Poster No:
554
Submission Type:
Abstract Submission
Authors:
Yuanyuan Li1, Xiqin LIU1, Qiyong Gong1
Institutions:
1Department of Radiology and Huaxi MR Research Center, West China Hospital of Sichuan University, Chengdu, Sichuan
First Author:
Yuanyuan Li
Department of Radiology and Huaxi MR Research Center, West China Hospital of Sichuan University
Chengdu, Sichuan
Co-Author(s):
Xiqin LIU
Department of Radiology and Huaxi MR Research Center, West China Hospital of Sichuan University
Chengdu, Sichuan
Qiyong Gong
Department of Radiology and Huaxi MR Research Center, West China Hospital of Sichuan University
Chengdu, Sichuan
Introduction:
Major depressive disorder (MDD) is characterized by cognitive impairments, emotional deficits, and somatic symptoms that imposing substantial personal and societal burdens.
Methods:
This study included 171 first-episode drug-naïve MDD patients and applied uncertainty-aware adaptive sparse multi-view canonical correlation analysis (UnAdaSMCCA) (Lei et al., 2021) to identify covariation patterns among four clinical symptom domains (anxiety, melancholia, insomnia or sleep difficulties, and general somatic complaints) (Shafer, 2006), 12 cognitive scores (including executive function, memory, and processing speed), and 30 dynamic brain features. The dynamic brain features were derived from resting-state functional magnetic resonance imaging (fMRI) using a sliding-window approach with a 30-TR (60s) window size and a 1-TR (2s) step. The top 1% of dynamic functional connectivity (dFC) features with the highest weight were selected using the Relief algorithm (Kira et al., 1992). Mediation analysis was conducted to investigate potential intermediary mechanisms linking clinical symptoms, brain dynamics, and cognitive function. The predictor variable (X) was dFC. The mediator variable (M) was clinical symptoms, and the outcome variable (Y) was cognitive function. The indirect effect was calculated as the product of path a (the relationship between X and M) and path b (the relationship between M and Y, after controlling for X). To estimate the significance of the indirect effect, we performed bootstrapping with 5000 samples to create a 95% confidence interval (CI). If the CI did not contain zero, the indirect effect was considered significant at p < 0.05. Sex, age, and education were included as nuisance variables in the analysis.
Results:
Our study identified a significant covariation pattern, with melancholic symptoms (w = 0.64), dFC between the left inferior parietal lobule and left insula (w = -0.52), and Stroop Color and Word Test (SCWT) performance (w = 0.63) occupying the highest weights across three dimensions. Specifically, more severe melancholic symptoms were associated with altered dFC in regions of the frontoparietal and default mode networks, as well as greater deficits in inhibitory control. In mediation analysis, we found that melancholic symptoms showed significant mediation effects on the dFC between the left IPL and left insula to SCWT performance (indirect effect = - 0.226, p < 0.05).
Conclusions:
These findings enhance our understanding of the complex interdependencies among symptoms, brain dynamics, and cognitive function in MDD. They also shed light on potential neurological mechanisms through which melancholic symptoms affect inhibitory control.
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 1
Higher Cognitive Functions:
Executive Function, Cognitive Control and Decision Making 2
Modeling and Analysis Methods:
fMRI Connectivity and Network Modeling
Novel Imaging Acquisition Methods:
BOLD fMRI
Keywords:
Cognition
Emotions
MRI
Psychiatric Disorders
1|2Indicates the priority used for review
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Please indicate below if your study was a "resting state" or "task-activation” study.
Resting state
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.
Not applicable
Were any animal research approved by the relevant IACUC or other animal research panel?
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Not applicable
Please indicate which methods were used in your research:
Functional MRI
Neuropsychological testing
For human MRI, what field strength scanner do you use?
3.0T
Provide references using APA citation style.
Kira, K. (1992). A practical approach to feature selection. Paper presented at the Proceedings of the ninth international workshop on Machine learning, Aberdeen, Scotland, United Kingdom.
Lei D. (2021). Identifying associations among genomic, proteomic and imaging biomarkers via adaptive sparse multi-view canonical correlation analysis. Medical Image Analysis, 70, 102003.
Shafer, AB. (2006). Meta-analysis of the factor structures of four depression questionnaires: Beck, CES-D, Hamilton, and Zung. Journal of Clinical Psychology, 62(1), 123–146.
No