Consistency of frontal cortex metabolites quantified by magnetic resonance spectroscopy within overlapping small and large voxels
Bibliographic record
Abstract
Single voxel magnetic resonance spectroscopy (MRS) quantifies metabolites within a specified volume of interest. MRS voxels are constrained to rectangular prism shapes. Therefore, they must define a small voxel contained within the anatomy of interest or include not of interest neighbouring tissue. When studying cortical regions without clearly demarcated boundaries, e.g. the dorsolateral prefrontal cortex (DLPFC), it is unclear how representative a larger voxel is of a smaller volume within it. To determine if a large voxel is representative of a small voxel placed within it, this study quantified total N-Acetylaspartate (tNAA), choline, glutamate, Glx (glutamate and glutamine combined), myo-inositol, and creatine in two overlapping MRS voxels in the DLPFC, a large (30×30x30 mm) and small (15×15x15 mm) voxel. Signal-to-noise ratio (SNR) and tissue type factors were specifically investigated. With water-referencing, only myo-inositol was significantly correlated between the two voxels, while all metabolites showed significant correlations with creatine-referencing. SNR had a minimal effect on the correspondence between voxels, while tissue type showed substantial influence. This study demonstrates substantial variability of metabolite estimates within the DLPFC. It suggests that when small anatomical structures are of interest, it may be valuable to spend additional acquisition time to obtain specific, localized data.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".