NeuropsychopharmacologyNeurosciencePeer-reviewed journal article
Intrinsic connectivity of the prefrontal cortex and striato-limbic system respectively differentiate Major Depressive from Generalized Anxiety Disorder
Xiaolei XuiD, Jing DaiiD, Yuanshu CheniD, Congcong LiuiD, Fei XiniD, Xinqi ZhouiD, Feng ZhouiD, Emmanuel Andreas StamatakisiD, Shuxia YaoiD, Lizhu LuoiD, Yulan HuangiD, Jinyu WangiD, Zhili ZouiD, Deniz VatanseveriD, Keith Maurice KendrickiD, Bo ZhouiD, Benjamin BeckeriD
Abstract
Major Depressive Disorder (MDD) and Generalized Anxiety Disorder (GAD) are highly prevalent and debilitating disorders. The high overlap on the symptomatic and neurobiological level led to ongoing debates about their diagnostic and neurobiological uniqueness. The present study aims to identify common and disorder-specific neuropathological mechanisms and treatment targets in MDD and GAD. The present study combined categorial and dimensional disorder models with a fully data-driven intrinsic network level analysis (Intrinsic Connectivity Contrast, ICC) to resting state fMRI data acquired in 108 partn = 35 and n = 38 unmedicated patients with first-episode GAD, MDD respectively and n=35 healthy controls). Convergent evidence from categorical and dimensional analyses revealed MDD-specific decreased whole-brain connectivity profiles of the medial prefrontal and dorsolateral prefrontal cortex while GAD was specifically characterized by decreased whole-brain connectivity profiles of the putamen and decreased communication of this region with the amygdala. Together, findings from the present data-driven analysis suggest that intrinsic communication of frontal regions engaged in executive functions and emotion regulation represent depression-specific neurofunctional markers and treatment targets whereas dysregulated intrinsic communication of the striato-amygdala system engaged in reinforcement-based and emotional learning processes represent GAD-specific markers and a promising treatment target.
- organism: human
- assay: imaging
- research focus: cell type annotation
- research focus: clustering
- research focus: dimensionality reduction
- approach: statistical model
- Citations
- 54
- FWCI
- 3.49
- Field percentile
- top 7%
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