ArticlePLoS computational biology2022
High-order functional redundancy in ageing explained via alterations in the connectome in a whole-brain model.
Article in PLoS computational biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed, 37 citations in OpenAlex.
- Charting higher-order models of brain function beyond pairwise interactions.Nature communications · 2026Article
- Astrocyte-mediated higher-order control of synaptic plasticity.Communications biology · 2026Article
- Structural signatures of synergy and redundancy in human brain function.bioRxiv : the preprint server for biology · 2026Article
- Spatiotemporal complexity in the psychotic brain.Molecular psychiatry · 2026Article
- Evolving reservoir computers reveal bidirectional coupling between predictive power and emergent dynamics.Patterns (New York, N.Y.) · 2026Article
- THOI: An efficient and accessible library for computing higher-order interactions enhanced by batch-processing.PloS one · 2026Article
- Predicting future risk of developing cognitive impairment using ambulatory sleep EEG: Integrating univariate analysis and multivariate information theory approach.Journal of Alzheimer's disease : JAD · 2025Article
- Null models for comparing information decomposition across complex systems.PLoS computational biology · 2025Article
- Understanding the high-order network plasticity mechanisms of ultrasound neuromodulation.PLoS computational biology · 2025Article
- Diversity-sensitive brain clocks linked to biophysical mechanisms in aging and dementia.Nature. Mental health · 2025Article
- Leveraging multivariate information for community detection in functional brain networks.Communications biology · 2025Article
- Time-varying synergy/redundancy dominance in the human cerebral cortex.Journal of physics. Complexity · 2025Article
- Viscous dynamics associated with hypoexcitation and structural disintegration in neurodegeneration via generative whole-brain modeling.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2024Article
- Higher-order functional connectivity analysis of resting-state functional magnetic resonance imaging data using multivariate cumulants.Human brain mapping · 2024Article
- Open datasets and code for multi-scale relations on structure, function and neuro-genetics in the human brain.Scientific data · 2024Article
- Generalized decomposition of multivariate information.PloS one · 2024Article
- Transcranial ultrasound stimulation effect in the redundant and synergistic networks consistent across macaques.Network neuroscience (Cambridge, Mass.) · 2024Article
- Neural mass modeling for the masses: Democratizing access to whole-brain biophysical modeling with FastDMF.Network neuroscience (Cambridge, Mass.) · 2024Article
- Elevating understanding: Linking high-altitude hypoxia to brain aging through EEG functional connectivity and spectral analyses.Network neuroscience (Cambridge, Mass.) · 2024Article
- Cognitive flexibility and brain network energy in healthy aging: An allostatic perspective from the SENECA model.Imaging neuroscience (Cambridge, Mass.)Article
Corrections and comments
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Authors and funding
8 authors at 8 institutions in 6 countries.
Funding
Abstract
The human brain generates a rich repertoire of spatio-temporal activity patterns, which support a wide variety of motor and cognitive functions. These patterns of activity change with age in a multi-factorial manner. One of these factors is the variations in the brain's connectomics that occurs along the lifespan. However, the precise relationship between high-order functional interactions and connnectomics, as well as their variations with age are largely unknown, in part due to the absence of mechanistic models that can efficiently map brain connnectomics to functional connectivity in aging. To investigate this issue, we have built a neurobiologically-realistic whole-brain computational model using both anatomical and functional MRI data from 161 participants ranging from 10 to 80 years old. We show that the differences in high-order functional interactions between age groups can be largely explained by variations in the connectome. Based on this finding, we propose a simple neurodegeneration model that is representative of normal physiological aging. As such, when applied to connectomes of young participant it reproduces the age-variations that occur in the high-order structure of the functional data. Overall, these results begin to disentangle the mechanisms by which structural changes in the connectome lead to functional differences in the ageing brain. Our model can also serve as a starting point for modeling more complex forms of pathological ageing or cognitive deficits.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.