Abstract
Mild cognitive impairment (MCI) is generally regarded as a prodromal stage of Alzheimer’s disease (AD). In coping with the challenges caused by AD, we analyzed resting-state functional magnetic resonance imaging data of 82 MCI subjects and 93 normal controls (NCs). The alteration of brain functional network in MCI was investigated on three scales, including global metrics, nodal characteristics, and modular properties. The results supported the existence of small worldness, hubs, and community structure in the brain functional networks of both groups. Compared with NCs, the network altered in MCI over all the three scales. In scale I, we found significantly decreased characteristic path length and increased global efficiency in MCI. Moreover, altered global network metrics were associated with cognitive level evaluated by neuropsychological assessments. In scale II, the nodal betweenness centrality of some global hubs, such as the right Crus II of cerebellar hemisphere (CERCRU2.R) and fusiform gyrus (FFG.R), changed significantly and associated with the severity and cognitive impairment in MCI. In scale III, although anatomically adjacent regions tended to be clustered into the same module regardless of group, discrepancies existed in the composition of modules in both groups, with a prominent separation of the cerebellum and a less localized organization of community structure in MCI compared with NC. Taking advantages of random forest approach, we achieved an accuracy of 91.4% to discriminate MCI patients from NCs by integrating cognitive assessments and network analysis. The importance of the used features fed into the classifier further validated the nodal characteristics of CERCRU2.R and FFG.R could be potential biomarkers in the identification of MCI. In conclusion, the present study demonstrated that the brain functional connectome data altered at the stage of MCI and could assist the automatic diagnosis of MCI patients.
Highlights
Alzheimer’s disease (AD), a neurodegenerative disease, represents the most common type of dementia (Ahmadlou et al, 2010; Li et al, 2011)
We observed a significant correlation between global network metrics and the clinical cognitive evaluations, suggesting that graph theory analysis could act as a strategy to differentiate Mild cognitive impairment (MCI) patients from normal controls (NCs) subjects
We investigated the alterations of brain functional network in MCI
Summary
Alzheimer’s disease (AD), a neurodegenerative disease, represents the most common type of dementia (Ahmadlou et al, 2010; Li et al, 2011). The human brain can be represented as a “connectome,” a large-scale network of interconnected regions that provides the anatomical substrate for neural communication, functional processing, and information integration (Fornito et al, 2013). Evidences from functional imaging studies have reported the involvement of the cerebellum in various cognitive tasks besides the traditional motor ones (Stoodley, 2012), and cerebellar abnormality has been reported in AD/MCI patients recently (Tabatabaei-Jafari et al, 2017; Pagen et al, 2020). Exploring the whole-brain functional network, including both cerebral and cerebellar regions, can disclose more comprehensive information of the abnormal brain connectome in MCI patients
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