Editorial: The role of the basal ganglia in somatosensory-motor interactions: evidence from neurophysiology and behavior, volume II
Bibliographic record
Abstract
possibility to record for longer periods and to apply adaptive stimulation paradigms (Nakajima et al. 2021), it is expected that the field will adapt this technology in the coming years.Next to the applied methods in the research topic, the diseases studied are also noteworthy. Although the role of the basal ganglia in movement disorders has been studied extensively, this is far less the case for (urologic) pain syndromes and traumatic brain injury (TBI) as respectively described by Lan et al. (Lan et al. 2022) and Pinky et al. (Pinky et al. 2022). The findings of the study by Lan et al. further illustrate the role of the basal ganglia in pathological somatosensory-motor interactions in abnormal sensations (i.e. pain) and Pinky et al. studied the role of the caudate as modulator in the recovery of TBI. These insights show how studying basal-ganglia functions beyond movement disorders can help in developing neuro-modulation strategies and the development of (image-based) biomarkers. The development of biomarkers is also relevant for the prodromal stages of Parkinson's disease (PD) such as in patients with rapid eye movement sleep behaviour disorder (RBD), which are at a higher risk for developing PD. By identifying these patients using advanced imaging approaches, such as described by Chen et al. (Chen et al. 2022), patients may benefit in the future from diseasemodifying therapies before the onset of motor symptoms (Athauda et al. 2019).Although these insights from imaging studies comparing patient populations with controls are valuable for the reasons mentioned above, they lack a behavioural component showing how altered (network) processing actually leads to disturbed basal-ganglia function. In their contribution Sengupta et al. (Sengupta et al. 2022) show the nature of response disinhibition in PD during natural movement performance whilst Filyushkina et al. (Filyushkina et al. 2022) were able to distinguish neural patterns of self-initiated vs externally cued response in the STN. With the dawn of DBS stimulation paradigms that influence decision making processes (Ghahremani et al. 2018;Herz et al. 2018), it is of utmost relevance to understand the basal-ganglia contributions underlying autonomous action selecting and its interference by either diseases (e.g. PD) or therapies (e.g. DBS).
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.006 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.005 | 0.001 |
| Research integrity | 0.016 | 0.021 |
| Insufficient payload (model declined to judge) | 0.016 | 0.014 |
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".