Evidence map›Paper›PMID 39487825›Full record

ArticleEpilepsia2025

Latent cognitive phenotypes in juvenile myoclonic epilepsy: Clinical, sociodemographic, and neuroimaging associations.

Aaron F Struck, Camille Garcia-Ramos, Vivek Prabhakaran, Veena Nair, Nagesh Adluru, Anusha Adluru, Dace Almane, Jana E Jones, Bruce P Hermann

Abstract read
In one paragraph

Article in Epilepsia, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

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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

9 authors.

Aaron F StruckDepartment of Neurology, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA.
Camille Garcia-RamosDepartment of Neurology, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA.ORCID 0000-0003-4365-9895
Vivek PrabhakaranDepartment of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA.
Veena NairDepartment of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA.
Nagesh AdluruDepartment of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA.
Anusha AdluruDepartment of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA.
Dace AlmaneDepartment of Neurology, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA.
Jana E JonesDepartment of Neurology, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA.
Bruce P HermannDepartment of Neurology, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA.ORCID 0000-0003-0133-4427

Funding

Waisman Center Intellectual and Developmental Disabilities Research CenterP50HD105353 · NICHD · UNIVERSITY OF WISCONSIN-MADISON · PI Brittany Gail Travers · 2021 to 2026
$8.5M
Stroke Connectome MRI Biomarkers for VCID Risk AssessmentR01NS123378 · NINDS · UNIVERSITY OF WISCONSIN-MADISON · PI Nagesh Adluru, Veena A Nair · 2022 to 2026
$3.6M
The Juvenile Myoclonic Epilepsy Connectome ProjectR01NS111022 · NINDS · UNIVERSITY OF WISCONSIN-MADISON · PI STRUCK, AARON F · 2020 to 2024
$3.1M
PET/MR Correlates of Accelerated Aging in Chronic EpilepsyR01NS117568 · NINDS · UNIVERSITY OF WISCONSIN-MADISON · PI Alan Blair McMillan, Vivek Prabhakaran · 2021 to 2026
$3.0M
Stroke Rehabilitation utilizing BCI technologyR01NS105646 · NINDS · UNIVERSITY OF WISCONSIN-MADISON · PI PRABHAKARAN, VIVEK · 2018 to 2022
$2.3M
National Institute of Child Health and Human Development P50HD105353NICHD NIH HHS P50 HD105353NINDS NIH HHS R01-1NS111022NINDS NIH HHS R01 NS105646NINDS NIH HHS R01 NS111022NINDS NIH HHS R01 NS117568NINDS NIH HHS R01 NS123378U.S. Department of Health and Human Services R01NS105646U.S. Department of Health and Human Services R01NS117568U.S. Department of Health and Human Services R01NS123378
6 · The paper itself

Abstract

objectiveApplication of cluster analytic procedures has advanced understanding of the cognitive heterogeneity inherent in diverse epilepsy syndromes and the associated clinical and neuroimaging features. Application of this unsupervised machine learning approach to the neuropsychological performance of persons with juvenile myoclonic epilepsy (JME) has yet to be attempted, which is the intent of this investigation.

methodsA total of 77 JME participants, 19 unaffected siblings, and 44 unrelated controls, 12 to 25 years of age, were administered a comprehensive neuropsychological battery (intelligence, language, memory, executive function, and processing speed), which was subjected to factor analysis followed by K-means clustering of the resultant factor scores. Identified cognitive phenotypes were characterized and related to clinical, family, sociodemographic, and cortical and subcortical imaging features.

resultsFactor analysis revealed three underlying cognitive dimensions (general ability, speed/response inhibition, and learning/memory), with JME participants performing worse than unrelated controls across all factor scores, and unaffected siblings performing worse than unrelated controls on the general mental ability and learning/memory factors, with no JME vs sibling differences. K-means clustering of the factor scores revealed three latent groups including above average (31.4% of participants), average (52.1%), and abnormal performance (16.4%). Participant groups differed in their distributions across the latent groups (p < 0.001), with 23% JME, 22% siblings, and 2% unrelated controls in the abnormal performance group; and 18% JME, 21% siblings, and 59% unrelated controls in the above average group. Clinical epilepsy variables were unassociated with cluster membership, whereas family factors (lower parental education) and abnormally increased thickness and/or volume in the frontal, parietal, and temporal-occipital regions were associated with the abnormal cognition group. SIGNIFICANCE: Distinct cognitive phenotypes characterize the spectrum of neuropsychological performance of patients with JME for which there is familial (sibling) aggregation. Phenotypic membership was associated with parental (education) and imaging characteristics (increased cortical thickness and volume) but not basic clinical seizure features.

Indexed as

CognitionCognition DisordersMyoclonic Epilepsy, JuvenileAdolescentAdultChildFemaleHumansMagnetic Resonance ImagingMaleNeuroimagingNeuropsychological TestsPhenotypeSiblingsYoung Adultcognitionjuvenile myoclonic epilepsyneuropsychologyphenotypes

Identifiers

PMID39487825
PMCPMC11742545

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.