TY - GEN
T1 - Aberrant Functional Network Connectivity Transition Probability in Major Depressive Disorder
AU - Zendehrouh, Elaheh
AU - Sendi, Mohammad S.E.
AU - Sui, Jing
AU - Fu, Zening
AU - Zhi, Dongmei
AU - Lv, Luxian
AU - Ma, Xiaohong
AU - Ke, Qing
AU - Li, Xianbin
AU - Wang, Chuanyue
AU - Abbott, Christopher C.
AU - Turner, Jessica A.
AU - Miller, Robyn L.
AU - Calhoun, Vince D.
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/7
Y1 - 2020/7
N2 - Major depressive disorder (MDD) is a common and serious mental disorder characterized by a persistent negative feeling and tremendous sadness. In recent decades, several studies used functional network connectivity (FNC), estimated from resting state functional magnetic resonance imaging (fMRI), to investigate the biological signature of MDD. However, the majority of them have ignored the temporal change of brain interaction by focusing on static FNC (sFNC). Dynamic functional network connectivity (dFNC) that explores temporal patterns of functional connectivity (FC) might provide additional information to its static counterpart. In the current study, by applying k-means clustering on dFNC of MDD and healthy subjects (HCs), we estimated 5 different states. Next, we use the hidden Markov model as a potential biomarker to differentiate the dFNC pattern of MDD patients from HCs. Comparing MDD and HC subjects' hidden Markov model (HMM) features, we have highlighted the role of transition probabilities between states as potential biomarkers and identified that transition probability from a lightly- connected state to highly connected one reduces as symptom severity increases in MDD subjects.Index Terms - Major depressive disorder, Dynamic functional network connectivity, Machine learning, Resting- state functional magnetic resonance imaging, Hidden Markov model.
AB - Major depressive disorder (MDD) is a common and serious mental disorder characterized by a persistent negative feeling and tremendous sadness. In recent decades, several studies used functional network connectivity (FNC), estimated from resting state functional magnetic resonance imaging (fMRI), to investigate the biological signature of MDD. However, the majority of them have ignored the temporal change of brain interaction by focusing on static FNC (sFNC). Dynamic functional network connectivity (dFNC) that explores temporal patterns of functional connectivity (FC) might provide additional information to its static counterpart. In the current study, by applying k-means clustering on dFNC of MDD and healthy subjects (HCs), we estimated 5 different states. Next, we use the hidden Markov model as a potential biomarker to differentiate the dFNC pattern of MDD patients from HCs. Comparing MDD and HC subjects' hidden Markov model (HMM) features, we have highlighted the role of transition probabilities between states as potential biomarkers and identified that transition probability from a lightly- connected state to highly connected one reduces as symptom severity increases in MDD subjects.Index Terms - Major depressive disorder, Dynamic functional network connectivity, Machine learning, Resting- state functional magnetic resonance imaging, Hidden Markov model.
UR - http://www.scopus.com/inward/record.url?scp=85091033151&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85091033151&partnerID=8YFLogxK
U2 - 10.1109/EMBC44109.2020.9175872
DO - 10.1109/EMBC44109.2020.9175872
M3 - Conference contribution
C2 - 33018274
AN - SCOPUS:85091033151
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 1493
EP - 1496
BT - 42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, EMBC 2020
Y2 - 20 July 2020 through 24 July 2020
ER -