Continuous theta burst TMS of area MT impairs attentive motion tracking
Bibliographic record
Abstract
Multiple object tracking (MOT) is impaired in amblyopia. This deficit has been associated with reduced MT activity during MOT task performance, suggesting that MT plays an important role in attentive tracking. To test this possibility, we assessed whether modulation of MT activity using inhibitory continuous theta burst stimulation (cTBS) would influence MOT performance in participants with normal vision. The MOT stimulus consisted of 4 targets and 4 distractors and was presented at 10° eccentricity (right and left hemifields). Functional MRI-guided cTBS was applied to left MT at 100% of active motor threshold intensity. Participants (n=15, age: 27±3) attended separate active and sham cTBS sessions. During cTBS, the MOT task was presented at each participant's speed threshold. Percent correct (based on partial report) for 40 trials was measured at baseline (before cTBS) and 5min and 30min after cTBS stimulation. Baseline accuracy did not vary between the right and left hemifields. There was a significant interaction between cTBS type (active/sham) and measurement time (baseline/post cTBS 5min or 30min) (F2,18=5.71, p=0.01). For active cTBS, there was a significant reduction in accuracy from baseline for the right hemifield after 5min (10 ± 2% reduction; t14 = 1.95, p = 0.03) and after 30min (15±3% reduction; t14=2.96, p=0.01). The left hemifield exhibited improved accuracy 30min after active cTBS (6±1.5% improvement, t14=-2.24, p=0.02). For sham cTBS, accuracy improved in both hemifields equally (right: 9 ± 2% improvement; t14=-2.94, p=0.02 and left: 9±1.5% improvement; t14=1.95, p=0.04). Our results demonstrate that cTBS of MT impaired MOT accuracy. There were improvements in MOT accuracy in the control hemifield and in the sham condition suggesting a task learning effect. These results highlight the importance of lower-level motion processing for MOT and support previous findings indicating that impaired MT function is responsible for MOT deficits in patients with amblyopia. Meeting abstract presented at VSS 2018
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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".