Multimodal assessment of the GABA system in patients with fragile-X syndrome and neurofibromatosis of type 1
Bibliographic record
Abstract
Fragile-X syndrome (FXS) and Neurofibromatosis of type 1 (NF-1) are two monogenic disorders sharing neurobehavioral symptoms and pathophysiological mechanisms. Namely, preclinical models of both conditions show overactivity of the mTOR signaling pathway as well as GABAergic alterations. However, despite its potential clinical relevance for these disorders, the GABAergic system has not been systematically studied in humans. In the present study, we used an extensive transcranial magnetic stimulation (TMS) assessment battery in combination with magnetic resonance spectroscopy (MRS) to provide a comprehensive picture of the main inhibitory neurotransmitter system in patients with FXS and NF1. Forty-three participants took part in the TMS session (15 FXS, 10 NF1, 18 controls) and 36 in the MRS session (11 FXS, 14 NF1, 11 controls). Results show that, in comparison to healthy control participants, individuals with FXS and NF1 display lower GABA concentration levels as measured with MRS. TMS result show that FXS patients present increased GABAB-mediated inhibition compared to controls and NF1 patients, and that GABAA-mediated intracortical inhibition was associated with increased excitability specifically in the FXS groups. In line with previous reports, correlational analyses between MRS and TMS measures did not show significant relationships between GABA-related metrics, but several TMS measures correlated with glutamate+glutamine (Glx) levels assessed with MRS. Overall, these results suggest a partial overlap in neurophysiological alterations involving the GABA system in NF1 and FXS, and support the hypothesis that MRS and TMS assess different aspects of the neurotransmitter systems.
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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 | 0.001 |
| 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) | 0.001 | 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".