Relevant bibliographies by topics / Brain – Models

Academic literature on the topic 'Brain – Models'

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Published: 4 June 2021
Last updated: 24 January 2023

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Journal articles on the topic "Brain – Models"

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Asaro, Peter. "Working Models and the Synthetic Method." Science & Technology Studies 19, no. 1 (January 1, 2006): 12–34. http://dx.doi.org/10.23987/sts.55200.

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This article examines the construction of electronic brain models in the 1940s as an instance of “working models” in science. It argues that the best way to understand the scientific role of these synthetic brains is through combining aspects of the “models as mediators” approach (Morgan and Morrison, 1999) and the “synthetic method” (Cordeschi, 2002). Taken together these approaches allow a fuller understanding of how working models functioned within the brain sciences of the time. This combined approach to understanding models is applied to an investigation of two electronic brains built in the late 1940s, the Homeostat of W. Ross Ashby, and the Tortoise of W. Grey Walter. It also examines the writings of Ashby, a psychiatrist and leading proponent of the synthetic brain models, and Walter, a brain electro-physiologist, and their ideas on the pragmatic values of such models. I conclude that rather than mere toys or publicity stunts, these electronic brains are best understood by considering the roles they played as mediators between disparate theories of brain function and animal behavior, and their combined metaphorical and material power.
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Willis, J. B. "Models of brain function." Endeavour 16, no. 1 (January 1992): 46. http://dx.doi.org/10.1016/0160-9327(92)90131-8.

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Barinaga, M. "Neuroscience models the brain." Science 247, no. 4942 (February 2, 1990): 524–26. http://dx.doi.org/10.1126/science.2300812.

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Scherg, M., and J. S. Ebersole. "Models of brain sources." Brain Topography 5, no. 4 (1993): 419–23. http://dx.doi.org/10.1007/bf01128700.

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Alicea, B., and J. Parent. "Meta-brain Models: biologically-inspired cognitive agents." IOP Conference Series: Materials Science and Engineering 1261, no. 1 (October 1, 2022): 012019. http://dx.doi.org/10.1088/1757-899x/1261/1/012019.

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Abstract Artificial Intelligence (AI) systems based solely on neural networks or symbolic computation present a representational complexity challenge. While minimal representations can produce behavioral outputs like locomotion or simple decision-making, more elaborate internal representations might offer a richer variety of behaviors. We propose that these issues can be addressed with a computational approach we call meta-brain models. Meta-brain models are embodied hybrid models that include layered components featuring varying degrees of representational complexity. We will propose combinations of layers composed using specialized types of models. Rather than using a generic black box approach to unify each component, this relationship mimics systems like the neocortical-thalamic system relationship of the mammalian brain, which utilizes both feedforward and feedback connectivity to facilitate functional communication. Importantly, the relationship between layers can be made anatomically explicit. This allows for structural specificity that can be incorporated into the model's function in interesting ways. We will propose several types of layers that might be functionally integrated into agents that perform unique types of tasks, from agents that simultaneously perform morphogenesis and perception, to agents that undergo morphogenesis and the acquisition of conceptual representations simultaneously. Our approach to meta-brain models involves creating models with different degrees of representational complexity, creating a layered meta-architecture that mimics the structural and functional heterogeneity of biological brains, and an input/output methodology flexible enough to accommodate cognitive functions, social interactions, and adaptive behaviors more generally. We will conclude by proposing next steps in the development of this flexible and open-source approach.
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Burke, David C. "Models of brain injury rehabilitation." Brain Injury 9, no. 7 (January 1995): 735–43. http://dx.doi.org/10.3109/02699059509008229.

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Miarka, Lauritz, and Manuel Valiente. "Animal models of brain metastasis." Neuro-Oncology Advances 3, Supplement_5 (November 1, 2021): v144—v156. http://dx.doi.org/10.1093/noajnl/vdab115.

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Abstract Modeling of metastatic disease in animal models is a critical resource to study the complexity of this multi-step process in a relevant system. Available models of metastatic disease to the brain are still far from ideal but they allow to address specific aspects of the biology or mimic clinically relevant scenarios. We not only review experimental models and their potential improvements but also discuss specific answers that could be obtained from them on unsolved aspects of clinical management.
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Aligholi, Hadi, and Maryam Safahani. "Experimental Models of Brain Injury." Neuroscience Journal of Shefaye Khatam 3, no. 2 (June 1, 2015): 69–76. http://dx.doi.org/10.18869/acadpub.shefa.3.2.69.

