Choice reaction time performance correlates with diffusion anisotropy in white matter pathways supporting visuospatial attention
Bibliographic record
Abstract
Humans exhibit significant interindividual variability in behavioral reaction time (RT) performance yet the underlying neural mechanisms for this variability remain largely unknown. It has been proposed that interindividual variability in RT performance may be due to differences in white matter (WM) physiological properties, although such a relationship has never been demonstrated in cortical projection or association pathways in healthy young adults. Using diffusion tensor MRI (DTI), we sought to test whether diffusion tensor fractional anisotropy (FA), a measure of the orientational coherence of water self-diffusion, is regionally correlated with RT on a visual self-paced choice RT (CRT) task. CRT was found to be significantly correlated with FA in projection and association pathways supporting visuospatial attention including the right optic radiation, right posterior thalamus, and right medial precuneus WM. Significant correlations were also observed in left superior temporal sulcus WM and the left parietal operculum. The lateralization of the CRT-FA correlation to right visual and parietal WM pathways is consistent with the specialization of right visual and parietal cortices for visuospatial attention. The localization of the CRT-FA correlations to predominantly visual and parietal WM pathways, but not to motor pathways or the corpus callosum indicates that individual differences in visual CRT performance are associated with variations in the WM underlying the visuospatial attention network as opposed to pathways supporting motor movement or interhemispheric transmission.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".