Functional Brain Network Alterations in a Korean Brain Aging Study of Alzheimer's Disease
Poster No:
223
Submission Type:
Abstract Submission
Authors:
Evgeny Chumin1, Lauren Hirschfield1, Sahith Peddireddy1, Rachael Deardorff1, Matt Tharp1, Dahyun Yi2, Min Soo Byun3, Jun-Young Lee3, Yu Kyeong Kim4, Koung Mi Kang5, Chul-Ho Sohn6, Shannon Risacher1, Olaf Sporns7, Kwangsik Nho1, Andrew Saykin1, Dong Young Lee2
Institutions:
1Indiana University School of Medicine, Indianapolis, IN, 2Seoul National University, Seoul, Korea, Republic of, 3Seoul National University College of Medicine, Seoul, Korea, Republic of, 4SMG-SNU Boramae Medical Center, Seoul, Korea, Republic of, 5Department of Radiology, Seoul National University Hospital, Seoul, Korea, Republic of, 6Seoul National University Hospital, Seoul, Korea, Republic of, 7Indiana University, Bloomington, IN
First Author:
Co-Author(s):
Introduction:
MRI studies of Alzheimer's disease (AD) have shown utility in quantifying neurodegeneration and alterations in brain structural (diffusion) and functional (task and resting state) connectivity. However, much of our knowledge comes predominantly from samples of European ancestry. In recent years, the field has begun to address the lack of diversity through initiatives that aim to recruit African American, Hispanic, and Asian participants into large multicenter studies such as the AD Neuroimaging Initiative (Weiner 2023). Here we analyze functional connectivity in 588 participants form the Korean Brain Aging Study for the Early Diagnosis and Prediction of AD (KBASE; Byun 2017) using methods previously applied to samples from the Indiana AD Research Center (Contreras 2019; Chumin 2021). We also apply edge community detection (Chumin 2022) to assess its potential as a metric of interest for AD studies/trials.
Methods:
Anatomical and resting state functional MRI from 70 younger and 284 older cognitively normal (yCN, mean age: 38yo and oCN, 69yo), 147 mild cognitive impairment (MCI, 73yo), and 87 AD dementia (72yo) participants were processed using a publicly available pipeline (Chumin 2021). Anatomical data were denoised and skull-stripped, with the Schaefer (2018) 200 node parcellation registered to each subject's T1 and then fMRI scan. FMRI data underwent standard preprocessing, with nuisance and global signal regressed out (ICA-AROMA and aCompCor) and average time series extracted. Network contingency analysis comparison (t-thresholds 2:0.25:6, 10,000 permutations) of oCN vs. AD connectivity among the 7 canonical resting state networks (Yeo 2011) and multiresolution consensus clustering (1000 partitions from group averaged data, from which agreement matrices and consensus partitions at α=.05 were estimated) were done as previously reported (Contreras 2019). Edge community detection (Faskowitz 2020; k-means algorithm k=2-20) was applied as in Chumin (2022), computing a group consensus partition, edge community similarity matrix, and node entropy.
Results:
Functional connectivity (Fig1A) showed a qualitative weakening of connectivity in group averaged data. Network contingency analysis showed that AD had lower connectivity within 6/7 networks (excluding limbic; Fig1C shows frontoparietal connectivity values by group) across multiple t-thresholds (p<0.0018, adjusted for 28 network blocks tested), and for interaction blocks that fell within primary (visual and attention) or heteromodal (frontoparietal and default mode; Fig1D) systems (Fig1B). Greater connectivity in AD was found in interaction blocks that connect the primary and heteromodal systems (Fig1E shows the frontoparietal-visual interaction). Multiresolution community detection derived agreement matrices showed a breakdown of organization across diagnosis (lower agreement) particularly in the higher order systems (frontoparietal and default mode, Fig2A). Similarity of consensus community partitions decreased with greater separation in diagnostic severity (Fig2B, oCN and MCI vs. AD). Edge community structure (Fig2C, lower triangles) supports this view of breakdown in organization, which can be seen in increasing between network similarity (Fig2C, upper triangles) and entropy (Fig2D).
Conclusions:
These results show overlap with prior research (Contreras 2019), replicating the findings within the frontoparietal system. However, an opposite relationship for frontoparietal/default mode interaction among other differences were observed, perhaps due to the greater sample size compared to prior work. Findings with edge community detection support its application in future AD research. Our characterization of functional alterations in a Korean participant sample showed both overlap and discrepancy relative to prior literature in samples of European ancestry, reinforcing the need for population diversity when studying aging and prodromal AD.
Disorders of the Nervous System:
Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s) 1
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 2
Task-Independent and Resting-State Analysis
Keywords:
Aging
Computational Neuroscience
Degenerative Disease
FUNCTIONAL MRI
MRI
Other - network neuroscience
1|2Indicates the priority used for review
Provide references using author date format
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Contreras, JA, Avena-Koenigsberger, A, Risacher, SL, West, JD, et al. (2019). Resting state network modularity along the prodromal late onset Alzheimer's disease continuum. Neuroimage: Clinical, 22:101687.
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