In Vivo Brain Magnetic Resonance Spectroscopy: A Measurement of Biomarker Sensitivity to Post-Processing Algorithms
Bibliographic record
Abstract
Clinical translation of reported biomarkers requires reliable and consistent algorithms to derive biomarkers. However, the literature reports statistically significant differences between 1-D MRS measurements from control groups and subjects with disease states but frequently provides little information on the algorithms and parameters used to process the data. The sensitivity of in vivo brain magnetic resonance spectroscopy biomarkers is investigated with respect to parameter values for two key stages of post-acquisitional processing. Our effort is specifically motivated by the lack of consensus on approaches and parameter values for the two critical operations, water resonance removal, and baseline correction. The different stages of data processing also introduce varying levels of uncertainty and arbitrary selection of parameter values can significantly underutilize the intrinsic differences between two classes of signals. The sensitivity of biomarkers points to the need for a better understanding of how all stages of post-acquisitional processing affect biomarker discovery and ultimately, clinical translation. Our results also highlight the possibility of optimizing biomarker discovery by the careful selection of parameters that best reveal class differences. Using previously reported data and biomarkers, our results demonstrate that small changes in parameter values affect the statistical significance and corresponding effect size of biomarkers. Consequently, it is possible to increase the strength of biomarkers by selecting optimal parameter values in different spectral intervals. Our analyses with a previously reported data set demonstrate an increase in effect sizes for wavelet-based biomarkers of up to 36%, with increases in classification performance of up to 12%.
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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 | 0.016 | 0.082 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".