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Finkel, Leif H. "Neuroengineering Models of Brain Disease." Annual Review of Biomedical Engineering 2, no. 1 (August 2000): 577–606. http://dx.doi.org/10.1146/annurev.bioeng.2.1.577.

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Tupper, D. E. "Models of Brain Injury Rehabilitation." Neurology 39, no. 12 (December 1, 1989): 1649. http://dx.doi.org/10.1212/wnl.39.12.1649-a.

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Dissertations / Theses on the topic "Brain – Models"

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Parsapoor, Mahboobeh. "Brain Emotional Learning-Inspired Models." Licentiate thesis, Högskolan i Halmstad, Centrum för forskning om inbyggda system (CERES), 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-25428.

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In this thesis the mammalian nervous system and mammalian brain have been used as inspiration to develop a computational intelligence model based on the neural structure of fear conditioning and to extend the structure of the previous proposed amygdala-orbitofrontal model. The proposed model can be seen as a framework for developing general computational intelligence based on the emotional system instead of traditional models on the rational system of the human brain. The suggested model can be considered a new data driven model and is referred to as the brain emotional learning-inspired model (BELIM). Structurally, a BELIM consists of four main parts to mimic those parts of the brain’s emotional system that are responsible for activating the fear response. In this thesis the model is initially investigated for prediction and classification. The performance has been evaluated using various benchmark data sets from prediction applications, e.g. sunspot numbers from solar activity prediction, auroral electroject (AE) index from geomagnetic storms prediction and Henon map, Lorenz time series. In most of these cases, the model was tested for both long-term and short-term prediction. The performance of BELIM has also been evaluated for classification, by classifying binary and multiclass benchmark data sets.
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Aida, Toru. "Study of human head impact brain tissue constitutive models /." Morgantown, W. Va. : [West Virginia University Libraries], 2000. http://etd.wvu.edu/templates/showETD.cfm?recnum=1402.

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Thesis (Ph. D.)--West Virginia University, 2000.
Title from document title page. Document formatted into pages; contains x, 133 p. : ill. Vita. Includes abstract. Includes bibliographical references (p. 122-130).
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Amerineni, Rajesh. "BRAIN-INSPIRED MACHINE LEARNING CLASSIFICATION MODELS." OpenSIUC, 2020. https://opensiuc.lib.siu.edu/dissertations/1806.

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This dissertation focuses on the development of three classes of brain-inspired machine learning classification models. The models attempt to emulate (a) multi-sensory integration, (b) context-integration, and (c) visual information processing in the brain.The multi-sensory integration models are aimed at enhancing object classification through the integration of semantically congruent unimodal stimuli. Two multimodal classification models are introduced: the feature integrating (FI) model and the decision integrating (DI) model. The FI model, inspired by multisensory integration in the subcortical superior colliculus, combines unimodal features which are subsequently classified by a multimodal classifier. The DI model, inspired by integration in primary cortical areas, classifies unimodal stimuli independently using unimodal classifiers and classifies the combined decisions using a multimodal classifier. The multimodal classifier models are be implemented using multilayer perceptrons and multivariate statistical classifiers. Experiments involving the classification of noisy and attenuated auditory and visual representations of ten digits are designed to demonstrate the properties of the multimodal classifiers and to compare the performances of multimodal and unimodal classifiers. The experimental results show that the multimodal classification systems exhibit an important aspect of the “inverse effectiveness principle” by yielding significantly higher classification accuracies when compared with those of the unimodal classifiers. Furthermore, the flexibility offered by the generalized models enables the simulations and evaluations of various combinations of multimodal stimuli and classifiers under varying uncertainty conditions. The context-integrating model emulates the brain’s ability to use contextual information to uniquely resolve the interpretation of ambiguous stimuli. A deep learning neural network classification model that emulates this ability by integrating weighted bidirectional context into the classification process is introduced. The model, referred to as the CINET, is implemented using a convolution neural network (CNN), which is shown to be ideal for combining target and context stimuli and for extracting coupled target-context features. The CINET parameters can be manipulated to simulate congruent and incongruent context environments and to manipulate target-context stimuli relationships. The formulation of the CINET is quite general; consequently, it is not restricted to stimuli in any particular sensory modality nor to the dimensionality of the stimuli. A broad range of experiments are designed to demonstrate the effectiveness of the CINET in resolving ambiguous visual stimuli and in improving the classification of non-ambiguous visual stimuli in various contextual environments. The fact that the performance improves through the inclusion of context can be exploited to design robust brain-inspired machine learning algorithms. It is interesting to note that the CINET is a classification model that is inspired by a combination of brain’s ability to integrate contextual information and the CNN, which is inspired by the hierarchical processing of visual information in the visual cortex. A convolution neural network (CNN) model, inspired by the hierarchical processing of visual information in the brain, is introduced to fuse information from an ensemble of multi-axial sensors in order to classify strikes such as boxing punches and taekwondo kicks in combat sports. Although CNNs are not an obvious choice for non-array data nor for signals with non-linear variations, it will be shown that CNN models can effectively classify multi-axial multi-sensor signals. Experiments involving the classification of three-axis accelerometer and three-axes gyroscope signals measuring boxing punches and taekwondo kicks showed that the performance of the fusion classifiers were significantly superior to the uni-axial classifiers. Interestingly, the classification accuracies of the CNN fusion classifiers were significantly higher than those of the DTW fusion classifiers. Through training with representative signals and the local feature extraction property, the CNNs tend to be invariant to the latency shifts and non-linear variations. Moreover, by increasing the number of network layers and the training set, the CNN classifiers offer the potential for even better performance as well as the ability to handle a larger number of classes. Finally, due to the generalized formulations, the classifier models can be easily adapted to classify multi-dimensional signals of multiple sensors in various other applications.
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Ahmad, Faysal B. "Computational and biophysical models of the brain." Thesis, University of Oxford, 2015. https://ora.ox.ac.uk/objects/uuid:7395e8af-0a12-4304-88a3-52e3a0d20ec5.

