TY - GEN
T1 - Classification of schizophrenia and bipolar patients using static and time-varying resting-state FMRI brain connectivity
AU - Rashid, Barnaly
AU - Arbabshirani, Mohammad Reza
AU - Damaraju, Eswar
AU - Millar, Robyn
AU - Cetin, Mustafa S.
AU - Pearlson, Godfrey D.
AU - Calhoun, Vince D.
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/7/21
Y1 - 2015/7/21
N2 - Recently, there is a growing interest in designing objective prognostic/diagnostic tools based on neuroimaging and other data that display high accuracy and robustness. Small training subjects and very large amount of high dimensional data make it a challenging task to design robust and accurate classifiers for heterogeneous disorders such as schizophrenia. Majority of previous works have focused on classification of schizophrenia from healthy controls while automatic differential diagnosis of schizophrenia from bipolar disorder has been rarely investigated. In this work, we propose a framework for automatic classification of schizophrenia, bipolar and healthy control subjects based on static and dynamic functional network connectivity (FNC) features. Our results show that disrupted functional integration in schizophrenia and bipolar patients as captured by FNC analysis reveal powerful information for automatic discriminative analysis.
AB - Recently, there is a growing interest in designing objective prognostic/diagnostic tools based on neuroimaging and other data that display high accuracy and robustness. Small training subjects and very large amount of high dimensional data make it a challenging task to design robust and accurate classifiers for heterogeneous disorders such as schizophrenia. Majority of previous works have focused on classification of schizophrenia from healthy controls while automatic differential diagnosis of schizophrenia from bipolar disorder has been rarely investigated. In this work, we propose a framework for automatic classification of schizophrenia, bipolar and healthy control subjects based on static and dynamic functional network connectivity (FNC) features. Our results show that disrupted functional integration in schizophrenia and bipolar patients as captured by FNC analysis reveal powerful information for automatic discriminative analysis.
KW - bipolar
KW - classification
KW - dynamic functional network connectivity
KW - fMRI
KW - schizophrenia
UR - http://www.scopus.com/inward/record.url?scp=84944324959&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84944324959&partnerID=8YFLogxK
U2 - 10.1109/ISBI.2015.7163861
DO - 10.1109/ISBI.2015.7163861
M3 - Conference contribution
AN - SCOPUS:84944324959
T3 - Proceedings - International Symposium on Biomedical Imaging
SP - 251
EP - 254
BT - 2015 IEEE 12th International Symposium on Biomedical Imaging, ISBI 2015
PB - IEEE Computer Society
T2 - 12th IEEE International Symposium on Biomedical Imaging, ISBI 2015
Y2 - 16 April 2015 through 19 April 2015
ER -