Pattern recognition of schizophrenia based on multidimensional spatial feature fusion

Abstract

Purpose

Schizophrenia (SCH) is a severe psychiatric disorder associated with brain connectivity abnormalities, and early diagnosis can significantly reduce the burden on the families of the patients. Though several classification methods have been created to identify SCH, a reliable method is yet to be found. In this study, we explore the performance of multidimensional spatial feature fusion in the recognition of SCH.

Materials and methods

Using an MRI connectomes dataset, we extract the spatial pattern network (SPN) and diffusion map embedding (DME) features from functional connectivity (FC) and structural connectivity (SC) networks of both schizophrenic patients and healthy subjects, and we use both single mode features and fused features to classify the two groups.

Results

Compared to the single mode features, the fused features showed superior performance in classification. By fusing the SPN and DME features of the structural network, we obtained the highest accuracy of 87.50%.

Conclusions

Multidimensional spatial feature fusion is promising as a reliable method for the recognition of SCH.

Keywords:

• Schizophrenia
• brain network
• pattern recognition
• multidimensional spatial feature fusion

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the STI 2030-Major Project (Grant No. 2022ZD0208500), the Sichuan Science and Technology Program (Grant No. 2023NSFSC1595), and the Hospital Fund of Sichuan Provincial People’s Hospital (Grant No. 2022QN15).

Notes on contributors

Shuqi Guo

Shuqi Guo, she is currently a PhD student of School of Life Science and Technology, University of Electronic Science and Technology of China. Her current research interests include pattern recognition, brain dynamics and multimodal brain image analysis.

Yuhang Lin

Yuhang Lin, he graduated from School of Life Science and Technology, University of Electronic Science and Technology of China in 2022. His research direction during master's degree is multimodal neural signal analysis and pattern recognition.

Shi Zhao

Shi Zhao, she graduated from School of Life Science and Technology, University of Electronic Science and Technology of China in 2023. Her research direction during master's degree is multimodal neural signal analysis.

Yan Cui

Yan Cui, he is currently with the Department of Neurosurgery, Sichuan Provincial People's Hospital. His research interests include pattern recognition, brain dynamics and multimodal neural signal analysis.

Yang Xia

Yang Xia, she is currently a full Professor with the Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China. Her current research interests include visual electrophysiology and computational neuroscience.

Ke Chen

Ke Chen, he is currently an associate professor at the School of Life Science and Technology, University of Electronic Science and Technology of China. His current research interests include visual electrophysiology and computational neuroscience.

Dezhong Yao

Dezhong Yao (Senior Member, IEEE), he is currently a full Professor of neuroengineering and neurodata with the University of Electronic Science and Technology of China (UEST C), Chengdu. He is also the Dean of the Sichuan Institute for Brain Science and Brain-Inspired Intelligence, Chengdu. His research interests include EEG, simultaneous EEG and functional magnetic resonance imaging (fMRI), and brain--apparatus communication.

Daqing Guo

Daqing Guo, he is currently a full Professor with the Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China. His current research interests include computational neuroscience, brain-inspired intelligence and digital twin brains.