTD that present this abnormality, the objective of the present study was to develop a surgical method of brain tissue heterotopia in the lung. We used 24 pregnant Swiss mice divided into two groups of 12 animals each, denoted 17GD and 18GD according to the gestational day (GD) when caesarean section was performed to collect the fetuses. Surgery was performed on the 15th GD, one fetus was removed by hysterectomy and its brain tissue was cut into small fragments and implanted in the lung of its litter mates. Thirty-four live fetuses were obtained from the 17GD group. Of these, eight (23.5%) were used as control (C), eight (23.5%) were sham operated (S) and 18 (52.9%) were used for pulmonary brain tissue implantation (PBI). Thirty live fetuses were obtained from the females of the 18GD group. Of these, eight (26.6%) were C, eight (26.6%) S and 14 (46.6%) were used for PBI. Histological examination of the fetal trunks showed implantation of GFAP-positive brain tissue in 85% of the fetuses of the 17GD group and in 100% of those of the 18GD group, with no significant difference between groups for any of the parameters analysed. The experimental model proved to be efficient and of relatively simple execution, showing complete integration of the brain tissue with pulmonary and pleural tissue and thus representing a model that will permit the study of different aspects of cell implantation and interaction. PMID:17877535

Modeling learning in brain stem and cerebellar sites responsible for VOR plasticity

NASA Technical Reports Server (NTRS)

Quinn, K. J.; Didier, A. J.; Baker, J. F.; Peterson, B. W.

1998-01-01

A simple model of vestibuloocular reflex (VOR) function was used to analyze several hypotheses currently held concerning the characteristics of VOR plasticity. The network included a direct vestibular pathway and an indirect path via the cerebellum. An optimization analysis of this model suggests that regulation of brain stem sites is critical for the proper modification of VOR gain. A more physiologically plausible learning rule was also applied to this network. Analysis of these simulation results suggests that the preferred error correction signal controlling gain modification of the VOR is the direct output of the accessory optic system (AOS) to the vestibular nuclei vs. a signal relayed through the cerebellum via floccular Purkinje cells. The potential anatomical and physiological basis for this conclusion is discussed, in relation to our current understanding of the latency of the adapted VOR response.

Critical dynamics on a large human Open Connectome network

NASA Astrophysics Data System (ADS)

Ódor, Géza

2016-12-01

Extended numerical simulations of threshold models have been performed on a human brain network with N =836 733 connected nodes available from the Open Connectome Project. While in the case of simple threshold models a sharp discontinuous phase transition without any critical dynamics arises, variable threshold models exhibit extended power-law scaling regions. This is attributed to fact that Griffiths effects, stemming from the topological or interaction heterogeneity of the network, can become relevant if the input sensitivity of nodes is equalized. I have studied the effects of link directness, as well as the consequence of inhibitory connections. Nonuniversal power-law avalanche size and time distributions have been found with exponents agreeing with the values obtained in electrode experiments of the human brain. The dynamical critical region occurs in an extended control parameter space without the assumption of self-organized criticality.

Pencil-and-Paper Neural Networks: An Undergraduate Laboratory Exercise in Computational Neuroscience

PubMed Central

Crisp, Kevin M.; Sutter, Ellen N.; Westerberg, Jacob A.

2015-01-01

Although it has been more than 70 years since McCulloch and Pitts published their seminal work on artificial neural networks, such models remain primarily in the domain of computer science departments in undergraduate education. This is unfortunate, as simple network models offer undergraduate students a much-needed bridge between cellular neurobiology and processes governing thought and behavior. Here, we present a very simple laboratory exercise in which students constructed, trained and tested artificial neural networks by hand on paper. They explored a variety of concepts, including pattern recognition, pattern completion, noise elimination and stimulus ambiguity. Learning gains were evident in changes in the use of language when writing about information processing in the brain. PMID:26557791

Corticonic models of brain mechanisms underlying cognition and intelligence

NASA Astrophysics Data System (ADS)

Farhat, Nabil H.

The concern of this review is brain theory or more specifically, in its first part, a model of the cerebral cortex and the way it: (a) interacts with subcortical regions like the thalamus and the hippocampus to provide higher-level-brain functions that underlie cognition and intelligence, (b) handles and represents dynamical sensory patterns imposed by a constantly changing environment, (c) copes with the enormous number of such patterns encountered in a lifetime by means of dynamic memory that offers an immense number of stimulus-specific attractors for input patterns (stimuli) to select from, (d) selects an attractor through a process of “conjugation” of the input pattern with the dynamics of the thalamo-cortical loop, (e) distinguishes between redundant (structured) and non-redundant (random) inputs that are void of information, (f) can do categorical perception when there is access to vast associative memory laid out in the association cortex with the help of the hippocampus, and (g) makes use of “computation” at the edge of chaos and information driven annealing to achieve all this. Other features and implications of the concepts presented for the design of computational algorithms and machines with brain-like intelligence are also discussed. The material and results presented suggest, that a Parametrically Coupled Logistic Map network (PCLMN) is a minimal model of the thalamo-cortical complex and that marrying such a network to a suitable associative memory with re-entry or feedback forms a useful, albeit, abstract model of a cortical module of the brain that could facilitate building a simple artificial brain. In the second part of the review, the results of numerical simulations and drawn conclusions in the first part are linked to the most directly relevant works and views of other workers. What emerges is a picture of brain dynamics on the mesoscopic and macroscopic scales that gives a glimpse of the nature of the long sought after brain code underlying intelligence and other higher level brain functions.

A distance constrained synaptic plasticity model of C. elegans neuronal network

NASA Astrophysics Data System (ADS)

Badhwar, Rahul; Bagler, Ganesh

2017-03-01

Brain research has been driven by enquiry for principles of brain structure organization and its control mechanisms. The neuronal wiring map of C. elegans, the only complete connectome available till date, presents an incredible opportunity to learn basic governing principles that drive structure and function of its neuronal architecture. Despite its apparently simple nervous system, C. elegans is known to possess complex functions. The nervous system forms an important underlying framework which specifies phenotypic features associated to sensation, movement, conditioning and memory. In this study, with the help of graph theoretical models, we investigated the C. elegans neuronal network to identify network features that are critical for its control. The 'driver neurons' are associated with important biological functions such as reproduction, signalling processes and anatomical structural development. We created 1D and 2D network models of C. elegans neuronal system to probe the role of features that confer controllability and small world nature. The simple 1D ring model is critically poised for the number of feed forward motifs, neuronal clustering and characteristic path-length in response to synaptic rewiring, indicating optimal rewiring. Using empirically observed distance constraint in the neuronal network as a guiding principle, we created a distance constrained synaptic plasticity model that simultaneously explains small world nature, saturation of feed forward motifs as well as observed number of driver neurons. The distance constrained model suggests optimum long distance synaptic connections as a key feature specifying control of the network.

Atypical Brain Activation during Simple & Complex Levels of Processing in Adult ADHD: An fMRI Study

ERIC Educational Resources Information Center

Hale, T. Sigi; Bookheimer, Susan; McGough, James J.; Phillips, Joseph M.; McCracken, James T.

2007-01-01

Objective: Executive dysfunction in ADHD is well supported. However, recent studies suggest that more fundamental impairments may be contributing. We assessed brain function in adults with ADHD during simple and complex forms of processing. Method: We used functional magnetic resonance imaging with forward and backward digit spans to investigate…

Increasingly diverse brain dynamics in the developmental arc: using Pareto-optimization to infer a mechanism

NASA Astrophysics Data System (ADS)

Tang, Evelyn; Giusti, Chad; Baum, Graham; Gu, Shi; Pollock, Eli; Kahn, Ari; Roalf, David; Moore, Tyler; Ruparel, Kosha; Gur, Ruben; Gur, Raquel; Satterthwaite, Theodore; Bassett, Danielle

Motivated by a recent demonstration that the network architecture of white matter supports emerging control of diverse neural dynamics as children mature into adults, we seek to investigate structural mechanisms that support these changes. Beginning from a network representation of diffusion imaging data, we simulate network evolution with a set of simple growth rules built on principles of network control. Notably, the optimal evolutionary trajectory displays a striking correspondence to the progression of child to adult brain, suggesting that network control is a driver of development. More generally, and in comparison to the complete set of available models, we demonstrate that all brain networks from child to adult are structured in a manner highly optimized for the control of diverse neural dynamics. Within this near-optimality, we observe differences in the predicted control mechanisms of the child and adult brains, suggesting that the white matter architecture in children has a greater potential to increasingly support brain state transitions, potentially underlying cognitive switching.

Brain network dysfunction in youth with obsessive-compulsive disorder induced by simple uni-manual behavior: The role of the dorsal anterior cingulate cortex

PubMed Central

Friedman, Amy L.; Burgess, Ashley; Ramaseshan, Karthik; Easter, Phil; Khatib, Dalal; Chowdury, Asadur; Arnold, Paul D.; Hanna, Gregory L.; Rosenberg, David R.; Diwadkar, Vaibhav A.

2017-01-01

In an effort to elucidate differences in functioning brain networks between youth with obsessive-compulsive disorder and controls, we used fMRI signals to analyze brain network interactions of the dorsal anterior cingulate cortex (dACC) during visually coordinated motor responses. Subjects made a uni-manual response to briefly presented probes, at periodic (allowing participants to maintain a “motor set”) or random intervals (demanding reactive responses). Network interactions were assessed using psycho-physiological interaction (PPI), a basic model of functional connectivity evaluating modulatory effects of the dACC in the context of each task condition. Across conditions, OCD were characterized by hyper-modulation by the dACC, with loci alternatively observed as both condition-general and condition-specific. Thus, dynamically driven task demands during simple uni-manual motor control induce compensatory network interactions in cortical-thalamic regions in OCD. These findings support previous research in OCD showing compensatory network interactions during complex memory tasks, but establish that these network effects are observed during basic sensorimotor processing. Thus, these patterns of network dysfunction may in fact be independent of the complexity of tasks used to induce brain network activity. Hypothesis-driven approaches coupled with sophisticated network analyses are a highly valuable approach in using fMRI to uncover mechanisms in disorders like OCD. PMID:27992792

FDTD analysis of a noninvasive hyperthermia system for brain tumors.

PubMed

Yacoob, Sulafa M; Hassan, Noha S

2012-08-14

Hyperthermia is considered one of the new therapeutic modalities for cancer treatment and is based on the difference in thermal sensitivity between healthy tissues and tumors. During hyperthermia treatment, the temperature of the tumor is raised to 40-45°C for a definite period resulting in the destruction of cancer cells. This paper investigates design, modeling and simulation of a new non-invasive hyperthermia applicator system capable of effectively heating deep seated as well as superficial brain tumors using inexpensive, simple, and easy to fabricate components without harming surrounding healthy brain tissues. The proposed hyperthermia applicator system is composed of an air filled partial half ellipsoidal chamber, a patch antenna, and a head model with an embedded tumor at an arbitrary location. The irradiating antenna is placed at one of the foci of the hyperthermia chamber while the center of the brain tumor is placed at the other focus. The finite difference time domain (FDTD) method is used to compute both the SAR patterns and the temperature distribution in three different head models due to two different patch antennas at a frequency of 915 MHz. The obtained results suggest that by using the proposed noninvasive hyperthermia system it is feasible to achieve sufficient and focused energy deposition and temperature rise to therapeutic values in deep seated as well as superficial brain tumors without harming surrounding healthy tissue. The proposed noninvasive hyperthermia system proved suitable for raising the temperature in tumors embedded in the brain to therapeutic values by carefully selecting the systems components. The operator of the system only needs to place the center of the brain tumor at a pre-specified location and excite the antenna at a single frequency of 915 MHz. Our study may provide a basis for a clinical applicator prototype capable of heating brain tumors.

Neuroendocrine control of seasonal plasticity in the auditory and vocal systems of fish

PubMed Central

Forlano, Paul M.; Sisneros, Joseph A.; Rohmann, Kevin N.; Bass, Andrew H.

2014-01-01

Seasonal changes in reproductive-related vocal behavior are widespread among fishes. This review highlights recent studies of the vocal plainfin midshipman fish, Porichthys notatus, a neuroethological model system used for the past two decades to explore neural and endocrine mechanisms of vocal-acoustic social behaviors shared with tetrapods. Integrative approaches combining behavior, neurophysiology, neuropharmacology, neuroanatomy, and gene expression methodologies have taken advantage of simple, stereotyped and easily quantifiable behaviors controlled by discrete neural networks in this model system to enable discoveries such as the first demonstration of adaptive seasonal plasticity in the auditory periphery of a vertebrate as well as rapid steroid and neuropeptide effects on vocal physiology and behavior. This simple model system has now revealed cellular and molecular mechanisms underlying seasonal and steroid-driven auditory and vocal plasticity in the vertebrate brain. PMID:25168757

Numerical simulation model of hyperacute/acute stage white matter infarction.

PubMed

Sakai, Koji; Yamada, Kei; Oouchi, Hiroyuki; Nishimura, Tsunehiko

2008-01-01

Although previous studies have revealed the mechanisms of changes in diffusivity (apparent diffusion coefficient [ADC]) in acute brain infarction, changes in diffusion anisotropy (fractional anisotropy [FA]) in white matter have not been examined. We hypothesized that membrane permeability as well as axonal swelling play important roles, and we therefore constructed a simulation model using random walk simulation to replicate the diffusion of water molecules. We implemented a numerical diffusion simulation model of normal and infarcted human brains using C++ language. We constructed this 2-pool model using simple tubes aligned in a single direction. Random walk simulation diffused water. Axon diameters and membrane permeability were then altered in step-wise fashion. To estimate the effects of axonal swelling, axon diameters were changed from 6 to 10 microm. Membrane permeability was altered from 0% to 40%. Finally, both elements were combined to explain increasing FA in the hyperacute stage of white matter infarction. The simulation demonstrated that simple water shift into the intracellular space reduces ADC and increases FA, but not to the extent expected from actual human cases (ADC approximately 50%; FA approximately +20%). Similarly, membrane permeability alone was insufficient to explain this phenomenon. However, a combination of both factors successfully replicated changes in diffusivity indices. Both axonal swelling and reduced membrane permeability appear important in explaining changes in ADC and FA based on eigenvalues in hyperacute-stage white matter infarction.

Toward a model-based predictive controller design in brain-computer interfaces.

PubMed

Kamrunnahar, M; Dias, N S; Schiff, S J

2011-05-01

A first step in designing a robust and optimal model-based predictive controller (MPC) for brain-computer interface (BCI) applications is presented in this article. An MPC has the potential to achieve improved BCI performance compared to the performance achieved by current ad hoc, nonmodel-based filter applications. The parameters in designing the controller were extracted as model-based features from motor imagery task-related human scalp electroencephalography. Although the parameters can be generated from any model-linear or non-linear, we here adopted a simple autoregressive model that has well-established applications in BCI task discriminations. It was shown that the parameters generated for the controller design can as well be used for motor imagery task discriminations with performance (with 8-23% task discrimination errors) comparable to the discrimination performance of the commonly used features such as frequency specific band powers and the AR model parameters directly used. An optimal MPC has significant implications for high performance BCI applications.

Using Case Studies as a Semester-Long Tool to Teach Neuroanatomy and Structure-Function Relationships to Undergraduates

PubMed Central

Kennedy, Susan

2013-01-01

In addition to being inherently interesting to students, case studies can serve as useful tools to teach neuroanatomy and demonstrate important relationships between brain structure and function. In most undergraduate courses, however, neuroanatomy is presented to students as a “unit” or chapter, much like other topics (e.g., receptors, pharmacology) covered in the course, over a period of a week or two. In this article, a relatively simple model of teaching neuroanatomy is described in which students are actively engaged in the presentation and discussion of case studies throughout the semester, following a general introduction to the structure of the nervous system. In this way, the teaching of neuroanatomy is “distributed” throughout the semester and put into a more user-friendly context for students as additional topics are introduced. Generally, students report enjoying learning brain structure using this method, and commented positively on the class activities associated with learning brain anatomy. Advantages and disadvantages of such a model are presented, as are suggestions for implementing similar models of undergraduate neuroanatomy education. PMID:24319386

The Effect of Herrmann Whole Brain Teaching Method on Students' Understanding of Simple Electric Circuits

ERIC Educational Resources Information Center

Bawaneh, Ali Khalid Ali; Nurulazam Md Zain, Ahmad; Salmiza, Saleh

2011-01-01

The purpose of this study was to investigate the effect of Herrmann Whole Brain Teaching Method over conventional teaching method on eight graders in their understanding of simple electric circuits in Jordan. Participants (N = 273 students; M = 139, F = 134) were randomly selected from Bani Kenanah region-North of Jordan and randomly assigned to…

The brainstem reticular formation is a small-world, not scale-free, network

PubMed Central

Humphries, M.D; Gurney, K; Prescott, T.J

2005-01-01

Recently, it has been demonstrated that several complex systems may have simple graph-theoretic characterizations as so-called ‘small-world’ and ‘scale-free’ networks. These networks have also been applied to the gross neural connectivity between primate cortical areas and the nervous system of Caenorhabditis elegans. Here, we extend this work to a specific neural circuit of the vertebrate brain—the medial reticular formation (RF) of the brainstem—and, in doing so, we have made three key contributions. First, this work constitutes the first model (and quantitative review) of this important brain structure for over three decades. Second, we have developed the first graph-theoretic analysis of vertebrate brain connectivity at the neural network level. Third, we propose simple metrics to quantitatively assess the extent to which the networks studied are small-world or scale-free. We conclude that the medial RF is configured to create small-world (implying coherent rapid-processing capabilities), but not scale-free, type networks under assumptions which are amenable to quantitative measurement. PMID:16615219

Decoding negative affect personality trait from patterns of brain activation to threat stimuli.

PubMed

Fernandes, Orlando; Portugal, Liana C L; Alves, Rita de Cássia S; Arruda-Sanchez, Tiago; Rao, Anil; Volchan, Eliane; Pereira, Mirtes; Oliveira, Letícia; Mourao-Miranda, Janaina

2017-01-15

Pattern recognition analysis (PRA) applied to functional magnetic resonance imaging (fMRI) has been used to decode cognitive processes and identify possible biomarkers for mental illness. In the present study, we investigated whether the positive affect (PA) or negative affect (NA) personality traits could be decoded from patterns of brain activation in response to a human threat using a healthy sample. fMRI data from 34 volunteers (15 women) were acquired during a simple motor task while the volunteers viewed a set of threat stimuli that were directed either toward them or away from them and matched neutral pictures. For each participant, contrast images from a General Linear Model (GLM) between the threat versus neutral stimuli defined the spatial patterns used as input to the regression model. We applied a multiple kernel learning (MKL) regression combining information from different brain regions hierarchically in a whole brain model to decode the NA and PA from patterns of brain activation in response to threat stimuli. The MKL model was able to decode NA but not PA from the contrast images between threat stimuli directed away versus neutral with a significance above chance. The correlation and the mean squared error (MSE) between predicted and actual NA were 0.52 (p-value=0.01) and 24.43 (p-value=0.01), respectively. The MKL pattern regression model identified a network with 37 regions that contributed to the predictions. Some of the regions were related to perception (e.g., occipital and temporal regions) while others were related to emotional evaluation (e.g., caudate and prefrontal regions). These results suggest that there was an interaction between the individuals' NA and the brain response to the threat stimuli directed away, which enabled the MKL model to decode NA from the brain patterns. To our knowledge, this is the first evidence that PRA can be used to decode a personality trait from patterns of brain activation during emotional contexts. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

Start Smart! Building Brain Power in the Early Years.

ERIC Educational Resources Information Center

Schiller, Pam

Noting current brain development research, this book offers simple, straightforward ways to boost children's brain power with active exploration, repetition, sensory exploration, laughter, and more. The chapters describe how and why the brain develops and explain how parents can give their children the best foundation for future learning.…

Probabilistic brains: knowns and unknowns

PubMed Central

Pouget, Alexandre; Beck, Jeffrey M; Ma, Wei Ji; Latham, Peter E

2015-01-01

There is strong behavioral and physiological evidence that the brain both represents probability distributions and performs probabilistic inference. Computational neuroscientists have started to shed light on how these probabilistic representations and computations might be implemented in neural circuits. One particularly appealing aspect of these theories is their generality: they can be used to model a wide range of tasks, from sensory processing to high-level cognition. To date, however, these theories have only been applied to very simple tasks. Here we discuss the challenges that will emerge as researchers start focusing their efforts on real-life computations, with a focus on probabilistic learning, structural learning and approximate inference. PMID:23955561

Synchronisation of chaos and its applications

NASA Astrophysics Data System (ADS)

Eroglu, Deniz; Lamb, Jeroen S. W.; Pereira, Tiago

2017-07-01

Dynamical networks are important models for the behaviour of complex systems, modelling physical, biological and societal systems, including the brain, food webs, epidemic disease in populations, power grids and many other. Such dynamical networks can exhibit behaviour in which deterministic chaos, exhibiting unpredictability and disorder, coexists with synchronisation, a classical paradigm of order. We survey the main theory behind complete, generalised and phase synchronisation phenomena in simple as well as complex networks and discuss applications to secure communications, parameter estimation and the anticipation of chaos.

Effect of Observation of Simple Hand Movement on Brain Activations in Patients with Unilateral Cerebral Palsy: An fMRI Study

ERIC Educational Resources Information Center

Dinomais, Mickael; Lignon, Gregoire; Chinier, Eva; Richard, Isabelle; Minassian, Aram Ter; The Tich, Sylvie N'Guyen

2013-01-01

The aim of this functional magnetic resonance imaging (fMRI) study was to examine and compare brain activation in patients with unilateral cerebral palsy (CP) during observation of simple hand movement performed by the paretic and nonparetic hand. Nineteen patients with clinical unilateral CP (14 male, mean age 14 years, 7-21 years) participated…

A simplified protocol employing elacridar in rodents: a screening model in drug discovery to assess P-gp mediated efflux at the blood brain barrier.

PubMed

Kallem, Rajareddy; Kulkarni, Chetan P; Patel, Dakshay; Thakur, Megha; Sinz, Michael; Singh, Sheelendra P; Mahammad, S Shahe; Mandlekar, Sandhya

2012-06-01

In the present study we have developed a simple, time, and cost effective in vivo rodent protocol to screen the susceptibility of a test compound for P-glycoprotein (P-gp) mediated efflux at the blood brain barrier (BBB) during early drug discovery. We used known P-gp substrates as test compounds (quinidine, digoxin, and talinolol) and elacridar (GF120918) as a chemical inhibitor to establish the model. The studies were carried out in both mice and rats. Elacridar was dosed intravenously at 5 mg/kg, 0.5 h prior to probe substrate administration. Plasma and brain samples were collected and analyzed using UPLC-MS/MS. In the presence of elacridar, the ratio of brain to plasma area under the curve (B/P) in mouse increased 2, 4, and 38-fold, respectively, for talinolol, digoxin, and quinidine; whereas in rat, a 70-fold increase was observed for quinidine. Atenolol, a non P-gp substrate, exhibited poor brain penetration in the presence or absence of elacridar in both species (B/P ratio ~ 0.1). Elacridar had no significant effect on the systemic clearance of digoxin or quinidine; however, a trend towards increasing volume of distribution and half life was observed. Our results support the utility of elacridar in evaluation of the influence of P-gp mediated efflux on drug distribution to the brain. Our protocol employing a single intravenous dose of elacridar and test compound provides a cost effective alternative to expensive P-gp knockout mice models during early drug discovery.

Histopathological and biochemical changes following fat embolism with administration of corn oil micelles: a new animal model for fat embolism syndrome.

PubMed

Liu, D D; Hsieh, N-K; Chen, H I

2008-11-01

Several experimental models have been used to produce intravascular fat embolism. We have developed a simple technique to induce fat embolism using corn oil emulsified with distilled water to form fatty micelles. Fat embolism was produced by intravenous administration of these fatty micelles in anaesthetised rats, causing alveolar oedema, haemorrhage and increased lung weight. Histopathological examination revealed fatty droplets and fibrin thrombi in the lung, kidney and brain. The arteriolar lumen was filled with fatty deposits. Following fat embolism, hypoxia and hypercapnia occurred. The plasma phospholipase A(2), nitrate/nitrite, methylguidanidine and proinflammatory cytokines were significantly increased. Mass spectrometry showed that the main ingredient of corn oil was oleic acid. This simple technique may be applied as a new animal model for the investigation of the mechanisms involved in the fat embolism syndrome.

The Cerebellum in Maintenance of a Motor Skill: A Hierarchy of Brain and Spinal Cord Plasticity Underlies H-Reflex Conditioning

ERIC Educational Resources Information Center

Wolpaw, Jonathan R.; Chen, Xiang Yang

2006-01-01

Operant conditioning of the H-reflex, the electrical analog of the spinal stretch reflex, is a simple model of skill acquisition and involves plasticity in the spinal cord. Previous work showed that the cerebellum is essential for down-conditioning the H-reflex. This study asks whether the cerebellum is also essential for maintaining…

On high b diffusion imaging in the human brain: ruminations and experimental insights.

PubMed

Mulkern, Robert V; Haker, Steven J; Maier, Stephan E

2009-10-01

Interest in the manner in which brain tissue signal decays with b factor in diffusion imaging schemes has grown in recent years following the observation that the decay curves depart from purely monoexponential decay behavior. Regardless of the model or fitting function proposed for characterizing sufficiently sampled decay curves (vide infra), the departure from monoexponentiality spells increased tissue characterization potential. The degree to which this potential can be harnessed to improve specificity, sensitivity and spatial localization of diseases in brain, and other tissues, largely remains to be explored. Furthermore, the degree to which currently popular diffusion tensor imaging methods, including visually impressive white matter fiber "tractography" results, have almost completely ignored the nonmonoexponential nature of the basic signal decay with b factor is worthy of communal introspection. Here we limit our attention to a review of the basic experimental features associated with brain water signal diffusion decay curves as measured over extended b-factor ranges, the simple few parameter fitting functions that have been proposed to characterize these decays and the more involved models, e.g.,"ruminations," which have been proposed to account for the nonmonoexponentiality to date.

On high b diffusion imaging in the human brain: ruminations and experimental insights✩

PubMed Central

Mulkern, Robert V.; Haker, Steven J.; Maier, Stephan E.

2010-01-01

Interest in the manner in which brain tissue signal decays with b factor in diffusion imaging schemes has grown in recent years following the observation that the decay curves depart from purely monoexponential decay behavior. Regardless of the model or fitting function proposed for characterizing sufficiently sampled decay curves (vide infra), the departure from monoexponentiality spells increased tissue characterization potential. The degree to which this potential can be harnessed to improve specificity, sensitivity and spatial localization of diseases in brain, and other tissues, largely remains to be explored. Furthermore, the degree to which currently popular diffusion tensor imaging methods, including visually impressive white matter fiber “tractography” results, have almost completely ignored the nonmonoexponential nature of the basic signal decay with b factor is worthy of communal introspection. Here we limit our attention to a review of the basic experimental features associated with brain water signal diffusion decay curves as measured over extended b-factor ranges, the simple few parameter fitting functions that have been proposed to characterize these decays and the more involved models, e.g.,“ruminations,” which have been proposed to account for the nonmonoexponentiality to date. PMID:19520535

Relative timing: from behaviour to neurons

PubMed Central

Aghdaee, S. Mehdi; Battelli, Lorella; Assad, John A.

2014-01-01

Processing of temporal information is critical to behaviour. Here, we review the phenomenology and mechanism of relative timing, ordinal comparisons between the timing of occurrence of events. Relative timing can be an implicit component of particular brain computations or can be an explicit, conscious judgement. Psychophysical measurements of explicit relative timing have revealed clues about the interaction of sensory signals in the brain as well as in the influence of internal states, such as attention, on those interactions. Evidence from human neurophysiological and functional imaging studies, neuropsychological examination in brain-lesioned patients, and temporary disruptive interventions such as transcranial magnetic stimulation (TMS), point to a role of the parietal cortex in relative timing. Relative timing has traditionally been modelled as a ‘race’ between competing neural signals. We propose an updated race process based on the integration of sensory evidence towards a decision threshold rather than simple signal propagation. The model suggests a general approach for identifying brain regions involved in relative timing, based on looking for trial-by-trial correlations between neural activity and temporal order judgements (TOJs). Finally, we show how the paradigm can be used to reveal signals related to TOJs in parietal cortex of monkeys trained in a TOJ task. PMID:24446505

Lipid transport and human brain development.

PubMed

Betsholtz, Christer

2015-07-01

How the human brain rapidly builds up its lipid content during brain growth and maintains its lipids in adulthood has remained elusive. Two new studies show that inactivating mutations in MFSD2A, known to be expressed specifically at the blood-brain barrier, lead to microcephaly, thereby offering a simple and surprising solution to an old enigma.

The application of brain-based learning principles aided by GeoGebra to improve mathematical representation ability

NASA Astrophysics Data System (ADS)

Priatna, Nanang

2017-08-01

The use of Information and Communication Technology (ICT) in mathematics instruction will help students in building conceptual understanding. One of the software products used in mathematics instruction is GeoGebra. The program enables simple visualization of complex geometric concepts and helps improve students' understanding of geometric concepts. Instruction applying brain-based learning principles is one oriented at the efforts of naturally empowering the brain potentials which enable students to build their own knowledge. One of the goals of mathematics instruction in school is to develop mathematical communication ability. Mathematical representation is regarded as a part of mathematical communication. It is a description, expression, symbolization, or modeling of mathematical ideas/concepts as an attempt of clarifying meanings or seeking for solutions to the problems encountered by students. The research aims to develop a learning model and teaching materials by applying the principles of brain-based learning aided by GeoGebra to improve junior high school students' mathematical representation ability. It adopted a quasi-experimental method with the non-randomized control group pretest-posttest design and the 2x3 factorial model. Based on analysis of the data, it is found that the increase in the mathematical representation ability of students who were treated with mathematics instruction applying the brain-based learning principles aided by GeoGebra was greater than the increase of the students given conventional instruction, both as a whole and based on the categories of students' initial mathematical ability.

A Simple Noise Correction Scheme for Diffusional Kurtosis Imaging

PubMed Central

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

2014-01-01

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

Visual cortical areas of the mouse: comparison of parcellation and network structure with primates

PubMed Central

Laramée, Marie-Eve; Boire, Denis

2015-01-01

Brains have evolved to optimize sensory processing. In primates, complex cognitive tasks must be executed and evolution led to the development of large brains with many cortical areas. Rodents do not accomplish cognitive tasks of the same level of complexity as primates and remain with small brains both in relative and absolute terms. But is a small brain necessarily a simple brain? In this review, several aspects of the visual cortical networks have been compared between rodents and primates. The visual system has been used as a model to evaluate the level of complexity of the cortical circuits at the anatomical and functional levels. The evolutionary constraints are first presented in order to appreciate the rules for the development of the brain and its underlying circuits. The organization of sensory pathways, with their parallel and cross-modal circuits, is also examined. Other features of brain networks, often considered as imposing constraints on the development of underlying circuitry, are also discussed and their effect on the complexity of the mouse and primate brain are inspected. In this review, we discuss the common features of cortical circuits in mice and primates and see how these can be useful in understanding visual processing in these animals. PMID:25620914

Visual cortical areas of the mouse: comparison of parcellation and network structure with primates.

PubMed

Laramée, Marie-Eve; Boire, Denis

2014-01-01

Brains have evolved to optimize sensory processing. In primates, complex cognitive tasks must be executed and evolution led to the development of large brains with many cortical areas. Rodents do not accomplish cognitive tasks of the same level of complexity as primates and remain with small brains both in relative and absolute terms. But is a small brain necessarily a simple brain? In this review, several aspects of the visual cortical networks have been compared between rodents and primates. The visual system has been used as a model to evaluate the level of complexity of the cortical circuits at the anatomical and functional levels. The evolutionary constraints are first presented in order to appreciate the rules for the development of the brain and its underlying circuits. The organization of sensory pathways, with their parallel and cross-modal circuits, is also examined. Other features of brain networks, often considered as imposing constraints on the development of underlying circuitry, are also discussed and their effect on the complexity of the mouse and primate brain are inspected. In this review, we discuss the common features of cortical circuits in mice and primates and see how these can be useful in understanding visual processing in these animals.

The Role of Neuromodulators in Cortical Plasticity. A Computational Perspective

PubMed Central

Pedrosa, Victor; Clopath, Claudia

2017-01-01

Neuromodulators play a ubiquitous role across the brain in regulating plasticity. With recent advances in experimental techniques, it is possible to study the effects of diverse neuromodulatory states in specific brain regions. Neuromodulators are thought to impact plasticity predominantly through two mechanisms: the gating of plasticity and the upregulation of neuronal activity. However, the consequences of these mechanisms are poorly understood and there is a need for both experimental and theoretical exploration. Here we illustrate how neuromodulatory state affects cortical plasticity through these two mechanisms. First, we explore the ability of neuromodulators to gate plasticity by reshaping the learning window for spike-timing-dependent plasticity. Using a simple computational model, we implement four different learning rules and demonstrate their effects on receptive field plasticity. We then compare the neuromodulatory effects of upregulating learning rate versus the effects of upregulating neuronal activity. We find that these seemingly similar mechanisms do not yield the same outcome: upregulating neuronal activity can lead to either a broadening or a sharpening of receptive field tuning, whereas upregulating learning rate only intensifies the sharpening of receptive field tuning. This simple model demonstrates the need for further exploration of the rich landscape of neuromodulator-mediated plasticity. Future experiments, coupled with biologically detailed computational models, will elucidate the diversity of mechanisms by which neuromodulatory state regulates cortical plasticity. PMID:28119596

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.

Bioreactivity: Studies on a Simple Brain Stem Reflex in Behaving Animals

DTIC Science & Technology

1990-08-10

problem in attempting to understand complex physiological processes, such as brain neuromodulation , or complex behavioral processes, such as arousal...containing only one synapse in brain, and receives dense inputs from two neurochemical systems important in neuromodulation and arousal. Initial

Bioreactivity: Studies on a Simple Brain Stem Reflex in Behaving Animals

DTIC Science & Technology

1990-01-04

attempting to understand complex physiological processes, such as brain neuromodulation , or complex behavioral processes, such as arousal, is finding a...one synapse in brain, and receives dense inputs from two neurochemical systems important in neuromodulation and arousal. Initial pharmacologic studies

A CCA+ICA based model for multi-task brain imaging data fusion and its application to schizophrenia.

PubMed

Sui, Jing; Adali, Tülay; Pearlson, Godfrey; Yang, Honghui; Sponheim, Scott R; White, Tonya; Calhoun, Vince D

2010-05-15

Collection of multiple-task brain imaging data from the same subject has now become common practice in medical imaging studies. In this paper, we propose a simple yet effective model, "CCA+ICA", as a powerful tool for multi-task data fusion. This joint blind source separation (BSS) model takes advantage of two multivariate methods: canonical correlation analysis and independent component analysis, to achieve both high estimation accuracy and to provide the correct connection between two datasets in which sources can have either common or distinct between-dataset correlation. In both simulated and real fMRI applications, we compare the proposed scheme with other joint BSS models and examine the different modeling assumptions. The contrast images of two tasks: sensorimotor (SM) and Sternberg working memory (SB), derived from a general linear model (GLM), were chosen to contribute real multi-task fMRI data, both of which were collected from 50 schizophrenia patients and 50 healthy controls. When examining the relationship with duration of illness, CCA+ICA revealed a significant negative correlation with temporal lobe activation. Furthermore, CCA+ICA located sensorimotor cortex as the group-discriminative regions for both tasks and identified the superior temporal gyrus in SM and prefrontal cortex in SB as task-specific group-discriminative brain networks. In summary, we compared the new approach to some competitive methods with different assumptions, and found consistent results regarding each of their hypotheses on connecting the two tasks. Such an approach fills a gap in existing multivariate methods for identifying biomarkers from brain imaging data.

Quantitative measurement of intact alpha-synuclein proteoforms from post-mortem control and Parkinson's disease brain tissue by intact protein mass spectrometry.

PubMed

Kellie, John F; Higgs, Richard E; Ryder, John W; Major, Anthony; Beach, Thomas G; Adler, Charles H; Merchant, Kalpana; Knierman, Michael D

2014-07-23

A robust top down proteomics method is presented for profiling alpha-synuclein species from autopsied human frontal cortex brain tissue from Parkinson's cases and controls. The method was used to test the hypothesis that pathology associated brain tissue will have a different profile of post-translationally modified alpha-synuclein than the control samples. Validation of the sample processing steps, mass spectrometry based measurements, and data processing steps were performed. The intact protein quantitation method features extraction and integration of m/z data from each charge state of a detected alpha-synuclein species and fitting of the data to a simple linear model which accounts for concentration and charge state variability. The quantitation method was validated with serial dilutions of intact protein standards. Using the method on the human brain samples, several previously unreported modifications in alpha-synuclein were identified. Low levels of phosphorylated alpha synuclein were detected in brain tissue fractions enriched for Lewy body pathology and were marginally significant between PD cases and controls (p = 0.03).

Influence of the implanted pulse generator as reference electrode in finite element model of monopolar deep brain stimulation.

PubMed

Walckiers, Grégoire; Fuchs, Benjamin; Thiran, Jean-Philippe; Mosig, Juan R; Pollo, Claudio

2010-01-30

Electrical deep brain stimulation (DBS) is an efficient method to treat movement disorders. Many models of DBS, based mostly on finite elements, have recently been proposed to better understand the interaction between the electrical stimulation and the brain tissues. In monopolar DBS, clinically widely used, the implanted pulse generator (IPG) is used as reference electrode (RE). In this paper, the influence of the RE model of monopolar DBS is investigated. For that purpose, a finite element model of the full electric loop including the head, the neck and the superior chest is used. Head, neck and superior chest are made of simple structures such as parallelepipeds and cylinders. The tissues surrounding the electrode are accurately modelled from data provided by the diffusion tensor magnetic resonance imaging (DT-MRI). Three different configurations of RE are compared with a commonly used model of reduced size. The electrical impedance seen by the DBS system and the potential distribution are computed for each model. Moreover, axons are modelled to compute the area of tissue activated by stimulation. Results show that these indicators are influenced by the surface and position of the RE. The use of a RE model corresponding to the implanted device rather than the usually simplified model leads to an increase of the system impedance (+48%) and a reduction of the area of activated tissue (-15%). (c) 2009 Elsevier B.V. All rights reserved.

Reducing Brain Signal Noise in the Prediction of Economic Choices: A Case Study in Neuroeconomics

PubMed Central

Sundararajan, Raanju R.; Palma, Marco A.; Pourahmadi, Mohsen

2017-01-01

In order to reduce the noise of brain signals, neuroeconomic experiments typically aggregate data from hundreds of trials collected from a few individuals. This contrasts with the principle of simple and controlled designs in experimental and behavioral economics. We use a frequency domain variant of the stationary subspace analysis (SSA) technique, denoted as DSSA, to filter out the noise (nonstationary sources) in EEG brain signals. The nonstationary sources in the brain signal are associated with variations in the mental state that are unrelated to the experimental task. DSSA is a powerful tool for reducing the number of trials needed from each participant in neuroeconomic experiments and also for improving the prediction performance of an economic choice task. For a single trial, when DSSA is used as a noise reduction technique, the prediction model in a food snack choice experiment has an increase in overall accuracy by around 10% and in sensitivity and specificity by around 20% and in AUC by around 30%, respectively. PMID:29311784

Reducing Brain Signal Noise in the Prediction of Economic Choices: A Case Study in Neuroeconomics.

PubMed

Sundararajan, Raanju R; Palma, Marco A; Pourahmadi, Mohsen

2017-01-01

In order to reduce the noise of brain signals, neuroeconomic experiments typically aggregate data from hundreds of trials collected from a few individuals. This contrasts with the principle of simple and controlled designs in experimental and behavioral economics. We use a frequency domain variant of the stationary subspace analysis (SSA) technique, denoted as DSSA, to filter out the noise (nonstationary sources) in EEG brain signals. The nonstationary sources in the brain signal are associated with variations in the mental state that are unrelated to the experimental task. DSSA is a powerful tool for reducing the number of trials needed from each participant in neuroeconomic experiments and also for improving the prediction performance of an economic choice task. For a single trial, when DSSA is used as a noise reduction technique, the prediction model in a food snack choice experiment has an increase in overall accuracy by around 10% and in sensitivity and specificity by around 20% and in AUC by around 30%, respectively.

A neural computational model for animal's time-to-collision estimation.

PubMed

Wang, Ling; Yao, Dezhong

2013-04-17

The time-to-collision (TTC) is the time elapsed before a looming object hits the subject. An accurate estimation of TTC plays a critical role in the survival of animals in nature and acts as an important factor in artificial intelligence systems that depend on judging and avoiding potential dangers. The theoretic formula for TTC is 1/τ≈θ'/sin θ, where θ and θ' are the visual angle and its variation, respectively, and the widely used approximation computational model is θ'/θ. However, both of these measures are too complex to be implemented by a biological neuronal model. We propose a new simple computational model: 1/τ≈Mθ-P/(θ+Q)+N, where M, P, Q, and N are constants that depend on a predefined visual angle. This model, weighted summation of visual angle model (WSVAM), can achieve perfect implementation through a widely accepted biological neuronal model. WSVAM has additional merits, including a natural minimum consumption and simplicity. Thus, it yields a precise and neuronal-implemented estimation for TTC, which provides a simple and convenient implementation for artificial vision, and represents a potential visual brain mechanism.

Toward an operational model of decision making, emotional regulation, and mental health impact.

PubMed

Collura, Thomas Francis; Zalaquett, Ronald P; Bonnstetter, Carlos Joyce; Chatters, Seria J

2014-01-01

Current brain research increasingly reveals the underlying mechanisms and processes of human behavior, cognition, and emotion. In addition to being of interest to a wide range of scientists, educators, and professionals, as well as laypeople, brain-based models are of particular value in a clinical setting. Psychiatrists, psychologists, counselors, and other mental health professionals are in need of operational models that integrate recent findings in the physical, cognitive, and emotional domains, and offer a common language for interdisciplinary understanding and communication. Based on individual traits, predispositions, and responses to stimuli, we can begin to identify emotional and behavioral pathways and mental processing patterns. The purpose of this article is to present a brain-path activation model to understand individual differences in decision making and psychopathology. The first section discusses the role of frontal lobe electroencephalography (EEG) asymmetry, summarizes state- and trait-based models of decision making, and provides a more complex analysis that supplements the traditional simple left-right brain model. Key components of the new model are the introduction of right hemisphere parallel and left hemisphere serial scanning in rendering decisions, and the proposition of pathways that incorporate both past experiences as well as future implications into the decision process. Main attributes of each decision-making mechanism are provided. The second section applies the model within the realm of clinical mental health as a tool to understand specific human behavior and pathology. Applications include general and chronic anxiety, depression, paranoia, risk taking, and the pathways employed when well-functioning operational integration is observed. Finally, specific applications such as meditation and mindfulness are offered to facilitate positive functioning.

125 Brain Games for Babies: Simple Games To Promote Early Brain Development.

ERIC Educational Resources Information Center

Silberg, Jackie

Scientists believe that the stimulation that infants and young children receive determines which synapses form in the brain. This book presents 125 games for infants from birth to 12 months and is designed to nurture brain development. The book is organized chronologically in 3-month increments. Each game description includes information from…

Skull and cerebrospinal fluid effects on microwave radiation propagation in human brain

NASA Astrophysics Data System (ADS)

Ansari, M. A.; Zarei, M.; Akhlaghipour, N.; Niknam, A. R.

2017-12-01

The determination of microwave absorption distribution in the human brain is necessary for the detection of brain tumors using thermo-acoustic imaging and for removing them using hyperthermia treatment. In contrast to ionizing radiation, hyperthermia treatment can be applied to remove tumors inside the brain without the concern of including secondary malignancies, which typically form from the neuronal cells of the septum pellucidum. The aim of this study is to determine the microwave absorption distribution in an adult human brain and to study the effects of skull and cerebrospinal fluid on the propagation of microwave radiation inside the brain. To this end, we simulate the microwave absorption distribution in a realistic adult brain model (Colin 27) using the mesh-based Monte Carlo (MMC) method. This is because in spite of there being other numerical methods, the MMC does not require a large memory, even for complicated geometries, and its algorithm is simple and easy to implement with low computational cost. The brain model is constructed using high-resolution (1 mm isotropic voxel) and low noise magnetic resonance imaging (MRI) scans and its volume contains 181×217×181 voxels, covering the brain completely. Using the MMC method, the radiative transport equation is solved and the absorbed microwave energy distribution in different brain regions is obtained without any fracture or anomaly. The simulation results show that the skull and cerebrospinal fluid guide the microwave radiation and suppress its penetration through deep brain compartments as a shielding factor. These results reveal that the MMC can be used to predict the amount of required energy to increase the temperature inside the tumour during hyperthermia treatment. Our results also show why a deep tumour inside an adult human brain cannot be efficiently treated using hyperthermia treatment. Finally, the accuracy of the presented numerical method is verified using the signal flow graph technique.

Visual task performance in the blind with the BrainPort V100 Vision Aid.

PubMed

Stronks, H Christiaan; Mitchell, Ellen B; Nau, Amy C; Barnes, Nick

2016-10-01

The BrainPort® V100 Vision Aid is a non-invasive assistive device for the blind based on sensory substitution. The device translates camera images into electrotactile stimuli delivered to the tongue. The BrainPort has recently received the CE mark and FDA approval and it is currently marketed to augment, rather than replace, the traditional assistive technologies such as the white cane or guide dog. Areas covered: In this work, we will review the functional studies performed to date with the BrainPort and we will highlight the critical factors that determine device performance, including the technology behind the BrainPort, the impediments to assessing device performance, and the impact of device training and rehabilitation. Expert commentary: The BrainPort enables blind people to perceive light, identify simple objects, recognize short words, localize simple objects, and detect motion and orientation of objects. To achieve this, proper rehabilitation and training regimes are crucial.

Dynamics of large-scale brain activity in normal arousal states and epileptic seizures

NASA Astrophysics Data System (ADS)

Robinson, P. A.; Rennie, C. J.; Rowe, D. L.

2002-04-01

Links between electroencephalograms (EEGs) and underlying aspects of neurophysiology and anatomy are poorly understood. Here a nonlinear continuum model of large-scale brain electrical activity is used to analyze arousal states and their stability and nonlinear dynamics for physiologically realistic parameters. A simple ordered arousal sequence in a reduced parameter space is inferred and found to be consistent with experimentally determined parameters of waking states. Instabilities arise at spectral peaks of the major clinically observed EEG rhythms-mainly slow wave, delta, theta, alpha, and sleep spindle-with each instability zone lying near its most common experimental precursor arousal states in the reduced space. Theta, alpha, and spindle instabilities evolve toward low-dimensional nonlinear limit cycles that correspond closely to EEGs of petit mal seizures for theta instability, and grand mal seizures for the other types. Nonlinear stimulus-induced entrainment and seizures are also seen, EEG spectra and potentials evoked by stimuli are reproduced, and numerous other points of experimental agreement are found. Inverse modeling enables physiological parameters underlying observed EEGs to be determined by a new, noninvasive route. This model thus provides a single, powerful framework for quantitative understanding of a wide variety of brain phenomena.

Development of a computational model on the neural activity patterns of a visual working memory in a hierarchical feedforward Network

NASA Astrophysics Data System (ADS)

An, Soyoung; Choi, Woochul; Paik, Se-Bum

2015-11-01

Understanding the mechanism of information processing in the human brain remains a unique challenge because the nonlinear interactions between the neurons in the network are extremely complex and because controlling every relevant parameter during an experiment is difficult. Therefore, a simulation using simplified computational models may be an effective approach. In the present study, we developed a general model of neural networks that can simulate nonlinear activity patterns in the hierarchical structure of a neural network system. To test our model, we first examined whether our simulation could match the previously-observed nonlinear features of neural activity patterns. Next, we performed a psychophysics experiment for a simple visual working memory task to evaluate whether the model could predict the performance of human subjects. Our studies show that the model is capable of reproducing the relationship between memory load and performance and may contribute, in part, to our understanding of how the structure of neural circuits can determine the nonlinear neural activity patterns in the human brain.

Measurement of Pressure Responses in a Physical Model of a Human Head with High Shape Fidelity Based on Ct/mri Data

NASA Astrophysics Data System (ADS)

Miyazaki, Yusuke; Tachiya, Hiroshi; Anata, Kenji; Hojo, Akihiro

This study discusses a head injury mechanism in case of a human head subjected to impact, from results of impact experiments by using a physical model of a human head with high-shape fidelity. The physical model was constructed by using rapid prototyping technology from the three-dimensional CAD data, which obtained from CT/MRI images of a subject's head. As results of the experiments, positive pressure responses occurred at the impacted site, whereas negative pressure responses occurred at opposite the impacted site. Moreover, the absolute maximum value of pressure occurring at the frontal region of the intracranial space of the head model resulted in same or higher than that at the occipital site in each case that the impact force was imposed on frontal or occipital region. This result has not been showed in other study using simple shape physical models. And, the result corresponds with clinical evidences that brain contusion mainly occurs at the frontal part in each impact direction. Thus, physical model with accurate skull shape is needed to clarify the mechanism of brain contusion.

Emerging category representation in the visual forebrain hierarchy of pigeons (Columba livia).

PubMed

Azizi, Amir Hossein; Pusch, Roland; Koenen, Charlotte; Klatt, Sebastian; Bröcker, Franziska; Thiele, Samuel; Kellermann, Janosch; Güntürkün, Onur; Cheng, Sen

2018-06-06

Recognizing and categorizing visual stimuli are cognitive functions vital for survival, and an important feature of visual systems in primates as well as in birds. Visual stimuli are processed along the ventral visual pathway. At every stage in the hierarchy, neurons respond selectively to more complex features, transforming the population representation of the stimuli. It is therefore easier to read-out category information in higher visual areas. While explicit category representations have been observed in the primate brain, less is known on equivalent processes in the avian brain. Even though their brain anatomies are radically different, it has been hypothesized that visual object representations are comparable across mammals and birds. In the present study, we investigated category representations in the pigeon visual forebrain using recordings from single cells responding to photographs of real-world objects. Using a linear classifier, we found that the population activity in the visual associative area mesopallium ventrolaterale (MVL) distinguishes between animate and inanimate objects, although this distinction is not required by the task. By contrast, a population of cells in the entopallium, a region that is lower in the hierarchy of visual areas and that is related to the primate extrastriate cortex, lacked this information. A model that pools responses of simple cells, which function as edge detectors, can account for the animate vs. inanimate categorization in the MVL, but performance in the model is based on different features than in MVL. Therefore, processing in MVL cells is very likely more abstract than simple computations on the output of edge detectors. Copyright © 2018. Published by Elsevier B.V.

Joint probabilities and quantum cognition

NASA Astrophysics Data System (ADS)

de Barros, J. Acacio

2012-12-01

In this paper we discuss the existence of joint probability distributions for quantumlike response computations in the brain. We do so by focusing on a contextual neural-oscillator model shown to reproduce the main features of behavioral stimulus-response theory. We then exhibit a simple example of contextual random variables not having a joint probability distribution, and describe how such variables can be obtained from neural oscillators, but not from a quantum observable algebra.

Self-identification with another person's face: the time relevant role of multimodal brain areas in the enfacement illusion.

PubMed

Bufalari, Ilaria; Porciello, Giuseppina; Sperduti, Marco; Minio-Paluello, Ilaria

2015-04-01

The illusory subjective experience of looking at one's own face while in fact looking at another person's face can surprisingly be induced by simple synchronized visuotactile stimulation of the two faces. A recent study (Apps MA, Tajadura-Jiménez A, Sereno M, Blanke O, Tsakiris M. Cereb Cortex. First published August 20, 2013; doi:10.1093/cercor/bht199) investigated for the first time the role of visual unimodal and temporoparietal multimodal brain areas in the enfacement illusion and suggested a model in which multisensory mechanisms are crucial to construct and update self-face representation. Copyright © 2015 the American Physiological Society.

Improved Targeting Through Collaborative Decision-Making and Brain Computer Interfaces

NASA Technical Reports Server (NTRS)

Stoica, Adrian; Barrero, David F.; McDonald-Maier, Klaus

2013-01-01

This paper reports a first step toward a brain-computer interface (BCI) for collaborative targeting. Specifically, we explore, from a broad perspective, how the collaboration of a group of people can increase the performance on a simple target identification task. To this end, we requested a group of people to identify the location and color of a sequence of targets appearing on the screen and measured the time and accuracy of the response. The individual results are compared to a collective identification result determined by simple majority voting, with random choice in case of drawn. The results are promising, as the identification becomes significantly more reliable even with this simple voting and a small number of people (either odd or even number) involved in the decision. In addition, the paper briefly analyzes the role of brain-computer interfaces in collaborative targeting, extending the targeting task by using a BCI instead of a mechanical response.

People Control Their Addictions: No matter how much the "chronic" brain disease model of addiction indicates otherwise, we know that people can quit addictions - with special reference to harm reduction and mindfulness.

PubMed

Peele, Stanton

2016-12-01

The world, led by the United States, is hell bent on establishing the absence of choice in addiction, as expressed by the defining statement that addiction is a " chronic relapsing brain disease" (my emphasis). The figure most associated with this model, the director of the American National Institute on Drug Abuse, Nora Volkow, claims that addiction vitiates free will through its effects on the brain. In reality, while by no means a simple task, people regularly quit their substance addictions, often by moderating their consumption, usually through mindfulness-mediated processes (Peele, 2007). Ironically, the brain disease model's ascendance in the U.S. corresponds with epidemic rises in opiate addiction, both painkillers (Brady et al., 2016) and heroin (CDC, n.d.), as well as heroin, painkiller, and tranquilizer poisoning deaths (Rudd et al., 2016). More to the point, the conceptual and treatment goal of eliminating choice in addiction and recovery is not only futile, but iatrogenic. Indeed, the National Institute on Alcohol Abuse and Alcoholism's epidemiological surveys, while finding natural recovery for both drug and alcohol disorders to be typical, has found a decline in natural recovery rates (Dawson et al., 2005) and a sharp increase in AUDs (Grant et al., 2015).

Dynamic functional brain networks involved in simple visual discrimination learning.

PubMed

Fidalgo, Camino; Conejo, Nélida María; González-Pardo, Héctor; Arias, Jorge Luis

2014-10-01

Visual discrimination tasks have been widely used to evaluate many types of learning and memory processes. However, little is known about the brain regions involved at different stages of visual discrimination learning. We used cytochrome c oxidase histochemistry to evaluate changes in regional brain oxidative metabolism during visual discrimination learning in a water-T maze at different time points during training. As compared with control groups, the results of the present study reveal the gradual activation of cortical (prefrontal and temporal cortices) and subcortical brain regions (including the striatum and the hippocampus) associated to the mastery of a simple visual discrimination task. On the other hand, the brain regions involved and their functional interactions changed progressively over days of training. Regions associated with novelty, emotion, visuo-spatial orientation and motor aspects of the behavioral task seem to be relevant during the earlier phase of training, whereas a brain network comprising the prefrontal cortex was found along the whole learning process. This study highlights the relevance of functional interactions among brain regions to investigate learning and memory processes. Copyright © 2014 Elsevier Inc. All rights reserved.

The coupling of cerebral blood flow and oxygen metabolism with brain activation is similar for simple and complex stimuli in human primary visual cortex.

PubMed

Griffeth, Valerie E M; Simon, Aaron B; Buxton, Richard B

2015-01-01

Quantitative functional MRI (fMRI) experiments to measure blood flow and oxygen metabolism coupling in the brain typically rely on simple repetitive stimuli. Here we compared such stimuli with a more naturalistic stimulus. Previous work on the primary visual cortex showed that direct attentional modulation evokes a blood flow (CBF) response with a relatively large oxygen metabolism (CMRO2) response in comparison to an unattended stimulus, which evokes a much smaller metabolic response relative to the flow response. We hypothesized that a similar effect would be associated with a more engaging stimulus, and tested this by measuring the primary human visual cortex response to two contrast levels of a radial flickering checkerboard in comparison to the response to free viewing of brief movie clips. We did not find a significant difference in the blood flow-metabolism coupling (n=%ΔCBF/%ΔCMRO2) between the movie stimulus and the flickering checkerboards employing two different analysis methods: a standard analysis using the Davis model and a new analysis using a heuristic model dependent only on measured quantities. This finding suggests that in the primary visual cortex a naturalistic stimulus (in comparison to a simple repetitive stimulus) is either not sufficient to provoke a change in flow-metabolism coupling by attentional modulation as hypothesized, that the experimental design disrupted the cognitive processes underlying the response to a more natural stimulus, or that the technique used is not sensitive enough to detect a small difference. Copyright © 2014 Elsevier Inc. All rights reserved.

A simple water-immersion condenser for imaging living brain slices on an inverted microscope.

PubMed

Prusky, G T

1997-09-05

Due to some physical limitations of conventional condensers, inverted compound microscopes are not optimally suited for imaging living brain slices with transmitted light. Herein is described a simple device that converts an inverted microscope into an effective tool for this application by utilizing an objective as a condenser. The device is mounted on a microscope in place of the condenser, is threaded to accept a water immersion objective, and has a slot for a differential interference contrast (DIC) slider. When combined with infrared video techniques, this device allows an inverted microscope to effectively image living cells within thick brain slices in an open perfusion chamber.

FDTD analysis of a noninvasive hyperthermia system for brain tumors

PubMed Central

2012-01-01

Background Hyperthermia is considered one of the new therapeutic modalities for cancer treatment and is based on the difference in thermal sensitivity between healthy tissues and tumors. During hyperthermia treatment, the temperature of the tumor is raised to 40–45°C for a definite period resulting in the destruction of cancer cells. This paper investigates design, modeling and simulation of a new non-invasive hyperthermia applicator system capable of effectively heating deep seated as well as superficial brain tumors using inexpensive, simple, and easy to fabricate components without harming surrounding healthy brain tissues. Methods The proposed hyperthermia applicator system is composed of an air filled partial half ellipsoidal chamber, a patch antenna, and a head model with an embedded tumor at an arbitrary location. The irradiating antenna is placed at one of the foci of the hyperthermia chamber while the center of the brain tumor is placed at the other focus. The finite difference time domain (FDTD) method is used to compute both the SAR patterns and the temperature distribution in three different head models due to two different patch antennas at a frequency of 915 MHz. Results The obtained results suggest that by using the proposed noninvasive hyperthermia system it is feasible to achieve sufficient and focused energy deposition and temperature rise to therapeutic values in deep seated as well as superficial brain tumors without harming surrounding healthy tissue. Conclusions The proposed noninvasive hyperthermia system proved suitable for raising the temperature in tumors embedded in the brain to therapeutic values by carefully selecting the systems components. The operator of the system only needs to place the center of the brain tumor at a pre-specified location and excite the antenna at a single frequency of 915 MHz. Our study may provide a basis for a clinical applicator prototype capable of heating brain tumors. PMID:22891953

Compartment models of the diffusion MR signal in brain white matter: a taxonomy and comparison.

PubMed

Panagiotaki, Eleftheria; Schneider, Torben; Siow, Bernard; Hall, Matt G; Lythgoe, Mark F; Alexander, Daniel C

2012-02-01

This paper aims to identify the minimum requirements for an accurate model of the diffusion MR signal in white matter of the brain. We construct a taxonomy of multi-compartment models of white matter from combinations of simple models for the intra- and the extra-axonal spaces. We devise a new diffusion MRI protocol that provides measurements with a wide range of imaging parameters for diffusion sensitization both parallel and perpendicular to white matter fibres. We use the protocol to acquire data from two fixed rat brains, which allows us to fit, study and compare the different models. The study examines a total of 47 analytic models, including several well-used models from the literature, which we place within the taxonomy. The results show that models that incorporate intra-axonal restriction, such as ball and stick or CHARMED, generally explain the data better than those that do not, such as the DT or the biexponential models. However, three-compartment models which account for restriction parallel to the axons and incorporate pore size explain the measurements most accurately. The best fit comes from combining a full diffusion tensor (DT) model of the extra-axonal space with a cylindrical intra-axonal component of single radius and a third spherical compartment of non-zero radius. We also measure the stability of the non-zero radius intra-axonal models and find that single radius intra-axonal models are more stable than gamma distributed radii models with similar fitting performance. Copyright © 2011 Elsevier Inc. All rights reserved.

Brain tumor detection and segmentation in a CRF (conditional random fields) framework with pixel-pairwise affinity and superpixel-level features.

PubMed

Wu, Wei; Chen, Albert Y C; Zhao, Liang; Corso, Jason J

2014-03-01

Detection and segmentation of a brain tumor such as glioblastoma multiforme (GBM) in magnetic resonance (MR) images are often challenging due to its intrinsically heterogeneous signal characteristics. A robust segmentation method for brain tumor MRI scans was developed and tested. Simple thresholds and statistical methods are unable to adequately segment the various elements of the GBM, such as local contrast enhancement, necrosis, and edema. Most voxel-based methods cannot achieve satisfactory results in larger data sets, and the methods based on generative or discriminative models have intrinsic limitations during application, such as small sample set learning and transfer. A new method was developed to overcome these challenges. Multimodal MR images are segmented into superpixels using algorithms to alleviate the sampling issue and to improve the sample representativeness. Next, features were extracted from the superpixels using multi-level Gabor wavelet filters. Based on the features, a support vector machine (SVM) model and an affinity metric model for tumors were trained to overcome the limitations of previous generative models. Based on the output of the SVM and spatial affinity models, conditional random fields theory was applied to segment the tumor in a maximum a posteriori fashion given the smoothness prior defined by our affinity model. Finally, labeling noise was removed using "structural knowledge" such as the symmetrical and continuous characteristics of the tumor in spatial domain. The system was evaluated with 20 GBM cases and the BraTS challenge data set. Dice coefficients were computed, and the results were highly consistent with those reported by Zikic et al. (MICCAI 2012, Lecture notes in computer science. vol 7512, pp 369-376, 2012). A brain tumor segmentation method using model-aware affinity demonstrates comparable performance with other state-of-the art algorithms.