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Widely distributed brain networks display highly coherent activity at rest. In this work, we combined bottom-up and top-down approaches to investigate the dynamics and underlying mechanisms of this spontaneous activity. We developed a realistic network model to simulate resting-state data, which incorporates biophysical regional dynamics, empirical brain connectivity, time delays, and background noise. At moderately weak coupling strengths, the model produces spontaneous metastable oscillatory states and a novel form of frequency depression, resulting in transient synchronizations between brain regions at reduced collective frequencies. We used fixed and sliding window correlation approaches on the power of band-limited MEG data, and show that brain regions exhibit significant functional connectivity (FC) in the alpha and beta frequency bands on slow ( > 1sec) time scales. We also show that temporal non-stationarity and bistability in FC occur in the same pairs of brain areas, and in the same frequency bands, as stationary measures of FC. We find that the network model reproduces the same frequency-dependency, time-scales, and non-stationary nature of FC as we found in real MEG data. Furthermore, seed-based correlations and independent component analysis also reveal a similar spatial profile of FC in empirical and simulated data, with the existence of widely distributed transient resting state networks in the same frequency bands. Finally, we used the network model simulations to evaluate a range of network estimation methods, and find that often the simplest linear measures perform best and some of the common non-linear measures can often give erroneous estimates. Overall, our results suggest that structured interactions between brain regions in the presence of delays and noise result in spontaneous synchronizations leading to the organized power fluctuations across brain regions, and some of the simplest statistical measures provide excellent estimates of this connectivity. Our work also highlights the potential of computational models in exploring neural mechanisms.
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Obando, Forero Catalina. "Statistical graph models of temporal brain networks." Electronic Thesis or Diss., Sorbonne université, 2018. https://accesdistant.sorbonne-universite.fr/login?url=https://theses-intra.sorbonne-universite.fr/2018SORUS454.pdf.

