ArticleiScience2024
The virtual multiple sclerosis patient.
Article in iScience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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
9 citing papers in PubMed.
- Review
- Functional Connectivity at Rest and During Cognitive Processing in Multiple Sclerosis: A Pilot Magnetoencephalography Study and Systematic Review.Brain sciences · 2026Article
- A digital twin approach for simultaneous reconstruction of brain anatomy and dynamics from neural data.PLOS digital health · 2026Article
- The neurotoxic legacy of CAR-T cells: where do we stand?Therapeutic advances in neurological disorders · 2026Review
- Article
- Local homeostasis preserves global neural dynamics compensating for structural loss during human lifespan aging.Communications biology · 2025Article
- Dynamic causal modelling in probabilistic programming languages.Journal of the Royal Society, Interface · 2025Article
- Mapping Brain Lesions to Conduction Delays: The Next Step for Personalized Brain Models in Multiple Sclerosis.Human brain mapping · 2025Article
- More Than the Sum of Its Parts: Disrupted Core Periphery of Multiplex Brain Networks in Multiple Sclerosis.Human brain mapping · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Multiple sclerosis (MS) diagnosis typically involves assessing clinical symptoms, MRI findings, and ruling out alternative explanations. While myelin damage broadly affects conduction speeds, traditional tests focus on specific white-matter tracts, which may not reflect overall impairment accurately. In this study, we integrate diffusion tensor immaging (DTI) and magnetoencephalography (MEG) data into individualized virtual brain models to estimate conduction velocities for MS patients and controls. Using Bayesian inference, we demonstrated a causal link between empirical spectral changes and inferred slower conduction velocities in patients. Remarkably, these velocities proved superior predictors of clinical disability compared to structural damage. Our findings underscore a nuanced relationship between conduction delays and large-scale brain dynamics, suggesting that individualized velocity alterations at the whole-brain level contribute causatively to clinical outcomes in MS.
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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.