Investigating GABA concentrations measured with macromolecule suppressed and unsuppressed MEGA‐PRESS MR spectroscopy and their relationship with BOLD responses in the occipital cortex
Bibliographic record
Abstract
BACKGROUND: A combination of magnetic resonance spectroscopy (MRS) and functional (f)MRI is a promising method for studying brain activity. Negative results have, however, produced uncertainty as to the validity of the approach. Using a MEGA-PRESS sequence adapted to suppress the macromolecule signal (GABA-) has been suggested as a key methodological improvement, but there is some doubt as to the relationship between such estimates and those from the standard sequence (GABA+), making interpretation difficult. PURPOSE: To investigate the relationship between GABA+ and GABA- estimates from the posterior cingulate and occipital cortices. The second aim was to test for a correlation between occipital GABA and blood oxygenation level-dependent (BOLD) responses in the visual cortex to establish which of the two MEGA-PRESS sequences was more related to the functional responses. STUDY TYPE: Prospective. SUBJECTS: Thirty-one healthy participants. FIELD STRENGTH/SEQUENCE: H-MRS and gradient-echo echo planar imaging (EPI). ASSESSMENT: GABA estimates were made using the Gannet toolbox. fMRI data were analyzed with FSL and Python scripts. STATISTICAL TEST: Relationships between different variables were tested with Pearson's correlation. RESULTS: >0.1). However, adding these results to those of prior studies in a meta-analysis of correlation coefficients did provide overall support for a negative correlation between GABA and BOLD response amplitudes (r = -0.39, 95% CI = -0.15-0.64). DATA CONCLUSION: The current findings highlight potential methodological issues that continue to interfere with relating MRS GABA estimates with fMRI responses but, taken in sum, provide support for this general approach. LEVEL OF EVIDENCE: 1 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2019;50:1285-1294.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".