Characterizing functional connectivity differences in aging adults using machine learning on resting state fMRI data

Frontiers in Computational Neuroscience

Volumn , Issue APR 2013, 2013, Pages

  Vergun, Svyatoslav ^a   Deshpande, Alok ^a   Meier, Timothy B ^a   Song, Jie ^a   Tudorascu, Dana L ^e   Nair, Veena A ^a   Singh, Vikas ^c   Biswal, Bharat B ^d   Meyerand, Mary Elizabeth ^a,b   Birn, Rasmus M ^a   Prabhakaran, Vivek ^a  

^a UNIVERSITY OF WISCONSIN MADISON   (United States)

^b UNIVERSITY OF WISCONSIN MADISON   (United States)

^c UNIVERSITY OF WISCONSIN SCHOOL OF MEDICINE AND PUBLIC HEALTH   (United States)

^d RUTGERS NEW JERSEY MEDICAL SCHOOL   (United States)

^e UNIVERSITY OF PITTSBURGH   (United States)

Author keywords

Aging; Reorganization; Resting state fMRI; Support vector machine

Indexed keywords

AGE-RELATED DIFFERENCES; CONNECTED REGION; FUNCTIONAL CONNECTIVITY; LINEAR CLASSIFIERS; REORGANIZATION; RESTING-STATE FMRI; RESTING-STATE FUNCTIONAL MAGNETIC RESONANCE IMAGING; TASK DIFFICULTY;

AGING OF MATERIALS; MAGNETIC RESONANCE IMAGING; SUPPORT VECTOR MACHINES;

FUNCTIONAL NEUROIMAGING;

ADULT; AGED; AGING; ARTICLE; BRAIN NETWORK; CLASSIFICATION; CLASSIFIER; FEMALE; FUNCTIONAL MAGNETIC RESONANCE IMAGING; HUMAN; MACHINE LEARNING; MALE; NERVE CELL NETWORK; NEUROIMAGING; PREDICTION; SUPPORT VECTOR MACHINE;

EID: 84875970739     PISSN: 16625188     EISSN: None     Source Type: Journal    
DOI: 10.3389/fncom.2013.00038     Document Type: Article

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