Transcallosal sensorimotor fiber tract structure‐function relationships
Bibliographic record
Abstract
Recent studies have demonstrated neuroanatomically selective relationships among white matter tract microstructure, physiological function, and task performance. Such findings suggest that the microstructure of transcallosal motor fibers may reflect the capacity for interhemispheric inhibition between the primary motor cortices, although full characterization of the transcallosal inhibitory sensorimotor network is lacking. Thus, the goal of this study was to provide a comprehensive description of transcallosal fibers connecting homologous sensorimotor cortical regions and to identify the relationship(s) between fiber tract microstructure and interhemispheric inhibition during voluntary cortical activity. To this end, we assessed microstructure of fiber tracts connecting homologous sensorimotor regions of the cortex with diffusion tensor imaging. We also assessed interhemispheric inhibition by eliciting the ipsilateral silent period (iSP) within the same participants. We mapped mutually exclusive transcallosal connections between homologous sensorimotor regions and computed quantitative metrics of each fiber tract. Paralleling work in non-human primates, we found the densest interhemispheric sensorimotor connections to be between the medial motor areas. Additionally, we provide a midsagittal callosal atlas in normalized Montreal Neurological Institute (MNI) space for future studies to use when investigating callosal fiber tracts connecting primary and secondary sensorimotor cortices. Finally, we report a strong, positive relationship (r = 0.76) between strength of interhemispheric inhibition (iSP) and microstructure of interhemispheric fibers that is specific to tracts connecting the primary motor cortices. Thus, increased fiber microstructure in young adults predicts interhemispheric inhibitory capacity.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".