Studying depression using imaging and machine learning methods

NeuroImage: Clinical

Volumn 10, Issue , 2016, Pages 115-123

  Patel, Meenal J ^a   Khalaf, Alexander ^b   Aizenstein, Howard J ^a,b  

^a University of Pittsburgh   (United States)

^b UNIVERSITY OF PITTSBURGH SCHOOL OF MEDICINE   (United States)

Author keywords

Depression; Machine learning; Prediction; Review; Treatment

Indexed keywords

AREA UNDER THE CURVE; DIFFUSION TENSOR IMAGING; ELECTROCONVULSIVE THERAPY; FUNCTIONAL MAGNETIC RESONANCE IMAGING; HUMAN; MACHINE LEARNING; MAJOR DEPRESSION; NEUROIMAGING; PRINCIPAL COMPONENT ANALYSIS; PRIORITY JOURNAL; RECEIVER OPERATING CHARACTERISTIC; REVIEW; SAMPLE SIZE; SUPPORT VECTOR MACHINE; BRAIN; COMPUTER ASSISTED DIAGNOSIS; DEPRESSIVE DISORDER, MAJOR; NUCLEAR MAGNETIC RESONANCE IMAGING; PATHOLOGY; PATHOPHYSIOLOGY; PROCEDURES;

BRAIN; DEPRESSIVE DISORDER, MAJOR; DIAGNOSIS, COMPUTER-ASSISTED; DIFFUSION TENSOR IMAGING; HUMANS; MACHINE LEARNING; MAGNETIC RESONANCE IMAGING;

EID: 84949505803     PISSN: None     EISSN: 22131582     Source Type: Journal    
DOI: 10.1016/j.nicl.2015.11.003     Document Type: Review

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