Exploring PET and MRI brain networks across healthy controls, mild cognitive impairment, and Alzheimer’s disease

This study aimed to characterize population-level and individual-level metabolic and radiomics-derived structural brain networks across healthy controls (HC), mild cognitive impairment (MCI), and Alzheimer’s disease (AD) using multimodal PET and MRI. A total of 651 participants with both [18F]FDG PET and MRI were retrospectively included, with an external cohort of 45 participants used for validation. Thirty-two AD-relevant regions of interest were retained as network nodes and characterized using PET standardized uptake value ratios (SUVRs) and a selected MRI radiomics descriptor.

Population-level networks were constructed using probability density function-based similarity with Jensen–Shannon divergence, and individualized perturbation networks were derived relative to an HC reference. Network-derived features were evaluated for prediction. Both SUVR-based and radiomics-based networks showed distinct patterns in MCI and AD compared with HC.

Fused network features improved discrimination compared with either modality alone, achieving one-vs-rest AUCs of 0.894 [0.868–0.917] for HC, 0.888 [0.862–0.912] for AD, and 0.814 [0.783–0.844] for MCI. These findings indicate graded network disorganization across the HC–MCI–AD spectrum and support multimodal prediction. This work was supported by the National Key Research and Development Program of China (2024YFE0202400), the National Natural Science Foundation of China (82372038, 12326607), the Research Instrument Development Project of the Chinese Academy of Sciences (PTYQ2026YZ0034), the Guangdong Basic and Applied Basic Research Foundation of China (2023B1515120007, 2024B1515040018), the Shenzhen Science and Technology Program of China (KJZD20240903101307010), and the Shenzhen Medical Research Funds of China (D260401001).

The funders had no role in the study design, data collection, analysis, interpretation, or preparation of the manuscript. These authors contributed equally: Bingjie Wang, Sheng Bi. Biomedical Imaging Laboratory (BIG), Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Taipa, Macau SAR, China Bingjie Wang, Runxiang Huang & Greta S.

Mok Research Center for Medical AI, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China Bingjie Wang, Xuetong Tao, Haiyan Wang, Runxiang Huang, Hairong Zheng, Dong Liang & Zhanli Hu Key Laboratory of Biomedical Imaging Science and System, Chinese Academy of Sciences, State Key Laboratory of Biomedical Imaging Science and System, Shenzhen, China Department of Radiology & Nuclear Medicine, Xuanwu Hospital, Capital Medical University, Beijing, China Department of Nuclear Medicine, Nanjing First Hospital, Nanjing Medical University, Nanjing, China Centre for Cognitive and Brain Sciences, Institute of Collaborative Innovation, University of Macau, Taipa, Macau SAR, China Correspondence to Greta S. Mok, Shaozhen Yan or Zhanli Hu. The authors declare no competing interests.

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