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Record W4412183484 · doi:10.1101/2025.07.07.662836

Alterations in Electroencephalography Signals in Female Fragile X Syndrome Mouse Model on a C57Bl/6J Background

2025· preprint· en· W4412183484 on OpenAlexafffund
Bosong Wang, Asim A. Ahmed, Kartikeya Murari, Ning Cheng

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2025
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetics and Neurodevelopmental Disorders
Canadian institutionsAlberta Children's HospitalUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaFRAXA Research Foundation
KeywordsElectroencephalographyFragile X syndromePsychologyAudiologyNeuroscienceMedicinePsychiatry

Abstract

fetched live from OpenAlex

Abstract Background Fragile X Syndrome (FXS), the most common monogenic cause of autism spectrum disorder, arises from FMR1 gene silencing and exhibits pronounced sex differences in prevalence and phenotypic severity. Electroencephalography (EEG) has emerged as a promising translational biomarker for FXS pathophysiology, yet prior research has predominantly focused on male cohorts. In the widely used C57Bl/6J (B6) mouse strain, male Fmr1 knockout (KO) models show increased absolute gamma power at both juvenile and adult stages, which may reflect cortical hyperexcitability. In contrast, little is known about female Fmr1 KO mice, except that they exhibit no gamma alterations in adulthood. This gap hinders understanding of sex-specific neurodevelopmental trajectories of EEG profile in FXS. Leveraging the genetic stability and translational relevance of the B6 strain, this study compares EEG profiles between juvenile female Fmr1 KO and wild-type (WT) B6 mice to address this critical gap. Methods Frontal-parietal differential EEG was recorded in freely behaving mice using the Open-Source Electrophysiology Recording system for Rodents. Neural activity was analyzed across three recording conditions: in the home cage, light-dark arena, and open field arena. Computed metrics included absolute/relative power, peak alpha frequency, theta-beta ratio, phase-amplitude coupling, amplitude-amplitude coupling, and multiscale entropy to assess signal complexity. Results In all recording conditions, Fmr1 KO mice exhibited reduced absolute power in theta, alpha, and beta frequency bands compared to WT controls. Relative power analysis revealed decreased alpha activity alongside increased gamma-band power, including both low and high gamma, in the KO mice. Cross-frequency coupling was disrupted, with diminished alpha-gamma phase-amplitude coupling. Amplitude-amplitude coupling between theta or alpha and gamma power displayed distinct changes in different recording conditions. Peak alpha frequency and theta-beta ratio were both reduced or unchanged in the KO mice, depending on the recording condition. Finally, EEG signal complexity remained comparable between the two genotypes across the conditions. Behaviorally, KO mice displayed hyper-exploration in the open field test, characterized by increased center time and entries. However, no overall robust correlations between EEG power in different frequency bands and behavioral parameters in the open field test were observed. Discussion and Conclusion Our results demonstrate that juvenile female Fmr1 KO mice on a B6 background exhibit EEG alterations highly consistent with those reported in FXS patients, particularly increased gamma and reduced alpha power. The robust increase in gamma activity reinforces its status as a reliable biomarker across preclinical and clinical studies, while alpha reductions and slowed peak alpha frequency implicate thalamocortical network involvement. Together, these findings highlight the translational value of this model for studying core circuit dysfunctions in FXS.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.014
GPT teacher head0.232
Teacher spread0.218 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

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