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La discipline encore naissante des réseaux complexes est vecteur d’un changement de paradigme dans la neuroscience. Les connectomes estimés à partir de mesures de neuroimagerie comme l’électroencéphalographie, la magnétoencéphalographie ou encore l’imagerie par résonance magnétique fonctionnelle fournissent une représentation abstraite du cerveau sous la forme d’un graphe, ce qui a permis des percées décisives dans la compréhension compacte et objective des propriétés topologiques et physiologiques des cerveaux sains. Cependant, les approches de pointe ignorent souvent l'incertitude et la nature temporelle de données de connectivité fonctionnelles. La plupart des méthodes disponibles dans la littérature ont en effet été développées pour caractériser les réseaux cérébraux fonctionnels comme des graphes statiques composés de nœuds (des régions cérébrales) et des liens (intensité de connectivité fonctionnelle) par métrique de réseau. En conséquence, la théorie des réseaux complexes a été principalement appliquée à des études transversales avec une unique mesure par sujet, produisant au final une caractérisation consistant en une moyenne de phénomènes neuronaux spatiotemporels. Nous avons implémenté des méthodes statistiques pour modéliser et simuler des réseaux cérébraux temporels. Nous avons utilisé des modèles de graphe qui permettent d'étudier simultanément à quel point les différentes propriétés des réseaux influencent la topologie observée dans les réseaux de connectivité cérébrale fonctionnelle. Nous avons identifié avec succès les mécanismes de connectivité locale fondamentaux qui gouvernent les propriétés des réseaux cérébraux. Nous avons proposé l'adaptation temporelle de ces mécanismes fondamentaux pour modéliser et simuler les changements physiologiques dynamiques d'un réseau cérébral. Plus spécifiquement, nous avons exploité des métriques temporelles pour construire des modèles temporels informatifs du rétablissement de patients ayant subit un accident vasculaire cérébral
The emerging area of complex networks has led to a paradigm shift in neuroscience. Connectomes estimated from neuroimaging techniques such as electroencephalography (EEG), magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI) results in an abstract representation of the brain as a graph, which has allowed a major breakthrough in the understanding of topological and physiological properties of healthy brains in a compact and objective way. However, state of the art approaches often ignore the uncertainty and temporal nature of functional connectivity data. Most of the available methods in the literature have been developed to characterize functional brain networks as static graphs composed of nodes (brain regions) and links (FC intensity) by network metrics. As a consequence, complex networks theory has been mainly applied to cross-sectional studies referring to a single point in time and the resulting characterization ultimately represents an average across spatiotemporal neural phenomena. Here, we implemented statistical methods to model and simulate temporal brain networks. We used graph models that allow to simultaneously study how different network properties influence the emergent topology observed in functional connectivity brain networks. We successfully identified fundamental local connectivity mechanisms that govern properties of brain networks. We proposed a temporal adaptation of such fundamental connectivity mechanisms to model and simulate physiological brain network dynamic changes. Specifically, we exploited the temporal metrics to build informative temporal models of recovery of patients after stroke
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Jaroudi, Rym. "Inverse Mathematical Models for Brain Tumour Growth." Licentiate thesis, Linköpings universitet, Tekniska fakulteten, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-141982.

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We study the following well-established model of reaction-diffusion type for brain tumour growth: This equation describes the change over time of the normalised tumour cell density u as a consequence of two biological phenomena: proliferation and diffusion. We discuss a mathematical method for the inverse problem of locating the brain tumour source (origin) based on the reaction-diffusion model. Our approach consists in recovering the initial spatial distribution of the tumour cells  starting from a later state , which can be given by a medical image. We use the nonlinear Landweber regularization method to solve the inverse problem as a sequence of well-posed forward problems. We give full 3-dimensional simulations of the tumour in time on two types of data, the 3d Shepp-Logan phantom and an MRI T1-weighted brain scan from the Internet Brain Segmentation Repository (IBSR). These simulations are obtained using standard finite difference discretisation of the space and time-derivatives, generating a simplistic approach that performs well. We also give a variational formulation for the model to open the possibility of alternative derivations and modifications of the model. Simulations with synthetic images show the accuracy of our approach for locating brain tumour sources.
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Robinson, Emma Claire. "Characterising population variability in brain structure through models of whole-brain structural connectivity." Thesis, Imperial College London, 2010. http://hdl.handle.net/10044/1/5875.

