Functional connectivity classification of autism identifies highly predictive brain features but falls short of biomarker standards

NeuroImage: Clinical

Volumn 7, Issue , 2015, Pages 359-366

  Plitt, Mark ^a   Barnes, Kelly Anne ^a   Martin, Alex ^a  

^a NATIONAL INSTITUTE OF MENTAL HEALTH   (United States)

Author keywords

Autism; Biomarkers; Machine learning classification; Social brain

Indexed keywords

ADULT; ARTICLE; ASSESSMENT OF HUMANS; AUTISM; BRAIN REGION; CLASSIFIER; COHORT ANALYSIS; CONNECTOME; DIAGNOSIS; DIAGNOSTIC ACCURACY; ELECTROENCEPHALOGRAM; FUNCTIONAL MAGNETIC RESONANCE IMAGING; FUNCTIONAL NEUROIMAGING; HUMAN; INTELLIGENCE QUOTIENT; K NEAREST NEIGHBOR; KERNEL METHOD; MACHINE LEARNING; MAJOR CLINICAL STUDY; MALE; NUCLEAR MAGNETIC RESONANCE SCANNER; PREDICTION; PRIORITY JOURNAL; RESTING STATE NETWORK; SIGNAL NOISE RATIO; SOCIAL INTERACTION; SOCIAL RESPONSIVENESS SCALE; SUPPORT VECTOR MACHINE; ADOLESCENT; BRAIN; CHILD DEVELOPMENT DISORDERS, PERVASIVE; CLINICAL TRIAL; COMPUTER ASSISTED DIAGNOSIS; NERVE TRACT; NUCLEAR MAGNETIC RESONANCE IMAGING; PATHOPHYSIOLOGY; YOUNG ADULT;

ADOLESCENT; BRAIN; CHILD DEVELOPMENT DISORDERS, PERVASIVE; HUMANS; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MAGNETIC RESONANCE IMAGING; MALE; NEURAL PATHWAYS; YOUNG ADULT;

EID: 84921627174     PISSN: None     EISSN: 22131582     Source Type: Journal    
DOI: 10.1016/j.nicl.2014.12.013     Document Type: Article

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