Transformation but not generation of motor images is disrupted following stimulation over the left inferior parietal lobe
Bibliographic record
Abstract
Motor imagery (MI) involves the generation, maintenance, and transformation of motor images; yet, the neural underpinnings of each stage are not well understood. Here, we investigated the role of the left inferior parietal lobe (IPL) in the stages of MI. Healthy participants (N = 20) engaged in a MI task (making judgments about hands presented on a screen; hand laterality judgment task) over two days. Past literature demonstrates the mental rotation of hands in this task involves implicit MI (i.e., where MI occurs spontaneously in the absence of explicit instructions). During the task, active (Day A; 120% resting motor threshold) or sham (Day B; placebo) neuronavigated transcranial magnetic stimulation (TMS) was applied to the left IPL (location determined from past neuroimaging work) on 50% of trials at 250, 500, or 750ms post-stimulus onset, corresponding to different stages of MI. A/B days were randomized across participants. Linear mixed effects (LME) modelling conducted on reaction time and accuracy revealed that longer reaction times were observed when TMS was delivered at 750ms after trial onset, and more greatly for active vs. sham stimulation. This effect was exacerbated for palm-vs. back-view stimuli and for left vs. right hands. Accuracy overall was decreased for active vs. sham stimulation, and to a greater extent for palm-vs. back-view stimuli. Findings suggest that the left IPL is involved in image transformation. Overall this work informs on the neural underpinnings of the stages of MI.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".