Identification of the Neural Circuit Underlying Episodic Memory Deficit in Amnestic Mild Cognitive Impairment via Machine Learning on Gray Matter Volume

Article type: Short Communication

Authors: Wang, Zan^{a; b; 1; *} | Zhang, Zhengsheng^{a; b; 1} | Xie, Chunming^{a; b} | Shu, Hao^{a; b} | Liu, Duan^a | Zhang, Zhijun^{a; b}

Affiliations: [a] School of Medicine, Southeast University, Nanjing, China | [b] Department of Neurology, Affiliated ZhongDa Hospital of Southeast University, Nanjing, China

Correspondence: [*] Correspondence to: Zan Wang, Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, 210009, China. E-mail: [email protected].

Note: [1] These authors contributed equally to this work.

Abstract: Based on whole-brain gray matter volume (GMV), we used relevance vector regression to predict the Rey’s Auditory Verbal Learning Test Delayed Recall (AVLT-DR) scores of individual amnestic mild cognitive impairment (aMCI) patient. The whole-brain GMV pattern could significantly predict the AVLT-DR scores (r = 0.54, p < 0.001). The most important GMV features mainly involved default-mode (e.g., posterior cingulate gyrus, angular gyrus, and middle temporal gyrus) and limbic systems (e.g., hippocampus and parahippocampal gyrus). Therefore, our results provide evidence supporting the idea that the episodic memory deficit in aMCI patients is associated with disruption of the default-mode and limbic systems.

Keywords: Amnestic mild cognitive impairment, episodic memory, gray matter volume, machine learning, relevance vector regression

DOI: 10.3233/JAD-210579

Journal: Journal of Alzheimer's Disease, vol. 84, no. 3, pp. 959-964, 2021