Emotionally Evoked Connectivity Alterations in Major Depressive Disorder

Poster No:

608 

Submission Type:

Abstract Submission 

Authors:

Qingjin Liu1,2, Jinpeng Niu1,2, Kangjia Chen1,2, Yaohui He1,2, Wei Li1,2, Huafu Chen1,2, Jiao Li1,2, Wei Liao1,2

Institutions:

1The Clinical Hospital of Chengdu Brain Science Institute, Chengdu, China, 2School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China

First Author:

Qingjin Liu  
The Clinical Hospital of Chengdu Brain Science Institute|School of Life Science and Technology, University of Electronic Science and Technology of China
Chengdu, China|Chengdu 611731, China

Co-Author(s):

Jinpeng Niu  
The Clinical Hospital of Chengdu Brain Science Institute|School of Life Science and Technology, University of Electronic Science and Technology of China
Chengdu, China|Chengdu 611731, China
Kangjia Chen  
The Clinical Hospital of Chengdu Brain Science Institute|School of Life Science and Technology, University of Electronic Science and Technology of China
Chengdu, China|Chengdu 611731, China
Yaohui He  
The Clinical Hospital of Chengdu Brain Science Institute|School of Life Science and Technology, University of Electronic Science and Technology of China
Chengdu, China|Chengdu 611731, China
Wei Li  
The Clinical Hospital of Chengdu Brain Science Institute|School of Life Science and Technology, University of Electronic Science and Technology of China
Chengdu, China|Chengdu 611731, China
Huafu Chen  
The Clinical Hospital of Chengdu Brain Science Institute|School of Life Science and Technology, University of Electronic Science and Technology of China
Chengdu, China|Chengdu 611731, China
Jiao Li  
The Clinical Hospital of Chengdu Brain Science Institute|School of Life Science and Technology, University of Electronic Science and Technology of China
Chengdu, China|Chengdu 611731, China
Wei Liao  
The Clinical Hospital of Chengdu Brain Science Institute|School of Life Science and Technology, University of Electronic Science and Technology of China
Chengdu, China|Chengdu 611731, China

Introduction:

Major depressive disorder (major depressive disorder (MDD) is associated with significant alterations in functional brain networks, which are modulated by the emotional context of stimuli(Reis et al., 2023). Previous studies often relied on static or artificial paradigms, limiting ecological validity(Schneck et al., 2021). Using naturalistic movie stimuli with robust emotional elicitation(Rajimehr et al., 2024; Sonkusare et al., 2019), we aims to investigate functional connectivity (FC) differences between MDD patients and healthy controls (HCs) in response to positive and negative emotional content. By exploring these FC patterns, we aim to elucidate the neural mechanisms underlying emotion-specific dysregulation in MDD and advance understanding of its pathophysiology. Additionally, this approach provides a more dynamic understanding of brain function by leveraging emotionally engaging and ecologically valid stimuli. Such insights could pave the way for targeted interventions addressing emotional dysregulation in MDD.

Methods:

We recruited 117 participants, including 55 MDD patients (female: 52, male: 15; mean age: 27.55 ± 11.17 years) and 62 HCs (female: 60, male: 18; mean age: 30.04 ± 5.38 years). Diagnoses were based on the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) structured clinical interviews, and exclusion criteria included severe neurological or psychiatric comorbidities.Participants viewed two 8-minute emotionally evocative naturalistic movies. Preprocessing included slice timing correction, head motion realignment, and normalization to MNI space using Data Processing Assistant for Resting-State fMRI (DPARSF). FC was calculated using the Brainnetome atlas (246 regions of interest), yielding 246 × 246 correlation matrices. Fisher's z-transformation was applied for statistical analyses. A 2×2 analysis of variance (ANOVA) was conducted to examine the effects of group (MDD vs. HCs), emotion type (positive vs. negative), and their interaction on whole-brain FC. Multiple comparisons were corrected using a false discovery rate (FDR) (p < 0.05).

