Reliability of resting state functional connectivity within intensive longitudinal designs
Poster No:
1243
Submission Type:
Abstract Submission
Authors:
Martin Gell1,2, Gracie Grimsrud2, Kristina Hufnagle2, Damien Fair3,2, Brenden Tervo-Clemmens1,2
Institutions:
1Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, 2Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, 3Institute of Child Development, University of Minnesota, Minneapolis, MN
First Author:
Martin Gell
Department of Psychiatry and Behavioral Sciences, University of Minnesota|Masonic Institute for the Developing Brain, University of Minnesota
Minneapolis, MN|Minneapolis, MN
Department of Psychiatry and Behavioral Sciences, University of Minnesota|Masonic Institute for the Developing Brain, University of Minnesota
Minneapolis, MN|Minneapolis, MN
Co-Author(s):
Kristina Hufnagle
Masonic Institute for the Developing Brain, University of Minnesota
Minneapolis, MN
Masonic Institute for the Developing Brain, University of Minnesota
Minneapolis, MN
Damien Fair, PhD
Institute of Child Development, University of Minnesota|Masonic Institute for the Developing Brain, University of Minnesota
Minneapolis, MN|Minneapolis, MN
Institute of Child Development, University of Minnesota|Masonic Institute for the Developing Brain, University of Minnesota
Minneapolis, MN|Minneapolis, MN
Brenden Tervo-Clemmens
Department of Psychiatry and Behavioral Sciences, University of Minnesota|Masonic Institute for the Developing Brain, University of Minnesota
Minneapolis, MN|Minneapolis, MN
Department of Psychiatry and Behavioral Sciences, University of Minnesota|Masonic Institute for the Developing Brain, University of Minnesota
Minneapolis, MN|Minneapolis, MN
Introduction:
Growing methodological concerns for typical neuroimaging studies to identify associations with behaviour have prompted calls for a shift toward population-level "big data" or targeted, often longitudinal, precision investigations (Gratton 2022). Of these, intensive longitudinal studies are more practical for a single lab as they can leverage statistical power from a smaller set of individuals through deep phenotyping and repeated scanning. Advances in the acquisition of functional imaging like multi-echo (ME) have demonstrated significant improvements in SNR, possibly enhancing the reliability of a single session to a level where tracking session-to-session dynamics in metrics like functional connectivity with behaviour become feasible. Concurently, research indicates that the functional connectome is more "trait-like", with relative stability over long periods of time (Ramduny 2024). Here we investigate session-to-session stability, as well as within and between person variance of functional connectivity in densely sampled individuals using state-of-the-art acquisition protocol to achieve highest possible reliability.
Methods:
We used a pediatric sample of 11 participants (3 males, ages=9-11, mean age=10), each scanned 3-4 times in 1 to 8 week intervals. The resting-state fMRI acquisition protocol in each session involved 3 scans (single 10 minute and two 16 minute) acquired using MBME4 sequence (TR=1761ms, 2.0 mm isotropic voxels). Each participant had at minimum 30 minutes of low motion data (all frames with FD > 0.02 were scrubbed) per session. Preprocessing was performed using fMRIprep and additional denoising (motion and tissue class regression, detrending and filtering) was done using XCPd. Finally, vertex wise time series were parcellated using the 1000 parcel Schaefer atlas together with 56 subcortical and cerebellar regions and functional connectivity between them calculated using pearson correlation. Interclass correlation coefficient (ICC) reliability between sessions of every connection (557040 edges) was calculated as the ratio of between and total variance estimated using linear mixed effects model with random intercepts.
Results:
The stability of functional connectivity was not uniform across the connectome (Fig.1a). The most stable connections between sessions were among nodes of the same network (ICCs=0.58-0.81), except for the limbic network (mean ICC=0.32) and subcortical regions (mean ICC=0.42) which displayed generally poor reliability. Between-person variance (i.e., stable individual differences between participants) for many edges in higher-order networks was up to an order of magnitude higher than within-person variance (i.e., session-to-session changes; Fig.1b) and followed the same spatial pattern as ICC. However, multiple edges, particularly between control and sensory networks, displayed minimal between-person variance. The spatial pattern of between-person variance was moderately related to group mean connectivity strength across sessions (r=0.34, p < 0.001). Crucially, within-person variance among sessions was generally very low (median=0.006) and had limited spatial consistency.
Conclusions:
Taken together, findings from this densely sampled cohort with field leading acquisition strongly support "trait-like" stability of the functional connectome across scanning sessions. Higher-order cortices exhibited substantial between-person variability supporting previous work (Mueller, 2013); however, within-person variability was uniformly low. Collectively, these results suggest that unstructured, naturally occurring within-person changes in functional connectivity over weeks to months are minimal. Therefore, tracking brain-behavior associations in densely sampled individuals will require large associations, sizable samples, or perturbations (e.g., task paradigm) to amplify this variability. Ongoing work aims to replicate these analyses in adults to explore age-related differences in connectivity variability and reliability.
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 1
Task-Independent and Resting-State Analysis 2
Keywords:
Data analysis
Design and Analysis
Experimental Design
FUNCTIONAL MRI
Modeling
Multivariate
Statistical Methods
Other - Reliability
1|2Indicates the priority used for review
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Provide references using APA citation style.
Mueller, S., Wang, D., Fox, M. D., Yeo, B. T. T., Sepulcre, J., Sabuncu, M. R., Shafee, R., Lu, J., & Liu, H. (2013). Individual Variability in Functional Connectivity Architecture of the Human Brain. Neuron, 77(3), 586. https://doi.org/10.1016/j.neuron.2012.12.028
Ramduny, J., & Kelly, C. (2024). Connectome-based fingerprinting: Reproducibility, precision, and behavioral prediction. Neuropsychopharmacology, 50(1), 114–123. https://doi.org/10.1038/s41386-024-01962-8
Yeo, B. T., Krienen, F. M., Sepulcre, J., Sabuncu, M. R., Lashkari, D., Hollinshead, M., Roffman, J. L., Smoller, J. W., Zöllei, L., Polimeni, J. R., Fischl, B., Liu, H., & Buckner, R. L. (2011). The organization of the human cerebral cortex estimated by intrinsic functional connectivity. Journal of Neurophysiology, 106(3), 1125–1165. https://doi.org/10.1152/jn.00338.2011