Automated detection of spreading depolarizations in electrocorticography.

Sreekar Puchala, Ethan Muchnik, Anca Ralescu, Jed A Hartings

Abstract read

In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing 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

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

4 authors.

Sreekar PuchalaDepartment of Computer Science, University of Cincinnati, Cincinnati, OH, 45267, USA.

Ethan MuchnikDepartment of Electrochemistry, University of Oregon, Eugene, OR, USA.

Anca RalescuDepartment of Computer Science, University of Cincinnati, Cincinnati, OH, 45267, USA.

Jed A HartingsDepartment of Neurosurgery, University of Cincinnati College of Medicine, 231 Albert Sabin Way, Cincinnati, OH, 45267, USA. jed.hartings@uc.edu.

Funding

Defense Medical Research and Development Program W81XWH-16-2-0020

6 · The paper itself

Abstract

Spreading depolarizations (SD) in the cerebral cortex are a novel mechanism of lesion development and worse outcomes after acute brain injury, but accurate diagnosis by neurophysiology is a barrier to more widespread application in neurocritical care. Here we developed an automated method for SD detection by training machine-learning models on electrocorticography data from a 14-patient cohort that included 1,548 examples of SD direct-current waveforms as identified in expert manual scoring. As determined by leave-one-patient-out cross-validation, optimal performance was achieved with a gradient-boosting model using 30 features computed from 400-s electrocorticography segments sampled at 0.1 Hz. This model was applied to continuous electrocorticography data by generating a time series of SD probability [P

Indexed as

Cerebral CortexCortical Spreading DepressionElectrocorticographyAdultAlgorithmsFemaleHumansMachine LearningMaleMiddle AgedAutomated detectionComputational algorithmElectroencephalographyMachine learningNeurocritical careSpreading depolarizationSpreading depression

Identifiers

PMID40074765

DOI10.1038/s41598-025-91623-7

PMCPMC11903781

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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.