Neocortical neurogenesis: a proneural gene perspective
Bibliographic record
Abstract
The neocortex, which is the site of higher-order cognitive functioning, is comprised of two main neuronal types: excitatory (E) and inhibitory (I). Neurodevelopmental disorders that disrupt the balance of E:I neurotransmission predispose individuals to atypical brain function, highlighting the importance of generating the correct numbers of each neuronal type. During development, neurons with E and I neurotransmission profiles are primarily generated from neural stem and progenitor cells (NPCs), located in the dorsal and ventral telencephalon, respectively. To ensure that correct numbers of each neuronal type are generated, NPC differentiation dynamics vary depending on positional and temporal information and host species. Despite variations in NPC differentiation kinetics and outcomes, proneural genes encoding basic helix-loop-helix (bHLH) transcription factors (TFs) have remained constant as the core drivers of neurogenesis and neuronal subtype specification from fly to human. This high degree of functional conservation raises the question of how proneural TF activity is regulated to control precise neurogenic patterns. In the neocortex, the proneural genes neurogenin 1 (Neurog1) and Neurog2 specify an excitatory neuronal identity in dorsal telencephalic NPCs, whereas achaete-scute family bHLH transcription factor 1 (Ascl1) specifies an inhibitory neurotransmission fate in ventral NPCs, generating interneurons that then migrate tangentially to enter the neocortex. Here, we review our current knowledge of how Neurog1/Neurog2 and Ascl1 functions are regulated to ensure that E:I balance is ultimately achieved in the lissencephalic murine cortex and in gyrencephalic species. Together, these studies point to emergent and conserved features of proneural gene regulation and function across evolutionary time.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".