Modulation of 7 T fMRI Signal in the Cerebellar Cortex and Nuclei During Acquisition, Extinction, and Reacquisition of Conditioned Eyeblink Responses
Bibliographic record
Abstract
Classical delay eyeblink conditioning is likely the most commonly used paradigm to study cerebellar learning. As yet, few studies have focused on extinction and savings of conditioned eyeblink responses (CRs). Saving effects, which are reflected in a reacquisition after extinction that is faster than the initial acquisition, suggest that learned associations are at least partly preserved during extinction. In this study, we tested the hypothesis that acquisition-related plasticity is nihilated during extinction in the cerebellar cortex, but retained in the cerebellar nuclei, allowing for faster reacquisition. Changes of 7 T functional magnetic resonance imaging (fMRI) signals were investigated in the cerebellar cortex and nuclei of young and healthy human subjects. Main effects of acquisition, extinction, and reacquisition against rest were calculated in conditioned stimulus-only trials. First-level β values were determined for a spherical region of interest (ROI) around the acquisition peak voxel in lobule VI, and dentate and interposed nuclei ipsilateral to the unconditioned stimulus. In the cerebellar cortex and nuclei, fMRI signals were significantly lower in extinction compared to acquisition and reacquisition, but not significantly different between acquisition and reacquisition. These findings are consistent with the theory of bidirectional learning in both the cerebellar cortex and nuclei. It cannot explain, however, why conditioned responses reappear almost immediately in reacquisition following extinction. Although the present data do not exclude that part of the initial memory remains in the cerebellum in extinction, future studies should also explore changes in extracerebellar regions as a potential substrate of saving effects. Hum Brain Mapp 38:3957-3974, 2017. © 2017 Wiley Periodicals, Inc.
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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".