Assignment Confidence in Localization of the Hand Motor Cortex: Comparison of Structural Imaging With Functional MRI
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to assign confidence levels to structural MRI and functional MRI (fMRI) for localization of the primary motor cortex. MATERIALS AND METHODS: Ninety-one fMRI studies with at least one motor task (178 hemispheres) were identified. Three anatomic assessments were used to localize the primary motor cortex: relation between the superior frontal sulcus and precentral sulcus; cortical thickness; and configuration of the precentral knob. In 105 hemispheres, interreader agreement was assessed for two investigators with different experience levels. Confidence ratings from 0 to 5 (0, no confidence; 5, 100% confidence) were assigned for fMRI and each anatomic localization method. RESULTS: Cortical thickness had the highest confidence rating (mean, 4.90 ± 0.47 [SD]) with only one failure. The relation between the superior frontal sulcus and precentral sulcus had the lowest confidence rating (4.33 ± 0.91) with three failures. The greatest statistical significance was observed for the cortical thickness and superior frontal sulcus-precentral sulcus methods (post hoc Bonferroni test, p < 0.001). Confidence rating scores were significantly higher for the cortical thickness sign than for fMRI results (4.72 ± 0.54) for a single motor task (post hoc Bonferroni test, p = 0.006); however, the mean confidence rating for fMRI improved to 4.87 ± 0.36 when additional motor tasks were performed. Interreader differences were least for the cortical thickness sign (paired t test, t = 4.25, p < 0.001). CONCLUSION: Cortical thickness is a better anatomic landmark than fMRI localization for assigning confidence regarding localization of the primary motor cortex; however, localization of motor function is more specific when combined with fMRI findings. Multiple techniques can be used to increase confidence in identifying the hand motor cortex.
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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 | 0.010 | 0.074 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 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 | 0.000 |
| 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".