Evaluating the diagnostic utility of applying a machine learning algorithm to diffusion tensor MRI measures in individuals with major depressive disorder

Psychiatry Research - Neuroimaging

Volumn 264, Issue , 2017, Pages 1-9

  Schnyer, David M ^a   Clasen, Peter C ^b   Gonzalez, Christopher ^c   Beevers, Christopher G ^a  

^a UNIVERSITY OF TEXAS AT AUSTIN   (United States)

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

^c UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ADULT; ARTICLE; BRAIN METABOLISM; BRAIN REGION; CLINICAL ARTICLE; CONTROLLED STUDY; CORPUS CALLOSUM; DIAGNOSTIC VALUE; DIFFUSION TENSOR IMAGING; DISEASE CLASSIFICATION; ECHO PLANAR IMAGING; FEMALE; FRACTIONAL ANISOTROPY; FUNCTIONAL MAGNETIC RESONANCE IMAGING; FUNCTIONAL NEUROIMAGING; HUMAN; INCOME; LEFT HEMISPHERE; MAJOR DEPRESSION; MALE; NUCLEAR MAGNETIC RESONANCE SCANNER; PATTERN RECOGNITION; PRIORITY JOURNAL; RIGHT HEMISPHERE; SUPPORT VECTOR MACHINE; WHITE MATTER; ADOLESCENT; ALGORITHM; BRAIN; BRAIN MAPPING; DIAGNOSTIC IMAGING; MACHINE LEARNING; PROCEDURES; PSYCHOLOGY; STANDARDS; YOUNG ADULT;

ADOLESCENT; ADULT; ALGORITHMS; BRAIN; BRAIN MAPPING; DEPRESSIVE DISORDER, MAJOR; DIFFUSION TENSOR IMAGING; FEMALE; HUMANS; MACHINE LEARNING; SUPPORT VECTOR MACHINE; WHITE MATTER; YOUNG ADULT;

EID: 85016980085     PISSN: 09254927     EISSN: 18727506     Source Type: Journal    
DOI: 10.1016/j.pscychresns.2017.03.003     Document Type: Article

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