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Sparse deep neural networks on imaging genetics for schizophrenia case-control classification.

Jiayu Chen¹, Xiang Li², Vince D Calhoun^1,2,3

¹Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): (Georgia State University, Georgia Institute of Technology and Emory University), Atlanta, Georgia, USA.

Human Brain Mapping

|March 16, 2021

Summary

This study introduces a sparse deep neural network (DNN) for interpretable schizophrenia (SZ) biomarker identification using brain imaging and genetic data. The approach effectively identifies key brain regions for SZ classification, aiding clinical applications.

Keywords:

deep neural network gray matter volume schizophrenia single nucleotide polymorphism sparse

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Area of Science:

Neuroscience
Artificial Intelligence
Biomarker Discovery

Background:

Current deep learning methods for psychiatric classification lack interpretability of original features.
Identifying reliable biomarkers for schizophrenia (SZ) remains a challenge for clinical application.

Purpose of the Study:

To develop a sparse deep neural network (DNN) for identifying interpretable biomarkers for schizophrenia (SZ) classification.
To evaluate the DNN's performance in classifying SZ using gray matter volume (GMV) and single nucleotide polymorphism (SNP) data.

Main Methods:

Implemented a sparse DNN with L0-norm regularization for interpretable feature selection.
Applied the approach to a large multi-study cohort including GMV and SNP data for SZ classification.
Validated the model's generalizability on three independent datasets.

Main Results:

The sparse DNN achieved an average error rate of 28.98% on external data for SZ classification.
The model effectively fused GMV and SNP features, outperforming the ICA+SVM framework.
Importance weights highlighted frontal and superior temporal gyrus as key regions for SZ classification.

Conclusions:

The proposed sparse DNN approach enables interpretable biomarker discovery for schizophrenia.
This method demonstrates potential for improved SZ classification and clinical utility.
The approach is promising for application to other data modalities and psychiatric traits.