Temporal dynamics of noradrenaline release at fine spatial scales during motor learning
Bibliographic record
Abstract
ABSTRACT Noradrenaline (NA) released from the locus coeruleus (LC) has been known to play pivotal roles in arousal, sensory processing, decision-making, and learning through global release across the entire neocortex. Recent studies have demonstrated heterogeneous and modular NA release in distinct brain regions and highlighted its spatiotemporal dynamics across the neocortex. However, the spatiotemporal specificity of NA release at fine scales within a single brain region remains unclear, and it has not been reported whether the release patterns evolve functionally throughout prolonged learning processes such as motor skill acquisition. Here, by employing in vivo two-photon imaging with various genetically encoded NA sensors, we reveal that behavior-induced NA release in the primary motor cortex (M1) is spatially heterogeneous at the scale of local microcircuitry. Over the course of learning, the release pattern is locally refined, achieving consistent spatial precision within M1. Intriguingly, pharmacological manipulations that disrupt the spatial specificity also alter local neurons’ activity and representations. Furthermore, LC-NA axonal calcium imaging uncovered two distinct temporal activity patterns, in which non-behavior-related ‘rapid’ axonal activity (sub-second duration events) profoundly affect the temporally precise behavior-induced ‘persistent’ axonal activity (seconds duration events). Closed-loop optogenetic manipulations that bi-directionally modulate non-behavior-related rapid events directly impacted the learning process. Together, our results provide novel insights into the temporal dynamics of NA release at fine spatial scales within one brain region and underscore the critical role of local NA specificity in regulating circuit plasticity during motor skill acquisition.
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 | 0.000 |
| 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".