Aberrant Functional Network Connectivity Transition Probability in Major Depressive Disorder

• Main Authors: Zendehrouh, Elaheh, Sendi, Mohammad. S. E., Sui, Jing, Fu, Zening, Zhi, Dongmei, Lv, Luxian, Ma, Xiaohong, Ke, Qing, Li, Xianbin, Wang, Chuanyue, Abbott, Christopher. C., Turner, Jessica A., Miller, Robyn. L, Calhoun, Vince D.
• Format: Conference Proceeding
• Language: English
• Subjects: Correlation; Dynamic functional network connectivity; Functional magnetic resonance imaging; Hidden Markov model; Hidden Markov models; Hospitals; Machine learning; Major depressive disorder; Microsoft Windows; Probability; Psychiatry; Resting-state functional magnetic resonance imaging
• ISSN: 2375-7477; 2694-0604
• DOI: 10.1109/EMBC44109.2020.9175872

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.