Predicting beneficial effects of atomoxetine and citalopram on response inhibition in Parkinson's disease with clinical and neuroimaging measures

PubMed Central

Ye, Zheng; Rae, Charlotte L.; Nombela, Cristina; Ham, Timothy; Rittman, Timothy; Jones, Peter Simon; Rodríguez, Patricia Vázquez; Coyle‐Gilchrist, Ian; Regenthal, Ralf; Altena, Ellemarije; Housden, Charlotte R.; Maxwell, Helen; Sahakian, Barbara J.; Barker, Roger A.; Robbins, Trevor W.

2016-01-01

Abstract Recent studies indicate that selective noradrenergic (atomoxetine) and serotonergic (citalopram) reuptake inhibitors may improve response inhibition in selected patients with Parkinson's disease, restoring behavioral performance and brain activity. We reassessed the behavioral efficacy of these drugs in a larger cohort and developed predictive models to identify patient responders. We used a double‐blind randomized three‐way crossover design to investigate stopping efficiency in 34 patients with idiopathic Parkinson's disease after 40 mg atomoxetine, 30 mg citalopram, or placebo. Diffusion‐weighted and functional imaging measured microstructural properties and regional brain activations, respectively. We confirmed that Parkinson's disease impairs response inhibition. Overall, drug effects on response inhibition varied substantially across patients at both behavioral and brain activity levels. We therefore built binary classifiers with leave‐one‐out cross‐validation (LOOCV) to predict patients’ responses in terms of improved stopping efficiency. We identified two optimal models: (1) a “clinical” model that predicted the response of an individual patient with 77–79% accuracy for atomoxetine and citalopram, using clinically available information including age, cognitive status, and levodopa equivalent dose, and a simple diffusion‐weighted imaging scan; and (2) a “mechanistic” model that explained the behavioral response with 85% accuracy for each drug, using drug‐induced changes of brain activations in the striatum and presupplementary motor area from functional imaging. These data support growing evidence for the role of noradrenaline and serotonin in inhibitory control. Although noradrenergic and serotonergic drugs have highly variable effects in patients with Parkinson's disease, the individual patient's response to each drug can be predicted using a pattern of clinical and neuroimaging features. Hum Brain Mapp 37:1026–1037, 2016. © 2016 Wiley Periodicals, Inc. PMID:26757216

Affective Brain-Computer Interfaces As Enabling Technology for Responsive Psychiatric Stimulation

PubMed Central

Widge, Alik S.; Dougherty, Darin D.; Moritz, Chet T.

2014-01-01

There is a pressing clinical need for responsive neurostimulators, which sense a patient’s brain activity and deliver targeted electrical stimulation to suppress unwanted symptoms. This is particularly true in psychiatric illness, where symptoms can fluctuate throughout the day. Affective BCIs, which decode emotional experience from neural activity, are a candidate control signal for responsive stimulators targeting the limbic circuit. Present affective decoders, however, cannot yet distinguish pathologic from healthy emotional extremes. Indiscriminate stimulus delivery would reduce quality of life and may be actively harmful. We argue that the key to overcoming this limitation is to specifically decode volition, in particular the patient’s intention to experience emotional regulation. Those emotion-regulation signals already exist in prefrontal cortex (PFC), and could be extracted with relatively simple BCI algorithms. We describe preliminary data from an animal model of PFC-controlled limbic brain stimulation and discuss next steps for pre-clinical testing and possible translation. PMID:25580443

A neural network model of memory and higher cognitive functions.

PubMed

Vogel, David D

2005-01-01

I first describe a neural network model of associative memory in a small region of the brain. The model depends, unconventionally, on disinhibition of inhibitory links between excitatory neurons rather than long-term potentiation (LTP) of excitatory projections. The model may be shown to have advantages over traditional neural network models both in terms of information storage capacity and biological plausibility. The learning and recall algorithms are independent of network architecture, and require no thresholds or finely graded synaptic strengths. Several copies of this local network are then connected by means of many, weak, reciprocal, excitatory projections that allow one region to control the recall of information in another to produce behaviors analogous to serial memory, classical and operant conditioning, secondary reinforcement, refabrication of memory, and fabrication of possible future events. The network distinguishes between perceived and recalled events, and can predicate its response on the absence as well as the presence of particular stimuli. Some of these behaviors are achieved in ways that seem to provide instances of self-awareness and imagination, suggesting that consciousness may emerge as an epiphenomenon in simple brains.

Improved color constancy in honey bees enabled by parallel visual projections from dorsal ocelli.

PubMed

Garcia, Jair E; Hung, Yu-Shan; Greentree, Andrew D; Rosa, Marcello G P; Endler, John A; Dyer, Adrian G

2017-07-18

How can a pollinator, like the honey bee, perceive the same colors on visited flowers, despite continuous and rapid changes in ambient illumination and background color? A hundred years ago, von Kries proposed an elegant solution to this problem, color constancy, which is currently incorporated in many imaging and technological applications. However, empirical evidence on how this method can operate on animal brains remains tenuous. Our mathematical modeling proposes that the observed spectral tuning of simple ocellar photoreceptors in the honey bee allows for the necessary input for an optimal color constancy solution to most natural light environments. The model is fully supported by our detailed description of a neural pathway allowing for the integration of signals originating from the ocellar photoreceptors to the information processing regions in the bee brain. These findings reveal a neural implementation to the classic color constancy problem that can be easily translated into artificial color imaging systems.

RM-SORN: a reward-modulated self-organizing recurrent neural network.

PubMed

Aswolinskiy, Witali; Pipa, Gordon

2015-01-01

Neural plasticity plays an important role in learning and memory. Reward-modulation of plasticity offers an explanation for the ability of the brain to adapt its neural activity to achieve a rewarded goal. Here, we define a neural network model that learns through the interaction of Intrinsic Plasticity (IP) and reward-modulated Spike-Timing-Dependent Plasticity (STDP). IP enables the network to explore possible output sequences and STDP, modulated by reward, reinforces the creation of the rewarded output sequences. The model is tested on tasks for prediction, recall, non-linear computation, pattern recognition, and sequence generation. It achieves performance comparable to networks trained with supervised learning, while using simple, biologically motivated plasticity rules, and rewarding strategies. The results confirm the importance of investigating the interaction of several plasticity rules in the context of reward-modulated learning and whether reward-modulated self-organization can explain the amazing capabilities of the brain.

Human performance on the temporal bisection task.

PubMed

Kopec, Charles D; Brody, Carlos D

2010-12-01

The perception and processing of temporal information are tasks the brain must continuously perform. These include measuring the duration of stimuli, storing duration information in memory, recalling such memories, and comparing two durations. How the brain accomplishes these tasks, however, is still open for debate. The temporal bisection task, which requires subjects to compare temporal stimuli to durations held in memory, is perfectly suited to address these questions. Here we perform a meta-analysis of human performance on the temporal bisection task collected from 148 experiments spread across 18 independent studies. With this expanded data set we are able to show that human performance on this task contains a number of significant peculiarities, which in total no single model yet proposed has been able to explain. Here we present a simple 2-step decision model that is capable of explaining all the idiosyncrasies seen in the data. Copyright © 2010 Elsevier Inc. All rights reserved.

Prefrontal and parietal activity is modulated by the rule complexity of inductive reasoning and can be predicted by a cognitive model.

PubMed

Jia, Xiuqin; Liang, Peipeng; Shi, Lin; Wang, Defeng; Li, Kuncheng

2015-01-01

In neuroimaging studies, increased task complexity can lead to increased activation in task-specific regions or to activation of additional regions. How the brain adapts to increased rule complexity during inductive reasoning remains unclear. In the current study, three types of problems were created: simple rule induction (i.e., SI, with rule complexity of 1), complex rule induction (i.e., CI, with rule complexity of 2), and perceptual control. Our findings revealed that increased activations accompany increased rule complexity in the right dorsal lateral prefrontal cortex (DLPFC) and medial posterior parietal cortex (precuneus). A cognitive model predicted both the behavioral and brain imaging results. The current findings suggest that neural activity in frontal and parietal regions is modulated by rule complexity, which may shed light on the neural mechanisms of inductive reasoning. Copyright © 2014. Published by Elsevier Ltd.

The point spread function of the human head and its implications for transcranial current stimulation

NASA Astrophysics Data System (ADS)

Dmochowski, Jacek P.; Bikson, Marom; Parra, Lucas C.

2012-10-01

Rational development of transcranial current stimulation (tCS) requires solving the ‘forward problem’: the computation of the electric field distribution in the head resulting from the application of scalp currents. Derivation of forward models has represented a major effort in brain stimulation research, with model complexity ranging from spherical shells to individualized head models based on magnetic resonance imagery. Despite such effort, an easily accessible benchmark head model is greatly needed when individualized modeling is either undesired (to observe general population trends as opposed to individual differences) or unfeasible. Here, we derive a closed-form linear system which relates the applied current to the induced electric potential. It is shown that in the spherical harmonic (Fourier) domain, a simple scalar multiplication relates the current density on the scalp to the electric potential in the brain. Equivalently, the current density in the head follows as the spherical convolution between the scalp current distribution and the point spread function of the head, which we derive. Thus, if one knows the spherical harmonic representation of the scalp current (i.e. the electrode locations and current intensity to be employed), one can easily compute the resulting electric field at any point inside the head. Conversely, one may also readily determine the scalp current distribution required to generate an arbitrary electric field in the brain (the ‘backward problem’ in tCS). We demonstrate the simplicity and utility of the model with a series of characteristic curves which sweep across a variety of stimulation parameters: electrode size, depth of stimulation, head size and anode-cathode separation. Finally, theoretically optimal montages for targeting an infinitesimal point in the brain are shown.

Adjusting to Random Demands of Patient Care: A Predictive Model for Nursing Staff Scheduling at Naval Medical Center San Diego

DTIC Science & Technology

2008-09-01

rich mix of medical services that range from simple ambulatory visits to plastic surgery , neuro- surgery , general surgery , bariatric , ophthalmology...CENTER SAN DIEGO NMCSD is a 266-bed tertiary care facility providing patient services ranging from same day surgery to brain surgery . The hospital...orthopedics, cardiology, thoracic surgery , vascular surgery , transient ischemic attack/cerebro vascular accident (TIA/CVA), OB/GYN, urology, non

Inhibitory motor control based on complex stopping goals relies on the same brain network as simple stopping

PubMed Central

Wessel, Jan R.; Aron, Adam R.

2014-01-01

Much research has modeled action-stopping using the stop-signal task (SST), in which an impending response has to be stopped when an explicit stop-signal occurs. A limitation of the SST is that real-world action-stopping rarely involves explicit stop-signals. Instead, the stopping-system engages when environmental features match more complex stopping goals. For example, when stepping into the street, one monitors path, velocity, size, and types of objects; and only stops if there is a vehicle approaching. Here, we developed a task in which participants compared the visual features of a multidimensional go-stimulus to a complex stopping-template, and stopped their go-response if all features matched the template. We used independent component analysis of EEG data to show that the same motor inhibition brain network that explains action-stopping in the SST also implements motor inhibition in the complex-stopping task. Furthermore, we found that partial feature overlap between go-stimulus and stopping-template lead to motor slowing, which also corresponded with greater stopping-network activity. This shows that the same brain system for action-stopping to explicit stop-signals is recruited to slow or stop behavior when stimuli match a complex stopping goal. The results imply a generalizability of the brain’s network for simple action-stopping to more ecologically valid scenarios. PMID:25270603

Predict or classify: The deceptive role of time-locking in brain signal classification

NASA Astrophysics Data System (ADS)

Rusconi, Marco; Valleriani, Angelo

2016-06-01

Several experimental studies claim to be able to predict the outcome of simple decisions from brain signals measured before subjects are aware of their decision. Often, these studies use multivariate pattern recognition methods with the underlying assumption that the ability to classify the brain signal is equivalent to predict the decision itself. Here we show instead that it is possible to correctly classify a signal even if it does not contain any predictive information about the decision. We first define a simple stochastic model that mimics the random decision process between two equivalent alternatives, and generate a large number of independent trials that contain no choice-predictive information. The trials are first time-locked to the time point of the final event and then classified using standard machine-learning techniques. The resulting classification accuracy is above chance level long before the time point of time-locking. We then analyze the same trials using information theory. We demonstrate that the high classification accuracy is a consequence of time-locking and that its time behavior is simply related to the large relaxation time of the process. We conclude that when time-locking is a crucial step in the analysis of neural activity patterns, both the emergence and the timing of the classification accuracy are affected by structural properties of the network that generates the signal.

Three-Dimensional Blood-Brain Barrier Model for in vitro Studies of Neurovascular Pathology

NASA Astrophysics Data System (ADS)

Cho, Hansang; Seo, Ji Hae; Wong, Keith H. K.; Terasaki, Yasukazu; Park, Joseph; Bong, Kiwan; Arai, Ken; Lo, Eng H.; Irimia, Daniel

2015-10-01

Blood-brain barrier (BBB) pathology leads to neurovascular disorders and is an important target for therapies. However, the study of BBB pathology is difficult in the absence of models that are simple and relevant. In vivo animal models are highly relevant, however they are hampered by complex, multi-cellular interactions that are difficult to decouple. In vitro models of BBB are simpler, however they have limited functionality and relevance to disease processes. To address these limitations, we developed a 3-dimensional (3D) model of BBB on a microfluidic platform. We verified the tightness of the BBB by showing its ability to reduce the leakage of dyes and to block the transmigration of immune cells towards chemoattractants. Moreover, we verified the localization at endothelial cell boundaries of ZO-1 and VE-Cadherin, two components of tight and adherens junctions. To validate the functionality of the BBB model, we probed its disruption by neuro-inflammation mediators and ischemic conditions and measured the protective function of antioxidant and ROCK-inhibitor treatments. Overall, our 3D BBB model provides a robust platform, adequate for detailed functional studies of BBB and for the screening of BBB-targeting drugs in neurological diseases.

Sensory Perception and Aging in Model Systems: From the Outside In

PubMed Central

Linford, Nancy J.; Kuo, Tsung-Han; Chan, Tammy P.; Pletcher, Scott D.

2014-01-01

Sensory systems provide organisms from bacteria to human with the ability to interact with the world. Numerous senses have evolved that allow animals to detect and decode cues from sources in both their external and internal environments. Recent advances in understanding the central mechanisms by which the brains of simple organisms evaluate different cues and initiate behavioral decisions, coupled with observations that sensory manipulations are capable of altering organism lifespan, have opened the door for powerful new research into aging. While direct links between sensory perception and aging have been established only recently, here we discuss these initial discoveries and evaluate the potential for different forms of sensory processing to modulate lifespan across taxa. Harnessing the neurobiology of simple model systems to study the biological impact of sensory experiences will yield insights into the broad influence of sensory perception in mammals and may help uncover new mechanisms of healthy aging. PMID:21756108

Sensory perception and aging in model systems: from the outside in.

PubMed

Linford, Nancy J; Kuo, Tsung-Han; Chan, Tammy P; Pletcher, Scott D

2011-01-01

Sensory systems provide organisms from bacteria to humans with the ability to interact with the world. Numerous senses have evolved that allow animals to detect and decode cues from sources in both their external and internal environments. Recent advances in understanding the central mechanisms by which the brains of simple organisms evaluate different cues and initiate behavioral decisions, coupled with observations that sensory manipulations are capable of altering organismal lifespan, have opened the door for powerful new research into aging. Although direct links between sensory perception and aging have been established only recently, here we discuss these initial discoveries and evaluate the potential for different forms of sensory processing to modulate lifespan across taxa. Harnessing the neurobiology of simple model systems to study the biological impact of sensory experiences will yield insights into the broad influence of sensory perception in mammals and may help uncover new mechanisms of healthy aging.

A simple behavioral test for locomotor function after brain injury in mice.

PubMed

Tabuse, Masanao; Yaguchi, Masae; Ohta, Shigeki; Kawase, Takeshi; Toda, Masahiro

2010-11-01

To establish a simple and reliable test for assessing locomotor function in mice with brain injury, we developed a new method, the rotarod slip test, in which the number of slips of the paralytic hind limb from a rotarod is counted. Brain injuries of different severity were created in adult C57BL/6 mice, by inflicting 1-point, 2-point and 4-point cryo-injuries. These mice were subjected to the rotarod slip test, the accelerating rotarod test and the elevated body swing test (EBST). Histological analyses were performed to assess the severity of the brain damage. Significant and consistent correlations between test scores and severity were observed for the rotarod slip test and the EBST. Only the rotarod slip test detected the mild hindlimb paresis in the acute and sub-acute phase after injury. Our results suggest that the rotarod slip test is the most sensitive and reliable method for assessing locomotor function after brain damage in mice. Copyright © 2010 Elsevier Ltd. All rights reserved.

Assessment of a brain-tumour-specific Patient Concerns Inventory in the neuro-oncology clinic.

PubMed

Rooney, Alasdair G; Netten, Anouk; McNamara, Shanne; Erridge, Sara; Peoples, Sharon; Whittle, Ian; Hacking, Belinda; Grant, Robin

2014-04-01

Brain tumour patients may struggle to express their concerns in the outpatient clinic, creating a physician-focused rather than a shared agenda. We created a simple, practical brain-tumour-specific holistic needs assessment (HNA) tool for use in the neuro-oncology outpatient clinic. We posted the brain tumour Patient Concerns Inventory (PCI) to a consecutive sample of adult brain tumour attendees to a neuro-oncology outpatient clinic. Participants brought the completed PCI to their clinic consultation. Patients and staff provided feedback. Seventy seven patients were eligible and 53 participated (response rate = 68%). The PCI captured many problems absent from general cancer checklists. The five most frequent concerns were fatigue, fear of tumour coming back, memory, concentration, and low mood. Respondents used the PCI to formulate 105 specific questions, usually about the meaning of physical or psychological symptoms. Patients and staff found the PCI to be useful, and satisfaction with the instrument was high. This study demonstrates the clinical utility of the brain tumour PCI in a neuro-oncology clinic. The combination of a brain-tumour-specific concerns checklist and an intervention to focus patient agenda creates a simple and efficient HNA tool.

The Eye of a Mathematical Physicist

NASA Astrophysics Data System (ADS)

Hepp, Klaus

2009-03-01

In this essay we are searching for neural correlates of `doing mathematical physics'. We introduce a toy model of a mathematical physicist, a brain connected with the outside world only by vision and saccadic eye movements and interacting with a computer screen. First, we describe the neuroanatomy of the visuo-saccadic system and Listing's law, which binds saccades and the optics of the eye. Then we explain space-time transformations in the superior colliculus, the performance of a canonical cortical circuit in the frontal eye field and finally the recurrent interaction of both areas, which leads to a coherent percept of space in spite of saccades. This sets the stage in the brain for doing mathematical physics, which is analyzed in simple examples.

How much detail is needed in modeling a transcranial magnetic stimulation figure-8 coil: Measurements and brain simulations

PubMed Central

Mandija, Stefano; Sommer, Iris E. C.; van den Berg, Cornelis A. T.; Neggers, Sebastiaan F. W.

2017-01-01

Background Despite TMS wide adoption, its spatial and temporal patterns of neuronal effects are not well understood. Although progress has been made in predicting induced currents in the brain using realistic finite element models (FEM), there is little consensus on how a magnetic field of a typical TMS coil should be modeled. Empirical validation of such models is limited and subject to several limitations. Methods We evaluate and empirically validate models of a figure-of-eight TMS coil that are commonly used in published modeling studies, of increasing complexity: simple circular coil model; coil with in-plane spiral winding turns; and finally one with stacked spiral winding turns. We will assess the electric fields induced by all 3 coil models in the motor cortex using a computer FEM model. Biot-Savart models of discretized wires were used to approximate the 3 coil models of increasing complexity. We use a tailored MR based phase mapping technique to get a full 3D validation of the incident magnetic field induced in a cylindrical phantom by our TMS coil. FEM based simulations on a meshed 3D brain model consisting of five tissues types were performed, using two orthogonal coil orientations. Results Substantial differences in the induced currents are observed, both theoretically and empirically, between highly idealized coils and coils with correctly modeled spiral winding turns. Thickness of the coil winding turns affect minimally the induced electric field, and it does not influence the predicted activation. Conclusion TMS coil models used in FEM simulations should include in-plane coil geometry in order to make reliable predictions of the incident field. Modeling the in-plane coil geometry is important to correctly simulate the induced electric field and to correctly make reliable predictions of neuronal activation PMID:28640923

Prenatal and pubertal testosterone affect brain lateralization.

PubMed

Beking, T; Geuze, R H; van Faassen, M; Kema, I P; Kreukels, B P C; Groothuis, T G G

2018-02-01

After decades of research, the influence of prenatal testosterone on brain lateralization is still elusive, whereas the influence of pubertal testosterone on functional brain lateralization has not been investigated, although there is increasing evidence that testosterone affects the brain in puberty. We performed a longitudinal study, investigating the relationship between prenatal testosterone concentrations in amniotic fluid, pubertal testosterone concentrations in saliva, and brain lateralization (measured with functional Transcranial Doppler ultrasonography (fTCD)) of the Mental Rotation, Chimeric Faces and Word Generation tasks. Thirty boys and 30 girls participated in this study at the age of 15 years. For boys, we found a significant interaction effect between prenatal and pubertal testosterone on lateralization of Mental Rotation and Chimeric Faces. In the boys with low prenatal testosterone levels, pubertal testosterone was positively related to the strength of lateralization in the right hemisphere, while in the boys with high prenatal testosterone levels, pubertal testosterone was negatively related to the strength of lateralization. For Word Generation, pubertal testosterone was negatively related to the strength of lateralization in the left hemisphere in boys. For girls, we did not find any significant effects, possibly because their pubertal testosterone levels were in many cases below quantification limit. To conclude, prenatal and pubertal testosterone affect lateralization in a task-specific way. Our findings cannot be explained by simple models of prenatal testosterone affecting brain lateralization in a similar way for all tasks. We discuss alternative models involving age dependent effects of testosterone, with a role for androgen receptor distribution and efficiency. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

Resting-State Functional Connectivity Emerges from Structurally and Dynamically Shaped Slow Linear Fluctuations

PubMed Central

Deco, Gustavo; Mantini, Dante; Romani, Gian Luca; Hagmann, Patric; Corbetta, Maurizio

2013-01-01

Brain fluctuations at rest are not random but are structured in spatial patterns of correlated activity across different brain areas. The question of how resting-state functional connectivity (FC) emerges from the brain's anatomical connections has motivated several experimental and computational studies to understand structure–function relationships. However, the mechanistic origin of resting state is obscured by large-scale models' complexity, and a close structure–function relation is still an open problem. Thus, a realistic but simple enough description of relevant brain dynamics is needed. Here, we derived a dynamic mean field model that consistently summarizes the realistic dynamics of a detailed spiking and conductance-based synaptic large-scale network, in which connectivity is constrained by diffusion imaging data from human subjects. The dynamic mean field approximates the ensemble dynamics, whose temporal evolution is dominated by the longest time scale of the system. With this reduction, we demonstrated that FC emerges as structured linear fluctuations around a stable low firing activity state close to destabilization. Moreover, the model can be further and crucially simplified into a set of motion equations for statistical moments, providing a direct analytical link between anatomical structure, neural network dynamics, and FC. Our study suggests that FC arises from noise propagation and dynamical slowing down of fluctuations in an anatomically constrained dynamical system. Altogether, the reduction from spiking models to statistical moments presented here provides a new framework to explicitly understand the building up of FC through neuronal dynamics underpinned by anatomical connections and to drive hypotheses in task-evoked studies and for clinical applications. PMID:23825427

Resting-state functional connectivity emerges from structurally and dynamically shaped slow linear fluctuations.

PubMed

Deco, Gustavo; Ponce-Alvarez, Adrián; Mantini, Dante; Romani, Gian Luca; Hagmann, Patric; Corbetta, Maurizio

2013-07-03

Brain fluctuations at rest are not random but are structured in spatial patterns of correlated activity across different brain areas. The question of how resting-state functional connectivity (FC) emerges from the brain's anatomical connections has motivated several experimental and computational studies to understand structure-function relationships. However, the mechanistic origin of resting state is obscured by large-scale models' complexity, and a close structure-function relation is still an open problem. Thus, a realistic but simple enough description of relevant brain dynamics is needed. Here, we derived a dynamic mean field model that consistently summarizes the realistic dynamics of a detailed spiking and conductance-based synaptic large-scale network, in which connectivity is constrained by diffusion imaging data from human subjects. The dynamic mean field approximates the ensemble dynamics, whose temporal evolution is dominated by the longest time scale of the system. With this reduction, we demonstrated that FC emerges as structured linear fluctuations around a stable low firing activity state close to destabilization. Moreover, the model can be further and crucially simplified into a set of motion equations for statistical moments, providing a direct analytical link between anatomical structure, neural network dynamics, and FC. Our study suggests that FC arises from noise propagation and dynamical slowing down of fluctuations in an anatomically constrained dynamical system. Altogether, the reduction from spiking models to statistical moments presented here provides a new framework to explicitly understand the building up of FC through neuronal dynamics underpinned by anatomical connections and to drive hypotheses in task-evoked studies and for clinical applications.

Towards a real-time interface between a biomimetic model of sensorimotor cortex and a robotic arm

PubMed Central

Dura-Bernal, Salvador; Chadderdon, George L; Neymotin, Samuel A; Francis, Joseph T; Lytton, William W

2015-01-01

Brain-machine interfaces can greatly improve the performance of prosthetics. Utilizing biomimetic neuronal modeling in brain machine interfaces (BMI) offers the possibility of providing naturalistic motor-control algorithms for control of a robotic limb. This will allow finer control of a robot, while also giving us new tools to better understand the brain’s use of electrical signals. However, the biomimetic approach presents challenges in integrating technologies across multiple hardware and software platforms, so that the different components can communicate in real-time. We present the first steps in an ongoing effort to integrate a biomimetic spiking neuronal model of motor learning with a robotic arm. The biomimetic model (BMM) was used to drive a simple kinematic two-joint virtual arm in a motor task requiring trial-and-error convergence on a single target. We utilized the output of this model in real time to drive mirroring motion of a Barrett Technology WAM robotic arm through a user datagram protocol (UDP) interface. The robotic arm sent back information on its joint positions, which was then used by a visualization tool on the remote computer to display a realistic 3D virtual model of the moving robotic arm in real time. This work paves the way towards a full closed-loop biomimetic brain-effector system that can be incorporated in a neural decoder for prosthetic control, to be used as a platform for developing biomimetic learning algorithms for controlling real-time devices. PMID:26709323

Combined computational-experimental approach to predict blood-brain barrier (BBB) permeation based on "green" salting-out thin layer chromatography supported by simple molecular descriptors.

PubMed

Ciura, Krzesimir; Belka, Mariusz; Kawczak, Piotr; Bączek, Tomasz; Markuszewski, Michał J; Nowakowska, Joanna

2017-09-05

The objective of this paper is to build QSRR/QSAR model for predicting the blood-brain barrier (BBB) permeability. The obtained models are based on salting-out thin layer chromatography (SOTLC) constants and calculated molecular descriptors. Among chromatographic methods SOTLC was chosen, since the mobile phases are free of organic solvent. As consequences, there are less toxic, and have lower environmental impact compared to classical reserved phases liquid chromatography (RPLC). During the study three stationary phase silica gel, cellulose plates and neutral aluminum oxide were examined. The model set of solutes presents a wide range of log BB values, containing compounds which cross the BBB readily and molecules poorly distributed to the brain including drugs acting on the nervous system as well as peripheral acting drugs. Additionally, the comparison of three regression models: multiple linear regression (MLR), partial least-squares (PLS) and orthogonal partial least squares (OPLS) were performed. The designed QSRR/QSAR models could be useful to predict BBB of systematically synthesized newly compounds in the drug development pipeline and are attractive alternatives of time-consuming and demanding directed methods for log BB measurement. The study also shown that among several regression techniques, significant differences can be obtained in models performance, measured by R 2 and Q 2 , hence it is strongly suggested to evaluate all available options as MLR, PLS and OPLS. Copyright © 2017 Elsevier B.V. All rights reserved.

Phylostratigraphic Profiles in Zebrafish Uncover Chordate Origins of the Vertebrate Brain

PubMed Central

Šestak, Martin Sebastijan; Domazet-Lošo, Tomislav

2015-01-01

An elaborated tripartite brain is considered one of the important innovations of vertebrates. Other extant chordate groups have a more basic brain organization. For instance, cephalochordates possess a relatively simple brain possibly homologous to the vertebrate forebrain and hindbrain, whereas tunicates display the tripartite organization, but without the specialized brain centers. The difference in anatomical complexity is even more pronounced if one compares chordates with other deuterostomes that have only a diffuse nerve net or alternatively a rather simple central nervous system. To gain a new perspective on the evolutionary roots of the complex vertebrate brain, we made here a phylostratigraphic analysis of gene expression patterns in the developing zebrafish (Danio rerio). The recovered adaptive landscape revealed three important periods in the evolutionary history of the zebrafish brain. The oldest period corresponds to preadaptive events in the first metazoans and the emergence of the nervous system at the metazoan–eumetazoan transition. The origin of chordates marks the next phase, where we found the overall strongest adaptive imprint in almost all analyzed brain regions. This finding supports the idea that the vertebrate brain evolved independently of the brains within the protostome lineage. Finally, at the origin of vertebrates we detected a pronounced signal coming from the dorsal telencephalon, in agreement with classical theories that consider this part of the cerebrum a genuine vertebrate innovation. Taken together, these results reveal a stepwise adaptive history of the vertebrate brain where most of its extant organization was already present in the chordate ancestor. PMID:25415965

Beer and bread to brains and beyond: can yeast cells teach us about neurodegenerative disease?

PubMed

Gitler, Aaron D

2008-01-01

For millennia, humans have harnessed the astonishing power of yeast, producing such culinary masterpieces as bread, beer and wine. Therefore, in this new millennium, is it very farfetched to ask if we can also use yeast to unlock some of the modern day mysteries of human disease? Remarkably, these seemingly simple cells possess most of the same basic cellular machinery as the neurons in the brain. We and others have been using the baker's yeast, Saccharomyces cerevisiae, as a model system to study the mechanisms of devastating neurodegenerative diseases such as Parkinson's, Huntington's, Alzheimer's and amyotrophic lateral sclerosis. While very different in their pathophysiology, they are collectively referred to as protein-misfolding disorders because of the presence of misfolded and aggregated forms of various proteins in the brains of affected individuals. Using yeast genetics and the latest high-throughput screening technologies, we have identified some of the potential causes underpinning these disorders and discovered conserved genes that have proven effective in preventing neuron loss in animal models. Thus, these genes represent new potential drug targets. In this review, I highlight recent work investigating mechanisms of cellular toxicity in a yeast Parkinson's disease model and discuss how similar approaches are being applied to additional neurodegenerative diseases.

Emotional arousal amplifies the effects of biased competition in the brain

PubMed Central

Lee, Tae-Ho; Sakaki, Michiko; Cheng, Ruth; Velasco, Ricardo

2014-01-01

The arousal-biased competition model predicts that arousal increases the gain on neural competition between stimuli representations. Thus, the model predicts that arousal simultaneously enhances processing of salient stimuli and impairs processing of relatively less-salient stimuli. We tested this model with a simple dot-probe task. On each trial, participants were simultaneously exposed to one face image as a salient cue stimulus and one place image as a non-salient stimulus. A border around the face cue location further increased its bottom-up saliency. Before these visual stimuli were shown, one of two tones played: one that predicted a shock (increasing arousal) or one that did not. An arousal-by-saliency interaction in category-specific brain regions (fusiform face area for salient faces and parahippocampal place area for non-salient places) indicated that brain activation associated with processing the salient stimulus was enhanced under arousal whereas activation associated with processing the non-salient stimulus was suppressed under arousal. This is the first functional magnetic resonance imaging study to demonstrate that arousal can enhance information processing for prioritized stimuli while simultaneously impairing processing of non-prioritized stimuli. Thus, it goes beyond previous research to show that arousal does not uniformly enhance perceptual processing, but instead does so selectively in ways that optimizes attention to highly salient stimuli. PMID:24532703

The genetics of mental illness: implications for practice.

PubMed Central

Hyman, S. E.

2000-01-01

Many of the comfortable and relatively simple models of the nature of mental disorders, their causes and their neural substrates now appear quite frayed. Gone is the idea that symptom clusters, course of illness, family history and treatment response would coalesce in a simple way to yield valid diagnoses. Also too simple was the concept, born of early pharmacological successes, that abnormal levels of one or more neurotransmitters would satisfactorily explain the pathogenesis of depression or schizophrenia. Gone is the notion that there is a single gene that causes any mental disorder or determines any behavioural variant. The concept of the causative gene has been replaced by that of genetic complexity, in which multiple genes act in concert with non-genetic factors to produce a risk of mental disorder. Discoveries in genetics and neuroscience can be expected to lead to better models that provide improved representation of the complexity of the brain and behaviour and the development of both. There are likely to be profound implications for clinical practice. The complex genetics of risk should reinvigorate research on the epidemiology and classification of mental disorders and explain the complex patterns of disease transmission within families. Knowledge of the timing of the expression of risk genes during brain development and of their function should not only contribute to an understanding of gene action and the pathophysiology of disease but should also help to direct the search for modifiable environmental risk factors that convert risk into illness. The function of risk genes can only become comprehensible in the context of advances at the molecular, cellular and systems levels in neuroscience and the behavioural sciences. Genetics should yield new therapies aimed not just at symptoms but also at pathogenic processes, thus permitting the targeting of specific therapies to individual patients. PMID:10885164

What's Next: Recruitment of a Grounded Predictive Body Model for Planning a Robot's Actions.

PubMed

Schilling, Malte; Cruse, Holk

2012-01-01

Even comparatively simple, reactive systems are able to control complex motor tasks, such as hexapod walking on unpredictable substrate. The capability of such a controller can be improved by introducing internal models of the body and of parts of the environment. Such internal models can be applied as inverse models, as forward models or to solve the problem of sensor fusion. Usually, separate models are used for these functions. Furthermore, separate models are used to solve different tasks. Here we concentrate on internal models of the body as the brain considers its own body the most important part of the world. The model proposed is formed by a recurrent neural network with the property of pattern completion. The model shows a hierarchical structure but nonetheless comprises a holistic system. One and the same model can be used as a forward model, as an inverse model, for sensor fusion, and, with a simple expansion, as a model to internally simulate (new) behaviors to be used for prediction. The model embraces the geometrical constraints of a complex body with many redundant degrees of freedom, and allows finding geometrically possible solutions. To control behavior such as walking, climbing, or reaching, this body model is complemented by a number of simple reactive procedures together forming a procedural memory. In this article, we illustrate the functioning of this network. To this end we present examples for solutions of the forward function and the inverse function, and explain how the complete network might be used for predictive purposes. The model is assumed to be "innate," so learning the parameters of the model is not (yet) considered.

Correlation analysis for the incubation period of prion disease.

PubMed

Bae, Se-Eun; Jung, Sunghoon; Kim, Ha-Yeon; Son, Hyeon S

2012-07-01

Previous studies have shown that genetic quantitative trait loci (QTL), strain barriers, inoculation dose and inoculation method modulate the incubation period of prion diseases. We examined the relationship between a diverse set of physical, genetic and immunological characteristics and the incubation period of prion disease using correlation analyses. We found that incubation period was highly correlated with brain weight. In addition, mean corpuscular volume and cell size were strongly correlated with incubation period, indicating that the physical magnitude of prion-infected organs or individual cells may be important in determining the incubation period. Given the same prion inoculation dose, animals with a lower brain weight, mean corpuscular volume or cell size may experience more virulent disease, as the effective concentration of abnormal prion, which might regulate the attachment rate of prions to aggregates, is increased with smaller capacity of brains and cells. This is partly consistent with previous theoretical modeling. The strong correlations between incubation period and physical properties of the brain and cells in this study suggest that the mechanism underlying prion disease pathology may be physical, indicating that the incubation process is governed by simple chemical stoichiometry.

Connectotyping: Model Based Fingerprinting of the Functional Connectome

PubMed Central

Miranda-Dominguez, Oscar; Mills, Brian D.; Carpenter, Samuel D.; Grant, Kathleen A.; Kroenke, Christopher D.; Nigg, Joel T.; Fair, Damien A.

2014-01-01

A better characterization of how an individual’s brain is functionally organized will likely bring dramatic advances to many fields of study. Here we show a model-based approach toward characterizing resting state functional connectivity MRI (rs-fcMRI) that is capable of identifying a so-called “connectotype”, or functional fingerprint in individual participants. The approach rests on a simple linear model that proposes the activity of a given brain region can be described by the weighted sum of its functional neighboring regions. The resulting coefficients correspond to a personalized model-based connectivity matrix that is capable of predicting the timeseries of each subject. Importantly, the model itself is subject specific and has the ability to predict an individual at a later date using a limited number of non-sequential frames. While we show that there is a significant amount of shared variance between models across subjects, the model’s ability to discriminate an individual is driven by unique connections in higher order control regions in frontal and parietal cortices. Furthermore, we show that the connectotype is present in non-human primates as well, highlighting the translational potential of the approach. PMID:25386919

A model for traumatic brain injury using laser induced shockwaves

NASA Astrophysics Data System (ADS)

Selfridge, A.; Preece, D.; Gomez, V.; Shi, L. Z.; Berns, M. W.

2015-08-01

Traumatic brain injury (TBI) represents a major treatment challenge in both civilian and military medicine; on the cellular level, its mechanisms are poorly understood. As a method to study the dysfunctional repair mechanisms following injury, laser induced shock waves (LIS) are a useful way to create highly precise, well characterized mechanical forces. We present a simple model for TBI using laser induced shock waves as a model for damage. Our objective is to develop an understanding of the processes responsible for neuronal death, the ways in which we can manipulate these processes to improve cell survival and repair, and the importance of these processes at different levels of biological organization. The physics of shock wave creation has been modeled and can be used to calculate forces acting on individual neurons. By ensuring that the impulse is in the same regime as that occurring in practical TBI, the LIS model can ensure that in vitro conditions and damage are similar to those experienced in TBI. This model will allow for the study of the biochemical response of neurons to mechanical stresses, and can be combined with microfluidic systems for cell growth in order to better isolate areas of damage.

Seeing the whole picture: A comprehensive imaging approach to functional mapping of circuits in behaving zebrafish.

PubMed

Feierstein, C E; Portugues, R; Orger, M B

2015-06-18

In recent years, the zebrafish has emerged as an appealing model system to tackle questions relating to the neural circuit basis of behavior. This can be attributed not just to the growing use of genetically tractable model organisms, but also in large part to the rapid advances in optical techniques for neuroscience, which are ideally suited for application to the small, transparent brain of the larval fish. Many characteristic features of vertebrate brains, from gross anatomy down to particular circuit motifs and cell-types, as well as conserved behaviors, can be found in zebrafish even just a few days post fertilization, and, at this early stage, the physical size of the brain makes it possible to analyze neural activity in a comprehensive fashion. In a recent study, we used a systematic and unbiased imaging method to record the pattern of activity dynamics throughout the whole brain of larval zebrafish during a simple visual behavior, the optokinetic response (OKR). This approach revealed the broadly distributed network of neurons that were active during the behavior and provided insights into the fine-scale functional architecture in the brain, inter-individual variability, and the spatial distribution of behaviorally relevant signals. Combined with mapping anatomical and functional connectivity, targeted electrophysiological recordings, and genetic labeling of specific populations, this comprehensive approach in zebrafish provides an unparalleled opportunity to study complete circuits in a behaving vertebrate animal. Copyright © 2014. Published by Elsevier Ltd.

Long-term disability progression in primary progressive multiple sclerosis: a 15-year study.

PubMed

Rocca, Maria A; Sormani, Maria Pia; Rovaris, Marco; Caputo, Domenico; Ghezzi, Angelo; Montanari, Enrico; Bertolotto, Antonio; Laroni, Alice; Bergamaschi, Roberto; Martinelli, Vittorio; Comi, Giancarlo; Filippi, Massimo

2017-11-01

Prognostic markers of primary progressive multiple sclerosis evolution are needed. We investigated the added value of magnetic resonance imaging measures of brain and cervical cord damage in predicting long-term clinical worsening of primary progressive multiple sclerosis compared to simple clinical assessment. In 54 patients, conventional and diffusion tensor brain scans and cervical cord T1-weighted scans were acquired at baseline and after 15 months. Clinical evaluation was performed after 5 and 15 years in 49 patients. Lesion load, brain and cord atrophy, mean diffusivity and fractional anisotropy values from the brain normal-appearing white matter and grey matter were obtained. Using linear regression models, we screened the clinical and imaging variables as independent predictors of 15-year disability change (measured on the expanded disability status scale). At 15 years, 90% of the patients had disability progression. Integrating clinical and imaging variables at 15 months predicted disability changes at 15 years better than clinical factors at 5 years (R2 = 61% versus R2 = 57%). The model predicted long-term disability change with a precision within one point in 38 of 49 patients (77.6%). Integration of clinical and imaging measures allows identification of primary progressive multiple sclerosis patients at risk of long-term disease progression 4 years earlier than when using clinical assessment alone. © 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.

Establishment of a simplified in vitro porcine blood–brain barrier model with high transendothelial electrical resistance

PubMed Central

Patabendige, Adjanie; Skinner, Robert A.; Abbott, N. Joan

2013-01-01

Good in vitro blood–brain barrier (BBB) models that mimic the in vivo BBB phenotype are essential for studies on BBB functionality and for initial screening in drug discovery programmes, as many potential therapeutic drug candidates have poor BBB permeation. Difficulties associated with the availability of human brain tissue, coupled with the time and cost associated with using animals for this kind of research have led to the development of non-human cell culture models. However, most BBB models display a low transendothelial electrical resistance (TEER), which is a measure of the tightness of the BBB. To address these issues we have established and optimised a robust, simple to use in vitro BBB model using porcine brain endothelial cells (PBECs). The PBEC model gives high TEER without the need for co-culture with astrocytes (up to 1300 Ω cm2 with a mean TEER of ∼800 Ω cm2) with well organised tight junctions as shown by immunostaining for occludin and claudin-5. Functional assays confirmed the presence of high levels of alkaline phosphatase (ALP), and presence of the efflux transporter, P-glycoprotein (P-gp, ABCB1). Presence of the breast cancer resistance protein (BCRP, ABCG2) was confirmed by TaqMan real-time RT-PCR assay. Real-time RT-PCR assays for BCRP, occludin and claudin-5 demonstrated no significant differences between batches of PBECs, and also between primary and passage 1 PBECs. A permeability screen of 10 compounds demonstrated the usefulness of the model as a tool for drug permeability studies. Qualitative and quantitative results from this study confirm that this in vitro porcine BBB model is reliable and robust; it is also simpler to generate than most other BBB models. This article is part of a Special Issue entitled Electrical Synapses. PMID:22789905

Biological and medical applications of a brain-on-a-chip

PubMed Central

2016-01-01

The desire to develop and evaluate drugs as potential countermeasures for biological and chemical threats requires test systems that can also substitute for the clinical trials normally crucial for drug development. Current animal models have limited predictivity for drug efficacy in humans as the large majority of drugs fails in clinical trials. We have limited understanding of the function of the central nervous system and the complexity of the brain, especially during development and neuronal plasticity. Simple in vitro systems do not represent physiology and function of the brain. Moreover, the difficulty of studying interactions between human genetics and environmental factors leads to lack of knowledge about the events that induce neurological diseases. Microphysiological systems (MPS) promise to generate more complex in vitro human models that better simulate the organ’s biology and function. MPS combine different cell types in a specific three-dimensional (3D) configuration to simulate organs with a concrete function. The final aim of these MPS is to combine different “organoids” to generate a human-on-a-chip, an approach that would allow studies of complex physiological organ interactions. The recent discovery of induced pluripotent stem cells (iPSCs) gives a range of possibilities allowing cellular studies of individuals with different genetic backgrounds (e.g., human disease models). Application of iPSCs from different donors in MPS gives the opportunity to better understand mechanisms of the disease and can be a novel tool in drug development, toxicology, and medicine. In order to generate a brain-on-a-chip, we have established a 3D model from human iPSCs based on our experience with a 3D rat primary aggregating brain model. After four weeks of differentiation, human 3D aggregates stain positive for different neuronal markers and show higher gene expression of various neuronal differentiation markers compared to 2D cultures. Here we present the applications and challenges of this emerging technology. PMID:24912505

Predicting haemodynamic networks using electrophysiology: The role of non-linear and cross-frequency interactions

PubMed Central

Tewarie, P.; Bright, M.G.; Hillebrand, A.; Robson, S.E.; Gascoyne, L.E.; Morris, P.G.; Meier, J.; Van Mieghem, P.; Brookes, M.J.

2016-01-01

Understanding the electrophysiological basis of resting state networks (RSNs) in the human brain is a critical step towards elucidating how inter-areal connectivity supports healthy brain function. In recent years, the relationship between RSNs (typically measured using haemodynamic signals) and electrophysiology has been explored using functional Magnetic Resonance Imaging (fMRI) and magnetoencephalography (MEG). Significant progress has been made, with similar spatial structure observable in both modalities. However, there is a pressing need to understand this relationship beyond simple visual similarity of RSN patterns. Here, we introduce a mathematical model to predict fMRI-based RSNs using MEG. Our unique model, based upon a multivariate Taylor series, incorporates both phase and amplitude based MEG connectivity metrics, as well as linear and non-linear interactions within and between neural oscillations measured in multiple frequency bands. We show that including non-linear interactions, multiple frequency bands and cross-frequency terms significantly improves fMRI network prediction. This shows that fMRI connectivity is not only the result of direct electrophysiological connections, but is also driven by the overlap of connectivity profiles between separate regions. Our results indicate that a complete understanding of the electrophysiological basis of RSNs goes beyond simple frequency-specific analysis, and further exploration of non-linear and cross-frequency interactions will shed new light on distributed network connectivity, and its perturbation in pathology. PMID:26827811

Mechanical characterization of human brain tissue.

PubMed

Budday, S; Sommer, G; Birkl, C; Langkammer, C; Haybaeck, J; Kohnert, J; Bauer, M; Paulsen, F; Steinmann, P; Kuhl, E; Holzapfel, G A

2017-01-15

Mechanics are increasingly recognized to play an important role in modulating brain form and function. Computational simulations are a powerful tool to predict the mechanical behavior of the human brain in health and disease. The success of these simulations depends critically on the underlying constitutive model and on the reliable identification of its material parameters. Thus, there is an urgent need to thoroughly characterize the mechanical behavior of brain tissue and to identify mathematical models that capture the tissue response under arbitrary loading conditions. However, most constitutive models have only been calibrated for a single loading mode. Here, we perform a sequence of multiple loading modes on the same human brain specimen - simple shear in two orthogonal directions, compression, and tension - and characterize the loading-mode specific regional and directional behavior. We complement these three individual tests by combined multiaxial compression/tension-shear tests and discuss effects of conditioning and hysteresis. To explore to which extent the macrostructural response is a result of the underlying microstructural architecture, we supplement our biomechanical tests with diffusion tensor imaging and histology. We show that the heterogeneous microstructure leads to a regional but not directional dependence of the mechanical properties. Our experiments confirm that human brain tissue is nonlinear and viscoelastic, with a pronounced compression-tension asymmetry. Using our measurements, we compare the performance of five common constitutive models, neo-Hookean, Mooney-Rivlin, Demiray, Gent, and Ogden, and show that only the isotropic modified one-term Ogden model is capable of representing the hyperelastic behavior under combined shear, compression, and tension loadings: with a shear modulus of 0.4-1.4kPa and a negative nonlinearity parameter it captures the compression-tension asymmetry and the increase in shear stress under superimposed compression but not tension. Our results demonstrate that material parameters identified for a single loading mode fail to predict the response under arbitrary loading conditions. Our systematic characterization of human brain tissue will lead to more accurate computational simulations, which will allow us to determine criteria for injury, to develop smart protection systems, and to predict brain development and disease progression. There is a pressing need to characterize the mechanical behavior of human brain tissue under multiple loading conditions, and to identify constitutive models that are able to capture the tissue response under these conditions. We perform a sequence of experimental tests on the same brain specimen to characterize the regional and directional behavior, and we supplement our tests with DTI and histology to explore to which extent the macrostructural response is a result of the underlying microstructure. Results demonstrate that human brain tissue is nonlinear and viscoelastic, with a pronounced compression-tension asymmetry, and we show that the multiaxial data can best be captured by a modified version of the one-term Ogden model. Copyright © 2016 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

Micromechanics of brain white matter tissue: A fiber-reinforced hyperelastic model using embedded element technique.

PubMed

Yousefsani, Seyed Abdolmajid; Shamloo, Amir; Farahmand, Farzam

2018-04-01

A transverse-plane hyperelastic micromechanical model of brain white matter tissue was developed using the embedded element technique (EET). The model consisted of a histology-informed probabilistic distribution of axonal fibers embedded within an extracellular matrix, both described using the generalized Ogden hyperelastic material model. A correcting method, based on the strain energy density function, was formulated to resolve the stiffness redundancy problem of the EET in large deformation regime. The model was then used to predict the homogenized tissue behavior and the associated localized responses of the axonal fibers under quasi-static, transverse, large deformations. Results indicated that with a sufficiently large representative volume element (RVE) and fine mesh, the statistically randomized microstructure implemented in the RVE exhibits directional independency in transverse plane, and the model predictions for the overall and local tissue responses, characterized by the normalized strain energy density and Cauchy and von Mises stresses, are independent from the modeling parameters. Comparison of the responses of the probabilistic model with that of a simple uniform RVE revealed that only the first one is capable of representing the localized behavior of the tissue constituents. The validity test of the model predictions for the corona radiata against experimental data from the literature indicated a very close agreement. In comparison with the conventional direct meshing method, the model provided almost the same results after correcting the stiffness redundancy, however, with much less computational cost and facilitated geometrical modeling, meshing, and boundary conditions imposing. It was concluded that the EET can be used effectively for detailed probabilistic micromechanical modeling of the white matter in order to provide more accurate predictions for the axonal responses, which are of great importance when simulating the brain trauma or tumor growth. Copyright © 2018 Elsevier Ltd. All rights reserved.

Note on the coefficient of variations of neuronal spike trains.

PubMed

Lengler, Johannes; Steger, Angelika

2017-08-01

It is known that many neurons in the brain show spike trains with a coefficient of variation (CV) of the interspike times of approximately 1, thus resembling the properties of Poisson spike trains. Computational studies have been able to reproduce this phenomenon. However, the underlying models were too complex to be examined analytically. In this paper, we offer a simple model that shows the same effect but is accessible to an analytic treatment. The model is a random walk model with a reflecting barrier; we give explicit formulas for the CV in the regime of excess inhibition. We also analyze the effect of probabilistic synapses in our model and show that it resembles previous findings that were obtained by simulation.

Brain activity during driving with distraction: an immersive fMRI study

PubMed Central

Schweizer, Tom A.; Kan, Karen; Hung, Yuwen; Tam, Fred; Naglie, Gary; Graham, Simon J.

2013-01-01

Introduction: Non-invasive measurements of brain activity have an important role to play in understanding driving ability. The current study aimed to identify the neural underpinnings of human driving behavior by visualizing the areas of the brain involved in driving under different levels of demand, such as driving while distracted or making left turns at busy intersections. Materials and Methods: To capture brain activity during driving, we placed a driving simulator with a fully functional steering wheel and pedals in a 3.0 Tesla functional magnetic resonance imaging (fMRI) system. To identify the brain areas involved while performing different real-world driving maneuvers, participants completed tasks ranging from simple (right turns) to more complex (left turns at busy intersections). To assess the effects of driving while distracted, participants were asked to perform an auditory task while driving analogous to speaking on a hands-free device and driving. Results: A widely distributed brain network was identified, especially when making left turns at busy intersections compared to more simple driving tasks. During distracted driving, brain activation shifted dramatically from the posterior, visual and spatial areas to the prefrontal cortex. Conclusions: Our findings suggest that the distracted brain sacrificed areas in the posterior brain important for visual attention and alertness to recruit enough brain resources to perform a secondary, cognitive task. The present findings offer important new insights into the scientific understanding of the neuro-cognitive mechanisms of driving behavior and lay down an important foundation for future clinical research. PMID:23450757

A simple brain atrophy measure improves the prediction of malignant middle cerebral artery infarction by acute DWI lesion volume.

PubMed

Beck, Christoph; Kruetzelmann, Anna; Forkert, Nils D; Juettler, Eric; Singer, Oliver C; Köhrmann, Martin; Kersten, Jan F; Sobesky, Jan; Gerloff, Christian; Fiehler, Jens; Schellinger, Peter D; Röther, Joachim; Thomalla, Götz

2014-06-01

In patients with malignant middle cerebral artery infarction (MMI) decompressive surgery within 48 h improves functional outcome. In this respect, early identification of patients at risk of developing MMI is crucial. While the acute diffusion weighted imaging (DWI) lesion volume was found to predict MMI with high predictive values, the potential impact of preexisting brain atrophy on the course of space-occupying middle cerebral artery (MCA) infarction and the development of MMI remains unclear. We tested the hypothesis that the combination of the acute DWI lesion volume with simple measures of brain atrophy improves the early prediction of MMI. Data from a prospective, multicenter, observational study, which included patients with acute middle cerebral artery main stem occlusion studied by MRI within 6 h of symptom onset, was analyzed retrospectively. The development of MMI was defined according to the European randomized controlled trials of decompressive surgery. Acute DWI lesion volume, as well as brain and cerebrospinal fluid volume (CSF) were delineated. The intercaudate distance (ICD) was assessed as a linear brain atrophy marker by measuring the hemi-ICD of the intact hemisphere to account for local brain swelling. Binary logistic regression analysis was used to identify significant predictors of MMI. Cut-off values were determined by Classification and Regression Trees analysis. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of the resulting models were calculated. Twenty-one (18 %) of 116 patients developed a MMI. Malignant middle cerebral artery infarctions patients had higher National Institutes of Health Stroke Scale scores on admission and presented more often with combined occlusion of the internal carotid artery and MCA. There were no differences in brain and CSF volume between the two groups. Diffusion weighted imaging lesion volume was larger (p < 0.001), while hemi-ICD was smaller (p = 0.029) in MMI patients. Inclusion of hemi-ICD improved the prediction of MMI. Best cut-off values to predict the development of MMI were DWI lesion volume > 87 ml and hemi-ICD ≤ 9.4 mm. The addition of hemi-ICD to the decision tree strongly increased PPV (0.93 vs. 0.70) resulting in a reduction of false positive findings from 7/23 (30 %) to 1/15 (7 %), while there were only slight changes in specificity, sensitivity and NPV. The absolute number of correct classifications increased by 4 (3.4 %). The integration of hemi-ICD as a linear marker of brain atrophy, that can easily be assessed in an emergency setting, may improve the prediction of MMI by lesion volume based predictive models.

Encoder-Decoder Optimization for Brain-Computer Interfaces

PubMed Central

Merel, Josh; Pianto, Donald M.; Cunningham, John P.; Paninski, Liam

2015-01-01

Neuroprosthetic brain-computer interfaces are systems that decode neural activity into useful control signals for effectors, such as a cursor on a computer screen. It has long been recognized that both the user and decoding system can adapt to increase the accuracy of the end effector. Co-adaptation is the process whereby a user learns to control the system in conjunction with the decoder adapting to learn the user's neural patterns. We provide a mathematical framework for co-adaptation and relate co-adaptation to the joint optimization of the user's control scheme ("encoding model") and the decoding algorithm's parameters. When the assumptions of that framework are respected, co-adaptation cannot yield better performance than that obtainable by an optimal initial choice of fixed decoder, coupled with optimal user learning. For a specific case, we provide numerical methods to obtain such an optimized decoder. We demonstrate our approach in a model brain-computer interface system using an online prosthesis simulator, a simple human-in-the-loop pyschophysics setup which provides a non-invasive simulation of the BCI setting. These experiments support two claims: that users can learn encoders matched to fixed, optimal decoders and that, once learned, our approach yields expected performance advantages. PMID:26029919

Encoder-decoder optimization for brain-computer interfaces.

PubMed

Merel, Josh; Pianto, Donald M; Cunningham, John P; Paninski, Liam

2015-06-01

Neuroprosthetic brain-computer interfaces are systems that decode neural activity into useful control signals for effectors, such as a cursor on a computer screen. It has long been recognized that both the user and decoding system can adapt to increase the accuracy of the end effector. Co-adaptation is the process whereby a user learns to control the system in conjunction with the decoder adapting to learn the user's neural patterns. We provide a mathematical framework for co-adaptation and relate co-adaptation to the joint optimization of the user's control scheme ("encoding model") and the decoding algorithm's parameters. When the assumptions of that framework are respected, co-adaptation cannot yield better performance than that obtainable by an optimal initial choice of fixed decoder, coupled with optimal user learning. For a specific case, we provide numerical methods to obtain such an optimized decoder. We demonstrate our approach in a model brain-computer interface system using an online prosthesis simulator, a simple human-in-the-loop pyschophysics setup which provides a non-invasive simulation of the BCI setting. These experiments support two claims: that users can learn encoders matched to fixed, optimal decoders and that, once learned, our approach yields expected performance advantages.

Biological basis of neuroprotection and neurotherapeutic effects of Whole Body Periodic Acceleration (pGz).

PubMed

Adams, Jose A; Uryash, Arkady; Bassuk, Jorge; Sackner, Marvin A; Kurlansky, Paul

2014-06-01

Exercise is a well known neuroprotective and neurotherapeutic strategy in animal models and humans with brain injury and cognitive dysfunction. In part, exercise induced beneficial effects relate to endothelial derived nitric oxide (eNO) production and induction of the neurotrophins; Brain Derived Neurotrophic Factor (BDNF) and Glial Derived Neurotrophic Factor (GDNF). Whole Body Periodic Acceleration (WBPA (pGz), is the motion of the supine body headward to footward in a sinusoidal fashion, at frequencies of 100-160 cycles/min, inducing pulsatile shear stress to the vascular endothelium. WBPA (pGz) increases eNO in the cardiovascular system in animal models and humans. We hypothesized that WBPA (pGz) has neuroprotective and neurotherapeutic effects due to enhancement of biological pathways that include eNOS, BDNF and GDNF. We discuss protein expression analysis of these in brain of rodents. Animal and observational human data affirm a neuroprotective and neurotherapeutic role for WBPA (pGz). These findings suggest that WBPA (pGz) in addition to its well known beneficial cardiovascular effects can be a simple non-invasive neuroprotective and neurotherapeutic strategy with far reaching health benefits. Copyright © 2014 Elsevier Ltd. All rights reserved.

Gaining Insight of Fetal Brain Development with Diffusion MRI and Histology

PubMed Central

Huang, Hao; Vasung, Lana

2013-01-01

Human brain is extraordinarily complex and yet its origin is a simple tubular structure. Its development during the fetal period is characterized by a series of accurately organized events which underlie the mechanisms of dramatic structural changes during fetal development. Revealing detailed anatomy at different stages of human fetal brain development provides insight on understanding not only this highly ordered process, but also the neurobiological foundations of cognitive brain disorders such as mental retardation, autism, schizophrenia, bipolar and language impairment. Diffusion tensor imaging (DTI) and histology are complementary tools which are capable of delineating the fetal brain structures at both macroscopic and microscopic level. In this review, the structural development of the fetal brains has been characterized with DTI and histology. Major components of the fetal brain, including cortical plate, fetal white matter and cerebral wall layer between the ventricle and subplate, have been delineated with DTI and histology. Anisotropic metrics derived from DTI were used to quantify the microstructural changes during the dynamic process of human fetal cortical development and prenatal development of other animal models. Fetal white matter pathways have been traced with DTI-based tractography to reveal growth patterns of individual white matter tracts and corticocortical connectivity. These detailed anatomical accounts of the structural changes during fetal period may provide the clues of detecting developmental and cognitive brain disorders at their early stages. The anatomical information from DTI and histology may also provide reference standards for diagnostic radiology of premature newborns. PMID:23796901

Linking minds and brains.

PubMed

Barlow, Horace

2013-11-01

When I first came across William James' dictum that " … this sense of sameness is the very keel and backbone of our thinking," I thought he had foreseen the importance of cross-correlation in the brain, and told myself to find out how he had reached this conclusion. When I finally did this a year or two ago, I slowly came to realize that I had completely misunderstood him; from the full quote it is absolutely clear that his dictum cannot be referring to the process by which a cortical simple cell responds selectively to the orientation of features in a visual image, as I had originally supposed. If one translates the original dictum into two more prosaic modern versions, his version would say: "Our minds could not think at all without neural circuits in our brains that compute auto-correlations," but in my mistaken interpretation the last word would be "cross-correlations." Others may have made the same mistake, but the difference is profound, and finding what he really meant has been a revelation to me. This essay explains the revelation, describes how to determine experimentally whether the brain does auto- or cross-correlation, and gives the result of preliminary experiments showing clearly that it does both. A revised view of the visual cortex as autocorrelator as well as cross-correlator claims to tell us what complex cells in the visual cortex do, and it assigns a role to its columnar structure that is as important to fulfilling that role as the concept of the receptive field has been to understanding the simple cells' fulfillment of theirs. The new view has compelling features, broad implications, and suggests a plausible model of how neural circuits in the cortex achieve thought, but it needs further testing.

A PDP model of the simultaneous perception of multiple objects

NASA Astrophysics Data System (ADS)

Henderson, Cynthia M.; McClelland, James L.