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Models of whole-brain connectivity are valuable for understanding neurological function. This thesis seeks to develop an optimal framework for extracting models of whole-brain connectivity from clinically acquired diffusion data. We propose new approaches for studying these models. The aim is to develop techniques which can take models of brain connectivity and use them to identify biomarkers or phenotypes of disease. The models of connectivity are extracted using a standard probabilistic tractography algorithm, modified to assess the structural integrity of tracts, through estimates of white matter anisotropy. Connections are traced between 77 regions of interest, automatically extracted by label propagation from multiple brain atlases followed by classifier fusion. The estimates of tissue integrity for each tract are input as indices in 77x77 ”connectivity” matrices, extracted for large populations of clinical data. These are compared in subsequent studies. To date, most whole-brain connectivity studies have characterised population differences using graph theory techniques. However these can be limited in their ability to pinpoint the locations of differences in the underlying neural anatomy. Therefore, this thesis proposes new techniques. These include a spectral clustering approach for comparing population differences in the clustering properties of weighted brain networks. In addition, machine learning approaches are suggested for the first time. These are particularly advantageous as they allow classification of subjects and extraction of features which best represent the differences between groups. One limitation of the proposed approach is that errors propagate from segmentation and registration steps prior to tractography. This can cumulate in the assignment of false positive connections, where the contribution of these factors may vary across populations, causing the appearance of population differences where there are none. The final contribution of this thesis is therefore to develop a common co-ordinate space approach. This combines probabilistic models of voxel-wise diffusion for each subject into a single probabilistic model of diffusion for the population. This allows tractography to be performed only once, ensuring that there is one model of connectivity. Cross-subject differences can then be identified by mapping individual subjects’ anisotropy data to this model. The approach is used to compare populations separated by age and gender.
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Rostami, Elham. "Traumatic brain injury in humans and animal models." Doctoral thesis, Stockholm : Reproprint AB, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-212088.

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Harbord, Ruth. "Time-varying brain connectivity with multiregression dynamic models." Thesis, University of Warwick, 2017. http://wrap.warwick.ac.uk/101426/.

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Functional magnetic resonance imaging (fMRI) is a non-invasive method for studying the human brain that is now widely used to study functional connectivity. Functional connectivity concerns how brain regions interact and how these interactions change over time, between subjects and in different experimental contexts and can provide deep insights into the underlying brain function. Multiregression Dynamic Models (MDMs) are dynamic Bayesian networks that describe contemporaneous, causal relationships between time series. They may therefore be applied to fMRI data to infer functional brain networks. This work focuses on the MDM Directed Graph Model (MDM-DGM) search algorithm for network discovery. The Log Predictive Likelihood (model evidence) factors by subject and by node, allowing a fast, parallelised model search. The estimated networks are directed and may contain the bidirectional edges and cycles that may be thought of as being representative of the true, reciprocal nature of brain connectivity. In Chapter 2, we use two datasets with 15 brain regions to demonstrate that the MDM-DGM can infer networks that are physiologically-interpretable. The estimated MDM-DGM networks are similar to networks estimated with the widely-used partial correlation method but advantageously also provide directional information. As the size of the model space prohibits an exhaustive search over networks with more than 20 nodes, in Chapter 3 we propose and evaluate stepwise model selection algorithms that reduce the number of models scored while optimising the networks. We show that computation time may be dramatically reduced for only a small trade-off in accuracy. In Chapter 4, we use non-local priors to derive new, closed-form expressions for the model evidence with a penalty on weaker, potentially spurious, edges. While the application of non-local priors poses a number of challenges, we argue that it has the potential to provide a flexible Bayesian framework to improve the robustness of the MDM-DGM networks.
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Venkataraman, Archana Ph D. Massachusetts Institute of Technology. "Generative models of brain connectivity for population studies." Thesis, Massachusetts Institute of Technology, 2012. http://hdl.handle.net/1721.1/78534.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 131-139).
Connectivity analysis focuses on the interaction between brain regions. Such relationships inform us about patterns of neural communication and may enhance our understanding of neurological disorders. This thesis proposes a generative framework that uses anatomical and functional connectivity information to find impairments within a clinical population. Anatomical connectivity is measured via Diffusion Weighted Imaging (DWI), and functional connectivity is assessed using resting-state functional Magnetic Resonance Imaging (fMRI). We first develop a probabilistic model to merge information from DWI tractography and resting-state fMRI correlations. Our formulation captures the interaction between hidden templates of anatomical and functional connectivity within the brain. We also present an intuitive extension to population studies and demonstrate that our model learns predictive differences between a control and a schizophrenia population. Furthermore, combining the two modalities yields better results than considering each one in isolation. Although our joint model identifies widespread connectivity patterns influenced by a neurological disorder, the results are difficult to interpret and integrate with our regioncentric knowledge of the brain. To alleviate this problem, we present a novel approach to identify regions associated with the disorder based on connectivity information. Specifically, we assume that impairments of the disorder localize to a small subset of brain regions, which we call disease foci, and affect neural communication to/from these regions. This allows us to aggregate pairwise connectivity changes into a region-based representation of the disease. Once again, we use a probabilistic formulation: latent variables specify a template organization of the brain, which we indirectly observe through resting-state fMRI correlations and DWI tractography. Our inference algorithm simultaneously identifies both the afflicted regions and the network of aberrant functional connectivity. Finally, we extend the region-based model to include multiple collections of foci, which we call disease clusters. Preliminary results suggest that as the number of clusters increases, the refined model explains progressively more of the functional differences between the populations.
by Archana Venkataraman.
Ph.D.
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Books on the topic "Brain – Models"

