Brain health clocks linked to biophysical mechanisms in dementia and Down syndrome
Poster No:
883
Submission Type:
Abstract Submission
Authors:
Carlos Coronel1, Joaquin Migeot1, Agustín Ibáñez1, Eimear Mc Glinchey2, Ruben Herzog3
Institutions:
1BrainLat Institute, Universidad Adolfo Ibañez, Santiago, Santiago, 2Trinity College Dublin, Dublin, Ireland, 3Paris Brain Institute, Paris, France
First Author:
Co-Author(s):
Introduction:
Brain clocks, which measure deviations between chronological age and predicted brain age (brain age gaps, or BAGs), reveal whether an individual's brain appears older or younger than expected. These models provide valuable insights into aging trajectories and are particularly useful for studying conditions like Alzheimer's Disease (AD), behavioral variant frontotemporal dementia (bvFTD), and Down Syndrome (DS), the last considered a genetic model of accelerated aging. However, challenges such as heterogeneous findings, limited computational methods, and a lack of diverse representation hinder progress in the field. Emerging generative biophysical brain models offer a promising solution, enabling robust analyses with moderate sample sizes and shedding light on unexplored causal mechanisms.
Methods:
We combined structural MRI and resting-state fMRI functional connectivity with generative brain modeling in healthy controls (HCs) from both the global south and north, alongside AD, bvFTD, and DS with dementia patients (N > 6000 in total). Using gray matter volume (derived from MRI), fMRI functional connectivity, and Support Vector Machine (SVM) regression models, we examined how BAGs reflect disparities at both aggregate (e.g., geographic and income differences), and individual levels (e.g., education and sex). We then explored the biophysical mechanisms ascribed to accelerated brain aging in healthy controls and dementia. We combined structural (DTI) and functional (fMRI) connectivity with a dynamic mean field model, to create personalized whole-brain models and inferred individual biophysical parameters. We explored mechanisms based on neural excitability and structural disintegration, by modulating the regional firing rates of nodes and the integration/segregation balance of structural networks, respectively.
·Figure 1. General pipeline for data preprocessing and analysys.
Results:
The BAGs in aging were modulated by diversity-related factors inducing accelerated aging, including geography (south>north), income (GPD, low > high), sex (female>male), and education (low > high). A larger BAG was observed in patients, with sex further increasing the effects in AD (female>male). Within the patients, we found a BAG gradient of DS with dementia > AD > bvFTD. Biophysical modeling shows that BAG is related to specific mechanisms: global hyperexcitability and reduced connectivity (structural disintegration) were implicated in aging's BAGs. Hypoexcitability and severe disintegration were related to dementia. We identified specific spatiotemporal patterns particular for each clinical condition, i.e., frontal hyperexcitability and posterior hypoexcitability in AD and DS with dementia, and temporal hypoexcitability in bvFTD. In clinical populations, BAGs correlated with brain excitability, PET amyloid-beta and tau, and neuropsychological assessments.
Conclusions:
Our study suggests that earlier hyperexcitation, linked to negative social determinants of brain health, can produce accelerated brain aging measured by higher BAGs. This sustained hyperexcitability would produce hypoexcitation and structural disintegration in dementia. Our work sheds light on biophysical mechanisms of accelerated aging in diverse and underserved populations and provides domain-independent and normative approach to characterize brain disorders from neuroimaging data.
Disorders of the Nervous System:
Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s)
Lifespan Development:
Aging 1
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural)
fMRI Connectivity and Network Modeling 2
Keywords:
Aging
Computational Neuroscience
FUNCTIONAL MRI
Modeling
1|2Indicates the priority used for review
Abstract Information
By submitting your proposal, you grant permission for the Organization for Human Brain Mapping (OHBM) to distribute your work in any format, including video, audio print and electronic text through OHBM OnDemand, social media channels, the OHBM website, or other electronic publications and media.
The Open Science Special Interest Group (OSSIG) is introducing a reproducibility challenge for OHBM 2025. This new initiative aims to enhance the reproducibility of scientific results and foster collaborations between labs. Teams will consist of a “source” party and a “reproducing” party, and will be evaluated on the success of their replication, the openness of the source work, and additional deliverables. Click here for more information. Propose your OHBM abstract(s) as source work for future OHBM meetings by selecting one of the following options:
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?
Which processing packages did you use for your study?
Provide references using APA citation style.
Moguilner, S., et al. (2024). Brain clocks capture diversity and disparities in aging and dementia across geographically diverse populations. Nature Medicine. https://doi.org/10.1038/s41591-024-03209-x
Clift, S., et al. (2024). WHO–Lancet Global Series on health and the arts. The Lancet, 403(10319), 1335. https://doi.org/10.1016/S0140-6736(23)02431-5
Bowling, D. L. (2023). Biological principles for music and mental health. Translational Psychiatry, 13, 374. https://doi.org/10.1038/s41398-023-02477-7
Tian, Y. E., et al. (2023). Heterogeneous aging across multiple organ systems and prediction of chronic disease and mortality. Nature Medicine, 29(7), 1221–1231. https://doi.org/10.1038/s41591-023-02296-6
Lynn, C. W., & Bassett, D. S. (2019). The physics of brain network structure, function and control. Nature Reviews Physics, 1(5), 318–332. https://doi.org/10.1038/s42254-019-0040-8
Bullmore, E., & Sporns, O. (2009). Complex brain networks: Graph theoretical analysis of structural and functional systems. Nature Reviews Neuroscience, 10(3), 186–198. https://doi.org/10.1038/nrn2575
Gerstner, W., Sprekeler, H., & Deco, G. (2012). Theory and simulation in neuroscience. Science, 338(6103), 60–65. https://doi.org/10.1126/science.1227356
Yarkoni, T., Poldrack, R. A., Nichols, T. E., Van Essen, D. C., & Wager, T. D. (2011). Large-scale automated synthesis of human functional neuroimaging data. Nature Methods, 8(8), 665–670. https://doi.org/10.1038/nmeth.1635
Dow, R., Warran, K., Letrondo, P., & Fancourt, D. (2023). The arts in public health policy: Progress and opportunities. The Lancet Public Health, 8(3), e155–e160. https://doi.org/10.1016/S2468-2667(23)00010-8