Transcriptomic and neuroimaging data integration enhances machine learning classification of schizophrenia

Abstract

Background: Schizophrenia is a polygenic disorder associated with changes in brain structure and function. Integrating macroscale brain features with microscale genetic data may provide a more complete overview of the disease etiology and may serve as potential diagnostic markers for schizophrenia.

Objective: We aim to systematically evaluate the impact of multi-scale neuroimaging and transcriptomic data fusion in schizophrenia classification models.

Methods: We collected brain imaging data and blood RNA sequencing data from 43 patients with schizophrenia and 60 age- and gender-matched healthy controls, and we extracted multi-omics features of macroscale brain morphology, brain structural and functional connectivity, and gene transcription of schizophrenia risk genes. Multi-scale data fusion was performed using a machine learning integration framework, together with several conventional machine learning methods and neural networks for patient classification.

Results: We found that multi-omics data fusion in conventional machine learning models achieved the highest accuracy (AUC ~0.76-0.92) in contrast to the single-modality models, with AUC improvements of 8.88 to 22.64%. Similar findings were observed for the neural network, showing an increase of 16.57% for the multimodal classification model (accuracy 71.43%) compared to the single-modal average. In addition, we identified several brain regions in the left posterior cingulate and right frontal pole that made a major contribution to disease classification.

Conclusion: We provide empirical evidence for the increased accuracy achieved by imaging genetic data integration in schizophrenia classification. Multi-scale data fusion holds promise for enhancing diagnostic precision, facilitating early detection and personalizing treatment regimens in schizophrenia.

Keywords

schizophrenia / machine learning / multi-omics / genomics / transcriptomics

Supplementary data

Supplementary data is available at PSYRAD Journal online.

Author contributions

Mengya Wang (Formal analysis, Investigation, Methodology, Writing - original draft, Writing - review & editing), Shu-Wan Zhao (Writing - review & editing), Di Wu (Funding acquisition, Data curation), Ya-Hong Zhang (Data curation), Yan-Kun Han (Data curation), Kun Zhao (Writing - review & editing), Ting Qi (Writing - review & editing), Yong Liu (Writing - review & editing), Long-Biao Cui (Conceptualization, Funding acquisition, Data curation, Writing - review & editing), and Yongbin Wei (Conceptualization, Funding acquisition, Project administration, Supervision, Writing - review & editing)

Conflict of interest

None declared.

Acknowledgement

Y.W. received grants from the Beijing Municipal Natural Science Foundation (7232341) and the National Natural Science Foundation of China (82202264) during the conduct of the study. L.-B.C. received a grant from the National Natural Science Foundation of China (82271949). D.W. received a grant from the Key Research and Development Program of Shaanxi Province (2023-YBSF-444). This study is supported by Super Computing Platform of Beijing University of Posts and Telecommunications.

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