Classification of schizophrenia and bipolar patients using static and dynamic resting-state fMRI brain connectivity.
Recently, functional network connectivity (FNC, defined as the temporal correlation among spatially distant brain networks) has been used to examine the functional organization of brain networks in various psychiatric illnesses. Dynamic FNC is a recent extension of the conventional FNC analysis that takes into account FNC changes over short periods of time. While such dynamic FNC measures may be more informative about various aspects of connectivity, there has been no detailed head-to-head comparison of the ability of static and dynamic FNC to perform classification in complex mental illnesses. This paper proposes a framework for automatic classification of schizophrenia, bipolar and healthy subjects based on their static and dynamic FNC features. Also, we compare cross-validated classification performance between static and dynamic FNC. Results show that the dynamic FNC significantly outperforms the static FNC in terms of predictive accuracy, indicating that features from dynamic FNC have distinct advantages over static FNC for classification purposes. Moreover, combining static and dynamic FNC features does not significantly improve the classification performance over the dynamic FNC features alone, suggesting that static FNC does not add any significant information when combined with dynamic FNC for classification purposes. A three-way classification methodology based on static and dynamic FNC features discriminates individual subjects into appropriate diagnostic groups with high accuracy. Our proposed classification framework is potentially applicable to additional mental disorders.
The Mind Research Network & LBERI, Albuquerque, New Mexico, USA; Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, New Mexico, USA.
Barnaly
B
Rashid
The Mind Research Network & LBERI, Albuquerque, New Mexico, USA; Geisinger Health System, Danville, Pennsylvania, USA.
Mohammad R
MR
Arbabshirani
The Mind Research Network & LBERI, Albuquerque, New Mexico, USA; Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, New Mexico, USA.
Eswar
E
Damaraju
The Mind Research Network & LBERI, Albuquerque, New Mexico, USA; Department of Computer Science, University of New Mexico, Albuquerque, New Mexico, USA.
Mustafa S
MS
Cetin
Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, New Mexico, USA.
Robyn
R
Miller
Olin Neuropsychiatry Research Center, Institute of Living, Hartford, Connecticut, USA; Departments of Psychiatry, Yale University School of Medicine, New Haven, Connecticut, USA; Neurobiology, Yale University School of Medicine, New Haven, Connecticut, USA.
Godfrey D
GD
Pearlson
The Mind Research Network & LBERI, Albuquerque, New Mexico, USA; Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, New Mexico, USA; Departments of Psychiatry, Yale University School of Medicine, New Haven, Connecticut, USA; Department of Computer Science, University of New Mexico, Albuquerque, New Mexico, USA. Electronic address: vcalhoun@mrn.org.
Vince D
VD
Calhoun
D000328:Adult; D000465:Algorithms; D001714:Bipolar Disorder / Q000145:classification / Q000000981:diagnostic imaging / Q000503:physiopathology*; D001921:Brain / Q000000981:diagnostic imaging / Q000503:physiopathology*; D001931:Brain Mapping / Q000379:methods*; D005260:Female; D006801:Humans; D008279:Magnetic Resonance Imaging; D008297:Male; D009434:Neural Pathways / Q000000981:diagnostic imaging / Q000503:physiopathology; D010363:Pattern Recognition, Automated / Q000379:methods; D012559:Schizophrenia / Q000145:classification / Q000000981:diagnostic imaging / Q000503:physiopathology*; D012680:Sensitivity and Specificity
Bipolar; Classification; Dynamic functional network connectivity; Resting-state; Schizophrenia; fMRI