Disruption in Cognitive-Affective Circuitry in MDD and its Predicative Power for TMS Efficacy
Poster No:
453
Submission Type:
Abstract Submission
Authors:
Na Zhao1, Liang Li2, Yi-Fan Ai3, Jian Liu4, Chun-Ying Zhu5, Yu-Feng Zang3, Hua-Ning Wang6, Bao-Juan Li7
Institutions:
1Hangzhou normal university, Hangzhou, Select a State or Province, 2The Brain Science Center, Beijing Institute of Basic Medical Sciences, Beijing, China, 3Hangzhou normal university, Hangzhou, China, 4Teaching and Research Support Center, Air Force Medical University, Xi'an, China, 5The Department of Psychology, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, China, 6Department of Psychiatry, Xijing Hospital, Air Force Medical University, Xi'an, China, 7School of Biomedical Engineering, Air Force Medical University, Xi'an, China
First Author:
Co-Author(s):
Chun-Ying Zhu
The Department of Psychology, The Affiliated Hospital of Hangzhou Normal University
Hangzhou, China
The Department of Psychology, The Affiliated Hospital of Hangzhou Normal University
Hangzhou, China
Introduction:
Major depression disorder (MDD) is one intricate psychiatric disease. Delving into objective aberrations is significant to enhance its diagnosis and facilitating targeted therapeutic interventions. It has been even postulated that MDD involves a breakdown in the coordination between the cognitive control network (CCN) and the affective network (AN). However, the heterogeneity pervaded previous studies without large multi-center dataset hampered the progress. Here we endeavor to unravel anomalies within the CCN-AN circuitry and identify a crucial predictive biomarker of TMS efficacy based on a large multi-center dataset and an independent dataset.
Methods:
Two datasets were utilized in this study. Dataset-1 was a multi-center dataset named REST-meta-MDD, which includes 848 MDD patients and 794 healthy controls (HCs) (Chen Q et al., 2022;Yan CG et al., 2019); Dataset-2 consists of 28 MDD patients with suicidal ideation who received individualized, neuro-navigation-guided transcranial Magnetic Stimulation (TMS) therapy (Li B et al., 2024). To identify the anomalies within MDD, a total of 9 ROIs within the CCN and AN were included to conduct functional connectivity (FC) and effective connectivity (EC) analysis based on spectral dynamic causal model (spDCM) (based on Dataset-1, i.e., REST-meta-MDD). Furthermore, since only one connection measured by FC displayed a significant difference after FDR correction (q < 0.05), we selected the EC connections with differences confirmed by parametric empirical bayes (PEB) analysis (p > 0.95) to conduct support vector machine (SVM) analysis and construct a classifier. Five-fold cross-validation and leave-one-site-out cross-validation (LOSOCV) was conducted to assess its classification performance. Furthermore, correlations between these abnormal connections and depression scores were calculated to detect the neural underpinnings of MDD. Additionally, correlations between these abnormal connections and depression and suicidal ideation reductions after TMS treatment (based on Dataset-2) were calculated to determine the predictive power of these connections for TMS treatment efficacy.
Results:
Overall increased connections from the CCN to the AN and decreased connections from the CCN to the AN in MDD were observed using effective connectivity (Figure 1). These disruptions drove the classification accuracy up to 77.3% (75% classification accuracy using LOSOCV) (Figure 2A, B). Furthermore, the connection from the right inferior parietal lobule (IPL. R) to the right amygdala (AMYG.R) was negatively correlated with depression scores (Figure 2C). Notably, the IPL connections to the anterior cingulate cortex (ACC) and the AMYG.R were closely correlated with depression and suicidal ideation alleviation following TMS treatment (Figure 2D). No corresponding significant results were detected using functional connectivity.
·The disrupted CCN-AN circuitry in MDD
·The constructed classification model based on abnormalities in CCN-AN circuitry and its predicative power for TMS therapy
Conclusions:
MDD exhibited disrupted circuits of the CCN-AN circuits, indicating disruptions in both bottom-top and top-bottom regulation systems. Moreover, these alterations of the AN-CCN circuits could predict the treatment efficacy of TMS in an independent dataset, providing more dependable biomarker of MDD, refining the MDD diagnosis and enabling more precise target interventions in the future.
Brain Stimulation:
Non-invasive Magnetic/TMS
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 1
Modeling and Analysis Methods:
Classification and Predictive Modeling
Connectivity (eg. functional, effective, structural) 2
fMRI Connectivity and Network Modeling
Keywords:
Affective Disorders
Emotions
FUNCTIONAL MRI
Machine Learning
Transcranial Magnetic Stimulation (TMS)
1|2Indicates the priority used for review
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Provide references using APA citation style.
Li, B., Zhao, N., Tang, N., Friston, K. J., Zhai, W., Wu, D., . . . Wang, H. (2024). Targeting suicidal ideation in major depressive disorder with MRI-navigated Stanford accelerated intelligent neuromodulation therapy. Transl Psychiatry, 14(1), 21. doi:10.1038/s41398-023-02707-9
Yan, C. G., Chen, X., Li, L., Castellanos, F. X., Bai, T. J., Bo, Q. J., . . . Zang, Y. F. (2019). Reduced default mode network functional connectivity in patients with recurrent major depressive disorder. Proc Natl Acad Sci U S A, 116(18), 9078-9083. doi:10.1073/pnas.1900390116