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Brain Connect. 2019 Aug 7. doi: 10.1089/brain.2019.0676. [Epub ahead of print]

Differentiation of Early Alzheimer's Disease, Mild Cognitive Impairment and Cognitively Healthy Elderly Samples using Multimodal Neuroimaging Indices.

Author information

1
National Institute of Mental Health and Neuro Sciences, 29148, Department of Psychiatry, Multimodal Brain Image Analysis Laboratory, Bangalore, Karnataka, India; anshuhim20@gmail.com.
2
National Institute of Mental Health and Neuro Sciences, 29148, Department of Psychiatry, Bangalore, Karnataka, India; srikala.bharath@gmail.com.
3
National Institute of Mental Health and Neuro Sciences, 29148, Department of Clinical Neurosciences, Bangalore, Karnataka, India; balachandar.rakesh@gmail.com.
4
National Institute of Mental Health and Neuro Sciences, 29148, Department of Psychiatry, Bangalore, Karnataka, India; shilpa848@gmail.com.
5
National Institute of Mental Health and Neuro Sciences, 29148, Department of Psychiatry, Bangalore, Karnataka, India; harshita.vishwakarma14@gmail.com.
6
National Institute of Mental Health and Neuro Sciences, 29148, Department of Psychiatry, Bangalore, Karnataka, India; subramoniam85@gmail.com.
7
National Institute of Mental Health and Neuro Sciences, 29148, Department of Neuroimaging & Interventional Radiology, Bangalore, Karnataka, India; jsaini76@gmail.com.
8
National Institute of Mental Health and Neuro Sciences, 29148, Department of Clinical Psychology, Bangalore, Karnataka, India; keshavjkapp@gmail.com.
9
National Institute of Mental Health and Neuro Sciences, 29148, Department of Psychiatry, Multimodal Brain Image Analysis Laboratory, Bangalore, Karnataka, India; jpj@nimhans.ac.in.
10
National Institute of Mental Health and Neuro Sciences, 29148, Department of Psychiatry, Bangalore, Karnataka, India; mat.varg@yahoo.com.

Abstract

Brain resting state functional connectivity, white matter integrity and cortical morphometry as well as neuropsychological performance have seldom been studied together to differentiate Alzheimer's disease (AD), mild cognitive impairment (MCI) and elderly cognitively healthy comparison (eCHC) samples in the context of the same study. We examined brain resting state functional connectivity (rsFC) in samples of patients with mild AD (n=50) and MCI (n=49) in comparison to eCHC samples (n=48) and then explored whether rsFC abnormalities can be linked to underlying gray matter volumetric and/or white matter microstructural abnormalities. The mild AD sample showed significantly increased rsFC in Executive control network (ECN) and Dorsal attention network (DAN) compared to eCHC sample, and increased rsFC in ECN compared to MCI. Brain regions corresponding to both these resting state networks (RSNs) showed significant reduction in fractional anisotropy in mild AD in comparison to eCHC. Significant gray matter volumetric reductions were observed in brain regions corresponding to both RSNs in the mild AD sample compared to MCI as well as eCHC samples. The association of Default mode network (DMN)-DAN anticorrelation with cognitive performances differentiated mild AD and MCI from eCHC sample. These findings highlight the association between brain structural and functional abnormalities as well as cognitive impairment that enables differentiation between early AD, MCI and eCHC samples.

KEYWORDS:

Alzheimer's Disease; Gray matter (also, grey matter); Mild cognitive impairment; Resting-state functional connectivity magnetic resonance imaging (R-fMRI); White matter

PMID:
31389245
DOI:
10.1089/brain.2019.0676

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