Computational modeling of prefrontal-amygdala circuits links functional connectivity alterations to circuit-level mechanisms in major depression.

Authors

Licong Li, Fukuan Zhang, Shuaiyang Zhang, Jinzhao Wei, Kun Wang, Bote Zheng, Jianli Yang, Xiuling Liu

Published in

Journal of computational neuroscience. Jul 23, 2026. Epub Jul 23, 2026.

Abstract

Altered connectivity in the prefrontal cortex-amygdala circuit in Major Depressive Disorder is associated with abnormal circuit states. Although substantial evidence from neuroimaging studies has demonstrated that changes in functional connectivity within this circuit during depressive states are commonly reported as a key feature, little is known about how these changes may relate to circuit functioning. In this study, we propose a biophysical computational model that incorporates an improved Jansen-Rit neural mass model to construct the circuit. Resting-state functional magnetic resonance imaging is used to calculate the functional connectivity of the circuit and simulate brain signals. The aim is to investigate how abnormalities in functional connectivity may contribute to or be associated with the underlying mechanisms of circuit dysfunction. The numerical simulation results indicate that under normal conditions, the amygdala plays a key regulatory role in this circuit. When the amygdala is strongly activated in the model, it suppresses the ventromedial prefrontal cortex and activates the rostral anterior cingulate cortex, leading to an abnormal circuit state. In a healthy state, the amygdala maintains activation through bottom-up regulation, supporting emotional regulation and network stability. The amygdala plays a key regulatory role in this circuit, and changes in functional connectivity are a potential factor that may contribute to the imbalance between cognitive control and emotional regulation. We also show within the modeling framework that alterations in functional connectivity are associated with an abnormal circuit state, which may be an important mechanism underlying the expression of depressive symptoms. This study combines data and theoretical modeling techniques, providing a computational perspective on the potential pathological mechanisms of depression and suggests potential therapeutic targets.

PMID:
42489759
Bibliographic data and abstract were imported from PubMed on 23 Jul 2026.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.