Quantitative Assessment of Resting‐State Functional Connectivity MRI to Differentiate Amnestic Mild Cognitive Impairment, Late‐Onset Alzheimer's Disease From Normal Subjects

• Published in: Journal of magnetic resonance imaging Vol. 57; no. 6; pp. 1702 - 1712
• Main Authors: Mohammadian, Fatemeh, Zare Sadeghi, Arash, Noroozian, Maryam, Malekian, Vahid, Abbasi Sisara, Majid, Hashemi, Hasan, Mobarak Salari, Hanieh, Valizadeh, Gelareh, Samadi, Fardin, Sodaei, Forough, Saligheh Rad, Hamidreza
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01.06.2023; Wiley Subscription Services, Inc
• Subjects: Alzheimer disease; Alzheimer Disease - diagnostic imaging; Alzheimer's disease; Attention; Bivariate analysis; Brain; Brain - diagnostic imaging; Brain Mapping; Cerebellum; Clustering; Cognitive ability; Cognitive Dysfunction - diagnostic imaging; Correlation coefficient; Correlation coefficients; Efficiency; Field strength; fMRI; functional connectivity; Functional magnetic resonance imaging; Graph theory; graph theory analysis; Humans; Impairment; Language; Magnetic Resonance Imaging; mild cognitive impairment; Networks; Neural networks; Neurodegenerative diseases; Neurological diseases; Prospective Studies; resting‐state networks; Statistical analysis; Statistical tests; Variance analysis; Visual observation; Visual perception; fMRI; mild cognitive impairment; functional connectivity; graph theory analysis; Alzheimer disease; resting-state networks
• ISSN: 1053-1807; 1522-2586; 1522-2586
• DOI: 10.1002/jmri.28469

Background Alzheimer disease (AD) is a neurological disorder with brain network dysfunction. Investigation of the brain network functional connectivity (FC) alterations using resting‐state functional MRI (rs‐fMRI) can provide valuable information about the brain network pattern in early AD diagnosis. Purpose To quantitatively assess FC patterns of resting‐state brain networks and graph theory metrics (GTMs) to identify potential features for differentiation of amnestic mild cognitive impairment (aMCI) and late‐onset AD from normal. Study Type Prospective. Subjects A total of 14 normal, 16 aMCI, and 13 late‐onset AD. Field Strength/Sequence A 3.0 T; rs‐fMRI: single‐shot 2D‐EPI and T1‐weighted structure: MPRAGE. Assessment By applying bivariate correlation coefficient and Fisher transformation on the time series of predefined ROIs' pairs, correlation coefficient matrixes and ROI‐to‐ROI connectivity (RRC) were extracted. By thresholding the RRC matrix (with a threshold of 0.15), a graph adjacency matrix was created to compute GTMs. Statistical Tests Region of interest (ROI)‐based analysis: parametric multivariable statistical analysis (PMSA) with a false discovery rate using (FDR)‐corrected P < 0.05 cluster‐level threshold together with posthoc uncorrected P < 0.05 connection‐level threshold. Graph‐theory analysis (GTA): P‐FDR‐corrected < 0.05. One‐way ANOVA and Chi‐square tests were used to compare clinical characteristics. Results PMSA differentiated AD from normal, with a significant decrease in FC of default mode, salience, dorsal attention, frontoparietal, language, visual, and cerebellar networks. Furthermore, significant increase in overall FC of visual and language networks was observed in aMCI compared to normal. GTA revealed a significant decrease in global‐efficiency (28.05 < 45), local‐efficiency (22.98 < 24.05), and betweenness‐centrality (14.60 < 17.39) for AD against normal. Moreover, a significant increase in local‐efficiency (33.46 > 24.05) and clustering‐coefficient (25 > 20.18) were found in aMCI compared to normal. Data Conclusion This study demonstrated resting‐state FC potential as an indicator to differentiate AD, aMCI, and normal. GTA revealed brain integration and breakdown by providing concise and comprehensible statistics. Evidence Level 1 Technical Efficacy Stage 2