Hierarchical multi-class Alzheimer's disease diagnostic framework using imaging and clinical features

Yao Qin; Jing Cui; Xiaoyan Ge; Yuling Tian; Hongjuan Han; Zhao Fan; Long Liu; Yanhong Luo; Hongmei Yu

doi:10.3389/fnagi.2022.935055

Hierarchical multi-class Alzheimer's disease diagnostic framework using imaging and clinical features

Front Aging Neurosci. 2022 Aug 10:14:935055. doi: 10.3389/fnagi.2022.935055. eCollection 2022.

Authors

Yao Qin¹, Jing Cui¹, Xiaoyan Ge¹, Yuling Tian², Hongjuan Han¹, Zhao Fan³, Long Liu¹, Yanhong Luo¹, Hongmei Yu^{1

4}

Affiliations

¹ Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, China.
² Department of Neurology, First Hospital of Shanxi Medical University, Taiyuan, China.
³ Center of Translational Medicine, School of Basic Medical Sciences, Shanxi Medical University, Taiyuan, China.
⁴ Shanxi Provincial Key Laboratory of Major Diseases Risk Assessment, Taiyuan, China.

Abstract

Due to the clinical continuum of Alzheimer's disease (AD), the accuracy of early diagnostic remains unsatisfactory and warrants further research. The objectives of this study were: (1) to develop an effective hierarchical multi-class framework for clinical populations, namely, normal cognition (NC), early mild cognitive impairment (EMCI), late mild cognitive impairment (LMCI), and AD, and (2) to explore the geometric properties of cognition-related anatomical structures in the cerebral cortex. A total of 1,670 participants were enrolled in the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, comprising 985 participants (314 NC, 208 EMCI, 258 LMCI, and 205 AD) in the model development set and 685 participants (417 NC, 110 EMCI, 83 LMCI, and 75 AD) after 2017 in the temporal validation set. Four cortical geometric properties for 148 anatomical structures were extracted, namely, cortical thickness (CTh), fractal dimension (FD), gyrification index (GI), and sulcus depth (SD). By integrating these imaging features with Mini-Mental State Examination (MMSE) scores at four-time points after the initial visit, we identified an optimal subset of 40 imaging features using the temporally constrained group sparse learning method. The combination of selected imaging features and clinical variables improved the multi-class performance using the AdaBoost algorithm, with overall accuracy rates of 0.877 in the temporal validation set. Clinical Dementia Rating (CDR) was the primary clinical variable associated with AD-related populations. The most discriminative imaging features included the bilateral CTh of the dorsal part of the posterior cingulate gyrus, parahippocampal gyrus (PHG), parahippocampal part of the medial occipito-temporal gyrus, and angular gyrus, the GI of the left inferior segment of the insula circular sulcus, and the CTh and SD of the left superior temporal sulcus (STS). Our hierarchical multi-class framework underscores the utility of combining cognitive variables with imaging features and the reliability of surface-based morphometry, facilitating more accurate early diagnosis of AD in clinical practice.

Keywords: Alzheimer’s disease; diagnosis; magnetic resonance imaging; multi-class classification; surface-based morphometry.