Incorporating Dynamic Brain States Improves Models of Processing Speed in the Oldest-Old
Presented During: Poster Session 3
Friday, June 27, 2025: 01:45 PM - 03:45 PM
Friday, June 27, 2025: 01:45 PM - 03:45 PM
Presented During: Poster Session 4
Saturday, June 28, 2025: 01:45 PM - 03:45 PM
Saturday, June 28, 2025: 01:45 PM - 03:45 PM
Poster No:
1675
Submission Type:
Abstract Submission
Authors:
George Ling1, Hannah Cowart1, Sara Nolin2, Paul Stewart1, Gerhard Hellemann1, Leland Fleming1, Mary Faulkner1, Clinton Wright3, Stacy Merritt4, Roxanne Rezaei5, Pradyumna Bharadwaj6, Mary Franchetti6, David Raichlen7, Cortney Jessup6, G Hishaw6, Emily Van Etten6, Theodore Trouard6, David Geldmacher1, Virginia Wadley1, Noam Alperin4, Eric Porges5, Adam Woods5, Ronald Cohen5, Bonnie Levin4, Tatjana Rundek4, Gene Alexander6, Kristina Visscher1
Institutions:
1University of Alabama at Birmingham, Birmingham, AL, 2Medical University of South Carolina, Charleston, SC, 3National Institute of Neurological Disorders and Stroke, Bethesda, MD, 4University of Miami, Miami, FL, 5University of Florida, Gainesville, FL, 6University of Arizona, Tucson, AZ, 7University of Southern California, Los Angeles, CA
First Author:
Co-Author(s):
Introduction:
Individual variability in cognitive performance is greater in oldest-old (85+ years) individuals than in younger adults. The neural underpinnings of cognitive variation remain largely unknown, but there is recent interest in the hypothesis that cognitive function is likely to rely on temporal aspects of brain activity, in comparison to static measures such as functional connectivity. We propose to explore temporal-dependent brain activity using functional magnetic resonance imaging (fMRI) on the individual time-point level to produce group-averaged "whole brain states," also known as "co-activation patterns" (Janes et. al., 2020). This innovation allows for improved spatial resolution over EEG and faster temporal resolution than typical dynamic functional connectivity methods. To our knowledge, this study marks the first attempt to explore the relationship between brain state dynamics and cognition within an oldest-old cohort.
Methods:
The McKnight Brain Aging Registry was collected across four sites in the United States of America: University of Alabama at Birmingham, University of Florida, University of Miami, and University of Arizona. 146 cognitively healthy participants aged 85-99 underwent an 8-minute fMRI scan while resting with eyes open. A parcellation atlas consisting of 386 3-mm centroids was calculated to best represent this unique neuroimaging dataset of 85-99-year-olds (Nolin et. al., 2022a). The atlas was then averaged into 9 networks (Power et. al., 2011) including the default mode, ventral attention, dorsal attention, etc. The NIH Toolbox Cognitive Battery was used to derive five cognitive domains (Nolin, et al., 2022b). Group-averaged whole brain states were defined using a k-means clustering algorithm on fMRI brain oxygen level dependent time-series data using the Python package "capcalc" (Frederick, 2023). All k-means models from k=2 to k=50 were examined and the most robust whole brain state across all models was selected for analysis. Dynamic measures calculated included fraction of occurrence (percentage of time spent in a state), persistence (consecutive time spent in a state), and transition entropy (probability distribution of directional movement from one state to another). These dynamic measures, as well as static measures of functional connectivity, including network segregation, were used as predictors in general linear models predicting cognitive performance. Akaike's information criterion (AIC) was used to determine the best model.
Results:
The whole brain state displaying the highest stability across models has a highly active default mode network (DMN) (z-score = 2.2), relatively high activation of the ventral attention network (z-score = 1.0) and low activation of every other area (z-score < -0.6). We examined how the dynamics of this state (referred to here as the DMN+ state) correlated to five cognitive performance measures. These DMN+ dynamics were strongly correlated with processing speed but not the other cognitive measures (binomial p < 0.001). Better processing speed was associated with greater transition entropy, longer persistence (Figure 1), and greater fraction of occurrence. Using AIC score, a combination of both DMN+ dynamics and network segregation was shown to best-fit processing speed in 44 of the 48 k-means models (Figure 2).
Conclusions:
The DMN+ brain state was identified as a stable, robust brain state in the oldest-old cohort. DMN+ dynamics were uniquely correlated to processing speed and no other cognitive domain, suggesting that processing speed is related to brain dynamics. Combining both static functional connectivity measures with dynamics improves the interpretability of the model over measures that examine static connections alone or dynamic measures alone. Thus, brain states add a dynamic dimension to fMRI that can improve our ability to describe aspects of cognitive performance.
Higher Cognitive Functions:
Space, Time and Number Coding
Lifespan Development:
Aging 2
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural)
Methods Development
Task-Independent and Resting-State Analysis 1
Keywords:
Aging
Cognition
Computational Neuroscience
Cortex
Data analysis
FUNCTIONAL MRI
1|2Indicates the priority used for review
Abstract Information
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Provide references using APA citation style.
Nolin, S. A., Faulkner, M. E., Stewart, P., Merritt, S., Rezaei, R. F., Bharadwaj, P. K., Franchetti, M. K., Raichlen, D. A., Jessup, C. J., Hishaw, G. A., Van Etten, E. J., Trouard, T. P., Geldmacher, D., Wadley, V. G., Alperin, N., Porges, E. S., Woods, A. J., Cohen, R. A., Levin, B. E., Rundek, T., Alexander, G. E., & Visscher, K. M. (2022a). Network Segregation Predicts Processing Speed in the Cognitively Healthy Oldest-old. BioRxiv.
Power, J. D., Cohen, A. L., Nelson, S. M., Wig, G. S., Barnes, K. A., Church, J. A., Vogel, A. C., Laumann, T. O., Miezin, F. M., Schlaggar, B. L., & Petersen, S. E. (2011). Functional network organization of the human brain. Neuron, 72(4), 665–678.
Nolin, S. A., Cowart, H., Merritt, S., McInerney, K., Bharadwaj, P. K., Franchetti, M. K., Raichlen, D. A., Jessup, C. J., Hishaw, G. A., Van Etten, E. J., Trouard, T. P., Geldmacher, D. S., Wadley, V. G., Porges, E. S., Woods, A. J., Cohen, R. A., Levin, B. E., Rundek, T., Alexander, G. E., & Visscher, K. M. (2022b). Validity of the NIH toolbox cognitive battery in a healthy oldest-old 85+ sample. Journal of the International Neuropsychological Society, 1–10.
Frederick, B. (2023). capcalc (Version 1.3.6) [Computer software].