Topological properties analysis and identification of mild cognitive impairment based on individual morphological brain network connectome

• Published in: Cerebral cortex (New York, N.Y. 1991) N.Y. 1991), 2024-01, Vol.34 (1)
• Main Authors: Xu, Xiaowen, Chen, Peiying, Li, Weikai, Xiang, Yongsheng, Xie, Zhongfeng, Yu, Qiang, Tang, Ying, Wang, Peijun
• Format: Article
• Language: English
• Subjects: Alzheimer Disease - diagnostic imaging; Alzheimer Disease - pathology; Brain - diagnostic imaging; Brain - pathology; Cognitive Dysfunction - diagnostic imaging; Cognitive Dysfunction - pathology; Connectome - methods; Humans; Magnetic Resonance Imaging - methods
• ISSN: 1047-3211; 1460-2199
• DOI: 10.1093/cercor/bhad450

Abstract Mild cognitive impairment is considered the prodromal stage of Alzheimer’s disease. Accurate diagnosis and the exploration of the pathological mechanism of mild cognitive impairment are extremely valuable for targeted Alzheimer’s disease prevention and early intervention. In all, 100 mild cognitive impairment patients and 86 normal controls were recruited in this study. We innovatively constructed the individual morphological brain networks and derived multiple brain connectome features based on 3D-T1 structural magnetic resonance imaging with the Jensen–Shannon divergence similarity estimation method. Our results showed that the most distinguishing morphological brain connectome features in mild cognitive impairment patients were consensus connections and nodal graph metrics, mainly located in the frontal, occipital, limbic lobes, and subcortical gray matter nuclei, corresponding to the default mode network. Topological properties analysis revealed that mild cognitive impairment patients exhibited compensatory changes in the frontal lobe, while abnormal cortical–subcortical circuits associated with cognition were present. Moreover, the combination of multidimensional brain connectome features using multiple kernel-support vector machine achieved the best classification performance in distinguishing mild cognitive impairment patients and normal controls, with an accuracy of 84.21%. Therefore, our findings are of significant importance for developing potential brain imaging biomarkers for early detection of Alzheimer’s disease and understanding the neuroimaging mechanisms of the disease.