Associations Between Altered Auditory EEG Markers and Clinical Impairments in Fragile X Syndrome
Bibliographic record
Abstract
PURPOSE: Individuals with Fragile X syndrome (FXS) manifest clinical impairments in several domains. Previous research has shown that auditory evoked potentials (AEPs), measured using electroencephalogram (EEG), are altered in FXS, but the associations between these alterations and the symptoms observed in FXS have not been thoroughly investigated. The aim of this study was to compare AEP markers between individuals with FXS and neurotypical (NT) controls, with the main purpose of exploring how these markers are related to various clinical symptoms present in FXS. METHODS: A passive auditory oddball paradigm was presented. P1, N1, P2, N2, P3 and mismatch negativity (MMN) amplitudes and latencies were compared between 41 children and adults with FXS and 46 age-matched NT controls. Amplitudes and latencies, as well as habituation and change detection effects were compared between the groups using mixed design ANOVAs. Pearson correlations were then performed to explore associations between AEP markers and symptoms in the FXS group. RESULTS: Our results showed that FXS participants had increased N1, P2 and MMN amplitudes and latencies, as well as lack of habituation and change detection effects compared to NT controls. Our correlational analyses revealed several associations between AEPs and phenotypic manifestations; notably, associations between exaggerated N1 and P2 amplitudes and more severe autistic and ADHD symptoms. CONCLUSIONS: These findings confirm that abnormalities of the N1 and P2 components are robust biomarkers of altered sensory processing in FXS and suggest that these alterations may present a dose-response relation to clinical impairments 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 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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".