Evidence map›Paper›PMID 39077376›Full record

ArticleBrain communications2024

Longitudinal network-based brain grey matter MRI measures are clinically relevant and sensitive to treatment effects in multiple sclerosis.

Elisa Colato, Jonathan Stutters, Sridar Narayanan, Douglas L Arnold, Jeremy Chataway, Claudia A M Gandini Wheeler-Kingshott, Frederik Barkhof, Olga Ciccarelli, Arman Eshaghi, Declan T Chard

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Brain communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Elisa ColatoQueen Square Multiple Sclerosis Centre, Department of Neuroinflammation, University College London (UCL) Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, WC1N 3BG, UK.ORCID https://orcid.org/0000-0002-4101-7058
Jonathan StuttersQueen Square Multiple Sclerosis Centre, Department of Neuroinflammation, University College London (UCL) Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, WC1N 3BG, UK.
Sridar NarayananMcConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, H3A 2B4, Canada.
Douglas L ArnoldMcConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, H3A 2B4, Canada.
Jeremy ChatawayQueen Square Multiple Sclerosis Centre, Department of Neuroinflammation, University College London (UCL) Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, WC1N 3BG, UK.ORCID https://orcid.org/0000-0001-7286-6901
Claudia A M Gandini Wheeler-KingshottQueen Square Multiple Sclerosis Centre, Department of Neuroinflammation, University College London (UCL) Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, WC1N 3BG, UK.ORCID https://orcid.org/0000-0002-4832-1300
Frederik BarkhofQueen Square Multiple Sclerosis Centre, Department of Neuroinflammation, University College London (UCL) Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, WC1N 3BG, UK.
Olga CiccarelliQueen Square Multiple Sclerosis Centre, Department of Neuroinflammation, University College London (UCL) Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, WC1N 3BG, UK.
Arman EshaghiQueen Square Multiple Sclerosis Centre, Department of Neuroinflammation, University College London (UCL) Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, WC1N 3BG, UK.
Declan T ChardQueen Square Multiple Sclerosis Centre, Department of Neuroinflammation, University College London (UCL) Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, WC1N 3BG, UK.ORCID https://orcid.org/0000-0003-3076-2682

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In multiple sclerosis clinical trials, MRI outcome measures are typically extracted at a whole-brain level, but pathology is not homogeneous across the brain and so whole-brain measures may overlook regional treatment effects. Data-driven methods, such as independent component analysis, have shown promise in identifying regional disease effects but can only be computed at a group level and cannot be applied prospectively. The aim of this work was to develop a technique to extract longitudinal independent component analysis network-based measures of co-varying grey matter volumes, derived from T

Indexed as

disabilitygrey matterindependent component analysismultiple sclerosistreatment effects

Identifiers

PMID39077376
PMCPMC11285187

What OpenQuestion holds

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Registered trials

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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.