Fine-Scale Dynamics of Age-Related Decline in Functional Network Segregation
Poster No:
1487
Submission Type:
Late-Breaking Abstract Submission
Authors:
Sarah Cutts1, Rudra Patel1, Colleen Hughes1, Roberto French1, Anne Krendl1, Olaf Sporns1
Institutions:
1Indiana University, Bloomington, IN
First Author:
Co-Author(s):
Late Breaking Reviewer(s):
Introduction:
Decreased segregation of functional brain networks in older adults is related to declines in cognitive function (Wig, 2017). These findings are based on measures of functional connectivity (FC) averaged across a scan, but previous work has shown that segregation varies over time (Shine, 2016). This temporal variation could be more sensitive to detecting individual differences in behavior or cognition. Moreover, averaging can result in shorter sessions and unequal amounts of data that can artificially exacerbate age differences in segregation (Han, 2024). Therefore, it is crucial to develop methods for equal comparison of FC measures and determine whether time-varying FC is associated with older adults' cognitive function.
Temporal variations in FC, measured by unwrapping FC into pairwise cofluctuation time series (Faskowitz, 2020), emerge as different functional systems that appear transiently during a scan (Sporns, 2021). When averaged together, these discrete systems are represented in full FC as a combination of all systems. Such moments have been related to variations in individualized signatures, behavioral associations, and disease-relevant relationships not found in standard FC (Cutts, 2023, 2025; Chumin, 2024). This suggests that transient changes in FC might better predict older adults' cognitive function. Here, we test multiple hypotheses as to how transient patterns in the data could contribute to age-related decreased segregation in full FC, and whether such moments reveal stronger associations with cognitive function.
Temporal variations in FC, measured by unwrapping FC into pairwise cofluctuation time series (Faskowitz, 2020), emerge as different functional systems that appear transiently during a scan (Sporns, 2021). When averaged together, these discrete systems are represented in full FC as a combination of all systems. Such moments have been related to variations in individualized signatures, behavioral associations, and disease-relevant relationships not found in standard FC (Cutts, 2023, 2025; Chumin, 2024). This suggests that transient changes in FC might better predict older adults' cognitive function. Here, we test multiple hypotheses as to how transient patterns in the data could contribute to age-related decreased segregation in full FC, and whether such moments reveal stronger associations with cognitive function.
Methods:
We analyzed eyes-open 15-minute resting-state fMRI scans of 91 younger adults (YA; mean age = 22.2; gender = 58, 31, 2 (female, male, non-binary) and 62 older adults (OA; mean age = 72.9; gender = 43, 19 (female, male) collected at Indiana University. Functional scans were collected on a Siemens 3.0T Prisma MRI Scanner with TR = 2000ms and data preprocessed for motion and distortion correction, co-registration, and normalization using fMRIPrep. Nuisance regressors were removed and censoring performed (FD threshold of 0.5mm (Hughes, 2020)).
First, we tested mean system segregation of full FC between YA and OA (Fig 1A-B). Second, we isolated moments with greatest similarity to a set of functional system templates to examine instances when specific systems emerge during a scan and their frequency of occurrence (Fig 1C-E). Third, we tested whether age differences in segregation were related to amount of time spent in segregated or integrated states. This was done by creating cartographic profiles by clustering joint histograms of nodal within-module degree and participation coefficient (Shine, 2016). Fourth, we examined system segregation found from full FC and connectivity components (FCc) made from the top 5% of moments that matched functional system expression (Cutts, 2023). Fifth, relationships were examined between age-regressed system segregation and a composite variable of cognition.
First, we tested mean system segregation of full FC between YA and OA (Fig 1A-B). Second, we isolated moments with greatest similarity to a set of functional system templates to examine instances when specific systems emerge during a scan and their frequency of occurrence (Fig 1C-E). Third, we tested whether age differences in segregation were related to amount of time spent in segregated or integrated states. This was done by creating cartographic profiles by clustering joint histograms of nodal within-module degree and participation coefficient (Shine, 2016). Fourth, we examined system segregation found from full FC and connectivity components (FCc) made from the top 5% of moments that matched functional system expression (Cutts, 2023). Fifth, relationships were examined between age-regressed system segregation and a composite variable of cognition.
Results:
Age-related differences in segregation were localized to transient system fluctuations and their frequencies of occurrence were similar between YA and OA (Fig 1). Additionally, we found that YA and OA have similar segregated and integrated states found from cartographic profiles, but OA spent less time in segregated patterns (Fig 2A-B). System segregation was higher for FCc than measures found from full FC (Fig 2C). Differences in segregation relate to cognition for the frontoparietal network (FPN) but were not found for individual systems in full FC (Fig 2D).
Conclusions:
Together this work suggests that desegregation of functional networks can be systematically found from transient fluctuations of functional systems at a localized scale. Additionally, OA spend time in globally defined segregated states, albeit less than YA. Moreover, localized measures using FCc reveal that older adults are briefly able to achieve higher segregation than suggested by full FC and these moments reveal stronger relationships with cognitive function.
Lifespan Development:
Aging 2
Modeling and Analysis Methods:
fMRI Connectivity and Network Modeling 1
Task-Independent and Resting-State Analysis
Keywords:
Aging
Computational Neuroscience
Data analysis
FUNCTIONAL MRI
Other - Network Neuroscience
1|2Indicates the priority used for review
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2. Cutts, S. A. (2023). Uncovering individual differences in fine-scale dynamics of functional connectivity. Cerebral Cortex, 33(5), 2375-2394.
3. Cutts, S. A. (2025). Temporal variability of brain–behavior relationships in fine-scale dynamics of edge time series. Imaging Neuroscience, 3, imag_a_00443.
4. Faskowitz, J. (2020). Edge-centric functional network representations of human cerebral cortex reveal overlapping system-level architecture. Nature Neuroscience, 23(12), 1644–1654.
5. Han, L. (2024). Measures of resting-state brain network segregation and integration vary in relation to data quantity: implications for within and between subject comparisons of functional brain network organization. Cerebral Cortex, 34(2), bhad506.
6. Hughes, C. (2020). Aging relates to a disproportionately weaker functional architecture of brain networks during rest and task states. NeuroImage, 209, 116521.
7. Satterthwaite, T. D. (2013). An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data. Neuroimage, 64, 240-256.
8. Shine, J. M. (2016). The dynamics of functional brain networks: integrated network states during cognitive task performance. Neuron, 92(2), 544-554.
9. Sporns, O. (2021). Dynamic expression of brain functional systems disclosed by fine-scale analysis of edge time series. Network Neuroscience, 5(2), 405-433.
10. Wig, G. S. (2017). Segregated systems of human brain networks. Trends in Cognitive Sciences, 21(12), 981-996.