2011-06-01

Illusory conjunctions in normal and simultanagnosic subjects are two instances where the visual features of multiple objects are incorrectly 'bound' together. A connectionist model explores how multiple objects could be perceived correctly in normal subjects given sufficient time, but could give rise to illusory conjunctions with damage or time pressure. In this model, perception of two objects benefits from lateral connections between hidden layers modelling aspects of the ventral and dorsal visual pathways. As with simultanagnosia, simulations of dorsal lesions impair multi-object recognition. In contrast, a large ventral lesion has minimal effect on dorsal functioning, akin to dissociations between simple object manipulation (retained in visual form agnosia and semantic dementia) and object discrimination (impaired in these disorders) [Hodges, J.R., Bozeat, S., Lambon Ralph, M.A., Patterson, K., and Spatt, J. (2000), 'The Role of Conceptual Knowledge: Evidence from Semantic Dementia', Brain, 123, 1913-1925; Milner, A.D., and Goodale, M.A. (2006), The Visual Brain in Action (2nd ed.), New York: Oxford]. It is hoped that the functioning of this model might suggest potential processes underlying dorsal and ventral contributions to the correct perception of multiple objects.

Phosphorylated recombinant HSP27 protects the brain and attenuates blood-brain barrier disruption following stroke in mice receiving intravenous tissue-plasminogen activator.

PubMed

Shimada, Yoshiaki; Shimura, Hideki; Tanaka, Ryota; Yamashiro, Kazuo; Koike, Masato; Uchiyama, Yasuo; Urabe, Takao; Hattori, Nobutaka

2018-01-01

Loss of integrity of the blood-brain barrier (BBB) in ischemic stroke victims initiates a devastating cascade of events causing brain damage. Maintaining the BBB is important to preserve brain function in ischemic stroke. Unfortunately, recombinant tissue plasminogen activator (tPA), the only effective fibrinolytic treatment at the acute stage of ischemic stroke, also injures the BBB and increases the risk of brain edema and secondary hemorrhagic transformation. Thus, it is important to identify compounds that maintain BBB integrity in the face of ischemic injury in patients with stroke. We previously demonstrated that intravenously injected phosphorylated recombinant heat shock protein 27 (prHSP27) protects the brains of mice with transient middle cerebral artery occlusion (tMCAO), an animal stroke-model. Here, we determined whether prHSP27, in addition to attenuating brain injury, also decreases BBB damage in hyperglycemic tMCAO mice that had received tPA. After induction of hyperglycemia and tMCAO, we examined 4 treatment groups: 1) bovine serum albumin (BSA), 2) prHSP27, 3) tPA, 4) tPA plus prHSP27. We examined the effects of prHSP27 by comparing the BSA and prHSP27 groups and the tPA and tPA plus prHSP27 groups. Twenty-four hours after injection, prHSP27 reduced infarct volume, brain swelling, neurological deficits, the loss of microvessel proteins and endothelial cell walls, and mortality. It also reduced the rates of hemorrhagic transformation, extravasation of endogenous IgG, and MMP-9 activity, signs of BBB damage. Therefore, prHSP27 injection attenuated brain damage and preserved the BBB in tPA-injected, hyperglycemic tMCAO experimental stroke-model mice, in which the BBB is even more severely damaged than in simple tMCAO mice. The attenuation of brain damage and BBB disruption in the presence of tPA suggests the effectiveness of prHSP27 and tPA as a combination therapy. prHSP27 may be a novel therapeutic agent for ischemic stroke patients whose BBBs are injured following tPA injections.

A systematic review finds methodological improvements necessary for prognostic models in determining traumatic brain injury outcomes.

PubMed

Mushkudiani, Nino A; Hukkelhoven, Chantal W P M; Hernández, Adrián V; Murray, Gordon D; Choi, Sung C; Maas, Andrew I R; Steyerberg, Ewout W

2008-04-01

To describe the modeling techniques used for early prediction of outcome in traumatic brain injury (TBI) and to identify aspects for potential improvements. We reviewed key methodological aspects of studies published between 1970 and 2005 that proposed a prognostic model for the Glasgow Outcome Scale of TBI based on admission data. We included 31 papers. Twenty-four were single-center studies, and 22 reported on fewer than 500 patients. The median of the number of initially considered predictors was eight, and on average five of these were selected for the prognostic model, generally including age, Glasgow Coma Score (or only motor score), and pupillary reactivity. The most common statistical technique was logistic regression with stepwise selection of predictors. Model performance was often quantified by accuracy rate rather than by more appropriate measures such as the area under the receiver-operating characteristic curve. Model validity was addressed in 15 studies, but mostly used a simple split-sample approach, and external validation was performed in only four studies. Although most models agree on the three most important predictors, many were developed on small sample sizes within single centers and hence lack generalizability. Modeling strategies have to be improved, and include external validation.

Systematic evaluation of a time-domain Monte Carlo fitting routine to estimate the adult brain optical properties

NASA Astrophysics Data System (ADS)

Selb, Juliette; Ogden, Tyler M.; Dubb, Jay; Fang, Qianqian; Boas, David A.

2013-03-01

Time-domain near-infrared spectroscopy (TD-NIRS) offers the ability to measure the absolute baseline optical properties of a tissue. Specifically, for brain imaging, the robust assessment of cerebral blood volume and oxygenation based on measurement of cerebral hemoglobin concentrations is essential for reliable cross-sectional and longitudinal studies. In adult heads, these baseline measurements are complicated by the presence of thick extra-cerebral tissue (scalp, skull, CSF). A simple semi-infinite homogeneous model of the head has proven to have limited use because of the large errors it introduces in the recovered brain absorption. Analytical solutions for layered media have shown improved performance on Monte-Carlo simulated data and layered phantom experiments, but their validity on real adult head data has never been demonstrated. With the advance of fast Monte Carlo approaches based on GPU computation, numerical methods to solve the radiative transfer equation become viable alternatives to analytical solutions of the diffusion equation. Monte Carlo approaches provide the additional advantage to be adaptable to any geometry, in particular more realistic head models. The goals of the present study were twofold: (1) to implement a fast and flexible Monte Carlo-based fitting routine to retrieve the brain optical properties; (2) to characterize the performances of this fitting method on realistic adult head data. We generated time-resolved data at various locations over the head, and fitted them with different models of light propagation: the homogeneous analytical model, and Monte Carlo simulations for three head models: a two-layer slab, the true subject's anatomy, and that of a generic atlas head. We found that the homogeneous model introduced a median 20 to 25% error on the recovered brain absorption, with large variations over the range of true optical properties. The two-layer slab model only improved moderately the results over the homogeneous one. On the other hand, using a generic atlas head registered to the subject's head surface decreased the error by a factor of 2. When the information is available, using the true subject anatomy offers the best performance.

Simple platform for chronic imaging of hippocampal activity during spontaneous behaviour in an awake mouse

PubMed Central

Villette, Vincent; Levesque, Mathieu; Miled, Amine; Gosselin, Benoit; Topolnik, Lisa

2017-01-01

Chronic electrophysiological recordings of neuronal activity combined with two-photon Ca2+ imaging give access to high resolution and cellular specificity. In addition, awake drug-free experimentation is required for investigating the physiological mechanisms that operate in the brain. Here, we developed a simple head fixation platform, which allows simultaneous chronic imaging and electrophysiological recordings to be obtained from the hippocampus of awake mice. We performed quantitative analyses of spontaneous animal behaviour, the associated network states and the cellular activities in the dorsal hippocampus as well as estimated the brain stability limits to image dendritic processes and individual axonal boutons. Ca2+ imaging recordings revealed a relatively stereotyped hippocampal activity despite a high inter-animal and inter-day variability in the mouse behavior. In addition to quiet state and locomotion behavioural patterns, the platform allowed the reliable detection of walking steps and fine speed variations. The brain motion during locomotion was limited to ~1.8 μm, thus allowing for imaging of small sub-cellular structures to be performed in parallel with recordings of network and behavioural states. This simple device extends the drug-free experimentation in vivo, enabling high-stability optophysiological experiments with single-bouton resolution in the mouse awake brain. PMID:28240275

Phylostratigraphic profiles in zebrafish uncover chordate origins of the vertebrate brain.

PubMed

Šestak, Martin Sebastijan; Domazet-Lošo, Tomislav

2015-02-01

An elaborated tripartite brain is considered one of the important innovations of vertebrates. Other extant chordate groups have a more basic brain organization. For instance, cephalochordates possess a relatively simple brain possibly homologous to the vertebrate forebrain and hindbrain, whereas tunicates display the tripartite organization, but without the specialized brain centers. The difference in anatomical complexity is even more pronounced if one compares chordates with other deuterostomes that have only a diffuse nerve net or alternatively a rather simple central nervous system. To gain a new perspective on the evolutionary roots of the complex vertebrate brain, we made here a phylostratigraphic analysis of gene expression patterns in the developing zebrafish (Danio rerio). The recovered adaptive landscape revealed three important periods in the evolutionary history of the zebrafish brain. The oldest period corresponds to preadaptive events in the first metazoans and the emergence of the nervous system at the metazoan-eumetazoan transition. The origin of chordates marks the next phase, where we found the overall strongest adaptive imprint in almost all analyzed brain regions. This finding supports the idea that the vertebrate brain evolved independently of the brains within the protostome lineage. Finally, at the origin of vertebrates we detected a pronounced signal coming from the dorsal telencephalon, in agreement with classical theories that consider this part of the cerebrum a genuine vertebrate innovation. Taken together, these results reveal a stepwise adaptive history of the vertebrate brain where most of its extant organization was already present in the chordate ancestor. © The Author 2014. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.

The Role of Competitive Inhibition and Top-Down Feedback in Binding during Object Recognition

PubMed Central

Wyatte, Dean; Herd, Seth; Mingus, Brian; O’Reilly, Randall

2012-01-01

How does the brain bind together visual features that are processed concurrently by different neurons into a unified percept suitable for processes such as object recognition? Here, we describe how simple, commonly accepted principles of neural processing can interact over time to solve the brain’s binding problem. We focus on mechanisms of neural inhibition and top-down feedback. Specifically, we describe how inhibition creates competition among neural populations that code different features, effectively suppressing irrelevant information, and thus minimizing illusory conjunctions. Top-down feedback contributes to binding in a similar manner, but by reinforcing relevant features. Together, inhibition and top-down feedback contribute to a competitive environment that ensures only the most appropriate features are bound together. We demonstrate this overall proposal using a biologically realistic neural model of vision that processes features across a hierarchy of interconnected brain areas. Finally, we argue that temporal synchrony plays only a limited role in binding – it does not simultaneously bind multiple objects, but does aid in creating additional contrast between relevant and irrelevant features. Thus, our overall theory constitutes a solution to the binding problem that relies only on simple neural principles without any binding-specific processes. PMID:22719733

Scales

ScienceCinema

Murray Gibson

2017-12-09

Musical scales involve notes that, sounded simultaneously (chords), sound good together. The result is the left brain meeting the right brain — a Pythagorean interval of overlapping notes. This synergy would suggest less difference between the working of the right brain and the left brain than common wisdom would dictate. The pleasing sound of harmony comes when two notes share a common harmonic, meaning that their frequencies are in simple integer ratios, such as 3/2 (G/C) or 5/4 (E/C).

The topology of large Open Connectome networks for the human brain.

PubMed

Gastner, Michael T; Ódor, Géza

2016-06-07

The structural human connectome (i.e. the network of fiber connections in the brain) can be analyzed at ever finer spatial resolution thanks to advances in neuroimaging. Here we analyze several large data sets for the human brain network made available by the Open Connectome Project. We apply statistical model selection to characterize the degree distributions of graphs containing up to nodes and edges. A three-parameter generalized Weibull (also known as a stretched exponential) distribution is a good fit to most of the observed degree distributions. For almost all networks, simple power laws cannot fit the data, but in some cases there is statistical support for power laws with an exponential cutoff. We also calculate the topological (graph) dimension D and the small-world coefficient σ of these networks. While σ suggests a small-world topology, we found that D < 4 showing that long-distance connections provide only a small correction to the topology of the embedding three-dimensional space.

The topology of large Open Connectome networks for the human brain

NASA Astrophysics Data System (ADS)

Gastner, Michael T.; Ódor, Géza

2016-06-01

The structural human connectome (i.e. the network of fiber connections in the brain) can be analyzed at ever finer spatial resolution thanks to advances in neuroimaging. Here we analyze several large data sets for the human brain network made available by the Open Connectome Project. We apply statistical model selection to characterize the degree distributions of graphs containing up to nodes and edges. A three-parameter generalized Weibull (also known as a stretched exponential) distribution is a good fit to most of the observed degree distributions. For almost all networks, simple power laws cannot fit the data, but in some cases there is statistical support for power laws with an exponential cutoff. We also calculate the topological (graph) dimension D and the small-world coefficient σ of these networks. While σ suggests a small-world topology, we found that D < 4 showing that long-distance connections provide only a small correction to the topology of the embedding three-dimensional space.

Multi-agent Simulations of Population Behavior: A Promising Tool for Systems Biology.

PubMed

Colosimo, Alfredo

2018-01-01

This contribution reports on the simulation of some dynamical events observed in the collective behavior of different kinds of populations, ranging from shape-changing cells in a Petri dish to functionally correlated brain areas in vivo. The unifying methodological approach, based upon a Multi-Agent Simulation (MAS) paradigm as incorporated in the NetLogo™ interpreter, is a direct consequence of the cornerstone that simple, individual actions within a population of interacting agents often give rise to complex, collective behavior.The discussion will mainly focus on the emergence and spreading of synchronous activities within the population, as well as on the modulation of the collective behavior exerted by environmental force-fields. A relevant section of this contribution is dedicated to the extension of the MAS paradigm to Brain Network models. In such a general framework some recent applications taken from the direct experience of the author, and exploring the activation patterns characteristic of specific brain functional states, are described, and their impact on the Systems-Biology universe underlined.

Estimation of State Transition Probabilities: A Neural Network Model

NASA Astrophysics Data System (ADS)

Saito, Hiroshi; Takiyama, Ken; Okada, Masato

2015-12-01

Humans and animals can predict future states on the basis of acquired knowledge. This prediction of the state transition is important for choosing the best action, and the prediction is only possible if the state transition probability has already been learned. However, how our brains learn the state transition probability is unknown. Here, we propose a simple algorithm for estimating the state transition probability by utilizing the state prediction error. We analytically and numerically confirmed that our algorithm is able to learn the probability completely with an appropriate learning rate. Furthermore, our learning rule reproduced experimentally reported psychometric functions and neural activities in the lateral intraparietal area in a decision-making task. Thus, our algorithm might describe the manner in which our brains learn state transition probabilities and predict future states.

No Evidence for an Item Limit in Change Detection (Open Access)

DTIC Science & Technology

2013-02-28

memory : a reconsideration of mental storage capacity. Behav Brain Sci 24: 87–114. 17. Eng HY, Chen D, Jiang Y (2005) Visual working memory for simple...working memory can hold no more than a fixed number of items (‘‘item-limit models’’). Recent findings force us to consider the alternative view that working... memory is limited by the precision in stimulus encoding, with mean precision decreasing with increasing set size (‘‘continuous-resource models

Bird song: in vivo, in vitro, in silico

NASA Astrophysics Data System (ADS)

Mukherjee, Aryesh; Mandre, Shreyas; Mahadevan, Lakshminarayan

2010-11-01

Bird song, long since an inspiration for artists, writers and poets also poses challenges for scientists interested in dissecting the mechanisms underlying the neural, motor, learning and behavioral systems behind the beak and brain, as a way to recreate and synthesize it. We use a combination of quantitative visualization experiments with physical models and computational theories to understand the simplest aspects of these complex musical boxes, focusing on using the controllable elastohydrodynamic interactions to mimic aural gestures and simple songs.

The detection of brain ischaemia in rats by inductive phase shift spectroscopy.

PubMed

González, C A; Villanueva, C; Vera, C; Flores, O; Reyes, R D; Rubinsky, B

2009-08-01

Ischaemia in the brain is an important clinical problem that is often monitored and studied with expensive devices such as MRI and PET, which are not readily available in low economical resource parts of the world. We have developed a new less expensive tool for non-invasive monitoring of ischaemia in the brain. This is a first feasibility study describing the concept. The system is based on the hypothesis that electromagnetic properties of the tissue change during ischaemia and that measuring the electromagnetic properties of the bulk of the brain with non-contact means can detect these changes. The apparatus we have built and whose design we describe here consists of two electromagnetic coils placed around the head. The system measures the bulk change in time of the phase difference between the electromagnetic signal on the two coils in a range of frequencies. A mathematical model simulating the device and the measurement is also introduced. Ischaemia was induced in the brain of rats by occlusion of the right cerebral and carotid arteries. Experimental subjects were monitored for 24 h. Inductive phase shift measurements were made at five frequencies in the range of 0.1-50 MHz eight times during the observation period. An ex vivo estimation of the percentage of necrosis in the ischemic subjects at t = 24 h was done. The mathematical model was also applied to the experimental tested situation. The results of both experiments and theory show significant phase shifts increase as a function of frequency and ischaemia time. The theoretical and experimental results suggest that the tested technique has the potential to detect the processes and level of ischaemia in the brain by non-invasive, continuous, bulk volumetric monitoring with a simple and inexpensive apparatus.

Noncoding origins of anthropoid traits and a new null model of transposon functionalization

PubMed Central

del Rosario, Ricardo C.H.; Rayan, Nirmala Arul

2014-01-01

Little is known about novel genetic elements that drove the emergence of anthropoid primates. We exploited the sequencing of the marmoset genome to identify 23,849 anthropoid-specific constrained (ASC) regions and confirmed their robust functional signatures. Of the ASC base pairs, 99.7% were noncoding, suggesting that novel anthropoid functional elements were overwhelmingly cis-regulatory. ASCs were highly enriched in loci associated with fetal brain development, motor coordination, neurotransmission, and vision, thus providing a large set of candidate elements for exploring the molecular basis of hallmark primate traits. We validated ASC192 as a primate-specific enhancer in proliferative zones of the developing brain. Unexpectedly, transposable elements (TEs) contributed to >56% of ASCs, and almost all TE families showed functional potential similar to that of nonrepetitive DNA. Three L1PA repeat-derived ASCs displayed coherent eye-enhancer function, thus demonstrating that the “gene-battery” model of TE functionalization applies to enhancers in vivo. Our study provides fundamental insights into genome evolution and the origins of anthropoid phenotypes and supports an elegantly simple new null model of TE exaptation. PMID:25043600

A mouse model of human repetitive mild traumatic brain injury

PubMed Central

Kane, Michael J.; Pérez, Mariana Angoa; Briggs, Denise I.; Viano, David C.; Kreipke, Christian W.; Kuhn, Donald M.

2011-01-01

A novel method for the study of repetitive mild traumatic brain injury (rmTBI) that models the most common form of head injury in humans is presented. Existing animal models of TBI impart focal, severe damage unlike that seen in repeated and mild concussive injuries, and few are configured for repetitive application. Our model is a modification of the Marmarou weight drop method and allows repeated head impacts to lightly anesthetized mice. A key facet of this method is the delivery of an impact to the cranium of an unrestrained subject allowing rapid acceleration of the free-moving head and torso, an essential characteristic known to be important for concussive injury in humans, and a factor that is missing from existing animal models of TBI. Our method does not require scalp incision, emplacement of protective skull helmets or surgery and the procedure can be completed in 1-2 minutes. Mice spontaneously recover the righting reflex and show no evidence of seizures, paralysis or impaired behavior. Skull fractures and intracranial bleeding are very rare. Minor deficits in motor coordination and locomotor hyperactivity recover over time. Histological analyses reveal mild astrocytic reactivity (increased expression of GFAP) and increased phospho-tau but a lack of blood-brain-barrier disruption, edema and microglial activation. This new animal model is simple and cost-effective and will facilitate characterization of the neurobiological and behavioral consequences of rmTBI. It is also ideal for high throughput screening of potential new therapies for mild concussive injuries as experienced by athletes and military personnel. PMID:21930157

On the space and time evolution of regular or irregular human heart or brain signals

NASA Astrophysics Data System (ADS)

Tuncay, Ç.

2009-01-01

A coupled map is suggested to investigate various spatial or temporal designs in biology: several cells (or tissues) in an organ are considered as connected to each other in terms of some molecular diffusions or electrical potential differences and so on. The biological systems (groups of cells) start from various initial conditions for spatial designs (or initial signals for temporal designs) and they evolve in time in terms of the mentioned interactions (connections) besides some individual feedings. The basic aim of the present contribution is to mimic various empirical data for the heart (in normal, quasi-stable, unstable and post operative physiological conditions) or brain (regular or irregular; for epilepsy) signals. The mentioned empirical data are borrowed from various works in the literature which are cited. The suggested model (to be used besides or instead of the artificial network models) involves simple mathematics and the related software is easy. The results may be considered as in good agreement with the mentioned real signals.

A Brain-wide Circuit Model of Heat-Evoked Swimming Behavior in Larval Zebrafish.

PubMed

Haesemeyer, Martin; Robson, Drew N; Li, Jennifer M; Schier, Alexander F; Engert, Florian

2018-05-16

Thermosensation provides crucial information, but how temperature representation is transformed from sensation to behavior is poorly understood. Here, we report a preparation that allows control of heat delivery to zebrafish larvae while monitoring motor output and imaging whole-brain calcium signals, thereby uncovering algorithmic and computational rules that couple dynamics of heat modulation, neural activity and swimming behavior. This approach identifies a critical step in the transformation of temperature representation between the sensory trigeminal ganglia and the hindbrain: A simple sustained trigeminal stimulus representation is transformed into a representation of absolute temperature as well as temperature changes in the hindbrain that explains the observed motor output. An activity constrained dynamic circuit model captures the most prominent aspects of these sensori-motor transformations and predicts both behavior and neural activity in response to novel heat stimuli. These findings provide the first algorithmic description of heat processing from sensory input to behavioral output. Copyright © 2018 Elsevier Inc. All rights reserved.

Brain-derived neurotrophic factor as a model system for examining gene by environment interactions across development.

PubMed

Casey, B J; Glatt, C E; Tottenham, N; Soliman, F; Bath, K; Amso, D; Altemus, M; Pattwell, S; Jones, R; Levita, L; McEwen, B; Magariños, A M; Gunnar, M; Thomas, K M; Mezey, J; Clark, A G; Hempstead, B L; Lee, F S

2009-11-24

There has been a dramatic rise in gene x environment studies of human behavior over the past decade that have moved the field beyond simple nature versus nurture debates. These studies offer promise in accounting for more variability in behavioral and biological phenotypes than studies that focus on genetic or experiential factors alone. They also provide clues into mechanisms of modifying genetic risk or resilience in neurodevelopmental disorders. Yet, it is rare that these studies consider how these interactions change over the course of development. In this paper, we describe research that focuses on the impact of a polymorphism in a brain-derived neurotrophic factor (BDNF) gene, known to be involved in learning and development. Specifically we present findings that assess the effects of genotypic and environmental loadings on neuroanatomic and behavioral phenotypes across development. The findings illustrate the use of a genetic mouse model that mimics the human polymorphism, to constrain the interpretation of gene-environment interactions across development in humans.

Robust short-term memory without synaptic learning.

PubMed

Johnson, Samuel; Marro, J; Torres, Joaquín J

2013-01-01

Short-term memory in the brain cannot in general be explained the way long-term memory can--as a gradual modification of synaptic weights--since it takes place too quickly. Theories based on some form of cellular bistability, however, do not seem able to account for the fact that noisy neurons can collectively store information in a robust manner. We show how a sufficiently clustered network of simple model neurons can be instantly induced into metastable states capable of retaining information for a short time (a few seconds). The mechanism is robust to different network topologies and kinds of neural model. This could constitute a viable means available to the brain for sensory and/or short-term memory with no need of synaptic learning. Relevant phenomena described by neurobiology and psychology, such as local synchronization of synaptic inputs and power-law statistics of forgetting avalanches, emerge naturally from this mechanism, and we suggest possible experiments to test its viability in more biological settings.

L-Phenylalanine preloading reduces the (10)B(n, α)(7)Li dose to the normal brain by inhibiting the uptake of boronophenylalanine in boron neutron capture therapy for brain tumours.

PubMed

Watanabe, Tsubasa; Tanaka, Hiroki; Fukutani, Satoshi; Suzuki, Minoru; Hiraoka, Masahiro; Ono, Koji

2016-01-01

Boron neutron capture therapy (BNCT) is a cellular-level particle radiation therapy that combines the selective delivery of boron compounds to tumour tissue with neutron irradiation. Previously, high doses of one of the boron compounds used for BNCT, L-BPA, were found to reduce the boron-derived irradiation dose to the central nervous system. However, injection with a high dose of L-BPA is not feasible in clinical settings. We aimed to find an alternative method to improve the therapeutic efficacy of this therapy. We examined the effects of oral preloading with various analogues of L-BPA in a xenograft tumour model and found that high-dose L-phenylalanine reduced the accumulation of L-BPA in the normal brain relative to tumour tissue. As a result, the maximum irradiation dose in the normal brain was 19.2% lower in the L-phenylalanine group relative to the control group. This study provides a simple strategy to improve the therapeutic efficacy of conventional boron compounds for BNCT for brain tumours and the possibility to widen the indication of BNCT to various kinds of other tumours. Copyright © 2015. Published by Elsevier Ireland Ltd.

Neurobiology as Information Physics

PubMed Central

Street, Sterling

2016-01-01

This article reviews thermodynamic relationships in the brain in an attempt to consolidate current research in systems neuroscience. The present synthesis supports proposals that thermodynamic information in the brain can be quantified to an appreciable degree of objectivity, that many qualitative properties of information in systems of the brain can be inferred by observing changes in thermodynamic quantities, and that many features of the brain’s anatomy and architecture illustrate relatively simple information-energy relationships. The brain may provide a unique window into the relationship between energy and information. PMID:27895560

Predictors for traumatic brain injuries evaluated through accident reconstructions.

PubMed

Kleiven, Svein

2007-10-01

The aim of this study is to evaluate all the 58 available NFL cases and compare various predictors for mild traumatic brain injuries using a detailed and extensively validated finite element model of the human head. Global injury measures such as magnitude in angular and translational acceleration, change in angular velocity, head impact power (HIP) and HIC were also investigated with regard to their ability to predict the intracranial pressure and strains associated with injury. The brain material properties were modeled using a hyperelastic and viscoelastic constitutive law. Also, three different stiffness parameters, encompassing a range of published brain tissue properties, were tested. 8 tissue injury predictors were evaluated for 6 different regions, covering the entire cerebrum, as well as for the whole brain. In addition, 10 head kinematics based predictors were evaluated both for correlation with injury as well as with strain and pressure. When evaluating the results, a statistical correlation between strain, strain rate, product of strain and strain rate, Cumulative Strain Damage Measure (CSDM), strain energy density, maximum pressure, magnitude of minimum pressure, as well as von Mises effective stress, with injury was found when looking into specific regions of the brain. However, the maximal pressure in the gray matter showed a higher correlation with injury than other evaluated measures. On the other hand, it was possible, through the reconstruction of a motocross accident, to re-create the injury pattern in the brain of the injured rider using maximal principal strain. It was also found that a simple linear combination of peak change in rotational velocity and HIC showed a high correlation (R=0.98) with the maximum principal strain in the brain, in addition to being a significant predictor of injury. When applying the rotational and translational kinematics separately for one of the cases, it was found that the translational kinematics contribute very little to the intracranial distortional strains while the rotational kinematics contributes insignificantly to the pressure response. This study underlines that the strain based brain tissue injury predictors are very sensitive to the choice of stiffness for the brain tissue.

Study on Control of Brain Temperature for Brain Hypothermia Treatment

NASA Astrophysics Data System (ADS)

Gaohua, Lu; Wakamatsu, Hidetoshi

The brain hypothermia treatment is an attractive therapy for the neurologist because of its neuroprotection in hypoxic-ischemic encephalopathy patients. The present paper deals with the possibility of controlling the brain and other viscera in different temperatures from the viewpoint of system control. It is theoretically attempted to realize the special brain hypothermia treatment to cool only the head but to warm the body by using the simple apparatus such as the cooling cap, muffler and warming blanket. For this purpose, a biothermal system concerning the temperature difference between the brain and the other thoracico-abdominal viscus is synthesized from the biothermal model of hypothermic patient. The output controllability and the asymptotic stability of the system are examined on the basis of its structure. Then, the maximum temperature difference to be realized is shown dependent on the temperature range of the apparatus and also on the maximum gain determined from the coefficient matrices A, B and C of the biothermal system. Its theoretical analysis shows the realization of difference of about 2.5°C, if there is absolutely no constraint of the temperatures of the cooling cap, muffler and blanket. It is, however, physically unavailable. Those are shown by simulation example of the optimal brain temperature regulation using a standard adult database. It is thus concluded that the surface cooling and warming apparatus do no make it possible to realize the special brain hypothermia treatment, because the brain temperature cannot be cooled lower than those of other viscera in an appropriate temperature environment. This study shows that the ever-proposed good method of clinical treatment is in principle impossible in the actual brain hypothermia treatment.

Gesturing with an injured brain: How gesture helps children with early brain injury learn linguistic constructions

PubMed Central

Özçalışkan, Şeyda; Levine, Susan C.; Goldin-Meadow, Susan

2013-01-01

Children with pre/perinatal unilateral brain lesions (PL) show remarkable plasticity for language development. Is this plasticity characterized by the same developmental trajectory that characterizes typically developing (TD) children, with gesture leading the way into speech? We explored this question, comparing 11 children with PL—matched to 30 TD children on expressive vocabulary—in the second year of life. Children with PL showed similarities to TD children for simple but not complex sentence types. Children with PL produced simple sentences across gesture and speech several months before producing them entirely in speech, exhibiting parallel delays in both gesture+speech and speech-alone. However, unlike TD children, children with PL produced complex sentence types first in speech-alone. Overall, the gesture-speech system appears to be a robust feature of language-learning for simple—but not complex—sentence constructions, acting as a harbinger of change in language development even when that language is developing in an injured brain. PMID:23217292

Inborn and experience-dependent models of categorical brain organization. A position paper

PubMed Central

Gainotti, Guido

2015-01-01

The present review aims to summarize the debate in contemporary neuroscience between inborn and experience-dependent models of conceptual representations that goes back to the description of category-specific semantic disorders for biological and artifact categories. Experience-dependent models suggest that categorical disorders are the by-product of the differential weighting of different sources of knowledge in the representation of biological and artifact categories. These models maintain that semantic disorders are not really category-specific, because they do not respect the boundaries between different categories. They also argue that the brain structures which are disrupted in a given type of category-specific semantic disorder should correspond to the areas of convergence of the sensory-motor information which play a major role in the construction of that category. Furthermore, they provide a simple interpretation of gender-related categorical effects and are supported by studies assessing the importance of prior experience in the cortical representation of objects On the other hand, inborn models maintain that category-specific semantic disorders reflect the disruption of innate brain networks, which are shaped by natural selection to allow rapid identification of objects that are very relevant for survival. From the empirical point of view, these models are mainly supported by observations of blind subjects, which suggest that visual experience is not necessary for the emergence of category-specificity in the ventral stream of visual processing. The weight of the data supporting experience-dependent and inborn models is thoroughly discussed, stressing the fact observations made in blind subjects are still the subject of intense debate. It is concluded that at the present state of knowledge it is not possible to choose between experience-dependent and inborn models of conceptual representations. PMID:25667570

Peripersonal Space and Margin of Safety around the Body: Learning Visuo-Tactile Associations in a Humanoid Robot with Artificial Skin.

PubMed

Roncone, Alessandro; Hoffmann, Matej; Pattacini, Ugo; Fadiga, Luciano; Metta, Giorgio

2016-01-01

This paper investigates a biologically motivated model of peripersonal space through its implementation on a humanoid robot. Guided by the present understanding of the neurophysiology of the fronto-parietal system, we developed a computational model inspired by the receptive fields of polymodal neurons identified, for example, in brain areas F4 and VIP. The experiments on the iCub humanoid robot show that the peripersonal space representation i) can be learned efficiently and in real-time via a simple interaction with the robot, ii) can lead to the generation of behaviors like avoidance and reaching, and iii) can contribute to the understanding the biological principle of motor equivalence. More specifically, with respect to i) the present model contributes to hypothesizing a learning mechanisms for peripersonal space. In relation to point ii) we show how a relatively simple controller can exploit the learned receptive fields to generate either avoidance or reaching of an incoming stimulus and for iii) we show how the robot can select arbitrary body parts as the controlled end-point of an avoidance or reaching movement.

Beyond Molecular Codes: Simple Rules to Wire Complex Brains

PubMed Central

Hassan, Bassem A.; Hiesinger, P. Robin

2015-01-01

Summary Molecular codes, like postal zip codes, are generally considered a robust way to ensure the specificity of neuronal target selection. However, a code capable of unambiguously generating complex neural circuits is difficult to conceive. Here, we re-examine the notion of molecular codes in the light of developmental algorithms. We explore how molecules and mechanisms that have been considered part of a code may alternatively implement simple pattern formation rules sufficient to ensure wiring specificity in neural circuits. This analysis delineates a pattern-based framework for circuit construction that may contribute to our understanding of brain wiring. PMID:26451480

Neuronal Correlation Parameter and the Idea of Thermodynamic Entropy of an N-Body Gravitationally Bounded System.

PubMed

Haranas, Ioannis; Gkigkitzis, Ioannis; Kotsireas, Ilias; Austerlitz, Carlos

2017-01-01

Understanding how the brain encodes information and performs computation requires statistical and functional analysis. Given the complexity of the human brain, simple methods that facilitate the interpretation of statistical correlations among different brain regions can be very useful. In this report we introduce a numerical correlation measure that may serve the interpretation of correlational neuronal data, and may assist in the evaluation of different brain states. The description of the dynamical brain system, through a global numerical measure may indicate the presence of an action principle which may facilitate a application of physics principles in the study of the human brain and cognition.

Robust estimation of cerebral hemodynamics in neonates using multilayered diffusion model for normal and oblique incidences

NASA Astrophysics Data System (ADS)

Steinberg, Idan; Harbater, Osnat; Gannot, Israel

2014-07-01

The diffusion approximation is useful for many optical diagnostics modalities, such as near-infrared spectroscopy. However, the simple normal incidence, semi-infinite layer model may prove lacking in estimation of deep-tissue optical properties such as required for monitoring cerebral hemodynamics, especially in neonates. To answer this need, we present an analytical multilayered, oblique incidence diffusion model. Initially, the model equations are derived in vector-matrix form to facilitate fast and simple computation. Then, the spatiotemporal reflectance predicted by the model for a complex neonate head is compared with time-resolved Monte Carlo (TRMC) simulations under a wide range of physiologically feasible parameters. The high accuracy of the multilayer model is demonstrated in that the deviation from TRMC simulations is only a few percent even under the toughest conditions. We then turn to solve the inverse problem and estimate the oxygen saturation of deep brain tissues based on the temporal and spatial behaviors of the reflectance. Results indicate that temporal features of the reflectance are more sensitive to deep-layer optical parameters. The accuracy of estimation is shown to be more accurate and robust than the commonly used single-layer diffusion model. Finally, the limitations of such approaches are discussed thoroughly.

Multilayer network modeling of integrated biological systems. Comment on "Network science of biological systems at different scales: A review" by Gosak et al.

NASA Astrophysics Data System (ADS)

De Domenico, Manlio

2018-03-01

Biological systems, from a cell to the human brain, are inherently complex. A powerful representation of such systems, described by an intricate web of relationships across multiple scales, is provided by complex networks. Recently, several studies are highlighting how simple networks - obtained by aggregating or neglecting temporal or categorical description of biological data - are not able to account for the richness of information characterizing biological systems. More complex models, namely multilayer networks, are needed to account for interdependencies, often varying across time, of biological interacting units within a cell, a tissue or parts of an organism.

On the biological plausibility of grandmother cells: implications for neural network theories in psychology and neuroscience.

PubMed

Bowers, Jeffrey S

2009-01-01

A fundamental claim associated with parallel distributed processing (PDP) theories of cognition is that knowledge is coded in a distributed manner in mind and brain. This approach rejects the claim that knowledge is coded in a localist fashion, with words, objects, and simple concepts (e.g. "dog"), that is, coded with their own dedicated representations. One of the putative advantages of this approach is that the theories are biologically plausible. Indeed, advocates of the PDP approach often highlight the close parallels between distributed representations learned in connectionist models and neural coding in brain and often dismiss localist (grandmother cell) theories as biologically implausible. The author reviews a range a data that strongly challenge this claim and shows that localist models provide a better account of single-cell recording studies. The author also contrast local and alternative distributed coding schemes (sparse and coarse coding) and argues that common rejection of grandmother cell theories in neuroscience is due to a misunderstanding about how localist models behave. The author concludes that the localist representations embedded in theories of perception and cognition are consistent with neuroscience; biology only calls into question the distributed representations often learned in PDP models.

The importance of situation-specific encodings: analysis of a simple connectionist model of letter transposition effects

NASA Astrophysics Data System (ADS)

Fang, Shin-Yi; Smith, Garrett; Tabor, Whitney

2018-04-01

This paper analyses a three-layer connectionist network that solves a translation-invariance problem, offering a novel explanation for transposed letter effects in word reading. Analysis of the hidden unit encodings provides insight into two central issues in cognitive science: (1) What is the novelty of claims of "modality-specific" encodings? and (2) How can a learning system establish a complex internal structure needed to solve a problem? Although these topics (embodied cognition and learnability) are often treated separately, we find a close relationship between them: modality-specific features help the network discover an abstract encoding by causing it to break the initial symmetries of the hidden units in an effective way. While this neural model is extremely simple compared to the human brain, our results suggest that neural networks need not be black boxes and that carefully examining their encoding behaviours may reveal how they differ from classical ideas about the mind-world relationship.

Correlation analysis for the incubation period of prion disease

PubMed Central

Bae, Se-Eun; Jung, Sunghoon; Kim, Ha-Yeon; Son, Hyeon S.

2012-01-01

Previous studies have shown that genetic quantitative trait loci (QTL), strain barriers, inoculation dose and inoculation method modulate the incubation period of prion diseases. We examined the relationship between a diverse set of physical, genetic and immunological characteristics and the incubation period of prion disease using correlation analyses. We found that incubation period was highly correlated with brain weight. In addition, mean corpuscular volume and cell size were strongly correlated with incubation period, indicating that the physical magnitude of prion-infected organs or individual cells may be important in determining the incubation period. Given the same prion inoculation dose, animals with a lower brain weight, mean corpuscular volume or cell size may experience more virulent disease, as the effective concentration of abnormal prion, which might regulate the attachment rate of prions to aggregates, is increased with smaller capacity of brains and cells. This is partly consistent with previous theoretical modeling. The strong correlations between incubation period and physical properties of the brain and cells in this study suggest that the mechanism underlying prion disease pathology may be physical, indicating that the incubation process is governed by simple chemical stoichiometry. PMID:22561168

Gaining insight of fetal brain development with diffusion MRI and histology.

PubMed

Huang, Hao; Vasung, Lana

2014-02-01

Human brain is extraordinarily complex and yet its origin is a simple tubular structure. Its development during the fetal period is characterized by a series of accurately organized events which underlie the mechanisms of dramatic structural changes during fetal development. Revealing detailed anatomy at different stages of human fetal brain development provides insight on understanding not only this highly ordered process, but also the neurobiological foundations of cognitive brain disorders such as mental retardation, autism, schizophrenia, bipolar and language impairment. Diffusion tensor imaging (DTI) and histology are complementary tools which are capable of delineating the fetal brain structures at both macroscopic and microscopic levels. In this review, the structural development of the fetal brains has been characterized with DTI and histology. Major components of the fetal brain, including cortical plate, fetal white matter and cerebral wall layer between the ventricle and subplate, have been delineated with DTI and histology. Anisotropic metrics derived from DTI were used to quantify the microstructural changes during the dynamic process of human fetal cortical development and prenatal development of other animal models. Fetal white matter pathways have been traced with DTI-based tractography to reveal growth patterns of individual white matter tracts and corticocortical connectivity. These detailed anatomical accounts of the structural changes during fetal period may provide the clues of detecting developmental and cognitive brain disorders at their early stages. The anatomical information from DTI and histology may also provide reference standards for diagnostic radiology of premature newborns. Copyright © 2013 ISDN. Published by Elsevier Ltd. All rights reserved.

Anesthetic management of a case of armored brain.

PubMed

Gupta, Surender Kumar; Pandia, Mihir Prakash

2015-01-01

Armored brain is condition, which occurs due to calcification in a chronic subdural hematoma (SDH). Here, we are reporting a case of armored brain due to chronic SDH as a complication of vetriculoperitoneal shunt (VP shunt). Patient had undergone major surgery for removal of calcified hematoma. VP shunt is a simple surgery, but can lead to catastrophic complications like this. In this report, we had described this condition and its aspects.

Oh! What a Smart Baby: What You Need to Know about Children's Brain Development

ERIC Educational Resources Information Center

Arnold, Renea; Colburn, Nell

2005-01-01

Brain research is complicated, but its message is simple: babies are born learning and what they learn is up to us. New research on infant brain development shows that a child's experiences in the first three years of life have a distinct impact on her later development and learning. Here's why. All babies are born with one organ that is not fully…

Fast learning of simple perceptual discriminations reduces brain activation in working memory and in high-level auditory regions.

PubMed

Daikhin, Luba; Ahissar, Merav

2015-07-01

Introducing simple stimulus regularities facilitates learning of both simple and complex tasks. This facilitation may reflect an implicit change in the strategies used to solve the task when successful predictions regarding incoming stimuli can be formed. We studied the modifications in brain activity associated with fast perceptual learning based on regularity detection. We administered a two-tone frequency discrimination task and measured brain activation (fMRI) under two conditions: with and without a repeated reference tone. Although participants could not explicitly tell the difference between these two conditions, the introduced regularity affected both performance and the pattern of brain activation. The "No-Reference" condition induced a larger activation in frontoparietal areas known to be part of the working memory network. However, only the condition with a reference showed fast learning, which was accompanied by a reduction of activity in two regions: the left intraparietal area, involved in stimulus retention, and the posterior superior-temporal area, involved in representing auditory regularities. We propose that this joint reduction reflects a reduction in the need for online storage of the compared tones. We further suggest that this change reflects an implicit strategic shift "backwards" from reliance mainly on working memory networks in the "No-Reference" condition to increased reliance on detected regularities stored in high-level auditory networks.

Weak Higher-Order Interactions in Macroscopic Functional Networks of the Resting Brain.

PubMed

Huang, Xuhui; Xu, Kaibin; Chu, Congying; Jiang, Tianzi; Yu, Shan

2017-10-25

Interactions among different brain regions are usually examined through functional connectivity (FC) analysis, which is exclusively based on measuring pairwise correlations in activities. However, interactions beyond the pairwise level, that is, higher-order interactions (HOIs), are vital in understanding the behavior of many complex systems. So far, whether HOIs exist among brain regions and how they can affect the brain's activities remains largely elusive. To address these issues, here, we analyzed blood oxygenation level-dependent (BOLD) signals recorded from six typical macroscopic functional networks of the brain in 100 human subjects (46 males and 54 females) during the resting state. Through examining the binarized BOLD signals, we found that HOIs within and across individual networks were both very weak regardless of the network size, topology, degree of spatial proximity, spatial scales, and whether the global signal was regressed. To investigate the potential mechanisms underlying the weak HOIs, we analyzed the dynamics of a network model and also found that HOIs were generally weak within a wide range of key parameters provided that the overall dynamic feature of the model was similar to the empirical data and it was operating close to a linear fluctuation regime. Our results suggest that weak HOI may be a general property of brain's macroscopic functional networks, which implies the dominance of pairwise interactions in shaping brain activities at such a scale and warrants the validity of widely used pairwise-based FC approaches. SIGNIFICANCE STATEMENT To explain how activities of different brain areas are coordinated through interactions is essential to revealing the mechanisms underlying various brain functions. Traditionally, such an interaction structure is commonly studied using pairwise-based functional network analyses. It is unclear whether the interactions beyond the pairwise level (higher-order interactions or HOIs) play any role in this process. Here, we show that HOIs are generally weak in macroscopic brain networks. We also suggest a possible dynamical mechanism that may underlie this phenomenon. These results provide plausible explanation for the effectiveness of widely used pairwise-based approaches in analyzing brain networks. More importantly, it reveals a previously unknown, simple organization of the brain's macroscopic functional systems. Copyright © 2017 the authors 0270-6474/17/3710481-17$15.00/0.

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

Honorio, J.; Goldstein, R.; Honorio, J.

We propose a simple, well grounded classification technique which is suited for group classification on brain fMRI data sets that have high dimensionality, small number of subjects, high noise level, high subject variability, imperfect registration and capture subtle cognitive effects. We propose threshold-split region as a new feature selection method and majority voteas the classification technique. Our method does not require a predefined set of regions of interest. We use average acros ssessions, only one feature perexperimental condition, feature independence assumption, and simple classifiers. The seeming counter-intuitive approach of using a simple design is supported by signal processing and statisticalmore » theory. Experimental results in two block design data sets that capture brain function under distinct monetary rewards for cocaine addicted and control subjects, show that our method exhibits increased generalization accuracy compared to commonly used feature selection and classification techniques.« less

Arm Nerve Conduction Velocity (NCV), Brain NCV, Reaction Time, and Intelligence.

ERIC Educational Resources Information Center

Reed, T. Edward; Jensen, Arthur R.

1991-01-01

Correlations among peripheral nerve conduction velocity (NCV), brain NCV, simple and choice reaction times, and a standard measure of intelligence were investigated for 200 male college students. No correlation was found between any arm NCV and the intelligence score. Neurophysiological bases of human information processing and intelligence are…

Pregnancy and Birth-Related Brain Disorders.

ERIC Educational Resources Information Center

Fink, Leslie

1986-01-01

Although it once seemed simple to say that a single event such as birth trauma or asphyxia caused brain disorders like cerebral palsy, mental retardation, and epilepsy, a recent study showed that it is nearly impossible to pinpoint a single cause and its effects. Recommendations for further research are made. (BB)

The rabbit blood-shunt model for the study of acute and late sequelae of subarachnoid hemorrhage: technical aspects.

PubMed

Andereggen, Lukas; Neuschmelting, Volker; von Gunten, Michael; Widmer, Hans Rudolf; Takala, Jukka; Jakob, Stephan M; Fandino, Javier; Marbacher, Serge

2014-10-02

Early brain injury and delayed cerebral vasospasm both contribute to unfavorable outcomes after subarachnoid hemorrhage (SAH). Reproducible and controllable animal models that simulate both conditions are presently uncommon. Therefore, new models are needed in order to mimic human pathophysiological conditions resulting from SAH. This report describes the technical nuances of a rabbit blood-shunt SAH model that enables control of intracerebral pressure (ICP). An extracorporeal shunt is placed between the arterial system and the subarachnoid space, which enables examiner-independent SAH in a closed cranium. Step-by-step procedural instructions and necessary equipment are described, as well as technical considerations to produce the model with minimal mortality and morbidity. Important details required for successful surgical creation of this robust, simple and consistent ICP-controlled SAH rabbit model are described.

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.

Simultaneous Determination of Seven Neuroactive Steroids Associated with Depression in Rat Plasma and Brain by High Performance Liquid Chromatography-Tandem Mass Spectrometry.

PubMed

Wang, Youqiong; Tang, Lipeng; Yin, Wei; Chen, Jiesi; Leng, Tiandong; Zheng, Xiaoke; Zhu, Wenbo; Zhang, Haipeng; Qiu, Pengxin; Yang, Xiaoxiao; Yan, Guangmei; Hu, Haiyan

2016-01-01

Sensitive and specific biomarkers are required for the diagnosis and treatment of depression because the existing diagnostic criteria are subjective and could produce false positives or negatives. Some endogenous neuroactive steroids that have shown either antidepressant effects or concentration changes in individuals with depression could provide potential biomarkers. In this study, a simple and specific method was developed to simultaneously determine seven endogenous neuroactive steroids in biological samples: cortisone, cortisol, dehydroepiandrosterone, estradiol, progesterone, pregnenolone, and testosterone. After liquid-liquid extraction, chromatographic separation was achieved on a C18 column with gradient elution using water-methanol at a flow rate of 300 μL min(-1). Detection and quantitation were performed by tandem mass spectrometry with atmospheric pressure chemical ionization and selected reaction monitoring. Plasma and brain neuroactive steroid levels were then determined in control rats and rats exposed to forced swimming, a classical rodent model of depression. The results showed that the plasma concentrations of testosterone, pregnenolone, and progesterone significantly increased in rats exposed to the forced swimming test. In contrast, brain homogenate levels of cortisol, estradiol, and progesterone decreased, while pregnenolone levels were elevated in this model of depression. In conclusion, a new method to quantify neuroactive steroids was successfully developed and applied to their investigation in rat plasma and brain. The findings of this study indicated that plasma testosterone, pregnenolone, and progesterone levels could provide potential biomarkers for the diagnosis and treatment of depression.

A Taxonomy of Network Centric Warfare Architectures

DTIC Science & Technology

2008-01-01

mound structure emerges as a result of the termites following very simple rules, and exchanging very simple pheromone signals (Solé & Goodwin 2000...only fairly simple decisions.” For example, in far northern Australia, “magnetic termites ” build large termite mounds which are oriented north-south...and contain a complex ventilation system which controls temperature, humidity, and oxygen levels. But termite brains are too small to store a plan

Mapping Network Centric Operational Architectures to C2 and Software Architectures

DTIC Science & Technology

2007-06-01

Instead, the termite mound structure emerges as a result of the termites following very simple rules, and exchanging very simple pheromone signals...Each worker need make only fairly simple decisions.” For example, in far northern Australia, “magnetic termites ” build large termite mounds which are...oriented north-south and contain a complex ventilation system which controls temperature, humidity, and oxygen levels. But termite brains are too

Anesthetic management of a case of armored brain

PubMed Central

Gupta, Surender Kumar; Pandia, Mihir Prakash

2015-01-01

Armored brain is condition, which occurs due to calcification in a chronic subdural hematoma (SDH). Here, we are reporting a case of armored brain due to chronic SDH as a complication of vetriculoperitoneal shunt (VP shunt). Patient had undergone major surgery for removal of calcified hematoma. VP shunt is a simple surgery, but can lead to catastrophic complications like this. In this report, we had described this condition and its aspects. PMID:25558206

Psychiatric aspects of chronic organic brain syndrome.

PubMed

Peterson, G C

1976-11-01

In chronic organic brain syndrome, or dementia, the patient generally retreats to simple, familiar situations and resists involvement in others. The symptoms represent both organic deficits due to brain damage and psychologic reactions to the deficits. Some causes are treatable. Because of progressive change in the total behavior of the patient, major rearrangement of life-style is often necessary. The physician should guide both patient and family in this process. Medication may also be helpful.

Physical and computational fluid dynamics models for the hemodynamics of the artiodactyl carotid rete.

PubMed

O'Brien, Haley D; Bourke, Jason

2015-12-07

In the mammalian order Artiodactyla, the majority of arterial blood entering the intracranial cavity is supplied by a large arterial meshwork called the carotid rete. This vascular structure functionally replaces the internal carotid artery. Extensive experimentation has demonstrated that the artiodactyl carotid rete drives one of the most effective selective brain cooling mechanisms among terrestrial vertebrates. Less well understood is the impact that the unique morphology of the carotid rete may have on the hemodynamics of blood flow to the cerebrum. It has been hypothesized that, relative to the tubular internal carotid arteries of most other vertebrates, the highly convoluted morphology of the carotid rete may increase resistance to flow during extreme changes in cerebral blood pressure, essentially protecting the brain by acting as a resistor. We test this hypothesis by employing simple and complex physical models to a 3D surface rendering of the carotid rete of the domestic goat, Capra hircus. First, we modeled the potential for increased resistance across the carotid rete using an electrical circuit analog. The extensive branching of the rete equates to a parallel circuit that is bound in series by single tubular arteries, both upstream and downstream. This method calculated a near-zero increase in resistance across the rete. Because basic equations do not incorporate drag, shear-stress, and turbulence, we used computational fluid dynamics to simulate the impact of these computationally intensive factors on resistance. Ultimately, both simple and complex models demonstrated negligible changes in resistance and blood pressure across the arterial meshwork. We further tested the resistive potential of the carotid rete by simulating blood pressures known to occur in giraffes. Based on these models, we found resistance (and blood pressure mitigation as a whole) to be an unlikely function for the artiodactyl carotid rete. Copyright © 2015 Elsevier Ltd. All rights reserved.

Coping with Trial-to-Trial Variability of Event Related Signals: A Bayesian Inference Approach

NASA Technical Reports Server (NTRS)

Ding, Mingzhou; Chen, Youghong; Knuth, Kevin H.; Bressler, Steven L.; Schroeder, Charles E.

2005-01-01

In electro-neurophysiology, single-trial brain responses to a sensory stimulus or a motor act are commonly assumed to result from the linear superposition of a stereotypic event-related signal (e.g. the event-related potential or ERP) that is invariant across trials and some ongoing brain activity often referred to as noise. To extract the signal, one performs an ensemble average of the brain responses over many identical trials to attenuate the noise. To date, h s simple signal-plus-noise (SPN) model has been the dominant approach in cognitive neuroscience. Mounting empirical evidence has shown that the assumptions underlying this model may be overly simplistic. More realistic models have been proposed that account for the trial-to-trial variability of the event-related signal as well as the possibility of multiple differentially varying components within a given ERP waveform. The variable-signal-plus-noise (VSPN) model, which has been demonstrated to provide the foundation for separation and characterization of multiple differentially varying components, has the potential to provide a rich source of information for questions related to neural functions that complement the SPN model. Thus, being able to estimate the amplitude and latency of each ERP component on a trial-by-trial basis provides a critical link between the perceived benefits of the VSPN model and its many concrete applications. In this paper we describe a Bayesian approach to deal with this issue and the resulting strategy is referred to as the differentially Variable Component Analysis (dVCA). We compare the performance of dVCA on simulated data with Independent Component Analysis (ICA) and analyze neurobiological recordings from monkeys performing cognitive tasks.

Behavior-specific changes in transcriptional modules lead to distinct and predictable neurogenomic states

PubMed Central

Chandrasekaran, Sriram; Ament, Seth A.; Eddy, James A.; Rodriguez-Zas, Sandra L.; Schatz, Bruce R.; Price, Nathan D.; Robinson, Gene E.

2011-01-01

Using brain transcriptomic profiles from 853 individual honey bees exhibiting 48 distinct behavioral phenotypes in naturalistic contexts, we report that behavior-specific neurogenomic states can be inferred from the coordinated action of transcription factors (TFs) and their predicted target genes. Unsupervised hierarchical clustering of these transcriptomic profiles showed three clusters that correspond to three ecologically important behavioral categories: aggression, maturation, and foraging. To explore the genetic influences potentially regulating these behavior-specific neurogenomic states, we reconstructed a brain transcriptional regulatory network (TRN) model. This brain TRN quantitatively predicts with high accuracy gene expression changes of more than 2,000 genes involved in behavior, even for behavioral phenotypes on which it was not trained, suggesting that there is a core set of TFs that regulates behavior-specific gene expression in the bee brain, and other TFs more specific to particular categories. TFs playing key roles in the TRN include well-known regulators of neural and behavioral plasticity, e.g., Creb, as well as TFs better known in other biological contexts, e.g., NF-κB (immunity). Our results reveal three insights concerning the relationship between genes and behavior. First, distinct behaviors are subserved by distinct neurogenomic states in the brain. Second, the neurogenomic states underlying different behaviors rely upon both shared and distinct transcriptional modules. Third, despite the complexity of the brain, simple linear relationships between TFs and their putative target genes are a surprisingly prominent feature of the networks underlying behavior. PMID:21960440

Extraversion and neuroticism relate to topological properties of resting-state brain networks.

PubMed

Gao, Qing; Xu, Qiang; Duan, Xujun; Liao, Wei; Ding, Jurong; Zhang, Zhiqiang; Li, Yuan; Lu, Guangming; Chen, Huafu

2013-01-01

With the advent and development of modern neuroimaging techniques, there is an increasing interest in linking extraversion and neuroticism to anatomical and functional brain markers. Here, we aimed to test the theoretically derived biological personality model as proposed by Eysenck using graph theoretical analyses. Specifically, the association between the topological organization of whole-brain functional networks and extraversion/neuroticism was explored. To construct functional brain networks, functional connectivity among 90 brain regions was measured by temporal correlation using resting-state functional magnetic resonance imaging (fMRI) data of 71 healthy subjects. Graph theoretical analysis revealed a positive association of extraversion scores and normalized clustering coefficient values. These results suggested a more clustered configuration in brain networks of individuals high in extraversion, which could imply a higher arousal threshold and higher levels of arousal tolerance in the cortex of extraverts. On a local network level, we observed that a specific nodal measure, i.e., betweenness centrality (BC), was positively associated with neuroticism scores in the right precentral gyrus (PreCG), right caudate nucleus, right olfactory cortex, and bilateral amygdala. For individuals high in neuroticism, these results suggested a more frequent participation of these specific regions in information transition within the brain network and, in turn, may partly explain greater regional activation levels and lower arousal thresholds in these regions. In contrast, extraversion scores were positively correlated with BC in the right insula, while negatively correlated with BC in the bilateral middle temporal gyrus (MTG), indicating that the relationship between extraversion and regional arousal is not as simple as proposed by Eysenck.

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

What lies beneath? Diffusion EAP-based study of brain tissue microstructure.

PubMed

Zucchelli, Mauro; Brusini, Lorenza; Andrés Méndez, C; Daducci, Alessandro; Granziera, Cristina; Menegaz, Gloria

2016-08-01

Diffusion weighted magnetic resonance signals convey information about tissue microstructure and cytoarchitecture. In the last years, many models have been proposed for recovering the diffusion signal and extracting information to constitute new families of numerical indices. Two main categories of reconstruction models can be identified in diffusion magnetic resonance imaging (DMRI): ensemble average propagator (EAP) models and compartmental models. From both, descriptors can be derived for elucidating the underlying microstructural architecture. While compartmental models indices directly quantify the fraction of different cell compartments in each voxel, EAP-derived indices are only a derivative measure and the effect of the different microstructural configurations on the indices is still unclear. In this paper, we analyze three EAP indices calculated using the 3D Simple Harmonic Oscillator based Reconstruction and Estimation (3D-SHORE) model and estimate their changes with respect to the principal microstructural configurations. We take advantage of the state of the art simulations to quantify the variations of the indices with the simulation parameters. Analysis of in-vivo data correlates the EAP indices with the microstructural parameters obtained from the Neurite Orientation Dispersion and Density Imaging (NODDI) model as a pseudo ground truth for brain data. Results show that the EAP derived indices convey information on the tissue microstructure and that their combined values directly reflect the configuration of the different compartments in each voxel. Copyright © 2016 Elsevier B.V. All rights reserved.

Eating breakfast enhances the efficiency of neural networks engaged during mental arithmetic in school-aged children

USDA-ARS?s Scientific Manuscript database

Are there effects of morning nutrition on brain functions important for learning and performance in children? We used time-frequency analyses of EEG activity recorded while children solved simple math problems to study how brain processes were influenced by eating or skipping breakfast. Participants...

Hemispheric Visual Attentional Imbalance in Patients with Traumatic Brain Injury

ERIC Educational Resources Information Center

Pavlovskaya, Marina; Groswasser, Zeev; Keren, Ofer; Mordvinov, Eugene; Hochstein, Shaul

2007-01-01

We find a spatially asymmetric allocation of attention in patients with traumatic brain injury (TBI) despite the lack of obvious asymmetry in neurological indicators. Identification performance was measured for simple spatial patterns presented briefly to a locus 5 degrees into the left or right hemifield, after precuing attention to the same…

Effective Connectivity Analysis of the Brain Network in Drivers during Actual Driving Using Near-Infrared Spectroscopy

PubMed Central

Liu, Zhian; Zhang, Ming; Xu, Gongcheng; Huo, Congcong; Tan, Qitao; Li, Zengyong; Yuan, Quan

2017-01-01

Driving a vehicle is a complex activity that requires high-level brain functions. This study aimed to assess the change in effective connectivity (EC) between the prefrontal cortex (PFC), motor-related areas (MA) and vision-related areas (VA) in the brain network among the resting, simple-driving and car-following states. Twelve young male right-handed adults were recruited to participate in an actual driving experiment. The brain delta [HbO2] signals were continuously recorded using functional near infrared spectroscopy (fNIRS) instruments. The conditional Granger causality (GC) analysis, which is a data-driven method that can explore the causal interactions among different brain areas, was performed to evaluate the EC. The results demonstrated that the hemodynamic activity level of the brain increased with an increase in the cognitive workload. The connection strength among PFC, MA and VA increased from the resting state to the simple-driving state, whereas the connection strength relatively decreased during the car-following task. The PFC in EC appeared as the causal target, while the MA and VA appeared as the causal sources. However, l-MA turned into causal targets with the subtask of car-following. These findings indicate that the hemodynamic activity level of the cerebral cortex increases linearly with increasing cognitive workload. The EC of the brain network can be strengthened by a cognitive workload, but also can be weakened by a superfluous cognitive workload such as driving with subtasks. PMID:29163083

Brain Events Underlying Episodic Memory Changes in Aging: A Longitudinal Investigation of Structural and Functional Connectivity.

PubMed

Fjell, Anders M; Sneve, Markus H; Storsve, Andreas B; Grydeland, Håkon; Yendiki, Anastasia; Walhovd, Kristine B

2016-03-01

Episodic memories are established and maintained by close interplay between hippocampus and other cortical regions, but degradation of a fronto-striatal network has been suggested to be a driving force of memory decline in aging. We wanted to directly address how changes in hippocampal-cortical versus striatal-cortical networks over time impact episodic memory with age. We followed 119 healthy participants (20-83 years) for 3.5 years with repeated tests of episodic verbal memory and magnetic resonance imaging for quantification of functional and structural connectivity and regional brain atrophy. While hippocampal-cortical functional connectivity predicted memory change in young, changes in cortico-striatal functional connectivity were related to change in recall in older adults. Within each age group, effects of functional and structural connectivity were anatomically closely aligned. Interestingly, the relationship between functional connectivity and memory was strongest in the age ranges where the rate of reduction of the relevant brain structure was lowest, implying selective impacts of the different brain events on memory. Together, these findings suggest a partly sequential and partly simultaneous model of brain events underlying cognitive changes in aging, where different functional and structural events are more or less important in various time windows, dismissing a simple uni-factorial view on neurocognitive aging. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

High-speed 3D imaging of cellular activity in the brain using axially-extended beams and light sheets.

