Calbindin Stratifies Midbrain Dopaminergic Neurons Governing Distinct Aspects of Locomotion
Bibliographic record
Abstract
Despite advances in delineating the molecular diversity and projection patterns of midbrain dopamine (DA) neurons, subtype-specific contributions to motor learning and movement execution remain poorly defined. Here, we applied intersectional ablation and inhibitory chemogenetics to dissect the roles of calbindin-expressing (CALB1 + ) and nonexpressing (CALB1 − ) DA neurons in locomotion. Using newly engineered intersectional autocleavable Caspase3 constructs, we ablated CALB1 + or CALB1 − DA neurons in the mouse midbrain. CALB1 − DA neuron ablation caused severe weight loss, whereas CALB1 + DA neuron ablation produced no overt health impairments. Nonetheless, loss of either subtype led to a bradykinetic-like phenotype on the initiation and vigor of voluntary movements. Only ablation of CALB1 − DA neurons impaired performance on the accelerated rotarod. To test if these phenotypes are the result of DA subtype activity, we silenced either population using the inhibitory DREADD hM4Di. Consistent with ablation, silencing CALB1 − DA neurons impacted the initial performance on the rotarod, whereas inhibition of CALB1 + DA neurons did not impact performance on the first day, but prevented across-day improvement. Silencing both populations impaired the initiation and vigor of voluntary movements. We next investigated whether this locomotor phenotype stemmed from reduced DA release in the dorsolateral striatum (DLS). While CALB1 − silencing abrogated DA transients in the DLS, CALB1 + silencing unexpectedly resulted in increased transients in DLS. Thus, our results demonstrate that DA transients in the DLS are not invariably coupled with movement execution. Altogether, these findings uncover both distinct and shared roles of molecularly defined DA subtypes in shaping different aspects of locomotion.
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 | 0.001 |
| 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".