ReviewNational science review2024
Virtual brain twins: from basic neuroscience to clinical use.
Review in National science review, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 56 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
56 citing papers in PubMed.
- Reconciling global collaboration and data sovereignty in neuroscience.Nature neuroscience · 2026Review
- Neurostimulation in the treatment of psychiatric disorders: Underlying mechanisms and critical analysis of circuit-based interventions.Molecular psychiatry · 2026Review
- Digital twin for neurological conditions: a systematic scoping review.Biomedical engineering letters · 2026Review
- Bladder Digital Twins for Neuromodulation: Current Platforms and a Roadmap to Closed-Loop Therapy.International neurourology journal · 2026Article
- Digital Twins as the Implementation Layer of Precision Medicine in Pediatric Neurosurgery.Journal of Korean Neurosurgical Society · 2026Review
- Cerebrovascular Imaging-to-Graph Reconstruction for Individualized Digital Twin Brains.bioRxiv : the preprint server for biology · 2026Article
- A digital twin approach for simultaneous reconstruction of brain anatomy and dynamics from neural data.PLOS digital health · 2026Article
- Digitizing Dignity: Analyzing Digital Twins Through the Lens of Multidimensional Human Dignity.Bioethics · 2026Article
- Co-simulation framework combining a microscopically detailed point neuron model of the hippocampal CA1 region with the macroscopic high-resolution virtual brain model.Journal of computational neuroscience · 2026Article
- The optimization of neuroprosthetic interfaces relying on biophysical and surrogate digital twins.npj biomedical innovations · 2026Review
- Six artificial intelligence innovation strategies applied to autism spectrum disorder research: A narrative review.Pediatric investigation · 2026Review
- Developmental and aging changes in brain network switching dynamics revealed by EEG phase synchronization.PLoS computational biology · 2026Article
- Hierarchical whole-brain modeling of critical synchronization dynamics in the human brain.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Digital Twin Brain simulation and manipulation of a functional brain network underlying mental illness.bioRxiv : the preprint server for biology · 2026Article
- The role of connectivity for the degeneracy of the brain's resting state dynamics.Journal of computational neuroscience · 2026Article
- Digital twin brain reveals state-specific stimulation targets for abnormal brain dynamics in tinnitus.BMC medicine · 2026Article
- Artificial intelligence as a surrogate brain: bridging neural dynamical models and data.National science review · 2026Review
- Personalized treatment approaches in neurocritical care.Acute and critical care · 2026Article
- Can digital brain twins dissolve the uncertainties surrounding unresponsive wakefulness?BMC medical ethics · 2026Article
- The neurotoxic legacy of CAR-T cells: where do we stand?Therapeutic advances in neurological disorders · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Virtual brain twins are personalized, generative and adaptive brain models based on data from an individual's brain for scientific and clinical use. After a description of the key elements of virtual brain twins, we present the standard model for personalized whole-brain network models. The personalization is accomplished using a subject's brain imaging data by three means: (1) assemble cortical and subcortical areas in the subject-specific brain space; (2) directly map connectivity into the brain models, which can be generalized to other parameters; and (3) estimate relevant parameters through model inversion, typically using probabilistic machine learning. We present the use of personalized whole-brain network models in healthy ageing and five clinical diseases: epilepsy, Alzheimer's disease, multiple sclerosis, Parkinson's disease and psychiatric disorders. Specifically, we introduce spatial masks for relevant parameters and demonstrate their use based on the physiological and pathophysiological hypotheses. Finally, we pinpoint the key challenges and future directions.
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What OpenQuestion holds
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.