Evidence map›Paper›PMID 42498895›Full record

ArticleNeurocritical care2026

Early Identification of Recovery Potential After Acute Brain Injury Using Functional Near-Infrared Spectroscopy.

Matthew Kolisnyk, Karnig Kazazian, Garima Gupta, Jack de Jeu, Androu Abdalmalak, Sergio L Novi, Laura E Gonzalez-Lara, Marat Slessarev, Teneille E Gofton, Loretta Norton and 2 more

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Article in Neurocritical care, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

12 authors.

Matthew KolisnykWestern Institute of Neuroscience, Western University, London, ON, Canada. mkolisny@uwo.ca.ORCID http://orcid.org/0000-0003-0914-6105
Karnig KazazianWestern Institute of Neuroscience, Western University, London, ON, Canada.
Garima GuptaWestern Institute of Neuroscience, Western University, London, ON, Canada.
Jack de JeuWestern Institute of Neuroscience, Western University, London, ON, Canada.
Androu AbdalmalakWestern Institute of Neuroscience, Western University, London, ON, Canada.
Sergio L NoviWestern Institute of Neuroscience, Western University, London, ON, Canada.
Laura E Gonzalez-LaraWestern Institute of Neuroscience, Western University, London, ON, Canada.
Marat SlessarevWestern Institute of Neuroscience, Western University, London, ON, Canada.
Teneille E GoftonWestern Institute of Neuroscience, Western University, London, ON, Canada.
Loretta NortonWestern Institute of Neuroscience, Western University, London, ON, Canada.
Derek B Debicki *Western Institute of Neuroscience, Western University, London, ON, Canada.
Adrian M Owen *Western Institute of Neuroscience, Western University, London, ON, Canada.

Funding

Academic Medical Organization of Southwestern Ontario INN24-001CIHR 203337CIHR 408004Wellcome Trust 215063
6 · The paper itself

Abstract

backgroundAccurate early prognostication in patients with acute brain injury remains a major challenge in neurocritical care. Conventional bedside assessments provide limited insight into long-term outcomes and may not fully capture preserved brain function that supports recovery. Functional neuroimaging can detect brain activity not evident at the bedside, but its use in intensive care remains constrained by cost, logistics, and the need for stronger evidence supporting its value. Functional near-infrared spectroscopy (fNIRS) offers a scalable, bedside-compatible approach for assessing brain function in critically ill patients, but its value for early prognostication has yet to be established.

methodsIn this prospective observational cohort study, 33 patients with acute brain injury in the intensive care unit (ICU) underwent fNIRS recording while listening to two audio-only movie clips. Functional connectivity features were used to train a machine learning model to classify 6-month functional outcome, defined by the Glasgow Outcome Scale-Extended (favorable ≥ 4, unfavorable < 4). Model performance was assessed using balanced accuracy and statistically evaluated using permutation testing. Performance was compared with validated behavioral assessments and clinical variables. Secondary analyses evaluated prediction of behavioral responsiveness (observable command-following after testing) and covert awareness (neural command-following).

resultsA total of 26 patients had an unfavorable outcome and 7 had a favorable outcome. The fNIRS-based model predicted 6-month outcome with a balanced accuracy of 81.3% (sensitivity = 85.7%, specificity = 76.9%; p = 0.006), outperforming clinical models (balanced accuracy = 67.6%; p = 0.018). The fNIRS-based model also predicted recovery of behavioral responsiveness (balanced accuracy 78.5%; p = 0.008) but not covert awareness (70.4%; p = 0.109).

conclusionsBedside fNIRS provides objective neural measures associated with later functional recovery and behavioral responsiveness in patients with acute brain injury. These findings suggest that fNIRS may capture clinically relevant brain function not detected by conventional assessments. With further validation in larger, multicenter cohorts, such approaches may complement existing methods for early prognostication.

Indexed as

Acute brain injuryFunctional near-infrared spectroscopyFunctional neuroimagingMachine learningPrognostication

Identifiers

PMID42498895

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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.