PubMed

Hillman, Elizabeth Mc; Voleti, Venkatakaushik; Patel, Kripa; Li, Wenze; Yu, Hang; Perez-Campos, Citlali; Benezra, Sam E; Bruno, Randy M; Galwaduge, Pubudu T

2018-06-01

As optical reporters and modulators of cellular activity have become increasingly sophisticated, the amount that can be learned about the brain via high-speed cellular imaging has increased dramatically. However, despite fervent innovation, point-scanning microscopy is facing a fundamental limit in achievable 3D imaging speeds and fields of view. A range of alternative approaches are emerging, some of which are moving away from point-scanning to use axially-extended beams or sheets of light, for example swept confocally aligned planar excitation (SCAPE) microscopy. These methods are proving effective for high-speed volumetric imaging of the nervous system of small organisms such as Drosophila (fruit fly) and D. Rerio (Zebrafish), and are showing promise for imaging activity in the living mammalian brain using both single and two-photon excitation. This article describes these approaches and presents a simple model that demonstrates key advantages of axially-extended illumination over point-scanning strategies for high-speed volumetric imaging, including longer integration times per voxel, improved photon efficiency and reduced photodamage. Copyright © 2018 Elsevier Ltd. All rights reserved.

Issues in localization of brain function: The case of lateralized frontal cortex in cognition, emotion, and psychopathology.

PubMed

Miller, Gregory A; Crocker, Laura D; Spielberg, Jeffrey M; Infantolino, Zachary P; Heller, Wendy

2013-01-01

The appeal of simple, sweeping portraits of large-scale brain mechanisms relevant to psychological phenomena competes with a rich, complex research base. As a prominent example, two views of frontal brain organization have emphasized dichotomous lateralization as a function of either emotional valence (positive/negative) or approach/avoidance motivation. Compelling findings support each. The literature has struggled to choose between them for three decades, without success. Both views are proving untenable as comprehensive models. Evidence of other frontal lateralizations, involving distinctions among dimensions of depression and anxiety, make a dichotomous view even more problematic. Recent evidence indicates that positive valence and approach motivation are associated with different areas in the left-hemisphere. Findings that appear contradictory at the level of frontal lobes as the units of analysis can be accommodated because hemodynamic and electromagnetic neuroimaging studies suggest considerable functional differentiation, in specialization and activation, of subregions of frontal cortex, including their connectivity to each other and to other regions. Such findings contribute to a more nuanced understanding of functional localization that accommodates aspects of multiple theoretical perspectives.

Issues in localization of brain function: The case of lateralized frontal cortex in cognition, emotion, and psychopathology

PubMed Central

Miller, Gregory A.; Crocker, Laura D.; Spielberg, Jeffrey M.; Infantolino, Zachary P.; Heller, Wendy

2013-01-01

The appeal of simple, sweeping portraits of large-scale brain mechanisms relevant to psychological phenomena competes with a rich, complex research base. As a prominent example, two views of frontal brain organization have emphasized dichotomous lateralization as a function of either emotional valence (positive/negative) or approach/avoidance motivation. Compelling findings support each. The literature has struggled to choose between them for three decades, without success. Both views are proving untenable as comprehensive models. Evidence of other frontal lateralizations, involving distinctions among dimensions of depression and anxiety, make a dichotomous view even more problematic. Recent evidence indicates that positive valence and approach motivation are associated with different areas in the left-hemisphere. Findings that appear contradictory at the level of frontal lobes as the units of analysis can be accommodated because hemodynamic and electromagnetic neuroimaging studies suggest considerable functional differentiation, in specialization and activation, of subregions of frontal cortex, including their connectivity to each other and to other regions. Such findings contribute to a more nuanced understanding of functional localization that accommodates aspects of multiple theoretical perspectives. PMID:23386814

PET Mapping for Brain-Computer Interface Stimulation of the Ventroposterior Medial Nucleus of the Thalamus in Rats with Implanted Electrodes.

PubMed

Zhu, Yunqi; Xu, Kedi; Xu, Caiyun; Zhang, Jiacheng; Ji, Jianfeng; Zheng, Xiaoxiang; Zhang, Hong; Tian, Mei

2016-07-01

Brain-computer interface (BCI) technology has great potential for improving the quality of life for neurologic patients. This study aimed to use PET mapping for BCI-based stimulation in a rat model with electrodes implanted in the ventroposterior medial (VPM) nucleus of the thalamus. PET imaging studies were conducted before and after stimulation of the right VPM. Stimulation induced significant orienting performance. (18)F-FDG uptake increased significantly in the paraventricular thalamic nucleus, septohippocampal nucleus, olfactory bulb, left crus II of the ansiform lobule of the cerebellum, and bilaterally in the lateral septum, amygdala, piriform cortex, endopiriform nucleus, and insular cortex, but it decreased in the right secondary visual cortex, right simple lobule of the cerebellum, and bilaterally in the somatosensory cortex. This study demonstrated that PET mapping after VPM stimulation can identify specific brain regions associated with orienting performance. PET molecular imaging may be an important approach for BCI-based research and its clinical applications. © 2016 by the Society of Nuclear Medicine and Molecular Imaging, Inc.

Rapid, simple and inexpensive production of custom 3D printed equipment for large-volume fluorescence microscopy

PubMed Central

Tyson, Adam L.; Hilton, Stephen T.; Andreae, Laura C.

2015-01-01

The cost of 3D printing has reduced dramatically over the last few years and is now within reach of many scientific laboratories. This work presents an example of how 3D printing can be applied to the development of custom laboratory equipment that is specifically adapted for use with the novel brain tissue clearing technique, CLARITY. A simple, freely available online software tool was used, along with consumer-grade equipment, to produce a brain slicing chamber and a combined antibody staining and imaging chamber. Using standard 3D printers we were able to produce research-grade parts in an iterative manner at a fraction of the cost of commercial equipment. 3D printing provides a reproducible, flexible, simple and cost-effective method for researchers to produce the equipment needed to quickly adopt new methods. PMID:25797056

Neural associative memories for the integration of language, vision and action in an autonomous agent.

PubMed

Markert, H; Kaufmann, U; Kara Kayikci, Z; Palm, G

2009-03-01

Language understanding is a long-standing problem in computer science. However, the human brain is capable of processing complex languages with seemingly no difficulties. This paper shows a model for language understanding using biologically plausible neural networks composed of associative memories. The model is able to deal with ambiguities on the single word and grammatical level. The language system is embedded into a robot in order to demonstrate the correct semantical understanding of the input sentences by letting the robot perform corresponding actions. For that purpose, a simple neural action planning system has been combined with neural networks for visual object recognition and visual attention control mechanisms.

Squids in the Study of Cerebral Magnetic Field

NASA Astrophysics Data System (ADS)

Romani, G. L.; Narici, L.

The following sections are included: * INTRODUCTION * HISTORICAL OVERVIEW * NEUROMAGNETIC FIELDS AND AMBIENT NOISE * DETECTORS * Room temperature sensors * SQUIDs * DETECTION COILS * Magnetometers * Gradiometers * Balancing * Planar gradiometers * Choice of the gradiometer parameters * MODELING * Current pattern due to neural excitations * Action potentials and postsynaptic currents * The current dipole model * Neural population and detected fields * Spherically bounded medium * SPATIAL CONFIGURATION OF THE SENSORS * SOURCE LOCALIZATION * Localization procedure * Experimental accuracy and reproducibility * SIGNAL PROCESSING * Analog Filtering * Bandpass filters * Line rejection filters * DATA ANALYSIS * Analysis of evoked/event-related responses * Simple average * Selected average * Recursive techniques * Similarity analysis * Analysis of spontaneous activity * Mapping and localization * EXAMPLES OF NEUROMAGNETIC STUDIES * Neuromagnetic measurements * Studies on the normal brain * Clinical applications * Epilepsy * Tinnitus * CONCLUSIONS * ACKNOWLEDGEMENTS * REFERENCES

Is Cycloxygenase-2 (COX-2) a Major Component of the Mechanism Responsible for Microvascular Remodeling in the Brain?

NASA Astrophysics Data System (ADS)

LaManna, Joseph C.; Sun, Xiaoyan; Ivy, Andre D.; Ward, Nicole L.

We have used a relatively simple model of hypoxia that triggers adaptive structural changes in the cerebral microvasculature to study the process of physiological angiogenesis. This model can be used to obtain mechanistic data for the processes that probably underlie the dynamic structural changes that occur in learning and the control of oxygen availability to the neurovascular unit. These mechanisms are broadly involved in a wide variety of pathophysiological processes. This is the vascular component to CNS functional plasticity, supporting learning and adaptation. The angiogenic process may wane with age, contributing to the decreasing ability to survive metabolic stress and the diminution of neuronal plasticity.

Big words, halved brains and small worlds: complex brain networks of figurative language comprehension.

PubMed

Arzouan, Yossi; Solomon, Sorin; Faust, Miriam; Goldstein, Abraham

2011-04-27

Language comprehension is a complex task that involves a wide network of brain regions. We used topological measures to qualify and quantify the functional connectivity of the networks used under various comprehension conditions. To that aim we developed a technique to represent functional networks based on EEG recordings, taking advantage of their excellent time resolution in order to capture the fast processes that occur during language comprehension. Networks were created by searching for a specific causal relation between areas, the negative feedback loop, which is ubiquitous in many systems. This method is a simple way to construct directed graphs using event-related activity, which can then be analyzed topologically. Brain activity was recorded while subjects read expressions of various types and indicated whether they found them meaningful. Slightly different functional networks were obtained for event-related activity evoked by each expression type. The differences reflect the special contribution of specific regions in each condition and the balance of hemispheric activity involved in comprehending different types of expressions and are consistent with the literature in the field. Our results indicate that representing event-related brain activity as a network using a simple temporal relation, such as the negative feedback loop, to indicate directional connectivity is a viable option for investigation which also derives new information about aspects not reflected in the classical methods for investigating brain activity.

Noncoding origins of anthropoid traits and a new null model of transposon functionalization.

PubMed

del Rosario, Ricardo C H; Rayan, Nirmala Arul; Prabhakar, Shyam

2014-09-01

Little is known about novel genetic elements that drove the emergence of anthropoid primates. We exploited the sequencing of the marmoset genome to identify 23,849 anthropoid-specific constrained (ASC) regions and confirmed their robust functional signatures. Of the ASC base pairs, 99.7% were noncoding, suggesting that novel anthropoid functional elements were overwhelmingly cis-regulatory. ASCs were highly enriched in loci associated with fetal brain development, motor coordination, neurotransmission, and vision, thus providing a large set of candidate elements for exploring the molecular basis of hallmark primate traits. We validated ASC192 as a primate-specific enhancer in proliferative zones of the developing brain. Unexpectedly, transposable elements (TEs) contributed to >56% of ASCs, and almost all TE families showed functional potential similar to that of nonrepetitive DNA. Three L1PA repeat-derived ASCs displayed coherent eye-enhancer function, thus demonstrating that the "gene-battery" model of TE functionalization applies to enhancers in vivo. Our study provides fundamental insights into genome evolution and the origins of anthropoid phenotypes and supports an elegantly simple new null model of TE exaptation. © 2014 del Rosario et al.; Published by Cold Spring Harbor Laboratory Press.

Let thy left brain know what thy right brain doeth: Inter-hemispheric compensation of functional deficits after brain damage.

PubMed

Bartolomeo, Paolo; Thiebaut de Schotten, Michel

2016-12-01

Recent evidence revealed the importance of inter-hemispheric communication for the compensation of functional deficits after brain damage. This review summarises the biological consequences observed using histology as well as the longitudinal findings measured with magnetic resonance imaging methods in brain damaged animals and patients. In particular, we discuss the impact of post-stroke brain hyperactivity on functional recovery in relation to time. The reviewed evidence also suggests that the proportion of the preserved functional network both in the lesioned and in the intact hemispheres, rather than the simple lesion location, determines the extent of functional recovery. Hence, future research exploring longitudinal changes in patients with brain damage may unveil potential biomarkers underlying functional recovery. Copyright © 2016 Elsevier Ltd. All rights reserved.

Pharmacological Rescue of Long-Term Potentiation in Alzheimer Diseased Synapses

PubMed Central

Berchtold, Nicole C.; Lynch, Gary; Cotman, Carl W.

2017-01-01

Long-term potentiation (LTP) is an activity-dependent and persistent increase in synaptic transmission. Currently available techniques to measure LTP are time-intensive and require highly specialized expertise and equipment, and thus are not well suited for screening of multiple candidate treatments, even in animal models. To expand and facilitate the analysis of LTP, here we use a flow cytometry-based method to track chemically induced LTP by detecting surface AMPA receptors in isolated synaptosomes: fluorescence analysis of single-synapse long-term potentiation (FASS-LTP). First, we demonstrate that FASS-LTP is simple, sensitive, and models electrically induced LTP recorded in intact circuitries. Second, we conducted FASS-LTP analysis in two well-characterized Alzheimer's disease (AD) mouse models (3xTg and Tg2576) and, importantly, in cryopreserved human AD brain samples. By profiling hundreds of synaptosomes, our data provide the first direct evidence to support the idea that synapses from AD brain are intrinsically defective in LTP. Third, we used FASS-LTP for drug evaluation in human synaptosomes. Testing a panel of modulators of cAMP and cGMP signaling pathways, FASS-LTP identified vardenafil and Bay-73–6691 (phosphodiesterase-5 and -9 inhibitors, respectively) as potent enhancers of LTP in synaptosomes from AD cases. These results indicate that our approach could provide the basis for protocols to study LTP in both healthy and diseased human brains, a previously unattainable goal. SIGNIFICANCE STATEMENT Learning and memory depend on the ability of synapses to strengthen in response to activity. Long-term potentiation (LTP) is a rapid and persistent increase in synaptic transmission that is thought to be affected in Alzheimer's disease (AD). However, direct evidence of LTP deficits in human AD brain has been elusive, primarily due to methodological limitations. Here, we analyze LTP in isolated synapses from AD brain using a novel approach that allows testing LTP in cryopreserved brain. Our analysis of hundreds of synapses supports the idea that AD-diseased synapses are intrinsically defective in LTP. Further, we identified pharmacological agents that rescue LTP in AD, thus opening up a new avenue for drug screening and evaluation of strategies for alleviating memory impairments. PMID:27986924

The ratio of acetate-to-glucose oxidation in astrocytes from a single 13C NMR spectrum of cerebral cortex.

PubMed

Marin-Valencia, Isaac; Hooshyar, M Ali; Pichumani, Kumar; Sherry, A Dean; Malloy, Craig R

2015-01-01

The (13) C-labeling patterns in glutamate and glutamine from brain tissue are quite different after infusion of a mixture of (13) C-enriched glucose and acetate. Two processes contribute to this observation, oxidation of acetate by astrocytes but not neurons, and preferential incorporation of α-ketoglutarate into glutamate in neurons, and incorporation of α-ketoglutarate into glutamine in astrocytes. The acetate:glucose ratio, introduced previously for analysis of a single (13) C NMR spectrum, provides a useful index of acetate and glucose oxidation in the brain tissue. However, quantitation of relative substrate oxidation at the cell compartment level has not been reported. A simple mathematical method is presented to quantify the ratio of acetate-to-glucose oxidation in astrocytes, based on the standard assumption that neurons do not oxidize acetate. Mice were infused with [1,2-(13) C]acetate and [1,6-(13) C]glucose, and proton decoupled (13) C NMR spectra of cortex extracts were acquired. A fit of those spectra to the model indicated that (13) C-labeled acetate and glucose contributed approximately equally to acetyl-CoA (0.96) in astrocytes. As this method relies on a single (13) C NMR spectrum, it can be readily applied to multiple physiologic and pathologic conditions. Differences in (13) C labeling of brain glutamate and glutamine have been attributed to metabolic compartmentation. The acetate:glucose ratio, introduced for description of a (13) C NMR (nuclear magnetic resonance) spectrum, is an index of glucose and acetate oxidation in brain tissue. A simple mathematical method is presented to quantify the ratio of acetate-to-glucose oxidation in astrocytes from a single NMR spectrum. As kinetic analysis is not required, the method is readily applicable to analysis of tissue extracts. α-KG = alpha-ketoglutarate; CAC = citric acid cycle; GLN = glutamine; GLU = glutamate. © 2014 International Society for Neurochemistry.

Harmonic Training and the Formation of Pitch Representation in a Neural Network Model of the Auditory Brain

PubMed Central

Ahmad, Nasir; Higgins, Irina; Walker, Kerry M. M.; Stringer, Simon M.

2016-01-01

Attempting to explain the perceptual qualities of pitch has proven to be, and remains, a difficult problem. The wide range of sounds which elicit pitch and a lack of agreement across neurophysiological studies on how pitch is encoded by the brain have made this attempt more difficult. In describing the potential neural mechanisms by which pitch may be processed, a number of neural networks have been proposed and implemented. However, no unsupervised neural networks with biologically accurate cochlear inputs have yet been demonstrated. This paper proposes a simple system in which pitch representing neurons are produced in a biologically plausible setting. Purely unsupervised regimes of neural network learning are implemented and these prove to be sufficient in identifying the pitch of sounds with a variety of spectral profiles, including sounds with missing fundamental frequencies and iterated rippled noises. PMID:27047368

Processing of visual semantic information to concrete words: temporal dynamics and neural mechanisms indicated by event-related brain potentials( ).

PubMed

van Schie, Hein T; Wijers, Albertus A; Mars, Rogier B; Benjamins, Jeroen S; Stowe, Laurie A

2005-05-01

Event-related brain potentials were used to study the retrieval of visual semantic information to concrete words, and to investigate possible structural overlap between visual object working memory and concreteness effects in word processing. Subjects performed an object working memory task that involved 5 s retention of simple 4-angled polygons (load 1), complex 10-angled polygons (load 2), and a no-load baseline condition. During the polygon retention interval subjects were presented with a lexical decision task to auditory presented concrete (imageable) and abstract (nonimageable) words, and pseudowords. ERP results are consistent with the use of object working memory for the visualisation of concrete words. Our data indicate a two-step processing model of visual semantics in which visual descriptive information of concrete words is first encoded in semantic memory (indicated by an anterior N400 and posterior occipital positivity), and is subsequently visualised via the network for object working memory (reflected by a left frontal positive slow wave and a bilateral occipital slow wave negativity). Results are discussed in the light of contemporary models of semantic memory.

Brain delivery of proteins via their fatty acid and block copolymer modifications

PubMed Central

Yi, Xiang; Kabanov, Alexander V.

2014-01-01

It is well known that hydrophobic small molecules penetrate cell membranes better than hydrophilic molecules. Amphiphilic molecules that dissolve both in lipid and aqueous phases are best suited for membrane transport. Transport of biomacromolecules across physiological barriers, e.g. the blood-brain barrier, is greatly complicated by the unique structure and function of such barriers. Two decades ago we adopted a simple philosophy that to increase protein delivery to the brain one needs to modify this protein with hydrophobic moieties. With this general idea we began modifying proteins (antibodies, enzymes, hormones, etc.) with either hydrophobic fatty acid residues or amphiphilic block copolymer moieties, such as poy(ethylene oxide)-poly(propylene oxide)-poly(ethylene oxide) (pluronics or poloxamers) and more recently, poly(2-oxasolines). This simple approach has resulted in impressive successes in CNS drug delivery. We present a retrospective overview of these works initiated in the Soviet Union in 1980s, and then continued in the United States and other countries. Notably some of the early findings were later corroborated by brain pharmacokinetic data. Industrial development of several drug candidates employing these strategies has followed. Overall modification by hydrophobic fatty acids residues or amphiphilic block copolymers represents a promising and relatively safe strategy to deliver proteins to the brain. PMID:24160902

Predictive Feedback Can Account for Biphasic Responses in the Lateral Geniculate Nucleus

PubMed Central

Jehee, Janneke F. M.; Ballard, Dana H.

2009-01-01

Biphasic neural response properties, where the optimal stimulus for driving a neural response changes from one stimulus pattern to the opposite stimulus pattern over short periods of time, have been described in several visual areas, including lateral geniculate nucleus (LGN), primary visual cortex (V1), and middle temporal area (MT). We describe a hierarchical model of predictive coding and simulations that capture these temporal variations in neuronal response properties. We focus on the LGN-V1 circuit and find that after training on natural images the model exhibits the brain's LGN-V1 connectivity structure, in which the structure of V1 receptive fields is linked to the spatial alignment and properties of center-surround cells in the LGN. In addition, the spatio-temporal response profile of LGN model neurons is biphasic in structure, resembling the biphasic response structure of neurons in cat LGN. Moreover, the model displays a specific pattern of influence of feedback, where LGN receptive fields that are aligned over a simple cell receptive field zone of the same polarity decrease their responses while neurons of opposite polarity increase their responses with feedback. This phase-reversed pattern of influence was recently observed in neurophysiology. These results corroborate the idea that predictive feedback is a general coding strategy in the brain. PMID:19412529

Peripersonal Space and Margin of Safety around the Body: Learning Visuo-Tactile Associations in a Humanoid Robot with Artificial Skin

PubMed Central

Roncone, Alessandro; Fadiga, Luciano; Metta, Giorgio

2016-01-01

This paper investigates a biologically motivated model of peripersonal space through its implementation on a humanoid robot. Guided by the present understanding of the neurophysiology of the fronto-parietal system, we developed a computational model inspired by the receptive fields of polymodal neurons identified, for example, in brain areas F4 and VIP. The experiments on the iCub humanoid robot show that the peripersonal space representation i) can be learned efficiently and in real-time via a simple interaction with the robot, ii) can lead to the generation of behaviors like avoidance and reaching, and iii) can contribute to the understanding the biological principle of motor equivalence. More specifically, with respect to i) the present model contributes to hypothesizing a learning mechanisms for peripersonal space. In relation to point ii) we show how a relatively simple controller can exploit the learned receptive fields to generate either avoidance or reaching of an incoming stimulus and for iii) we show how the robot can select arbitrary body parts as the controlled end-point of an avoidance or reaching movement. PMID:27711136

Possible biomechanical origins of the long-range correlations in stride intervals of walking

NASA Astrophysics Data System (ADS)

Gates, Deanna H.; Su, Jimmy L.; Dingwell, Jonathan B.

2007-07-01

When humans walk, the time duration of each stride varies from one stride to the next. These temporal fluctuations exhibit long-range correlations. It has been suggested that these correlations stem from higher nervous system centers in the brain that control gait cycle timing. Existing proposed models of this phenomenon have focused on neurophysiological mechanisms that might give rise to these long-range correlations, and generally ignored potential alternative mechanical explanations. We hypothesized that a simple mechanical system could also generate similar long-range correlations in stride times. We modified a very simple passive dynamic model of bipedal walking to incorporate forward propulsion through an impulsive force applied to the trailing leg at each push-off. Push-off forces were varied from step to step by incorporating both “sensory” and “motor” noise terms that were regulated by a simple proportional feedback controller. We generated 400 simulations of walking, with different combinations of sensory noise, motor noise, and feedback gain. The stride time data from each simulation were analyzed using detrended fluctuation analysis to compute a scaling exponent, α. This exponent quantified how each stride interval was correlated with previous and subsequent stride intervals over different time scales. For different variations of the noise terms and feedback gain, we obtained short-range correlations (α<0.5), uncorrelated time series (α=0.5), long-range correlations (0.5<α<1.0), or Brownian motion (α>1.0). Our results indicate that a simple biomechanical model of walking can generate long-range correlations and thus perhaps these correlations are not a complex result of higher level neuronal control, as has been previously suggested.

Brain Hyper-Connectivity and Operation-Specific Deficits during Arithmetic Problem Solving in Children with Developmental Dyscalculia

ERIC Educational Resources Information Center

Rosenberg-Lee, Miriam; Ashkenazi, Sarit; Chen, Tianwen; Young, Christina B.; Geary, David C.; Menon, Vinod

2015-01-01

Developmental dyscalculia (DD) is marked by specific deficits in processing numerical and mathematical information despite normal intelligence (IQ) and reading ability. We examined how brain circuits used by young children with DD to solve simple addition and subtraction problems differ from those used by typically developing (TD) children who…

Teaching Kids or Training Brains?

ERIC Educational Resources Information Center

Thorp, Carmany

2009-01-01

Learning style, emotional health, and short term memory all act in concert to affect one's capacity to learn on any given day. However, with a few simple rules, lessons can be structured and delivered to meet more kids' needs more often. Current brain research gives teachers a new way to understand the "best practices" they have been taught. The…

The Impact of Brain-Based Instruction on Reading Achievement in a Second-Grade Classroom

ERIC Educational Resources Information Center

McNamee, Merideth M.

2011-01-01

School accountability and high-stakes testing often shift classroom focus from the use of engaging learning activities that promote critical thinking and creativity to simple test preparation practices. Using brain research as a guide, educators may be able to improve test scores, while still providing a balanced education that promotes critical…

Compact and mobile high resolution PET brain imager

DOEpatents

Majewski, Stanislaw [Yorktown, VA; Proffitt, James [Newport News, VA

2011-02-08

A brain imager includes a compact ring-like static PET imager mounted in a helmet-like structure. When attached to a patient's head, the helmet-like brain imager maintains the relative head-to-imager geometry fixed through the whole imaging procedure. The brain imaging helmet contains radiation sensors and minimal front-end electronics. A flexible mechanical suspension/harness system supports the weight of the helmet thereby allowing for patient to have limited movements of the head during imaging scans. The compact ring-like PET imager enables very high resolution imaging of neurological brain functions, cancer, and effects of trauma using a rather simple mobile scanner with limited space needs for use and storage.

A Biologically Constrained, Mathematical Model of Cortical Wave Propagation Preceding Seizure Termination

PubMed Central

González-Ramírez, Laura R.; Ahmed, Omar J.; Cash, Sydney S.; Wayne, C. Eugene; Kramer, Mark A.

2015-01-01

Epilepsy—the condition of recurrent, unprovoked seizures—manifests in brain voltage activity with characteristic spatiotemporal patterns. These patterns include stereotyped semi-rhythmic activity produced by aggregate neuronal populations, and organized spatiotemporal phenomena, including waves. To assess these spatiotemporal patterns, we develop a mathematical model consistent with the observed neuronal population activity and determine analytically the parameter configurations that support traveling wave solutions. We then utilize high-density local field potential data recorded in vivo from human cortex preceding seizure termination from three patients to constrain the model parameters, and propose basic mechanisms that contribute to the observed traveling waves. We conclude that a relatively simple and abstract mathematical model consisting of localized interactions between excitatory cells with slow adaptation captures the quantitative features of wave propagation observed in the human local field potential preceding seizure termination. PMID:25689136

From homeostasis to behavior: Balanced activity in an exploration of embodied dynamic environmental-neural interaction.

PubMed

Hellyer, Peter John; Clopath, Claudia; Kehagia, Angie A; Turkheimer, Federico E; Leech, Robert

2017-08-01

In recent years, there have been many computational simulations of spontaneous neural dynamics. Here, we describe a simple model of spontaneous neural dynamics that controls an agent moving in a simple virtual environment. These dynamics generate interesting brain-environment feedback interactions that rapidly destabilize neural and behavioral dynamics demonstrating the need for homeostatic mechanisms. We investigate roles for homeostatic plasticity both locally (local inhibition adjusting to balance excitatory input) as well as more globally (regional "task negative" activity that compensates for "task positive", sensory input in another region) balancing neural activity and leading to more stable behavior (trajectories through the environment). Our results suggest complementary functional roles for both local and macroscale mechanisms in maintaining neural and behavioral dynamics and a novel functional role for macroscopic "task-negative" patterns of activity (e.g., the default mode network).

Trace conditioning in insects—keep the trace!

PubMed Central

Dylla, Kristina V.; Galili, Dana S.; Szyszka, Paul; Lüdke, Alja

2013-01-01

Trace conditioning is a form of associative learning that can be induced by presenting a conditioned stimulus (CS) and an unconditioned stimulus (US) following each other, but separated by a temporal gap. This gap distinguishes trace conditioning from classical delay conditioning, where the CS and US overlap. To bridge the temporal gap between both stimuli and to form an association between CS and US in trace conditioning, the brain must keep a neural representation of the CS after its termination—a stimulus trace. Behavioral and physiological studies on trace and delay conditioning revealed similarities between the two forms of learning, like similar memory decay and similar odor identity perception in invertebrates. On the other hand differences were reported also, like the requirement of distinct brain structures in vertebrates or disparities in molecular mechanisms in both vertebrates and invertebrates. For example, in commonly used vertebrate conditioning paradigms the hippocampus is necessary for trace but not for delay conditioning, and Drosophila delay conditioning requires the Rutabaga adenylyl cyclase (Rut-AC), which is dispensable in trace conditioning. It is still unknown how the brain encodes CS traces and how they are associated with a US in trace conditioning. Insects serve as powerful models to address the mechanisms underlying trace conditioning, due to their simple brain anatomy, behavioral accessibility and established methods of genetic interference. In this review we summarize the recent progress in insect trace conditioning on the behavioral and physiological level and emphasize similarities and differences compared to delay conditioning. Moreover, we examine proposed molecular and computational models and reassess different experimental approaches used for trace conditioning. PMID:23986710

Methods for Generating Complex Networks with Selected Structural Properties for Simulations: A Review and Tutorial for Neuroscientists

PubMed Central

Prettejohn, Brenton J.; Berryman, Matthew J.; McDonnell, Mark D.

2011-01-01

Many simulations of networks in computational neuroscience assume completely homogenous random networks of the Erdös–Rényi type, or regular networks, despite it being recognized for some time that anatomical brain networks are more complex in their connectivity and can, for example, exhibit the “scale-free” and “small-world” properties. We review the most well known algorithms for constructing networks with given non-homogeneous statistical properties and provide simple pseudo-code for reproducing such networks in software simulations. We also review some useful mathematical results and approximations associated with the statistics that describe these network models, including degree distribution, average path length, and clustering coefficient. We demonstrate how such results can be used as partial verification and validation of implementations. Finally, we discuss a sometimes overlooked modeling choice that can be crucially important for the properties of simulated networks: that of network directedness. The most well known network algorithms produce undirected networks, and we emphasize this point by highlighting how simple adaptations can instead produce directed networks. PMID:21441986

Compartmental analysis of [11C]flumazenil kinetics for the estimation of ligand transport rate and receptor distribution using positron emission tomography.

PubMed

Koeppe, R A; Holthoff, V A; Frey, K A; Kilbourn, M R; Kuhl, D E

1991-09-01

The in vivo kinetic behavior of [11C]flumazenil ([11C]FMZ), a non-subtype-specific central benzodiazepine antagonist, is characterized using compartmental analysis with the aim of producing an optimized data acquisition protocol and tracer kinetic model configuration for the assessment of [11C]FMZ binding to benzodiazepine receptors (BZRs) in human brain. The approach presented is simple, requiring only a single radioligand injection. Dynamic positron emission tomography data were acquired on 18 normal volunteers using a 60- to 90-min sequence of scans and were analyzed with model configurations that included a three-compartment, four-parameter model, a three-compartment, three-parameter model, with a fixed value for free plus nonspecific binding; and a two-compartment, two-parameter model. Statistical analysis indicated that a four-parameter model did not yield significantly better fits than a three-parameter model. Goodness of fit was improved for three- versus two-parameter configurations in regions with low receptor density, but not in regions with moderate to high receptor density. Thus, a two-compartment, two-parameter configuration was found to adequately describe the kinetic behavior of [11C]FMZ in human brain, with stable estimates of the model parameters obtainable from as little as 20-30 min of data. Pixel-by-pixel analysis yields functional images of transport rate (K1) and ligand distribution volume (DV"), and thus provides independent estimates of ligand delivery and BZR binding.

Is Rest Really Rest? Resting State Functional Connectivity during Rest and Motor Task Paradigms.

PubMed

Jurkiewicz, Michael T; Crawley, Adrian P; Mikulis, David J

2018-04-18

Numerous studies have identified the default mode network (DMN) within the brain of healthy individuals, which has been attributed to the ongoing mental activity of the brain during the wakeful resting-state. While engaged during specific resting-state fMRI paradigms, it remains unclear as to whether traditional block-design simple movement fMRI experiments significantly influence the default mode network or other areas. Using blood-oxygen level dependent (BOLD) fMRI we characterized the pattern of functional connectivity in healthy subjects during a resting-state paradigm and compared this to the same resting-state analysis performed on motor task data residual time courses after regressing out the task paradigm. Using seed-voxel analysis to define the DMN, the executive control network (ECN), and sensorimotor, auditory and visual networks, the resting-state analysis of the residual time courses demonstrated reduced functional connectivity in the motor network and reduced connectivity between the insula and the ECN compared to the standard resting-state datasets. Overall, performance of simple self-directed motor tasks does little to change the resting-state functional connectivity across the brain, especially in non-motor areas. This would suggest that previously acquired fMRI studies incorporating simple block-design motor tasks could be mined retrospectively for assessment of the resting-state connectivity.

Interaction vs. observation: distinctive modes of social cognition in human brain and behavior? A combined fMRI and eye-tracking study.

PubMed

Tylén, Kristian; Allen, Micah; Hunter, Bjørk K; Roepstorff, Andreas

2012-01-01

Human cognition has usually been approached on the level of individual minds and brains, but social interaction is a challenging case. Is it best thought of as a self-contained individual cognitive process aiming at an "understanding of the other," or should it rather be approached as an collective, inter-personal process where individual cognitive components interact on a moment-to-moment basis to form coupled dynamics? In a combined fMRI and eye-tracking study we directly contrasted these models of social cognition. We found that the perception of situations affording social contingent responsiveness (e.g., someone offering or showing you an object) elicited activations in regions of the right posterior temporal sulcus and yielded greater pupil dilation corresponding to a model of coupled dynamics (joint action). In contrast, the social-cognitive perception of someone "privately" manipulating an object elicited activation in medial prefrontal cortex, the right inferior frontal gyrus and right inferior parietal lobus, regions normally associated with Theory of Mind and with the mirror neuron system. Our findings support a distinction in social cognition between social observation and social interaction, and demonstrate that simple ostensive cues may shift participants' experience, behavior, and brain activity between these modes. The identification of a distinct, interactive mode has implications for research on social cognition, both in everyday life and in clinical conditions.

Controversies about the enhanced vulnerability of the adolescent brain to develop addiction.

PubMed

Bernheim, Aurélien; Halfon, Olivier; Boutrel, Benjamin

2013-11-28

Adolescence, defined as a transition phase toward autonomy and independence, is a natural time of learning and adjustment, particularly in the setting of long-term goals and personal aspirations. It also is a period of heightened sensation seeking, including risk taking and reckless behaviors, which is a major cause of morbidity and mortality among teenagers. Recent observations suggest that a relative immaturity in frontal cortical neural systems may underlie the adolescent propensity for uninhibited risk taking and hazardous behaviors. However, converging preclinical and clinical studies do not support a simple model of frontal cortical immaturity, and there is substantial evidence that adolescents engage in dangerous activities, including drug abuse, despite knowing and understanding the risks involved. Therefore, a current consensus considers that much brain development during adolescence occurs in brain regions and systems that are critically involved in the perception and evaluation of risk and reward, leading to important changes in social and affective processing. Hence, rather than naive, immature and vulnerable, the adolescent brain, particularly the prefrontal cortex, should be considered as prewired for expecting novel experiences. In this perspective, thrill seeking may not represent a danger but rather a window of opportunities permitting the development of cognitive control through multiple experiences. However, if the maturation of brain systems implicated in self-regulation is contextually dependent, it is important to understand which experiences matter most. In particular, it is essential to unveil the underpinning mechanisms by which recurrent adverse episodes of stress or unrestricted access to drugs can shape the adolescent brain and potentially trigger life-long maladaptive responses.

A Neurosemantic Theory of Concrete Noun Representation Based on the Underlying Brain Codes

PubMed Central

Just, Marcel Adam; Cherkassky, Vladimir L.; Aryal, Sandesh; Mitchell, Tom M.

2010-01-01

This article describes the discovery of a set of biologically-driven semantic dimensions underlying the neural representation of concrete nouns, and then demonstrates how a resulting theory of noun representation can be used to identify simple thoughts through their fMRI patterns. We use factor analysis of fMRI brain imaging data to reveal the biological representation of individual concrete nouns like apple, in the absence of any pictorial stimuli. From this analysis emerge three main semantic factors underpinning the neural representation of nouns naming physical objects, which we label manipulation, shelter, and eating. Each factor is neurally represented in 3–4 different brain locations that correspond to a cortical network that co-activates in non-linguistic tasks, such as tool use pantomime for the manipulation factor. Several converging methods, such as the use of behavioral ratings of word meaning and text corpus characteristics, provide independent evidence of the centrality of these factors to the representations. The factors are then used with machine learning classifier techniques to show that the fMRI-measured brain representation of an individual concrete noun like apple can be identified with good accuracy from among 60 candidate words, using only the fMRI activity in the 16 locations associated with these factors. To further demonstrate the generativity of the proposed account, a theory-based model is developed to predict the brain activation patterns for words to which the algorithm has not been previously exposed. The methods, findings, and theory constitute a new approach of using brain activity for understanding how object concepts are represented in the mind. PMID:20084104

A neurosemantic theory of concrete noun representation based on the underlying brain codes.

PubMed

Just, Marcel Adam; Cherkassky, Vladimir L; Aryal, Sandesh; Mitchell, Tom M

2010-01-13

This article describes the discovery of a set of biologically-driven semantic dimensions underlying the neural representation of concrete nouns, and then demonstrates how a resulting theory of noun representation can be used to identify simple thoughts through their fMRI patterns. We use factor analysis of fMRI brain imaging data to reveal the biological representation of individual concrete nouns like apple, in the absence of any pictorial stimuli. From this analysis emerge three main semantic factors underpinning the neural representation of nouns naming physical objects, which we label manipulation, shelter, and eating. Each factor is neurally represented in 3-4 different brain locations that correspond to a cortical network that co-activates in non-linguistic tasks, such as tool use pantomime for the manipulation factor. Several converging methods, such as the use of behavioral ratings of word meaning and text corpus characteristics, provide independent evidence of the centrality of these factors to the representations. The factors are then used with machine learning classifier techniques to show that the fMRI-measured brain representation of an individual concrete noun like apple can be identified with good accuracy from among 60 candidate words, using only the fMRI activity in the 16 locations associated with these factors. To further demonstrate the generativity of the proposed account, a theory-based model is developed to predict the brain activation patterns for words to which the algorithm has not been previously exposed. The methods, findings, and theory constitute a new approach of using brain activity for understanding how object concepts are represented in the mind.

Theory of cortical function

PubMed Central

Heeger, David J.

2017-01-01

Most models of sensory processing in the brain have a feedforward architecture in which each stage comprises simple linear filtering operations and nonlinearities. Models of this form have been used to explain a wide range of neurophysiological and psychophysical data, and many recent successes in artificial intelligence (with deep convolutional neural nets) are based on this architecture. However, neocortex is not a feedforward architecture. This paper proposes a first step toward an alternative computational framework in which neural activity in each brain area depends on a combination of feedforward drive (bottom-up from the previous processing stage), feedback drive (top-down context from the next stage), and prior drive (expectation). The relative contributions of feedforward drive, feedback drive, and prior drive are controlled by a handful of state parameters, which I hypothesize correspond to neuromodulators and oscillatory activity. In some states, neural responses are dominated by the feedforward drive and the theory is identical to a conventional feedforward model, thereby preserving all of the desirable features of those models. In other states, the theory is a generative model that constructs a sensory representation from an abstract representation, like memory recall. In still other states, the theory combines prior expectation with sensory input, explores different possible perceptual interpretations of ambiguous sensory inputs, and predicts forward in time. The theory, therefore, offers an empirically testable framework for understanding how the cortex accomplishes inference, exploration, and prediction. PMID:28167793

Directionality analysis on functional magnetic resonance imaging during motor task using Granger causality.

PubMed

Anwar, A R; Muthalib, M; Perrey, S; Galka, A; Granert, O; Wolff, S; Deuschl, G; Raethjen, J; Heute, U; Muthuraman, M

2012-01-01

Directionality analysis of signals originating from different parts of brain during motor tasks has gained a lot of interest. Since brain activity can be recorded over time, methods of time series analysis can be applied to medical time series as well. Granger Causality is a method to find a causal relationship between time series. Such causality can be referred to as a directional connection and is not necessarily bidirectional. The aim of this study is to differentiate between different motor tasks on the basis of activation maps and also to understand the nature of connections present between different parts of the brain. In this paper, three different motor tasks (finger tapping, simple finger sequencing, and complex finger sequencing) are analyzed. Time series for each task were extracted from functional magnetic resonance imaging (fMRI) data, which have a very good spatial resolution and can look into the sub-cortical regions of the brain. Activation maps based on fMRI images show that, in case of complex finger sequencing, most parts of the brain are active, unlike finger tapping during which only limited regions show activity. Directionality analysis on time series extracted from contralateral motor cortex (CMC), supplementary motor area (SMA), and cerebellum (CER) show bidirectional connections between these parts of the brain. In case of simple finger sequencing and complex finger sequencing, the strongest connections originate from SMA and CMC, while connections originating from CER in either direction are the weakest ones in magnitude during all paradigms.

Human Time-Frequency Acuity Beats the Fourier Uncertainty Principle

NASA Astrophysics Data System (ADS)

Oppenheim, Jacob N.; Magnasco, Marcelo O.

2013-01-01

The time-frequency uncertainty principle states that the product of the temporal and frequency extents of a signal cannot be smaller than 1/(4π). We study human ability to simultaneously judge the frequency and the timing of a sound. Our subjects often exceeded the uncertainty limit, sometimes by more than tenfold, mostly through remarkable timing acuity. Our results establish a lower bound for the nonlinearity and complexity of the algorithms employed by our brains in parsing transient sounds, rule out simple “linear filter” models of early auditory processing, and highlight timing acuity as a central feature in auditory object processing.

Auditory Power-Law Activation Avalanches Exhibit a Fundamental Computational Ground State

NASA Astrophysics Data System (ADS)

Stoop, Ruedi; Gomez, Florian

2016-07-01

The cochlea provides a biological information-processing paradigm that we are only beginning to understand in its full complexity. Our work reveals an interacting network of strongly nonlinear dynamical nodes, on which even a simple sound input triggers subnetworks of activated elements that follow power-law size statistics ("avalanches"). From dynamical systems theory, power-law size distributions relate to a fundamental ground state of biological information processing. Learning destroys these power laws. These results strongly modify the models of mammalian sound processing and provide a novel methodological perspective for understanding how the brain processes information.

Becoming a "Wiz" at Brain-Based Teaching: From Translation to Application. How To Make Every Year Your Best Year.

ERIC Educational Resources Information Center

Sprenger, Marilee B.

This book uses an analogy of "The Wizard of Oz" to offer information about cognitive research, sharing simple tactics for implementing these ideas in the classroom. The book discusses expert findings about brain growth, structure, and functions to help teachers and administrators foster a love of learning in all students. Key features…

Familiarity Affects Entrainment of EEG in Music Listening.

PubMed

Kumagai, Yuiko; Arvaneh, Mahnaz; Tanaka, Toshihisa

2017-01-01

Music perception involves complex brain functions. The relationship between music and brain such as cortical entrainment to periodic tune, periodic beat, and music have been well investigated. It has also been reported that the cerebral cortex responded more strongly to the periodic rhythm of unfamiliar music than to that of familiar music. However, previous works mainly used simple and artificial auditory stimuli like pure tone or beep. It is still unclear how the brain response is influenced by the familiarity of music. To address this issue, we analyzed electroencelphalogram (EEG) to investigate the relationship between cortical response and familiarity of music using melodies produced by piano sounds as simple natural stimuli. The cross-correlation function averaged across trials, channels, and participants showed two pronounced peaks at time lags around 70 and 140 ms. At the two peaks the magnitude of the cross-correlation values were significantly larger when listening to unfamiliar and scrambled music compared to those when listening to familiar music. Our findings suggest that the response to unfamiliar music is stronger than that to familiar music. One potential application of our findings would be the discrimination of listeners' familiarity with music, which provides an important tool for assessment of brain activity.

Deep-Learning Technique To Convert a Crude Piezoresistive Carbon Nanotube-Ecoflex Composite Sheet into a Smart, Portable, Disposable, and Extremely Flexible Keypad.

PubMed

Lee, Jin-Woong; Chung, Jiyong; Cho, Min-Young; Timilsina, Suman; Sohn, Keemin; Kim, Ji Sik; Sohn, Kee-Sun

2018-06-20

An extremely simple bulk sheet made of a piezoresistive carbon nanotube (CNT)-Ecoflex composite can act as a smart keypad that is portable, disposable, and flexible enough to be carried crushed inside the pocket of a pair of trousers. Both a rigid-button-imbedded, rollable (or foldable) pad and a patterned flexible pad have been introduced for use as portable keyboards. Herein, we suggest a bare, bulk, macroscale piezoresistive sheet as a replacement for these complex devices that are achievable only through high-cost fabrication processes such as patterning-based coating, printing, deposition, and mounting. A deep-learning technique based on deep neural networks (DNN) enables this extremely simple bulk sheet to play the role of a smart keypad without the use of complicated fabrication processes. To develop this keypad, instantaneous electrical resistance change was recorded at several locations on the edge of the sheet along with the exact information on the touch position and pressure for a huge number of random touches. The recorded data were used for training a DNN model that could eventually act as a brain for a simple sheet-type keypad. This simple sheet-type keypad worked perfectly and outperformed all of the existing portable keypads in terms of functionality, flexibility, disposability, and cost.

A brain electrical signature of left-lateralized semantic activation from single words.

PubMed

Koppehele-Gossel, Judith; Schnuerch, Robert; Gibbons, Henning

2016-01-01

Lesion and imaging studies consistently indicate a left-lateralization of semantic language processing in human temporo-parietal cortex. Surprisingly, electrocortical measures, which allow a direct assessment of brain activity and the tracking of cognitive functions with millisecond precision, have not yet been used to capture this hemispheric lateralization, at least with respect to posterior portions of this effect. Using event-related potentials, we employed a simple single-word reading paradigm to compare neural activity during three tasks requiring different degrees of semantic processing. As expected, we were able to derive a simple temporo-parietal left-right asymmetry index peaking around 300ms into word processing that neatly tracks the degree of semantic activation. The validity of this measure in specifically capturing verbal semantic activation was further supported by a significant relation to verbal intelligence. We thus posit that it represents a promising tool to monitor verbal semantic processing in the brain with little technological effort and in a minimal experimental setup. Copyright © 2016 Elsevier Inc. All rights reserved.

Underlying neural mechanisms of mirror therapy: Implications for motor rehabilitation in stroke.

PubMed

Arya, Kamal Narayan

2016-01-01

Mirror therapy (MT) is a valuable method for enhancing motor recovery in poststroke hemiparesis. The technique utilizes the mirror-illusion created by the movement of sound limb that is perceived as the paretic limb. MT is a simple and economical technique than can stimulate the brain noninvasively. The intervention unquestionably has neural foundation. But the underlying neural mechanisms inducing motor recovery are still unclear. In this review, the neural-modulation due to MT has been explored. Multiple areas of the brain such as the occipital lobe, dorsal frontal area and corpus callosum are involved during the simple MT regime. Bilateral premotor cortex, primary motor cortex, primary somatosensory cortex, and cerebellum also get reorganized to enhance the function of the damaged brain. The motor areas of the lesioned hemisphere receive visuo-motor processing information through the parieto-occipital lobe. The damaged motor cortex responds variably to the MT and may augment true motor recovery. Mirror neurons may also play a possible role in the cortico-stimulatory mechanisms occurring due to the MT.

Prediction of brain deformations and risk of traumatic brain injury due to closed-head impact: quantitative analysis of the effects of boundary conditions and brain tissue constitutive model.

PubMed

Wang, Fang; Han, Yong; Wang, Bingyu; Peng, Qian; Huang, Xiaoqun; Miller, Karol; Wittek, Adam

2018-05-12

In this study, we investigate the effects of modelling choices for the brain-skull interface (layers of tissues between the brain and skull that determine boundary conditions for the brain) and the constitutive model of brain parenchyma on the brain responses under violent impact as predicted using computational biomechanics model. We used the head/brain model from Total HUman Model for Safety (THUMS)-extensively validated finite element model of the human body that has been applied in numerous injury biomechanics studies. The computations were conducted using a well-established nonlinear explicit dynamics finite element code LS-DYNA. We employed four approaches for modelling the brain-skull interface and four constitutive models for the brain tissue in the numerical simulations of the experiments on post-mortem human subjects exposed to violent impacts reported in the literature. The brain-skull interface models included direct representation of the brain meninges and cerebrospinal fluid, outer brain surface rigidly attached to the skull, frictionless sliding contact between the brain and skull, and a layer of spring-type cohesive elements between the brain and skull. We considered Ogden hyperviscoelastic, Mooney-Rivlin hyperviscoelastic, neo-Hookean hyperviscoelastic and linear viscoelastic constitutive models of the brain tissue. Our study indicates that the predicted deformations within the brain and related brain injury criteria are strongly affected by both the approach of modelling the brain-skull interface and the constitutive model of the brain parenchyma tissues. The results suggest that accurate prediction of deformations within the brain and risk of brain injury due to violent impact using computational biomechanics models may require representation of the meninges and subarachnoidal space with cerebrospinal fluid in the model and application of hyperviscoelastic (preferably Ogden-type) constitutive model for the brain tissue.

Neuronal and network computation in the brain

NASA Astrophysics Data System (ADS)

Babloyantz, A.

1999-03-01

The concepts and methods of non-linear dynamics have been a powerful tool for studying some gamow aspects of brain dynamics. In this paper we show how, from time series analysis of electroencepholograms in sick and healthy subjects, chaotic nature of brain activity could be unveiled. This finding gave rise to the concept of spatiotemporal cortical chaotic networks which in turn was the foundation for a simple brain-like device which is able to become attentive, perform pattern recognition and motion detection. A new method of time series analysis is also proposed which demonstrates for the first time the existence of neuronal code in interspike intervals of coclear cells.

Speech and language outcomes of very preterm infants.

PubMed

Vohr, Betty

2014-04-01

Speech and language impairments of both simple and complex language functions are common among former preterm infants. Risk factors include lower gestational age and increasing illness severity including severe brain injury. Even in the absence of brain injury, however, altered brain maturation and vulnerability imposed by premature entrance to the extrauterine environment is associated with brain structural and microstructural changes. These alterations are associated with language impairments with lasting effects in childhood and adolescence and increased needs for speech therapy and education supports. Studies are needed to investigate language interventions which begin in the neonatal intensive care unit. Copyright © 2013 Elsevier Ltd. All rights reserved.

Recruitment of prefrontal-striatal circuit in response to skilled motor challenge.

PubMed

Guo, Yumei; Wang, Zhuo; Prathap, Sandhya; Holschneider, Daniel P

2017-12-13

A variety of physical fitness regimens have been shown to improve cognition, including executive function, yet our understanding of which parameters of motor training are important in optimizing outcomes remains limited. We used functional brain mapping to compare the ability of two motor challenges to acutely recruit the prefrontal-striatal circuit. The two motor tasks - walking in a complex running wheel with irregularly spaced rungs or walking in a running wheel with a smooth internal surface - differed only in the extent of skill required for their execution. Cerebral perfusion was mapped in rats by intravenous injection of [C]-iodoantipyrine during walking in either a motorized complex wheel or in a simple wheel. Regional cerebral blood flow (rCBF) was quantified by whole-brain autoradiography and analyzed in three-dimensional reconstructed brains by statistical parametric mapping and seed-based functional connectivity. Skilled or simple walking compared with rest, increased rCBF in regions of the motor circuit, somatosensory and visual cortex, as well as the hippocampus. Significantly greater rCBF increases were noted during skilled walking than for simple walking. Skilled walking, unlike simple walking or the resting condition, was associated with a significant positive functional connectivity in the prefrontal-striatal circuit (prelimbic cortex-dorsomedial striatum) and greater negative functional connectivity in the prefrontal-hippocampal circuit. Our findings suggest that the level of skill of a motor training task determines the extent of functional recruitment of the prefrontal-corticostriatal circuit, with implications for a new approach in neurorehabilitation that uses circuit-specific neuroplasticity to improve motor and cognitive functions.

Noise-driven neuromorphic tuned amplifier.

PubMed

Fanelli, Duccio; Ginelli, Francesco; Livi, Roberto; Zagli, Niccoló; Zankoc, Clement

2017-12-01

We study a simple stochastic model of neuronal excitatory and inhibitory interactions. The model is defined on a directed lattice and internodes couplings are modulated by a nonlinear function that mimics the process of synaptic activation. We prove that such a system behaves as a fully tunable amplifier: the endogenous component of noise, stemming from finite size effects, seeds a coherent (exponential) amplification across the chain generating giant oscillations with tunable frequencies, a process that the brain could exploit to enhance, and eventually encode, different signals. On a wider perspective, the characterized amplification process could provide a reliable pacemaking mechanism for biological systems. The device extracts energy from the finite size bath and operates as an out of equilibrium thermal machine, under stationary conditions.

LIMO EEG: a toolbox for hierarchical LInear MOdeling of ElectroEncephaloGraphic data.

PubMed

Pernet, Cyril R; Chauveau, Nicolas; Gaspar, Carl; Rousselet, Guillaume A

2011-01-01

Magnetic- and electric-evoked brain responses have traditionally been analyzed by comparing the peaks or mean amplitudes of signals from selected channels and averaged across trials. More recently, tools have been developed to investigate single trial response variability (e.g., EEGLAB) and to test differences between averaged evoked responses over the entire scalp and time dimensions (e.g., SPM, Fieldtrip). LIMO EEG is a Matlab toolbox (EEGLAB compatible) to analyse evoked responses over all space and time dimensions, while accounting for single trial variability using a simple hierarchical linear modelling of the data. In addition, LIMO EEG provides robust parametric tests, therefore providing a new and complementary tool in the analysis of neural evoked responses.

LIMO EEG: A Toolbox for Hierarchical LInear MOdeling of ElectroEncephaloGraphic Data

PubMed Central

Pernet, Cyril R.; Chauveau, Nicolas; Gaspar, Carl; Rousselet, Guillaume A.

2011-01-01

Magnetic- and electric-evoked brain responses have traditionally been analyzed by comparing the peaks or mean amplitudes of signals from selected channels and averaged across trials. More recently, tools have been developed to investigate single trial response variability (e.g., EEGLAB) and to test differences between averaged evoked responses over the entire scalp and time dimensions (e.g., SPM, Fieldtrip). LIMO EEG is a Matlab toolbox (EEGLAB compatible) to analyse evoked responses over all space and time dimensions, while accounting for single trial variability using a simple hierarchical linear modelling of the data. In addition, LIMO EEG provides robust parametric tests, therefore providing a new and complementary tool in the analysis of neural evoked responses. PMID:21403915

A high-definition fiber tracking report for patients with traumatic brain injury and their doctors.

PubMed

Chmura, Jon; Presson, Nora; Benso, Steven; Puccio, Ava M; Fissel, Katherine; Hachey, Rebecca; Braun, Emily; Okonkwo, David O; Schneider, Walter

2015-03-01

We have developed a tablet-based application, the High-Definition Fiber Tracking Report App, to enable clinicians and patients in research studies to see and understand damage from Traumatic Brain Injury (TBI) by viewing 2-dimensional and 3-dimensional images of their brain, with a focus on white matter tracts with quantitative metrics. The goal is to visualize white matter fiber tract injury like bone fractures; that is, to make the "invisible wounds of TBI" understandable for patients. Using mobile computing technology (iPad), imaging data for individual patients can be downloaded remotely within hours of a magnetic resonance imaging brain scan. Clinicians and patients can view the data in the form of images of each tract, rotating animations of the tracts, 3-dimensional models, and graphics. A growing number of tracts can be examined for asymmetry, gaps in streamline coverage, reduced arborization (branching), streamline volume, and standard quantitative metrics (e.g., Fractional Anisotropy (FA)). Novice users can learn to effectively navigate and interact with the application (explain the figures and graphs representing normal and injured brain tracts) within 15 minutes of simple orientation with high accuracy (96%). The architecture supports extensive graphics, configurable reports, provides an easy-to-use, attractive interface with a smooth user experience, and allows for securely serving cases from a database. Patients and clinicians have described the application as providing dramatic benefits in understanding their TBI and improving their lives. Reprint & Copyright © 2015 Association of Military Surgeons of the U.S.

Accuracy of magnetic resonance based susceptibility measurements

NASA Astrophysics Data System (ADS)

Erdevig, Hannah E.; Russek, Stephen E.; Carnicka, Slavka; Stupic, Karl F.; Keenan, Kathryn E.

2017-05-01

Magnetic Resonance Imaging (MRI) is increasingly used to map the magnetic susceptibility of tissue to identify cerebral microbleeds associated with traumatic brain injury and pathological iron deposits associated with neurodegenerative diseases such as Parkinson's and Alzheimer's disease. Accurate measurements of susceptibility are important for determining oxygen and iron content in blood vessels and brain tissue for use in noninvasive clinical diagnosis and treatment assessments. Induced magnetic fields with amplitude on the order of 100 nT, can be detected using MRI phase images. The induced field distributions can then be inverted to obtain quantitative susceptibility maps. The focus of this research was to determine the accuracy of MRI-based susceptibility measurements using simple phantom geometries and to compare the susceptibility measurements with magnetometry measurements where SI-traceable standards are available. The susceptibilities of paramagnetic salt solutions in cylindrical containers were measured as a function of orientation relative to the static MRI field. The observed induced fields as a function of orientation of the cylinder were in good agreement with simple models. The MRI susceptibility measurements were compared with SQUID magnetometry using NIST-traceable standards. MRI can accurately measure relative magnetic susceptibilities while SQUID magnetometry measures absolute magnetic susceptibility. Given the accuracy of moment measurements of tissue mimicking samples, and the need to look at small differences in tissue properties, the use of existing NIST standard reference materials to calibrate MRI reference structures is problematic and better reference materials are required.

NeuronDepot: keeping your colleagues in sync by combining modern cloud storage services, the local file system, and simple web applications

PubMed Central

Rautenberg, Philipp L.; Kumaraswamy, Ajayrama; Tejero-Cantero, Alvaro; Doblander, Christoph; Norouzian, Mohammad R.; Kai, Kazuki; Jacobsen, Hans-Arno; Ai, Hiroyuki; Wachtler, Thomas; Ikeno, Hidetoshi

2014-01-01

Neuroscience today deals with a “data deluge” derived from the availability of high-throughput sensors of brain structure and brain activity, and increased computational resources for detailed simulations with complex output. We report here (1) a novel approach to data sharing between collaborating scientists that brings together file system tools and cloud technologies, (2) a service implementing this approach, called NeuronDepot, and (3) an example application of the service to a complex use case in the neurosciences. The main drivers for our approach are to facilitate collaborations with a transparent, automated data flow that shields scientists from having to learn new tools or data structuring paradigms. Using NeuronDepot is simple: one-time data assignment from the originator and cloud based syncing—thus making experimental and modeling data available across the collaboration with minimum overhead. Since data sharing is cloud based, our approach opens up the possibility of using new software developments and hardware scalabitliy which are associated with elastic cloud computing. We provide an implementation that relies on existing synchronization services and is usable from all devices via a reactive web interface. We are motivating our solution by solving the practical problems of the GinJang project, a collaboration of three universities across eight time zones with a complex workflow encompassing data from electrophysiological recordings, imaging, morphological reconstructions, and simulations. PMID:24971059

NeuronDepot: keeping your colleagues in sync by combining modern cloud storage services, the local file system, and simple web applications.

PubMed

Rautenberg, Philipp L; Kumaraswamy, Ajayrama; Tejero-Cantero, Alvaro; Doblander, Christoph; Norouzian, Mohammad R; Kai, Kazuki; Jacobsen, Hans-Arno; Ai, Hiroyuki; Wachtler, Thomas; Ikeno, Hidetoshi

2014-01-01

Neuroscience today deals with a "data deluge" derived from the availability of high-throughput sensors of brain structure and brain activity, and increased computational resources for detailed simulations with complex output. We report here (1) a novel approach to data sharing between collaborating scientists that brings together file system tools and cloud technologies, (2) a service implementing this approach, called NeuronDepot, and (3) an example application of the service to a complex use case in the neurosciences. The main drivers for our approach are to facilitate collaborations with a transparent, automated data flow that shields scientists from having to learn new tools or data structuring paradigms. Using NeuronDepot is simple: one-time data assignment from the originator and cloud based syncing-thus making experimental and modeling data available across the collaboration with minimum overhead. Since data sharing is cloud based, our approach opens up the possibility of using new software developments and hardware scalabitliy which are associated with elastic cloud computing. We provide an implementation that relies on existing synchronization services and is usable from all devices via a reactive web interface. We are motivating our solution by solving the practical problems of the GinJang project, a collaboration of three universities across eight time zones with a complex workflow encompassing data from electrophysiological recordings, imaging, morphological reconstructions, and simulations.

Scores Obtained from a Simple Cognitive Test of Visuospatial Episodic Memory Performed Decades before Death Are Associated with the Ultimate Presence of Alzheimer Disease Pathology.

