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. 2020 Sep 1:274:38-47.
doi: 10.1016/j.jad.2020.05.005. Epub 2020 May 22.

Anterior cingulate cortex, insula and amygdala seed-based whole brain resting-state functional connectivity differentiates bipolar from unipolar depression

Affiliations

Anterior cingulate cortex, insula and amygdala seed-based whole brain resting-state functional connectivity differentiates bipolar from unipolar depression

Hua Yu et al. J Affect Disord. .

Abstract

Objective: The frontal-limbic circuit is hypothesized as sub-serving emotional regulation. We performed whole brain resting-state functional connectivity (rs-FC) analysis by studying the key hubs of frontal-limbic circuit: anterior cingulate cortex (ACC), bilateral insula subregions, bilateral amygdala (Amy) as seeds, separately, to discriminate bipolar depression (BipD) from unipolar depression (UniD).

Methods: We compared seed-based rs-FC of the frontal-limbic seeds with whole brain among 23 BipD participants; 23 age, gender, and depression severity matched patients with UniD, and 23 healthy controls (HCs). We also used support vector machine learning to study classification based on the rs-FC of ACC, bilateral insula subregions, and bilateral Amy seeds with whole brain.

Results: BipD showed increased rs-FC between the left ventral anterior insula (vAI) seed and the left anterior supramarginal gyrus (aSMG) and left postcentral gyrus, as well as increased rs-FC between left amygdala seed and the left aSMG when compared to HCs and UniD. Compared to UniD, BipD was associated with increased rs-FC between right dorsal anterior insula seed and right superior frontal gyrus, as well as increased rs-FC between left posterior insula seed and right precentral gyrus and right thalamus. Combined rs-FC of ACC, bilateral insula subregions and bilateral Amy seeds with the whole brain discriminated BipD from UniD with an accuracy of 91.30%.

Conclusions: Rs-FC of the emotional regulation circuit is more widely disturbed in BipD than UniD. Using rs-FC with this circuit may lead to further developments in diagnostic decision-making.

Keywords: Bipolar depression; Different patterns; Functional connectivity; Support vector machine learning; Unipolar depression.

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Conflict of interest statement

Declaration of Competing Interest There are no known conflicts of interest associated with this publication and there has been no significant financial support for this work that could have influenced its outcome.

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