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Joint Sparse Collaborative Regression on Imaging Genetics Study of Schizophrenia.

Xueli Song, Rongpeng Li, Kaiming Wang

    IEEE/ACM Transactions on Computational Biology and Bioinformatics
    |May 3, 2022
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    This study introduces Joint Sparse Collaborative Regression (JSCoReg) for analyzing complex schizophrenia data. JSCoReg effectively identifies significant biomarkers, improving upon existing methods for this mental health condition.

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    Area of Science:

    • Neuroimaging
    • Genetics
    • Computational Psychiatry

    Background:

    • Schizophrenia research faces challenges due to high-dimensional, multi-modal imaging genetics data.
    • Integrating diverse data structures, resolutions, and formats for schizophrenia analysis is complex.

    Purpose of the Study:

    • To propose a novel model, Joint Sparse Collaborative Regression (JSCoReg), for extracting class-specific features from multi-modal data.
    • To enhance the identification of biomarkers associated with schizophrenia.

    Main Methods:

    • Developed the Joint Sparse Collaborative Regression (JSCoReg) model.
    • Evaluated feature selection performance using Receiver Operating Characteristic (ROC) curves and Area Under the ROC Curve (AUC) in simulations.
    • Applied JSCoReg to a schizophrenia dataset from the Mind Clinical Imaging Consortium.

    Main Results:

    • JSCoReg demonstrated higher accuracy in feature selection compared to Joint Sparse Canonical Correlation Analysis and Sparse Collaborative Regression.
    • The model successfully identified biologically and statistically significant biomarkers for schizophrenia.
    • JSCoReg facilitates a more comprehensive study of schizophrenia through integrative data analysis.

    Conclusions:

    • JSCoReg is an effective tool for analyzing complex, multi-modal imaging genetics data in schizophrenia research.
    • The identified biomarkers offer new insights into the biological underpinnings of schizophrenia.
    • This approach advances the integrative study of complex mental diseases.