Hippocampal single‐voxel MR spectroscopy with a long echo time at 3 T using semi‐LASER sequence
Bibliographic record
Abstract
The hippocampus is one of the most challenging brain regions for proton MR spectroscopy (MRS) applications. Moreover, quantification of J ‐coupled species such as myo ‐inositol (m‐Ins) and glutamate + glutamine (Glx) is affected by the presence of macromolecular background. While long echo time (TE) MRS eliminates the macromolecules, it also decreases the m‐Ins and Glx signal and, as a result, these metabolites are studied mainly with short TE. Here, we investigate the feasibility of reproducibly measuring their concentrations at a long TE of 120 ms, using a semi‐adiabatic localization by adiabatic selective refocusing (sLASER) sequence, as this sequence was recently recommended as a standard for clinical MRS. Gradient offset‐independent adiabatic refocusing pulses were implemented, and an optimal long TE for the detection of m‐Ins and Glx was determined using the T 2 relaxation times of macromolecules. Metabolite concentrations and their coefficients of variation (CVs) were obtained for a 3.4‐mL voxel centered on the left hippocampus on 3‐T MR systems at two different sites with three healthy subjects (aged 32.5 ± 10.2 years [mean ± standard deviation]) per site, with each subject scanned over two sessions, and with each session comprising three scans. Concentrations of m‐Ins, choline, creatine, Glx and N ‐acetyl‐aspartate were 5.4 ± 1.5, 1.7 ± 0.2, 5.8 ± 0.3, 11.6 ± 1.2 and 5.9 ± 0.4 mM (mean ± standard deviation), respectively. Their respective mean within‐session CVs were 14.5% ± 5.9%, 6.5% ± 5.3%, 6.0% ± 3.4%, 10.6% ± 6.2% and 3.5% ± 1.4%, and their mean within‐subject CVs were 17.8% ± 18.2%, 7.5% ± 6.3%, 7.4% ± 6.4%, 12.4% ± 5.3% and 4.8% ± 3.0%. The between‐subject CVs were 25.0%, 12.3%, 5.3%, 10.7% and 6.4%, respectively. Hippocampal long‐TE sLASER single voxel spectroscopy can provide macromolecule‐independent assessment of all major metabolites including Glx and m‐Ins.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 teacher head, 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".