Evolutionary neural architecture search for automated MDD diagnosis using multimodal MRI imaging
Tongtong Li,
Ning Hou,
Jiandong Yu,
Ziyang Zhao,
Qi Sun,
Miao Chen,
Zhijun Yao,
Sujie Ma,
Jiansong Zhou,
Bin Hu
Affiliations
Tongtong Li
School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China; Gansu Provincial Key Laboratory of Wearable Computing, Lanzhou University, Lanzhou 730000, China
Ning Hou
Medical Department, The Third People’s Hospital of Tianshui, Tianshui 741000, China
Jiandong Yu
School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China; Gansu Provincial Key Laboratory of Wearable Computing, Lanzhou University, Lanzhou 730000, China
Ziyang Zhao
School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China; Gansu Provincial Key Laboratory of Wearable Computing, Lanzhou University, Lanzhou 730000, China
Qi Sun
School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China; Gansu Provincial Key Laboratory of Wearable Computing, Lanzhou University, Lanzhou 730000, China
Miao Chen
School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China; Gansu Provincial Key Laboratory of Wearable Computing, Lanzhou University, Lanzhou 730000, China
Zhijun Yao
School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China; Gansu Provincial Key Laboratory of Wearable Computing, Lanzhou University, Lanzhou 730000, China; Corresponding author
Sujie Ma
Sleep Department, The Third People’s Hospital of Tianshui, Tianshui 741000, China; Corresponding author
Jiansong Zhou
National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410000, China; Corresponding author
Bin Hu
School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China; Gansu Provincial Key Laboratory of Wearable Computing, Lanzhou University, Lanzhou 730000, China; School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China; CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China; Joint Research Center for Cognitive Neurosensor Technology of Lanzhou University & Institute of Semiconductors, Chinese Academy of Sciences, Lanzhou 730000, China; Corresponding author
Summary: 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.
