ArticleeLife2025
Virtual Brain Inference (VBI), a flexible and integrative toolkit for efficient probabilistic inference on whole-brain models.
Article in eLife, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Dynamical system modeling in schizophrenia: a narrative review of computational psychiatry frameworks.Dialogues in clinical neuroscience · 2026Review
- Arousal-driven critical roaming reproduces human functional connectivity dynamics.PLoS biology · 2026Article
- Neural population models for EEG: From Canonical models to alternative model structures.PLoS computational biology · 2026Article
- Virtual brain and electroencephalography explain the variance of memory alterations in mild cognitive impairment.Alzheimer's research & therapy · 2026Article
- Alterations in topological and dynamical parameters correlate with disease biomarkers and neuropsychological scores in prodromic stages of dementia.Scientific reports · 2026Article
- New directions for complex systems in contemporary neuroscience: a morphodynamic and emergent function approach.Frontiers in computational neuroscience · 2026Review
- The neuroscience education readiness checklist for digital tools and methods.Frontiers in computational neuroscience · 2026Article
- cuBNM: GPU-Accelerated Brain Network Modeling.bioRxiv : the preprint server for biology · 2025Article
- Region-specific mean field models enhance simulations of local and global brain dynamics.NPJ systems biology and applications · 2025Article
- Dynamic causal modelling in probabilistic programming languages.Journal of the Royal Society, Interface · 2025Article
- A Python toolbox for neural circuit parameter inference.NPJ systems biology and applications · 2025Article
- Mapping Brain Lesions to Conduction Delays: The Next Step for Personalized Brain Models in Multiple Sclerosis.Human brain mapping · 2025Article
- Mapping global brain reconfigurations following local targeted manipulations.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
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Authors and funding
6 authors.
Funding
Abstract
Network neuroscience has proven essential for understanding the principles and mechanisms underlying complex brain (dys)function and cognition. In this context, whole-brain network modeling-also known as virtual brain modeling-combines computational models of brain dynamics (placed at each network node) with individual brain imaging data (to coordinate and connect the nodes), advancing our understanding of the complex dynamics of the brain and its neurobiological underpinnings. However, there remains a critical need for automated model inversion tools to estimate control (bifurcation) parameters at large scales associated with neuroimaging modalities, given their varying spatio-temporal resolutions. This study aims to address this gap by introducing a flexible and integrative toolkit for efficient Bayesian inference on virtual brain models, called Virtual Brain Inference (VBI). This open-source toolkit provides fast simulations, taxonomy of feature extraction, efficient data storage and loading, and probabilistic machine learning algorithms, enabling biophysically interpretable inference from non-invasive and invasive recordings. Through in-silico testing, we demonstrate the accuracy and reliability of inference for commonly used whole-brain network models and their associated neuroimaging data. VBI shows potential to improve hypothesis evaluation in network neuroscience through uncertainty quantification and contribute to advances in precision medicine by enhancing the predictive power of virtual brain models.
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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.