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Record W4394616895 · doi:10.1101/2024.04.04.588163

Electroencephalography signals in a female Fragile X Syndrome mouse model

2024· preprint· en· W4394616895 on OpenAlexafffund
Asim A. Ahmed, Veronica Rasheva, Moonyoung Bae, Kartikeya Murari, Ning Cheng

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2024
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetics and Neurodevelopmental Disorders
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of CalgaryFRAXA Research Foundation
KeywordsJuvenileElectroencephalographyFragile X syndromeFragile xBiologyPsychologyNeuroscienceGenetics

Abstract

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Abstract Background Fragile X syndrome (FXS) is the leading monogenic cause of Autism. No broadly effective support option currently exists for FXS, and drug development has suffered many failures in clinical trials based on promising preclinical findings. Thus, effective translational biomarkers of treatment outcomes are needed. Recently, electroencephalography (EEG) has been proposed as a translational biomarker in FXS. Recent years have seen an exciting emergence of novel EEG signal analyses from FXS patients. However, there is a notable gap in corresponding analyses conducted on animal models of the disorder. Being X-linked, FXS is more prevalent in males than females, and there exist significant phenotype differences between males and females with FXS. Recent studies involving male FXS participants and rodent models have identified an increase in absolute gamma EEG power, while alpha power is found to be either decreased or unchanged. However, there is not enough research on female FXS patients or models. In addition, studying EEG activity in both young and adult FXS patients or rodent models is crucial for better understanding of the disorder’s effects on brain development. Therefore, using the well established fmr1 knockout (KO) mouse model of FXS, we aim to compare EEG signal between female wild-type (WT) and female model mice at both juvenile and adult ages. Methods Frontal-parietal differential EEG was recorded using a stand-alone Open-Source Electrophysiology Recording system for Rodents (OSERR). EEG activity was recorded in three different conditions: a) in the subject’s home cage, and in the arenas for b) light -dark test and c) open field test. Absolute and relative EEG power as well as peak alpha frequency, theta-beta ratio, phase-amplitude and amplitude-amplitude coupling, and EEG signal complexity were computed for each condition. Results In our study, we found absolute alpha, beta, gamma and total EEG power is increased in the female model compared to WT controls at the juvenile and adult ages. Alongside, relative theta power is decreased in the model. Additionally, phase-amplitude and amplitude-amplitude coupling is altered in the model. Furthermore, peak alpha frequency is increase, and theta-beta ratio is decreased in the model. Lastly, no change in EEG signal complexity is found. Discussion and Conclusion Consistent with most findings from FXS patients and rodent models, our results demonstrated an increase in gamma power in fmr1 KO female mice, reinforcing gamma power as a robust and reliable EEG phenotype across FXS models. Additionally, theta-gamma cross frequency amplitude coupling is inversely coupled in female FXS model, which is similar to what has been reported in FXS patients. Overall, our findings reveal that not all EEG biomarkers observed in FXS patients are replicated in the female FXS model. For example, peak alpha frequency, theta-beta ratio, and brain signal complexity showed discrepancies between the mouse models and FXS patients. Additionally, when compared to previously reported EEG changes in male FXS mouse models, our results highlight the presence of a potential sex-based difference in EEG phenotypes at both juvenile and adult stages of fmr1 KO mouse models. Together, our study indicates that certain EEG parameters may be more translatable between rodent models and FXS patients than others and underscore the importance of considering sex and developmental stage as a critical factor when using EEG as a biomarker in FXS research.

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 score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.010
GPT teacher head0.213
Teacher spread0.203 · 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".

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Citations1
Published2024
Admission routes2
Has abstractyes

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