ArticleeNeuro
Virtual Connectomic Datasets in Alzheimer's Disease and Aging Using Whole-Brain Network Dynamics Modelling.
Article in eNeuro. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Alterations in topological and dynamical parameters correlate with disease biomarkers and neuropsychological scores in prodromic stages of dementia.Scientific reports · 2026Article
- Competitive interactions shape mammalian brain network dynamics and computation.Nature neuroscience · 2026Article
- Attractor dynamics of a whole-cortex network model predicts emergence and structure of fMRI co-activation patterns in the mouse brain.PLoS computational biology · 2026Article
- Excitation-inhibition homeostasis in Alzheimer's disease: a selective multiscale review of mechanisms, sex differences, and therapeutic opportunities.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2026Review
- Creative experiences and brain clocks.Nature communications · 2025Article
- Diversity-sensitive brain clocks linked to biophysical mechanisms in aging and dementia.Nature. Mental health · 2025Article
- Recent Advancements in Neuroimaging-Based Alzheimer's Disease Prediction Using Deep Learning Approaches in e-Health: A Systematic Review.Health science reports · 2025Review
- A computational approach to evaluate how molecular mechanisms impact large-scale brain activity.Nature computational science · 2025Article
- A Shift Toward Supercritical Brain Dynamics Predicts Alzheimer's Disease Progression.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Article
- Deep learning techniques for Alzheimer's disease detection in 3D imaging: A systematic review.Health science reports · 2024Review
- Gaming expertise induces meso‑scale brain plasticity and efficiency mechanisms as revealed by whole-brain modeling.NeuroImage · 2024Article
- Biophysical models applied to dementia patients reveal links between geographical origin, gender, disease duration, and loss of neural inhibition.Alzheimer's research & therapy · 2024Article
- Brain dynamics predictive of response to psilocybin for treatment-resistant depression.Brain communications · 2024Article
- Gaming expertise induces meso-scale brain plasticity and efficiency mechanisms as revealed by whole-brain modeling Gaming expertise, neuroplasticity and functional dynamics.bioRxiv : the preprint server for biology · 2023Article
- Alzheimer's Disease: Insights from Large-Scale Brain Dynamics Models.Brain sciences · 2023Review
- Computational modelling in disorders of consciousness: Closing the gap towards personalised models for restoring consciousness.NeuroImage · 2023Article
- Complex spatiotemporal oscillations emerge from transverse instabilities in large-scale brain networks.PLoS computational biology · 2023Article
- Scale-Free Functional Brain Networks Exhibit Increased Connectivity, Are More Integrated and Less Segregated in Patients with Parkinson's Disease following Dopaminergic Treatment.Fractal and fractional · 2022Article
- Predicting the future of neuroimaging predictive models in mental health.Molecular psychiatry · 2022Review
- Disentangling the critical signatures of neural activity.Scientific reports · 2022Article
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Authors and funding
8 authors.
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Abstract
Large neuroimaging datasets, including information about structural connectivity (SC) and functional connectivity (FC), play an increasingly important role in clinical research, where they guide the design of algorithms for automated stratification, diagnosis or prediction. A major obstacle is, however, the problem of missing features [e.g., lack of concurrent DTI SC and resting-state functional magnetic resonance imaging (rsfMRI) FC measurements for many of the subjects]. We propose here to address the missing connectivity features problem by introducing strategies based on computational whole-brain network modeling. Using two datasets, the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset and a healthy aging dataset, for proof-of-concept, we demonstrate the feasibility of virtual data completion (i.e., inferring "virtual FC" from empirical SC or "virtual SC" from empirical FC), by using self-consistent simulations of linear and nonlinear brain network models. Furthermore, by performing machine learning classification (to separate age classes or control from patient subjects), we show that algorithms trained on virtual connectomes achieve discrimination performance comparable to when trained on actual empirical data; similarly, algorithms trained on virtual connectomes can be used to successfully classify novel empirical connectomes. Completion algorithms can be combined and reiterated to generate realistic surrogate connectivity matrices in arbitrarily large number, opening the way to the generation of virtual connectomic datasets with network connectivity information comparable to the one of the original data.
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Registered trials
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