Evidence map›Paper›PMID 37665382›Full record

ArticleJournal of neurology2023

Predicting neurologic recovery after severe acute brain injury using resting-state networks.

Matthew Kolisnyk, Karnig Kazazian, Karina Rego, Sergio L Novi, Conor J Wild, Teneille E Gofton, Derek B Debicki, Adrian M Owen, Loretta Norton

Erratum issuedAbstract read
PubMed Publisher
In one paragraph

Article in Journal of neurology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
3.2field-weighted citation impact, top 7% of its field
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

12 citing papers in PubMed, 15 citations in OpenAlex.

  1. Article
  2. Article
  3. Preservation of anticorrelated brain networks predicts recovery after traumatic brain injury.Proceedings of the National Academy of Sciences of the United States of America · 2025
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  5. Review
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  7. Article
  8. Review
  9. Article
  10. Review
  11. Functional near-infrared spectroscopy: A novel tool for detecting consciousness after acute severe brain injury.Proceedings of the National Academy of Sciences of the United States of America · 2024
    Article
  12. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors at 2 institutions in 1 country.

Matthew Kolisnyk *Western Institute of Neuroscience, Western Interdisciplinary Research Building, Western University, 1151 Richmond Street, London, ON, N6A 3K7, Canada.
Karnig Kazazian *Western Institute of Neuroscience, Western Interdisciplinary Research Building, Western University, 1151 Richmond Street, London, ON, N6A 3K7, Canada. kkazazia@uwo.ca.ORCID http://orcid.org/0000-0001-6157-601X
Karina RegoTemerty Faculty of Medicine, University of Toronto, Toronto, Canada.
Sergio L NoviWestern Institute of Neuroscience, Western Interdisciplinary Research Building, Western University, 1151 Richmond Street, London, ON, N6A 3K7, Canada.
Conor J WildWestern Institute of Neuroscience, Western Interdisciplinary Research Building, Western University, 1151 Richmond Street, London, ON, N6A 3K7, Canada.
Teneille E GoftonDepartment of Clinical Neurological Sciences, Schulich School of Medicine and Dentistry, Western University, London, Canada.
Derek B DebickiDepartment of Clinical Neurological Sciences, Schulich School of Medicine and Dentistry, Western University, London, Canada.
Adrian M OwenWestern Institute of Neuroscience, Western Interdisciplinary Research Building, Western University, 1151 Richmond Street, London, ON, N6A 3K7, Canada.
Loretta NortonWestern Institute of Neuroscience, Western Interdisciplinary Research Building, Western University, 1151 Richmond Street, London, ON, N6A 3K7, Canada.
Western University · CAUniversity of Toronto · CA

Funding

Wellcome Trust 215063
6 · The paper itself

Abstract

objectiveThere is a lack of reliable tools used to predict functional recovery in unresponsive patients following a severe brain injury. The objective of the study is to evaluate the prognostic utility of resting-state functional magnetic resonance imaging for predicting good neurologic recovery in unresponsive patients with severe brain injury in the intensive-care unit.

methodsEach patient underwent a 5.5-min resting-state scan and ten resting-state networks were extracted via independent component analysis. The Glasgow Outcome Scale was used to classify patients into good and poor outcome groups. The Nearest Centroid classifier used each patient's ten resting-state network values to predict best neurologic outcome within 6 months post-injury.

resultsOf the 25 patients enrolled (mean age = 43.68, range = [19-69]; GCS ≤ 9; 6 females), 10 had good and 15 had poor outcome. The classifier correctly and confidently predicted 8/10 patients with good and 12/15 patients with poor outcome (mean = 0.793, CI = [0.700, 0.886], Z = 2.843, p = 0.002). The prediction performance was largely determined by three visual (medial: Z = 3.11, p = 0.002; occipital pole: Z = 2.44, p = 0.015; lateral: Z = 2.85, p = 0.004) and the left frontoparietal network (Z = 2.179, p = 0.029). DISCUSSION: Our approach correctly identified good functional outcome with higher sensitivity (80%) than traditional prognostic measures. By revealing preserved networks in the absence of discernible behavioral signs, functional connectivity may aid in the prognostic process and affect the outcome of discussions surrounding withdrawal of life-sustaining measures.

Indexed as

Brain InjuriesMagnetic Resonance ImagingAdultBrainFemaleHumansOccipital LobePrognosisBrain injuryComaConsciousnessfMRIMachine learningResting state

Identifiers

PMID37665382
OpenAlexW4386407042

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.