Evidence map›Paper›PMID 40894539›Full record

ArticlebioRxiv : the preprint server for biology2025

Interpretable EEG biomarkers for neurological disease models in mice using bag-of-waves classifiers.

Maria Isabel Cano Achuri, Montana Kay Lara, Khalil Abed Rabbo, Benjamin T Wilson, Austin Meek, J Matthew Mahoney, Amanda E Hernan, Austin J Brockmeier

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in bioRxiv : the preprint server for biology, 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

5 · Who and what money

Authors and funding

8 authors.

Maria Isabel Cano AchuriDepartment of Electrical and Computer Engineering, University of Delaware, Newark, Delaware, USA.
Montana Kay LaraDepartment of Psychiatry, University of California San Diego, La Jolla, California, USA.
Khalil Abed RabboNeuroscience Program, University of Vermont, Burlington, Vermont, USA.
Benjamin T WilsonUniversity of Vermont, Burlington, Vermont, USA.
Austin MeekDepartment of Computer and Information Sciences, University of Delaware, Newark, Delaware, USA.
J Matthew MahoneyThe Jackson Laboratory, Bar Harbor, Maine, USA.
Amanda E HernanDivision of Neuroscience, Nemours Children's Health, Wilmington, Delaware, USA.
Austin J BrockmeierDepartment of Electrical and Computer Engineering, Department of Computer and Information Sciences, University of Delaware, Newark, Delaware, USA.

Funding

Leveraging genetically-encoded heterogeneity to understand TANDs and seizures in novel models of TSCR01NS134491 · NINDS · NEMOURS CHILDREN'S HOSPITAL, DELAWARE · PI Amanda Hernan · 2024 to 2026
$1.0M
Mechanisms for Improving Cognitive Outcome in Pediatric Epilepsy with ACTHK22NS104230 · NINDS · UNIVERSITY OF VERMONT & ST AGRIC COLLEGE · PI HERNAN, AMANDA · 2017 to 2022
$962k
NINDS NIH HHS K22 NS104230NINDS NIH HHS R01 NS134491
6 · The paper itself

Abstract

Electroencephalograms (EEGs) are time-series records of the electrical potential from collective neural activity in the brain. EEG waveform patterns-rhythmic and irregular oscillations and transient patterns of sharp waves or spikes-are potential phenotypical biomarkers, reflecting genotype-specific neural activity. This is especially relevant to diagnosing epilepsy without direct seizure observations, which is common in clinical settings, as well as in animal models, which often have subtle neurological phenotypes without overt epilepsy. Herein, we investigate genotypic prediction from long-term EEG signals of freely behaving mice belonging to six groups defined by the presence or absence of a neurological disease-genotype (

Identifiers

PMID40894539
PMCPMC12393263

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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.