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P4‐260: A framework for performing multi‐resolution statistical analysis of brain connectivity graphs for preclinical Alzheimer's disease
Author(s) -
Kim Won Hwa,
Adluru Nagesh,
Chung Moo K.,
Okonkwo Ozioma C.,
Johnson Sterling C.,
Bendlin Barbara B.,
Singh Vikas
Publication year - 2015
Publication title -
alzheimer's and dementia
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.713
H-Index - 118
eISSN - 1552-5279
pISSN - 1552-5260
DOI - 10.1016/j.jalz.2015.08.089
Subject(s) - wavelet , tractography , connectomics , computer science , pattern recognition (psychology) , graph theory , graph , adjacency list , diffusion imaging , statistical hypothesis testing , artificial intelligence , connectome , diffusion mri , functional connectivity , mathematics , neuroscience , algorithm , medicine , psychology , theoretical computer science , statistics , magnetic resonance imaging , combinatorics , radiology
Background: There is significant interest in understanding variations in structural brain connectivity across groups to explain the pathology of a neurodegenerative disease such as Alzheimer’s disease (AD). Brain connectivity is usually encoded as a graph with nodes (regions of interest (ROI)) and edges (interactions between ROIs) by diffusion imaging which infers the connectivity via tractography. Typically, statistical hypothesis testing is applied at each edge to identify the group differences, however, this is not suitable