Neural and behavioral dynamics of encoding, production and synchronization with external rhythms in subcortical lesion patients
Bibliographic record
Abstract
Abstract Acting in and adapting to a dynamically changing environment necessitates precise encoding of the timing of unfolding sensory events in our environment, and to time of our own (re-)actions to them. Cerebellar (CE) and basal ganglia (BG) circuitries play fundamental and complementary roles in timing processes. While the CE seems to encode the precise timing of sensory events ( when an event occurs), the BG engage in generating temporal predictions ( when a next event occurs). However, their contributions are rarely investigated in combination, as it is generally difficult to record data from respective patient groups in parallel. Here we investigated the causal roles of CE and BG in sensory and sensorimotor timing processes. Healthy controls and patients with CE or BG lesions listened to isochronous auditory sequences while their EEG was recorded and later performed a tapping synchronization task. We assessed intra- and inter-individual variabilities, as well as group differences, using event-related responses, delta-band inter-trial phase-coherence and acceleration dynamics while tuning to the stimulation frequency ( Sf ). CE and BG lesions increased variability in ERP latency and reduced the coherence of delta-band activity. CE but not BG lesions further impacted the stability of delta-band oscillations while tuning to the Sf . These findings show a causal link between subcortical lesions and the capacity to encode and synchronize ongoing neural activity with temporal regularities in the acoustic environment, but do not fully dissociate the specific contributions of the BG and the CE to processing sound in isochronous contexts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".