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1933-, Cotterill Rodney, ed. Models of brain function. Cambridge: Cambridge University Press, 1989.

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Ll, Wood Rodger, and Eames Peter, eds. Models of brain injury rehabilitation. Baltimore: Johns Hopkins University Press, 1989.

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Estrin, Moisey. Models of the brain functions. San Diego: M. Estrin, 1994.

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Martínez Murillo, Ricardo, and Alfredo Martínez, eds. Animal Models of Brain Tumors. Totowa, NJ: Humana Press, 2013. http://dx.doi.org/10.1007/978-1-62703-209-4.

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Cutsuridis, Vassilis, ed. Multiscale Models of Brain Disorders. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-18830-6.

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Moustafa, Ahmed A., ed. Computational Models of Brain and Behavior. Chichester, UK: John Wiley & Sons, Ltd, 2017. http://dx.doi.org/10.1002/9781119159193.

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L, Kitterle Frederick, ed. Hemispheric communication: Mechanisms and models. Hillsdale, N.J: Lawrence Erlbaum Associates, 1995.

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Chalidze, Valeriĭ. On the linguistic brain code. Benson, VT: Chalidze's Research Diary, 1985.

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Sandro, Mussa-Ivaldi, ed. Biological learning and control: How the brain forms representations, predicts events, and makes decisions. Cambridge, Mass: MIT Press, 2012.

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S, Jog Mandar, ed. Neuroelectrodynamics: Understanding the brain language. Amsterdam, Netherlands: IOS Press BV, 2010.

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Book chapters on the topic "Brain – Models"

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Peterson, James K. "Building Brain Models." In BioInformation Processing, 461–91. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-287-871-7_20.

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Jacobson, Marcus. "Making Brain Models." In Foundations of Neuroscience, 1–96. Boston, MA: Springer US, 1993. http://dx.doi.org/10.1007/978-1-4899-1781-2_1.

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Eggermont, Jos J. "Single-Neuron Models." In The Correlative Brain, 57–77. Berlin, Heidelberg: Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/978-3-642-51033-5_5.

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Eggermont, Jos J. "Neural Network Models." In The Correlative Brain, 78–93. Berlin, Heidelberg: Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/978-3-642-51033-5_6.

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Biessels, Geert Jan. "Animal Models." In Diabetes and the Brain, 387–408. Totowa, NJ: Humana Press, 2009. http://dx.doi.org/10.1007/978-1-60327-850-8_16.

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De Wilde, Philippe. "Neurons in the Brain." In Neural Network Models, 53–70. London: Springer London, 1997. http://dx.doi.org/10.1007/978-1-84628-614-8_3.

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Wong, Christina S. "In Vivo Models of Brain Metastases." In Brain Tumors, 59–84. New York, NY: Springer US, 2020. http://dx.doi.org/10.1007/978-1-0716-0856-2_3.

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Rish, Irina, Guillermo A. Cecchi, Marwan N. Baliki, and A. Vania Apkarian. "Sparse Regression Models of Pain Perception." In Brain Informatics, 212–23. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15314-3_20.

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Rose, Mark, and Ian Frampton. "Conceptual Models." In Eating Disorders and the Brain, 142–63. Chichester, UK: John Wiley & Sons, Ltd, 2011. http://dx.doi.org/10.1002/9781119998402.ch7.

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van Ments, Laila, Jan Treur, Jan Klein, and Peter Roelofsma. "A Computational Network Model for Shared Mental Models in Hospital Operation Rooms." In Brain Informatics, 67–78. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86993-9_7.

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Conference papers on the topic "Brain – Models"

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Hu, Hao, William S. Rosenberg, and Adnan H. Nayfeh. "Modeling Human Brain Movability Effect on Brain Response During Impact." In ASME 1998 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 1998. http://dx.doi.org/10.1115/imece1998-0980.

