Neuroimage Biomarker Identification of the Conversion of Mild Cognitive Impairment to Alzheimer’s Disease

Frontiers in Neuroscience

Yu-Min Kuo²  Te-Han Kung³  Gwo Giun Chris Lee³  Yi-Ru Xie³  Tzu-Cheng Chao⁴  Ming-Chyi Pai⁶ 

[1] Alzheimer’s Disease Research Center, National Cheng Kung University Hospital, Tainan, Taiwan;Department of Cell Biology and Anatomy, College of Medicine, National Cheng Kung University, Tainan, Taiwan;Department of Electrical Engineering, National Cheng Kung University, Tainan, Taiwan;Department of Radiology, Mayo Clinic, Rochester, MN, United States;Division of Behavioral Neurology, Department of Neurology, College of Medicine, National Cheng Kung University Hospital, National Cheng Kung University, Tainan, Taiwan;Institute of Gerontology, College of Medicine, National Cheng Kung University, Tainan, Taiwan;MediaTek Inc., Hsinchu, Taiwan;

关键词: mild cognitive impairment;    Alzheimer’s disease;    magnetic resonance imaging;    hippocampal subfields;    multilayer perceptron;   

DOI  :  10.3389/fnins.2021.584641

来源: DOAJ

【 摘 要 】

An efficient method to identify whether mild cognitive impairment (MCI) has progressed to Alzheimer’s disease (AD) will be beneficial to patient care. Previous studies have shown that magnetic resonance imaging (MRI) has enabled the assessment of AD progression based on imaging findings. The present work aimed to establish an algorithm based on three features, namely, volume, surface area, and surface curvature within the hippocampal subfields, to model variations, including atrophy and structural changes to the cortical surface. In this study, a new biomarker, the ratio of principal curvatures (RPC), was proposed to characterize the folding patterns of the cortical gyrus and sulcus. Along with volumes and surface areas, these morphological features associated with the hippocampal subfields were assessed in terms of their sensitivity to the changes in cognitive capacity by two different feature selection methods. Either the extracted features were statistically significantly different, or the features were selected through a random forest model. The identified subfields and their structural indices that are sensitive to the changes characteristic of the progression from MCI to AD were further assessed with a multilayer perceptron classifier to help facilitate the diagnosis. The accuracy of the classification based on the proposed method to distinguish whether a MCI patient enters the AD stage amounted to 79.95%, solely using the information from the features selected by a logical feature selection method.

【 授权许可】

Unknown