Network controllability in younger & older adults, and pre-symptomatic change in Alzheimer's disease
Poster No:
1228
Submission Type:
Abstract Submission
Authors:
Paulina Skolasinska1, Alfie Wearn1, Daniel O'Sullivan1, Isabella Stallworthy2, Kate Brynildsen2, Ilana Leppert1,3, Jennifer Tremblay-Mercier4, Judes Poirier4, John Breitner4, Sylvia Villeneuve4,3, Christine Tardif1,3, Gary Turner5, Dani Bassett2, Nathan Spreng1,3,4
Institutions:
1Montreal Neurological Institute, McGill University, Montreal, Canada, 2University of Pennsylvania, Philadelphia, PA, 3McConnell Brain Imaging Centre, McGill University, Montreal, Canada, 4Douglas Mental Health University Institute, McGill University, Montreal, Canada, 5York University, Toronto, Canada
First Author:
Co-Author(s):
Ilana Leppert
Montreal Neurological Institute, McGill University|McConnell Brain Imaging Centre, McGill University
Montreal, Canada|Montreal, Canada
Montreal Neurological Institute, McGill University|McConnell Brain Imaging Centre, McGill University
Montreal, Canada|Montreal, Canada
Jennifer Tremblay-Mercier
Douglas Mental Health University Institute, McGill University
Montreal, Canada
Douglas Mental Health University Institute, McGill University
Montreal, Canada
Sylvia Villeneuve, PhD
Douglas Mental Health University Institute, McGill University|McConnell Brain Imaging Centre, McGill University
Montreal, Canada|Montreal, Canada
Douglas Mental Health University Institute, McGill University|McConnell Brain Imaging Centre, McGill University
Montreal, Canada|Montreal, Canada
Christine Tardif, PhD
Montreal Neurological Institute, McGill University|McConnell Brain Imaging Centre, McGill University
Montreal, Canada|Montreal, Canada
Montreal Neurological Institute, McGill University|McConnell Brain Imaging Centre, McGill University
Montreal, Canada|Montreal, Canada
Nathan Spreng
Montreal Neurological Institute, McGill University|McConnell Brain Imaging Centre, McGill University|Douglas Mental Health University Institute, McGill University
Montreal, Canada|Montreal, Canada|Montreal, Canada
Montreal Neurological Institute, McGill University|McConnell Brain Imaging Centre, McGill University|Douglas Mental Health University Institute, McGill University
Montreal, Canada|Montreal, Canada|Montreal, Canada
Introduction:
The ability of brain networks to control neural dynamics might be affected by healthy and pathological aging. We used network control theory to characterize the efficacy of structural brain networks in a) driving neural dynamics toward all possible states, measured by average controllability (AveC), and b) toward difficult-to-reach states, using modal controllability (ModC; Gu et al., 2015). Differences in average and modal controllability of brain networks have been observed in the context of aging and Alzheimer's disease (Stanford et al., 2024; Zheng et al., 2024). In this study, we obtained these metrics for younger adults and a unique cohort of longitudinally followed older adults at risk of Alzheimer's disease, with ~30% conversion rate to mild cognitive impairment (MCI). We used a novel measure of structural connectivity: axonal caliber, shown to be superior to the commonly used number of streamlines (Nelson et al., 2023). We compared network controllability metrics in younger and older adults (cross-sectional), and their longitudinal change in healthy aging and prodromal MCI. We expected lower/decreased average controllability and higher/increased modal controllability in older and prodromal MCI adults compared to younger and non-MCI older adults, respectively.
Methods:
38 younger adults at a single time point (Mage =24 y, 58% female). Older adults at two time points: 73 healthy (Mage #1=67 y, Mdelay=3 y, 73% female), 26 prodromal MCI (Mage #1=68 y, Mdelay=3 y, 56% female). Older adults were part of the PREVENT-AD cohort (Tremblay-Mercier et al., 2021).
3T MRI sequences included: Diffusion-weighted imaging (2mm isotropic, b=[300,1000,2000] s/mm2, 109 directions, TR/TE=3000/66ms). Multiparametric mapping, including sequence weighed for magnetization transfer (MTsat; 1mm isotropic, TR=27ms, 6 echoes TE=2.04-14.89ms, FA 6°, MT pulse: 4ms Gaussian, 2 kHz off-resonance, FA 220°).
MTsat maps were computed using hMRI toolbox (Tabelow et al., 2019). Other processing was performed via micapipe (Cruces et al., 2022) and COMMIT (Daducci et al., 2015) to obtain axonal volume fraction tractograms reflecting axonal caliber, used to generate structural connectomes in the Schaefer-400 atlas. The controllability metrics were obtained via the nctpy toolbox (Parkes et al., 2024). AveC was computed as the trace of the controllability Gramian. ModC was calculated from the eigenvectors and eigenvalues of the decomposed matrix.
The Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) was used to test global cognition of older adults at each timepoint. ANCOVAs and linear mixed models were used to test the age group and cognitive status by time effects, controlling for total brain volumes.
3T MRI sequences included: Diffusion-weighted imaging (2mm isotropic, b=[300,1000,2000] s/mm2, 109 directions, TR/TE=3000/66ms). Multiparametric mapping, including sequence weighed for magnetization transfer (MTsat; 1mm isotropic, TR=27ms, 6 echoes TE=2.04-14.89ms, FA 6°, MT pulse: 4ms Gaussian, 2 kHz off-resonance, FA 220°).
MTsat maps were computed using hMRI toolbox (Tabelow et al., 2019). Other processing was performed via micapipe (Cruces et al., 2022) and COMMIT (Daducci et al., 2015) to obtain axonal volume fraction tractograms reflecting axonal caliber, used to generate structural connectomes in the Schaefer-400 atlas. The controllability metrics were obtained via the nctpy toolbox (Parkes et al., 2024). AveC was computed as the trace of the controllability Gramian. ModC was calculated from the eigenvectors and eigenvalues of the decomposed matrix.
The Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) was used to test global cognition of older adults at each timepoint. ANCOVAs and linear mixed models were used to test the age group and cognitive status by time effects, controlling for total brain volumes.
Results:
Younger adults showed greater AveC and lower ModC of the control network, esp., in the prefrontal cortex, and marginally greater AveC of the limbic network, while older adults had higher AveC in the visual system (Fig 1). In older adults, the decrease in AveC and increase in ModC was moderated by cognitive status, so that prodromal MCI showed greater AveC decrease and ModC increase in the limbic network. Marginal interaction effects were found for control, dorsal attention (both AveC) and visual networks (ModC). ModC of the visual network correlated negatively with RBANS at the 2nd timepoint (Fig 2).
·Cross-sectional comparisons between cognitively healthy older adults and younger adults.
·Longitudinal comparisons between cognitively healthy older adults and prodromal MCI.
Conclusions:
In line with past findings, the controllability of the limbic network was different in older adults with vs. without prodromal MCI. These and past findings showing age-related decrease in AveC of the limbic system, but its greater AveC in Alzheimer's disease compared to healthy controls hint at non-linear trajectories of controllability in dementia stages. While we did not observe substantial effects of age(ing) in the DMN, the limbic network has been suggested to belong to the wider DMN (Girn et al., 2024). We found functionally relevant effects in the visual system, indicating a possible shift in the control strategy in older age and prodromal MCI.
Disorders of the Nervous System:
Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s) 2
Lifespan Development:
Aging
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 1
Novel Imaging Acquisition Methods:
Diffusion MRI
Keywords:
ADULTS
Aging
Cognition
Data analysis
Degenerative Disease
Open Data
Plasticity
STRUCTURAL MRI
Tractography
Other - network control
1|2Indicates the priority used for review
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2. Daducci, A. et al. (2015). COMMIT: Convex Optimization Modeling for Microstructure Informed Tractography. IEEE Transactions on Medical Imaging, 34(1), 246–257.
3. Girn, M. et al. (2024). The “limbic network,” comprising orbitofrontal and anterior temporal cortex, is part of an extended default network: Evidence from multi-echo fMRI. Network Neuroscience, 8(3), 860–882.
4. Gu, S. et al. (2015). Controllability of structural brain networks. Nature Communications, 6(1), 8414.
5. Nelson, M. C. et al. (2023). The human brain connectome weighted by the myelin content and total intra-axonal cross-sectional area of white matter tracts. Network Neuroscience, 7(4), 1363–1388.
6. Parkes, L. et al. (2024). A network control theory pipeline for studying the dynamics of the structural connectome. Nature Protocols.
7. Stanford, W. et al. (2024). Age-related differences in network controllability are mitigated by redundancy in large-scale brain networks. Communications Biology, 7(1), 1–13.
8. Tabelow, K. et al. (2019). hMRI – A toolbox for quantitative MRI in neuroscience and clinical research. NeuroImage, 194, 191–210.
9. Tremblay-Mercier, J. et al. (2021). Open science datasets from PREVENT-AD, a longitudinal cohort of pre-symptomatic Alzheimer’s disease. NeuroImage. Clinical, 31, 102733.
10. Zheng, C. et al. (2024). Functional brain network controllability dysfunction in Alzheimer’s disease and its relationship with cognition and gene expression profiling. Journal of Neural Engineering, 21(2), 026018.