Specific sensorimotor interneuron circuits are sensitive to cerebellar-attention interactions
Bibliographic record
Abstract
Background: Short latency afferent inhibition (SAI) provides a method to investigate mechanisms of sensorimotor integration. Cholinergic involvement in the SAI phenomena suggests that SAI may provide a marker of cognitive influence over implicit sensorimotor processes. Consistent with this hypothesis, we previously demonstrated that visual attention load suppresses SAI circuits preferentially recruited by anterior-to-posterior (AP)-, but not posterior-to-anterior (PA)-current induced by transcranial magnetic stimulation. However, cerebellar modulation can also modulate these same AP-sensitive SAI circuits. Yet, the consequences of concurrent cognitive and implicit cerebellar influences over these AP circuits are unknown. Objective: We used cerebellar intermittent theta-burst stimulation (iTBS) to determine whether the cerebellar modulation of sensory to motor projections interacts with the attentional modulation of sensory to motor circuits probed by SAI. Methods: We assessed AP-SAI and PA-SAI during a concurrent visual detection task of varying attention load before and after cerebellar iTBS. Results: Before cerebellar iTBS, a higher visual attention load suppressed AP-SAI, but not PA-SAI, compared to a lower visual attention load. Post-cerebellar iTBS, the pattern of AP-SAI in response to visual attention load, was reversed; a higher visual attention load enhanced AP-SAI compared to a lower visual attention load. Cerebellar iTBS did not affect PA-SAI regardless of visual attention load. Conclusion: These findings suggest that attention and cerebellar networks converge on overlapping AP-sensitive circuitry to influence motor output by controlling the strength of the afferent projections to the motor cortex. This interaction has important implications for understanding the mechanisms of motor performance and learning.
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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 | 0.001 |
| 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".