PubMed

Robinson, Andrew C; McNamee, Roseanne; Davidson, Yvonne S; Horan, Michael A; Snowden, Julie S; McInnes, Lynn; Pendleton, Neil; Mann, David M A

2018-04-25

Community- or population-based longitudinal studies of cognitive ability with a brain donation end point offer an opportunity to examine relationships between pathology and cognitive state prior to death. Discriminating the earliest signs of dementing disorders, such as Alzheimer disease (AD), is necessary to undertake early interventions and treatments. The neuropathological profile of brains donated from The University of Manchester Longitudinal Study of Cognition in Normal Healthy Old Age, including CERAD (Consortium to Establish a Registry for Alzheimer's Disease) and Braak stage, was assessed by immunohistochemistry. Cognitive test scores collected 20 years prior to death were correlated with the extent of AD pathology present at death. Baseline scores from the Memory Circle test had the ability to distinguish between individuals who developed substantial AD pathology and those with no, or low, AD pathology. Predicted test scores at the age of 65 years also discriminated between these pathology groups. The addition of APOE genotype further improved the discriminatory ability of the model. The results raise the possibility of identifying individuals at future risk of the neuropathological changes associated with AD over 20 years before death using a simple cognitive test. This work may facilitate early interventions, therapeutics and treatments for AD by identifying at-risk and minimally affected (in pathological terms) individuals. © 2018 S. Karger AG, Basel.

Graph Frequency Analysis of Brain Signals

PubMed Central

Huang, Weiyu; Goldsberry, Leah; Wymbs, Nicholas F.; Grafton, Scott T.; Bassett, Danielle S.; Ribeiro, Alejandro

2016-01-01

This paper presents methods to analyze functional brain networks and signals from graph spectral perspectives. The notion of frequency and filters traditionally defined for signals supported on regular domains such as discrete time and image grids has been recently generalized to irregular graph domains, and defines brain graph frequencies associated with different levels of spatial smoothness across the brain regions. Brain network frequency also enables the decomposition of brain signals into pieces corresponding to smooth or rapid variations. We relate graph frequency with principal component analysis when the networks of interest denote functional connectivity. The methods are utilized to analyze brain networks and signals as subjects master a simple motor skill. We observe that brain signals corresponding to different graph frequencies exhibit different levels of adaptability throughout learning. Further, we notice a strong association between graph spectral properties of brain networks and the level of exposure to tasks performed, and recognize the most contributing and important frequency signatures at different levels of task familiarity. PMID:28439325

Comparison of two integration methods for dynamic causal modeling of electrophysiological data.

PubMed

Lemaréchal, Jean-Didier; George, Nathalie; David, Olivier

2018-06-01

Dynamic causal modeling (DCM) is a methodological approach to study effective connectivity among brain regions. Based on a set of observations and a biophysical model of brain interactions, DCM uses a Bayesian framework to estimate the posterior distribution of the free parameters of the model (e.g. modulation of connectivity) and infer architectural properties of the most plausible model (i.e. model selection). When modeling electrophysiological event-related responses, the estimation of the model relies on the integration of the system of delay differential equations (DDEs) that describe the dynamics of the system. In this technical note, we compared two numerical schemes for the integration of DDEs. The first, and standard, scheme approximates the DDEs (more precisely, the state of the system, with respect to conduction delays among brain regions) using ordinary differential equations (ODEs) and solves it with a fixed step size. The second scheme uses a dedicated DDEs solver with adaptive step sizes to control error, making it theoretically more accurate. To highlight the effects of the approximation used by the first integration scheme in regard to parameter estimation and Bayesian model selection, we performed simulations of local field potentials using first, a simple model comprising 2 regions and second, a more complex model comprising 6 regions. In these simulations, the second integration scheme served as the standard to which the first one was compared. Then, the performances of the two integration schemes were directly compared by fitting a public mismatch negativity EEG dataset with different models. The simulations revealed that the use of the standard DCM integration scheme was acceptable for Bayesian model selection but underestimated the connectivity parameters and did not allow an accurate estimation of conduction delays. Fitting to empirical data showed that the models systematically obtained an increased accuracy when using the second integration scheme. We conclude that inference on connectivity strength and delay based on DCM for EEG/MEG requires an accurate integration scheme. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

Poster — Thur Eve — 33: The Influence of a Modeled Treatment Couch on Dose Distributions During IMRT and RapidArc Treatment Delivery

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

Aldosary, Ghada; Nobah, Ahmad; Al-Zorkani, Faisal

2014-08-15

Treatment couches have been known to perturb dose delivery in patients. This effect is most pronounced in techniques such as IMRT and RapidArc. Although modern treatment planning systems (TPS) include data for a “default” treatment couch, actual couches are not manufactured identically. Thus, variations in their Hounsfield Unit (HU) values may exist. This study demonstrates a practical and simple method of acquiring reliable HU data for any treatment couch. We also investigate the effects of both the default and modeled treatment couches on absorbed dose. Experimental verifications show that by neglecting to incorporate the treatment couch in the TPS, dosemore » differences of up to 9.5% and 7.3% were present for 4 MV and 10 MV photon beams, respectively. Furthermore, a clinical study based on a cohort of 20 RapidArc and IMRT (brain, pelvis and abdominal) cases is performed. 2D dose distributions show that without the couch in the planning phase, differences ≤ 4.6% and 5.9% for RapidArc and IMRT cases are present for the same cases that the default couch was added to. Additionally, in comparison to the default couch, employing the modeled couch in the calculation process influences dose distributions by ≤ 2.7% and 8% for RapidArc and IMRT cases, respectively. This result was found to be site specific; where an accurate couch proves to be preferable for IMRT brain plans. As such, adding the couch during dose calculation decreases dose calculation errors, and a precisely modeled treatment couch offers higher dose delivery accuracy for brain treatment using IMRT.« less

A pragmatic clinicopathobiological grouping/staging system for gliomas: proposal of the Indian TNM subcommittee on brain tumors.

PubMed

Gupta, Tejpal; Sarin, Rajiv; Jalali, Rakesh; Sharma, Suash; Kurkure, Purna; Goel, Atul

2009-01-01

There is no universally accepted staging system for primary brain tumors wherein prognostication is mainly based on complex composite indices. To develop a simple, pragmatic, and widely applicable grouping/staging system for gliomas, the most common primary brain tumor. An expert neurooncology panel with representation from radiation oncology, neurosurgery, pathology, radiology, and medical oncology had several rounds of discussion on issues pertinent to brain tumor staging. The trade off was between the accuracy of prognostic categorization and a pragmatic, widely applicable approach. The Tumor-Node-Metastasis staging was considered irrelevant for gliomas that seldom metastasize to lymphatics or outside the neuraxis. Instead, a 4-point staging/grouping system is proposed, using histological grade as the main prognostic variable and at least one stage migration based on other unfavorable features such as tumor location (brainstem); age (<5 years for all grades, >50 years for high-grade, and >40 years for low-grade gliomas); poor neurological performance status (NPS 2-4); multicentricity and/or gliomatosis; and adverse biological parameters (proliferative index, angiogenesis markers, apoptotic index, cytogenetic abnormalities, and molecular markers). In absence of a grouping/staging system for primary brain tumors, prognostification is mostly based on complex composite indices. The proposed clinicopathobiological grouping/staging system for gliomas is a simple, pragmatic, and user-friendly tool with a potential to fulfill the objectives of staging classification.

Relating Structure and Function in the Human Brain: Relative Contributions of Anatomy, Stationary Dynamics, and Non-stationarities

PubMed Central

Messé, Arnaud; Rudrauf, David; Benali, Habib; Marrelec, Guillaume

2014-01-01

Investigating the relationship between brain structure and function is a central endeavor for neuroscience research. Yet, the mechanisms shaping this relationship largely remain to be elucidated and are highly debated. In particular, the existence and relative contributions of anatomical constraints and dynamical physiological mechanisms of different types remain to be established. We addressed this issue by systematically comparing functional connectivity (FC) from resting-state functional magnetic resonance imaging data with simulations from increasingly complex computational models, and by manipulating anatomical connectivity obtained from fiber tractography based on diffusion-weighted imaging. We hypothesized that FC reflects the interplay of at least three types of components: (i) a backbone of anatomical connectivity, (ii) a stationary dynamical regime directly driven by the underlying anatomy, and (iii) other stationary and non-stationary dynamics not directly related to the anatomy. We showed that anatomical connectivity alone accounts for up to 15% of FC variance; that there is a stationary regime accounting for up to an additional 20% of variance and that this regime can be associated to a stationary FC; that a simple stationary model of FC better explains FC than more complex models; and that there is a large remaining variance (around 65%), which must contain the non-stationarities of FC evidenced in the literature. We also show that homotopic connections across cerebral hemispheres, which are typically improperly estimated, play a strong role in shaping all aspects of FC, notably indirect connections and the topographic organization of brain networks. PMID:24651524

The epidemic spreading model and the direction of information flow in brain networks.

PubMed

Meier, J; Zhou, X; Hillebrand, A; Tewarie, P; Stam, C J; Van Mieghem, P

2017-05-15

The interplay between structural connections and emerging information flow in the human brain remains an open research problem. A recent study observed global patterns of directional information flow in empirical data using the measure of transfer entropy. For higher frequency bands, the overall direction of information flow was from posterior to anterior regions whereas an anterior-to-posterior pattern was observed in lower frequency bands. In this study, we applied a simple Susceptible-Infected-Susceptible (SIS) epidemic spreading model on the human connectome with the aim to reveal the topological properties of the structural network that give rise to these global patterns. We found that direct structural connections induced higher transfer entropy between two brain regions and that transfer entropy decreased with increasing distance between nodes (in terms of hops in the structural network). Applying the SIS model, we were able to confirm the empirically observed opposite information flow patterns and posterior hubs in the structural network seem to play a dominant role in the network dynamics. For small time scales, when these hubs acted as strong receivers of information, the global pattern of information flow was in the posterior-to-anterior direction and in the opposite direction when they were strong senders. Our analysis suggests that these global patterns of directional information flow are the result of an unequal spatial distribution of the structural degree between posterior and anterior regions and their directions seem to be linked to different time scales of the spreading process. Copyright © 2017 Elsevier Inc. All rights reserved.

Endocranial morphology of Palaeocene Plesiadapis tricuspidens and evolution of the early primate brain.

PubMed

Orliac, Maeva J; Ladevèze, Sandrine; Gingerich, Philip D; Lebrun, Renaud; Smith, Thierry

2014-04-22

Expansion of the brain is a key feature of primate evolution. The fossil record, although incomplete, allows a partial reconstruction of changes in primate brain size and morphology through time. Palaeogene plesiadapoids, closest relatives of Euprimates (or crown-group primates), are crucial for understanding early evolution of the primate brain. However, brain morphology of this group remains poorly documented, and major questions remain regarding the initial phase of euprimate brain evolution. Micro-CT investigation of the endocranial morphology of Plesiadapis tricuspidens from the Late Palaeocene of Europe--the most complete plesiadapoid cranium known--shows that plesiadapoids retained a very small and simple brain. Plesiadapis has midbrain exposure, and minimal encephalization and neocorticalization, making it comparable with that of stem rodents and lagomorphs. However, Plesiadapis shares a domed neocortex and downwardly shifted olfactory-bulb axis with Euprimates. If accepted phylogenetic relationships are correct, then this implies that the euprimate brain underwent drastic reorganization during the Palaeocene, and some changes in brain structure preceded brain size increase and neocortex expansion during evolution of the primate brain.

The brain MRI classification problem from wavelets perspective

NASA Astrophysics Data System (ADS)

Bendib, Mohamed M.; Merouani, Hayet F.; Diaba, Fatma

2015-02-01

Haar and Daubechies 4 (DB4) are the most used wavelets for brain MRI (Magnetic Resonance Imaging) classification. The former is simple and fast to compute while the latter is more complex and offers a better resolution. This paper explores the potential of both of them in performing Normal versus Pathological discrimination on the one hand, and Multiclassification on the other hand. The Whole Brain Atlas is used as a validation database, and the Random Forest (RF) algorithm is employed as a learning approach. The achieved results are discussed and statistically compared.

Bayesian function-on-function regression for multilevel functional data.

PubMed

Meyer, Mark J; Coull, Brent A; Versace, Francesco; Cinciripini, Paul; Morris, Jeffrey S

2015-09-01

Medical and public health research increasingly involves the collection of complex and high dimensional data. In particular, functional data-where the unit of observation is a curve or set of curves that are finely sampled over a grid-is frequently obtained. Moreover, researchers often sample multiple curves per person resulting in repeated functional measures. A common question is how to analyze the relationship between two functional variables. We propose a general function-on-function regression model for repeatedly sampled functional data on a fine grid, presenting a simple model as well as a more extensive mixed model framework, and introducing various functional Bayesian inferential procedures that account for multiple testing. We examine these models via simulation and a data analysis with data from a study that used event-related potentials to examine how the brain processes various types of images. © 2015, The International Biometric Society.

Serotonin shapes risky decision making in monkeys.

PubMed

Long, Arwen B; Kuhn, Cynthia M; Platt, Michael L

2009-12-01

Some people love taking risks, while others avoid gambles at all costs. The neural mechanisms underlying individual variation in preference for risky or certain outcomes, however, remain poorly understood. Although behavioral pathologies associated with compulsive gambling, addiction and other psychiatric disorders implicate deficient serotonin signaling in pathological decision making, there is little experimental evidence demonstrating a link between serotonin and risky decision making, in part due to the lack of a good animal model. We used dietary rapid tryptophan depletion (RTD) to acutely lower brain serotonin in three macaques performing a simple gambling task for fluid rewards. To confirm the efficacy of RTD experiments, we measured total plasma tryptophan using high-performance liquid chromatography (HPLC) with electrochemical detection. Reducing brain serotonin synthesis decreased preference for the safe option in a gambling task. Moreover, lowering brain serotonin function significantly decreased the premium required for monkeys to switch their preference to the risky option, suggesting that diminished serotonin signaling enhances the relative subjective value of the risky option. These results implicate serotonin in risk-sensitive decision making and, further, suggest pharmacological therapies for treating pathological risk preferences in disorders such as problem gambling and addiction.

Quantum neural network-based EEG filtering for a brain-computer interface.

PubMed

Gandhi, Vaibhav; Prasad, Girijesh; Coyle, Damien; Behera, Laxmidhar; McGinnity, Thomas Martin

2014-02-01

A novel neural information processing architecture inspired by quantum mechanics and incorporating the well-known Schrodinger wave equation is proposed in this paper. The proposed architecture referred to as recurrent quantum neural network (RQNN) can characterize a nonstationary stochastic signal as time-varying wave packets. A robust unsupervised learning algorithm enables the RQNN to effectively capture the statistical behavior of the input signal and facilitates the estimation of signal embedded in noise with unknown characteristics. The results from a number of benchmark tests show that simple signals such as dc, staircase dc, and sinusoidal signals embedded within high noise can be accurately filtered and particle swarm optimization can be employed to select model parameters. The RQNN filtering procedure is applied in a two-class motor imagery-based brain-computer interface where the objective was to filter electroencephalogram (EEG) signals before feature extraction and classification to increase signal separability. A two-step inner-outer fivefold cross-validation approach is utilized to select the algorithm parameters subject-specifically for nine subjects. It is shown that the subject-specific RQNN EEG filtering significantly improves brain-computer interface performance compared to using only the raw EEG or Savitzky-Golay filtered EEG across multiple sessions.

A microinjection technique for targeting regions of embryonic and neonatal mouse brain in vivo

PubMed Central

Davidson, Steve; Truong, Hai; Nakagawa, Yasushi; Giesler, Glenn J

2009-01-01

A simple pressure injection technique was developed to deliver substances into specific regions of the embryonic and neonatal mouse brain in vivo. The retrograde tracers Fluorogold and cholera toxin B subunit were used to test the validity of the technique. Injected animals survived the duration of transport (24–48 hrs) and then were sacrificed and perfused with fixative. Small injections (≤ 50 nL) were contained within targeted structures of the perinatal brain and labeled distant cells of origin in several model neural pathways. Traced neural pathways in the perinatal mouse were further examined with immunohistochemical methods to test the feasibility of double labeling experiments during development. Several experimental situations in which this technique would be useful are discussed, for example, to label projection neurons in slice or culture preparations of mouse embryos and neonates. The administration of pharmacological or genetic vectors directly into specific neural targets during development should also be feasible. An examination of the form of neural pathways during early stages of life may lead to insights regarding the functional changes that occur during critical periods of development and provide an anatomic basis for some neurodevelopmental disorders. PMID:19840780

Gap junctions mediate large-scale Turing structures in a mean-field cortex driven by subcortical noise

NASA Astrophysics Data System (ADS)

Steyn-Ross, Moira L.; Steyn-Ross, D. A.; Wilson, M. T.; Sleigh, J. W.

2007-07-01

One of the grand puzzles in neuroscience is establishing the link between cognition and the disparate patterns of spontaneous and task-induced brain activity that can be measured clinically using a wide range of detection modalities such as scalp electrodes and imaging tomography. High-level brain function is not a single-neuron property, yet emerges as a cooperative phenomenon of multiply-interacting populations of neurons. Therefore a fruitful modeling approach is to picture the cerebral cortex as a continuum characterized by parameters that have been averaged over a small volume of cortical tissue. Such mean-field cortical models have been used to investigate gross patterns of brain behavior such as anesthesia, the cycles of natural sleep, memory and erasure in slow-wave sleep, and epilepsy. There is persuasive and accumulating evidence that direct gap-junction connections between inhibitory neurons promote synchronous oscillatory behavior both locally and across distances of some centimeters, but, to date, continuum models have ignored gap-junction connectivity. In this paper we employ simple mean-field arguments to derive an expression for D2 , the diffusive coupling strength arising from gap-junction connections between inhibitory neurons. Using recent neurophysiological measurements reported by Fukuda [J. Neurosci. 26, 3434 (2006)], we estimate an upper limit of D2≈0.6cm2 . We apply a linear stability analysis to a standard mean-field cortical model, augmented with gap-junction diffusion, and find this value for the diffusive coupling strength to be close to the critical value required to destabilize the homogeneous steady state. Computer simulations demonstrate that larger values of D2 cause the noise-driven model cortex to spontaneously crystalize into random mazelike Turing structures: centimeter-scale spatial patterns in which regions of high-firing activity are intermixed with regions of low-firing activity. These structures are consistent with the spatial variations in brain activity patterns detected with the BOLD (blood oxygen-level-dependent) signal detected with magnetic resonance imaging, and may provide a natural substrate for synchronous gamma-band rhythms observed across separated EEG (electroencephalogram) electrodes.

Rough Set Based Splitting Criterion for Binary Decision Tree Classifiers

DTIC Science & Technology

2006-09-26

Alata O. Fernandez-Maloigne C., and Ferrie J.C. (2001). Unsupervised Algorithm for the Segmentation of Three-Dimensional Magnetic Resonance Brain ...instinctual and learned responses in the brain , causing it to make decisions based on patterns in the stimuli. Using this deceptively simple process...2001. [2] Bohn C. (1997). An Incremental Unsupervised Learning Scheme for Function Approximation. In: Proceedings of the 1997 IEEE International

Hardware-based Artificial Neural Networks for Size, Weight, and Power Constrained Platforms (Preprint)

DTIC Science & Technology

2012-11-01

few sensors/complex computations, and many sensors/simple computation. II. CHALLENGES WITH NANO-ENABLED NEUROMORPHIC CHIPS A wide variety of...scenarios. Neuromorphic processors, which are based on the highly parallelized computing architecture of the mammalian brain, show great promise in...in the brain. This fundamentally different approach, frequently referred to as neuromorphic computing, is thought to be better able to solve fuzzy

Hierarchical Heteroclinics in Dynamical Model of Cognitive Processes: Chunking

NASA Astrophysics Data System (ADS)

Afraimovich, Valentin S.; Young, Todd R.; Rabinovich, Mikhail I.

Combining the results of brain imaging and nonlinear dynamics provides a new hierarchical vision of brain network functionality that is helpful in understanding the relationship of the network to different mental tasks. Using these ideas it is possible to build adequate models for the description and prediction of different cognitive activities in which the number of variables is usually small enough for analysis. The dynamical images of different mental processes depend on their temporal organization and, as a rule, cannot be just simple attractors since cognition is characterized by transient dynamics. The mathematical image for a robust transient is a stable heteroclinic channel consisting of a chain of saddles connected by unstable separatrices. We focus here on hierarchical chunking dynamics that can represent several cognitive activities. Chunking is the dynamical phenomenon that means dividing a long information chain into shorter items. Chunking is known to be important in many processes of perception, learning, memory and cognition. We prove that in the phase space of the model that describes chunking there exists a new mathematical object — heteroclinic sequence of heteroclinic cycles — using the technique of slow-fast approximations. This new object serves as a skeleton of motions reflecting sequential features of hierarchical chunking dynamics and is an adequate image of the chunking processing.

The Central Neural Foundations of Awareness and Self-Awareness

NASA Astrophysics Data System (ADS)

Pfaff, D.; Martin, E. M.; Weingarten, W.; Vimal, V.

In the past, neuroscientists have done very well to concentrate onexplaining the mechanisms for very specific, simple behaviors. For example, our laboratory's work with molecular and neural mechanisms of a simple sex behavior proved for the first time that specific biochemical reactions in specific parts of the brain govern a specific behavior [D. W. Pfaff, Drive: Neurobiological and Molecular Mechanisms of Sexual Motivation (The MIT Press, Cambridge, 1999)]. Now, advances in our field coupled with new techniques permit us to attack the problems of explaining global changes of state in the central nervous system. For example, how does a simple sex behavior depend on sexual arousal, and in turn, how does that sexual arousal depend on other forms of CNS arousal? Of surpassing interest is the explanation of the primary causes of brain arousal [D. W. Pfaff, textit{Brain Arousal and Information Theory: Neural and Genetic Mechanisms} (Harvard University Press, Cambridg e, 2006)]. We have hypothesized that the earliest and most elementary event in waking up the brain is the activation of certain primitive nerve cells in the hindbrain reticular formation. Hypothesizing a `generalized arousal' force emanating from these cells puts forth an idea roughly analogous to the hypothesis of a `big bang' in astrophysics, or to our ideas about the magma of the earth in geophysics. Following the activation of this primitive arousal force we are able to be alert and aware. The neuroanatomical pathways serving brain arousal are fairly well known: they are Bilateral, Bidirectional, Universal among vertebrate animals including humans, and they are always involved in Response Potentiation, approach or avoidance responses (BBURP theory). More than 120 genes are involved in the regulation of brain arousal. In theoretical terms, the discussion so far has dealt with `bottoms up' approaches to awareness -- from mechanisms in the hindbrain working through several phylogenetically ancient pathways, to higher levels of awareness. However, we must also consider `top down' approaches. Based on our thinking and our fantasies, arousal of the central nervous system may be modulated up or down to produce more or less awareness. And then, self-awareness results from our memory of our own behavioral activity.

You prime what you code: The fAIM model of priming of pop-out

PubMed Central

Meeter, Martijn

2017-01-01

Our visual brain makes use of recent experience to interact with the visual world, and efficiently select relevant information. This is exemplified by speeded search when target- and distractor features repeat across trials versus when they switch, a phenomenon referred to as intertrial priming. Here, we present fAIM, a computational model that demonstrates how priming can be explained by a simple feature-weighting mechanism integrated into an established model of bottom-up vision. In fAIM, such modulations in feature gains are widespread and not just restricted to one or a few features. Consequentially, priming effects result from the overall tuning of visual features to the task at hand. Such tuning allows the model to reproduce priming for different types of stimuli, including for typical stimulus dimensions such as ‘color’ and for less obvious dimensions such as ‘spikiness’ of shapes. Moreover, the model explains some puzzling findings from the literature: it shows how priming can be found for target-distractor stimulus relations rather than for their absolute stimulus values per se, without an explicit representation of relations. Similarly, it simulates effects that have been taken to reflect a modulation of priming by an observers’ goals—without any representation of goals in the model. We conclude that priming is best considered as a consequence of a general adaptation of the brain to visual input, and not as a peculiarity of visual search. PMID:29166386

Decompression sickness in breath-hold diving, and its probable connection to the growth and dissolution of small arterial gas emboli.

PubMed

Goldman, Saul; Solano-Altamirano, J M

2015-04-01

We solved the Laplace equation for the radius of an arterial gas embolism (AGE), during and after breath-hold diving. We used a simple three-region diffusion model for the AGE, and applied our results to two types of breath-hold dives: single, very deep competitive-level dives and repetitive shallower breath-hold dives similar to those carried out by indigenous commercial pearl divers in the South Pacific. Because of the effect of surface tension, AGEs tend to dissolve in arterial blood when arteries remote from supersaturated tissue. However if, before fully dissolving, they reach the capillary beds that perfuse the brain and the inner ear, they may become inflated with inert gas that is transferred into them from these contiguous temporarily supersaturated tissues. By using simple kinetic models of cerebral and inner ear tissue, the nitrogen tissue partial pressures during and after the dive(s) were determined. These were used to theoretically calculate AGE growth and dissolution curves for AGEs lodged in capillaries of the brain and inner ear. From these curves it was found that both cerebral and inner ear decompression sickness are expected to occur occasionally in single competitive-level dives. It was also determined from these curves that for the commercial repetitive dives considered, the duration of the surface interval (the time interval separating individual repetitive dives from one another) was a key determinant, as to whether inner ear and/or cerebral decompression sickness arose. Our predictions both for single competitive-level and repetitive commercial breath-hold diving were consistent with what is known about the incidence of cerebral and inner ear decompression sickness in these forms of diving. Copyright © 2015 Elsevier Inc. All rights reserved.

A physiologically based pharmacokinetic model for atrazine and its main metabolites in the adult male C57BL/6 mouse

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

Lin Zhoumeng; Interdisciplinary Toxicology Program, University of Georgia, Athens, GA 30602; Fisher, Jeffrey W.

Atrazine (ATR) is a chlorotriazine herbicide that is widely used and relatively persistent in the environment. In laboratory rodents, excessive exposure to ATR is detrimental to the reproductive, immune, and nervous systems. To better understand the toxicokinetics of ATR and to fill the need for a mouse model, a physiologically based pharmacokinetic (PBPK) model for ATR and its main chlorotriazine metabolites (Cl-TRIs) desethyl atrazine (DE), desisopropyl atrazine (DIP), and didealkyl atrazine (DACT) was developed for the adult male C57BL/6 mouse. Taking advantage of all relevant and recently made available mouse-specific data, a flow-limited PBPK model was constructed. The ATR andmore » DACT sub-models included blood, brain, liver, kidney, richly and slowly perfused tissue compartments, as well as plasma protein binding and red blood cell binding, whereas the DE and DIP sub-models were constructed as simple five-compartment models. The model adequately simulated plasma levels of ATR and Cl-TRIs and urinary dosimetry of Cl-TRIs at four single oral dose levels (250, 125, 25, and 5 mg/kg). Additionally, the model adequately described the dose dependency of brain and liver ATR and DACT concentrations. Cumulative urinary DACT amounts were accurately predicted across a wide dose range, suggesting the model's potential use for extrapolation to human exposures by performing reverse dosimetry. The model was validated using previously reported data for plasma ATR and DACT in mice and rats. Overall, besides being the first mouse PBPK model for ATR and its Cl-TRIs, this model, by analogy, provides insights into tissue dosimetry for rats. The model could be used in tissue dosimetry prediction and as an aid in the exposure assessment to this widely used herbicide.« less

Mirroring and beyond: coupled dynamics as a generalized framework for modelling social interactions

PubMed Central

Hasson, Uri; Frith, Chris D.

2016-01-01

When people observe one another, behavioural alignment can be detected at many levels, from the physical to the mental. Likewise, when people process the same highly complex stimulus sequences, such as films and stories, alignment is detected in the elicited brain activity. In early sensory areas, shared neural patterns are coupled to the low-level properties of the stimulus (shape, motion, volume, etc.), while in high-order brain areas, shared neural patterns are coupled to high-levels aspects of the stimulus, such as meaning. Successful social interactions require such alignments (both behavioural and neural), as communication cannot occur without shared understanding. However, we need to go beyond simple, symmetric (mirror) alignment once we start interacting. Interactions are dynamic processes, which involve continuous mutual adaptation, development of complementary behaviour and division of labour such as leader–follower roles. Here, we argue that interacting individuals are dynamically coupled rather than simply aligned. This broader framework for understanding interactions can encompass both processes by which behaviour and brain activity mirror each other (neural alignment), and situations in which behaviour and brain activity in one participant are coupled (but not mirrored) to the dynamics in the other participant. To apply these more sophisticated accounts of social interactions to the study of the underlying neural processes we need to develop new experimental paradigms and novel methods of data analysis PMID:27069044

Parametric analysis of the biomechanical response of head subjected to the primary blast loading--a data mining approach.

PubMed

Zhu, Feng; Kalra, Anil; Saif, Tal; Yang, Zaihan; Yang, King H; King, Albert I

2016-01-01

Traumatic brain injury due to primary blast loading has become a signature injury in recent military conflicts and terrorist activities. Extensive experimental and computational investigations have been conducted to study the interrelationships between intracranial pressure response and intrinsic or 'input' parameters such as the head geometry and loading conditions. However, these relationships are very complicated and are usually implicit and 'hidden' in a large amount of simulation/test data. In this study, a data mining method is proposed to explore such underlying information from the numerical simulation results. The heads of different species are described as a highly simplified two-part (skull and brain) finite element model with varying geometric parameters. The parameters considered include peak incident pressure, skull thickness, brain radius and snout length. Their interrelationship and coupling effect are discovered by developing a decision tree based on the large simulation data-set. The results show that the proposed data-driven method is superior to the conventional linear regression method and is comparable to the nonlinear regression method. Considering its capability of exploring implicit information and the relatively simple relationships between response and input variables, the data mining method is considered to be a good tool for an in-depth understanding of the mechanisms of blast-induced brain injury. As a general method, this approach can also be applied to other nonlinear complex biomechanical systems.

Engineering a High-Throughput 3-D In Vitro Glioblastoma Model

PubMed Central

Fan, Yantao; Avci, Naze G.; Nguyen, Duong T.; Dragomir, Andrei; Xu, Feng; Akay, Metin

2015-01-01

Glioblastoma multiforme (GBM) is the most common and malignant primary brain tumor in adults because of its highly invasive behavior. The existing treatment for GBM, which involves a combination of resection, chemotherapy, and radiotherapy, has a very limited success rate with a median survival rate of <1 year. This is mainly because of the failure of early detection and effective treatment. We designed a novel 3-D GBM cell culture model based on microwells that could mimic in vitro environment and help to bypass the lack of suitable animal models for preclinical toxicity tests. Microwells were fabricated from simple and inexpensive polyethylene glycol material for the control of in vitro 3-D culture. We applied the 3-D micropatterning system to GBM (U-87) cells using the photolithography technique to control the cell spheroids’ shape, size, and thickness. Our preliminary results suggested that uniform GBM spheroids can be formed in 3-D, and the size of these GBM spheroids depends on the size of microwells. The viability of the spheroids generated in this manner was quantitatively evaluated using live/dead assay and shown to improve over 21 days. We believe that in vitro 3-D cell culture model could help to reduce the time of the preclinical brain tumor growth studies. The proposed novel platform could be useful and cost-effective for high-throughput screening of cancer drugs and assessment of treatment responses. PMID:27170911

Absent without leave; a neuroenergetic theory of mind wandering

PubMed Central

Killeen, Peter R.

2013-01-01

Absent minded people are not under the control of task-relevant stimuli. According to the Neuroenergetics Theory of attention (NeT), this lack of control is often due to fatigue of the relevant processing units in the brain caused by insufficient resupply of the neuron's preferred fuel, lactate, from nearby astrocytes. A simple drift model of information processing accounts for response-time statistics in a paradigm often used to study inattention, the Sustained Attention to Response Task (SART). It is suggested that errors and slowing in this fast-paced, response-engaging task may have little to due with inattention. Slower-paced and less response-demanding tasks give greater license for inattention—aka absent-mindedness, mind-wandering. The basic NeT is therefore extended with an ancillary model of attentional drift and recapture. This Markov model, called NEMA, assumes probability λ of lapses of attention from 1 s to the next, and probability α of drifting back to the attentional state. These parameters measure the strength of attraction back to the task (α), or away to competing mental states or action patterns (λ); their proportion determines the probability of the individual being inattentive at any point in time over the long run. Their values are affected by the fatigue of the brain units they traffic between. The deployment of the model is demonstrated with a data set involving paced responding. PMID:23847559

Self-control with spiking and non-spiking neural networks playing games.

PubMed

Christodoulou, Chris; Banfield, Gaye; Cleanthous, Aristodemos

2010-01-01

Self-control can be defined as choosing a large delayed reward over a small immediate reward, while precommitment is the making of a choice with the specific aim of denying oneself future choices. Humans recognise that they have self-control problems and attempt to overcome them by applying precommitment. Problems in exercising self-control, suggest a conflict between cognition and motivation, which has been linked to competition between higher and lower brain functions (representing the frontal lobes and the limbic system respectively). This premise of an internal process conflict, lead to a behavioural model being proposed, based on which, we implemented a computational model for studying and explaining self-control through precommitment behaviour. Our model consists of two neural networks, initially non-spiking and then spiking ones, representing the higher and lower brain systems viewed as cooperating for the benefit of the organism. The non-spiking neural networks are of simple feed forward multilayer type with reinforcement learning, one with selective bootstrap weight update rule, which is seen as myopic, representing the lower brain and the other with the temporal difference weight update rule, which is seen as far-sighted, representing the higher brain. The spiking neural networks are implemented with leaky integrate-and-fire neurons with learning based on stochastic synaptic transmission. The differentiating element between the two brain centres in this implementation is based on the memory of past actions determined by an eligibility trace time constant. As the structure of the self-control problem can be likened to the Iterated Prisoner's Dilemma (IPD) game in that cooperation is to defection what self-control is to impulsiveness or what compromising is to insisting, we implemented the neural networks as two players, learning simultaneously but independently, competing in the IPD game. With a technique resembling the precommitment effect, whereby the payoffs for the dilemma cases in the IPD payoff matrix are differentially biased (increased or decreased), it is shown that increasing the precommitment effect (through increasing the differential bias) increases the probability of cooperating with oneself in the future, irrespective of whether the implementation is with spiking or non-spiking neural networks. Copyright (c) 2009 Elsevier Ltd. All rights reserved.

On imputing function to structure from the behavioural effects of brain lesions.

PubMed

Young, M P; Hilgetag, C C; Scannell, J W

2000-01-29

What is the link, if any, between the patterns of connections in the brain and the behavioural effects of localized brain lesions? We explored this question in four related ways. First, we investigated the distribution of activity decrements that followed simulated damage to elements of the thalamocortical network, using integrative mechanisms that have recently been used to successfully relate connection data to information on the spread of activation, and to account simultaneously for a variety of lesion effects. Second, we examined the consequences of the patterns of decrement seen in the simulation for each type of inference that has been employed to impute function to structure on the basis of the effects of brain lesions. Every variety of conventional inference, including double dissociation, readily misattributed function to structure. Third, we tried to derive a more reliable framework of inference for imputing function to structure, by clarifying concepts of function, and exploring a more formal framework, in which knowledge of connectivity is necessary but insufficient, based on concepts capable of mathematical specification. Fourth, we applied this framework to inferences about function relating to a simple network that reproduces intact, lesioned and paradoxically restored orientating behaviour. Lesion effects could be used to recover detailed and reliable information on which structures contributed to particular functions in this simple network. Finally, we explored how the effects of brain lesions and this formal approach could be used in conjunction with information from multiple neuroscience methodologies to develop a practical and reliable approach to inferring the functional roles of brain structures.

Modeling ultrasound propagation through material of increasing geometrical complexity.

PubMed

Odabaee, Maryam; Odabaee, Mostafa; Pelekanos, Matthew; Leinenga, Gerhard; Götz, Jürgen

2018-06-01

Ultrasound is increasingly being recognized as a neuromodulatory and therapeutic tool, inducing a broad range of bio-effects in the tissue of experimental animals and humans. To achieve these effects in a predictable manner in the human brain, the thick cancellous skull presents a problem, causing attenuation. In order to overcome this challenge, as a first step, the acoustic properties of a set of simple bone-modeling resin samples that displayed an increasing geometrical complexity (increasing step sizes) were analyzed. Using two Non-Destructive Testing (NDT) transducers, we found that Wiener deconvolution predicted the Ultrasound Acoustic Response (UAR) and attenuation caused by the samples. However, whereas the UAR of samples with step sizes larger than the wavelength could be accurately estimated, the prediction was not accurate when the sample had a smaller step size. Furthermore, a Finite Element Analysis (FEA) performed in ANSYS determined that the scattering and refraction of sound waves was significantly higher in complex samples with smaller step sizes compared to simple samples with a larger step size. Together, this reveals an interaction of frequency and geometrical complexity in predicting the UAR and attenuation. These findings could in future be applied to poro-visco-elastic materials that better model the human skull. Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.

Gait-Related Brain Activity in People with Parkinson Disease with Freezing of Gait

PubMed Central

Peterson, Daniel S.; Pickett, Kristen A.; Duncan, Ryan; Perlmutter, Joel; Earhart, Gammon M.

2014-01-01

Approximately 50% of people with Parkinson disease experience freezing of gait, described as a transient inability to produce effective stepping. Complex gait tasks such as turning typically elicit freezing more commonly than simple gait tasks, such as forward walking. Despite the frequency of this debilitating and dangerous symptom, the brain mechanisms underlying freezing remain unclear. Gait imagery during functional magnetic resonance imaging permits investigation of brain activity associated with locomotion. We used this approach to better understand neural function during gait-like tasks in people with Parkinson disease who experience freezing- “FoG+” and people who do not experience freezing- ”FoG−“. Nine FoG+ and nine FoG− imagined complex gait tasks (turning, backward walking), simple gait tasks (forward walking), and quiet standing during measurements of blood oxygen level dependent (BOLD) signal. Changes in BOLD signal (i.e. beta weights) during imagined walking and imagined standing were analyzed across FoG+ and FoG− groups in locomotor brain regions including supplementary motor area, globus pallidus, putamen, mesencephalic locomotor region, and cerebellar locomotor region. Beta weights in locomotor regions did not differ for complex tasks compared to simple tasks in either group. Across imagined gait tasks, FoG+ demonstrated significantly lower beta weights in the right globus pallidus with respect to FoG−. FoG+ also showed trends toward lower beta weights in other right-hemisphere locomotor regions (supplementary motor area, mesencephalic locomotor region). Finally, during imagined stand, FoG+ exhibited lower beta weights in the cerebellar locomotor region with respect to FoG−. These data support previous results suggesting FoG+ exhibit dysfunction in a number of cortical and subcortical regions, possibly with asymmetric dysfunction towards the right hemisphere. PMID:24595265

An Automated, Adaptive Framework for Optimizing Preprocessing Pipelines in Task-Based Functional MRI

PubMed Central

Churchill, Nathan W.; Spring, Robyn; Afshin-Pour, Babak; Dong, Fan; Strother, Stephen C.

2015-01-01

BOLD fMRI is sensitive to blood-oxygenation changes correlated with brain function; however, it is limited by relatively weak signal and significant noise confounds. Many preprocessing algorithms have been developed to control noise and improve signal detection in fMRI. Although the chosen set of preprocessing and analysis steps (the “pipeline”) significantly affects signal detection, pipelines are rarely quantitatively validated in the neuroimaging literature, due to complex preprocessing interactions. This paper outlines and validates an adaptive resampling framework for evaluating and optimizing preprocessing choices by optimizing data-driven metrics of task prediction and spatial reproducibility. Compared to standard “fixed” preprocessing pipelines, this optimization approach significantly improves independent validation measures of within-subject test-retest, and between-subject activation overlap, and behavioural prediction accuracy. We demonstrate that preprocessing choices function as implicit model regularizers, and that improvements due to pipeline optimization generalize across a range of simple to complex experimental tasks and analysis models. Results are shown for brief scanning sessions (<3 minutes each), demonstrating that with pipeline optimization, it is possible to obtain reliable results and brain-behaviour correlations in relatively small datasets. PMID:26161667

Implicit knowledge of visual uncertainty guides decisions with asymmetric outcomes.

PubMed

Whiteley, Louise; Sahani, Maneesh

2008-03-06

Perception is an "inverse problem," in which the state of the world must be inferred from the sensory neural activity that results. However, this inference is both ill-posed (Helmholtz, 1856; Marr, 1982) and corrupted by noise (Green & Swets, 1989), requiring the brain to compute perceptual beliefs under conditions of uncertainty. Here we show that human observers performing a simple visual choice task under an externally imposed loss function approach the optimal strategy, as defined by Bayesian probability and decision theory (Berger, 1985; Cox, 1961). In concert with earlier work, this suggests that observers possess a model of their internal uncertainty and can utilize this model in the neural computations that underlie their behavior (Knill & Pouget, 2004). In our experiment, optimal behavior requires that observers integrate the loss function with an estimate of their internal uncertainty rather than simply requiring that they use a modal estimate of the uncertain stimulus. Crucially, they approach optimal behavior even when denied the opportunity to learn adaptive decision strategies based on immediate feedback. Our data thus support the idea that flexible representations of uncertainty are pre-existing, widespread, and can be propagated to decision-making areas of the brain.

Validation and Development of a Modified Breast Graded Prognostic Assessment As a Tool for Survival in Patients With Breast Cancer and Brain Metastases.

PubMed

Subbiah, Ishwaria M; Lei, Xiudong; Weinberg, Jeffrey S; Sulman, Erik P; Chavez-MacGregor, Mariana; Tripathy, Debu; Gupta, Rohan; Varma, Ankur; Chouhan, Jay; Guevarra, Richard P; Valero, Vicente; Gilbert, Mark R; Gonzalez-Angulo, Ana M

2015-07-10

Several indices have been developed to predict overall survival (OS) in patients with breast cancer with brain metastases, including the breast graded prognostic assessment (breast-GPA), comprising age, tumor subtype, and Karnofsky performance score. However, number of brain metastases-a highly relevant clinical variable-is less often incorporated into the final model. We sought to validate the existing breast-GPA in an independent larger cohort and refine it integrating number of brain metastases. Data were retrospectively gathered from a prospectively maintained institutional database. Patients with newly diagnosed brain metastases from 1996 to 2013 were identified. After validating the breast-GPA, multivariable Cox regression and recursive partitioning analysis led to the development of the modified breast-GPA. The performances of the breast-GPA and modified breast-GPA were compared using the concordance index. In our cohort of 1,552 patients, the breast-GPA was validated as a prognostic tool for OS (P < .001). In multivariable analysis of the breast-GPA and number of brain metastases (> three v ≤ three), both were independent predictors of OS. We therefore developed the modified breast-GPA integrating a fourth clinical parameter. Recursive partitioning analysis reinforced the prognostic significance of these four factors. Concordance indices were 0.78 (95% CI, 0.77 to 0.80) and 0.84 (95% CI, 0.83 to 0.85) for the breast-GPA and modified breast-GPA, respectively (P < .001). The modified breast-GPA incorporates four simple clinical parameters of high prognostic significance. This index has an immediate role in the clinic as a formative part of the clinician's discussion of prognosis and direction of care and as a potential patient selection tool for clinical trials. © 2015 by American Society of Clinical Oncology.

Radiotracer properties determined by high performance liquid chromatography: a potential tool for brain radiotracer discovery.

PubMed

Tavares, Adriana Alexandre S; Lewsey, James; Dewar, Deborah; Pimlott, Sally L

2012-01-01

Previously, development of novel brain radiotracers has largely relied on simple screening tools. Improved selection methods at the early stages of radiotracer discovery and an increased understanding of the relationships between in vitro physicochemical and in vivo radiotracer properties are needed. We investigated if high performance liquid chromatography (HPLC) methodologies could provide criteria for lead candidate selection by comparing HPLC measurements with radiotracer properties in humans. Ten molecules, previously used as radiotracers in humans, were analysed to obtain the following measures: partition coefficient (Log P); permeability (P(m)); percentage of plasma protein binding (%PPB); and membrane partition coefficient (K(m)). Relationships between brain entry measurements (Log P, P(m) and %PPB) and in vivo brain percentage injected dose (%ID); and K(m) and specific binding in vivo (BP(ND)) were investigated. Log P values obtained using in silico packages and flask methods were compared with Log P values obtained using HPLC. The modelled associations with %ID were stronger for %PPB (r(2)=0.65) and P(m) (r(2)=0.77) than for Log P (r(2)=0.47) while 86% of BP(ND) variance was explained by K(m). Log P values were variable dependant on the methodology used. Log P should not be relied upon as a predictor of blood-brain barrier penetration during brain radiotracer discovery. HPLC measurements of permeability, %PPB and membrane interactions may be potentially useful in predicting in vivo performance and hence allow evaluation and ranking of compound libraries for the selection of lead radiotracer candidates at early stages of radiotracer discovery. Copyright © 2012 Elsevier Inc. All rights reserved.

Intrinsic Brain Activity of Cognitively Normal Older Persons Resembles More That of Patients Both with and at Risk for Alzheimer's Disease Than That of Healthy Younger Persons

PubMed Central

Pasquini, Lorenzo; Tonch, Annika; Plant, Claudia; Zherdin, Andrew; Ortner, Marion; Kurz, Alexander; Förstl, Hans; Zimmer, Claus; Grimmer, Timo; Wohlschäger, Afra; Riedl, Valentin

2014-01-01

Abstract In Alzheimer's disease (AD), recent findings suggest that amyloid-β (Aβ)-pathology might start 20–30 years before first cognitive symptoms arise. To account for age as most relevant risk factor for sporadic AD, it has been hypothesized that lifespan intrinsic (i.e., ongoing) activity of hetero-modal brain areas with highest levels of functional connectivity triggers Aβ-pathology. This model induces the simple question whether in older persons without any cognitive symptoms intrinsic activity of hetero-modal areas is more similar to that of symptomatic patients with AD or to that of younger healthy persons. We hypothesize that due to advanced age and therefore potential impact of pre-clinical AD, intrinsic activity of older persons resembles more that of patients than that of younger controls. We tested this hypothesis in younger (ca. 25 years) and older healthy persons (ca. 70 years) and patients with mild cognitive impairment and AD-dementia (ca. 70 years) by the use of resting-state functional magnetic resonance imaging, distinct measures of intrinsic brain activity, and different hierarchical clustering approaches. Independently of applied methods and involved areas, healthy older persons' intrinsic brain activity was consistently more alike that of patients than that of younger controls. Our result provides evidence for larger similarity in intrinsic brain activity between healthy older persons and patients with or at-risk for AD than between older and younger ones, suggesting a significant proportion of pre-clinical AD cases in the group of cognitively normal older people. The observed link of aging and AD with intrinsic brain activity supports the view that lifespan intrinsic activity may contribute critically to the pathogenesis of AD. PMID:24689864

A simple and effective process for noise reduction of multichannel cortical field potential recordings in freely moving rats.

PubMed

Shaw, Fu-Zen; Yen, Chen Tung; Chen, Ruei Feng

2003-04-15

Simple and useful steps, i.e. placing a grounded plate under the recording chamber as well as using multiple reference electrodes, are introduced here for obtaining reliable low-noise recordings of brain activity in freely moving rats. A general circuit model was built to analyze the electrical interference of both single-grounded and two-reference ground-free recording configurations. In both simulated and realistic conditions under two recording states, 60-Hz magnitude was in the microvolt range. Moreover, the noise was significantly reduced by shortening the distance between the subject and the grounded plate under the recording chamber. Furthermore, in chronically implanted rats, average 60-Hz interference of multichannel electroencephalograms of two-reference ground-free recordings (3.74 +/- 0.18 microV) was significantly smaller than that of the single-grounded condition (9.03 +/- 1.98 microV). Thus, we demonstrated that a lower-noise recording can be achieved by a two-reference configuration and a closely-placed metal grounded plate in an open-field circumstance. As compared to the use of a Faraday cage, this simple procedure is of benefit for long-term behavioral tracking with a video camera and for pharmacological experiments.

Interaction vs. observation: distinctive modes of social cognition in human brain and behavior? A combined fMRI and eye-tracking study

PubMed Central

Tylén, Kristian; Allen, Micah; Hunter, Bjørk K.; Roepstorff, Andreas

2012-01-01

Human cognition has usually been approached on the level of individual minds and brains, but social interaction is a challenging case. Is it best thought of as a self-contained individual cognitive process aiming at an “understanding of the other,” or should it rather be approached as an collective, inter-personal process where individual cognitive components interact on a moment-to-moment basis to form coupled dynamics? In a combined fMRI and eye-tracking study we directly contrasted these models of social cognition. We found that the perception of situations affording social contingent responsiveness (e.g., someone offering or showing you an object) elicited activations in regions of the right posterior temporal sulcus and yielded greater pupil dilation corresponding to a model of coupled dynamics (joint action). In contrast, the social-cognitive perception of someone “privately” manipulating an object elicited activation in medial prefrontal cortex, the right inferior frontal gyrus and right inferior parietal lobus, regions normally associated with Theory of Mind and with the mirror neuron system. Our findings support a distinction in social cognition between social observation and social interaction, and demonstrate that simple ostensive cues may shift participants' experience, behavior, and brain activity between these modes. The identification of a distinct, interactive mode has implications for research on social cognition, both in everyday life and in clinical conditions. PMID:23267322

Selectionist and Evolutionary Approaches to Brain Function: A Critical Appraisal

PubMed Central

Fernando, Chrisantha; Szathmáry, Eörs; Husbands, Phil

2012-01-01

We consider approaches to brain dynamics and function that have been claimed to be Darwinian. These include Edelman’s theory of neuronal group selection, Changeux’s theory of synaptic selection and selective stabilization of pre-representations, Seung’s Darwinian synapse, Loewenstein’s synaptic melioration, Adam’s selfish synapse, and Calvin’s replicating activity patterns. Except for the last two, the proposed mechanisms are selectionist but not truly Darwinian, because no replicators with information transfer to copies and hereditary variation can be identified in them. All of them fit, however, a generalized selectionist framework conforming to the picture of Price’s covariance formulation, which deliberately was not specific even to selection in biology, and therefore does not imply an algorithmic picture of biological evolution. Bayesian models and reinforcement learning are formally in agreement with selection dynamics. A classification of search algorithms is shown to include Darwinian replicators (evolutionary units with multiplication, heredity, and variability) as the most powerful mechanism for search in a sparsely occupied search space. Examples are given of cases where parallel competitive search with information transfer among the units is more efficient than search without information transfer between units. Finally, we review our recent attempts to construct and analyze simple models of true Darwinian evolutionary units in the brain in terms of connectivity and activity copying of neuronal groups. Although none of the proposed neuronal replicators include miraculous mechanisms, their identification remains a challenge but also a great promise. PMID:22557963

Abnormal Brain Dynamics Underlie Speech Production in Children with Autism Spectrum Disorder.

PubMed

Pang, Elizabeth W; Valica, Tatiana; MacDonald, Matt J; Taylor, Margot J; Brian, Jessica; Lerch, Jason P; Anagnostou, Evdokia

2016-02-01

A large proportion of children with autism spectrum disorder (ASD) have speech and/or language difficulties. While a number of structural and functional neuroimaging methods have been used to explore the brain differences in ASD with regards to speech and language comprehension and production, the neurobiology of basic speech function in ASD has not been examined. Magnetoencephalography (MEG) is a neuroimaging modality with high spatial and temporal resolution that can be applied to the examination of brain dynamics underlying speech as it can capture the fast responses fundamental to this function. We acquired MEG from 21 children with high-functioning autism (mean age: 11.43 years) and 21 age- and sex-matched controls as they performed a simple oromotor task, a phoneme production task and a phonemic sequencing task. Results showed significant differences in activation magnitude and peak latencies in primary motor cortex (Brodmann Area 4), motor planning areas (BA 6), temporal sequencing and sensorimotor integration areas (BA 22/13) and executive control areas (BA 9). Our findings of significant functional brain differences between these two groups on these simple oromotor and phonemic tasks suggest that these deficits may be foundational and could underlie the language deficits seen in ASD. © 2015 The Authors Autism Research published by Wiley Periodicals, Inc. on behalf of International Society for Autism Research.

Human Brain Modeling with Its Anatomical Structure and Realistic Material Properties for Brain Injury Prediction.

PubMed

Atsumi, Noritoshi; Nakahira, Yuko; Tanaka, Eiichi; Iwamoto, Masami

2018-05-01

Impairments of executive brain function after traumatic brain injury (TBI) due to head impacts in traffic accidents need to be obviated. Finite element (FE) analyses with a human brain model facilitate understanding of the TBI mechanisms. However, conventional brain FE models do not suitably describe the anatomical structure in the deep brain, which is a critical region for executive brain function, and the material properties of brain parenchyma. In this study, for better TBI prediction, a novel brain FE model with anatomical structure in the deep brain was developed. The developed model comprises a constitutive model of brain parenchyma considering anisotropy and strain rate dependency. Validation was performed against postmortem human subject test data associated with brain deformation during head impact. Brain injury analyses were performed using head acceleration curves obtained from reconstruction analysis of rear-end collision with a human whole-body FE model. The difference in structure was found to affect the regions of strain concentration, while the difference in material model contributed to the peak strain value. The injury prediction result by the proposed model was consistent with the characteristics in the neuroimaging data of TBI patients due to traffic accidents.

Hydrograph matching method for measuring model performance

NASA Astrophysics Data System (ADS)

Ewen, John

2011-09-01

SummaryDespite all the progress made over the years on developing automatic methods for analysing hydrographs and measuring the performance of rainfall-runoff models, automatic methods cannot yet match the power and flexibility of the human eye and brain. Very simple approaches are therefore being developed that mimic the way hydrologists inspect and interpret hydrographs, including the way that patterns are recognised, links are made by eye, and hydrological responses and errors are studied and remembered. In this paper, a dynamic programming algorithm originally designed for use in data mining is customised for use with hydrographs. It generates sets of "rays" that are analogous to the visual links made by the hydrologist's eye when linking features or times in one hydrograph to the corresponding features or times in another hydrograph. One outcome from this work is a new family of performance measures called "visual" performance measures. These can measure differences in amplitude and timing, including the timing errors between simulated and observed hydrographs in model calibration. To demonstrate this, two visual performance measures, one based on the Nash-Sutcliffe Efficiency and the other on the mean absolute error, are used in a total of 34 split-sample calibration-validation tests for two rainfall-runoff models applied to the Hodder catchment, northwest England. The customised algorithm, called the Hydrograph Matching Algorithm, is very simple to apply; it is given in a few lines of pseudocode.

Two possible driving forces supporting the evolution of animal communication. Comment on "Towards a Computational Comparative Neuroprimatology: Framing the language-ready brain" by Michael A. Arbib

NASA Astrophysics Data System (ADS)

Moulin-Frier, Clément; Verschure, Paul F. M. J.

2016-03-01

In the target paper [1], M.A. Arbib proposes a quite exhaustive review of the (often computational) models developed during the last decades that support his detailed scenario on language evolution (the Mirror System Hypothesis, MSH). The approach considers that language evolved from a mirror system for grasping already present in LCA-m (the last common ancestor of macaques and humans), to a simple imitation system for grasping present in LCA-c (the last common ancestor of chimpanzees and humans), to a complex imitation system for grasping that developed in the hominid line since that ancestor. MSH considers that this complex imitation system is a key evolutionary step for a language-ready brain, providing all the required elements for an open-ended gestural communication system. The transition from the gestural (bracchio-manual and visual) to the vocal (articulatory and auditory) domain is supposed to be a less important evolutionary step.

Oscillatory neural network for pattern recognition: trajectory based classification and supervised learning.

PubMed

Miller, Vonda H; Jansen, Ben H

2008-12-01

Computer algorithms that match human performance in recognizing written text or spoken conversation remain elusive. The reasons why the human brain far exceeds any existing recognition scheme to date in the ability to generalize and to extract invariant characteristics relevant to category matching are not clear. However, it has been postulated that the dynamic distribution of brain activity (spatiotemporal activation patterns) is the mechanism by which stimuli are encoded and matched to categories. This research focuses on supervised learning using a trajectory based distance metric for category discrimination in an oscillatory neural network model. Classification is accomplished using a trajectory based distance metric. Since the distance metric is differentiable, a supervised learning algorithm based on gradient descent is demonstrated. Classification of spatiotemporal frequency transitions and their relation to a priori assessed categories is shown along with the improved classification results after supervised training. The results indicate that this spatiotemporal representation of stimuli and the associated distance metric is useful for simple pattern recognition tasks and that supervised learning improves classification results.

Artificial consciousness, artificial emotions, and autonomous robots.

PubMed

Cardon, Alain

2006-12-01

Nowadays for robots, the notion of behavior is reduced to a simple factual concept at the level of the movements. On another hand, consciousness is a very cultural concept, founding the main property of human beings, according to themselves. We propose to develop a computable transposition of the consciousness concepts into artificial brains, able to express emotions and consciousness facts. The production of such artificial brains allows the intentional and really adaptive behavior for the autonomous robots. Such a system managing the robot's behavior will be made of two parts: the first one computes and generates, in a constructivist manner, a representation for the robot moving in its environment, and using symbols and concepts. The other part achieves the representation of the previous one using morphologies in a dynamic geometrical way. The robot's body will be seen for itself as the morphologic apprehension of its material substrata. The model goes strictly by the notion of massive multi-agent's organizations with a morphologic control.

Electro-Quasistatic Simulations in Bio-Systems Engineering and Medical Engineering

NASA Astrophysics Data System (ADS)

van Rienen, U.; Flehr, J.; Schreiber, U.; Schulze, S.; Gimsa, U.; Baumann, W.; Weiss, D. G.; Gimsa, J.; Benecke, R.; Pau, H.-W.

2005-05-01

Slowly varying electromagnetic fields play a key role in various applications in bio-systems and medical engineering. Examples are the electric activity of neurons on neurochips used as biosensors, the stimulating electric fields of implanted electrodes used for deep brain stimulation in patients with Morbus Parkinson and the stimulation of the auditory nerves in deaf patients, respectively. In order to simulate the neuronal activity on a chip it is necessary to couple Maxwell's and Hodgkin-Huxley's equations. First numerical results for a neuron coupling to a single electrode are presented. They show a promising qualitative agreement with the experimentally recorded signals. Further, simulations are presented on electrodes for deep brain stimulation in animal experiments where the question of electrode ageing and energy deposition in the surrounding tissue are of major interest. As a last example, electric simulations for a simple cochlea model are presented comparing the field in the skull bones for different electrode types and stimulations in different positions.

Neural computations underlying inverse reinforcement learning in the human brain

PubMed Central

Pauli, Wolfgang M; Bossaerts, Peter; O'Doherty, John

2017-01-01

In inverse reinforcement learning an observer infers the reward distribution available for actions in the environment solely through observing the actions implemented by another agent. To address whether this computational process is implemented in the human brain, participants underwent fMRI while learning about slot machines yielding hidden preferred and non-preferred food outcomes with varying probabilities, through observing the repeated slot choices of agents with similar and dissimilar food preferences. Using formal model comparison, we found that participants implemented inverse RL as opposed to a simple imitation strategy, in which the actions of the other agent are copied instead of inferring the underlying reward structure of the decision problem. Our computational fMRI analysis revealed that anterior dorsomedial prefrontal cortex encoded inferences about action-values within the value space of the agent as opposed to that of the observer, demonstrating that inverse RL is an abstract cognitive process divorceable from the values and concerns of the observer him/herself. PMID:29083301

Indocyanine Green Liposomes for Diagnosis and Therapeutic Monitoring of Cerebral Malaria.

PubMed

Portnoy, Emma; Vakruk, Natalia; Bishara, Ameer; Shmuel, Miriam; Magdassi, Shlomo; Golenser, Jacob; Eyal, Sara

2016-01-01

Cerebral malaria (CM) is a major cause of death of Plasmodium falciparum infection. Misdiagnosis of CM often leads to treatment delay and mortality. Conventional brain imaging technologies are rarely applicable in endemic areas. Here we address the unmet need for a simple, non-invasive imaging methodology for early diagnosis of CM. This study presents the diagnostic and therapeutic monitoring using liposomes containing the FDA-approved fluorescent dye indocyanine green (ICG) in a CM murine model. Increased emission intensity of liposomal ICG was demonstrated in comparison with free ICG. The Liposomal ICG's emission was greater in the brains of the infected mice compared to naïve mice and drug treated mice (where CM was prevented). Histological analyses suggest that the accumulation of liposomal ICG in the cerebral vasculature is due to extensive uptake mediated by activated phagocytes. Overall, liposomal ICG offers a valuable diagnostic tool and a biomarker for effectiveness of CM treatment, as well as other diseases that involve inflammation and blood vessel occlusion.

A Patch-Based Approach for the Segmentation of Pathologies: Application to Glioma Labelling.

PubMed

Cordier, Nicolas; Delingette, Herve; Ayache, Nicholas

2016-04-01

In this paper, we describe a novel and generic approach to address fully-automatic segmentation of brain tumors by using multi-atlas patch-based voting techniques. In addition to avoiding the local search window assumption, the conventional patch-based framework is enhanced through several simple procedures: an improvement of the training dataset in terms of both label purity and intensity statistics, augmented features to implicitly guide the nearest-neighbor-search, multi-scale patches, invariance to cube isometries, stratification of the votes with respect to cases and labels. A probabilistic model automatically delineates regions of interest enclosing high-probability tumor volumes, which allows the algorithm to achieve highly competitive running time despite minimal processing power and resources. This method was evaluated on Multimodal Brain Tumor Image Segmentation challenge datasets. State-of-the-art results are achieved, with a limited learning stage thus restricting the risk of overfit. Moreover, segmentation smoothness does not involve any post-processing.

