Exploring Brain Structural and Functional Biomarkers in Schizophrenia via Brain-Network-Constrained Multi-View SCCA - Song, Peilun; Wang, Yaping; Yuan, Xiuxia; Wang, Shuying; Song, Xueqin (Frontiers in Neuroscience, 2022)
Recent studies have proved that dynamic regional measures extracted from the resting-state functional magnetic resonance imaging, such as the dynamic fractional amplitude of low-frequency fluctuations (d-fALFF), could provide a great insight into brain dynamic characteristics of the schizophrenia. However, such unimodal feature is limited for depicting the complex brain defective pattern. Thus functional and structural imaging data are usually analyzed together for uncovering the neural mechanism of schizophrenia. Investigation of neural function-structure coupling enables to find the potential biomarkers and further helps to understand the biological basis of schizophrenia. Here, a brain-network-constrained multi-view sparse canonical correlation analysis (BN-MSCCA) was proposed to explore the intrinsic associations between brain structure and dynamic brain function. Specifically, the d-fALFF was firstly acquired based on the sliding-window method, while the gray matter map was computed based on voxel-based morphometry analysis. Then, the region-of-interest (ROI) based features were extracted, and further selected by performing the multi-view sparse canonical correlation analysis jointly with the diagnosis information. Moreover, the brain-network-based structural constraint was introduced to prompt the detected biomarkers interpretable. The experiments were conducted on 191 schizophrenia patients and 191 matched healthy controls. Results showed that the BN-MSCCA could identify the critical ROIs with more sparse canonical weight patterns, which are corresponding to the specific brain networks. These are biologically meaningful findings and could be treated as potential biomarkers. The proposed method also obtained a higher canonical correlation coefficient for the testing data, which is more consistent with the results on training data, demonstrating its promising capability for association identification. To demonstrate the effectiveness for the potential clinical applications, the detected biomarkers were further analyzed on a schizophrenia-control classification task and a correlation analysis task. The experimental results showed that our method had a superior performance with a 5%~8% increment on accuracy and 6%~10% improvement on area under the curve. Furthermore, two of the top-rank biomarkers were significantly negatively correlated with the PANSS positive score. Overall, the proposed method could find the association between brain structure and dynamic brain function, and help identify the biological meaningful biomarkers of schizophrenia. The findings enable our further understanding of this disease.