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Structural covariance network disruptions linked to behavior and gene in major depressive disorder

Poster No:

552

Submission Type:

Abstract Submission

Authors:

Qian Zhang¹, Aoxiang Zhang¹, Youjin Zhao¹, Xiaoqi Huang¹, Qiyong Gong Qiyong Gong¹

Institutions:

¹Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan

First Author:

Qian Zhang
Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University
Chengdu, Sichuan

Co-Author(s):

Aoxiang Zhang
Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University
Chengdu, Sichuan

Youjin Zhao
Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University
Chengdu, Sichuan

Xiaoqi Huang
Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University
Chengdu, Sichuan

Qiyong Gong Qiyong Gong
Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University
Chengdu, Sichuan

Introduction:

Major depressive disorder (MDD) has been increasingly understood as a disruption of brain connectome. Growing evidence suggests that disruptions in connectivity patterns and topology of the brain structural covariance network contribute to the clinical symptoms and cognitive impairments in MDD (Chen et al., 2022). However, previous studies have primarily focused on group-level analyses. Constructing individualized structural covariance networks (iSCNs) can better account for inter-individual differences in brain morphological covariation and offers an opportunity to explore its relationships with behavioral and transcriptional profiles (Yun, Kim, Lee, Chon, & Kwon, 2016).

Methods:

Individualized structural covariance networks were constructed based on grey matter volume maps for 193 first-episode drug-naive MDD patients and 121 healthy controls (HCs). We first investigated differences in interregional connectivity within iSCNs between MDD and HCs using network-based statistics analysis. For regions showing significant connectivity abnormalities, we examined between-group differences in degree centrality (Dc) and explored the interactions between these network alterations in MDD patients. Furthermore, partial least squares regressions were employed to explore the relationships between these network profiles and the clinical symptoms and cognitive function of MDD patients, as well as the associations between connectivity differences (measured by t-statistic map) and gene expression in both functional and developmental perspectives (Dougherty, Schmidt, Nakajima, & Heintz, 2010).

Results:

Relative to HCs, MDD patients showed reduced iSCN connectivity between the left post cingulate gyrus (PCG) with the left superior temporal gyrus (STG), thalamus, calcarine, and the right medial orbitofrontal gyrus (MOG), supplementary motor area, gyrus rectus, and lingual gyrus, as well as between the left calcarine and right thalamus (Figure 1a). MDD patients also exhibited lower Dc in the left calcarine, thalamus, STG and right MOG than HCs. Significant positive correlations were observed between the connectivity of left PCG with the MOG, STG, calcarine, and lingual gyrus (Figure 1b). Imaging-behavioral PLS analyses revealed that reductions in PCG-related connectivity were primarily associated with worsened somatic symptoms, while decreased connectivity between the left PCG and thalamus and reduced Dc in right MOG were mainly linked to worse emotional symptoms and poorer performance on the trail making tests in MDD patients (Figure 1c). Furthermore, connectome-transcriptome PLS analyses identified that positively and negatively weighted genes associated with connectivity differences (Figure 2a) were enriched in pathways related to centrosome dynamics and developmental regulation (Figure 2b), with positively weighted genes primarily expressed in the cortex from early mid-fetal to young adulthood and negatively weighted genes in subcortical regions from late infancy to young adulthood (Figure 2c).

·* indicates corrected-P<0.05.

·PLS1+/- refers to genes with positive/negative PLS1 weights.

Conclusions:

Weakened interregional connectivity, along with reduced degree centrality, primarily involves the left posterior cingulate gyrus (PCG) and key regions within sensory, cognitive, and emotional networks, indicating a disruption in the coordination of gray matter volume covariation and a decline in the integrative capacity of the iSCN in MDD patients. This disruption contributes to emotional and somatic symptoms, as well as cognitive flexibility impairments in MDD. Additionally, transcriptional profiles linked to abnormal iSCN connectivity involve genes enriched for centrosome dynamics and developmental regulation, with differential expression across cortical and subcortical regions at different developmental stages. These findings enhance our understanding of the neurobiological mechanisms of MDD and may provide targeted neurobiomarkers for modulating specific symptom dimensions and cognitive functions in MDD patients.

Disorders of the Nervous System:

Psychiatric (eg. Depression, Anxiety, Schizophrenia) ¹

Genetics:

Genetic Association Studies

Modeling and Analysis Methods:

Connectivity (eg. functional, effective, structural) ²

Neuroanatomy, Physiology, Metabolism and Neurotransmission:

Cortical Anatomy and Brain Mapping

Novel Imaging Acquisition Methods:

Anatomical MRI

Keywords:

ADULTS

Affective Disorders

Cognition

MRI

Psychiatric

STRUCTURAL MRI

Other - Major depressive disorder, Gene, Graph theory

^1|2Indicates the priority used for review

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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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Structural MRI

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3.0T

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SPM

Provide references using APA citation style.

1. Chen, C., Liu, Z., Xi, C., Tan, W., Fan, Z., Cheng, Y., . . . Yang, J. (2022). Multimetric structural covariance in first-episode major depressive disorder: a graph theoretical analysis. J Psychiatry Neurosci, 47(3), E176-e185. doi:10.1503/jpn.210204
2. Dougherty, J. D., Schmidt, E. F., Nakajima, M., & Heintz, N. (2010). Analytical approaches to RNA profiling data for the identification of genes enriched in specific cells. Nucleic Acids Res, 38(13), 4218-4230. doi:10.1093/nar/gkq130
3. Yun, J. Y., Kim, S. N., Lee, T. Y., Chon, M. W., & Kwon, J. S. (2016). Individualized covariance profile of cortical morphology for auditory hallucinations in first-episode psychosis. Hum Brain Mapp, 37(3), 1051-1065. doi:10.1002/hbm.23083

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