Brain Model of Information Based Exchange
Here we describe an "information based exchange" model of brain function that ascribes to neocortex, basal ganglia, and thalamus distinct network functions. The model allows us to analyze whole brain system set point measures, such as the rate and heterogeneity of transitions in striatum and neocortex, in the context of disease perturbations. Our closed-loop model invokes different forms of plasticity at specific tissue interfaces and their principle cell synapses to achieve these transitions. By modulating information based exchange of action potentials between modeled neocortical areas, we observe changes to these measures in simulation. We hypothesize that similar dynamic set points and modulations exist in the brain's resting state activity, and that germ line modifications of information based exchange may increase the risk of diseases such as Huntington's, Parkinson's, and Alzheimer's. Disturbances in synaptic plasticity at distinct tissue interfaces in the model may be used to estimate risks of system dysfunction and neuronal cell death from quantitative analyses of the global dynamics that maintain system set points. The model is targeted for further development using IBM's Neural Tissue Simulator, which allows scalable elaboration of networks, tissues, and their neural and synaptic components towards ever greater complexity and biological realism. Elaboration of these simulations within each modeled neural tissue allows in silico study of therapeutic interventions in living brain tissue.
Code (0)
등록된 구현이 없습니다.
Tasks
modelSimilar Papers 제목 키워드 기반
Microstructure estimation from diffusion-MRI: Compartmentalized models in permeable cellular tissue
Diffusion-weighted magnetic resonance imaging (DW-MRI) is used to characterize brain tissue microstructure employing tissue-specific biophysical models. A current limitation, however, is that most of the proposed models …
Diffusion MRIAccelerated and Quantitative 3D Semisolid MT/CEST Imaging using a Generative Adversarial Network (GAN-CEST)
Purpose: To substantially shorten the acquisition time required for quantitative 3D chemical exchange saturation transfer (CEST) and semisolid magnetization transfer (MT) imaging and allow for rapid chemical exchange par…
Generative Adversarial NetworkMagnetic Resonance FingerprintingSSIMAutism Classification Using Brain Functional Connectivity Dynamics and Machine Learning
The goal of the present study is to identify autism using machine learning techniques and resting-state brain imaging data, leveraging the temporal variability of the functional connections (FC) as the only information. …
BIG-bench Machine LearningFunctional ConnectivityGeneral ClassificationMulti-Atlas Brain Network Classification through Consistency Distillation and Complementary Information Fusion
In the realm of neuroscience, identifying distinctive patterns associated with neurological disorders via brain networks is crucial. Resting-state functional magnetic resonance imaging (fMRI) serves as a primary tool for…
Multi-Stage Graph Learning for fMRI Analysis to Diagnose Neuro-Developmental Disorders
The insufficient supervision limit the performance of the deep supervised models for brain disease diagnosis. It is important to develop a learning framework that can capture more information in limited data and insuffic…
Graph Learning