Comparing within-subject classification and regularization methods in fMRI for large and small sample sizes

Human Brain Mapping

Volumn 35, Issue 9, 2014, Pages 4499-4517

  Churchill, Nathan W ^a,b   Yourganov, Grigori ^a,b   Strother, Stephen C ^a,b  

^a ROTMAN RESEARCH INSTITUTE   (Canada)

^b UNIVERSITY OF TORONTO   (Canada)

Author keywords

BOLD fMRI; Data driven metrics; Head motion; Model optimization; Multivariate analysis; Physiological noise; Preprocessing

Indexed keywords

OXYGEN;

ADULT; BLOOD; BRAIN; BRAIN CIRCULATION; BRAIN MAPPING; COMPARATIVE STUDY; FEMALE; HUMAN; MALE; NUCLEAR MAGNETIC RESONANCE IMAGING; PHYSIOLOGY; PRINCIPAL COMPONENT ANALYSIS; PROCEDURES; REPRODUCIBILITY; SAMPLE SIZE; SIGNAL NOISE RATIO; SIGNAL PROCESSING; STATISTICAL MODEL; SUPPORT VECTOR MACHINE; VASCULARIZATION; YOUNG ADULT;

ADULT; BRAIN; BRAIN MAPPING; CEREBROVASCULAR CIRCULATION; FEMALE; HUMANS; LOGISTIC MODELS; MAGNETIC RESONANCE IMAGING; MALE; OXYGEN; PRINCIPAL COMPONENT ANALYSIS; REPRODUCIBILITY OF RESULTS; SAMPLE SIZE; SIGNAL PROCESSING, COMPUTER-ASSISTED; SIGNAL-TO-NOISE RATIO; SUPPORT VECTOR MACHINES; YOUNG ADULT;

EID: 84904515523     PISSN: 10659471     EISSN: 10970193     Source Type: Journal    
DOI: 10.1002/hbm.22490     Document Type: Article

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