Effective connectivity model differences of emotion regulation in major depressive disorder: dynamic causal modeling analysis

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2021-9

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Özaydın, Mustafa

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Major Depressive Disorder (MDD) is a mental disorder and one of the most critical and prevalent disorders in the world. Unlike other diseases, the diagnosis of mental disorders does not easily conform to objective tests. Medical experts consider several indicators to be able to distinguish a depressed person from a normal individual, and finding robust markers to aid diagnosis is still an active area of research. The recent proliferation of neuroimaging methods has brought up new opportunities in that regard. This study aims to contribute to these efforts by investigating the utility of Dynamic Causal Modeling (DCM) based effective connectivity measures for distinguishing MDD patients and healthy controls based on their responses to emotional stimuli. The analysis was conducted over an open fMRI dataset, including the brain responses of MDD patients and healthy controls to an emotional musical stimuli task. The results of the DCM effective connectivity model reveal an increasing sgACC to Amygdala connectivity and reduced dlPFC to Amygdala connectivity in the healthy controls compared to MDD patients. Thus, the findings of this study suggest that such differences in effective connectivity patterns in response to emotional stimuli can be useful in distinguishing MDD cases from healthy subjects.

Subject Keywords

Major depressive disorder, Effective connectivity, fMRI, Dynamic causal modeling

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https://hdl.handle.net/11511/93174

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Graduate School of Informatics, Thesis

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Citation Formats

M. Özaydın, “Effective connectivity model differences of emotion regulation in major depressive disorder: dynamic causal modeling analysis,” M.S. - Master of Science, Middle East Technical University, 2021.