Decoding State-Dependent Cortical-Cerebellar Cellular Functional Connectivity in the Mouse Brain
Bibliographic record
Abstract
ABSTRACT The cerebellum participates in motor tasks, but also a broad spectrum of cognitive functions. However, cerebellar connections with higher areas such as cortex are not direct and the mechanisms by which the cerebellum integrates and processes diverse information streams are not clear. We investigated the functional connectivity between single cerebellar neurons and population activity of the dorsal cortex using mesoscale imaging. Our findings revealed dynamic coupling between individual cerebellar neurons and diverse cortical networks, and such functional association can be influenced by local excitatory and inhibitory connections. While the cortical representations of individual cerebellar neurons displayed marked changes across different brain states, the overall assignments to specific cortical topographic areas at the population level remained stable. Simple spikes and complex spikes of the same Purkinje cells displayed either similar or distinct cortical functional connectivity patterns. Moreover, the spontaneous functional connectivity patterns aligned with cerebellar neurons’ functional responses to external stimuli in a modality-specific manner. Importantly, the tuning properties of subsets of cerebellar neurons differed between anesthesia and awake states, mirrored by state-dependent changes in their long-range functional connectivity patterns. Collectively, our results provide a comprehensive view of the state-dependent cortical-cerebellar functional connectivity landscape and demonstrate that remapping of long-range functional network association could underlie state-dependent change in sensory processing.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| 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".