A comparison of the effectiveness of functional MRI analysis methods for pain research: The new normal
Bibliographic record
Abstract
Studies of the neural basis of human pain processing present many challenges because of the subjective and variable nature of pain, and the inaccessibility of the central nervous system. Neuroimaging methods, such as functional magnetic resonance imaging (fMRI), have provided the ability to investigate these neural processes, and yet commonly used analysis methods may not be optimally adapted for studies of pain. Here we present a comparison of model-driven and data-driven analysis methods, specifically for the study of human pain processing. Methods are tested using data from healthy control participants in two previous studies, with separate data sets spanning the brain, and the brainstem and spinal cord. Data are analyzed by fitting time-series responses to predicted BOLD responses in order to identify significantly responding regions (model-driven), as well as with connectivity analyses (data-driven) based on temporal correlations between responses in spatially separated regions, and with connectivity analyses based on structural equation modeling, allowing for multiple source regions to explain the signal variations in each target region. The results are assessed in terms of the amount of signal variance that can be explained in each region, and in terms of the regions and connections that are identified as having BOLD responses of interest. The characteristics of BOLD responses in identified regions are also investigated. The results demonstrate that data-driven approaches are more effective than model-driven approaches for fMRI studies of pain.
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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 | 0.065 | 0.148 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 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".