Filopodial dynamics and growth cone stabilization in Drosophila visual circuit development

PubMed Central

Özel, Mehmet Neset; Langen, Marion; Hassan, Bassem A; Hiesinger, P Robin

2015-01-01

Filopodial dynamics are thought to control growth cone guidance, but the types and roles of growth cone dynamics underlying neural circuit assembly in a living brain are largely unknown. To address this issue, we have developed long-term, continuous, fast and high-resolution imaging of growth cone dynamics from axon growth to synapse formation in cultured Drosophila brains. Using R7 photoreceptor neurons as a model we show that >90% of the growth cone filopodia exhibit fast, stochastic dynamics that persist despite ongoing stepwise layer formation. Correspondingly, R7 growth cones stabilize early and change their final position by passive dislocation. N-Cadherin controls both fast filopodial dynamics and growth cone stabilization. Surprisingly, loss of N-Cadherin causes no primary targeting defects, but destabilizes R7 growth cones to jump between correct and incorrect layers. Hence, growth cone dynamics can influence wiring specificity without a direct role in target recognition and implement simple rules during circuit assembly. DOI: http://dx.doi.org/10.7554/eLife.10721.001 PMID:26512889

Allen Brain Atlas: an integrated spatio-temporal portal for exploring the central nervous system

PubMed Central

Sunkin, Susan M.; Ng, Lydia; Lau, Chris; Dolbeare, Tim; Gilbert, Terri L.; Thompson, Carol L.; Hawrylycz, Michael; Dang, Chinh

2013-01-01

The Allen Brain Atlas (http://www.brain-map.org) provides a unique online public resource integrating extensive gene expression data, connectivity data and neuroanatomical information with powerful search and viewing tools for the adult and developing brain in mouse, human and non-human primate. Here, we review the resources available at the Allen Brain Atlas, describing each product and data type [such as in situ hybridization (ISH) and supporting histology, microarray, RNA sequencing, reference atlases, projection mapping and magnetic resonance imaging]. In addition, standardized and unique features in the web applications are described that enable users to search and mine the various data sets. Features include both simple and sophisticated methods for gene searches, colorimetric and fluorescent ISH image viewers, graphical displays of ISH, microarray and RNA sequencing data, Brain Explorer software for 3D navigation of anatomy and gene expression, and an interactive reference atlas viewer. In addition, cross data set searches enable users to query multiple Allen Brain Atlas data sets simultaneously. All of the Allen Brain Atlas resources can be accessed through the Allen Brain Atlas data portal. PMID:23193282

Examining the volume efficiency of the cortical architecture in a multi-processor network model.

PubMed

Ruppin, E; Schwartz, E L; Yeshurun, Y

1993-01-01

The convoluted form of the sheet-like mammalian cortex naturally raises the question whether there is a simple geometrical reason for the prevalence of cortical architecture in the brains of higher vertebrates. Addressing this question, we present a formal analysis of the volume occupied by a massively connected network or processors (neurons) and then consider the pertaining cortical data. Three gross macroscopic features of cortical organization are examined: the segregation of white and gray matter, the circumferential organization of the gray matter around the white matter, and the folded cortical structure. Our results testify to the efficiency of cortical architecture.

Transcriptome analysis by strand-specific sequencing of complementary DNA

PubMed Central

Parkhomchuk, Dmitri; Borodina, Tatiana; Amstislavskiy, Vyacheslav; Banaru, Maria; Hallen, Linda; Krobitsch, Sylvia; Lehrach, Hans; Soldatov, Alexey

2009-01-01

High-throughput complementary DNA sequencing (RNA-Seq) is a powerful tool for whole-transcriptome analysis, supplying information about a transcript's expression level and structure. However, it is difficult to determine the polarity of transcripts, and therefore identify which strand is transcribed. Here, we present a simple cDNA sequencing protocol that preserves information about a transcript's direction. Using Saccharomyces cerevisiae and mouse brain transcriptomes as models, we demonstrate that knowing the transcript's orientation allows more accurate determination of the structure and expression of genes. It also helps to identify new genes and enables studying promoter-associated and antisense transcription. The transcriptional landscapes we obtained are available online. PMID:19620212

Transcriptome analysis by strand-specific sequencing of complementary DNA.

PubMed

Parkhomchuk, Dmitri; Borodina, Tatiana; Amstislavskiy, Vyacheslav; Banaru, Maria; Hallen, Linda; Krobitsch, Sylvia; Lehrach, Hans; Soldatov, Alexey

2009-10-01

High-throughput complementary DNA sequencing (RNA-Seq) is a powerful tool for whole-transcriptome analysis, supplying information about a transcript's expression level and structure. However, it is difficult to determine the polarity of transcripts, and therefore identify which strand is transcribed. Here, we present a simple cDNA sequencing protocol that preserves information about a transcript's direction. Using Saccharomyces cerevisiae and mouse brain transcriptomes as models, we demonstrate that knowing the transcript's orientation allows more accurate determination of the structure and expression of genes. It also helps to identify new genes and enables studying promoter-associated and antisense transcription. The transcriptional landscapes we obtained are available online.

Interpreting fMRI data: maps, modules and dimensions

PubMed Central

Op de Beeck, Hans P.; Haushofer, Johannes; Kanwisher, Nancy G.

2009-01-01

Neuroimaging research over the past decade has revealed a detailed picture of the functional organization of the human brain. Here we focus on two fundamental questions that are raised by the detailed mapping of sensory and cognitive functions and illustrate these questions with findings from the object-vision pathway. First, are functionally specific regions that are located close together best understood as distinct cortical modules or as parts of a larger-scale cortical map? Second, what functional properties define each cortical map or module? We propose a model in which overlapping continuous maps of simple features give rise to discrete modules that are selective for complex stimuli. PMID:18200027

Does a prosocial-selfish distinction help explain the biological affects? Comment on Buck (1999).

PubMed

Gray, Jeremy R

2002-10-01

R. Buck (1999) argued that a conceptual distinction between prosocial and selfish motivation is necessary to understand the biological affects (consciously experienced feelings and desires having an innate neurochemical basis). However, at a biological level of analysis, a prosocial-selfish distinction is doubtful empirically and conceptually. For this reason, Buck's proposed typology of biological affects is unclear. Moreover, a prosocial-selfish distinction is not necessary to explain hemispheric differences in brain activity associated with affect. In contrast, an approach-withdrawal distinction explains some data uniquely well, although numerous exceptions imply that simple models are inadequate. To extend hemispheric models of experienced emotion, a prosocial-selfish distinction is unlikely to be explanatory, whereas an alternative account based on a distinction between verbal and nonverbal working memory may be useful.

The role of cerebral spinal fluid in light propagation through the mouse head: improving fluorescence tomography with Monte Carlo modeling

NASA Astrophysics Data System (ADS)

Ancora, Daniele; Zacharopoulos, Athanasios; Ripoll, Jorge; Zacharakis, Giannis

2016-03-01

Optical Neuroimaging is a highly dynamical field of research owing to the combination of many advanced imaging techniques and computational tools that uncovered unexplored paths through the functioning of the brain. Light propagation modelling through such complicated structures has always played a crucial role as the basis for a high resolution and quantitative imaging where even the slightest improvement could lead to significant results. Fluorescence Diffuse Optical Tomography (fDOT), a widely used technique for three dimensional imaging of small animals and tissues, has been proved to be inaccurate for neuroimaging the mouse head without the knowledge of a-priori anatomical information of the subject. Commonly a normalized Born approximation model is used in fDOT reconstruction based on forward photon propagation using Diffusive Equation (DE) which has strong limitations in the optically clear regime. The presence of the Cerebral Spinal Fluid (CSF) instead, a thin optically clear layer surrounding the brain, can be more accurately taken into account using Monte Carlo approaches which nowadays is becoming more usable thanks to parallelized GPU algorithms. In this work we discuss the results of a synthetic experimental comparison, resulting to the increase of the accuracy for the Born approximation by introducing the CSF layer in a realistic mouse head structure with respect to the current model. We point out the importance of such clear layer for complex geometrical models, while for simple slab phantoms neglecting it does not introduce a significant error.

Graph analysis of functional brain networks: practical issues in translational neuroscience

PubMed Central

De Vico Fallani, Fabrizio; Richiardi, Jonas; Chavez, Mario; Achard, Sophie

2014-01-01

The brain can be regarded as a network: a connected system where nodes, or units, represent different specialized regions and links, or connections, represent communication pathways. From a functional perspective, communication is coded by temporal dependence between the activities of different brain areas. In the last decade, the abstract representation of the brain as a graph has allowed to visualize functional brain networks and describe their non-trivial topological properties in a compact and objective way. Nowadays, the use of graph analysis in translational neuroscience has become essential to quantify brain dysfunctions in terms of aberrant reconfiguration of functional brain networks. Despite its evident impact, graph analysis of functional brain networks is not a simple toolbox that can be blindly applied to brain signals. On the one hand, it requires the know-how of all the methodological steps of the pipeline that manipulate the input brain signals and extract the functional network properties. On the other hand, knowledge of the neural phenomenon under study is required to perform physiologically relevant analysis. The aim of this review is to provide practical indications to make sense of brain network analysis and contrast counterproductive attitudes. PMID:25180301

Planar implantable sensor for in vivo measurement of cellular oxygen metabolism in brain tissue.

PubMed

Tsytsarev, Vassiliy; Akkentli, Fatih; Pumbo, Elena; Tang, Qinggong; Chen, Yu; Erzurumlu, Reha S; Papkovsky, Dmitri B

2017-04-01

Brain imaging methods are continually improving. Imaging of the cerebral cortex is widely used in both animal experiments and charting human brain function in health and disease. Among the animal models, the rodent cerebral cortex has been widely used because of patterned neural representation of the whiskers on the snout and relative ease of activating cortical tissue with whisker stimulation. We tested a new planar solid-state oxygen sensor comprising a polymeric film with a phosphorescent oxygen-sensitive coating on the working side, to monitor dynamics of oxygen metabolism in the cerebral cortex following sensory stimulation. Sensory stimulation led to changes in oxygenation and deoxygenation processes of activated areas in the barrel cortex. We demonstrate the possibility of dynamic mapping of relative changes in oxygenation in live mouse brain tissue with such a sensor. Oxygenation-based functional magnetic resonance imaging (fMRI) is very effective method for functional brain mapping but have high costs and limited spatial resolution. Optical imaging of intrinsic signal (IOS) does not provide the required sensitivity, and voltage-sensitive dye optical imaging (VSDi) has limited applicability due to significant toxicity of the voltage-sensitive dye. Our planar solid-state oxygen sensor imaging approach circumvents these limitations, providing a simple optical contrast agent with low toxicity and rapid application. The planar solid-state oxygen sensor described here can be used as a tool in visualization and real-time analysis of sensory-evoked neural activity in vivo. Further, this approach allows visualization of local neural activity with high temporal and spatial resolution. Copyright © 2017 Elsevier B.V. All rights reserved.

Monte Carlo study for physiological interference reduction in near-infrared spectroscopy based on empirical mode decomposition

NASA Astrophysics Data System (ADS)

Zhang, Yan; Sun, JinWei; Rolfe, Peter

2010-12-01

Near-infrared spectroscopy (NIRS) can be used as the basis of non-invasive neuroimaging that may allow the measurement of haemodynamic changes in the human brain evoked by applied stimuli. Since this technique is very sensitive, physiological interference arising from the cardiac cycle and breathing can significantly affect the signal quality. Such interference is difficult to remove by conventional techniques because it occurs not only in the extracerebral layer but also in the brain tissue itself. Previous work on this problem employing temporal filtering, spatial filtering, and adaptive filtering have exhibited good performance for recovering brain activity data in evoked response studies. However, in this study, we present a time-frequency adaptive method for physiological interference reduction based on the combination of empirical mode decomposition (EMD) and Hilbert spectral analysis (HSA). Monte Carlo simulations based on a five-layered slab model of a human adult head were implemented to evaluate our methodology. We applied an EMD algorithm to decompose the NIRS time series derived from Monte Carlo simulations into a series of intrinsic mode functions (IMFs). In order to identify the IMFs associated with symmetric interference, the extracted components were then Hilbert transformed from which the instantaneous frequencies could be acquired. By reconstructing the NIRS signal by properly selecting IMFs, we determined that the evoked brain response is effectively filtered out with even higher signal-to-noise ratio (SNR). The results obtained demonstrated that EMD, combined with HSA, can effectively separate, identify and remove the contamination from the evoked brain response obtained with NIRS using a simple single source-detector pair.

The correlated evolution of antipredator defences and brain size in mammals

PubMed Central

Romero, Ashly N.

2017-01-01

Mammals that possess elaborate antipredator defences such as body armour, spines and quills are usually well protected, intermediate in size, primarily insectivorous and live in simple open environments. The benefits of such defences seem clear and may relax selection on maintaining cognitive abilities that aid in vigilance and predator recognition, and their bearers may accrue extensive production and maintenance costs. Here, in this comparative phylogenetic analysis of measurements of encephalization quotient and morphological defence scores of 647 mammal species representing nearly every order, we found that as lineages evolve stronger defences, they suffer a correlated reduction in encephalization. The only exceptions were those that live in trees—a complex three-dimensional world probably requiring greater cognitive abilities. At the proximate level, because brain tissue is extremely energetically expensive to build, mammals may be trading off spending more on elaborate defences and saving by building less powerful brains. At the ultimate level, having greater defences may also reduce the need for advanced cognitive abilities for constant assessment of environmental predation risk, especially in simple open environments. PMID:28077771

The correlated evolution of antipredator defences and brain size in mammals.

PubMed

Stankowich, Theodore; Romero, Ashly N

2017-01-11

Mammals that possess elaborate antipredator defences such as body armour, spines and quills are usually well protected, intermediate in size, primarily insectivorous and live in simple open environments. The benefits of such defences seem clear and may relax selection on maintaining cognitive abilities that aid in vigilance and predator recognition, and their bearers may accrue extensive production and maintenance costs. Here, in this comparative phylogenetic analysis of measurements of encephalization quotient and morphological defence scores of 647 mammal species representing nearly every order, we found that as lineages evolve stronger defences, they suffer a correlated reduction in encephalization. The only exceptions were those that live in trees-a complex three-dimensional world probably requiring greater cognitive abilities. At the proximate level, because brain tissue is extremely energetically expensive to build, mammals may be trading off spending more on elaborate defences and saving by building less powerful brains. At the ultimate level, having greater defences may also reduce the need for advanced cognitive abilities for constant assessment of environmental predation risk, especially in simple open environments. © 2017 The Author(s).

A theory of how active behavior stabilises neural activity: Neural gain modulation by closed-loop environmental feedback

PubMed Central

2018-01-01

During active behaviours like running, swimming, whisking or sniffing, motor actions shape sensory input and sensory percepts guide future motor commands. Ongoing cycles of sensory and motor processing constitute a closed-loop feedback system which is central to motor control and, it has been argued, for perceptual processes. This closed-loop feedback is mediated by brainwide neural circuits but how the presence of feedback signals impacts on the dynamics and function of neurons is not well understood. Here we present a simple theory suggesting that closed-loop feedback between the brain/body/environment can modulate neural gain and, consequently, change endogenous neural fluctuations and responses to sensory input. We support this theory with modeling and data analysis in two vertebrate systems. First, in a model of rodent whisking we show that negative feedback mediated by whisking vibrissa can suppress coherent neural fluctuations and neural responses to sensory input in the barrel cortex. We argue this suppression provides an appealing account of a brain state transition (a marked change in global brain activity) coincident with the onset of whisking in rodents. Moreover, this mechanism suggests a novel signal detection mechanism that selectively accentuates active, rather than passive, whisker touch signals. This mechanism is consistent with a predictive coding strategy that is sensitive to the consequences of motor actions rather than the difference between the predicted and actual sensory input. We further support the theory by re-analysing previously published two-photon data recorded in zebrafish larvae performing closed-loop optomotor behaviour in a virtual swim simulator. We show, as predicted by this theory, that the degree to which each cell contributes in linking sensory and motor signals well explains how much its neural fluctuations are suppressed by closed-loop optomotor behaviour. More generally we argue that our results demonstrate the dependence of neural fluctuations, across the brain, on closed-loop brain/body/environment interactions strongly supporting the idea that brain function cannot be fully understood through open-loop approaches alone. PMID:29342146

A theory of how active behavior stabilises neural activity: Neural gain modulation by closed-loop environmental feedback.

PubMed

Buckley, Christopher L; Toyoizumi, Taro

2018-01-01

During active behaviours like running, swimming, whisking or sniffing, motor actions shape sensory input and sensory percepts guide future motor commands. Ongoing cycles of sensory and motor processing constitute a closed-loop feedback system which is central to motor control and, it has been argued, for perceptual processes. This closed-loop feedback is mediated by brainwide neural circuits but how the presence of feedback signals impacts on the dynamics and function of neurons is not well understood. Here we present a simple theory suggesting that closed-loop feedback between the brain/body/environment can modulate neural gain and, consequently, change endogenous neural fluctuations and responses to sensory input. We support this theory with modeling and data analysis in two vertebrate systems. First, in a model of rodent whisking we show that negative feedback mediated by whisking vibrissa can suppress coherent neural fluctuations and neural responses to sensory input in the barrel cortex. We argue this suppression provides an appealing account of a brain state transition (a marked change in global brain activity) coincident with the onset of whisking in rodents. Moreover, this mechanism suggests a novel signal detection mechanism that selectively accentuates active, rather than passive, whisker touch signals. This mechanism is consistent with a predictive coding strategy that is sensitive to the consequences of motor actions rather than the difference between the predicted and actual sensory input. We further support the theory by re-analysing previously published two-photon data recorded in zebrafish larvae performing closed-loop optomotor behaviour in a virtual swim simulator. We show, as predicted by this theory, that the degree to which each cell contributes in linking sensory and motor signals well explains how much its neural fluctuations are suppressed by closed-loop optomotor behaviour. More generally we argue that our results demonstrate the dependence of neural fluctuations, across the brain, on closed-loop brain/body/environment interactions strongly supporting the idea that brain function cannot be fully understood through open-loop approaches alone.

Improving cerebellar segmentation with statistical fusion

NASA Astrophysics Data System (ADS)

Plassard, Andrew J.; Yang, Zhen; Prince, Jerry L.; Claassen, Daniel O.; Landman, Bennett A.

2016-03-01

The cerebellum is a somatotopically organized central component of the central nervous system well known to be involved with motor coordination and increasingly recognized roles in cognition and planning. Recent work in multiatlas labeling has created methods that offer the potential for fully automated 3-D parcellation of the cerebellar lobules and vermis (which are organizationally equivalent to cortical gray matter areas). This work explores the trade offs of using different statistical fusion techniques and post hoc optimizations in two datasets with distinct imaging protocols. We offer a novel fusion technique by extending the ideas of the Selective and Iterative Method for Performance Level Estimation (SIMPLE) to a patch-based performance model. We demonstrate the effectiveness of our algorithm, Non- Local SIMPLE, for segmentation of a mixed population of healthy subjects and patients with severe cerebellar anatomy. Under the first imaging protocol, we show that Non-Local SIMPLE outperforms previous gold-standard segmentation techniques. In the second imaging protocol, we show that Non-Local SIMPLE outperforms previous gold standard techniques but is outperformed by a non-locally weighted vote with the deeper population of atlases available. This work advances the state of the art in open source cerebellar segmentation algorithms and offers the opportunity for routinely including cerebellar segmentation in magnetic resonance imaging studies that acquire whole brain T1-weighted volumes with approximately 1 mm isotropic resolution.

Defining Optimal Brain Health in Adults: A Presidential Advisory From the American Heart Association/American Stroke Association.

PubMed

Gorelick, Philip B; Furie, Karen L; Iadecola, Costantino; Smith, Eric E; Waddy, Salina P; Lloyd-Jones, Donald M; Bae, Hee-Joon; Bauman, Mary Ann; Dichgans, Martin; Duncan, Pamela W; Girgus, Meighan; Howard, Virginia J; Lazar, Ronald M; Seshadri, Sudha; Testai, Fernando D; van Gaal, Stephen; Yaffe, Kristine; Wasiak, Hank; Zerna, Charlotte

2017-10-01

Cognitive function is an important component of aging and predicts quality of life, functional independence, and risk of institutionalization. Advances in our understanding of the role of cardiovascular risks have shown them to be closely associated with cognitive impairment and dementia. Because many cardiovascular risks are modifiable, it may be possible to maintain brain health and to prevent dementia in later life. The purpose of this American Heart Association (AHA)/American Stroke Association presidential advisory is to provide an initial definition of optimal brain health in adults and guidance on how to maintain brain health. We identify metrics to define optimal brain health in adults based on inclusion of factors that could be measured, monitored, and modified. From these practical considerations, we identified 7 metrics to define optimal brain health in adults that originated from AHA's Life's Simple 7: 4 ideal health behaviors (nonsmoking, physical activity at goal levels, healthy diet consistent with current guideline levels, and body mass index <25 kg/m 2 ) and 3 ideal health factors (untreated blood pressure <120/<80 mm Hg, untreated total cholesterol <200 mg/dL, and fasting blood glucose <100 mg/dL). In addition, in relation to maintenance of cognitive health, we recommend following previously published guidance from the AHA/American Stroke Association, Institute of Medicine, and Alzheimer's Association that incorporates control of cardiovascular risks and suggest social engagement and other related strategies. We define optimal brain health but recognize that the truly ideal circumstance may be uncommon because there is a continuum of brain health as demonstrated by AHA's Life's Simple 7. Therefore, there is opportunity to improve brain health through primordial prevention and other interventions. Furthermore, although cardiovascular risks align well with brain health, we acknowledge that other factors differing from those related to cardiovascular health may drive cognitive health. Defining optimal brain health in adults and its maintenance is consistent with the AHA's Strategic Impact Goal to improve cardiovascular health of all Americans by 20% and to reduce deaths resulting from cardiovascular disease and stroke by 20% by the year 2020. This work in defining optimal brain health in adults serves to provide the AHA/American Stroke Association with a foundation for a new strategic direction going forward in cardiovascular health promotion and disease prevention. © 2017 American Heart Association, Inc.

Defining Optimal Brain Health in Adults

PubMed Central

Gorelick, Philip B.; Furie, Karen L.; Iadecola, Costantino; Smith, Eric E.; Waddy, Salina P.; Lloyd-Jones, Donald M.; Bae, Hee-Joon; Bauman, Mary Ann; Dichgans, Martin; Duncan, Pamela W.; Girgus, Meighan; Howard, Virginia J.; Lazar, Ronald M.; Seshadri, Sudha; Testai, Fernando D.; van Gaal, Stephen; Yaffe, Kristine; Wasiak, Hank; Zerna, Charlotte

2017-01-01

Cognitive function is an important component of aging and predicts quality of life, functional independence, and risk of institutionalization. Advances in our understanding of the role of cardiovascular risks have shown them to be closely associated with cognitive impairment and dementia. Because many cardiovascular risks are modifiable, it may be possible to maintain brain health and to prevent dementia in later life. The purpose of this American Heart Association (AHA)/American Stroke Association presidential advisory is to provide an initial definition of optimal brain health in adults and guidance on how to maintain brain health. We identify metrics to define optimal brain health in adults based on inclusion of factors that could be measured, monitored, and modified. From these practical considerations, we identified 7 metrics to define optimal brain health in adults that originated from AHA’s Life’s Simple 7: 4 ideal health behaviors (nonsmoking, physical activity at goal levels, healthy diet consistent with current guideline levels, and body mass index <25 kg/m2) and 3 ideal health factors (untreated blood pressure <120/<80 mm Hg, untreated total cholesterol <200 mg/dL, and fasting blood glucose <100 mg/dL). In addition, in relation to maintenance of cognitive health, we recommend following previously published guidance from the AHA/American Stroke Association, Institute of Medicine, and Alzheimer’s Association that incorporates control of cardiovascular risks and suggest social engagement and other related strategies. We define optimal brain health but recognize that the truly ideal circumstance may be uncommon because there is a continuum of brain health as demonstrated by AHA’s Life’s Simple 7. Therefore, there is opportunity to improve brain health through primordial prevention and other interventions. Furthermore, although cardiovascular risks align well with brain health, we acknowledge that other factors differing from those related to cardiovascular health may drive cognitive health. Defining optimal brain health in adults and its maintenance is consistent with the AHA’s Strategic Impact Goal to improve cardiovascular health of all Americans by 20% and to reduce deaths resulting from cardiovascular disease and stroke by 20% by the year 2020. This work in defining optimal brain health in adults serves to provide the AHA/American Stroke Association with a foundation for a new strategic direction going forward in cardiovascular health promotion and disease prevention. PMID:28883125

Parkinson's Disease

MedlinePlus

Parkinson's disease (PD) is a type of movement disorder. It happens when nerve cells in the brain don't ... coordination As symptoms get worse, people with the disease may have trouble walking, talking, or doing simple ...

Human area MT+ shows load-dependent activation during working memory maintenance with continuously morphing stimulation.

PubMed

Galashan, Daniela; Fehr, Thorsten; Kreiter, Andreas K; Herrmann, Manfred

2014-07-11

Initially, human area MT+ was considered a visual area solely processing motion information but further research has shown that it is also involved in various different cognitive operations, such as working memory tasks requiring motion-related information to be maintained or cognitive tasks with implied or expected motion.In the present fMRI study in humans, we focused on MT+ modulation during working memory maintenance using a dynamic shape-tracking working memory task with no motion-related working memory content. Working memory load was systematically varied using complex and simple stimulus material and parametrically increasing retention periods. Activation patterns for the difference between retention of complex and simple memorized stimuli were examined in order to preclude that the reported effects are caused by differences in retrieval. Conjunction analysis over all delay durations for the maintenance of complex versus simple stimuli demonstrated a wide-spread activation pattern. Percent signal change (PSC) in area MT+ revealed a pattern with higher values for the maintenance of complex shapes compared to the retention of a simple circle and with higher values for increasing delay durations. The present data extend previous knowledge by demonstrating that visual area MT+ presents a brain activity pattern usually found in brain regions that are actively involved in working memory maintenance.

Computing local edge probability in natural scenes from a population of oriented simple cells

PubMed Central

Ramachandra, Chaithanya A.; Mel, Bartlett W.

2013-01-01

A key computation in visual cortex is the extraction of object contours, where the first stage of processing is commonly attributed to V1 simple cells. The standard model of a simple cell—an oriented linear filter followed by a divisive normalization—fits a wide variety of physiological data, but is a poor performing local edge detector when applied to natural images. The brain's ability to finely discriminate edges from nonedges therefore likely depends on information encoded by local simple cell populations. To gain insight into the corresponding decoding problem, we used Bayes's rule to calculate edge probability at a given location/orientation in an image based on a surrounding filter population. Beginning with a set of ∼ 100 filters, we culled out a subset that were maximally informative about edges, and minimally correlated to allow factorization of the joint on- and off-edge likelihood functions. Key features of our approach include a new, efficient method for ground-truth edge labeling, an emphasis on achieving filter independence, including a focus on filters in the region orthogonal rather than tangential to an edge, and the use of a customized parametric model to represent the individual filter likelihood functions. The resulting population-based edge detector has zero parameters, calculates edge probability based on a sum of surrounding filter influences, is much more sharply tuned than the underlying linear filters, and effectively captures fine-scale edge structure in natural scenes. Our findings predict nonmonotonic interactions between cells in visual cortex, wherein a cell may for certain stimuli excite and for other stimuli inhibit the same neighboring cell, depending on the two cells' relative offsets in position and orientation, and their relative activation levels. PMID:24381295

The Unique Propulsive Wake Pattern of the Swimming Sea Slug Aplysia

NASA Astrophysics Data System (ADS)

Zhou, Zhuoyu; Mittal, Rajat

2017-11-01

The Aplysia, also sometimes referred to as the `Sea Hare,' is a sea slug that swims elegantly using large-amplitude flapping of its mantle. The Sea Hare has become a very valuable laboratory animal for investigation into nervous systems and brain behavior due to its simple neural system with large neurons and axons. Recently, attempts have also been made to develop biohybrid robots with both organic actuation and organic motor-pattern control inspired by the locomotion of Aplysia. While extensive works have been done to investigate this animal's neurobiology, relatively little is known about its propulsive mechanisms and swimming energetics. In this study, incompressible flow simulations with a simple kinematical model are used to gain insights into vortex dynamics, thrust generation and energetics of locomotion. The effect of mantle kinematics on the propulsive performance is examined, and simulations indicate a unique vortex wake pattern that is responsible for thrust generation. The research is supported by NSF Grant PLR-1246317 and NSF XSEDE Grant TG-CTS100002.

Functional brain connectivity is predictable from anatomic network's Laplacian eigen-structure.

PubMed

Abdelnour, Farras; Dayan, Michael; Devinsky, Orrin; Thesen, Thomas; Raj, Ashish

2018-05-15

How structural connectivity (SC) gives rise to functional connectivity (FC) is not fully understood. Here we mathematically derive a simple relationship between SC measured from diffusion tensor imaging, and FC from resting state fMRI. We establish that SC and FC are related via (structural) Laplacian spectra, whereby FC and SC share eigenvectors and their eigenvalues are exponentially related. This gives, for the first time, a simple and analytical relationship between the graph spectra of structural and functional networks. Laplacian eigenvectors are shown to be good predictors of functional eigenvectors and networks based on independent component analysis of functional time series. A small number of Laplacian eigenmodes are shown to be sufficient to reconstruct FC matrices, serving as basis functions. This approach is fast, and requires no time-consuming simulations. It was tested on two empirical SC/FC datasets, and was found to significantly outperform generative model simulations of coupled neural masses. Copyright © 2018. Published by Elsevier Inc.

A simple and efficient method to enhance audiovisual binding tendencies

PubMed Central

Wozny, David R.; Shams, Ladan

2017-01-01

Individuals vary in their tendency to bind signals from multiple senses. For the same set of sights and sounds, one individual may frequently integrate multisensory signals and experience a unified percept, whereas another individual may rarely bind them and often experience two distinct sensations. Thus, while this binding/integration tendency is specific to each individual, it is not clear how plastic this tendency is in adulthood, and how sensory experiences may cause it to change. Here, we conducted an exploratory investigation which provides evidence that (1) the brain’s tendency to bind in spatial perception is plastic, (2) that it can change following brief exposure to simple audiovisual stimuli, and (3) that exposure to temporally synchronous, spatially discrepant stimuli provides the most effective method to modify it. These results can inform current theories about how the brain updates its internal model of the surrounding sensory world, as well as future investigations seeking to increase integration tendencies. PMID:28462016

Simple adaptive sparse representation based classification schemes for EEG based brain-computer interface applications.

PubMed

Shin, Younghak; Lee, Seungchan; Ahn, Minkyu; Cho, Hohyun; Jun, Sung Chan; Lee, Heung-No

2015-11-01

One of the main problems related to electroencephalogram (EEG) based brain-computer interface (BCI) systems is the non-stationarity of the underlying EEG signals. This results in the deterioration of the classification performance during experimental sessions. Therefore, adaptive classification techniques are required for EEG based BCI applications. In this paper, we propose simple adaptive sparse representation based classification (SRC) schemes. Supervised and unsupervised dictionary update techniques for new test data and a dictionary modification method by using the incoherence measure of the training data are investigated. The proposed methods are very simple and additional computation for the re-training of the classifier is not needed. The proposed adaptive SRC schemes are evaluated using two BCI experimental datasets. The proposed methods are assessed by comparing classification results with the conventional SRC and other adaptive classification methods. On the basis of the results, we find that the proposed adaptive schemes show relatively improved classification accuracy as compared to conventional methods without requiring additional computation. Copyright © 2015 Elsevier Ltd. All rights reserved.

Predicting institutionalization after traumatic brain injury inpatient rehabilitation.

PubMed

Eum, Regina S; Seel, Ronald T; Goldstein, Richard; Brown, Allen W; Watanabe, Thomas K; Zasler, Nathan D; Roth, Elliot J; Zafonte, Ross D; Glenn, Mel B

2015-02-15

Risk factors contributing to institutionalization after inpatient rehabilitation for people with traumatic brain injury (TBI) have not been well studied and need to be better understood to guide clinicians during rehabilitation. We aimed to develop a prognostic model that could be used at admission to inpatient rehabilitation facilities to predict discharge disposition. The model could be used to provide the interdisciplinary team with information regarding aspects of patients' functioning and/or their living situation that need particular attention during inpatient rehabilitation if institutionalization is to be avoided. The study population included 7219 patients with moderate-severe TBI in the Traumatic Brain Injury Model Systems (TBIMS) National Database enrolled from 2002-2012 who had not been institutionalized prior to injury. Based on institutionalization predictors in other populations, we hypothesized that among people who had lived at a private residence prior to injury, greater dependence in locomotion, bed-chair-wheelchair transfers, bladder and bowel continence, feeding, and comprehension at admission to inpatient rehabilitation programs would predict institutionalization at discharge. Logistic regression was used, with adjustment for demographic factors, proxy measures for TBI severity, and acute-care length-of-stay. C-statistic and predictiveness curves validated a five-variable model. Higher levels of independence in bladder management (adjusted odds ratio [OR], 0.88; 95% CI 0.83, 0.93), bed-chair-wheelchair transfers (OR, 0.81 [95% CI, 0.83-0.93]), and comprehension (OR, 0.78 [95% CI, 0.68, 0.89]) at admission were associated with lower risks of institutionalization on discharge. For every 10-year increment in age was associated with a 1.38 times higher risk for institutionalization (95% CI, 1.29, 1.48) and living alone was associated with a 2.34 times higher risk (95% CI, 1.86, 2.94). The c-statistic was 0.780. We conclude that this simple model can predict risk of institutionalization after inpatient rehabilitation for patients with TBI.

A Mathematical Model for Storage and Recall of Images using Targeted Synchronization of Coupled Maps.

PubMed

Palaniyandi, P; Rangarajan, Govindan

2017-08-21

We propose a mathematical model for storage and recall of images using coupled maps. We start by theoretically investigating targeted synchronization in coupled map systems wherein only a desired (partial) subset of the maps is made to synchronize. A simple method is introduced to specify coupling coefficients such that targeted synchronization is ensured. The principle of this method is extended to storage/recall of images using coupled Rulkov maps. The process of adjusting coupling coefficients between Rulkov maps (often used to model neurons) for the purpose of storing a desired image mimics the process of adjusting synaptic strengths between neurons to store memories. Our method uses both synchronisation and synaptic weight modification, as the human brain is thought to do. The stored image can be recalled by providing an initial random pattern to the dynamical system. The storage and recall of the standard image of Lena is explicitly demonstrated.

Free Will, Physics, Biology, and the Brain

NASA Astrophysics Data System (ADS)

Koch, Christof

This introduction reviews the traditionally conceived question of free will from the point of view of a physicist turned neurobiologist. I discuss the quantum mechanic evidence that has brought us to the view that the world, including our brains, is not completely determined by physics and that even very simple nervous systems are subject to deterministic chaos. However, it is unclear how consciousness or any other extra-physical agent could take advantage of this situation to effect a change in the world, except possibly by realizing one quantum possibility over another. While the brain is a highly nonlinear and stochastic system, it remains unclear to what extent individual quantum effects can affect its output behavior. Finally, I discuss several cognitive neuroscience experiments suggesting that in many instances, our brain decides prior to our conscious mind, and that we often ignorant of our brain's decisions.

The cyanobacterial neurotoxin beta-N-methylamino-L-alanine (BMAA) induces neuronal and behavioral changes in honeybees

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

Okle, Oliver, E-mail: oliver.okle@uni-konstanz.de; Rath, Lisa; Galizia, C. Giovanni

The cyanobacterially produced neurotoxin beta-N-methylamino-L-alanine (BMAA) is thought to induce amyotrophic lateral sclerosis/Parkinsonism dementia complex (ALS/PDC)-like symptoms. However, its mechanism of action and its pathway of intoxication are yet unknown. In vivo animal models suitable for investigating the neurotoxic effect of BMAA with applicability to the human are scarce. Hence, we used the honeybee (Apis mellifera) since its nervous system is relatively simple, yet having cognitive capabilities. Bees fed with BMAA-spiked sugar water had an increased mortality rate and a reduced ability to learn odors in a classical conditioning paradigm. Using {sup 14}C-BMAA we demonstrated that BMAA is biologically availablemore » to the bee, and is found in the head, thorax and abdomen with little to no excretion. BMAA is also transferred from one bee to the next via trophallaxis resulting in an exposure of the whole beehive. BMAA bath application directly onto the brain leads to an altered Ca{sup 2+} homeostasis and to generation of reactive oxygen species. These behavioral and physiological observations suggest that BMAA may have effects on bee brains similar to those assumed to occur in humans. Therefore the bee could serve as a surrogate model system for investigating the neurological effects of BMAA. - Highlights: • Investigating of neurotoxic effects of BMAA in honeybees • BMAA impairs ALS markers (ROS, Ca{sup 2+}, learning, memory, odor) in bees. • A method for the observation of ROS development in living bees brain was established. • Honeybees are a suitable model to explore neurodegenerative processes. • Neurotoxic BMAA can be spread in bee populations by trophallaxis.« less

The role of prediction in social neuroscience

PubMed Central

Brown, Elliot C.; Brüne, Martin

2012-01-01

Research has shown that the brain is constantly making predictions about future events. Theories of prediction in perception, action and learning suggest that the brain serves to reduce the discrepancies between expectation and actual experience, i.e., by reducing the prediction error. Forward models of action and perception propose the generation of a predictive internal representation of the expected sensory outcome, which is matched to the actual sensory feedback. Shared neural representations have been found when experiencing one's own and observing other's actions, rewards, errors, and emotions such as fear and pain. These general principles of the “predictive brain” are well established and have already begun to be applied to social aspects of cognition. The application and relevance of these predictive principles to social cognition are discussed in this article. Evidence is presented to argue that simple non-social cognitive processes can be extended to explain complex cognitive processes required for social interaction, with common neural activity seen for both social and non-social cognitions. A number of studies are included which demonstrate that bottom-up sensory input and top-down expectancies can be modulated by social information. The concept of competing social forward models and a partially distinct category of social prediction errors are introduced. The evolutionary implications of a “social predictive brain” are also mentioned, along with the implications on psychopathology. The review presents a number of testable hypotheses and novel comparisons that aim to stimulate further discussion and integration between currently disparate fields of research, with regard to computational models, behavioral and neurophysiological data. This promotes a relatively new platform for inquiry in social neuroscience with implications in social learning, theory of mind, empathy, the evolution of the social brain, and potential strategies for treating social cognitive deficits. PMID:22654749

Development of three-dimensional brain arteriovenous malformation model for patient communication and young neurosurgeon education.

PubMed

Dong, Mengqi; Chen, Guangzhong; Qin, Kun; Ding, Xiaowen; Zhou, Dong; Peng, Chao; Zeng, Shaojian; Deng, Xianming

2018-01-15

Rapid prototyping technology is used to fabricate three-dimensional (3D) brain arteriovenous malformation (AVM) models and facilitate presurgical patient communication and medical education for young surgeons. Two intracranial AVM cases were selected for this study. Using 3D CT angiography or 3D rotational angiography images, the brain AVM models were reconstructed on personal computer and the rapid prototyping process was completed using a 3D printer. The size and morphology of the models were compared to brain digital subtraction arteriography of the same patients. 3D brain AVM models were used for preoperative patient communication and young neurosurgeon education. Two brain AVM models were successfully produced. By neurosurgeons' evaluation, the printed models have high fidelity with the actual brain AVM structures of the patients. The patient responded positively toward the brain AVM model specific to himself. Twenty surgical residents from residency programs tested the brain AVM models and provided positive feedback on their usefulness as educational tool and resemblance to real brain AVM structures. Patient-specific 3D printed models of brain AVM can be constructed with high fidelity. 3D printed brain AVM models are proved to be helpful in preoperative patient consultation, surgical planning and resident training.

Observation of Simple Intransitive Actions: The Effect of Familiarity

PubMed Central

Plata Bello, Julio; Modroño, Cristián; Marcano, Francisco; González–Mora, José Luis

2013-01-01

Introduction Humans are more familiar with index – thumb than with any other finger to thumb grasping. The effect of familiarity has been previously tested with complex, specialized and/or transitive movements, but not with simple intransitive ones. The aim of this study is to evaluate brain activity patterns during the observation of simple and intransitive finger movements with differing degrees of familiarity. Methodology A functional Magnetic Resonance Imaging (fMRI) study was performed using a paradigm consisting of the observation of 4 videos showing a finger opposition task between the thumb and the other fingers (index, middle, ring and little) in a repetitive manner with a fixed frequency (1 Hz). This movement is considered as the pantomime of a precision grasping action. Results Significant activity was identified in the bilateral Inferior Parietal Lobule and premotor regions with the selected level of significance (FDR [False Discovery Rate] = 0.01). The extent of the activation in both regions tended to decrease when the finger that performed the action was further from the thumb. More specifically, this effect showed a linear trend (index>middle>ring>little) in the right parietal and premotor regions. Conclusions The observation of less familiar simple intransitive movements produces less activation of parietal and premotor areas than familiar ones. The most important implication of this study is the identification of differences in brain activity during the observation of simple intransitive movements with different degrees of familiarity. PMID:24073213

Model-Based Comparison of Deep Brain Stimulation Array Functionality with Varying Number of Radial Electrodes and Machine Learning Feature Sets.

PubMed

Teplitzky, Benjamin A; Zitella, Laura M; Xiao, YiZi; Johnson, Matthew D

2016-01-01

Deep brain stimulation (DBS) leads with radially distributed electrodes have potential to improve clinical outcomes through more selective targeting of pathways and networks within the brain. However, increasing the number of electrodes on clinical DBS leads by replacing conventional cylindrical shell electrodes with radially distributed electrodes raises practical design and stimulation programming challenges. We used computational modeling to investigate: (1) how the number of radial electrodes impact the ability to steer, shift, and sculpt a region of neural activation (RoA), and (2) which RoA features are best used in combination with machine learning classifiers to predict programming settings to target a particular area near the lead. Stimulation configurations were modeled using 27 lead designs with one to nine radially distributed electrodes. The computational modeling framework consisted of a three-dimensional finite element tissue conductance model in combination with a multi-compartment biophysical axon model. For each lead design, two-dimensional threshold-dependent RoAs were calculated from the computational modeling results. The models showed more radial electrodes enabled finer resolution RoA steering; however, stimulation amplitude, and therefore spatial extent of the RoA, was limited by charge injection and charge storage capacity constraints due to the small electrode surface area for leads with more than four radially distributed electrodes. RoA shifting resolution was improved by the addition of radial electrodes when using uniform multi-cathode stimulation, but non-uniform multi-cathode stimulation produced equivalent or better resolution shifting without increasing the number of radial electrodes. Robust machine learning classification of 15 monopolar stimulation configurations was achieved using as few as three geometric features describing a RoA. The results of this study indicate that, for a clinical-scale DBS lead, more than four radial electrodes minimally improved in the ability to steer, shift, and sculpt axonal activation around a DBS lead and a simple feature set consisting of the RoA center of mass and orientation enabled robust machine learning classification. These results provide important design constraints for future development of high-density DBS arrays.

Model-Based Comparison of Deep Brain Stimulation Array Functionality with Varying Number of Radial Electrodes and Machine Learning Feature Sets

PubMed Central

Teplitzky, Benjamin A.; Zitella, Laura M.; Xiao, YiZi; Johnson, Matthew D.

2016-01-01

Deep brain stimulation (DBS) leads with radially distributed electrodes have potential to improve clinical outcomes through more selective targeting of pathways and networks within the brain. However, increasing the number of electrodes on clinical DBS leads by replacing conventional cylindrical shell electrodes with radially distributed electrodes raises practical design and stimulation programming challenges. We used computational modeling to investigate: (1) how the number of radial electrodes impact the ability to steer, shift, and sculpt a region of neural activation (RoA), and (2) which RoA features are best used in combination with machine learning classifiers to predict programming settings to target a particular area near the lead. Stimulation configurations were modeled using 27 lead designs with one to nine radially distributed electrodes. The computational modeling framework consisted of a three-dimensional finite element tissue conductance model in combination with a multi-compartment biophysical axon model. For each lead design, two-dimensional threshold-dependent RoAs were calculated from the computational modeling results. The models showed more radial electrodes enabled finer resolution RoA steering; however, stimulation amplitude, and therefore spatial extent of the RoA, was limited by charge injection and charge storage capacity constraints due to the small electrode surface area for leads with more than four radially distributed electrodes. RoA shifting resolution was improved by the addition of radial electrodes when using uniform multi-cathode stimulation, but non-uniform multi-cathode stimulation produced equivalent or better resolution shifting without increasing the number of radial electrodes. Robust machine learning classification of 15 monopolar stimulation configurations was achieved using as few as three geometric features describing a RoA. The results of this study indicate that, for a clinical-scale DBS lead, more than four radial electrodes minimally improved in the ability to steer, shift, and sculpt axonal activation around a DBS lead and a simple feature set consisting of the RoA center of mass and orientation enabled robust machine learning classification. These results provide important design constraints for future development of high-density DBS arrays. PMID:27375470

End of Life Care (Brain Tumors)

MedlinePlus

... directives once you better understand your condition. Healthcare Power of Attorney This document names a person who ... simple form that covers both advanced directives and power of health care attorney. This Five Wishes form, ...

A multi-purpose electromagnetic actuator for magnetic resonance elastography.

PubMed

Feng, Yuan; Zhu, Mo; Qiu, Suhao; Shen, Ping; Ma, Shengyuan; Zhao, Xuefeng; Hu, Chun-Hong; Guo, Liang

2018-04-19

An electromagnetic actuator was designed for magnetic resonance elastography (MRE). The actuator is unique in that it is simple, portable, and capable of brain, abdomen, and phantom imagings. A custom-built control unit was used for controlling the vibration frequency and synchronizing the trigger signals. An actuation unit was built and mounted on the specifically designed clamp and holders for different imaging applications. MRE experiments with respect to gel phantoms, brain, and liver showed that the actuator could produce stable and consistent mechanical waves. Estimated shear modulus using local frequency estimate method demonstrated that the measurement results were in line with that from MRE studies using different actuation systems. The relatively easy setup procedure and simple design indicated that the actuator system had the potential to be applied in many different clinical studies. Copyright © 2018 Elsevier Inc. All rights reserved.

Origin of hyperbolicity in brain-to-brain coordination networks

NASA Astrophysics Data System (ADS)

Tadić, Bosiljka; Andjelković, Miroslav; Šuvakov, Milovan

2018-02-01

Hyperbolicity or negative curvature of complex networks is the intrinsic geometric proximity of nodes in the graph metric space, which implies an improved network function. Here, we investigate hidden combinatorial geometries in brain-to-brain coordination networks arising through social communications. The networks originate from correlations among EEG signals previously recorded during spoken communications comprising of 14 individuals with 24 speaker-listener pairs. We find that the corresponding networks are delta-hyperbolic with delta_max=1 and the graph diameter D=3 in each brain. While the emergent hyperbolicity in the two-brain networks satisfies delta_max/D/2 < 1 and can be attributed to the topology of the subgraph formed around the cross-brains linking channels. We identify these subgraphs in each studied two-brain network and decompose their structure into simple geometric descriptors (triangles, tetrahedra and cliques of higher orders) that contribute to hyperbolicity. Considering topologies that exceed two separate brain networks as a measure of coordination synergy between the brains, we identify different neuronal correlation patterns ranging from weak coordination to super-brain structure. These topology features are in qualitative agreement with the listener’s self-reported ratings of own experience and quality of the speaker, suggesting that studies of the cross-brain connector networks can reveal new insight into the neural mechanisms underlying human social behavior.

The "Skull Flap" a new conceived device for decompressive craniectomy experimental study on dogs to evaluate the safety and efficacy in reducing intracranial pressure and subsequent impact on brain perfusion.

PubMed

Salvatore, Chibbaro; Fabrice, Vallee; Marco, Marsella; Leonardo, Tigan; Thomas, Lilin; Benoit, Lecuelle; Bernard, George; Pierre, Kehrli; Eric, Vicaut; Paolo, Diemidio

2013-10-01

Decompressive craniectomy (DC) is a procedure performed increasingly often in current neurosurgical practice. Significant perioperative morbidity may be associated to this procedure because of the large skull defect; also, later closure of the skull defect (cranioplasty) may be associated to post-operative morbidity as much as any other reconstructive operation. The authors present a newly conceived/developed device: The "Skull Flap" (SF). This system, placed at the time of the craniectomy, offers the possibility to provide cranial reconstruction sparing patients a second operation. In other words, DC and cranioplasty essentially take place at the same time and in addition, patients retain their own bone flap. The current study conducted on animal models, represents the logical continuation of a prior recent study, realized on cadaver specimens, to assess the efficacy and safety of this recently developed device. This is an experimental pilot study on dogs to assess both safety and efficacy of the SF device. Two groups of experimental raised intracranial pressure animal models underwent DC; in the first group of dogs, the bone flap was left in raised position above the skull defect using the SF device; on the second group the flap was discarded. All dogs underwent transcranial Doppler (TCD) to assess brain perfusion. Head computed tomography (CT) scan to determine flap position was also obtained in the group in which the SF device was placed. SF has proved to be a strong fixation device that allows satisfactory brain decompression by keeping the bone flap elevated from the swollen brain; later on, the SF allows cranial reconstruction in a simple way without requiring a second staged operation. In addition, it is relevant to note that brain perfusion was measured and found to be better in the group receiving the SF (while the flap being in a raised as well as in its natural position) comparing to the other group. The SF device has proved to be very easy to place, well-adaptable to a different type of flaps and ultimately very effective in maintaining satisfactory brain decompression and later on, making easy bone flap repositioning after brain swelling has subsided.

Polyamine catabolism is enhanced after traumatic brain injury.

PubMed

Zahedi, Kamyar; Huttinger, Francis; Morrison, Ryan; Murray-Stewart, Tracy; Casero, Robert A; Strauss, Kenneth I

2010-03-01

Polyamines spermine and spermidine are highly regulated, ubiquitous aliphatic cations that maintain DNA structure and function as immunomodulators and as antioxidants. Polyamine homeostasis is disrupted after brain injuries, with concomitant generation of toxic metabolites that may contribute to secondary injuries. To test the hypothesis of increased brain polyamine catabolism after traumatic brain injury (TBI), we determined changes in catabolic enzymes and polyamine levels in the rat brain after lateral controlled cortical impact TBI. Spermine oxidase (SMO) catalyzes the degradation of spermine to spermidine, generating H2O2 and aminoaldehydes. Spermidine/spermine-N(1)-acetyltransferase (SSAT) catalyzes acetylation of these polyamines, and both are further oxidized in a reaction that generates putrescine, H2O2, and aminoaldehydes. In a rat cortical impact model of TBI, SSAT mRNA increased subacutely (6-24 h) after TBI in ipsilateral cortex and hippocampus. SMO mRNA levels were elevated late, from 3 to 7 days post-injury. Polyamine catabolism increased as well. Spermine levels were normal at 6 h and decreased slightly at 24 h, but were normal again by 72 h post-injury. Spermidine levels also decreased slightly (6-24 h), then increased by approximately 50% at 72 h post-injury. By contrast, normally low putrescine levels increased up to sixfold (6-72 h) after TBI. Moreover, N-acetylspermidine (but not N-acetylspermine) was detectable (24-72 h) near the site of injury, consistent with increased SSAT activity. None of these changes were seen in the contralateral hemisphere. Immunohistochemical confirmation indicated that SSAT and SMO were expressed throughout the brain. SSAT-immunoreactivity (SSAT-ir) increased in both neuronal and nonneuronal (likely glial) populations ipsilateral to injury. Interestingly, bilateral increases in cortical SSAT-ir neurons occurred at 72 h post-injury, whereas hippocampal changes occurred only ipsilaterally. Prolonged increases in brain polyamine catabolism are the likely cause of loss of homeostasis in this pathway. The potential for simple therapeutic interventions (e.g., polyamine supplementation or inhibition of polyamine oxidation) is an exciting implication of these studies.

Congenital heart disease affects cerebral size but not brain growth.

PubMed

Ortinau, Cynthia; Inder, Terrie; Lambeth, Jennifer; Wallendorf, Michael; Finucane, Kirsten; Beca, John

2012-10-01

Infants with congenital heart disease (CHD) have delayed brain maturation and alterations in brain volume. Brain metrics is a simple measurement technique that can be used to evaluate brain growth. This study used brain metrics to test the hypothesis that alterations in brain size persist at 3 months of age and that infants with CHD have slower rates of brain growth than control infants. Fifty-seven infants with CHD underwent serial brain magnetic resonance imaging (MRI). To evaluate brain growth across the first 3 months of life, brain metrics were undertaken using 19 tissue and fluid spaces shown on MRIs performed before surgery and again at 3 months of age. Before surgery, infants with CHD have smaller frontal, parietal, cerebellar, and brain stem measures (p < 0.001). At 3 months of age, alterations persisted in all measures except the cerebellum. There was no difference between control and CHD infants in brain growth. However, the cerebellum trended toward greater growth in infants with CHD. Somatic growth was the primary factor that related to brain growth. Presence of focal white matter lesions before and after surgery did not relate to alterations in brain size or growth. Although infants with CHD have persistent alterations in brain size at 3 months of age, rates of brain growth are similar to that of healthy term infants. Somatic growth was the primary predictor of brain growth, emphasizing the importance of optimal weight gain in this population.

Robust Nonlinear Neural Codes

NASA Astrophysics Data System (ADS)

Yang, Qianli; Pitkow, Xaq

2015-03-01

Most interesting natural sensory stimuli are encoded in the brain in a form that can only be decoded nonlinearly. But despite being a core function of the brain, nonlinear population codes are rarely studied and poorly understood. Interestingly, the few existing models of nonlinear codes are inconsistent with known architectural features of the brain. In particular, these codes have information content that scales with the size of the cortical population, even if that violates the data processing inequality by exceeding the amount of information entering the sensory system. Here we provide a valid theory of nonlinear population codes by generalizing recent work on information-limiting correlations in linear population codes. Although these generalized, nonlinear information-limiting correlations bound the performance of any decoder, they also make decoding more robust to suboptimal computation, allowing many suboptimal decoders to achieve nearly the same efficiency as an optimal decoder. Although these correlations are extremely difficult to measure directly, particularly for nonlinear codes, we provide a simple, practical test by which one can use choice-related activity in small populations of neurons to determine whether decoding is suboptimal or optimal and limited by correlated noise. We conclude by describing an example computation in the vestibular system where this theory applies. QY and XP was supported by a grant from the McNair foundation.

A transfer of technology from engineering: use of ROC curves from signal detection theory to investigate information processing in the brain during sensory difference testing.

PubMed

Wichchukit, Sukanya; O'Mahony, Michael

2010-01-01

This article reviews a beneficial effect of technology transfer from Electrical Engineering to Food Sensory Science. Specifically, it reviews the recent adoption in Food Sensory Science of the receiver operating characteristic (ROC) curve, a tool that is incorporated in the theory of signal detection. Its use allows the information processing that takes place in the brain during sensory difference testing to be studied and understood. The review deals with how Signal Detection Theory, also called Thurstonian modeling, led to the adoption of a more sophisticated way of analyzing the data from sensory difference tests, by introducing the signal-to-noise ratio, d', as a fundamental measure of perceived small sensory differences. Generally, the method of computation of d' is a simple matter for some of the better known difference tests like the triangle, duo-trio and 2-AFC. However, there are occasions when these tests are not appropriate and other tests like the same-different and the A Not-A test are more suitable. Yet, for these, it is necessary to understand how the brain processes information during the test before d' can be computed. It is for this task that the ROC curve has a particular use. © 2010 Institute of Food Technologists®

Minimizing brain shift during functional neurosurgical procedures - a simple burr hole technique that can decrease CSF loss and intracranial air.

PubMed

Coenen, V A; Abdel-Rahman, A; McMaster, J; Bogod, N; Honey, C R

2011-11-01

Exact stereotactic placement of deep brain stimulation electrodes during functional stereotactic neurosurgical procedures can be impeded by intraoperative brain shift. Brain shift has been shown to correlate with the amount of intracranial (subdural) air detected on early postoperative imaging studies. We report a simple burr hole technique that reduces the loss of cerebrospinal fluid (CSF) and has the potential to significantly reduce the amount of postoperative intracranial air. A total of 16 patients were studied with half (group 2) receiving the burr hole technique designed to seal the CSF space and thereby reducing CSF loss. The other 8 patients (group 1) received the standard burr hole technique. The 2 groups were of similar age, gender, diagnosis (Parkinson's disease, n=14; cervical dystonia n=2), and surgical targets. All patients received bilateral electrodes either in the subthalamic nucleus (STN, n=14) or in the globus pallidum internus (GPi, n=2) avoiding transventricular trajectories. Early postoperative 3-dimensional computed tomography (3D CT) was used to check for possible bleeding, DBS lead location, and the amount of intracranial air. Intracranial air was assessed manually in a volumetric slice-by-slice approach in the individual postoperative CT and the groups compared by t-test. Group 2 showed significantly lower postoperative intracranial air volumes (4.86 ± 4.35cc) as compared to group 1 (27.59 ± 17.80 cc, p=0.0083*). The duration of surgery, however, was significantly longer for group 1 (435 ± 56.05 min) as compared to group 2 (316 ± 34.79 min,p=0.00015*).The time span between the conclusion of the operation and postoperative 3DCT was similar for both groups. This new and simple burr hole technique was associated with a significant reduction in postoperative intracranial air. Reduction of intracranial air will ultimately reduce brain shift. That total operation time does not influence intracranial air is discussed as well as the limitations of this pilot series. In the authors' opinion, this straightforward and cost-effective technique has the potential to reduce brain shift and to increase DBS placement accuracy during functional stereotactic neurosurgical procedures performed in the seated or half-sitting position. A larger more standardized patient series is necessary to substantiate the findings. © Georg Thieme Verlag KG Stuttgart · New York.

The adaptive significance of adult neurogenesis: an integrative approach

PubMed Central

Konefal, Sarah; Elliot, Mick; Crespi, Bernard

2013-01-01

Adult neurogenesis in mammals is predominantly restricted to two brain regions, the dentate gyrus (DG) of the hippocampus and the olfactory bulb (OB), suggesting that these two brain regions uniquely share functions that mediate its adaptive significance. Benefits of adult neurogenesis across these two regions appear to converge on increased neuronal and structural plasticity that subserves coding of novel, complex, and fine-grained information, usually with contextual components that include spatial positioning. By contrast, costs of adult neurogenesis appear to center on potential for dysregulation resulting in higher risk of brain cancer or psychological dysfunctions, but such costs have yet to be quantified directly. The three main hypotheses for the proximate functions and adaptive significance of adult neurogenesis, pattern separation, memory consolidation, and olfactory spatial, are not mutually exclusive and can be reconciled into a simple general model amenable to targeted experimental and comparative tests. Comparative analysis of brain region sizes across two major social-ecological groups of primates, gregarious (mainly diurnal haplorhines, visually-oriented, and in large social groups) and solitary (mainly noctural, territorial, and highly reliant on olfaction, as in most rodents) suggest that solitary species, but not gregarious species, show positive associations of population densities and home range sizes with sizes of both the hippocampus and OB, implicating their functions in social-territorial systems mediated by olfactory cues. Integrated analyses of the adaptive significance of adult neurogenesis will benefit from experimental studies motivated and structured by ecologically and socially relevant selective contexts. PMID:23882188

Using a Simple Neural Network to Delineate Some Principles of Distributed Economic Choice.

PubMed

Balasubramani, Pragathi P; Moreno-Bote, Rubén; Hayden, Benjamin Y

2018-01-01

The brain uses a mixture of distributed and modular organization to perform computations and generate appropriate actions. While the principles under which the brain might perform computations using modular systems have been more amenable to modeling, the principles by which the brain might make choices using distributed principles have not been explored. Our goal in this perspective is to delineate some of those distributed principles using a neural network method and use its results as a lens through which to reconsider some previously published neurophysiological data. To allow for direct comparison with our own data, we trained the neural network to perform binary risky choices. We find that value correlates are ubiquitous and are always accompanied by non-value information, including spatial information (i.e., no pure value signals). Evaluation, comparison, and selection were not distinct processes; indeed, value signals even in the earliest stages contributed directly, albeit weakly, to action selection. There was no place, other than at the level of action selection, at which dimensions were fully integrated. No units were specialized for specific offers; rather, all units encoded the values of both offers in an anti-correlated format, thus contributing to comparison. Individual network layers corresponded to stages in a continuous rotation from input to output space rather than to functionally distinct modules. While our network is likely to not be a direct reflection of brain processes, we propose that these principles should serve as hypotheses to be tested and evaluated for future studies.

Identifying disease-related subnetwork connectome biomarkers by sparse hypergraph learning.

PubMed

Zu, Chen; Gao, Yue; Munsell, Brent; Kim, Minjeong; Peng, Ziwen; Cohen, Jessica R; Zhang, Daoqiang; Wu, Guorong

2018-06-14

The functional brain network has gained increased attention in the neuroscience community because of its ability to reveal the underlying architecture of human brain. In general, majority work of functional network connectivity is built based on the correlations between discrete-time-series signals that link only two different brain regions. However, these simple region-to-region connectivity models do not capture complex connectivity patterns between three or more brain regions that form a connectivity subnetwork, or subnetwork for short. To overcome this current limitation, a hypergraph learning-based method is proposed to identify subnetwork differences between two different cohorts. To achieve our goal, a hypergraph is constructed, where each vertex represents a subject and also a hyperedge encodes a subnetwork with similar functional connectivity patterns between different subjects. Unlike previous learning-based methods, our approach is designed to jointly optimize the weights for all hyperedges such that the learned representation is in consensus with the distribution of phenotype data, i.e. clinical labels. In order to suppress the spurious subnetwork biomarkers, we further enforce a sparsity constraint on the hyperedge weights, where a larger hyperedge weight indicates the subnetwork with the capability of identifying the disorder condition. We apply our hypergraph learning-based method to identify subnetwork biomarkers in Autism Spectrum Disorder (ASD) and Attention Deficit Hyperactivity Disorder (ADHD). A comprehensive quantitative and qualitative analysis is performed, and the results show that our approach can correctly classify ASD and ADHD subjects from normal controls with 87.65 and 65.08% accuracies, respectively.

Using a Simple Neural Network to Delineate Some Principles of Distributed Economic Choice

PubMed Central

Balasubramani, Pragathi P.; Moreno-Bote, Rubén; Hayden, Benjamin Y.

