Signal Complexity of Cortical Functional Connectivity Networks Across the Lifespan
Poster No:
1202
Submission Type:
Abstract Submission
Authors:
Dong Yin1, Kamen Tsvetanov1, Emmanuel Stamatakis1
Institutions:
1University of Cambridge, Cambridge, Cambridgeshire
First Author:
Co-Author(s):
Introduction:
Healthy ageing leads to changes in functional brain organisation, notably in the segregation and integration of large-scale networks (Chan et al., 2014; Cohen & D'Esposito, 2016). Early adulthood is characterised by stronger within-network connectivity, which declines in ageing and neurodegeneration (Tsvetanov et al., 2016). However, the neurobiological mechanism underlying network segregation is not well understood. Signal complexity, which captures the richness and irregularity of neural time series, has emerged as a measure distinguishing states of consciousness, cognitive impairments, and ageing-related adaptations (Sun et al., 2020; Varley, Luppi, et al., 2020). It may also serve as an early marker of age-related network reorganisation, potentially underlying shifts in segregation (Zheng et al., 2020). We investigate how the complexity of coactivations within- and between-canonical resting-state networks differs across the adult lifespan and relates to differences in system segregation with age.
Methods:
We analysed resting-state fMRI data from 654 adults aged 18 to 90 years (330 females) from the Cambridge Centre for Ageing and Neuroscience (CAM-CAN) dataset (Cam-CAN et al., 2014; Taylor et al., 2017). Standard processing including motion correction, noise regression and temporal filtering was applied. Using the Brainnetome atlas, with 246 regions assigned to one of seven canonical networks (Yeo-7), we estimated pairwise coactivations between regional BOLD signals using linear fits. The complexity of the resulting coactivation time series was computed via Higuchi's fractal dimension (Higuchi, 1988; Varley, Craig, et al., 2020), using a max k value of 16. Complexity values were categorised into within-network and between-network interactions (depending on whether the two ROIs belong to the same Yeo-7-defined network). The two distributions are compared across 5 age groups determined by equally spaced quantiles of age.
Results:
Both within-network and between-network coactivation complexities progressively 3 increased with age. Notably, between-network complexity exhibited a steeper age-related increase, surpassing within-network complexity around 46-50 years of age. Figure 1 shows the crossover in the joint distributions of complexity and age. Figure 2 A shows the upward trend and highlights when between-network complexity exceeds within-network complexity. Figure 2 B shows the effect size dynamics where initially positive difference between within- and between-network complexities becomes lower and more variable with age.
Conclusions:
Our findings show that, parallel to the decreasing of system segregation with age, the brain involves increased complexity in cross-network communication, transitioning from simple, segregated interactions to richer between-network dynamics. This shift in complexity and segregation around midlife corresponds with the age-related decline in system segregation typically observed around the age of 50 (Chan et al., 2014; Cohen & D'Esposito, 2016; Tsvetanov et al., 2016). Before overt disruptions in network architecture emerge (Tsvetanov et al., 2016), changes in neural complexity might precede integration-driven adaptations, offering a potential mechanistic explanation and serving as early markers of age-related brain reorganisation.
Lifespan Development:
Aging 2
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 1
fMRI Connectivity and Network Modeling
Task-Independent and Resting-State Analysis
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Anatomy and Functional Systems
Keywords:
Cognition
Cortex
Data analysis
FUNCTIONAL MRI
Other - System Segregation; Brain Network Connectivity; Signal Complexity; Coactivation Complexity; Higuchi's Fractal Dimension; Between-network Dynamics
1|2Indicates the priority used for review
Abstract Information
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Please indicate below if your study was a "resting state" or "task-activation” study.
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Was this research conducted in the United States?
Were any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.
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.
Please indicate which methods were used in your research:
For human MRI, what field strength scanner do you use?
Provide references using APA citation style.
Chan, et al. (2014). Decreased segregation of brain systems across the healthy adult lifespan. Proceedings of the National Academy of Sciences, 111(46).
Cohen, et al. (2016). The segregation and integration of distinct brain networks and their relationship to cognition. The Journal of Neuroscience, 36(48), 12083–12094.
Higuchi, T. (1988). Approach to an irregular time series on the basis of the fractal theory. Physica D: Nonlinear Phenomena, 31(2), 277–283.
Sun, et al. (2020). Complexity Analysis of EEG, MEG, and fMRI in Mild Cognitive Impairment and Alzheimer's Disease: A Review. Entropy, 22(2), 239.
Taylor, et al. (2017). The Cambridge Centre for Ageing and Neuroscience (Cam-CAN) data repository: Structural and functional MRI, MEG, and cognitive data from a cross-sectional adult lifespan sample. NeuroImage, 144, 262–269.
Tsvetanov, et al. (2016). Extrinsic and intrinsic brain network connectivity maintains cognition across the lifespan despite accelerated decay of regional brain activation. The Journal of Neuroscience, 36(11), 3115–3126.
Varley, et al. (2020). Fractal dimension of cortical functional connectivity networks & severity of disorders of consciousness (F. J. Esteban, Ed.). PLOS ONE, 15(2), e0223812.
Varley, et al. (2020). Consciousness & brain functional complexity in propofol anaesthesia. Scientific Reports, 10(1), 1018.
Zheng, et al. (2020). Reduced dynamic complexity of BOLD signals differentiates mild cognitive impairment from normal aging. Frontiers in Aging Neuroscience, 12, 90.