Poster No:
968
Submission Type:
Abstract Submission
Authors:
Isabella Stallworthy1, Sophie Loman1, Theodore Satterthwaite1, Raquel Gur1, Ruben Gur1, Dani Bassett1
Institutions:
1University of Pennsylvania, Philadelphia, PA
First Author:
Co-Author(s):
Raquel Gur
University of Pennsylvania
Philadelphia, PA
Ruben Gur
University of Pennsylvania
Philadelphia, PA
Introduction:
Neural communities of functional connectivity are dynamic, flexibly reconfiguring at multiple timescales to support complex behaviors (e.g., Bassett et al., 2011; Telesford et al., 2017). During rest, whole-brain neural flexibility –the rate at which regions switch functional communities –has been found to decrease across childhood and adolescence (Gu et al., 2022). Although existing work in adults suggests that neural flexibility facilitates higher-order cognitive functions (Pedersen et al., 2018), it remains unknown whether neural flexibility may support the development of socio-emotional capacities.
Methods:
We capitalized upon a sample of n= 1,361 participants ages 8-22 years who completed imaging as part of The Philadelphia Neurodevelopmental Cohort (Satterthwaite et al., 2014). fMRI scans included a resting state scan, an emotion recognition task, and an n-back working memory task. fMRI timeseries were processed using fMRIPrep (Esteban et al., 2018) and XCP-D (Mehta et al., 2024) with standard settings. Regions were parcellated into ten canonical functional networks (seven Schaefer networks as well as thalamus, cerebellar, and subcortical networks; Schaefer et al., 2018). An iterated general Louvain algorithm (Mucha et al., 2010) performed time-resolved community detection at a 30-second sliding window. Analyses were conducted using linear mixed effects models that included sex and in-scanner motion covariates.
Results:
On average, neural flexibility decreased with age (Bstd = -0.10, p < 0.01) across tasks and most but not all canonical networks (Figure 1). In comparison to rest, neural flexibility was higher during both working memory (Bstd = 0.81, p < 0.001) and emotion recognition (Bstd = 0.53, p = 0.014). Age-related flexibility decreases were more pronounced during emotion recognition compared to both rest (Bstd = -0.15, p < 0.001) and working memory (Bstd = -0.17, p < 0.001).
Further, neural flexibility was differentially related to performance on working memory and emotion recognition tasks (Figure 2). On average, neural flexibility was positively associated with working memory hit rate (Bstd = 0.17, p < 0.001), evident across most networks. On average, for younger children only (Bstd = -0.07, p = 0.03), greater neural flexibility was associated with longer emotion recognition response times (Bstd = 0.04, p = 0.02), primarily within the cerebellar, salience and ventral attention, and visual networks.


Conclusions:
Extending existing work, we report heterogenous developmental patterns of neural flexibility across task and network. Further, holding age constant, neural flexibility may function differently within different task contexts. Replicating previous findings, more flexible community structure may facilitate the integrated processing required for higher-order capacities such as working memory. However, particularly earlier in development, neural flexibility may reflect less efficient processing of socio-emotional cues such as recognizing basic emotions. Together, findings suggest that most functional communities stabilize with development, over which neural flexibility exhibits varying relations to emerging complex behaviors.
Emotion, Motivation and Social Neuroscience:
Emotional Perception 2
Learning and Memory:
Working Memory
Lifespan Development:
Early life, Adolescence, Aging 1
Normal Brain Development: Fetus to Adolescence
Keywords:
Development
Emotions
FUNCTIONAL MRI
Memory
1|2Indicates the priority used for review
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Please indicate below if your study was a "resting state" or "task-activation” study.
Resting state
Task-activation
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Healthy subjects
Was this research conducted in the United States?
Yes
Are you Internal Review Board (IRB) certified?
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Yes, I have IRB or AUCC approval
Were any human subjects research approved by the relevant Institutional Review Board or ethics panel?
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Yes
Were any animal research approved by the relevant IACUC or other animal research panel?
NOTE: Any animal studies without IACUC approval will be automatically rejected.
No
Please indicate which methods were used in your research:
Functional MRI
Behavior
For human MRI, what field strength scanner do you use?
3.0T
Which processing packages did you use for your study?
Other, Please list
-
fMRIPrep; XCP-D
Provide references using APA citation style.
Bassett, D. S., Wymbs, N. F., Porter, M. A., Mucha, P. J., Carlson, J. M., & Grafton, S. T. (2011). Dynamic reconfiguration of human brain networks during learning. Proceedings of the National Academy of Sciences of the United States of America, 108(18), 7641–7646. https://doi.org/10.1073/pnas.1018985108
Esteban, O., Markiewicz, C. J., Blair, R. W., Moodie, C. A., Isik, A. I., Erramuzpe, A., Kent, J. D., Goncalves, M., DuPre, E., Snyder, M., Oya, H., Ghosh, S. S., Wright, J., Durnez, J., Poldrack, R. A., & Gorgolewski, K. J. (2019). fMRIPrep: A robust preprocessing pipeline for functional MRI. Nature Methods, 16(1), 111–116. https://doi.org/10.1038/s41592-018-0235-4
Gu, S., Fotiadis, P., Parkes, L., Xia, C. H., Gur, R. C., Gur, R. E., Roalf, D. R., Satterthwaite, T. D., & Bassett, D. S. (2022). Network controllability mediates the relationship between rigid structure and flexible dynamics. Network Neuroscience, 6(1), 275–297. https://doi.org/10.1162/netn_a_00225
Mehta, K., Salo, T., Madison, T. J., Adebimpe, A., Bassett, D. S., Bertolero, M., … & Satterthwaite, T. D. (2024). XCP-D: A Robust Pipeline for the post-processing of fMRI data. Imaging Neuroscience, 2, 1-26. doi:10.1162/imag_a_00257.
Mucha, P. J., Richardson, T., Macon, K., Porter, M. A., & Onnela, J.-P. (2010). Community Structure in Time-Dependent, Multiscale, and Multiplex Networks. Science, 328(5980), 876–878. https://doi.org/10.1126/science.1184819
Pedersen, M., Zalesky, A., Omidvarnia, A., & Jackson, G. D. (2018). Multilayer network switching rate predicts brain performance. Proceedings of the National Academy of Sciences, 115(52), 13376–13381. https://doi.org/10.1073/pnas.1814785115
Satterthwaite, T. D., Elliott, M. A., Ruparel, K., Loughead, J., Prabhakaran, K., Calkins, M. E.,
Hopson, R., Jackson, C., Keefe, J., Riley, M., Mentch, F. D., Sleiman, P., Verma, R., Davatzikos, C., Hakonarson, H., Gur, R. C., & Gur, R. E. (2014). Neuroimaging of the Philadelphia Neurodevelopmental Cohort. NeuroImage, 86, 544–553. https://doi.org/10.1016/j.neuroimage.2013.07.064
Schaefer, A., Kong, R., Gordon, E. M., Laumann, T. O., Zuo, X. N., Holmes, A. J., Eickhoff, S. B., & Yeo, B. T. (2018). Local-global parcellation of the human cerebral cortex from intrinsic functional connectivity MRI. Cerebral cortex, 28(9), 3095-3114.
Telesford, Q. K., Lynall, M.-E., Vettel, J., Miller, M. B., Grafton, S. T., & Bassett, D. S. (2016). Detection of functional brain network reconfiguration during
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