Information Flow Pattern in Early Mild Cognitive Impairment Patients

Abstract

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 (HCs) with available resting-state functional MRI images and neurological measures [including the neuropsychological evaluation and cerebrospinal fluid (CSF) biomarkers] were included from the 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 the Atlas of Intrinsic Connectivity of Homotopic Areas (AICHA) were calculated by 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 was performed to differentiate EMCI from HC. The least absolute shrinkage and selection operator (lasso) regression were used to evaluate the predictive ability of information flow measures for the neurological state. Results: Compared to HC, disturbed preferred information flow directions between brain regions involving default mode network (DMN), executive control network (ECN), somatomotor network (SMN), and visual network (VN) were observed in patients with EMCI. An altered preferred information flow index in several brain regions (including the thalamus, posterior cingulate, and precentral gyrus) was also observed. Classification accuracy of 80% for differentiating patients with EMCI from HC was achieved by 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 the high Pearson's correlation coefficients (r > 0.7) between predictive and actual neurological measures. Conclusion: Patients with EMCI were presented with a disturbed brain information flow pattern, which could help clinicians to identify patients with EMCI and assess their neurological state.

Keywords: early mild cognitive impairment; information flow; resting state functional MRI; support vector classification; support vector regression.

Figure 1

Functional MRI (fMRI) processing and analysis flowchart of the study.

Figure 2

The information flow patterns in HC and EMCI. (A) The preferred information flow direction and preferred information flow index in HC and EMCI. The node size indicated the value of the preferred information flow index. The color of the directed edge indicated the value of preferred information flow direction (only directed edges with values > 0.5 were presented); (B) The alterations of information flow patterns in EMCI compared to HC. The first row presented the edge-wise statistical results of preferred information flow direction, only decreased preferred information flow direction in EMCI was displayed as the opposite preferred information flow direction was increased, which implies the identical information flow changes between the brain nodes; the Second row presented the node-wise statistical results of preferred information flow index, red nodes indicated the increased preferred information flow index and the blue nodes indicated the decreased preferred information flow index. HC, healthy controls; EMCI, early mild cognitive impairment; G, gyrus; S, sulcus.

Figure 3

Neurological assessment by using information flow measures by lasso regression. The first and second columns presented the selected information flow directions and a good prediction ability for the ADNI-MEM, ADNI-EF, Aβ, tau, and pTau evaluation. The color of the directed edge indicated the value of lasso regression coefficient (beta); the third and fourth columns presented the selected information flow index and a relative decreased prediction ability for the ADNI-MEM, ADNI-EF, Aβ, tau, and pTau evaluation. The size of the node indicated the absolute value of the lasso regression coefficient and the color of the node indicated the positive (red) or negative (blue) lasso regression coefficient. Aβ, amyloid β; pTau, phosphorylated Tau; ADNI-MEM, Alzheimer's Disease Neuroimaging Initiative-composite assessment of memory; ADNI-EF, ADNI-executive function; FN, feature number; G, gyrus; S, sulcus.