2018-01-01

The brain uses a mixture of distributed and modular organization to perform computations and generate appropriate actions. While the principles under which the brain might perform computations using modular systems have been more amenable to modeling, the principles by which the brain might make choices using distributed principles have not been explored. Our goal in this perspective is to delineate some of those distributed principles using a neural network method and use its results as a lens through which to reconsider some previously published neurophysiological data. To allow for direct comparison with our own data, we trained the neural network to perform binary risky choices. We find that value correlates are ubiquitous and are always accompanied by non-value information, including spatial information (i.e., no pure value signals). Evaluation, comparison, and selection were not distinct processes; indeed, value signals even in the earliest stages contributed directly, albeit weakly, to action selection. There was no place, other than at the level of action selection, at which dimensions were fully integrated. No units were specialized for specific offers; rather, all units encoded the values of both offers in an anti-correlated format, thus contributing to comparison. Individual network layers corresponded to stages in a continuous rotation from input to output space rather than to functionally distinct modules. While our network is likely to not be a direct reflection of brain processes, we propose that these principles should serve as hypotheses to be tested and evaluated for future studies. PMID:29643773

In vitro models of the blood–brain barrier: An overview of commonly used brain endothelial cell culture models and guidelines for their use

PubMed Central

Helms, Hans C; Abbott, N Joan; Burek, Malgorzata; Cecchelli, Romeo; Couraud, Pierre-Olivier; Deli, Maria A; Förster, Carola; Galla, Hans J; Romero, Ignacio A; Shusta, Eric V; Stebbins, Matthew J; Vandenhaute, Elodie; Weksler, Babette

2016-01-01

The endothelial cells lining the brain capillaries separate the blood from the brain parenchyma. The endothelial monolayer of the brain capillaries serves both as a crucial interface for exchange of nutrients, gases, and metabolites between blood and brain, and as a barrier for neurotoxic components of plasma and xenobiotics. This “blood-brain barrier” function is a major hindrance for drug uptake into the brain parenchyma. Cell culture models, based on either primary cells or immortalized brain endothelial cell lines, have been developed, in order to facilitate in vitro studies of drug transport to the brain and studies of endothelial cell biology and pathophysiology. In this review, we aim to give an overview of established in vitro blood–brain barrier models with a focus on their validation regarding a set of well-established blood–brain barrier characteristics. As an ideal cell culture model of the blood–brain barrier is yet to be developed, we also aim to give an overview of the advantages and drawbacks of the different models described. PMID:26868179

Declarative and nondeclarative memory: multiple brain systems supporting learning and memory.

PubMed

Squire, L R

1992-01-01

Abstract The topic of multiple forms of memory is considered from a biological point of view. Fact-and-event (declarative, explicit) memory is contrasted with a collection of non conscious (non-declarative, implicit) memory abilities including skills and habits, priming, and simple conditioning. Recent evidence is reviewed indicating that declarative and non declarative forms of memory have different operating characteristics and depend on separate brain systems. A brain-systems framework for understanding memory phenomena is developed in light of lesion studies involving rats, monkeys, and humans, as well as recent studies with normal humans using the divided visual field technique, event-related potentials, and positron emission tomography (PET).

Inflammation, neurodegeneration and protein aggregation in the retina as ocular biomarkers for Alzheimer's disease in the 3xTg-AD mouse model.

PubMed

Grimaldi, Alfonso; Brighi, Carlo; Peruzzi, Giovanna; Ragozzino, Davide; Bonanni, Valentina; Limatola, Cristina; Ruocco, Giancarlo; Di Angelantonio, Silvia

2018-06-07

Alzheimer's disease (AD) is the most common cause of dementia in the elderly. In the pathogenesis of AD a pivotal role is played by two neurotoxic proteins that aggregate and accumulate in the central nervous system: amyloid beta and hyper-phosphorylated tau. Accumulation of extracellular amyloid beta plaques and intracellular hyper-phosphorylated tau tangles, and consequent neuronal loss begins 10-15 years before any cognitive impairment. In addition to cognitive and behavioral deficits, sensorial abnormalities have been described in AD patients and in some AD transgenic mouse models. Retina can be considered a simple model of the brain, as some pathological changes and therapeutic strategies from the brain may be observed or applicable to the retina. Here we propose new retinal biomarkers that could anticipate the AD diagnosis and help the beginning and the follow-up of possible future treatments. We analyzed retinal tissue of triple-transgenic AD mouse model (3xTg-AD) for the presence of pathological hallmarks during disease progression. We found the presence of amyloid beta plaques, tau tangles, neurodegeneration, and astrogliosis in the retinal ganglion cell layer of 3xTg-AD mice, already at pre-symptomatic stage. Moreover, retinal microglia in pre-symptomatic mice showed a ramified, anti-inflammatory phenotype which, during disease progression, switches to a pro-inflammatory, less ramified one, becoming neurotoxic. We hypothesize retina as a window through which monitor AD-related neurodegeneration process.

Quest for the basic plan of nervous system circuitry

PubMed Central

Swanson, Larry W.

2007-01-01

The basic plan of nervous system organization has been investigated since classical antiquity. The first model centered on pneumas pumped from sensory nerves through the ventricular system and out motor nerves to muscles. It was popular well into the seventeenth century and diverted attention from the organization of brain parenchyma itself. Willis focused on gray matter production and white matter conduction of pneumas in 1664, and by the late nineteenth century a clear cellular model of nervous system organization based on sensory, motor, and association neuron classes transmitting nerve impulses was elaborated by Cajal and his contemporaries. Today, revolutionary advances in experimental pathway tracing methods, molecular genetics, and computer science inspire systems neuroscience. Seven minimal requirements are outlined for knowledge management systems capable of describing, analyzing, and modeling the basic plan of nervous system circuitry in general, and the plan evolved for vertebrates, for mammals, and ultimately for humans in particular. The goal remains a relatively simple, easy to understand model analogous to the one Harvey elaborated in 1628 for circulation in the cardiovascular system. As Cajal wrote in 1909, “To extend our understanding of neural function to the most complex human physiological and psychological activities, it is essential that we first generate a clear and accurate view of the structure of the relevant centers, and of the human brain itself, so that the basic plan—the overview—can be grasped in the blink of an eye.” PMID:17267046

Modular Neuronal Assemblies Embodied in a Closed-Loop Environment: Toward Future Integration of Brains and Machines

PubMed Central

Tessadori, Jacopo; Bisio, Marta; Martinoia, Sergio; Chiappalone, Michela

2012-01-01

Behaviors, from simple to most complex, require a two-way interaction with the environment and the contribution of different brain areas depending on the orchestrated activation of neuronal assemblies. In this work we present a new hybrid neuro-robotic architecture based on a neural controller bi-directionally connected to a virtual robot implementing a Braitenberg vehicle aimed at avoiding obstacles. The robot is characterized by proximity sensors and wheels, allowing it to navigate into a circular arena with obstacles of different sizes. As neural controller, we used hippocampal cultures dissociated from embryonic rats and kept alive over Micro Electrode Arrays (MEAs) for 3–8 weeks. The developed software architecture guarantees a bi-directional exchange of information between the natural and the artificial part by means of simple linear coding/decoding schemes. We used two different kinds of experimental preparation: “random” and “modular” populations. In the second case, the confinement was assured by a polydimethylsiloxane (PDMS) mask placed over the surface of the MEA device, thus defining two populations interconnected via specific microchannels. The main results of our study are: (i) neuronal cultures can be successfully interfaced to an artificial agent; (ii) modular networks show a different dynamics with respect to random culture, both in terms of spontaneous and evoked electrophysiological patterns; (iii) the robot performs better if a reinforcement learning paradigm (i.e., a tetanic stimulation delivered to the network following each collision) is activated, regardless of the modularity of the culture; (iv) the robot controlled by the modular network further enhances its capabilities in avoiding obstacles during the short-term plasticity trial. The developed paradigm offers a new framework for studying, in simplified model systems, neuro-artificial bi-directional interfaces for the development of new strategies for brain-machine interaction. PMID:23248586

[Prolonged clinical pattern of brain death in patients under barbiturate sedation: usefulness of transcranial Doppler].

PubMed

Segura, T; Jiménez, P; Jerez, P; García, F; Córcoles, V

2002-04-01

Throughout the world, is fully accepted that a person is dead when brain death exists. In most situations, neurological criteria permit the diagnosis of brain death, but in some instances, as when high-dose barbiturate therapy has been used, confirmatory testing are required by law. We report the case of a 17 year-old women who suffered high-dose barbiturate therapy due to post traumatic intracranial hypertension. During the period of the barbiturate infusion and until six days after the suppression of this therapy, neurological exploration and EEG findings seem to confirm brain death, while transcranial Doppler (TCD) study remained normal. TCD is a fast, simple and accurate confirmatory testing in the determination of brain death and its findings are not affected by high-dose barbiturate therapy. We think that TCD must be present in all hospitals where mechanical ventilation and support of patients are carried out.

The Identification of Aluminum in Human Brain Tissue Using Lumogallion and Fluorescence Microscopy

PubMed Central

Mirza, Ambreen; King, Andrew; Troakes, Claire; Exley, Christopher

2016-01-01

Aluminum in human brain tissue is implicated in the etiologies of neurodegenerative diseases including Alzheimer’s disease. While methods for the accurate and precise measurement of aluminum in human brain tissue are widely acknowledged, the same cannot be said for the visualization of aluminum. Herein we have used transversely-heated graphite furnace atomic absorption spectrometry to measure aluminum in the brain of a donor with Alzheimer’s disease, and we have developed and validated fluorescence microscopy and the fluor lumogallion to show the presence of aluminum in the same tissue. Aluminum is observed as characteristic orange fluorescence that is neither reproduced by other metals nor explained by autofluorescence. This new and relatively simple method to visualize aluminum in human brain tissue should enable more rigorous testing of the aluminum hypothesis of Alzheimer’s disease (and other neurological conditions) in the future. PMID:27472886

Whole-brain activity mapping onto a zebrafish brain atlas.

PubMed

Randlett, Owen; Wee, Caroline L; Naumann, Eva A; Nnaemeka, Onyeka; Schoppik, David; Fitzgerald, James E; Portugues, Ruben; Lacoste, Alix M B; Riegler, Clemens; Engert, Florian; Schier, Alexander F

2015-11-01

In order to localize the neural circuits involved in generating behaviors, it is necessary to assign activity onto anatomical maps of the nervous system. Using brain registration across hundreds of larval zebrafish, we have built an expandable open-source atlas containing molecular labels and definitions of anatomical regions, the Z-Brain. Using this platform and immunohistochemical detection of phosphorylated extracellular signal–regulated kinase (ERK) as a readout of neural activity, we have developed a system to create and contextualize whole-brain maps of stimulus- and behavior-dependent neural activity. This mitogen-activated protein kinase (MAP)-mapping assay is technically simple, and data analysis is completely automated. Because MAP-mapping is performed on freely swimming fish, it is applicable to studies of nearly any stimulus or behavior. Here we demonstrate our high-throughput approach using pharmacological, visual and noxious stimuli, as well as hunting and feeding. The resultant maps outline hundreds of areas associated with behaviors.

Habit formation.

PubMed

Smith, Kyle S; Graybiel, Ann M

2016-03-01

Habits, both good ones and bad ones, are pervasive in animal behavior. Important frameworks have been developed to understand habits through psychological and neurobiological studies. This work has given us a rich understanding of brain networks that promote habits, and has also helped us to understand what constitutes a habitual behavior as opposed to a behavior that is more flexible and prospective. Mounting evidence from studies using neural recording methods suggests that habit formation is not a simple process. We review this evidence and take the position that habits could be sculpted from multiple dissociable changes in neural activity. These changes occur across multiple brain regions and even within single brain regions. This strategy of classifying components of a habit based on different brain signals provides a potentially useful new way to conceive of disorders that involve overly fixed behaviors as arising from different potential dysfunctions within the brain's habit network.

Habit formation

PubMed Central

Smith, Kyle S.; Graybiel, Ann M.

2016-01-01

Habits, both good ones and bad ones, are pervasive in animal behavior. Important frameworks have been developed to understand habits through psychological and neurobiological studies. This work has given us a rich understanding of brain networks that promote habits, and has also helped us to understand what constitutes a habitual behavior as opposed to a behavior that is more flexible and prospective. Mounting evidence from studies using neural recording methods suggests that habit formation is not a simple process. We review this evidence and take the position that habits could be sculpted from multiple dissociable changes in neural activity. These changes occur across multiple brain regions and even within single brain regions. This strategy of classifying components of a habit based on different brain signals provides a potentially useful new way to conceive of disorders that involve overly fixed behaviors as arising from different potential dysfunctions within the brain's habit network. PMID:27069378

Bayesian deconvolution of [corrected] fMRI data using bilinear dynamical systems.

PubMed

Makni, Salima; Beckmann, Christian; Smith, Steve; Woolrich, Mark

2008-10-01

In Penny et al. [Penny, W., Ghahramani, Z., Friston, K.J. 2005. Bilinear dynamical systems. Philos. Trans. R. Soc. Lond. B Biol. Sci. 360(1457) 983-993], a particular case of the Linear Dynamical Systems (LDSs) was used to model the dynamic behavior of the BOLD response in functional MRI. This state-space model, called bilinear dynamical system (BDS), is used to deconvolve the fMRI time series in order to estimate the neuronal response induced by the different stimuli of the experimental paradigm. The BDS model parameters are estimated using an expectation-maximization (EM) algorithm proposed by Ghahramani and Hinton [Ghahramani, Z., Hinton, G.E. 1996. Parameter Estimation for Linear Dynamical Systems. Technical Report, Department of Computer Science, University of Toronto]. In this paper we introduce modifications to the BDS model in order to explicitly model the spatial variations of the haemodynamic response function (HRF) in the brain using a non-parametric approach. While in Penny et al. [Penny, W., Ghahramani, Z., Friston, K.J. 2005. Bilinear dynamical systems. Philos. Trans. R. Soc. Lond. B Biol. Sci. 360(1457) 983-993] the relationship between neuronal activation and fMRI signals is formulated as a first-order convolution with a kernel expansion using basis functions (typically two or three), in this paper, we argue in favor of a spatially adaptive GLM in which a local non-parametric estimation of the HRF is performed. Furthermore, in order to overcome the overfitting problem typically associated with simple EM estimates, we propose a full Variational Bayes (VB) solution to infer the BDS model parameters. We demonstrate the usefulness of our model which is able to estimate both the neuronal activity and the haemodynamic response function in every voxel of the brain. We first examine the behavior of this approach when applied to simulated data with different temporal and noise features. As an example we will show how this method can be used to improve interpretability of estimates from an independent component analysis (ICA) analysis of fMRI data. We finally demonstrate its use on real fMRI data in one slice of the brain.

A new approach for simple radioisotope cisternography examination in cerebrospinal fluid leakage detection.

PubMed

Hoshino, Hiromitsu; Higuchi, Tetsuya; Achmad, Arifudin; Taketomi-Takahashi, Ayako; Fujimaki, Hiroya; Tsushima, Yoshito

2016-01-01

We developed a new quantitative interpretation technique of radioisotope cisternography (RIC) for the diagnosis of spontaneous cerebrospinal fluid hypovolemia (SCH). RIC studies performed for suspected SCH were evaluated. (111)In-DTPA RIC images were taken at 0, 1, 3, 6, and 24-h after radioisotope injection following the current protocol. Regions of interest (ROI) were selected on 3-h images to include brain, spine, bladder or the whole body. The accumulative radioactivity counts were calculated for quantitative analysis. Final diagnoses of SCH were established based on the diagnostic criteria recently proposed by Schievink and colleagues. Thirty-five patients were focused on. Twenty-one (60.0%) patients were diagnosed as having SCH according to the Schievink criteria. On the 3-h images, direct cerebrospinal fluid leakage sign was detected in nine of 21 SCH patients (42.9%), as well as three patients with suspected iatrogenic leakage. Compared to non-SCH patients, SCH patients showed higher bladder accumulation at 3-h images (P = 0.0002), and higher brain clearance between the 6- and 24-h images (P < 0.0001). In particular, the 24-h brain clearance was more conclusive for the diagnosis than 24-h whole cistern clearance. The combination of direct sign and 24-h brain accumulation resulted in 100% of accuracy in the 32 patients in whom iatrogenic leakage was not observed. 1- and 6-h images did not provide any additional information in any patients. A new simple ROI setting method, in which only the 3-h whole body and 24-h brain images were necessary, was sufficient to diagnose SCH.

Magnetic bead-quantum dot assay for detection of a biomarker for traumatic brain injury

NASA Astrophysics Data System (ADS)

Kim, Chloe; Searson, Peter C.

2015-10-01

Current diagnostic methods for traumatic brain injury (TBI), which accounts for 15% of all emergency room visits, are limited to neuroimaging modalities. The challenges of accurate diagnosis and monitoring of TBI have created the need for a simple and sensitive blood test to detect brain-specific biomarkers. Here we report on an assay for detection of S100B, a putative biomarker for TBI, using antibody-conjugated magnetic beads for capture of the protein, and antibody-conjugated quantum dots for optical detection. From Western Blot, we show efficient antigen capture and concentration by the magnetic beads. Using magnetic bead capture and quantum dot detection in serum samples, we show a wide detection range and detection limit below the clinical cut-off level.Current diagnostic methods for traumatic brain injury (TBI), which accounts for 15% of all emergency room visits, are limited to neuroimaging modalities. The challenges of accurate diagnosis and monitoring of TBI have created the need for a simple and sensitive blood test to detect brain-specific biomarkers. Here we report on an assay for detection of S100B, a putative biomarker for TBI, using antibody-conjugated magnetic beads for capture of the protein, and antibody-conjugated quantum dots for optical detection. From Western Blot, we show efficient antigen capture and concentration by the magnetic beads. Using magnetic bead capture and quantum dot detection in serum samples, we show a wide detection range and detection limit below the clinical cut-off level. Electronic supplementary information (ESI) available. See DOI: 10.1039/c5nr05608j

Early Cerebral Small Vessel Disease and Brain Volume, Cognition, and Gait

PubMed Central

Smith, Eric E; O'Donnell, Martin; Dagenais, Gilles; Lear, Scott A; Wielgosz, Andreas; Sharma, Mukul; Poirier, Paul; Stotts, Grant; Black, Sandra E; Strother, Stephen; Noseworthy, Michael D; Benavente, Oscar; Modi, Jayesh; Goyal, Mayank; Batool, Saima; Sanchez, Karla; Hill, Vanessa; McCreary, Cheryl R; Frayne, Richard; Islam, Shofiqul; DeJesus, Jane; Rangarajan, Sumathy; Teo, Koon; Yusuf, Salim

2015-01-01

Objective Decline in cognitive function begins by the 40s, and may be related to future dementia risk. We used data from a community-representative study to determine whether there are age-related differences in simple cognitive and gait tests by the 40s, and whether these differences were associated with covert cerebrovascular disease on magnetic resonance imaging (MRI). Methods Between 2010 and 2012, 803 participants aged 40 to 75 years in the Prospective Urban Rural Epidemiological (PURE) study, recruited from prespecified postal code regions centered on 4 Canadian cities, underwent brain MRI and simple tests of cognition and gait as part of a substudy (PURE-MIND). Results Mean age was 58 ± 8 years. Linear decreases in performance on the Montreal Cognitive Assessment, Digit Symbol Substitution Test (DSST), and Timed Up and Go test of gait were seen with each age decade from the 40s to the 70s. Silent brain infarcts were observed in 3% of 40- to 49-year-olds, with increasing prevalence up to 18.9% in 70-year-olds. Silent brain infarcts were associated with slower timed gait and lower volume of supratentorial white matter. Higher volume of supratentorial MRI white matter hyperintensity was associated with slower timed gait and worse performance on DSST, and lower volumes of the supratentorial cortex and white matter, and cerebellum. Interpretation Covert cerebrovascular disease and its consequences on cognitive and gait performance and brain atrophy are manifest in some clinically asymptomatic persons as early as the 5th decade of life. Ann Neurol 2015;77:251–261 PMID:25428654

[The drama of hyperextension trauma--chronic whiplash syndrome with its neuropsychological and psychiatric findings. An analysis of 80 cases from expert assessment].

PubMed

Kissel, W

1999-10-28

The problem "whiplash associated disorder" was studied in a multidisciplinary analysis of 80 patients who all had a "simple whiplash-accident", this means a whiplash-accident without concomitant head trauma apart from contact with the car seat and without unconsciousness. The opinions of a rheumatologist and of a psychiatrist were considered in each case, and in 47 patients, a neuropsychological expertise was present. 43% of the patients who had been neuropsychologically tested revealed specific cognitive deficits as they are described after mild traumatic brain injuries and after whiplash accidents. Symptoms related to a pretraumatic cognitive disease were not found in any of the cases. Most patients had been professionally active at the time of the accident, some performing activities requiring a high level of cognitive skill. 66% percent of the study group showed psychological disturbances reducing the working capacity. There was no evidence for preexisting traumatic psychiatric symptoms. In many cases the psychiatric disturbances were accident-related, either reactive to the cognitive deficiencies or resulting from chronic pain. We assume that the "simple whiplash-accident" can cause chronic disturbances of brain function. In the etiology, a mild traumatic brain damage must be considered, this means an organic damage and not only a functional brain disorder. These brain function disturbances are often masked by the pure psychiatric symptoms, therefore they must be carefully searched for. Injured patients, who do not regain their working capacity after the accident, should be explored in a neuropsychological as well as in a psychiatric mode as early as possible after the accident.

Brain activity during auditory and visual phonological, spatial and simple discrimination tasks.

PubMed

Salo, Emma; Rinne, Teemu; Salonen, Oili; Alho, Kimmo

2013-02-16

We used functional magnetic resonance imaging to measure human brain activity during tasks demanding selective attention to auditory or visual stimuli delivered in concurrent streams. Auditory stimuli were syllables spoken by different voices and occurring in central or peripheral space. Visual stimuli were centrally or more peripherally presented letters in darker or lighter fonts. The participants performed a phonological, spatial or "simple" (speaker-gender or font-shade) discrimination task in either modality. Within each modality, we expected a clear distinction between brain activations related to nonspatial and spatial processing, as reported in previous studies. However, within each modality, different tasks activated largely overlapping areas in modality-specific (auditory and visual) cortices, as well as in the parietal and frontal brain regions. These overlaps may be due to effects of attention common for all three tasks within each modality or interaction of processing task-relevant features and varying task-irrelevant features in the attended-modality stimuli. Nevertheless, brain activations caused by auditory and visual phonological tasks overlapped in the left mid-lateral prefrontal cortex, while those caused by the auditory and visual spatial tasks overlapped in the inferior parietal cortex. These overlapping activations reveal areas of multimodal phonological and spatial processing. There was also some evidence for intermodal attention-related interaction. Most importantly, activity in the superior temporal sulcus elicited by unattended speech sounds was attenuated during the visual phonological task in comparison with the other visual tasks. This effect might be related to suppression of processing irrelevant speech presumably distracting the phonological task involving the letters. Copyright © 2012 Elsevier B.V. All rights reserved.

Model-based analysis of multi-shell diffusion MR data for tractography: How to get over fitting problems

PubMed Central

Jbabdi, Saad; Sotiropoulos, Stamatios N; Savio, Alexander M; Graña, Manuel; Behrens, Timothy EJ

2012-01-01

In this article, we highlight an issue that arises when using multiple b-values in a model-based analysis of diffusion MR data for tractography. The non-mono-exponential decay, commonly observed in experimental data, is shown to induce over-fitting in the distribution of fibre orientations when not considered in the model. Extra fibre orientations perpendicular to the main orientation arise to compensate for the slower apparent signal decay at higher b-values. We propose a simple extension to the ball and stick model based on a continuous Gamma distribution of diffusivities, which significantly improves the fitting and reduces the over-fitting. Using in-vivo experimental data, we show that this model outperforms a simpler, noise floor model, especially at the interfaces between brain tissues, suggesting that partial volume effects are a major cause of the observed non-mono-exponential decay. This model may be helpful for future data acquisition strategies that may attempt to combine multiple shells to improve estimates of fibre orientations in white matter and near the cortex. PMID:22334356

Clinician accessible tools for GUI computational models of transcranial electrical stimulation: BONSAI and SPHERES.

PubMed

Truong, Dennis Q; Hüber, Mathias; Xie, Xihe; Datta, Abhishek; Rahman, Asif; Parra, Lucas C; Dmochowski, Jacek P; Bikson, Marom

2014-01-01

Computational models of brain current flow during transcranial electrical stimulation (tES), including transcranial direct current stimulation (tDCS) and transcranial alternating current stimulation (tACS), are increasingly used to understand and optimize clinical trials. We propose that broad dissemination requires a simple graphical user interface (GUI) software that allows users to explore and design montages in real-time, based on their own clinical/experimental experience and objectives. We introduce two complimentary open-source platforms for this purpose: BONSAI and SPHERES. BONSAI is a web (cloud) based application (available at neuralengr.com/bonsai) that can be accessed through any flash-supported browser interface. SPHERES (available at neuralengr.com/spheres) is a stand-alone GUI application that allow consideration of arbitrary montages on a concentric sphere model by leveraging an analytical solution. These open-source tES modeling platforms are designed go be upgraded and enhanced. Trade-offs between open-access approaches that balance ease of access, speed, and flexibility are discussed. Copyright © 2014 Elsevier Inc. All rights reserved.

Sleep on your memory traces: How sleep effects can be explained by Act-In, a functional memory model.

PubMed

Cherdieu, Mélaine; Versace, Rémy; Rey, Amandine E; Vallet, Guillaume T; Mazza, Stéphanie

2018-06-01

Numerous studies have explored the effect of sleep on memory. It is well known that a period of sleep, compared to a similar period of wakefulness, protects memories from interference, improves performance, and might also reorganize memory traces in a way that encourages creativity and rule extraction. It is assumed that these benefits come from the reactivation of brain networks, mainly involving the hippocampal structure, as well as from their synchronization with neocortical networks during sleep, thereby underpinning sleep-dependent memory consolidation and reorganization. However, this memory reorganization is difficult to explain within classical memory models. The present paper aims to describe whether the influence of sleep on memory could be explained using a multiple trace memory model that is consistent with the concept of embodied cognition: the Act-In (activation-integration) memory model. We propose an original approach to the results observed in sleep research on the basis of two simple mechanisms, namely activation and integration. Copyright © 2017 Elsevier Ltd. All rights reserved.

Modeling Brain Dynamics in Brain Tumor Patients Using the Virtual Brain.

PubMed

Aerts, Hannelore; Schirner, Michael; Jeurissen, Ben; Van Roost, Dirk; Achten, Eric; Ritter, Petra; Marinazzo, Daniele

2018-01-01

Presurgical planning for brain tumor resection aims at delineating eloquent tissue in the vicinity of the lesion to spare during surgery. To this end, noninvasive neuroimaging techniques such as functional MRI and diffusion-weighted imaging fiber tracking are currently employed. However, taking into account this information is often still insufficient, as the complex nonlinear dynamics of the brain impede straightforward prediction of functional outcome after surgical intervention. Large-scale brain network modeling carries the potential to bridge this gap by integrating neuroimaging data with biophysically based models to predict collective brain dynamics. As a first step in this direction, an appropriate computational model has to be selected, after which suitable model parameter values have to be determined. To this end, we simulated large-scale brain dynamics in 25 human brain tumor patients and 11 human control participants using The Virtual Brain, an open-source neuroinformatics platform. Local and global model parameters of the Reduced Wong-Wang model were individually optimized and compared between brain tumor patients and control subjects. In addition, the relationship between model parameters and structural network topology and cognitive performance was assessed. Results showed (1) significantly improved prediction accuracy of individual functional connectivity when using individually optimized model parameters; (2) local model parameters that can differentiate between regions directly affected by a tumor, regions distant from a tumor, and regions in a healthy brain; and (3) interesting associations between individually optimized model parameters and structural network topology and cognitive performance.

Estimation of interhemispheric dynamics from simple unimanual reaction time to extrafoveal stimuli.

PubMed

Braun, C M

1992-12-01

This essay reviews research on interhemispheric transfer time derived from simple unimanual reaction time to hemitachistoscopically presented visual stimuli. Part 1 reviews major theoretical themes including (a) the significance of the eccentricity effect on interhemispheric transfer time in the context of proposed underlying neurohistological constraints; (b) the significance of gender differences in interhemispheric transfer time and findings in dyslexics and left-handers in the context of a fetal brain testosterone model; and (c) the significance of complexity effects on interhemispheric transfer time in a context of "dynamic" vs. "hard-wired" concepts of the underlying interhemispheric communication systems. Part 2 consists of a meta-analysis of 49 published behavioral experiments, in view of drawing a portrait of the best set of experimental conditions apt to produce salient, reliable, and statistically significant measures of interhemispheric transfer time, namely (a) index rather than thumb response, (b) low rather than high target luminance, (c) short rather than prolonged target display, and (d) very eccentric rather than near-foveal stimulus location. Part 3 proposes a theoretical model of interhemispheric transfer time, postulating the measurable existence of fast and slow interhemispheric channels. The proposed mechanism's evolutionary adaptive value, the neurophysiological evidence in its support, and favorable functional evidence from studies of callosotomized patients are then presented followed by proposals for critical experimental tests of the model.

Instant transformation of learned repulsion into motivational "wanting".

PubMed

Robinson, Mike J F; Berridge, Kent C

2013-02-18

Learned cues for pleasant reward often elicit desire, which, in addicts, may become compulsive. According to the dominant view in addiction neuroscience and reinforcement modeling, such desires are the simple products of learning, coming from a past association with reward outcome. We demonstrate that cravings are more than merely the products of accumulated pleasure memories-even a repulsive learned cue for unpleasantness can become suddenly desired via the activation of mesocorticolimbic circuitry. Rats learned repulsion toward a Pavlovian cue (a briefly-inserted metal lever) that always predicted an unpleasant Dead Sea saltiness sensation. Yet, upon first reencounter in a novel sodium-depletion state to promote mesocorticolimbic reactivity (reflected by elevated Fos activation in ventral tegmentum, nucleus accumbens, ventral pallidum, and the orbitofrontal prefrontal cortex), the learned cue was instantly transformed into an attractive and powerful motivational magnet. Rats jumped and gnawed on the suddenly attractive Pavlovian lever cue, despite never having tasted intense saltiness as anything other than disgusting. Instant desire transformation of a learned cue contradicts views that Pavlovian desires are essentially based on previously learned values (e.g., prediction error or temporal difference models). Instead desire is recomputed at reencounter by integrating Pavlovian information with the current brain/physiological state. This powerful brain transformation reverses strong learned revulsion into avid attraction. When applied to addiction, related mesocorticolimbic transformations (e.g., drugs or neural sensitization) of cues for already-pleasant drug experiences could create even more intense cravings. This cue/state transformation helps define what it means to say that addiction hijacks brain limbic circuits of natural reward. Copyright © 2013 Elsevier Ltd. All rights reserved.

Invertebrate hematopoiesis: an anterior proliferation center as a link between the hematopoietic tissue and the brain.

PubMed

Noonin, Chadanat; Lin, Xionghui; Jiravanichpaisal, Pikul; Söderhäll, Kenneth; Söderhäll, Irene

2012-11-20

During evolution, the innate and adaptive immune systems were developed to protect organisms from non-self substances. The innate immune system is phylogenetically more ancient and is present in most multicellular organisms, whereas adaptive responses are restricted to vertebrates. Arthropods lack the blood cells of the lymphoid lineage and oxygen-carrying erythrocytes, making them suitable model animals for studying the regulation of the blood cells of the innate immune system. Many crustaceans have a long life span and need to continuously synthesize blood cells, in contrast to many insects. The hematopoietic tissue (HPT) of Pacifastacus leniusculus provides a simple model for studying hematopoiesis, because the tissue can be isolated, and the proliferation of stem cells and their differentiation can be studied both in vivo and in vitro. Here, we demonstrate new findings of a physical link between the HPT and the brain. Actively proliferating cells were localized to an anterior proliferation center (APC) in the anterior part of the tissue near the area linking the HPT to the brain, whereas more differentiated cells were detected in the posterior part. The central areas of HPT expand in response to lipopolysaccharide-induced blood loss. Cells isolated from the APC divide rapidly and form cell clusters in vitro; conversely, the cells from the remaining HPT form monolayers, and they can be induced to differentiate in vitro. Our findings offer an opportunity to learn more about invertebrate hematopoiesis and its connection to the central nervous system, thereby obtaining new information about the evolution of different blood and nerve cell lineages.

Neurons derived from different brain regions are inherently different in vitro: a novel multiregional brain-on-a-chip.

PubMed

Dauth, Stephanie; Maoz, Ben M; Sheehy, Sean P; Hemphill, Matthew A; Murty, Tara; Macedonia, Mary Kate; Greer, Angie M; Budnik, Bogdan; Parker, Kevin Kit

2017-03-01

Brain in vitro models are critically important to developing our understanding of basic nervous system cellular physiology, potential neurotoxic effects of chemicals, and specific cellular mechanisms of many disease states. In this study, we sought to address key shortcomings of current brain in vitro models: the scarcity of comparative data for cells originating from distinct brain regions and the lack of multiregional brain in vitro models. We demonstrated that rat neurons from different brain regions exhibit unique profiles regarding their cell composition, protein expression, metabolism, and electrical activity in vitro. In vivo, the brain is unique in its structural and functional organization, and the interactions and communication between different brain areas are essential components of proper brain function. This fact and the observation that neurons from different areas of the brain exhibit unique behaviors in vitro underline the importance of establishing multiregional brain in vitro models. Therefore, we here developed a multiregional brain-on-a-chip and observed a reduction of overall firing activity, as well as altered amounts of astrocytes and specific neuronal cell types compared with separately cultured neurons. Furthermore, this multiregional model was used to study the effects of phencyclidine, a drug known to induce schizophrenia-like symptoms in vivo, on individual brain areas separately while monitoring downstream effects on interconnected regions. Overall, this work provides a comparison of cells from different brain regions in vitro and introduces a multiregional brain-on-a-chip that enables the development of unique disease models incorporating essential in vivo features. NEW & NOTEWORTHY Due to the scarcity of comparative data for cells from different brain regions in vitro, we demonstrated that neurons isolated from distinct brain areas exhibit unique behaviors in vitro. Moreover, in vivo proper brain function is dependent on the connection and communication of several brain regions, underlining the importance of developing multiregional brain in vitro models. We introduced a novel brain-on-a-chip model, implementing essential in vivo features, such as different brain areas and their functional connections. Copyright © 2017 the American Physiological Society.

Neurons derived from different brain regions are inherently different in vitro: a novel multiregional brain-on-a-chip

PubMed Central

Dauth, Stephanie; Maoz, Ben M.; Sheehy, Sean P.; Hemphill, Matthew A.; Murty, Tara; Macedonia, Mary Kate; Greer, Angie M.; Budnik, Bogdan

2017-01-01

Brain in vitro models are critically important to developing our understanding of basic nervous system cellular physiology, potential neurotoxic effects of chemicals, and specific cellular mechanisms of many disease states. In this study, we sought to address key shortcomings of current brain in vitro models: the scarcity of comparative data for cells originating from distinct brain regions and the lack of multiregional brain in vitro models. We demonstrated that rat neurons from different brain regions exhibit unique profiles regarding their cell composition, protein expression, metabolism, and electrical activity in vitro. In vivo, the brain is unique in its structural and functional organization, and the interactions and communication between different brain areas are essential components of proper brain function. This fact and the observation that neurons from different areas of the brain exhibit unique behaviors in vitro underline the importance of establishing multiregional brain in vitro models. Therefore, we here developed a multiregional brain-on-a-chip and observed a reduction of overall firing activity, as well as altered amounts of astrocytes and specific neuronal cell types compared with separately cultured neurons. Furthermore, this multiregional model was used to study the effects of phencyclidine, a drug known to induce schizophrenia-like symptoms in vivo, on individual brain areas separately while monitoring downstream effects on interconnected regions. Overall, this work provides a comparison of cells from different brain regions in vitro and introduces a multiregional brain-on-a-chip that enables the development of unique disease models incorporating essential in vivo features. NEW & NOTEWORTHY Due to the scarcity of comparative data for cells from different brain regions in vitro, we demonstrated that neurons isolated from distinct brain areas exhibit unique behaviors in vitro. Moreover, in vivo proper brain function is dependent on the connection and communication of several brain regions, underlining the importance of developing multiregional brain in vitro models. We introduced a novel brain-on-a-chip model, implementing essential in vivo features, such as different brain areas and their functional connections. PMID:28031399

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

PubMed Central

Laszlo, I.

1963-01-01

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

Sub-Network Kernels for Measuring Similarity of Brain Connectivity Networks in Disease Diagnosis.

PubMed

Jie, Biao; Liu, Mingxia; Zhang, Daoqiang; Shen, Dinggang

2018-05-01

As a simple representation of interactions among distributed brain regions, brain networks have been widely applied to automated diagnosis of brain diseases, such as Alzheimer's disease (AD) and its early stage, i.e., mild cognitive impairment (MCI). In brain network analysis, a challenging task is how to measure the similarity between a pair of networks. Although many graph kernels (i.e., kernels defined on graphs) have been proposed for measuring the topological similarity of a pair of brain networks, most of them are defined using general graphs, thus ignoring the uniqueness of each node in brain networks. That is, each node in a brain network denotes a particular brain region, which is a specific characteristics of brain networks. Accordingly, in this paper, we construct a novel sub-network kernel for measuring the similarity between a pair of brain networks and then apply it to brain disease classification. Different from current graph kernels, our proposed sub-network kernel not only takes into account the inherent characteristic of brain networks, but also captures multi-level (from local to global) topological properties of nodes in brain networks, which are essential for defining the similarity measure of brain networks. To validate the efficacy of our method, we perform extensive experiments on subjects with baseline functional magnetic resonance imaging data obtained from the Alzheimer's disease neuroimaging initiative database. Experimental results demonstrate that the proposed method outperforms several state-of-the-art graph-based methods in MCI classification.

Simplifying the use of prognostic information in traumatic brain injury. Part 1: The GCS-Pupils score: an extended index of clinical severity.

PubMed

Brennan, Paul M; Murray, Gordon D; Teasdale, Graham M

2018-06-01

OBJECTIVE Glasgow Coma Scale (GCS) scores and pupil responses are key indicators of the severity of traumatic brain damage. The aim of this study was to determine what information would be gained by combining these indicators into a single index and to explore the merits of different ways of achieving this. METHODS Information about early GCS scores, pupil responses, late outcomes on the Glasgow Outcome Scale, and mortality were obtained at the individual patient level by reviewing data from the CRASH (Corticosteroid Randomisation After Significant Head Injury; n = 9,045) study and the IMPACT (International Mission for Prognosis and Clinical Trials in TBI; n = 6855) database. These data were combined into a pooled data set for the main analysis. Methods of combining the Glasgow Coma Scale and pupil response data varied in complexity from using a simple arithmetic score (GCS score [range 3-15] minus the number of nonreacting pupils [0, 1, or 2]), which we call the GCS-Pupils score (GCS-P; range 1-15), to treating each factor as a separate categorical variable. The content of information about patient outcome in each of these models was evaluated using Nagelkerke's R 2 . RESULTS Separately, the GCS score and pupil response were each related to outcome. Adding information about the pupil response to the GCS score increased the information yield. The performance of the simple GCS-P was similar to the performance of more complex methods of evaluating traumatic brain damage. The relationship between decreases in the GCS-P and deteriorating outcome was seen across the complete range of possible scores. The additional 2 lowest points offered by the GCS-Pupils scale (GCS-P 1 and 2) extended the information about injury severity from a mortality rate of 51% and an unfavorable outcome rate of 70% at GCS score 3 to a mortality rate of 74% and an unfavorable outcome rate of 90% at GCS-P 1. The paradoxical finding that GCS score 4 was associated with a worse outcome than GCS score 3 was not seen when using the GCS-P. CONCLUSIONS A simple arithmetic combination of the GCS score and pupillary response, the GCS-P, extends the information provided about patient outcome to an extent comparable to that obtained using more complex methods. The greater range of injury severities that are identified and the smoothness of the stepwise pattern of outcomes across the range of scores may be useful in evaluating individual patients and identifying patient subgroups. The GCS-P may be a useful platform onto which information about other key prognostic features can be added in a simple format likely to be useful in clinical practice.

Geometry-aware multiscale image registration via OBBTree-based polyaffine log-demons.

PubMed

Seiler, Christof; Pennec, Xavier; Reyes, Mauricio

2011-01-01

Non-linear image registration is an important tool in many areas of image analysis. For instance, in morphometric studies of a population of brains, free-form deformations between images are analyzed to describe the structural anatomical variability. Such a simple deformation model is justified by the absence of an easy expressible prior about the shape changes. Applying the same algorithms used in brain imaging to orthopedic images might not be optimal due to the difference in the underlying prior on the inter-subject deformations. In particular, using an un-informed deformation prior often leads to local minima far from the expected solution. To improve robustness and promote anatomically meaningful deformations, we propose a locally affine and geometry-aware registration algorithm that automatically adapts to the data. We build upon the log-domain demons algorithm and introduce a new type of OBBTree-based regularization in the registration with a natural multiscale structure. The regularization model is composed of a hierarchy of locally affine transformations via their logarithms. Experiments on mandibles show improved accuracy and robustness when used to initialize the demons, and even similar performance by direct comparison to the demons, with a significantly lower degree of freedom. This closes the gap between polyaffine and non-rigid registration and opens new ways to statistically analyze the registration results.

The Mechanism of Extracellular Stimulation of Nerve Cells on an Electrolyte-Oxide-Semiconductor Capacitor

PubMed Central

Schoen, Ingmar; Fromherz, Peter

2007-01-01

Extracellular excitation of neurons is applied in studies of cultured networks and brain tissue, as well as in neuroprosthetics. We elucidate its mechanism in an electrophysiological approach by comparing voltage-clamp and current-clamp recordings of individual neurons on an insulated planar electrode. Noninvasive stimulation of neurons from pedal ganglia of Lymnaea stagnalis is achieved by defined voltage ramps applied to an electrolyte/HfO2/silicon capacitor. Effects on the smaller attached cell membrane and the larger free membrane are distinguished in a two-domain-stimulation model. Under current-clamp, we study the polarization that is induced for closed ion channels. Under voltage-clamp, we determine the capacitive gating of ion channels in the attached membrane by falling voltage ramps and for comparison also the gating of all channels by conventional variation of the intracellular voltage. Neuronal excitation is elicited under current-clamp by two mechanisms: Rising voltage ramps depolarize the free membrane such that an action potential is triggered. Falling voltage ramps depolarize the attached membrane such that local ion currents are activated that depolarize the free membrane and trigger an action potential. The electrophysiological analysis of extracellular stimulation in the simple model system is a basis for its systematic optimization in neuronal networks and brain tissue. PMID:17098803

View-tolerant face recognition and Hebbian learning imply mirror-symmetric neural tuning to head orientation

PubMed Central

Leibo, Joel Z.; Liao, Qianli; Freiwald, Winrich A.; Anselmi, Fabio; Poggio, Tomaso

2017-01-01

SUMMARY The primate brain contains a hierarchy of visual areas, dubbed the ventral stream, which rapidly computes object representations that are both specific for object identity and robust against identity-preserving transformations like depth-rotations [1, 2]. Current computational models of object recognition, including recent deep learning networks, generate these properties through a hierarchy of alternating selectivity-increasing filtering and tolerance-increasing pooling operations, similar to simple-complex cells operations [3, 4, 5, 6]. Here we prove that a class of hierarchical architectures and a broad set of biologically plausible learning rules generate approximate invariance to identity-preserving transformations at the top level of the processing hierarchy. However, all past models tested failed to reproduce the most salient property of an intermediate representation of a three-level face-processing hierarchy in the brain: mirror-symmetric tuning to head orientation [7]. Here we demonstrate that one specific biologically-plausible Hebb-type learning rule generates mirror-symmetric tuning to bilaterally symmetric stimuli like faces at intermediate levels of the architecture and show why it does so. Thus the tuning properties of individual cells inside the visual stream appear to result from group properties of the stimuli they encode and to reflect the learning rules that sculpted the information-processing system within which they reside. PMID:27916522

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.

Cultural influences on neural substrates of attentional control.

PubMed

Hedden, Trey; Ketay, Sarah; Aron, Arthur; Markus, Hazel Rose; Gabrieli, John D E

2008-01-01

Behavioral research has shown that people from Western cultural contexts perform better on tasks emphasizing independent (absolute) dimensions than on tasks emphasizing interdependent (relative) dimensions, whereas the reverse is true for people from East Asian contexts. We assessed functional magnetic resonance imaging responses during performance of simple visuospatial tasks in which participants made absolute judgments (ignoring visual context) or relative judgments (taking visual context into account). In each group, activation in frontal and parietal brain regions known to be associated with attentional control was greater during culturally nonpreferred judgments than during culturally preferred judgments. Also, within each group, activation differences in these regions correlated strongly with scores on questionnaires measuring individual differences in culture-typical identity. Thus, the cultural background of an individual and the degree to which the individual endorses cultural values moderate activation in brain networks engaged during even simple visual and attentional tasks.

Temporal lobe epilepsy: origin and significance of simple and complex auras.

PubMed Central

Taylor, D C; Lochery, M

1987-01-01

The aura experience of 88 patients with temporal lobe epilepsy was recorded, classified and analysed. Despite the great richness of the 215 experiences described, correlations with left or right brain, nature of lesion, age of onset, etc. were only apparent when a classification into three aura groups was used. "Simple primitive" auras as sole auras were more likely with early onset epilepsy, in lower IQ patients, in males, from the right temporal lobe, and with mesial temporal sclerosis. Exclusively "intellectual" auras were confined to a group of high IQ males. The number of aura experiences described per person correlated with Verbal IQ for males but not females, but also varied with side, sex, and nature of lesion. The results are discussed in terms of the necessary conditions for aura and their relevance and in relationship to the results of brain stimulation studies by Penfield and others. PMID:3612148

The Topographical Mapping in Drosophila Central Complex Network and Its Signal Routing

PubMed Central

Chang, Po-Yen; Su, Ta-Shun; Shih, Chi-Tin; Lo, Chung-Chuan

2017-01-01

Neural networks regulate brain functions by routing signals. Therefore, investigating the detailed organization of a neural circuit at the cellular levels is a crucial step toward understanding the neural mechanisms of brain functions. To study how a complicated neural circuit is organized, we analyzed recently published data on the neural circuit of the Drosophila central complex, a brain structure associated with a variety of functions including sensory integration and coordination of locomotion. We discovered that, except for a small number of “atypical” neuron types, the network structure formed by the identified 194 neuron types can be described by only a few simple mathematical rules. Specifically, the topological mapping formed by these neurons can be reconstructed by applying a generation matrix on a small set of initial neurons. By analyzing how information flows propagate with or without the atypical neurons, we found that while the general pattern of signal propagation in the central complex follows the simple topological mapping formed by the “typical” neurons, some atypical neurons can substantially re-route the signal pathways, implying specific roles of these neurons in sensory signal integration. The present study provides insights into the organization principle and signal integration in the central complex. PMID:28443014

Neural correlates in the processing of phoneme-level complexity in vowel production.

PubMed

Park, Haeil; Iverson, Gregory K; Park, Hae-Jeong

2011-12-01

We investigated how articulatory complexity at the phoneme level is manifested neurobiologically in an overt production task. fMRI images were acquired from young Korean-speaking adults as they pronounced bisyllabic pseudowords in which we manipulated phonological complexity defined in terms of vowel duration and instability (viz., COMPLEX: /tiɯi/ > MID-COMPLEX: /tiye/ > SIMPLE: /tii/). Increased activity in the left inferior frontal gyrus (Brodmann Areas (BA) 44 and 47), supplementary motor area and anterior insula was observed for the articulation of COMPLEX sequences relative to MID-COMPLEX; this was the case with the articulation of MID-COMPLEX relative to SIMPLE, except that the pars orbitalis (BA 47) was dominantly identified in the Broca's area. The differentiation indicates that phonological complexity is reflected in the neural processing of distinct phonemic representations, both by recruiting brain regions associated with retrieval of phonological information from memory and via articulatory rehearsal for the production of COMPLEX vowels. In addition, the finding that increased complexity engages greater areas of the brain suggests that brain activation can be a neurobiological measure of articulo-phonological complexity, complementing, if not substituting for, biomechanical measurements of speech motor activity. 2011 Elsevier Inc. All rights reserved.

A Simple and Efficient Methodology To Improve Geometric Accuracy in Gamma Knife Radiation Surgery: Implementation in Multiple Brain Metastases

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

Karaiskos, Pantelis, E-mail: pkaraisk@med.uoa.gr; Gamma Knife Department, Hygeia Hospital, Athens; Moutsatsos, Argyris

Purpose: To propose, verify, and implement a simple and efficient methodology for the improvement of total geometric accuracy in multiple brain metastases gamma knife (GK) radiation surgery. Methods and Materials: The proposed methodology exploits the directional dependence of magnetic resonance imaging (MRI)-related spatial distortions stemming from background field inhomogeneities, also known as sequence-dependent distortions, with respect to the read-gradient polarity during MRI acquisition. First, an extra MRI pulse sequence is acquired with the same imaging parameters as those used for routine patient imaging, aside from a reversal in the read-gradient polarity. Then, “average” image data are compounded from data acquiredmore » from the 2 MRI sequences and are used for treatment planning purposes. The method was applied and verified in a polymer gel phantom irradiated with multiple shots in an extended region of the GK stereotactic space. Its clinical impact in dose delivery accuracy was assessed in 15 patients with a total of 96 relatively small (<2 cm) metastases treated with GK radiation surgery. Results: Phantom study results showed that use of average MR images eliminates the effect of sequence-dependent distortions, leading to a total spatial uncertainty of less than 0.3 mm, attributed mainly to gradient nonlinearities. In brain metastases patients, non-eliminated sequence-dependent distortions lead to target localization uncertainties of up to 1.3 mm (mean: 0.51 ± 0.37 mm) with respect to the corresponding target locations in the “average” MRI series. Due to these uncertainties, a considerable underdosage (5%-32% of the prescription dose) was found in 33% of the studied targets. Conclusions: The proposed methodology is simple and straightforward in its implementation. Regarding multiple brain metastases applications, the suggested approach may substantially improve total GK dose delivery accuracy in smaller, outlying targets.« less

A Brief Period of Postnatal Visual Deprivation Alters the Balance between Auditory and Visual Attention.

PubMed

de Heering, Adélaïde; Dormal, Giulia; Pelland, Maxime; Lewis, Terri; Maurer, Daphne; Collignon, Olivier

2016-11-21

Is a short and transient period of visual deprivation early in life sufficient to induce lifelong changes in how we attend to, and integrate, simple visual and auditory information [1, 2]? This question is of crucial importance given the recent demonstration in both animals and humans that a period of blindness early in life permanently affects the brain networks dedicated to visual, auditory, and multisensory processing [1-16]. To address this issue, we compared a group of adults who had been treated for congenital bilateral cataracts during early infancy with a group of normally sighted controls on a task requiring simple detection of lateralized visual and auditory targets, presented alone or in combination. Redundancy gains obtained from the audiovisual conditions were similar between groups and surpassed the reaction time distribution predicted by Miller's race model. However, in comparison to controls, cataract-reversal patients were faster at processing simple auditory targets and showed differences in how they shifted attention across modalities. Specifically, they were faster at switching attention from visual to auditory inputs than in the reverse situation, while an opposite pattern was observed for controls. Overall, these results reveal that the absence of visual input during the first months of life does not prevent the development of audiovisual integration but enhances the salience of simple auditory inputs, leading to a different crossmodal distribution of attentional resources between auditory and visual stimuli. Copyright © 2016 Elsevier Ltd. All rights reserved.

Mind Operational Semantics and Brain Operational Architectonics: A Putative Correspondence

PubMed Central

Benedetti, Giulio; Marchetti, Giorgio; Fingelkurts, Alexander A; Fingelkurts, Andrew A

2010-01-01

Despite allowing for the unprecedented visualization of brain functional activity, modern neurobiological techniques have not yet been able to provide satisfactory answers to important questions about the relationship between brain and mind. The aim of this paper is to show how two different but complementary approaches, Mind Operational Semantics (OS) and Brain Operational Architectonics (OA), can help bridge the gap between a specific kind of mental activity—the higher-order reflective thought or linguistic thought—and brain. The fundamental notion that allows the two different approaches to be jointly used under a common framework is that of operation. According to OS, which is based on introspection and linguistic data, the meanings of words can be analyzed in terms of elemental mental operations (EOMC), amongst which those of attention play a key role. Linguistic thought is made possible by special kinds of elements, which OS calls “correlators”, which have the function of tying together the other elements of thought, which OS calls “correlata” (a "correlational network” that is, a sentence, is so formed). Therefore, OS conceives of linguistic thought as a hierarchy of operations of increasing complexity. Likewise, according to OA, which is based on the joint analysis of cognitive and electromagnetic data (EEG and MEG), every conscious phenomenon is brought to existence by the joint operations of many functional and transient neuronal assemblies in the brain. According to OA, the functioning of the brain is always operational (made up of operations), and its structure is characterized by a hierarchy of operations of increasing complexity: single neurons, single assemblies of neurons, synchronized neuronal assemblies or Operational Modules (OM), integrated or complex OMs. The authors put forward the hypothesis that the whole level of OS’s description (EOMC, correlators, and correlational networks) corresponds to the level of OMs (or set of them) of different complexity within OA’s theory: EOMC could correspond to simple OMs, correlators to complex OMs and the correlational network to a set of simple and complex OMs. Finally, a set of experiments is proposed to verify the putative correspondence between OS and OA and prove the existence of an integrated continuum between brain and mind. PMID:21113277

Plasticity in single neuron and circuit computations

NASA Astrophysics Data System (ADS)

Destexhe, Alain; Marder, Eve

2004-10-01

Plasticity in neural circuits can result from alterations in synaptic strength or connectivity, as well as from changes in the excitability of the neurons themselves. To better understand the role of plasticity in the brain, we need to establish how brain circuits work and the kinds of computations that different circuit structures achieve. By linking theoretical and experimental studies, we are beginning to reveal the consequences of plasticity mechanisms for network dynamics, in both simple invertebrate circuits and the complex circuits of mammalian cerebral cortex.

Carbohydrates as a cerebral metabolic fuel.

PubMed

Evans, M; Amiel, S A

1998-03-01

The human brain is an extremely active metabolic organ with little endogenous stores of energy. It is thus dependent on circulating glucose to fuel metabolism and support cognitive functioning. However there is growing evidence that the human brain is able to utilise other non-glucose fuels during times of glucose lack. We review the evidence for the potential of the human brain to use the alternate fuels lactate and beta-hydroxybutyrate, and some recent studies examining the ability of regions of brain to use non-glucose lipid fuels. The human brain does not seem to have the ability to use the gluconeogenic precursor alanine to any significant degree. Regionality within the brain can be examined in vivo by the use of positron emission tomography, which offers the exciting prospect of studying human brain metabolism in vivo using a simple and non-interventional technique. Increased understanding of the brain's metabolism, the way in which hypoglycaemia is recognised and the manner in which this can be altered in the syndrome of hypoglycaemia unawareness and deficient counterregulation will help develop further strategies to prevent the clinical problems associated with hypoglycaemia in insulin-dependent diabetic adults and children.

In Vitro Studies of Neuronal Networks and Synaptic Plasticity in Invertebrates and in Mammals Using Multielectrode Arrays

PubMed Central

Tessadori, Jacopo; Ghirardi, Mirella

2015-01-01

Brain functions are strictly dependent on neural connections formed during development and modified during life. The cellular and molecular mechanisms underlying synaptogenesis and plastic changes involved in learning and memory have been analyzed in detail in simple animals such as invertebrates and in circuits of mammalian brains mainly by intracellular recordings of neuronal activity. In the last decades, the evolution of techniques such as microelectrode arrays (MEAs) that allow simultaneous, long-lasting, noninvasive, extracellular recordings from a large number of neurons has proven very useful to study long-term processes in neuronal networks in vivo and in vitro. In this work, we start off by briefly reviewing the microelectrode array technology and the optimization of the coupling between neurons and microtransducers to detect subthreshold synaptic signals. Then, we report MEA studies of circuit formation and activity in invertebrate models such as Lymnaea, Aplysia, and Helix. In the following sections, we analyze plasticity and connectivity in cultures of mammalian dissociated neurons, focusing on spontaneous activity and electrical stimulation. We conclude by discussing plasticity in closed-loop experiments. PMID:25866681

Traumatic Brain Injury and Neuronal Functionality Changes in Sensory Cortex

PubMed Central

Carron, Simone F.; Alwis, Dasuni S.; Rajan, Ramesh

2016-01-01

Traumatic brain injury (TBI), caused by direct blows to the head or inertial forces during relative head-brain movement, can result in long-lasting cognitive and motor deficits which can be particularly consequential when they occur in young people with a long life ahead. Much is known of the molecular and anatomical changes produced in TBI but much less is known of the consequences of these changes to neuronal functionality, especially in the cortex. Given that much of our interior and exterior lives are dependent on responsiveness to information from and about the world around us, we have hypothesized that a significant contributor to the cognitive and motor deficits seen after TBI could be changes in sensory processing. To explore this hypothesis, and to develop a model test system of the changes in neuronal functionality caused by TBI, we have examined neuronal encoding of simple and complex sensory input in the rat’s exploratory and discriminative tactile system, the large face macrovibrissae, which feeds to the so-called “barrel cortex” of somatosensory cortex. In this review we describe the short-term and long-term changes in the barrel cortex encoding of whisker motion modeling naturalistic whisker movement undertaken by rats engaged in a variety of tasks. We demonstrate that the most common form of TBI results in persistent neuronal hyperexcitation specifically in the upper cortical layers, likely due to changes in inhibition. We describe the types of cortical inhibitory neurons and their roles and how selective effects on some of these could produce the particular forms of neuronal encoding changes described in TBI, and then generalize to compare the effects on inhibition seen in other forms of brain injury. From these findings we make specific predictions as to how non-invasive extra-cranial electrophysiology can be used to provide the high-precision information needed to monitor and understand the temporal evolution of changes in neuronal functionality in humans suffering TBI. Such detailed understanding of the specific changes in an individual patient’s cortex can allow for treatment to be tailored to the neuronal changes in that particular patient’s brain in TBI, a precision that is currently unavailable with any technique. PMID:27313514

Martian 'Brain'

NASA Technical Reports Server (NTRS)

2004-01-01

5 May 2004 Most middle-latitude craters on Mars have strange landforms on their floors. Often, the floors have pitted and convoluted features that lack simple explanation. In this case, the central part of the crater floor shown in this 2004 Mars Global Surveyor (MGS) Mars Orbiter Camera (MOC) image bears some resemblance to the folded nature of a brain. Or not. It depends upon the 'eye of the beholder,' perhaps. The light-toned 'ring' around the 'brain' feature is more easily explained--windblown ripples and dunes. The crater occurs near 33.1oS, 91.2oW, and is illuminated from the upper left. The picture covers an area about 3 km (1.9 mi) across.

Simple wavefront correction framework for two-photon microscopy of in-vivo brain

PubMed Central

Galwaduge, P. T.; Kim, S. H.; Grosberg, L. E.; Hillman, E. M. C.

2015-01-01

We present an easily implemented wavefront correction scheme that has been specifically designed for in-vivo brain imaging. The system can be implemented with a single liquid crystal spatial light modulator (LCSLM), which makes it compatible with existing patterned illumination setups, and provides measurable signal improvements even after a few seconds of optimization. The optimization scheme is signal-based and does not require exogenous guide-stars, repeated image acquisition or beam constraint. The unconstrained beam approach allows the use of Zernike functions for aberration correction and Hadamard functions for scattering correction. Low order corrections performed in mouse brain were found to be valid up to hundreds of microns away from the correction location. PMID:26309763

Studying the Brain in a Dish: 3D Cell Culture Models of Human Brain Development and Disease.

PubMed

Brown, Juliana; Quadrato, Giorgia; Arlotta, Paola

2018-01-01

The study of the cellular and molecular processes of the developing human brain has been hindered by access to suitable models of living human brain tissue. Recently developed 3D cell culture models offer the promise of studying fundamental brain processes in the context of human genetic background and species-specific developmental mechanisms. Here, we review the current state of 3D human brain organoid models and consider their potential to enable investigation of complex aspects of human brain development and the underpinning of human neurological disease. © 2018 Elsevier Inc. All rights reserved.

Recognition unit-free and self-cleaning photoelectrochemical sensing platform on TiO2 nanotube photonic crystals for sensitive and selective detection of dopamine release from mouse brain.

PubMed

Xin, Yanmei; Li, Zhenzhen; Wu, Wenlong; Fu, Baihe; Wu, Hongjun; Zhang, Zhonghai

2017-01-15

For implementing sensitive and selective detection of biological molecules, the biosensors are been designed more and more complicated. The exploration of detection platform in a simple way without loss their sensitivity and selectivity is always a big challenge. Herein, a prototype of recognition biomolecule unit-free photoelectrochemical (PEC) sensing platform with self-cleaning activity is proposed with TiO 2 nanotube photonic crystal (TiO 2 NTPCs) materials as photoelectrode, and dopamine (DA) molecule as both sensitizer and target analyte. The unique adsorption between DA and TiO 2 NTPCs induces the formation of charge transfer complex, which not only expends the optical absorption of TiO 2 into visible light region, thus significantly boosts the PEC performance under illumination of visible light, but also implements the selective detection of DA on TiO 2 photoelectrode. This simple but efficient PEC analysis platform presents a low detection limit of 0.15nm for detection of DA, which allows to realize the sensitive and selective determination of DA release from the mouse brain for its practical application after coupled with a microdialysis probe. The DA functionalized TiO 2 NTPCs PEC sensing platform opens up a new PEC detection model, without using extra-biomolecule auxiliary, just with target molecule naturally adsorbed on the electrode for sensitive and selective detection, and paves a new avenue for biosensors design with minimalism idea. Copyright © 2016 Elsevier B.V. All rights reserved.

