Biophysical network models and the human connectome

NeuroImage

Volumn 80, Issue , 2013, Pages 330-338

  Woolrich, Mark W ^a,b   Stephan, Klaas E ^c,d  

^a WARNEFORD HOSPITAL   (United Kingdom)

^b UNIVERSITY OF OXFORD   (United Kingdom)

^c UNIVERSITY OF ZURICH   (Switzerland)

^d UNIVERSITY COLLEGE LONDON   (United Kingdom)

Author keywords

Bayes; Biophysical model; Bottom up model; Connectivity; Connectome; DCM; Diffusion; EEG; FMRI; Generative embedding; MEG; Multi modal; Networks

Indexed keywords

ARTICLE; ARTIFICIAL NEURAL NETWORK; BAYES THEOREM; BIOPHYSICS; BRAIN FUNCTION; CONNECTOME; CORRELATION ANALYSIS; DIFFUSION TENSOR IMAGING; ELECTROENCEPHALOGRAPHY; FUNCTIONAL MAGNETIC RESONANCE IMAGING; INFORMATION PROCESSING; MAGNETOENCEPHALOGRAPHY; MATHEMATICAL ANALYSIS; MATHEMATICAL MODEL; MONTE CARLO METHOD; NERVE CELL NETWORK; NERVE CELL PLASTICITY; NEUROANATOMY; NON INVASIVE MEASUREMENT; PRIORITY JOURNAL; PROBABILITY; STRUCTURE ACTIVITY RELATION; TRACTOGRAPHY; WHITE MATTER;

BAYES; BIOPHYSICAL MODEL; BOTTOM-UP MODEL; CONNECTIVITY; CONNECTOME; DCM; DIFFUSION; EEG; FMRI; GENERATIVE EMBEDDING; MEG; MULTI-MODAL; NETWORKS;

ANIMALS; BRAIN; CONNECTOME; DIFFUSION TENSOR IMAGING; HUMANS; MODELS, ANATOMIC; MODELS, NEUROLOGICAL; NERVE FIBERS, MYELINATED; NERVE NET;

EID: 84880330489     PISSN: 10538119     EISSN: 10959572     Source Type: Journal    
DOI: 10.1016/j.neuroimage.2013.03.059     Document Type: Article

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