Connectome-based prediction of future episodic memory performance for individual amnestic mild cognitive impairment patients.

• Tác giả: Tong Lu, Qingguo Ren, Yachen Shi, Mengxue Wang, Zan Wang, Chunming Xie, Zhengsheng Zhang
• Ngôn ngữ: eng
• Ký hiệu phân loại: 333.822 Coal
• Thông tin xuất bản: England : Brain communications , 2025
• Bộ sưu tập: NCBI
• ID: 197328

Tóm tắt

The amnestic mild cognitive impairment progression to probable Alzheimer's disease is a continuous phenomenon. Here we conduct a cohort study and apply machine learning to generate a model of predicting episodic memory development for individual amnestic mild cognitive impairment patient that incorporates whole-brain functional connectivity. Fifty amnestic mild cognitive impairment patients completed baseline and 3-year follow-up visits including episodic memory assessments (e.g. Rey Auditory Verbal Learning Test Delayed Recall) and resting-state functional MRI scanning. Using a multivariate analytical method known as relevance vector regression, we found that the baseline whole-brain functional connectivity features failed to predict the baseline Rey Auditory Verbal Learning Test Delayed Recall scores (