Multiple-network classification of childhood autism using functional connectivity dynamics

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 8675 LNCS, Issue PART 3, 2014, Pages 177-184

  Price, True ^a   Wee, Chong Yaw ^a   Gao, Wei ^a   Shen, Dinggang ^a  

^a UNIVERSITY OF NORTH CAROLINA SCHOOL OF MEDICINE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

MAGNETIC RESONANCE IMAGING; SUPPORT VECTOR MACHINES;

ADDITIONAL KNOWLEDGE; DYNAMIC APPROACHES; FUNCTIONAL CONNECTIVITY; MULTI-SCALE APPROACHES; MULTIPLE NETWORKS; MULTIPLE TIME SCALE; RESTING-STATE FUNCTIONAL CONNECTIVITIES; RESTING-STATE FUNCTIONAL MAGNETIC RESONANCE IMAGING;

DISEASES;

ARTICLE; AUTISM; AUTOMATED PATTERN RECOGNITION; BRAIN; CHILD; COMPUTER ASSISTED DIAGNOSIS; CONNECTOME; FEMALE; HUMAN; IMAGE ENHANCEMENT; MALE; METHODOLOGY; NERVE CELL NETWORK; NUCLEAR MAGNETIC RESONANCE IMAGING; PATHOPHYSIOLOGY; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; SUPPORT VECTOR MACHINE;

AUTISTIC DISORDER; BRAIN; CHILD; CONNECTOME; FEMALE; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MAGNETIC RESONANCE IMAGING; MALE; NERVE NET; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; SUPPORT VECTOR MACHINES;

EID: 84906977910     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-10443-0_23     Document Type: Conference Paper

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