Unbalanced amygdala communication in major depressive disorder

Xiaotong Wen; Bukui Han; Huanhuan Li; Fengyu Dou; Guodong Wei; Gangqiang Hou; Xia Wu

doi:10.1016/j.jad.2023.02.091

Unbalanced amygdala communication in major depressive disorder

J Affect Disord. 2023 May 15:329:192-206. doi: 10.1016/j.jad.2023.02.091. Epub 2023 Feb 24.

Authors

Xiaotong Wen¹, Bukui Han², Huanhuan Li³, Fengyu Dou⁴, Guodong Wei⁴, Gangqiang Hou⁵, Xia Wu⁶

Affiliations

¹ Department of Psychology, Renmin University of China, Beijing 100872, China; Laboratory of the Department of Psychology, Renmin University of China, Beijing 100872, China; Interdisciplinary Platform of Philosophy and Cognitive Science, Renmin University of China, 100872, China. Electronic address: wenxiaotong@163.com.
² Department of Psychology, Renmin University of China, Beijing 100872, China; Laboratory of the Department of Psychology, Renmin University of China, Beijing 100872, China.
³ Department of Psychology, Renmin University of China, Beijing 100872, China; Laboratory of the Department of Psychology, Renmin University of China, Beijing 100872, China; Interdisciplinary Platform of Philosophy and Cognitive Science, Renmin University of China, 100872, China. Electronic address: psyli@ruc.edu.cn.
⁴ Department of Psychology, Renmin University of China, Beijing 100872, China.
⁵ Shenzhen Mental Health Center, Shenzhen Kangning Hospital, Shenzhen 518020, China.
⁶ School of Artificial Intelligence, Beijing Normal University, Beijing 100093, China.

PMID: 36841299
DOI: 10.1016/j.jad.2023.02.091

Abstract

Background: Previous studies suggested an association between functional alteration of the amygdala and typical major depressive disorder (MDD) symptoms. Examining whether and how the interaction between the amygdala and regions/functional networks is altered in patients with MDD is important for understanding its neural basis.

Methods: Resting-state functional magnetic resonance imaging data were recorded from 67 patients with MDD and 74 age- and sex-matched healthy controls (HCs). A framework for large-scale network analysis based on seed mappings of amygdala sub-regions, using a multi-connectivity-indicator strategy (cross-correlation, total interdependencies (TI), Granger causality (GC), and machine learning), was employed. Multiple indicators were compared between the two groups. The altered indicators were ranked in a supporting-vector machine-based procedure and associated with the Hamilton Rating Scale for Depression scores.

Results: The amygdala connectivity with the default mode network and ventral attention network regions was enhanced and that with the somatomotor network, dorsal frontoparietal network, and putamen regions in patients with MDD was reduced. The machine learning analysis highlighted altered indicators that were most conducive to the classification between the two groups.

Limitations: Most patients with MDD received different pharmacological treatments. It is difficult to illustrate the medication state's effect on the alteration model because of its complex situation.

Conclusion: The results indicate an unbalanced interaction model between the amygdala and functional networks and regions essential for various emotional and cognitive functions. The model can help explain potential aberrancy in the neural mechanisms that underlie the functional impairments observed across various domains in patients with MDD.

Keywords: Amygdala; Functional magnetic resonance imaging; Major depressive disorder; Multi-connectivity-indicator analysis.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Amygdala / diagnostic imaging
Brain
Brain Mapping / methods
Cognition
Depressive Disorder, Major* / diagnostic imaging
Emotions
Humans
Magnetic Resonance Imaging / methods