Information Flow Pattern in Early Mild Cognitive Impairment Patients - He, Haijuan; Ding, Shuang; Jiang, Chunhui; Wang, Yuanyuan; Luo, Qiaoya; Wang, Yunling, Alzheimer's Disease Neuroimaging Initiative (Frontiers in Neurology, 2021)
Purpose To investigate the brain information flow pattern in patients with early mild cognitive impairment (EMCI) and explore its potential ability of differentiation and prediction for EMCI. Methods In this study, 49 patients with EMCI and 40 age- and sex-matched healthy controls (HC) with available resting-state functional MRI images and neurological measures (including neuropsychological evaluation and cerebrospinal fluid [CSF] biomarkers) were included from Alzheimer’s Disease Neuroimaging Initiative. Functional MRI measures including preferred information flow direction between brain regions and preferred information flow index of each brain region parcellated by Atlas of Intrinsic Connectivity of Homotopic Areas (AICHA) were calculated using Non-Parametric Multiplicative Regression Granger Causality Analysis (NPMR-GCA). Edge- and node-wise Student’s t-test was conducted for between-group comparison. Support vector classification and regression analyses were performed to differentiate EMCI from HC, and to evaluate the predictive ability of information flow measures for neurological state. Results Compared to HC, disturbed preferred information flow directions between brain regions involving default network (DMN), executive control network (ECN), somatomotor network (SMN) and visual network (VN) were observed in EMCI patients. Altered preferred information flow index in several brain regions (including thalamus, posterior cingulate and precentral gyrus) were also observed. A classification accuracy of 80% for differentiating EMCI patients from HC were achieved using the preferred information flow directions. The preferred information flow directions have a good ability to predict memory and executive function, level of amyloid β, tau protein and phosphorylated tau protein with high Pearson’s correlation coefficients (R>0.7) between predictive and actual neurological measures. Conclusion Patients with EMCI presented with a disturbed brain information flow pattern, which could help clinicians identify patients with EMCI and assess their neurological state.