MEG signatures of arm posture coding and integration into movement plans
Bibliographic record
Abstract
To plan a visually guided movement, the brain must calculate an extrinsic movement vector and then convert this into intrinsic muscle commands for current posture. Where, how and when this happens in the human cortical arm movement planning network remains largely unknown. Here, we use high spatiotemporal resolution magnetoencephalography (MEG) combined with a pro-/anti-wrist pointing task with 3 different forearm postures to investigate this question. First, we then computed cortical source activity in 16 previously identified bilateral cortical areas (Alikhanian, et al., Frontiers in Neuroscience 2013). We then compared pro/anti trials to identify brain areas coding for stimulus direction vs. movement direction. Sensory activity in α / β bands progressed from posterior to anterior cortical areas, culminating in a β-band movement plan in primary motor cortex. During the delay, movement codes then retroactively replaced the sensory code in more posterior areas (Blohm, et al., Cerebral Cortex 2019). We then contrasted oscillatory activity related to opposing wrist postures to find posture coding and test when and where the extrinsic-to-intrinsic transformation occurred. We found a distinct pair of overlapping networks coding for posture (in γ band) vs. posture-specific movement plans (β). Together with previous results showing a yet again different sub-network specifying motor effector and it’s integration (Blohm, et al., J Neurophysiol 2022), we demonstrate that distinct cortical sub-networks carry out different spatiotemporal computations for movement planning.
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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".