Results:

ANOVA revealed a significant group effect (F = 10.38, p < 0.05) and a group-by-emotion interaction (F = 11.13, p < 0.05). MDD patients exhibited heightened FC in regions such as the fusiform gyrus, temporal lobe, parahippocampal gyrus, and limbic network during negative emotional stimuli compared to HCs (t = 2.25, FDR-corrected, p < 0.05). Conversely, no significant FC differences were observed for positive stimuli (Figure 1 A). Edge-based analysis identified significant group-by-emotion interaction effects (F = 11.02, p < 0.01). During positive stimuli, MDD patients showed reduced connectivity between the right inferior temporal gyrus and the left inferior parietal lobule and increased connectivity between the lateral and medial prefrontal cortices (t = 3.25, FDR-corrected, p < 0.01). In contrast, during negative stimuli, MDD patients exhibited enhanced connectivity between the primary motor cortex and posterior cingulate cortex (Figure 1 B).
Supporting Image: FC_txt.jpg
 

Conclusions:

This study provides compelling evidence that MDD patients undergo significant changes in FC when exposed to naturalistic emotional stimuli, highlighting a specific pattern of dysfunctional connectivity in sadness. The results suggest that MDD patients have increased sensitivity to negative emotional stimuli, as reflected by increased FC in limbic and associative brain regions, whereas positive stimuli elicit decreased frontal-parietal connectivity in associative limbic analyses. These findings not only deepen the understanding of the neural basis of MDD, but also highlight the potential of naturalistic paradigms in advancing psychoneuroscienc(Xu et al., 2023). Future studies should explore longitudinal and intervention designs to further elucidate these mechanisms and their clinical implications.

Emotion, Motivation and Social Neuroscience:

Emotional Perception 1

Higher Cognitive Functions:

Higher Cognitive Functions Other

Modeling and Analysis Methods:

Activation (eg. BOLD task-fMRI)
Connectivity (eg. functional, effective, structural) 2
fMRI Connectivity and Network Modeling

Keywords:

Cognition
DISORDERS
Emotions
FUNCTIONAL MRI
Other - Major depressive disorder

1|2Indicates the priority used for review

Abstract Information

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Please indicate below if your study was a "resting state" or "task-activation” study.

Task-activation

Healthy subjects only or patients (note that patient studies may also involve healthy subjects):

Patients

Was this research conducted in the United States?

No

Were any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.

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Were any animal research approved by the relevant IACUC or other animal research panel? NOTE: Any animal studies without IACUC approval will be automatically rejected.

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Please indicate which methods were used in your research:

Functional MRI

For human MRI, what field strength scanner do you use?

3.0T

Which processing packages did you use for your study?

SPM

Provide references using APA citation style.

Chen, J., & Bornstein, A. M. J. T. i. C. S. (2024). The causal structure and computational value of narratives.
Javaheripour, N., Colic, L., Opel, N., Li, M., Maleki Balajoo, S., Chand, T., . . . Meller, T. J. T. P. (2023). Altered brain dynamic in major depressive disorder: state and trait features. 13(1), 261.
Luo, A. C., Sydnor, V. J., Pines, A., Larsen, B., Alexander-Bloch, A. F., Cieslak, M., . . . Feczko, E. J. N. C. (2024). Functional connectivity development along the sensorimotor-association axis enhances the cortical hierarchy. 15(1), 3511.
Seah, T., Sidney, P. G., Taber, J. M., Thompson, C. A., & Coifman, K. G. J. E. (2023). Emotional complexity under high stress: Do protective associations for risk behaviors persist even during a pandemic? , 23(3), 879.
Sheu, Y.-h., Magdamo, C., Miller, M., Das, S., Blacker, D., & Smoller, J. W. J. N. D. M. (2023). AI-assisted prediction of differential response to antidepressant classes using electronic health records. 6(1), 73.

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