Preserved microstructure supports functional integration for cognition across the lifespan
Poster No:
893
Submission Type:
Abstract Submission
Authors:
Kamen Tsvetanov1, Casey Paquola2, Richard Bethlehem3, James Rowe4
Institutions:
1University of Cambridge, Cambridge, NA, 22Institute for Neuroscience and Medicine, INM-7, Forschungszentrum Jülich, Jülich, North Rhine-Westphalia, 3Department of Psychology, University of Cambridge, Cambridge, Cambridge, 4University of Cambridge, Cambridge, United Kingdom
First Author:
Co-Author(s):
Casey Paquola
2Institute for Neuroscience and Medicine, INM-7, Forschungszentrum Jülich
Jülich, North Rhine-Westphalia
2Institute for Neuroscience and Medicine, INM-7, Forschungszentrum Jülich
Jülich, North Rhine-Westphalia
Introduction:
Functional network integrity is important for maintaining cognition across the lifespan [1–3]. Given aging is associated with substantial structural changes in the brain, we test whether functional integrity relies on the preservation of intracortical microstructure. We investigated the relationship between functional integration and myelin-sensitive microstructural profiling, and examined whether its link to cognition varies with age.
Methods:
Participants include 509 healthy adults (age 18-88) from the Cambridge Centre for Ageing and Neuroscience [4]. T1w, T2w and functional magnetic resonance imaging were acquired and analysed using established approaches [5–7]. Microstructure profile covariance (MPC) and functional connectomes (FC) were mapped between parcels5 (Figure 1a-b). To quantify participant-level integration across microstructural and functional gradients, we calculated 'node integration' separately for each modality within their 3D connectivity space (Figure 1c) [3,8]. First, we determined the community of each node by selecting the top 10% of nodes in the group-average centre of gravity. Then, we estimated node integration based on the inverse of the sum squared of Euclidean distances from each community node to the centre of gravity of its corresponding community such that high values indicate high integration, and low values indicate low integration. Node integration measures were concatenated in 2D arrays (subject X MPC integration across regions and subject x FC integration across regions), Figure 1d. An inter-subject regularized canonical correlation analysis (CCA) with permutation-based cross-validations was used to jointly analyse the MPCs and FCs (Figure 1e). Second-level analysis employing robust multiple linear regression model tested for age-related differences in the strength of the association between functional CCA subject scores and performance on a fluid intelligence test, while controlling for sex (Equation: Cognition ~ CCA_Function*Age + Sex).
·Figure 1. Analytical strategy (top panel) and Results (lower centre panel).
Results:
Canonical correlation analysis identified one significant component linking MPC and FC patterns (r=0.270, p<0.001). This component was associated with functional integration in regions of the multiple demand network (Figure 1g). Subjects' expression of functional integration within these regions was associated with higher fluid abilities (r=0.08, p<0.028), and this relationship became stronger for older adults (r=0.09, p<0.008, Figure 1-h). This component was also associated with MPC of limbic and inferior temporal regions (Figure 1f), which are typically distinguished by relatively flat microstructure profiles (Figure 1i). The shape of the profile differed for older individuals though (Figure 1j), suggesting age-related microstructural changes, especially in mid-layers of the cortex, may be related to functional integration of the multiple demand network. The results are discussed in the context of linking MPC and FC integration maps to receptor/metabolic imaging and gene transcription profiles, allowing us to generate hypotheses about the genetic and neurometabolic basis of resilience.
Conclusions:
Our findings suggest that cognition depends on functional integration, more so for older people, which in turn may be supported by preserved microstructure in limbic regions. These results have implications for understanding brain resilience mechanisms, highlighting the value of multimodal imaging-based surrogate markers of neural systems coupling rather than reliance on a single modality or cognitive performance alone.
Lifespan Development:
Aging 1
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 2
fMRI Connectivity and Network Modeling
Methods Development
Multivariate Approaches
Keywords:
Aging
Cognition
Cortical Layers
FUNCTIONAL MRI
Multivariate
Myelin
Statistical Methods
1|2Indicates the priority used for review
Abstract Information
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2. Tsvetanov KA, Gazzina S, Jones SP, Swieten J van, Borroni B, Sanchez-Valle R, Moreno F, Laforce R, Graff C, Synofzik M, et al. Brain functional network integrity sustains cognitive function despite atrophy in presymptomatic genetic frontotemporal dementia. Alzheimer’s & Dementia. 2020;
3. Bethlehem RAI, Paquola C, Seidlitz J, Ronan L, Bernhardt B, Consortium C-C, Tsvetanov KA. Dispersion of functional gradients across the adult lifespan. NeuroImage. 2020;117299.
4. Shafto MA, Tyler LK, Dixon M, Taylor JR, Rowe JB, Cusack R, Calder AJ, Marslen-Wilson WD, Duncan J, Dalgleish T, et al. The Cambridge Centre for Ageing and Neuroscience (Cam-CAN) study protocol: a cross-sectional, lifespan, multidisciplinary examination of healthy cognitive ageing. BMC neurology. 2014;14:204.
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6. Geerligs L, Tsvetanov KA, Cam-Can, Henson RN. Challenges in measuring individual differences in functional connectivity using fMRI: The case of healthy aging. Human Brain Mapping. 2017;
7. Taylor JR, Williams N, Cusack R, Auer T, Shafto MA, Dixon M, Tyler LK, Cam-Can, Henson RN. 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 [Internet]. 2015;Available from: http://www.sciencedirect.com/science/article/pii/S1053811915008150 http://www.ncbi.nlm.nih.gov/pubmed/26375206
8. Paquola C, Wael RVD, Wagstyl K, Bethlehem RAI, Hong S-J, Seidlitz J, Bullmore ET, Evans AC, Misic B, Margulies DS, et al. Microstructural and functional gradients are increasingly dissociated in transmodal cortices. PLOS Biology. 2019;17:e3000284.