Alterations in Gray Matter Structural Networks in Amnestic Mild Cognitive Impairment: A Source-Based Morphometry Study

Author:

Setiadi Tania M.1,Marsman Jan-Bernard C.1,Martens Sander1,Tumati Shankar12,Opmeer Esther M.13,Reesink Fransje E.4,De Deyn Peter P.45,Atienza Mercedes67,Aleman André18,Cantero Jose L.67

Affiliation:

1. Department of Biomedical Sciences of Cells & Systems, Cognitive Neuroscience Center, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands

2. Neuropsychopharmacology Research Group, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada

3. Department of Health and Welfare, Windesheim University of Applied Sciences, Zwolle, The Netherlands

4. Department of Neurology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands

5. Laboratory of Neurochemistry and Behavior, Experimental Neurobiology Group, University of Antwerp, Antwerp, Belgium

6. Laboratory of Functional Neuroscience, Pablo de Olavide University, Seville, Spain

7. CIBER de Enfermedades Neurodegenerativas (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain

8. Department of Psychology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands

Abstract

Background: Amnestic mild cognitive impairment (aMCI), considered as the prodromal stage of Alzheimer’s disease, is characterized by isolated memory impairment and cerebral gray matter volume (GMV) alterations. Previous structural MRI studies in aMCI have been mainly based on univariate statistics using voxel-based morphometry. Objective: We investigated structural network differences between aMCI patients and cognitively normal older adults by using source-based morphometry, a multivariate approach that considers the relationship between voxels of various parts of the brain. Methods: Ninety-one aMCI patients and 80 cognitively normal controls underwent structural MRI and neuropsychological assessment. Spatially independent components (ICs) that covaried between participants were estimated and a multivariate analysis of covariance was performed with ICs as dependent variables, diagnosis as independent variable, and age, sex, education level, and site as covariates. Results: aMCI patients exhibited reduced GMV in the precentral, temporo-cerebellar, frontal, and temporal network, and increased GMV in the left superior parietal network compared to controls (pFWER < 0.05, Holm-Bonferroni correction). Moreover, we found that diagnosis, more specifically aMCI, moderated the positive relationship between occipital network and Mini-Mental State Examination scores (pFWER < 0.05, Holm-Bonferroni correction). Conclusions: Our results showed GMV alterations in temporo-fronto-parieto-cerebellar networks in aMCI, extending previous results obtained with univariate approaches.

Publisher

IOS Press

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