Evolutionary neural architecture search for automated MDD diagnosis using multimodal MRI imaging
Major depressive disorder (MDD) is a prevalent mental disorder with serious impacts on life and health. Neuroimaging offers valuable diagnostic insights. However, traditional computer-aided diagnosis methods are limited by reliance on researchers' experience. To address this, we proposed an evolutionary neural architecture search (M-ENAS) framework for automatically diagnosing MDD using multi-modal magnetic resonance imaging (MRI). M-ENAS determines the optimal weight and network architecture through a two-stage search method. Specifically, we designed a one-shot network architecture search (NAS) strategy to train supernet weights and a self-defined evolutionary search to optimize the network structure. Finally, M-ENAS was evaluated on two datasets, demonstrating that M-ENAS outperforms existing hand-designed methods. Additionally, our findings reveal that brain regions within the somatomotor network play important roles in the diagnosis of MDD, providing additional insight into the biological mechanisms underlying the disorder.
Authors
- Ziyang Zhao (ORCID: https://orcid.org/0000-0001-7716-3439)
- Zhijun Yao (ORCID: https://orcid.org/0000-0003-0057-0831)
- Jiansong Zhou (ORCID: https://orcid.org/0000-0003-2135-2139)
- Miao Chen (ORCID: https://orcid.org/0009-0002-2686-9647)
- Tongtong Li (ORCID: https://orcid.org/0009-0001-6370-7332)
- Jiandong Yu
- Ning Hou (ORCID: https://orcid.org/0000-0001-7030-9415)
- Bin Hu
- Qi Sun
- Sujie Ma
Institutions
- Beijing Institute of Technology (CN)
- Central South University (CN)
- Chinese Academy of Sciences (CN)
- Center for Excellence in Brain Science and Intelligence Technology (CN)
- Shanghai Institutes for Biological Sciences (CN)
- National Clinical Research (US)
- The First People's Hospital of Tianmen (CN)
- Institute of Semiconductors (CN)
- Second Xiangya Hospital of Central South University (CN)
- Lanzhou University (CN)
Publication Details
- Journal
- iScience
- Published
- 2024-09-23
- DOI
- https://doi.org/10.1016/j.isci.2024.111020
- Citations
- 4
- Primary Topic
- Advanced MRI Techniques and Applications
- Type
- article
- Field-Weighted Citation Impact
- 1.62
Funders
- National Natural Science Foundation of China
- National Key Research and Development Program of China
- Fundamental Research Funds for the Central Universities
- Science and Technology Program of Gansu Province