Evidence map›Paper›PMID 42114271›Full record

ArticleClinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology2026

Resting-state EEG for continuous prognostic monitoring and prediction of coma recovery after acute brain injury.

Morteza Zabihi, Sophie E Ack, Daniel B Rubin, Emily J Gilmore, Valdery Moura Junior, Quanzheng Li, Michael J Young, Brian L Edlow, Yelena G Bodien, Eric S Rosenthal

Abstract read
In one paragraph

Article in Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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.

Morteza ZabihiDepartment of Neurology, Massachusetts General Hospital, Boston, MA, USA. Electronic address: mzabihi@mgh.harvard.edu.
Sophie E AckDepartment of Neurology, Massachusetts General Hospital, Boston, MA, USA; Department of Neurology, Northwestern University, Chicago, IL, USA.
Daniel B RubinDepartment of Neurology, Massachusetts General Hospital, Boston, MA, USA.
Emily J GilmoreDepartment of Neurology, Yale Medical School, New Haven, CT, USA.
Valdery Moura JuniorDepartment of Neurology, Massachusetts General Hospital, Boston, MA, USA.
Quanzheng LiDepartment of Neurology, Massachusetts General Hospital, Boston, MA, USA.
Michael J YoungDepartment of Neurology, Massachusetts General Hospital, Boston, MA, USA.
Brian L EdlowDepartment of Neurology, Massachusetts General Hospital, Boston, MA, USA.
Yelena G BodienDepartment of Neurology, Massachusetts General Hospital, Boston, MA, USA; Department of Physical Medicine and Rehabilitation, Spaulding Rehabilitation Hospital, Harvard Medical School, Boston, MA, USA.
Eric S RosenthalDepartment of Neurology, Massachusetts General Hospital, Boston, MA, USA.

Funding

The Late Effects of TBI (LETBI) Study: Injury Patterns and Biological Markers of Post-Traumatic NeurodegenerationRF1NS115268 · NINDS · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI Kristen Dams-O'Connor · 2019 to 2026
$10.3M
ELECTRO-BOOST: Electroencephalography for cerebral trauma recovery and oxygenationR01NS117904 · NINDS · YALE UNIVERSITY · PI DING, KAN, GILMORE, EMILY · 2020 to 2025
$3.2M
A Connectome-Based Clinical Trial Platform to Promote Early Recovery of Consciousness after Traumatic ComaDP2HD101400 · NICHD · MASSACHUSETTS GENERAL HOSPITAL · PI EDLOW, BRIAN L. · 2019 to 2019
$2.5M
Real-Time Prediction of Delayed Cerebral Ischemia after Subarachnoid HemorrhageK23NS105950 · NINDS · MASSACHUSETTS GENERAL HOSPITAL · PI ROSENTHAL, ERIC S. · 2018 to 2022
$1.0M
Brainstem Connectome Mapping to Guide Targeted Stimulant Therapy in Patients with Traumatic ComaR21NS109627 · NINDS · MASSACHUSETTS GENERAL HOSPITAL · PI EDLOW, BRIAN L. · 2019 to 2020
$480k
NICHD NIH HHS DP2 HD101400NINDS NIH HHS K23 NS105950NINDS NIH HHS R01 NS117904NINDS NIH HHS R21 NS109627NINDS NIH HHS RF1 NS115268
6 · The paper itself

Abstract

objectiveWe developed a continuous prognostic monitoring tool to predict recovery from disorders of consciousness (DoC) following acute brain injury (ABI), utilizing resting-state EEG recorded during routine clinical care.

methodsPredictive models updating every 5 min were developed using serial neurologic assessments and continuous resting-state EEG to predict future consciousness level at 24-, 48-, and 72-hour time horizons. An ensemble of CatBoost classifiers was utilized for multi-class DoC grade prediction, leveraging a comprehensive set of 242 computed EEG features encoding time, frequency, and time-frequency characteristics. Conventional and confound-isolating cross-validation mitigated biases and increased robustness. Performance was compared across multiple ordinal DoC grade cut-points and time horizons.

results201 patients met inclusion criteria. Models incorporating EEG outperformed behavioral assessments alone, achieving a mean one-vs-rest AUROC of 0.88-0.89 (EEG + GCS) across 24-72-hour horizons and various trichotomized cut-points, with 95% bootstrap confidence intervals. The most robust features included global field power, theta-band (4-8 Hz) bandpower, beta-band (13-30 Hz) phase-locking value, and spectral entropy.

conclusionsEEG-augmented models enabled continuous prediction of future DoC grade after ABI. SIGNIFICANCE: Combining EEG with other serial measures during routine clinical care creates a novel paradigm for improved shared decision-making through continuous prognostic monitoring and serial assessment.

Indexed as

Brain InjuriesComaElectroencephalographyRecovery of FunctionRestAdultAgedFemaleHumansMaleMiddle AgedPrognosisAcute brain injuryComa recovery predictionConfound-isolating cross-validationDisorders of consciousnessEEG

Identifiers

PMID42114271
PMCPMC13242683

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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.