Identifying Mild Cognitive Impairment with Random Forest by Integrating Multiple MRI Morphological Metrics

Article type: Research Article

Authors: Ma, Zhe^{a; b; 1} | Jing, Bin^{a; b; 1} | Li, Yuxia^c | Yan, Huagang^{a; b} | Li, Zhaoxia^d | Ma, Xiangyu^{a; b} | Zhuo, Zhizheng^{a; b} | Wei, Lijiang^{a; b} | Li, Haiyun^{a; b; *} | for the Alzheimer’s Disease Neuroimaging Initiative²

Affiliations: [a] School of Biomedical Engineering, Capital Medical University, Beijing, China | [b] Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Capital Medical University, Beijing, China | [c] Department of Neurology, XuanWu Hospital of Capital Medical University, Beijing, China | [d] School of Chinese Medicine, Capital Medical University, Beijing, China

Correspondence: [*] Correspondence to: Prof. Haiyun Li, School of Biomedical Engineering, Capital Medical University, Beijing 100069, China. Tel.: +86 13520973464; E-mail: haiyunli@ccmu.edu.cn.

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

Note: [2] Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (http://adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete list of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf

Abstract: Mild cognitive impairment (MCI) exhibits a high risk of progression to Alzheimer’s disease (AD), and it is commonly deemed as the precursor of AD. It is important to find effective and robust ways for the early diagnosis of MCI. In this paper, a random forest-based method combining multiple morphological metrics was proposed to identify MCI from normal controls (NC). Voxel-based morphometry, deformation-based morphometry, and surface-based morphometry were utilized to extract morphological metrics such as gray matter volume, Jacobian determinant value, cortical thickness, gyrification index, sulcus depth, and fractal dimension. An initial discovery dataset (56 MCI/55 NC) from the ADNI were used to construct classification models and the performances were testified with 10-fold cross validation. To test the generalization of the proposed method, two extra validation datasets including longitudinal ADNI data (30 MCI/16 NC) and collected data from Xuanwu Hospital (27 MCI/32 NC) were employed respectively to evaluate the performance. No matter whether testing was done on the discovery dataset or the extra validation datasets, the accuracies were about 80% with the combined morphological metrics, which were significantly superior to single metric (accuracy: 45% ∼76%) and also displayed good generalization across datasets. Additionally, gyrification index and cortical thickness derived from surface-based morphometry outperformed other features in MCI identification, suggesting they were some key morphological biomarkers for early MCI diagnosis. Combining the multiple morphological metrics together resulted in a significantly better and reliable identification model, which may be helpful to assist in the clinical diagnosis of MCI.

Keywords: Deformation-based morphometry, mild cognitive impairment, random forest, surface-based morphometry, voxel-based morphometry

DOI: 10.3233/JAD-190715

Journal: Journal of Alzheimer's Disease, vol. 73, no. 3, pp. 991-1002, 2020