Computational Neuroscience.

ERIC Educational Resources Information Center

Sejnowski, Terrence J.; And Others

1988-01-01

Describes the use of brain models to connect the microscopic level accessible by molecular and cellular techniques with the systems level accessible by the study of behavior. Discusses classes of brain models, and specific examples of such models. Evaluates the strengths and weaknesses of using brain modelling to understand human brain function.…

MRI anatomy of schizophrenia.

PubMed

McCarley, R W; Wible, C G; Frumin, M; Hirayasu, Y; Levitt, J J; Fischer, I A; Shenton, M E

1999-05-01

Structural magnetic resonance imaging (MRI) data have provided much evidence in support of our current view that schizophrenia is a brain disorder with altered brain structure, and consequently involving more than a simple disturbance in neurotransmission. This review surveys 118 peer-reviewed studies with control group from 1987 to May 1998. Most studies (81%) do not find abnormalities of whole brain/intracranial contents, while lateral ventricle enlargement is reported in 77%, and third ventricle enlargement in 67%. The temporal lobe was the brain parenchymal region with the most consistently documented abnormalities. Volume decreases were found in 62% of 37 studies of whole temporal lobe, and in 81% of 16 studies of the superior temporal gyrus (and in 100% with gray matter separately evaluated). Fully 77% of the 30 studies of the medial temporal lobe reported volume reduction in one or more of its constituent structures (hippocampus, amygdala, parahippocampal gyrus). Despite evidence for frontal lobe functional abnormalities, structural MRI investigations less consistently found abnormalities, with 55% describing volume reduction. It may be that frontal lobe volume changes are small, and near the threshold for MRI detection. The parietal and occipital lobes were much less studied; about half of the studies showed positive findings. Most studies of cortical gray matter (86%) found volume reductions were not diffuse, but more pronounced in certain areas. About two thirds of the studies of subcortical structures of thalamus, corpus callosum and basal ganglia (which tend to increase volume with typical neuroleptics), show positive findings, as do almost all (91%) studies of cavum septi pellucidi (CSP). Most data were consistent with a developmental model, but growing evidence was compatible also with progressive, neurodegenerative features, suggesting a "two-hit" model of schizophrenia, for which a cellular hypothesis is discussed. The relationship of clinical symptoms to MRI findings is reviewed, as is the growing evidence suggesting structural abnormalities differ in affective (bipolar) psychosis and schizophrenia.

Brain and eyes of Kerygmachela reveal protocerebral ancestry of the panarthropod head.

PubMed

Park, Tae-Yoon S; Kihm, Ji-Hoon; Woo, Jusun; Park, Changkun; Lee, Won Young; Smith, M Paul; Harper, David A T; Young, Fletcher; Nielsen, Arne T; Vinther, Jakob

2018-03-09

Recent discoveries of fossil nervous tissue in Cambrian fossils have allowed researchers to trace the origin and evolution of the complex arthropod head and brain based on stem groups close to the origin of the clade, rather than on extant, highly derived members. Here we show that Kerygmachela from Sirius Passet, North Greenland, a primitive stem-group euarthropod, exhibits a diminutive (protocerebral) brain that innervates both the eyes and frontal appendages. It has been surmised, based on developmental evidence, that the ancestor of vertebrates and arthropods had a tripartite brain, which is refuted by the fossil evidence presented here. Furthermore, based on the discovery of eyes in Kerygmachela, we suggest that the complex compound eyes in arthropods evolved from simple ocelli, present in onychophorans and tardigrades, rather than through the incorporation of a set of modified limbs.

Computational Flux Balance Analysis Predicts that Stimulation of Energy Metabolism in Astrocytes and their Metabolic Interactions with Neurons Depend on Uptake of K+ Rather than Glutamate.

PubMed

DiNuzzo, Mauro; Giove, Federico; Maraviglia, Bruno; Mangia, Silvia

2017-01-01

Brain activity involves essential functional and metabolic interactions between neurons and astrocytes. The importance of astrocytic functions to neuronal signaling is supported by many experiments reporting high rates of energy consumption and oxidative metabolism in these glial cells. In the brain, almost all energy is consumed by the Na + /K + ATPase, which hydrolyzes 1 ATP to move 3 Na + outside and 2 K + inside the cells. Astrocytes are commonly thought to be primarily involved in transmitter glutamate cycling, a mechanism that however only accounts for few % of brain energy utilization. In order to examine the participation of astrocytic energy metabolism in brain ion homeostasis, here we attempted to devise a simple stoichiometric relation linking glutamatergic neurotransmission to Na + and K + ionic currents. To this end, we took into account ion pumps and voltage/ligand-gated channels using the stoichiometry derived from available energy budget for neocortical signaling and incorporated this stoichiometric relation into a computational metabolic model of neuron-astrocyte interactions. We aimed at reproducing the experimental observations about rates of metabolic pathways obtained by 13 C-NMR spectroscopy in rodent brain. When simulated data matched experiments as well as biophysical calculations, the stoichiometry for voltage/ligand-gated Na + and K + fluxes generated by neuronal activity was close to a 1:1 relationship, and specifically 63/58 Na + /K + ions per glutamate released. We found that astrocytes are stimulated by the extracellular K + exiting neurons in excess of the 3/2 Na + /K + ratio underlying Na + /K + ATPase-catalyzed reaction. Analysis of correlations between neuronal and astrocytic processes indicated that astrocytic K + uptake, but not astrocytic Na + -coupled glutamate uptake, is instrumental for the establishment of neuron-astrocytic metabolic partnership. Our results emphasize the importance of K + in stimulating the activation of astrocytes, which is relevant to the understanding of brain activity and energy metabolism at the cellular level.

Use of the Oxford Handicap Scale at hospital discharge to predict Glasgow Outcome Scale at 6 months in patients with traumatic brain injury.

PubMed

Perel, Pablo; Edwards, Phil; Shakur, Haleema; Roberts, Ian

2008-11-06

Traumatic brain injury (TBI) is an important cause of acquired disability. In evaluating the effectiveness of clinical interventions for TBI it is important to measure disability accurately. The Glasgow Outcome Scale (GOS) is the most widely used outcome measure in randomised controlled trials (RCTs) in TBI patients. However GOS measurement is generally collected at 6 months after discharge when loss to follow up could have occurred. The objectives of this study were to evaluate the association and predictive validity between a simple disability scale at hospital discharge, the Oxford Handicap Scale (OHS), and the GOS at 6 months among TBI patients. The study was a secondary analysis of a randomised clinical trial among TBI patients (MRC CRASH Trial). A Spearman correlation was estimated to evaluate the association between the OHS and GOS. The validity of different dichotomies of the OHS for predicting GOS at 6 months was assessed by calculating sensitivity, specificity and the C statistic. Uni and multivariate logistic regression models were fitted including OHS as explanatory variable. For each model we analysed its discrimination and calibration. We found that the OHS is highly correlated with GOS at 6 months (spearman correlation 0.75) with evidence of a linear relationship between the two scales. The OHS dichotomy that separates patients with severe dependency or death showed the greatest discrimination (C statistic: 84.3). Among survivors at hospital discharge the OHS showed a very good discrimination (C statistic 0.78) and excellent calibration when used to predict GOS outcome at 6 months. We have shown that the OHS, a simple disability scale available at hospital discharge can predict disability accurately, according to the GOS, at 6 months. OHS could be used to improve the design and analysis of clinical trials in TBI patients and may also provide a valuable clinical tool for physicians to improve communication with patients and relatives when assessing a patient's prognosis at hospital discharge.

Effective electric fields along realistic DTI-based neural trajectories for modelling the stimulation mechanisms of TMS

NASA Astrophysics Data System (ADS)

De Geeter, N.; Crevecoeur, G.; Leemans, A.; Dupré, L.

2015-01-01

In transcranial magnetic stimulation (TMS), an applied alternating magnetic field induces an electric field in the brain that can interact with the neural system. It is generally assumed that this induced electric field is the crucial effect exciting a certain region of the brain. More specifically, it is the component of this field parallel to the neuron’s local orientation, the so-called effective electric field, that can initiate neuronal stimulation. Deeper insights on the stimulation mechanisms can be acquired through extensive TMS modelling. Most models study simple representations of neurons with assumed geometries, whereas we embed realistic neural trajectories computed using tractography based on diffusion tensor images. This way of modelling ensures a more accurate spatial distribution of the effective electric field that is in addition patient and case specific. The case study of this paper focuses on the single pulse stimulation of the left primary motor cortex with a standard figure-of-eight coil. Including realistic neural geometry in the model demonstrates the strong and localized variations of the effective electric field between the tracts themselves and along them due to the interplay of factors such as the tract’s position and orientation in relation to the TMS coil, the neural trajectory and its course along the white and grey matter interface. Furthermore, the influence of changes in the coil orientation is studied. Investigating the impact of tissue anisotropy confirms that its contribution is not negligible. Moreover, assuming isotropic tissues lead to errors of the same size as rotating or tilting the coil with 10 degrees. In contrast, the model proves to be less sensitive towards the not well-known tissue conductivity values.

Transition between Functional Regimes in an Integrate-And-Fire Network Model of the Thalamus

PubMed Central

Barardi, Alessandro; Mazzoni, Alberto

2016-01-01

The thalamus is a key brain element in the processing of sensory information. During the sleep and awake states, this brain area is characterized by the presence of two distinct dynamical regimes: in the sleep state activity is dominated by spindle oscillations (7 − 15 Hz) weakly affected by external stimuli, while in the awake state the activity is primarily driven by external stimuli. Here we develop a simple and computationally efficient model of the thalamus that exhibits two dynamical regimes with different information-processing capabilities, and study the transition between them. The network model includes glutamatergic thalamocortical (TC) relay neurons and GABAergic reticular (RE) neurons described by adaptive integrate-and-fire models in which spikes are induced by either depolarization or hyperpolarization rebound. We found a range of connectivity conditions under which the thalamic network composed by these neurons displays the two aforementioned dynamical regimes. Our results show that TC-RE loops generate spindle-like oscillations and that a minimum level of clustering (i.e. local connectivity density) in the RE-RE connections is necessary for the coexistence of the two regimes. We also observe that the transition between the two regimes occurs when the external excitatory input on TC neurons (mimicking sensory stimulation) is large enough to cause a significant fraction of them to switch from hyperpolarization-rebound-driven firing to depolarization-driven firing. Overall, our model gives a novel and clear description of the role that the two types of neurons and their connectivity play in the dynamical regimes observed in the thalamus, and in the transition between them. These results pave the way for the development of efficient models of the transmission of sensory information from periphery to cortex. PMID:27598260

Conflict Resolution as Near-Threshold Decision-Making: A Spiking Neural Circuit Model with Two-Stage Competition for Antisaccadic Task

PubMed Central

Wang, Xiao-Jing

2016-01-01

Automatic responses enable us to react quickly and effortlessly, but they often need to be inhibited so that an alternative, voluntary action can take place. To investigate the brain mechanism of controlled behavior, we investigated a biologically-based network model of spiking neurons for inhibitory control. In contrast to a simple race between pro- versus anti-response, our model incorporates a sensorimotor remapping module, and an action-selection module endowed with a “Stop” process through tonic inhibition. Both are under the modulation of rule-dependent control. We tested the model by applying it to the well known antisaccade task in which one must suppress the urge to look toward a visual target that suddenly appears, and shift the gaze diametrically away from the target instead. We found that the two-stage competition is crucial for reproducing the complex behavior and neuronal activity observed in the antisaccade task across multiple brain regions. Notably, our model demonstrates two types of errors: fast and slow. Fast errors result from failing to inhibit the quick automatic responses and therefore exhibit very short response times. Slow errors, in contrast, are due to incorrect decisions in the remapping process and exhibit long response times comparable to those of correct antisaccade responses. The model thus reveals a circuit mechanism for the empirically observed slow errors and broad distributions of erroneous response times in antisaccade. Our work suggests that selecting between competing automatic and voluntary actions in behavioral control can be understood in terms of near-threshold decision-making, sharing a common recurrent (attractor) neural circuit mechanism with discrimination in perception. PMID:27551824

Conflict Resolution as Near-Threshold Decision-Making: A Spiking Neural Circuit Model with Two-Stage Competition for Antisaccadic Task.

PubMed

Lo, Chung-Chuan; Wang, Xiao-Jing

2016-08-01

Automatic responses enable us to react quickly and effortlessly, but they often need to be inhibited so that an alternative, voluntary action can take place. To investigate the brain mechanism of controlled behavior, we investigated a biologically-based network model of spiking neurons for inhibitory control. In contrast to a simple race between pro- versus anti-response, our model incorporates a sensorimotor remapping module, and an action-selection module endowed with a "Stop" process through tonic inhibition. Both are under the modulation of rule-dependent control. We tested the model by applying it to the well known antisaccade task in which one must suppress the urge to look toward a visual target that suddenly appears, and shift the gaze diametrically away from the target instead. We found that the two-stage competition is crucial for reproducing the complex behavior and neuronal activity observed in the antisaccade task across multiple brain regions. Notably, our model demonstrates two types of errors: fast and slow. Fast errors result from failing to inhibit the quick automatic responses and therefore exhibit very short response times. Slow errors, in contrast, are due to incorrect decisions in the remapping process and exhibit long response times comparable to those of correct antisaccade responses. The model thus reveals a circuit mechanism for the empirically observed slow errors and broad distributions of erroneous response times in antisaccade. Our work suggests that selecting between competing automatic and voluntary actions in behavioral control can be understood in terms of near-threshold decision-making, sharing a common recurrent (attractor) neural circuit mechanism with discrimination in perception.

Brain repair after stroke—a novel neurological model

PubMed Central

Small, Steven L.; Buccino, Giovanni; Solodkin, Ana

2017-01-01

Following stroke, patients are commonly left with debilitating motor and speech impairments. This article reviews the state of the art in neurological repair for stroke and proposes a new model for the future. We suggest that stroke treatment—from the time of the ictus itself to living with the consequences—must be fundamentally neurological, from limiting the extent of injury at the outset, to repairing the consequent damage. Our model links brain and behaviour by targeting brain circuits, and we illustrate the model though action observation treatment, which aims to enhance brain network connectivity. The model is based on the assumptions that the mechanisms of neural repair inherently involve cellular and circuit plasticity, that brain plasticity is a synaptic phenomenon that is largely stimulus-dependent, and that brain repair required both physical and behavioural interventions that are tailored to reorganize specific brain circuits. We review current approaches to brain repair after stroke and present our new model, and discuss the biological foundations, rationales, and data to support our novel approach to upper-extremity and language rehabilitation. We believe that by enhancing plasticity at the level of brain network interactions, this neurological model for brain repair could ultimately lead to a cure for stroke. PMID:24217509

Individual brain structure and modelling predict seizure propagation

PubMed Central

Proix, Timothée; Bartolomei, Fabrice; Guye, Maxime; Jirsa, Viktor K.

2017-01-01

Abstract See Lytton (doi:10.1093/awx018) for a scientific commentary on this article. Neural network oscillations are a fundamental mechanism for cognition, perception and consciousness. Consequently, perturbations of network activity play an important role in the pathophysiology of brain disorders. When structural information from non-invasive brain imaging is merged with mathematical modelling, then generative brain network models constitute personalized in silico platforms for the exploration of causal mechanisms of brain function and clinical hypothesis testing. We here demonstrate with the example of drug-resistant epilepsy that patient-specific virtual brain models derived from diffusion magnetic resonance imaging have sufficient predictive power to improve diagnosis and surgery outcome. In partial epilepsy, seizures originate in a local network, the so-called epileptogenic zone, before recruiting other close or distant brain regions. We create personalized large-scale brain networks for 15 patients and simulate the individual seizure propagation patterns. Model validation is performed against the presurgical stereotactic electroencephalography data and the standard-of-care clinical evaluation. We demonstrate that the individual brain models account for the patient seizure propagation patterns, explain the variability in postsurgical success, but do not reliably augment with the use of patient-specific connectivity. Our results show that connectome-based brain network models have the capacity to explain changes in the organization of brain activity as observed in some brain disorders, thus opening up avenues towards discovery of novel clinical interventions. PMID:28364550

Brain-wide pathway for waste clearance captured by contrast-enhanced MRI.

PubMed

Iliff, Jeffrey J; Lee, Hedok; Yu, Mei; Feng, Tian; Logan, Jean; Nedergaard, Maiken; Benveniste, Helene

2013-03-01

The glymphatic system is a recently defined brain-wide paravascular pathway for cerebrospinal fluid (CSF) and interstitial fluid (ISF) exchange that facilitates efficient clearance of solutes and waste from the brain. CSF enters the brain along para-arterial channels to exchange with ISF, which is in turn cleared from the brain along para-venous pathways. Because soluble amyloid β clearance depends on glymphatic pathway function, we proposed that failure of this clearance system contributes to amyloid plaque deposition and Alzheimer's disease progression. Here we provide proof of concept that glymphatic pathway function can be measured using a clinically relevant imaging technique. Dynamic contrast-enhanced MRI was used to visualize CSF-ISF exchange across the rat brain following intrathecal paramagnetic contrast agent administration. Key features of glymphatic pathway function were confirmed, including visualization of para-arterial CSF influx and molecular size-dependent CSF-ISF exchange. Whole-brain imaging allowed the identification of two key influx nodes at the pituitary and pineal gland recesses, while dynamic MRI permitted the definition of simple kinetic parameters to characterize glymphatic CSF-ISF exchange and solute clearance from the brain. We propose that this MRI approach may provide the basis for a wholly new strategy to evaluate Alzheimer's disease susceptibility and progression in the live human brain.

Brain-wide pathway for waste clearance captured by contrast-enhanced MRI

PubMed Central

Iliff, Jeffrey J.; Lee, Hedok; Yu, Mei; Feng, Tian; Logan, Jean; Nedergaard, Maiken; Benveniste, Helene

2013-01-01

The glymphatic system is a recently defined brain-wide paravascular pathway for cerebrospinal fluid (CSF) and interstitial fluid (ISF) exchange that facilitates efficient clearance of solutes and waste from the brain. CSF enters the brain along para-arterial channels to exchange with ISF, which is in turn cleared from the brain along para-venous pathways. Because soluble amyloid β clearance depends on glymphatic pathway function, we proposed that failure of this clearance system contributes to amyloid plaque deposition and Alzheimer’s disease progression. Here we provide proof of concept that glymphatic pathway function can be measured using a clinically relevant imaging technique. Dynamic contrast-enhanced MRI was used to visualize CSF-ISF exchange across the rat brain following intrathecal paramagnetic contrast agent administration. Key features of glymphatic pathway function were confirmed, including visualization of para-arterial CSF influx and molecular size-dependent CSF-ISF exchange. Whole-brain imaging allowed the identification of two key influx nodes at the pituitary and pineal gland recesses, while dynamic MRI permitted the definition of simple kinetic parameters to characterize glymphatic CSF-ISF exchange and solute clearance from the brain. We propose that this MRI approach may provide the basis for a wholly new strategy to evaluate Alzheimer’s disease susceptibility and progression in the live human brain. PMID:23434588

Enhanced permeability of blood-brain barrier and targeting function of brain via borneol-modified chemically solid lipid nanoparticle.

PubMed

Song, Hui; Wei, Man; Zhang, Nan; Li, He; Tan, Xiaochuan; Zhang, Yujia; Zheng, Wensheng

2018-01-01

The incidence of central nervous system disease has increased in recent years. However, the transportation of drug is restricted by the blood-brain barrier, contributing to the poor therapeutic effect in the brain. Therefore, the development of a new brain-targeting drug delivery system has become the hotspot of pharmacy. Borneol, a simple bicyclic monoterpene extracted from Dryobalanops aromatica , can direct drugs to the upper body parts according to the theory of traditional Chinese medicine. Dioleoyl phosphoethanolamine (DOPE) was chemically modified by borneol as one of the lipid materials of solid lipid nanoparticle (SLN) in the present study. The borneol-modified chemically solid lipid nanoparticle (BO-SLN/CM), borneol-modified physically solid lipid nanoparticle (BO-SLN/PM), and SLN have similar diameter (of about 87 nm) and morphological characteristics. However, BO-SLN/CM has a lower cytotoxicity, higher cell uptake, and better blood-brain barrier permeability compared with BO-SLN/PM and SLN. BO-SLN/CM has a remarkable targeting function to the brain, while BO-SLN/ PM and SLNs are concentrated at the lung. The present study provides an excellent drug delivery carrier, BO-SLN/CM, having the application potential of targeting to the brain and permeating to the blood-brain barrier.

Influence of irradiation on development of Caribbean fruit fly (diptera: tephritidae) larvae

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

Nation, J.L.; Milne, K.; Dykstra, T.M.

1995-05-01

Larvae of the Caribbean fruit fly, Anastrepha suspensa (Loew), were irradiated at hatching with 0, 5, 10, 20, 50, 75, 100 and 150 Gy doses from a Cesium-137 source and dissected for measurements of the supraesophageal ganglion (brain) and proventriculus (B/Prv) as mature third instars. Cross-sectional area of a plane through the brain and proventriculus, and simple dorsal width measurements of the two organs were evaluated as indicators of radiation exposure. Brain area, brain width, and brain/proventriculus (B/Prv) ratios were significantly different from controls in insects treated with a dose {ge}20 Gy. Detailed dissections of hatching larvae exposed to 50more » Gy revealed reductions in brain growth, small and misshapen compound eye and leg imaginal disks, and a ventral nerve cord that was elongated and sinuous. Larvae irradiated on the 1st d of each of the three instars had smaller brains, with the percentage of reduction in brain size being greater the younger the larvae were at the time of exposure. Brain and proventriculus measurements and calculated B/Prv values are indicative of irradiation in Caribbean fruit fly larvae, but the procedure may not be adaptable for routine use by quarantine inspectors. 14 refs., 4 figs., 2 tabs.« less

Individual brain structure and modelling predict seizure propagation.

PubMed

Proix, Timothée; Bartolomei, Fabrice; Guye, Maxime; Jirsa, Viktor K

2017-03-01

See Lytton (doi:10.1093/awx018) for a scientific commentary on this article.Neural network oscillations are a fundamental mechanism for cognition, perception and consciousness. Consequently, perturbations of network activity play an important role in the pathophysiology of brain disorders. When structural information from non-invasive brain imaging is merged with mathematical modelling, then generative brain network models constitute personalized in silico platforms for the exploration of causal mechanisms of brain function and clinical hypothesis testing. We here demonstrate with the example of drug-resistant epilepsy that patient-specific virtual brain models derived from diffusion magnetic resonance imaging have sufficient predictive power to improve diagnosis and surgery outcome. In partial epilepsy, seizures originate in a local network, the so-called epileptogenic zone, before recruiting other close or distant brain regions. We create personalized large-scale brain networks for 15 patients and simulate the individual seizure propagation patterns. Model validation is performed against the presurgical stereotactic electroencephalography data and the standard-of-care clinical evaluation. We demonstrate that the individual brain models account for the patient seizure propagation patterns, explain the variability in postsurgical success, but do not reliably augment with the use of patient-specific connectivity. Our results show that connectome-based brain network models have the capacity to explain changes in the organization of brain activity as observed in some brain disorders, thus opening up avenues towards discovery of novel clinical interventions. © The Author (2017). Published by Oxford University Press on behalf of the Guarantors of Brain.

A Simple and Reproducible Method to Prepare Membrane Samples from Freshly Isolated Rat Brain Microvessels.

PubMed

Brzica, Hrvoje; Abdullahi, Wazir; Reilly, Bianca G; Ronaldson, Patrick T

2018-05-07

The blood-brain barrier (BBB) is a dynamic barrier tissue that responds to various pathophysiological and pharmacological stimuli. Such changes resulting from these stimuli can greatly modulate drug delivery to the brain and, by extension, cause considerable challenges in the treatment of central nervous system (CNS) diseases. Many BBB changes that affect pharmacotherapy, involve proteins that are localized and expressed at the level of endothelial cells. Indeed, such knowledge on BBB physiology in health and disease has sparked considerable interest in the study of these membrane proteins. From a basic science research standpoint, this implies a requirement for a simple but robust and reproducible method for isolation of microvessels from brain tissue harvested from experimental animals. In order to prepare membrane samples from freshly isolated microvessels, it is essential that sample preparations be enriched in endothelial cells but limited in the presence of other cell types of the neurovascular unit (i.e., astrocytes, microglia, neurons, pericytes). An added benefit is the ability to prepare samples from individual animals in order to capture the true variability of protein expression in an experimental population. In this manuscript, details regarding a method that is utilized for isolation of rat brain microvessels and preparation of membrane samples are provided. Microvessel enrichment, from samples derived, is achieved by using four centrifugation steps where dextran is included in the sample buffer. This protocol can easily be adapted by other laboratories for their own specific applications. Samples generated from this protocol have been shown to yield robust experimental data from protein analysis experiments that can greatly aid the understanding of BBB responses to physiological, pathophysiological, and pharmacological stimuli.

Analysis of brain patterns using temporal measures

DOEpatents

Georgopoulos, Apostolos

2015-08-11

A set of brain data representing a time series of neurophysiologic activity acquired by spatially distributed sensors arranged to detect neural signaling of a brain (such as by the use of magnetoencephalography) is obtained. The set of brain data is processed to obtain a dynamic brain model based on a set of statistically-independent temporal measures, such as partial cross correlations, among groupings of different time series within the set of brain data. The dynamic brain model represents interactions between neural populations of the brain occurring close in time, such as with zero lag, for example. The dynamic brain model can be analyzed to obtain the neurophysiologic assessment of the brain. Data processing techniques may be used to assess structural or neurochemical brain pathologies.

Linking Theoretical Decision-making Mechanisms in the Simon Task with Electrophysiological Data: A Model-based Neuroscience Study in Humans.

PubMed

Servant, Mathieu; White, Corey; Montagnini, Anna; Burle, Borís

2016-10-01

A current challenge for decision-making research is in extending models of simple decisions to more complex and ecological choice situations. Conflict tasks (e.g., Simon, Stroop, Eriksen flanker) have been the focus of much interest, because they provide a decision-making context representative of everyday life experiences. Modeling efforts have led to an elaborated drift diffusion model for conflict tasks (DMC), which implements a superimposition of automatic and controlled decision activations. The DMC has proven to capture the diversity of behavioral conflict effects across various task contexts. This study combined DMC predictions with EEG and EMG measurements to test a set of linking propositions that specify the relationship between theoretical decision-making mechanisms involved in the Simon task and brain activity. Our results are consistent with a representation of the superimposed decision variable in the primary motor cortices. The decision variable was also observed in the EMG activity of response agonist muscles. These findings provide new insight into the neurophysiology of human decision-making. In return, they provide support for the DMC model framework.

Role of mechanical factors in cortical folding development

NASA Astrophysics Data System (ADS)

Razavi, Mir Jalil; Zhang, Tuo; Li, Xiao; Liu, Tianming; Wang, Xianqiao

2015-09-01

Deciphering mysteries of the structure-function relationship in cortical folding has emerged as the cynosure of recent research on brain. Understanding the mechanism of convolution patterns can provide useful insight into the normal and pathological brain function. However, despite decades of speculation and endeavors the underlying mechanism of the brain folding process remains poorly understood. This paper focuses on the three-dimensional morphological patterns of a developing brain under different tissue specification assumptions via theoretical analyses, computational modeling, and experiment verifications. The living human brain is modeled with a soft structure having outer cortex and inner core to investigate the brain development. Analytical interpretations of differential growth of the brain model provide preliminary insight into the critical growth ratio for instability and crease formation of the developing brain followed by computational modeling as a way to offer clues for brain's postbuckling morphology. Especially, tissue geometry, growth ratio, and material properties of the cortex are explored as the most determinant parameters to control the morphogenesis of a growing brain model. As indicated in results, compressive residual stresses caused by the sufficient growth trigger instability and the brain forms highly convoluted patterns wherein its gyrification degree is specified with the cortex thickness. Morphological patterns of the developing brain predicted from the computational modeling are consistent with our neuroimaging observations, thereby clarifying, in part, the reason of some classical malformation in a developing brain.

A common polymorphism in the brain-derived neurotrophic factor gene (BDNF) modulates human cortical plasticity and the response to rTMS.

PubMed

Cheeran, Binith; Talelli, Penelope; Mori, Francesco; Koch, Giacomo; Suppa, Antonio; Edwards, Mark; Houlden, Henry; Bhatia, Kailash; Greenwood, Richard; Rothwell, John C

2008-12-01

The brain-derived neurotrophic factor gene (BDNF) is one of many genes thought to influence synaptic plasticity in the adult brain and shows a common single nucleotide polymorphism (BDNF Val66Met) in the normal population that is associated with differences in hippocampal volume and episodic memory. It is also thought to influence possible synaptic changes in motor cortex following a simple motor learning task. Here we extend these studies by using new non-invasive transcranial magnetic stimulation (TMS) and transcranial direct current stimulation (TDCS) techniques that directly test the excitability and plasticity of neuronal circuits in human motor cortex in subjects at rest. We investigated whether the susceptibility to TMS probes of plasticity is significantly influenced by the BDNF polymorphism. Val66Met carriers were matched with Val66Val individuals and tested on the following protocols: continuous and intermittent theta burst TMS; median nerve paired associative stimulation; and homeostatic plasticity in the TDCS/1 Hz rTMS model. The response of Met allele carriers differed significantly in all protocols compared with the response of Val66Val individuals. We suggest that this is due to the effect of BNDF on the susceptibility of synapses to undergo LTP/LTD. The circuits tested here are implicated in the pathophysiology of movement disorders such as dystonia and are being assessed as potential new targets in the treatment of stroke. Thus the polymorphism may be one factor that influences the natural response of the brain to injury and disease.

Validated UPLC-MS/MS method for determination of moclobemide in human brain cell supernatant and its application to bidirectional transport study.

PubMed

Li-Bo, Dai; Miao, Yan; Huan-De, Li; Ping-Fei, Fang; Feng, Wang; Yang, Deng

2013-09-01

A simple and sensitive analytical method based on ultraperformance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) has been developed for determination of moclobemide in human brain cell monolayer as an in vitro model of blood-brain barrier. Brucine was employed as the internal standard. Moclobemide and internal standard were extracted from cell supernatant by ethyl acetate after alkalinizing with sodium hydroxide. The UPLC separation was performed on an Acquity UPLC(TM) BEH C18 column (50 × 2.1 mm, 1.7 µm, Waters, USA) with a mobile phase consisting of methanol-water (29.5:70.5, v/v); the water in the mobile phase contained 0.05% ammonium acetate and 0.1% formic acid. Detection of the analytes was achieved using positive ion electrospray via multiple reaction monitoring mode. The mass transitions were m/z 269.16 → 182.01 for moclobemide and m/z 395.24 → 324.15 for brucine. The extraction recovery was 83.0-83.4% and the lower limit of quantitation (LLOQ) was 1.0 ng/mL for moclobemide. The method was validated from LLOQ to 1980 ng/mL with a coefficient of determination greater than 0.999. Intra- and inter-day accuracies of the method at three concentrations ranged from 89.1 to 100.9% for moclobemide with precision of 1.1-9.6%. This validated method was successfully applied to bidirectional transport study of moclobemide blood-brain barrier permeability. Copyright © 2013 John Wiley & Sons, Ltd.

Robust Skull-Stripping Segmentation Based on Irrational Mask for Magnetic Resonance Brain Images.

PubMed

Moldovanu, Simona; Moraru, Luminița; Biswas, Anjan

2015-12-01

This paper proposes a new method for simple, efficient, and robust removal of the non-brain tissues in MR images based on an irrational mask for filtration within a binary morphological operation framework. The proposed skull-stripping segmentation is based on two irrational 3 × 3 and 5 × 5 masks, having the sum of its weights equal to the transcendental number π value provided by the Gregory-Leibniz infinite series. It allows maintaining a lower rate of useful pixel loss. The proposed method has been tested in two ways. First, it has been validated as a binary method by comparing and contrasting with Otsu's, Sauvola's, Niblack's, and Bernsen's binary methods. Secondly, its accuracy has been verified against three state-of-the-art skull-stripping methods: the graph cuts method, the method based on Chan-Vese active contour model, and the simplex mesh and histogram analysis skull stripping. The performance of the proposed method has been assessed using the Dice scores, overlap and extra fractions, and sensitivity and specificity as statistical methods. The gold standard has been provided by two neurologist experts. The proposed method has been tested and validated on 26 image series which contain 216 images from two publicly available databases: the Whole Brain Atlas and the Internet Brain Segmentation Repository that include a highly variable sample population (with reference to age, sex, healthy/diseased). The approach performs accurately on both standardized databases. The main advantage of the proposed method is its robustness and speed.

Microstructural changes in memory and reticular formation neural pathway after simple concussion☆

PubMed Central

Ouyang, Lin; Shi, Rongyue; Xiao, Yuhui; Meng, Jiarong; Guo, Yihe; Lu, Guangming

2012-01-01

Patients with concussion often present with temporary disturbance of consciousness. The microstructural and functional changes in the brain associated with concussion, as well as the relationship with transient cognitive disorders, are currently unclear. In the present study, a rabbit model of simple concussion was established. Magnetic resonance-diffusion tensor imaging results revealed that the corona radiata and midbrain exhibited significantly decreased fractional anisotropy values in the neural pathways associated with memory and the reticular formation. In addition, the apparent diffusion coefficient values were significantly increased following injury compared with those before injury. Following a 1-hour period of quiet rest, the fractional anisotropy values significantly increased, and apparent diffusion coefficient values significantly decreased, returning to normal pre-injury levels. In contrast, the fractional anisotropy values and apparent diffusion coefficient values in the corpus callosum, thalamus and hippocampus showed no statistical significant alterations following injury. These findings indicate that the neural pathways associated with memory and the reticular formation pathway exhibit reversible microstructural white matter changes when concussion occurs, and these changes are exhibited to a different extent in different regions. PMID:25538741

Test of a potential link between analytic and nonanalytic category learning and automatic, effortful processing.

PubMed

Tracy, J I; Pinsk, M; Helverson, J; Urban, G; Dietz, T; Smith, D J

2001-08-01

The link between automatic and effortful processing and nonanalytic and analytic category learning was evaluated in a sample of 29 college undergraduates using declarative memory, semantic category search, and pseudoword categorization tasks. Automatic and effortful processing measures were hypothesized to be associated with nonanalytic and analytic categorization, respectively. Results suggested that contrary to prediction strong criterion-attribute (analytic) responding on the pseudoword categorization task was associated with strong automatic, implicit memory encoding of frequency-of-occurrence information. Data are discussed in terms of the possibility that criterion-attribute category knowledge, once established, may be expressed with few attentional resources. The data indicate that attention resource requirements, even for the same stimuli and task, vary depending on the category rule system utilized. Also, the automaticity emerging from familiarity with analytic category exemplars is very different from the automaticity arising from extensive practice on a semantic category search task. The data do not support any simple mapping of analytic and nonanalytic forms of category learning onto the automatic and effortful processing dichotomy and challenge simple models of brain asymmetries for such procedures. Copyright 2001 Academic Press.

Microstructural changes in memory and reticular formation neural pathway after simple concussion.

PubMed

Ouyang, Lin; Shi, Rongyue; Xiao, Yuhui; Meng, Jiarong; Guo, Yihe; Lu, Guangming

2012-10-05

Patients with concussion often present with temporary disturbance of consciousness. The microstructural and functional changes in the brain associated with concussion, as well as the relationship with transient cognitive disorders, are currently unclear. In the present study, a rabbit model of simple concussion was established. Magnetic resonance-diffusion tensor imaging results revealed that the corona radiata and midbrain exhibited significantly decreased fractional anisotropy values in the neural pathways associated with memory and the reticular formation. In addition, the apparent diffusion coefficient values were significantly increased following injury compared with those before injury. Following a 1-hour period of quiet rest, the fractional anisotropy values significantly increased, and apparent diffusion coefficient values significantly decreased, returning to normal pre-injury levels. In contrast, the fractional anisotropy values and apparent diffusion coefficient values in the corpus callosum, thalamus and hippocampus showed no statistical significant alterations following injury. These findings indicate that the neural pathways associated with memory and the reticular formation pathway exhibit reversible microstructural white matter changes when concussion occurs, and these changes are exhibited to a different extent in different regions.

Meeting the memory challenges of brain-scale network simulation.

PubMed

Kunkel, Susanne; Potjans, Tobias C; Eppler, Jochen M; Plesser, Hans Ekkehard; Morrison, Abigail; Diesmann, Markus

2011-01-01

The development of high-performance simulation software is crucial for studying the brain connectome. Using connectome data to generate neurocomputational models requires software capable of coping with models on a variety of scales: from the microscale, investigating plasticity, and dynamics of circuits in local networks, to the macroscale, investigating the interactions between distinct brain regions. Prior to any serious dynamical investigation, the first task of network simulations is to check the consistency of data integrated in the connectome and constrain ranges for yet unknown parameters. Thanks to distributed computing techniques, it is possible today to routinely simulate local cortical networks of around 10(5) neurons with up to 10(9) synapses on clusters and multi-processor shared-memory machines. However, brain-scale networks are orders of magnitude larger than such local networks, in terms of numbers of neurons and synapses as well as in terms of computational load. Such networks have been investigated in individual studies, but the underlying simulation technologies have neither been described in sufficient detail to be reproducible nor made publicly available. Here, we discover that as the network model sizes approach the regime of meso- and macroscale simulations, memory consumption on individual compute nodes becomes a critical bottleneck. This is especially relevant on modern supercomputers such as the Blue Gene/P architecture where the available working memory per CPU core is rather limited. We develop a simple linear model to analyze the memory consumption of the constituent components of neuronal simulators as a function of network size and the number of cores used. This approach has multiple benefits. The model enables identification of key contributing components to memory saturation and prediction of the effects of potential improvements to code before any implementation takes place. As a consequence, development cycles can be shorter and less expensive. Applying the model to our freely available Neural Simulation Tool (NEST), we identify the software components dominant at different scales, and develop general strategies for reducing the memory consumption, in particular by using data structures that exploit the sparseness of the local representation of the network. We show that these adaptations enable our simulation software to scale up to the order of 10,000 processors and beyond. As memory consumption issues are likely to be relevant for any software dealing with complex connectome data on such architectures, our approach and our findings should be useful for researchers developing novel neuroinformatics solutions to the challenges posed by the connectome project.

Wireless power using magnetic resonance coupling for neural sensing applications

NASA Astrophysics Data System (ADS)

Yoon, Hargsoon; Kim, Hyunjung; Choi, Sang H.; Sanford, Larry D.; Geddis, Demetris; Lee, Kunik; Kim, Jaehwan; Song, Kyo D.

2012-04-01

Various wireless power transfer systems based on electromagnetic coupling have been investigated and applied in many biomedical applications including functional electrical stimulation systems and physiological sensing in humans and animals. By integrating wireless power transfer modules with wireless communication devices, electronic systems can deliver data and control system operation in untethered freely-moving conditions without requiring access through the skin, a potential source of infection. In this presentation, we will discuss a wireless power transfer module using magnetic resonance coupling that is specifically designed for neural sensing systems and in-vivo animal models. This research presents simple experimental set-ups and circuit models of magnetic resonance coupling modules and discusses advantages and concerns involved in positioning and sizing of source and receiver coils compared to conventional inductive coupling devices. Furthermore, the potential concern of tissue heating in the brain during operation of the wireless power transfer systems will also be addressed.

Image-Based Focusing

NASA Astrophysics Data System (ADS)

Selker, Ted

1983-05-01

Lens focusing using a hardware model of a retina (Reticon RL256 light sensitive array) with a low cost processor (8085 with 512 bytes of ROM and 512 bytes of RAM) was built. This system was developed and tested on a variety of visual stimuli to demonstrate that: a)an algorithm which moves a lens to maximize the sum of the difference of light level on adjacent light sensors will converge to best focus in all but contrived situations. This is a simpler algorithm than any previously suggested; b) it is feasible to use unmodified video sensor arrays with in-expensive processors to aid video camera use. In the future, software could be developed to extend the processor's usefulness, possibly to track an actor by panning and zooming to give a earners operator increased ease of framing; c) lateral inhibition is an adequate basis for determining best focus. This supports a simple anatomically motivated model of how our brain focuses our eyes.

The evolution of contralateral control of the body by the brain: is it a protective mechanism?

PubMed

Whitehead, Lorne; Banihani, Saleh

2014-01-01

Contralateral control, the arrangement whereby most of the human motor and sensory fibres cross the midline in order to provide control for contralateral portions of the body, presents a puzzle from an evolutionary perspective. What caused such a counterintuitive and complex arrangement to become dominant? In this paper we offer a new perspective on this question by showing that in a complex interactive control system there could be a significant net survival advantage with contralateral control, associated with the effect of injuries of intermediate severity. In such cases an advantage could arise from a combination of non-linear system response combined with correlations between injuries on the same side of the head and body. We show that a simple mathematical model of these ideas emulates such an advantage. Based on this model, we conclude that effects of this kind are a plausible driving force for the evolution of contralateral control.

A pilot study into the effects of music therapy on different areas of the brain of individuals with unresponsive wakefulness syndrome

PubMed Central

Steinhoff, Nikolaus; Heine, Astrid M.; Vogl, Julia; Weiss, Konrad; Aschraf, Asita; Hajek, Paul; Schnider, Peter; Tucek, Gerhard

2015-01-01

The global cerebral network allows music “ to do to us what it does.” While the same music can cause different emotions, the basic emotion of happy and sad songs can, nevertheless, be understood by most people. Consequently, the individual experience of music and its common effect on the human brain is a challenging subject for research. Various activities such as hearing, processing, and performing music provide us with different pictures of cerebral centers in PET. In comparison to these simple acts of experiencing music, the interaction and the therapeutic relationship between the patient and the therapist in Music Therapy (MT) provide us with an additional element in need of investigation. In the course of a pilot study, these problems were approached and reduced to the simple observation of pattern alteration in the brains of four individuals with Unresponsive Wakefulness Syndrome (UWS) during MT. Each patient had three PET investigations: (i) during a resting state, (ii) during the first exposure to MT, and (iii) during the last exposure to MT. Two patients in the MT group received MT for 5 weeks between the 2nd and the 3rd PET (three times a week), while two other patients in the control group had no MT in between. Tracer uptake was measured in the frontal, hippocampal, and cerebellar region of the brain. With certain differences in these three observed brain areas, the tracer uptake in the MT group was higher (34%) than in the control group after 5 weeks. The preliminary results suggest that MT activates the three brain regions described above. In this article, we present our approach to the neuroscience of MT and discuss the impact of our hypothesis on music therapy practice, neurological rehabilitation of individuals in UWS and additional neuroscientific research. PMID:26347603

A pilot study into the effects of music therapy on different areas of the brain of individuals with unresponsive wakefulness syndrome.

PubMed

Steinhoff, Nikolaus; Heine, Astrid M; Vogl, Julia; Weiss, Konrad; Aschraf, Asita; Hajek, Paul; Schnider, Peter; Tucek, Gerhard

2015-01-01

The global cerebral network allows music " to do to us what it does." While the same music can cause different emotions, the basic emotion of happy and sad songs can, nevertheless, be understood by most people. Consequently, the individual experience of music and its common effect on the human brain is a challenging subject for research. Various activities such as hearing, processing, and performing music provide us with different pictures of cerebral centers in PET. In comparison to these simple acts of experiencing music, the interaction and the therapeutic relationship between the patient and the therapist in Music Therapy (MT) provide us with an additional element in need of investigation. In the course of a pilot study, these problems were approached and reduced to the simple observation of pattern alteration in the brains of four individuals with Unresponsive Wakefulness Syndrome (UWS) during MT. Each patient had three PET investigations: (i) during a resting state, (ii) during the first exposure to MT, and (iii) during the last exposure to MT. Two patients in the MT group received MT for 5 weeks between the 2nd and the 3rd PET (three times a week), while two other patients in the control group had no MT in between. Tracer uptake was measured in the frontal, hippocampal, and cerebellar region of the brain. With certain differences in these three observed brain areas, the tracer uptake in the MT group was higher (34%) than in the control group after 5 weeks. The preliminary results suggest that MT activates the three brain regions described above. In this article, we present our approach to the neuroscience of MT and discuss the impact of our hypothesis on music therapy practice, neurological rehabilitation of individuals in UWS and additional neuroscientific research.

Measurement of regional cerebral blood flow with copper-62-PTSM and a three-compartment model.

PubMed

Okazawa, H; Yonekura, Y; Fujibayashi, Y; Mukai, T; Nishizawa, S; Magata, Y; Ishizu, K; Tamaki, N; Konishi, J

1996-07-01

We evaluated quantitatively 62Cu-labeled pyruvaldehyde bis(N4-methylthiosemicarbazone) copper II (62Cu-PTSM) as a brain perfusion tracer for positron emission tomography (PET). For quantitative measurement, the octanol extraction method is needed to correct for arterial radioactivity in estimating the lipophilic input function, but the procedure is not practical for clinical studies. To measure regional cerebral blood flow (rCBF) by 62Cu-PTSM with simple arterial blood sampling, a standard curve of the octanol extraction ratio and a three-compartment model were applied. We performed both 15O-labeled water PET and 62 Cu-PTSM PET with dynamic data acquisition and arterial sampling in six subjects. Data obtained in 10 subjects studied previously were used for the standard octanol extraction curve. Arterial activity was measured and corrected to obtain the true input function using the standard curve. Graphical analysis (Gjedde-Patlak plot) with the data for each subject fitted by a straight regression line suggested that 62Cu-PTSM can be analyzed by the three-compartment model with negligible K4. Using this model, K1-K3 were estimated from curve fitting of the cerebral time-activity curve and the corrected input function. The fractional uptake of 62Cu-PTSM was corrected to rCBF with the individual extraction at steady state calculated from K1-K3. The influx rates (Ki) obtained from three-compartment model and graphical analyses were compared for the validation of the model. A comparison of rCBF values obtained from 62Cu-PTSM and 150-water studies demonstrated excellent correlation. The results suggest the potential feasibility of quantitation of cerebral perfusion with 62Cu-PTSM accompanied by dynamic PET and simple arterial sampling.

Computational model for perception of objects and motions.

PubMed

Yang, WenLu; Zhang, LiQing; Ma, LiBo

2008-06-01

Perception of objects and motions in the visual scene is one of the basic problems in the visual system. There exist 'What' and 'Where' pathways in the superior visual cortex, starting from the simple cells in the primary visual cortex. The former is able to perceive objects such as forms, color, and texture, and the latter perceives 'where', for example, velocity and direction of spatial movement of objects. This paper explores brain-like computational architectures of visual information processing. We propose a visual perceptual model and computational mechanism for training the perceptual model. The computational model is a three-layer network. The first layer is the input layer which is used to receive the stimuli from natural environments. The second layer is designed for representing the internal neural information. The connections between the first layer and the second layer, called the receptive fields of neurons, are self-adaptively learned based on principle of sparse neural representation. To this end, we introduce Kullback-Leibler divergence as the measure of independence between neural responses and derive the learning algorithm based on minimizing the cost function. The proposed algorithm is applied to train the basis functions, namely receptive fields, which are localized, oriented, and bandpassed. The resultant receptive fields of neurons in the second layer have the characteristics resembling that of simple cells in the primary visual cortex. Based on these basis functions, we further construct the third layer for perception of what and where in the superior visual cortex. The proposed model is able to perceive objects and their motions with a high accuracy and strong robustness against additive noise. Computer simulation results in the final section show the feasibility of the proposed perceptual model and high efficiency of the learning algorithm.

A first-in-man PET study of [18F]PSS232, a fluorinated ABP688 derivative for imaging metabotropic glutamate receptor subtype 5.

PubMed

Warnock, Geoffrey; Sommerauer, Michael; Mu, Linjing; Pla Gonzalez, Gloria; Geistlich, Susanne; Treyer, Valerie; Schibli, Roger; Buck, Alfred; Krämer, Stefanie D; Ametamey, Simon M

2018-06-01

Non-invasive imaging of metabotropic glutamate receptor 5 (mGlu 5 ) in the brain using PET is of interest in e.g., anxiety, depression, and Parkinson's disease. Widespread application of the most widely used mGlu 5 tracer, [ 11 C]ABP688, is limited by the short physical half-life of carbon-11. [ 18 F]PSS232 is a fluorinated analog with promising preclinical properties and high selectivity and specificity for mGlu 5 . In this first-in-man study, we evaluated the brain uptake pattern and kinetics of [ 18 F]PSS232 in healthy volunteers. [ 18 F]PSS232 PET was performed with ten healthy male volunteers aged 20-40 years. Seven of the subjects received a bolus injection and the remainder a bolus/infusion protocol. Cerebral blood flow was determined in seven subjects using [ 15 O]water PET. Arterial blood activity was measured using an online blood counter. Tracer kinetics were evaluated by compartment modeling and parametric maps were generated for both tracers. At 90 min post-injection, 59.2 ± 11.1% of total radioactivity in plasma corresponded to intact tracer. The regional first pass extraction fraction of [ 18 F]PSS232 ranged from 0.41 ± 0.06 to 0.55 ± 0.03 and brain distribution pattern matched that of [ 11 C]ABP688. Uptake kinetics followed a simple two-tissue compartment model. The volume of distribution of total tracer (V T , ml/cm 3 ) ranged from 1.18 ± 0.20 for white matter to 2.91 ± 0.51 for putamen. The respective mean distribution volume ratios (DVR) with cerebellum as the reference tissue were 0.88 ± 0.06 and 2.12 ± 0.10, respectively. The tissue/cerebellum ratios of a bolus/infusion protocol (30/70 dose ratio) were close to the DVR values. Brain uptake of [ 18 F]PSS232 matched the distribution of mGlu 5 and followed a two-tissue compartment model. The well-defined kinetics and the possibility to use reference tissue models, obviating the need for arterial blood sampling, make [ 18 F]PSS232 a promising fluorine-18 labeled radioligand for measuring mGlu 5 density in humans.

Probabilistic Inference in General Graphical Models through Sampling in Stochastic Networks of Spiking Neurons

PubMed Central

Pecevski, Dejan; Buesing, Lars; Maass, Wolfgang

2011-01-01

An important open problem of computational neuroscience is the generic organization of computations in networks of neurons in the brain. We show here through rigorous theoretical analysis that inherent stochastic features of spiking neurons, in combination with simple nonlinear computational operations in specific network motifs and dendritic arbors, enable networks of spiking neurons to carry out probabilistic inference through sampling in general graphical models. In particular, it enables them to carry out probabilistic inference in Bayesian networks with converging arrows (“explaining away”) and with undirected loops, that occur in many real-world tasks. Ubiquitous stochastic features of networks of spiking neurons, such as trial-to-trial variability and spontaneous activity, are necessary ingredients of the underlying computational organization. We demonstrate through computer simulations that this approach can be scaled up to neural emulations of probabilistic inference in fairly large graphical models, yielding some of the most complex computations that have been carried out so far in networks of spiking neurons. PMID:22219717

Multiple memory stores and operant conditioning: a rationale for memory's complexity.

PubMed

Meeter, Martijn; Veldkamp, Rob; Jin, Yaochu

2009-02-01

Why does the brain contain more than one memory system? Genetic algorithms can play a role in elucidating this question. Here, model animals were constructed containing a dorsal striatal layer that controlled actions, and a ventral striatal layer that controlled a dopaminergic learning signal. Both layers could gain access to three modeled memory stores, but such access was penalized as energy expenditure. Model animals were then selected on their fitness in simulated operant conditioning tasks. Results suggest that having access to multiple memory stores and their representations is important in learning to regulate dopamine release, as well as in contextual discrimination. For simple operant conditioning, as well as stimulus discrimination, hippocampal compound representations turned out to suffice, a counterintuitive result given findings that hippocampal lesions tend not to affect performance in such tasks. We argue that there is in fact evidence to support a role for compound representations and the hippocampus in even the simplest conditioning tasks.

Preliminary pilot fMRI study of neuropostural optimization with a noninvasive asymmetric radioelectric brain stimulation protocol in functional dysmetria

PubMed Central

Mura, Marco; Castagna, Alessandro; Fontani, Vania; Rinaldi, Salvatore

2012-01-01

Purpose This study assessed changes in functional dysmetria (FD) and in brain activation observable by functional magnetic resonance imaging (fMRI) during a leg flexion-extension motor task following brain stimulation with a single radioelectric asymmetric conveyer (REAC) pulse, according to the precisely defined neuropostural optimization (NPO) protocol. Population and methods Ten healthy volunteers were assessed using fMRI conducted during a simple motor task before and immediately after delivery of a single REAC-NPO pulse. The motor task consisted of a flexion-extension movement of the legs with the knees bent. FD signs and brain activation patterns were compared before and after REAC-NPO. Results A single 250-millisecond REAC-NPO treatment alleviated FD, as evidenced by patellar asymmetry during a sit-up motion, and modulated activity patterns in the brain, particularly in the cerebellum, during the performance of the motor task. Conclusion Activity in brain areas involved in motor control and coordination, including the cerebellum, is altered by administration of a REAC-NPO treatment and this effect is accompanied by an alleviation of FD. PMID:22536071

Male and female voices activate distinct regions in the male brain.

PubMed

Sokhi, Dilraj S; Hunter, Michael D; Wilkinson, Iain D; Woodruff, Peter W R

2005-09-01

In schizophrenia, auditory verbal hallucinations (AVHs) are likely to be perceived as gender-specific. Given that functional neuro-imaging correlates of AVHs involve multiple brain regions principally including auditory cortex, it is likely that those brain regions responsible for attribution of gender to speech are invoked during AVHs. We used functional magnetic resonance imaging (fMRI) and a paradigm utilising 'gender-apparent' (unaltered) and 'gender-ambiguous' (pitch-scaled) male and female voice stimuli to test the hypothesis that male and female voices activate distinct brain areas during gender attribution. The perception of female voices, when compared with male voices, affected greater activation of the right anterior superior temporal gyrus, near the superior temporal sulcus. Similarly, male voice perception activated the mesio-parietal precuneus area. These different gender associations could not be explained by either simple pitch perception or behavioural response because the activations that we observed were conjointly activated by both 'gender-apparent' and 'gender-ambiguous' voices. The results of this study demonstrate that, in the male brain, the perception of male and female voices activates distinct brain regions.

Ex vivo mouse brain microscopy at 15T with loop-gap RF coil.

PubMed

Cohen, Ouri; Ackerman, Jerome L

2018-04-18

The design of a loop-gap-resonator RF coil optimized for ex vivo mouse brain microscopy at ultra high fields is described and its properties characterized using simulations, phantoms and experimental scans of mouse brains fixed in 10% formalin containing 4 mM Magnevist™. The RF (B 1 ) and magnetic field (B 0 ) homogeneities are experimentally quantified and compared to electromagnetic simulations of the coil. The coil's performance is also compared to a similarly sized surface coil and found to yield double the sensitivity. A three-dimensional gradient-echo (GRE) sequence is used to acquire high resolution mouse brain scans at (47 μm) 3 resolution in 1.8 h and a 20 × 20 × 19 μm 3 resolution in 27 h. The high resolution obtained permitted clear visualization and identification of multiple structures in the ex vivo mouse brain and represents, to our knowledge, the highest resolution ever achieved for a whole mouse brain. Importantly, the coil design is simple and easy to construct. Copyright © 2018 Elsevier Inc. All rights reserved.

The smartphone brain scanner: a portable real-time neuroimaging system.

PubMed

Stopczynski, Arkadiusz; Stahlhut, Carsten; Larsen, Jakob Eg; Petersen, Michael Kai; Hansen, Lars Kai

2014-01-01

Combining low-cost wireless EEG sensors with smartphones offers novel opportunities for mobile brain imaging in an everyday context. Here we present the technical details and validation of a framework for building multi-platform, portable EEG applications with real-time 3D source reconstruction. The system--Smartphone Brain Scanner--combines an off-the-shelf neuroheadset or EEG cap with a smartphone or tablet, and as such represents the first fully portable system for real-time 3D EEG imaging. We discuss the benefits and challenges, including technical limitations as well as details of real-time reconstruction of 3D images of brain activity. We present examples of brain activity captured in a simple experiment involving imagined finger tapping, which shows that the acquired signal in a relevant brain region is similar to that obtained with standard EEG lab equipment. Although the quality of the signal in a mobile solution using an off-the-shelf consumer neuroheadset is lower than the signal obtained using high-density standard EEG equipment, we propose mobile application development may offset the disadvantages and provide completely new opportunities for neuroimaging in natural settings.

Comparative analysis of brain EEG signals generated from the right and left hand while writing

NASA Astrophysics Data System (ADS)

Sardesai, Neha; Jamali Mahabadi, S. E.; Meng, Qinglei; Choa, Fow-Sen

2016-05-01

This paper provides a comparative analysis of right handed people and left handed people when they write with both their hands. Two left handed and one right handed subject were asked to write their respective names on a paper using both, their left and right handed, and their brain signals were measured using EEG. Similarly, they were asked to perform simple mathematical calculations using both their hand. The data collected from the EEG from writing with both hands is compared. It is observed that though it is expected that the right brain only would contribute to left handed writing and vice versa, it is not so. When a right handed person writes with his/her left hand, the initial instinct is to go for writing with the right hand. Hence, both parts of the brain are active when a subject writes with the other hand. However, when the activity is repeated, the brain learns to expect to write with the other hand as the activity is repeated and then only the expected part of the brain is active.

Epigenetic mechanisms in sexual differentiation of the brain and behaviour.

PubMed

Forger, Nancy G

2016-02-19

Circumstantial evidence alone argues that the establishment and maintenance of sex differences in the brain depend on epigenetic modifications of chromatin structure. More direct evidence has recently been obtained from two types of studies: those manipulating a particular epigenetic mechanism, and those examining the genome-wide distribution of specific epigenetic marks. The manipulation of histone acetylation or DNA methylation disrupts the development of several neural sex differences in rodents. Taken together, however, the evidence suggests there is unlikely to be a simple formula for masculine or feminine development of the brain and behaviour; instead, underlying epigenetic mechanisms may vary by brain region or even by dependent variable within a region. Whole-genome studies related to sex differences in the brain have only very recently been reported, but suggest that males and females may use different combinations of epigenetic modifications to control gene expression, even in cases where gene expression does not differ between the sexes. Finally, recent findings are discussed that are likely to direct future studies on the role of epigenetic mechanisms in sexual differentiation of the brain and behaviour. © 2016 The Author(s).

An adaptive model approach for quantitative wrist rigidity evaluation during deep brain stimulation surgery.

PubMed

Assis, Sofia; Costa, Pedro; Rosas, Maria Jose; Vaz, Rui; Silva Cunha, Joao Paulo

2016-08-01

Intraoperative evaluation of the efficacy of Deep Brain Stimulation includes evaluation of the effect on rigidity. A subjective semi-quantitative scale is used, dependent on the examiner perception and experience. A system was proposed previously, aiming to tackle this subjectivity, using quantitative data and providing real-time feedback of the computed rigidity reduction, hence supporting the physician decision. This system comprised of a gyroscope-based motion sensor in a textile band, placed in the patients hand, which communicated its measurements to a laptop. The latter computed a signal descriptor from the angular velocity of the hand during wrist flexion in DBS surgery. The first approach relied on using a general rigidity reduction model, regardless of the initial severity of the symptom. Thus, to enhance the performance of the previously presented system, we aimed to develop models for high and low baseline rigidity, according to the examiner assessment before any stimulation. This would allow a more patient-oriented approach. Additionally, usability was improved by having in situ processing in a smartphone, instead of a computer. Such system has shown to be reliable, presenting an accuracy of 82.0% and a mean error of 3.4%. Relatively to previous results, the performance was similar, further supporting the importance of considering the cogwheel rigidity to better infer about the reduction in rigidity. Overall, we present a simple, wearable, mobile system, suitable for intra-operatory conditions during DBS, supporting a physician in decision-making when setting stimulation parameters.

Chapter 36: history of aphasia: from brain to language.

PubMed

Eling, Paul; Whitaker, Harry

2010-01-01

An historical overview is presented that focuses on the changes both in approach and topics with respect to language disturbances due to brain lesions. Early cases of language disorders were described without any theorizing about language or its relation to the brain. Also, three forms of speech disorder were distinguished: traulotes, psellotes and ischophonia, which are only marginally related to aphasia. In the 18th century some authors, in particular Gesner and Crichton, attempted to explain language disorders in terms of mental processes. The great debate on both the anatomical (Broca, Wernicke) and functional (Wernicke, Lichtheim) aspects of aphasia dominated late 19th century discussion of localization of function, leading to the development of what we now call the cognitive neurosciences. In this period, language processing was described in terms of a simple functional model of word recognition and production; linguistic principles played no role. At the beginning of the 20th century the discussion on language disorders waned due to a decrease of interest in the issue of localization; aphasia became primarily a clinical issue of how best to classify patients. In the second half of the 20th century, the field of aphasia developed rapidly due to studies performed at the Boston Aphasia Unit and, more importantly, to a change of orientation to linguistic notions of language structure, as introduced by Chomsky.

A simple method for fabricating microwire tetrode with sufficient rigidity and integrity without a heat-fusing process.

PubMed

Liao, Yi-Fang; Tsai, Meng-Li; Yen, Chen-Tung; Cheng, Chiung-Hsiang

2011-02-15

Heat-fusing is a common process for fabricating microwire tetrodes. However, it is time-consuming, and the high-temperature treatment can easily cause the insulation of the microwire to overheat leading to short circuits. We herein provide a simple, fast method to fabricate microwire tetrodes without the heat-fusion process. By increasing the twisting density, we were able to fabricate tetrodes with good rigidity and integrity. This kind of tetrode showed good recording quality, penetrated the brain surface easily, and remained intact after chronic implantation. This method requires only general laboratory tools and is relatively simple even for inexperienced workers. © 2010 Elsevier B.V. All rights reserved.