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Abstract Brain responses due to its movability during impact was investigated by using sliding interface approach. A new 3D 50th percentile human head finite element model has been generated in which sliding interfaces totally separate the brains and cerebrospinal fluid (CSF)/cranium. So, the brains can move to some extent. It becomes an equivalent one to most widely used brain/CSF (cranium) coupled models by switching interface type from sliding to tied. The model was partially validated by using available experimental and computed data in frontal impact. Compared with brain/CSF (cranium) coupled models, the new model predicts higher brain stress levels at sites such as corpus callosum, brain stem, and the vicinity of the ventricles etc. and more realistic deformation patterns. The results suggest that a fluid-solid interaction approach should be used to better model brain movement during impact to correctly interpret the brain injuries and to evaluate proposed head injury mechanisms.
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Bacskai, Brian. "Multiphoton Imaging of Structure and Function in Mouse Models of Alzheimer's Disease." In Optics and the Brain. Washington, D.C.: OSA, 2016. http://dx.doi.org/10.1364/brain.2016.bth3d.1.

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Amunts, Katrin. "Ultra-high Resolution Models of the Human Brain – Computational and Neuroscientific Challenges." In Optics and the Brain. Washington, D.C.: OSA, 2016. http://dx.doi.org/10.1364/brain.2016.btu2d.1.

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Raj, Ashish. "Graph models of brain diseases." In 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI 2015). IEEE, 2015. http://dx.doi.org/10.1109/isbi.2015.7164174.

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Wu, Melissa M., Katherine Perdue, Suk-Tak Chan, Kimberly A. Stephens, Bin Deng, Maria Angela Franceschini, and Stefan A. Carp. "Complete head cerebral sensitivity mapping for diffuse correlation spectroscopy using subject-specific MRI models." In Optics and the Brain. Washington, D.C.: Optica Publishing Group, 2022. http://dx.doi.org/10.1364/brain.2022.bw1c.5.

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"Monitoring Depth of Hypnosis under Propofol General Anaesthesia - Granger Causality and Hidden Markov Models." In Special Session on Brain-computer Interfaces and Brain Stimulation for Neurorehabilitation. SCITEPRESS - Science and and Technology Publications, 2013. http://dx.doi.org/10.5220/0004679402560261.

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Eslaminejad, Ashkan, Hesam Sarvghad-Moghaddam, Asghar Rezaei, Mariusz Ziejewski, and Ghodrat Karami. "Comparison of Brain Tissue Material Finite Element Models Based on Threshold for Traumatic Brain Injury." In ASME 2016 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/imece2016-67505.

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Blast traumatic brain injury (bTBI) may happen due to sudden blast and high-frequency loads. Due to the moral issues and the burden of experimental approaches, using computational methods such as finite element analysis (FEA) can be effective. Several finite element studies have focused on the effects of TBI to anticipate and understand the brain dynamic response. One of the most important factors in every FEA study of bTBI is the accurate modeling of brain tissue material properties. The main goal of this study is a comparison of different brain tissue constitutive models to understand the dynamic response of brain under an identical blast load. The multi-material FE modeling of the human head has several limitations such as its complexity and consequently high computational costs. Therefore, a spherical head model is modeled which suggests more straightforward observation/understanding of the FE modeling of skull (solid), CSF (fluid), and the brain tissue. Three different material models are considered for the brain tissue, namely hyperelastic, viscoelastic, and hyperviscoelastic. Brain dynamic responses are studied in terms of the head kinematics (linear acceleration), intracranial pressure (ICP), shear stress, and maximum mechanical strain. Our results showed that the hyperelastic model predicts larger ICP and shear than other constitutive brain tissue models. However, all material models predicted similar shear strain and head accelerations.
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Yates, Keegan, Elizabeth Fievisohn, Warren Hardy, and Costin Untaroiu. "Development and Validation of a Göttingen Miniature Pig Brain Finite Element Model." In ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/detc2016-60217.

