Basal ganglia and cerebellar lesions causally impact the neural encoding of temporal regularities
Bibliographic record
Abstract
Abstract Acting in and adapting to a dynamically changing environment necessitates to precisely encode the timing of sensory events, and to time 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 use precise timing (when an event occurs) and temporal intervals to generate temporal predictions (when a next event occurs), the BG uses relative timing to extract the beat in rhythmic sequences. As it is generally difficult to record data from respective patient groups in parallel, CE and BG contributions to timing processes are rarely investigated in combination. Here, we let healthy controls and patients with CE or BG lesions listen to isochronous auditory sequences while their EEG was recorded. We assessed intra- and inter-individual variabilities, as well as group differences, using event-related potentials (ERP), 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. While most standard metrics of neural entrainment do not dissociate specific contributions of BG and CE to sound processing in isochronous sequences, the newly introduced ‘stability’ metric isolated distinct changes in delta-band tuning dynamics in CE patients. This observation highlights the fundamental role of the CE in generating and maintaining stable neural representations of event onsets in the sensory environment.
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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 | 0.001 |
| 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".