ADHD-200 Global Competition: Diagnosing ADHD using personal characteristic data can outperform resting state fMRI measurements

Frontiers in Systems Neuroscience

Volumn , Issue SEPTEMBER, 2012, Pages 1-22

  Brown, Matthew R G ^a   Sidhu, Gagan S ^a,b   Greiner, Russell ^a,b   Asgarian, Nasimeh ^a,b   Bastani, Meysam ^a,b   Silverstone, Peter H ^a   Greenshaw, Andrew J ^a   Dursun, Serdar M ^a  

^a UNIVERSITY OF ALBERTA   (Canada)

^b Alberta Machine Intelligence Institute   (Canada)

Author keywords

ADHD; Children; Classifier; Diagnosis; Functional connectivity; ICA; Machine learning; Multivoxel pattern analysis

Indexed keywords

AGE; ARTICLE; ATTENTION DEFICIT DISORDER; CHILD; CLASSIFIER; CLINICAL RESEARCH; CONTROLLED STUDY; DATA ANALYSIS; DIAGNOSTIC ACCURACY; DIAGNOSTIC IMAGING; DIAGNOSTIC PROCEDURE; DISEASE CLASSIFICATION; FEMALE; FUNCTIONAL MAGNETIC RESONANCE IMAGING; GENDER; HANDEDNESS; HUMAN; INTELLIGENCE QUOTIENT; MAJOR CLINICAL STUDY; MALE; NEUROIMAGING; PATIENT INFORMATION; PREDICTION; RESTING STATE FUNCTIONAL MAGNETIC RESONANCE IMAGING; SCHOOL CHILD; SCORING SYSTEM;

EID: 84867122690     PISSN: 16625137     EISSN: None     Source Type: Journal    
DOI: 10.3389/fnsys.2012.00069     Document Type: Article

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