Network, anatomical, and non-imaging measures for the prediction of ADHD diagnosis in individual subjects

Frontiers in Systems Neuroscience

Volumn , Issue NOV, 2012, Pages 1-36

  Bohland, Jason W ^a   Saperstein, Sara ^a   Pereira, Francisco ^b   Rapin, Jérémy ^b   Grady, Leo ^b  

^a BOSTON UNIVERSITY   (United States)

^b SIEMENS CORPORATE RESEARCH   (United States)

Author keywords

ADHD; fMRI; Functional connectivity; Machine learning; Network analysis; Resting state

Indexed keywords

ADOLESCENT; ADULT; AGE DISTRIBUTION; AREA UNDER THE CURVE; ARTICLE; ATTENTION DEFICIT DISORDER; BRAIN FUNCTION; BRAIN REGION; BRAIN SIZE; CHILD; CLINICAL FEATURE; CONCEPTUAL FRAMEWORK; CORRELATIONAL STUDY; DATA ANALYSIS; DIAGNOSTIC ACCURACY; DIAGNOSTIC TEST ACCURACY STUDY; DIAGNOSTIC VALUE; DISEASE MARKER; FALSE POSITIVE RESULT; FEMALE; FUNCTIONAL MAGNETIC RESONANCE IMAGING; GENDER; HANDEDNESS; HUMAN; INTELLIGENCE; INTELLIGENCE QUOTIENT; INTERMETHOD COMPARISON; LEARNING ALGORITHM; MALE; NERVE CELL NETWORK; NEUROANATOMY; NEUROIMAGING; PATHOLOGICAL ANATOMY; PERFORMANCE MEASUREMENT SYSTEM; PHENOTYPE; PREDICTIVE VALUE; QUANTITATIVE STUDY; RECEIVER OPERATING CHARACTERISTIC; SCHOOL CHILD; STANDARD; VALIDATION STUDY;

EID: 84870041182     PISSN: 16625137     EISSN: None     Source Type: Journal    
DOI: 10.3389/fnsys.2012.00078     Document Type: Article

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