Exploring Neural Activities in the Prefrontal Cortex of Fragile X Syndrome Mouse Models Using Electroencephalography
Bibliographic record
Abstract
Fragile X syndrome (FXS) is the most common inherited cause of intellectual disability and a leading monogenic contributor to autism spectrum disorder. Human EEG studies have identified alterations in gamma power, alpha slowing, cross-frequency coupling, and reduced signal complexity, suggesting potential translational biomarkers of network dysfunction. However, the extent to which these signatures are conserved in preclinical models—especially in female mice—remains unclear. This thesis aimed to characterize oscillatory alterations in female Fmr1 knockout (KO) mice on a C57BL/6J background and to evaluate whether EEG-derived metrics align with human findings. Analyses of spectral power, peak alpha frequency, theta–beta ratio, cross-frequency coupling, and signal complexity revealed reduced alpha power, slowed peak alpha frequency, disrupted coupling, and altered gamma activity. These features partially mirror abnormalities reported in FXS patients, underscoring EEG’s translational relevance. By extending analysis beyond traditional spectral power, this work establishes a bridge between preclinical and clinical EEG metrics and highlights the importance of including female models to advance biomarker development and translational research in FXS.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".