A comprehensive exploration of longitudinal white matter microstructure and cognitive trajectories
Presented During:
Wednesday, June 26, 2024: 11:30 AM - 12:45 PM
COEX
Room:
Grand Ballroom 103
Poster No:
156
Submission Type:
Abstract Submission
Authors:
Derek Archer1, Chris Peter2, Aditi Sathe2, Yisu Yang2, Alaina Durant2, Niranjana Shashikumar3, Kimberly Pechman2, Katherine Gifford2, Shubhabrata Mukherjee4, Brandon Klinedinst5, Michael Lee4, Seo-Eun Choi4, Phoebe Scollard6, Emily Trittschuh4, Shannon Risacher7, Lori Beason-Held8, Yang An9, Kurt Schilling1, Bennett Landman1, Lisa Barnes10, Julie Schneider10, David Bennett10, Paul Crane11, Walter Kukull4, Sterling Johnson12, Marilyn Albert13, Angela Jefferson2, Susan Resnick8, Andrew Saykin7, Timothy Hohman2
Institutions:
1Vanderbilt University Medical Center, Nashville, TN, 2Vanderbilt Memory & Alzheimer's Center, Nashville, TN, 3Vanderbilt memo, Nashville, TN, 4University of Washington School of Medicine, Seattle, WA, 5Unive, Seattle, WA, 6Uni, Seattle, WA, 7Indiana University School of Medicine, Indianapolis, IN, 8National Institute on Aging, Baltimore, MD, 9Nation, Baltimore, MD, 10Rush University Medical Center, Chicago, IL, 11Univer, Seatt, WA, 12University of Wisconsin, Madison, WI, 13Johns Hopkins University School of Medicine, Baltimore, MD
First Author:
Co-Author(s):
Introduction:
The primary clinical manifestation of Alzheimer's disease (AD) is cognitive impairment and longitudinal cognitive decline, and several prior diffusion MRI studies have investigated the association between white matter microstructure and cognitive decline in normal aging and AD1–7. Recent work from our group explored the free-water (FW)-corrected associations with longitudinal scores of memory and executive function and found that medial temporal lobe tracts were significantly associated with both domains8. One interesting finding from this prior study is that the FW component, which is a separate 3D map which is created in the FW-correction pipeline, is particularly sensitive to cognitive impairment and decline. This is in line with several prior studies which have demonstrated similar findings in other neurodegenerative diseases. While these studies have been foundational to our understanding of white matter contributions to cognitive impairment and decline, large-scale studies using harmonized scores of cognitive function would drastically enhance our understanding by elucidating which white matter tracts are most vulnerable in individuals with cognitive decline.
Methods:
The dataset used in this study was collated from seven longitudinal cohorts of aging (ADNI, BIOCARD, BLSA, NACC, ROS/MAP/MARS, VMAP, WRAP). In total, this dataset included 2,220 participants aged 50+ who had both diffusion MRI and harmonized composites of memory performance and executive function. This dataset included a total of 4,918 imaging sessions with corresponding cognitive data (mean number of visits per participant: 1.69 ± 1.67, interval range: 1-10 years).
Diffusion MRI data was preprocessed using the PreQual pipeline and free-water (FW) correction was conducted to obtain FW and FW-corrected fractional anisotropy (FAFWcorr) maps. Conventional diffusion MRI (FAconv, MDconv, AxDconv, RDconv and FW-corrected (FW, FAfwcorr, MDfwcorr, AxDfwcorr, RDfwcorr) measures were quantified within 48 white matter tracts consistent with prior publications9,10, which were subsequently harmonized using the Longitudinal ComBat package. Linear mixed effects regression was used for longitudinal analysis, in which we covaried for age, age squared, education, sex, race/ethnicity, diagnosis at baseline, APOE-ε4 status, and APOE-ε2 status. We also controlled for age x diagnosis converter and age squared x diagnosis converter interactions. Separate models were conducted to determine the association with longitudinal memory performance and executive function performance. All models were corrected for multiple comparisons using the FDR approach.
Diffusion MRI data was preprocessed using the PreQual pipeline and free-water (FW) correction was conducted to obtain FW and FW-corrected fractional anisotropy (FAFWcorr) maps. Conventional diffusion MRI (FAconv, MDconv, AxDconv, RDconv and FW-corrected (FW, FAfwcorr, MDfwcorr, AxDfwcorr, RDfwcorr) measures were quantified within 48 white matter tracts consistent with prior publications9,10, which were subsequently harmonized using the Longitudinal ComBat package. Linear mixed effects regression was used for longitudinal analysis, in which we covaried for age, age squared, education, sex, race/ethnicity, diagnosis at baseline, APOE-ε4 status, and APOE-ε2 status. We also controlled for age x diagnosis converter and age squared x diagnosis converter interactions. Separate models were conducted to determine the association with longitudinal memory performance and executive function performance. All models were corrected for multiple comparisons using the FDR approach.
Results:
For longitudinal memory performance, we found global associations with conventional diffusion MRI metrics, in which lower FAconv was associated with lower memory performance. In contrast, higher ADconv, RDconv, and MDconv were associated with lower memory performance. Following FW correction, we found that the FW metric itself was strongly associated with memory performance, in which higher FW was associated with lower memory performance and decline. Interestingly, following FW-correction the intracellular contributions were largely mitigated. As illustrated in Figure 1A, the most significant effects were found in the limbic tracts, with the most significant associations found for cingulum bundle FW (p=5.80x10-45). Figure 1B illustrates the association between cingulum FW and longitudinal memory performance. Findings for longitudinal executive function performance are shown in Figure 2.
Conclusions:
To date, this is the largest study combining FW corrected diffusion MRI data and harmonized cognitive composites to understand cognitive trajectories in aging. Future studies evaluating how white matter microstructure may be incorporated into models of AD may further our knowledge into the neurodegenerative cascade of AD.
Disorders of the Nervous System:
Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s) 1
Modeling and Analysis Methods:
Diffusion MRI Modeling and Analysis 2
Keywords:
Aging
Cognition
Computational Neuroscience
Memory
White Matter
WHITE MATTER IMAGING - DTI, HARDI, DSI, ETC
1|2Indicates the priority used for review
Provide references using author date format
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