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The Center for Disease Control and Prevention reports that there are approximately 1.4 million emergency department visits, hospitalizations, or deaths per year in the USA due to traumatic brain injuries (TBI) [1]. In order to lessen the severity or prevent TBIs, accurate dummy models, simulations, and injury risk metrics must be used. Ideally, these models and metrics would be designed with the use of human data. However, available human data is sparse, so animal study data must be applied to the human brain. Animal data must be scaled before it can be applied, and current scaling methods are very simplified. The objective of our study was to develop a finite element (FE) model of a Göttingen mini-pig to allow study of the tissue level response under impact loading. A hexahedral FE model of a miniature pig brain was created from MRI images. The cerebrum, cerebellum, corpus callosum, midbrain, brainstem, and ventricles were modeled and assigned properties as a Kelvin-Maxwell viscoelastic material. To validate the model, tests were conducted using mini-pigs in an injury device that subjected the pig brain to both linear and angular motion. These pigs are commonly used for brain testing because the brains are well developed with folds and the material properties are similar to human brain. The pigs’ brains were embedded with neutral density radio-opaque markers to track the motion of the brain relative to the skull with a biplanar X-ray system. The impact was then simulated, and the motion of nodes closest to the marker locations was recorded and used to optimize material parameters and the skull-brain interface. The injuries were defined at a tissue level with damage measures such as cumulative strain damage measure (CSDM). In future the animal FE model could be used with a human FE model to determine an accurate animal-to-human transfer function.
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Iftekharuddin, Khan M. "Texture Models for Brain Tumor Segmentation." In Quantitative Medical Imaging. Washington, D.C.: OSA, 2013. http://dx.doi.org/10.1364/qmi.2013.qw2g.2.

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Calvetti, D. "Multiscale multiphysiology predictive models of brain." In 9th edition of the International Conference on Computational Methods for Coupled Problems in Science and Engineering. CIMNE, 2021. http://dx.doi.org/10.23967/coupled.2021.064.

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Reports on the topic "Brain – Models"

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Nicholson, Katherine L. Opioid Abuse after Traumatic Brain Injury: Evaluation Using Rodent Models. Fort Belvoir, VA: Defense Technical Information Center, July 2012. http://dx.doi.org/10.21236/ada609949.

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Nicholson, Katherine L. Opioid Abuse after Traumatic Brain Injury: Evaluation Using Rodent Models. Fort Belvoir, VA: Defense Technical Information Center, July 2013. http://dx.doi.org/10.21236/ada586101.

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Harth, Erich. Mechanisms of Higher Brain Functions: A Study of Models of Perception. Fort Belvoir, VA: Defense Technical Information Center, December 1990. http://dx.doi.org/10.21236/ada232389.

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Rivera, Rodrigo, Juan Pablo Cruz, Catalina Merino-Osorio, Aymeric Rouchaud, and Charbel Mounayer. Brain Arteriovenous Malformation Models for Clinical Practice: Protocol for a Scoping Review. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, October 2020. http://dx.doi.org/10.37766/inplasy2020.10.0064.

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Lee, Stefan. Are Blast Brain Injuries Fundamentally Different Than Traditional Experimental Models of TBI? Fort Belvoir, VA: Defense Technical Information Center, July 2011. http://dx.doi.org/10.21236/ada555071.

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Leonessa, Fabio. Rat Models and Identification of Candidate Early Serum Biomarkers of Battlefield Traumatic Brain Injury. Fort Belvoir, VA: Defense Technical Information Center, July 2007. http://dx.doi.org/10.21236/ada482298.

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Lamb, Bruce T., and Olga Kokiko-Cochran. Novel Genetic Models to Study the Role of Inflammation in Brain Injury-Induced Alzheimer's Pathology. Fort Belvoir, VA: Defense Technical Information Center, December 2014. http://dx.doi.org/10.21236/ada612110.

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Lamb, Bruce T., and Olga Kokiko-Cochran. Novel Genetic Models to Study the Role of Inflammation in Brain Injury-Induced Alzheimer's Pathology. Fort Belvoir, VA: Defense Technical Information Center, October 2013. http://dx.doi.org/10.21236/ada591118.

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Baker, John L., James L. Olds, and Joel L. Davis. A Novel Approach to Large Scale Brain Network Models: An Algorithmic Model for Place Cell Emergence With Robotic Sensor Input. Fort Belvoir, VA: Defense Technical Information Center, June 2004. http://dx.doi.org/10.21236/ada425321.

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Doerschuk, Peter C. University LDRD student progress report on descriptions and comparisons of brain microvasculature via random graph models. Office of Scientific and Technical Information (OSTI), October 2012. http://dx.doi.org/10.2172/1055646.

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