Stochastic geometric network models for groups of functional and structural connectomes

NeuroImage

Volumn 101, Issue , 2014, Pages 473-484

  Friedman, Eric J ^a,e   Landsberg, Adam S ^b   Owen, Julia P ^c,d   Li, Yi Ou ^c,d   Mukherjee, Pratik ^c,d  

^a INTERNATIONAL COMPUTER SCIENCE INSTITUTE   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

^c CLAREMONT MCKENNA COLLEGE   (United States)

^d UNIVERSITY OF CALIFORNIA   (United States)

^e UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Connectome; DMRI; FMRI; Geometric networks; Graph analysis; Network

Indexed keywords

ADULT; ARTICLE; CONNECTOME; CONSENSUS MODEL; CONTROLLED STUDY; CORPUS CALLOSUM; DEGREE DISTRIBUTED MODEL; ERDOS RENYI MODEL; FEMALE; FUNCTIONAL CONNECTOME; GEOMETRIC DEGREE DISTRIBUTED MODEL; GEOMETRIC HEMISPHERE MODEL; GEOMETRIC MODEL; GEOMETRY; HUMAN; HUMAN EXPERIMENT; IMAGE ANALYSIS; LEFT HEMISPHERE; MALE; MEASUREMENT ACCURACY; NORMAL HUMAN; NORMALIZED GEOMETRIC DEGREE DISTRIBUTED MODEL; NUCLEAR MAGNETIC RESONANCE IMAGING; PROBABILITY; RIGHT HEMISPHERE; STOCHASTIC MODEL; STRUCTURAL CONNECTOME; ANATOMY AND HISTOLOGY; NERVE CELL NETWORK; PHYSIOLOGY; PROCEDURES; STATISTICAL MODEL; YOUNG ADULT;

ADULT; CONNECTOME; FEMALE; HUMANS; MAGNETIC RESONANCE IMAGING; MALE; MODELS, STATISTICAL; NERVE NET; YOUNG ADULT;

EID: 84907022097     PISSN: 10538119     EISSN: 10959572     Source Type: Journal    
DOI: 10.1016/j.neuroimage.2014.07.039     Document Type: Article

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