Causal Structural Covariance Network Revealing Progressive Changes in Major Depressive Disorder
Poster No:
1256
Submission Type:
Abstract Submission
Authors:
Changmin Chen1, Yuhan Liu1, Wenhao Jiang2, Zhao Qing1
Institutions:
1Southeast University, Nanjing, Jiangsu, 2Zhongda Hospital, Medical School, Southeast University, Nanjing, Jiangsu
First Author:
Co-Author(s):
Introduction:
Major depressive disorder (MDD) is a prevalent mental disorder that can lead to disability. Morphometric MRI has been the most favorable tool to study progression of structural alteration of human brain in MDD. A few recent studies have employed the structural covariance network (SCN) to delineate gray matter alterations in MDD. However, correlation analysis in SCN is zero‐time lagged and cannot reflect temporal progression of neural incidences. Granger causality analysis (GCA) is an effective approach for describing the direction of information flow and analyzes whether neural activity in one region precedes that in another region. The causal structural covariance network (CaSCN) analysis has been used to study the brain's morphological progression patterns in various neuropsychiatric disorders. This study uses a recently established large-sample, multi-center brain imaging database to explore CaSCN alterations in MDD.
Methods:
A total of 798 T1-weighted MRI images from patients with MDD and 974 T1-weighted MRI images from health controls from 24 sites of the REST-meta-MDD consortium were utilized after quality control. Voxel-based morphometry was first performed for all the images to generate a voxel-wise GM volume map for each subject. With the preprocessed GM images, we carried out the source-based morphometry processing using the GIFT toolbox to extract components. The scores for each component across subjects represent individual differences in the volume of this component. Two-sample t-tests with covariates were used to examine whether there were significant group differences in each component between groups. Results were corrected with the false discovery rate method at p < 0.05.
We calculated the CaSCN based on the SBM components. CaSCN was first conducted on 431 subjects with data on the 17 items of the HAMD scale. We sorted all these subjects according to the severity of the disease, using the following rules: sort by disease duration, and if the disease duration was the same, sort by the HAMD-17 item scale processed by canonical correlation analysis. The same CaSCN analysis was repeated in 199 first-episode MDD patients and 152 recurrent MDD patients, respectively. Positive and negative GC values indicated the same or opposite GMV changes in Y, occurring after X. We identify positive driving nodes among 20 components based on GCA coefficients and verify them in 1000 permutation tests, P<0.05. We take components with significant inter-group differences and positive driving components as nodes of interest to study the causal relationship between them and other components.
We calculated the CaSCN based on the SBM components. CaSCN was first conducted on 431 subjects with data on the 17 items of the HAMD scale. We sorted all these subjects according to the severity of the disease, using the following rules: sort by disease duration, and if the disease duration was the same, sort by the HAMD-17 item scale processed by canonical correlation analysis. The same CaSCN analysis was repeated in 199 first-episode MDD patients and 152 recurrent MDD patients, respectively. Positive and negative GC values indicated the same or opposite GMV changes in Y, occurring after X. We identify positive driving nodes among 20 components based on GCA coefficients and verify them in 1000 permutation tests, P<0.05. We take components with significant inter-group differences and positive driving components as nodes of interest to study the causal relationship between them and other components.
Results:
In Figure 1, the results of Two-sample t-test after FDR correction revealed that 3 of the 20 components showed significant group differences among the MDD and NC group: J (p = 0.001), R (p = 0.001), T (P < 0.0001). One component showed significant subgroup differences between FEDN patients and recurrent patients: I (p = 0.001).
In both the overall MDD group and the recurrent group, none of the 20 components exhibited significant positive driving scores. In the first-episode group, the positive drive of Component C is significant and affects component D. We focus on components with significant inter-group differences and components with significant positive drive, and draw their significant causal effects on other brain regions. In Figure 2, we show the results of CaSCN conducted respectively in the three groups.
In both the overall MDD group and the recurrent group, none of the 20 components exhibited significant positive driving scores. In the first-episode group, the positive drive of Component C is significant and affects component D. We focus on components with significant inter-group differences and components with significant positive drive, and draw their significant causal effects on other brain regions. In Figure 2, we show the results of CaSCN conducted respectively in the three groups.
.png
·FIGURE 1. Group differences of the components of the source-based morphometry.
·FIGURE 2. Results of causal structural covariance.
Conclusions:
We used causal structural covariance networks to estimate interregional causal influence of structural alterations with disease progression. We found that the superior frontal gyrus, cingulate cortex, and middle temporal gyrus are key brain areas in a series of causal effects, and their alterations may predict subsequent widespread alterations in other brain regions. This work may provide new evidence of the gradual brain structural abnormalities from the view of the causal effects driven by GMV changes in MDD.
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 2
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 1
Novel Imaging Acquisition Methods:
Anatomical MRI
Keywords:
Affective Disorders
Morphometrics
STRUCTURAL MRI
Other - Causal Structural Covariance Network
1|2Indicates the priority used for review
Abstract Information
By submitting your proposal, you grant permission for the Organization for Human Brain Mapping (OHBM) to distribute your work in any format, including video, audio print and electronic text through OHBM OnDemand, social media channels, the OHBM website, or other electronic publications and media.
The Open Science Special Interest Group (OSSIG) is introducing a reproducibility challenge for OHBM 2025. This new initiative aims to enhance the reproducibility of scientific results and foster collaborations between labs. Teams will consist of a “source” party and a “reproducing” party, and will be evaluated on the success of their replication, the openness of the source work, and additional deliverables. Click here for more information. Propose your OHBM abstract(s) as source work for future OHBM meetings by selecting one of the following options:
Please indicate below if your study was a "resting state" or "task-activation” study.
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Was this research conducted in the United States?
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.
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.
Please indicate which methods were used in your research:
For human MRI, what field strength scanner do you use?
Which processing packages did you use for your study?
Provide references using APA citation style.
Yan, C. G. (2019). Reduced default mode network functional connectivity in patients with recurrent major depressive disorder. Proceedings of the National Academy of Sciences of the United States of America, 116(18), 9078–9083.
Zhang, Z. (2017). Hippocampus-associated causal network of structural covariance measuring structural damage progression in temporal lobe epilepsy. Human brain mapping, 38(2), 753–766.
Li, R. (2022). Basal ganglia atrophy-associated causal structural network degeneration in Parkinson's disease. Human brain mapping, 43(3), 1145–1156.