Brain Regional Structural-Functional Connectivity Coupling in Bipolar Disorder for Diagnosis
Poster No:
571
Submission Type:
Late-Breaking Abstract Submission
Authors:
Yi Ye1, Kaitong Ma1, Erni Ji2, Li Zhang1
Institutions:
1Shenzhen University, Shenzhen, China, 2Shenzhen Kangning Hospital, Shenzhen, China
First Author:
Co-Author(s):
Introduction:
Bipolar disorder (BD) is a complex psychiatric illness characterized by mood dysregulation and cognitive impairments (Grande et al., 2016). Recent studies report disruptions in both functional (FC) and structural connectivity (SC) in BD, including altered FC in the default mode network and fronto-limbic circuits (Wang et al., 2017). Structural abnormalities, such as reduced white matter integrity and gray matter volume loss, are also observed, particularly in the prefrontal cortex and hippocampus (Brosch et al., 2022; Johnston-Wilson et al., 2000). However, the interplay between SC and FC remains unclear. This study aims to explore differences in SC-FC coupling features between BD patients and healthy controls (HC) and to investigate whether these characteristics can serve as an imaging marker for BD diagnosis.
Methods:
This study recruited 61 BD patients and 54 HCs. Resting-state functional magnetic resonance imaging (rs-fMRI) and diffusion tensor imaging (DTI) data were collected using a Siemens Trio 3.0T MRI scanner, with participants remaining awake with their eyes open during scanning. The rs-fMRI data were processed using DPABI, and Pearson correlation coefficients between BOLD signals of brain region pairs were calculated based on the Brainnetome Atlas to construct the FC matrix. Meanwhile, DTI data were processed using FSL, where fiber tracking assessed connectivity between brain regions. The SC matrix was generated through volume normalization, 25th percentile thresholding, and symmetrization, with 35 additional SC-based feature matrices derived (Xu et al., 2024). Principal component analysis (PCA) was performed on these 36 SC-based matrices, identifying the top-k principal components (PCs) explaining over 80% of the variance. A multiple linear regression model was then constructed for each brain region, with regional FC as the dependent variable and corresponding PC-based structural features as independent variables. The Pearson correlation between true and predicted regional FC was defined as the SC-FC coupling value. Statistical analysis of SC-FC coupling values between BD patients and HCs identified brain regions with significant differences after FDR correction.
To investigate the usefulness of SC-FC coupling for BD diagnosis, a two-layer graph convolutional network (GCN) model was constructed (Kipf et al., 2016). Each layer consisted of a convolutional layer, a ReLU activation function, and a dropout layer, with the final fully connected layer outputting disease probability predictions. The SC, FC, and SC-FC coupling features were used as inputs to the GCN. A 10-fold cross-validation was applied to optimize the model. Accuracy, precision, recall, and area under the curve (AUC) were used as performance metrics.
To investigate the usefulness of SC-FC coupling for BD diagnosis, a two-layer graph convolutional network (GCN) model was constructed (Kipf et al., 2016). Each layer consisted of a convolutional layer, a ReLU activation function, and a dropout layer, with the final fully connected layer outputting disease probability predictions. The SC, FC, and SC-FC coupling features were used as inputs to the GCN. A 10-fold cross-validation was applied to optimize the model. Accuracy, precision, recall, and area under the curve (AUC) were used as performance metrics.
Results:
PCA analysis showed that the top 7 PCs accounted for over 80% of the variance and were used to calculate SC-FC coupling. Fig. 1 illustrates significant SC-FC coupling differences between BD and HC, primarily in the frontal lobe, temporal lobe, insula, and subcortical nuclei. Notable alterations were found in the left dorsal agranular insula (Cohen's d = -0.709, FDR-corrected p = 0.040), left ventral dysgranular and granular insula (Cohen's d = -0.671, FDR-corrected p = 0.040), and left dorsal dysgranular insula (Cohen's d = -0.688, FDR-corrected p = 0.040). Significant changes were also identified in the ventral attention network after FDR correction. Fig. 2 presents the results of the GCN-based BD diagnosis comparison. The SC-FC coupling features demonstrated superior classification performance compared to using FC or SC alone, highlighting the potential of SC-FC coupling as a promising neuroimaging marker for BD diagnosis.
Conclusions:
This study reveals distinct SC-FC coupling patterns in BD patients compared to HCs, demonstrating the need to integrate structural and functional connectivity in BD research. The GCN-based analysis further highlights SC-FC coupling as a promising diagnostic tool.
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 1
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 2
Diffusion MRI Modeling and Analysis
fMRI Connectivity and Network Modeling
Keywords:
Data analysis
FUNCTIONAL MRI
Machine Learning
Psychiatric Disorders
WHITE MATTER IMAGING - DTI, HARDI, DSI, ETC
1|2Indicates the priority used for review
Abstract Information
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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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Provide references using APA citation style.
Wang, Y., Wang, J., Jia, Y., Zhong, S., Zhong, M., Sun, Y., Niu, M., Zhao, L., Zhao, L., Pan, J., Huang, L., & Huang, R. (2017). Topologically convergent and divergent functional connectivity patterns in unmedicated unipolar depression and bipolar disorder. Translational Psychiatry, 7(7), e1165.
Brosch, K., Stein, F., Schmitt, S., Pfarr, J. K., Ringwald, K. G., Thomas-Odenthal, F., Meller, T., Steinsträter, O., Waltemate, L., Lemke, H., Meinert, S., Winter, A., Breuer, F., Thiel, K., Grotegerd, D., Hahn, T., Jansen, A., Dannlowski, U., Krug, A., … Kircher, T. (2022). Reduced hippocampal gray matter volume is a common feature of patients with major depression, bipolar disorder, and schizophrenia spectrum disorders. Molecular Psychiatry, 27(10), 4234–4243.
Johnston-Wilson, N. L., Sims, C. D., Hofmann, J. P., Anderson, L., Shore, A. D., Torrey, E. F., & Yolken, R. H. (2000). Disease-specific alterations in frontal cortex brain proteins in schizophrenia, bipolar disorder, and major depressive disorder. Molecular psychiatry, 5(2), 142-149.
Xu, M., Li, X., Teng, T., Huang, Y., Liu, M., Long, Y., Lv, F., Zhi, D., Li, X., Feng, A., Yu, S., Calhoun, V., Zhou, X., & Sui, J. (2024). Reconfiguration of structural and functional connectivity coupling in patient subgroups with adolescent depression. JAMA Network Open, 7(3), e241933.
Kipf, T. N., & Welling, M. (2016). Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907.