Resting GABA and glutamate concentrations do not predict visual gamma frequency or amplitude
Bibliographic record
Abstract
Gamma band oscillations arise in neuronal networks of interconnected GABAergic interneurons and excitatory pyramidal cells. A previous study found a correlation between visual gamma peak frequency, as measured with magnetoencephalography, and resting GABA levels, as measured with magnetic resonance spectroscopy (MRS), in 12 healthy volunteers. If true, this would allow studies in clinical populations testing modulation of this relationship, but this finding has not been replicated. We addressed this important question by measuring gamma oscillations and GABA, as well as glutamate, in 50 healthy volunteers. Visual gamma activity was evoked using an established gratings paradigm, and we applied a beamformer spatial filtering technique to extract source-reconstructed gamma peak frequency and amplitude from the occipital lobe. We determined gamma peak frequency and amplitude from the location with maximal activation and from the location of the MRS voxel to assess the relationship of GABA with gamma. Gamma peak frequency was estimated from the highest value of the raw spectra and by a Gaussian fit to the spectra. MRS data were acquired from occipital cortex. We did not replicate the previously found correlation between gamma peak frequency and GABA concentration. Calculation of a Bayes factor provided strong evidence in favor of the null hypothesis. We also did not find a correlation between gamma activity and glutamate or between gamma and the ratio of GABA/glutamate. Our results suggest that cortical gamma oscillations do not have a consistent, demonstrable relationship to excitatory/inhibitory network activity as proxied by MRS measurements of GABA and glutamate.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".