Consensus Recommendations to Establish Reporting Standards in fMRI of Migraine
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Migraine is a multifaceted primary headache disorder. In neuroimaging of migraine, fMRI has been used to elucidate pathophysiology or monitor treatment effects. The current literature, however, is highly heterogeneous regarding reported variables and methodologies. This begets a lack of comparability and complicates synthesis of results across studies. We developed a framework for standardized reporting of fMRI studies in migraine. METHODS: Experts on fMRI in migraine were identified from the literature and subjected to structured questionnaires in 2 iterations of 3 rounds according to the DELPHI method. A total of 157 statements across 17 reporting domains were rated on 5-point Likert scales (strong support to strong opposition). The first iteration covered demographic data, migraine-specific factors, medication, scan timing, healthy controls (HCs), participant sampling/recruiting, standardized forms, study preregistration, region of interest (ROI) analyses, validation data sets, data sharing, preprocessing documentation, and analysis software. The second iteration of the questionnaire covered scanner-related factors, sequence-related factors, physiology monitoring, and stimulation-related factors. Items showing strong consensus/consensus (≥90%/≥75% of participants indicating scores 4 or 5) were included as standard reporting items. RESULTS: All 3 rounds of the first/second iteration were completed by 29 and 26 researchers (age 46 ± 11 years; 38% female/age 46 ± 12 years; 44% female) from 23 and 21 institutions. Across both iterations, strong consensus and consensus was achieved for 34 (3 scanner-related factors, 9 sequence-related factors, 1 stimulation-related factor, 2 demographic factors, 7 migraine-specific factors, 2 medication-factors, 2 scan timing factors, 4 HC factors, 1 preregistration factor, 1 analysis software factor, and 2 ROI analyses factors) and 33 (1 scanner-related factors, 4 sequence related factors, 1 factor related to physiology monitoring, 1 stimulation-related factor, 3 demographic factors, 6 migraine-specific factors, 4 medication factors, 3 HC factors, 2 sampling factors, 1 standardized form, 1 preregistration factor, 1 data sharing factor, 2 analysis software factors, and 3 ROI analyses factors) items, respectively. From these, a checklist covering 63 items from 14 reporting domains was created. DISCUSSION: We present an expert-based framework for reporting standards in fMRI studies of migraine, which can be used for future studies to homogenize cohort characterization, fMRI acquisitions, and analysis protocols.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.658 | 0.735 |
| Meta-epidemiology (narrow) | 0.004 | 0.006 |
| Meta-epidemiology (broad) | 0.008 | 0.023 |
| Bibliometrics | 0.024 | 0.017 |
| Science and technology studies | 0.010 | 0.013 |
| Scholarly communication | 0.019 | 0.018 |
| Open science | 0.032 | 0.021 |
| Research integrity | 0.035 | 0.032 |
| Insufficient payload (model declined to judge) | 0.008 | 0.009 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".