Stability of tactile‐ and pain‐related fMRI brain activations: An examination of threshold‐dependent and threshold‐independent methods
Bibliographic record
Abstract
Functional MRI can be used to assess brain plasticity over time. To confidently attribute changes in activation patterns to cortical plasticity, it is important to establish the stability of cortical activation patterns. Because little is known concerning the stability of somatosensory-evoked brain responses, we assessed the reproducibility of within-subject responses in key somatosensory regions [thalamus, primary and secondary cortex (S1, S2)] to tactile and painful stimuli using threshold-dependent and threshold-independent analyses. Six subjects underwent four biweekly scanning sessions during which tactile and painful stimuli were applied to the hand. Standard thresholding and voxel counting techniques were compared with a novel threshold-independent method utilizing percent signal change within the regions of interest. Contralateral S1 and S2 were qualitatively reproducible during tactile stimulation, with overlapping activations >85% of the time. S2 was also highly reproducible during painful stimulation (88%), whereas S1 was less reproducible (44%). However, activation in the thalamus to both tactile and painful stimulation was highly variable. Ipsilateral activation was consistent within S2 but sparse within S1 and thalamus. Deactivations within ipsilateral S1 occurred 48% of the time with tactile stimuli, and 90% of the time with painful stimuli. Within contralaterally activated regions intraclass correlations (ICCs) were very high using the unthresholded method regardless of the type of stimulation, whereas much lower ICCs arose from the thresholded analyses. These data indicate that a threshold-independent analysis can produce more reproducible outcomes than a standard threshold-dependent analysis.
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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 | 0.009 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".