Second-Scale Neural Dynamics Shape Hormonal Outputs in Hypothalamic CRH Neurons
Bibliographic record
Abstract
Abstract The elevation of glucocorticoids is a hallmark of stress, arising from the integration of rapid neuronal signals into sustained hormonal outputs. A key interface for this neuroendocrine signal translation is corticotropin releasing hormone (CRH) neurons in the paraventricular nucleus of the hypothalamus (PVN), which release CRH at the median eminence (ME). We recently discovered that CRH PVN neurons exhibit a characteristic shift in firing patterns from rhythmic short-bursts (low-activity state) to tonic firing (high-activity state) in response to stress. This raised a critical question: how are these distinct firing patterns are integrated into slower, sustained hormonal outputs at neuroendocrine terminals? Here, we implemented optical approaches to detect CRH release ex vivo and in vivo. Using newly developed sniffer cells for CRH, we measured CRH release at ME ex vivo , triggered by the distinct, in vivo -like firing patterns. Our results demonstrated that the primary determinant of neuroendocrine CRH release was the firing rate sustained over the timescale of seconds, with little contribution from specific firing patterns. These results collaborated with second-scale increase in firing rate triggered by stress stimuli. Additionally, we recorded the dynamics of CRH at the ME in the freely moving mice using genetically-encoded GPCR-activation based (GRAB) sensors for CRH. Foot shock stress triggered transient, time-locked increases in CRH release on the timescale of seconds. Importantly, these second-scale CRH pulses, when elicited during repeated foot shocks, were integrated over minutes to scale downstream hormone releases. Together, our data revealed critical roles of second-scale dynamics in CRH PVN neuron activity for the neuroendocrine translation of stress signals.
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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 | 0.000 | 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".