Prediction of pediatric bipolar disorder using neuroanatomical signatures of the amygdala

Bipolar Disorders

Volumn 16, Issue 7, 2014, Pages 713-721

  Mwangi, Benson ^a   Spiker, Danielle ^a   Zunta Soares, Giovana B ^a   Soares, Jair C ^a  

^a UNIVERSITY OF TEXAS MEDICAL SCHOOL   (United States)

Author keywords

Amygdala; Machine learning; Multivariate pattern analysis; Neuroimaging; Pediatric bipolar disorder

Indexed keywords

ADOLESCENT; AMYGDALOID NUCLEUS; AREA UNDER THE CURVE; ARTICLE; BIPOLAR DISORDER; CHILDHOOD DISEASE; CLINICAL ARTICLE; CONTROLLED STUDY; DIAGNOSTIC ACCURACY; DIAGNOSTIC TEST ACCURACY STUDY; DSM-IV; FEMALE; GRAY MATTER; HUMAN; MALE; NEUROANATOMY; NEUROIMAGING; NUCLEAR MAGNETIC RESONANCE IMAGING; NUCLEAR MAGNETIC RESONANCE SCANNER; PEDIATRIC BIPOLAR DISORDER; PREDICTIVE VALUE; PRIORITY JOURNAL; RECEIVER OPERATING CHARACTERISTIC; REGION OF INTEREST; SENSITIVITY AND SPECIFICITY; STATISTICAL PARAMETERS; VOXEL BASED MORPHOMETRY; ARTIFICIAL INTELLIGENCE; CHILD; IMAGE PROCESSING; PATHOLOGY; PROCEDURES;

ADOLESCENT; AMYGDALA; ARTIFICIAL INTELLIGENCE; BIPOLAR DISORDER; CHILD; FEMALE; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; MAGNETIC RESONANCE IMAGING; MALE; NEUROANATOMY; ROC CURVE;

EID: 84925946988     PISSN: 13985647     EISSN: 13995618     Source Type: Journal    
DOI: 10.1111/bdi.12222     Document Type: Article

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