MRS-BIDS, an extension to the Brain Imaging Data Structure for magnetic resonance spectroscopy
Bibliographic record
Abstract
Magnetic resonance spectroscopy (MRS), an application of the nuclear magnetic resonance phenomenon, for the discovery of which the 1944 Nobel Prize in Physics was awarded to Isidor Isaac Rabi, preceded the development of magnetic resonance imaging (MRI) by several decades. Its ability to noninvasively detect and estimate the concentration of endogenous chemical compounds in vivo has made it a powerful tool for studying metabolism in many contexts. Today, in vivo MRS is employed to interrogate the molecular underpinnings of many diseases and disorders, such as autism spectrum disorder 1 , cancer 2 , multiple sclerosis 3 , and schizophrenia 4 , to name a few. It is also used to study the neurochemical correlates of behavior, cognition, and perception, such as memory 5 , motor performance 6 , learning 7 , and vision 8 . However, a perennial issue with MRS, particularly compared to MRI, is the lack of standardization in terminology, acquisition approaches, data preprocessing, data analysis, metabolite quantification, and reporting of results. Recently, the MRS community came together to reach consensus recommendations for various aspects of MRS science. These recommendations were published as a series of articles in a special issue of NMR in Biomedicine 9 . In parallel, the Code and Data Sharing Committee of the MR Spectroscopy Study Group of the International Society for Magnetic Resonance in Medicine (ISMRM) was formed 10 . The Committee promotes the creation, curation, and sharing of openly available MRS datasets, software tools, educational materials, and expert knowledge for new and experienced users in the wider MRS community. A website was subsequently created to serve as a curated resource and hub for data, code, and resource sharing, as well as a virtual space for community interaction to discuss relevant topics of proposed and ongoing MRS research ( https://mrshub.org/ ).
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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 | 0.011 | 0.042 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.006 | 0.016 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.037 | 0.041 |
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".