Hormonal Regulation of Oligodendrogenesis I: Effects across the Lifespan
Bibliographic record
Abstract
The brain’s capacity to respond to changing environments via hormonal signaling is critical to fine-tuned function. An emerging body of literature highlights a role for myelin plasticity as a prominent type of experience-dependent plasticity in the adult brain. Myelin plasticity is driven by oligodendrocytes (OLs) and their precursor cells (OPCs). OPC differentiation regulates the trajectory of myelin production throughout development, and importantly, OPCs maintain the ability to proliferate and generate new OLs throughout adulthood. The process of oligodendrogenesis (OLgenesis), the creation of new OLs, can be dramatically influenced during early development and in adulthood by internal and environmental conditions such as hormones. Here, we review the current literature describing hormonal regulation of OLgenesis within physiological conditions, focusing on several classes of hormones: steroid, peptide, and thyroid hormones. We discuss hormonal regulation at each stage of OLgenesis and describe mechanisms of action, where known. Overall, the majority of hormones enhance OLgenesis, increasing OPC differentiation and inducing maturation and myelin production in OLs. The mechanisms underlying these processes vary for each hormone but may ultimately converge upon common signaling pathways, mediated by specific receptors expressed across the OL lineage. However, not all of the mechanisms have been fully elucidated, and here, we note the remaining gaps in the literature, including the complex interactions between hormonal systems and with the immune system. In the companion manuscript in this issue [1], we discuss the implications of hormonal regulation of OLgenesis for neurological and psychiatric disorders characterized by white matter loss. Ultimately, a better understanding of the fundamental mechanisms of hormonal regulation of OLgenesis across the entire lifespan, especially in vivo, will progress both basic and translational research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".