Continuous theta burst TMS of area MT+ impairs attentive motion tracking
Bibliographic record
Abstract
Attentive motion tracking deficits measured using multiple object tracking (MOT) tasks have been identified in a number of neurodevelopmental disorders such as amblyopia and autism. These deficits are often attributed to the abnormal development of high-level attentional networks. However, neuroimaging evidence from amblyopia suggests that reduced MOT performance can be explained by impaired function in motion-sensitive area MT+ alone. To test the hypothesis that a subtle disruption of MT+ function could cause MOT impairment, we assessed whether continuous theta burst stimulation (cTBS) of MT+ influenced MOT task accuracy in individuals with normal vision. The MOT stimulus consisted of four target and four distractor dots and was presented at ±10° eccentricity (right/left hemifield). fMRI-guided cTBS was applied to left MT+. Participants (n = 13, age: 27 ± 3) attended separate active and sham cTBS sessions where the MOT task was completed before, 5-min post- and 30-min post-cTBS. Active cTBS significantly impaired MOT task accuracy relative to baseline for the right (stimulated) hemifield 5-min (10 ± 2% reduction) and 30-min (14 ± 3% reduction) post-stimulation. No impairment occurred within the left (control) hemifield after active cTBS or for either hemifield after sham cTBS. These results highlight the importance of lower level motion processing for MOT, suggesting that a minor disruption of MT+ function alone is sufficient to cause a deficit in MOT performance.
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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 | 0.000 |
| 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) | 0.002 | 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".