Journal
JOURNAL OF PSYCHIATRIC RESEARCH
Volume 153, Issue -, Pages 1-10Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.jpsychires.2022.06.059
Keywords
Antidepressant efficacy; Default mode network; Functional gradient; Functional connectivity diversity; Major depressive disorder
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Funding
- Zhejiang Medical and Health Science and Technology Project [2022KY1055]
- Natural Science Foundation of Zhejiang Province [LY17H180007]
- National Natural Science Founda-tion of China [81271503]
- Key Medical Disciplines of Hangzhou and Affiliated Hospital of Hangzhou Normal University [KY21085]
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This study investigates the functional connectivity diversity of the default mode network (DMN) in patients with major depressive disorder (MDD) and how it can be affected by antidepressant therapy. The results show that MDD patients have abnormal functional gradients in the DMN, but antidepressant treatment can normalize these abnormalities. The study also finds that the baseline DMN gradient features can predict the effectiveness of antidepressant treatment, with the medial prefrontal cortex gradient playing a significant role.
Major depressive disorder (MDD) is a common and disabling psychiatric condition associated with aberrant functional activity of the default mode network (DMN). However, it is unclear how the DMN dysfunction in MDD patients is characterized by functional connectivity diversity or gradient and whether antidepressant therapy causes the abnormal functional gradient of the DMN to change toward normalization. In current work, we estimated the functional gradient of the DMN derived from resting state functional magnetic resonance imaging in MDD patients (n = 70) and matching healthy controls (n = 43) and identified MDD-related functional connectivity diversity of the DMN. The longitudinal changes of the DMN functional gradient in 36 MDD patients were assessed before and after 12-week antidepressant treatment. Compared to the healthy controls, the functional gradient of the DMN exhibited relatively relative compression along the dorsal-medial axis in MDD patients at baseline and antidepressant treatment could normalize these DMN gradient abnormalities. A regularized least-squares regression model based on DMN gradient features at baseline significantly predicted the change of Hamilton Depression Rating (HAMD) Scale scores after antidepressant treatment. The medial prefrontal cortex gradient had a more contribution to prediction of antidepressant efficacy. Our findings provided a novel insight into the neurobiological mechanism underlying MDD from the perspective of the DMN functional gradient.
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