Interneuron odyssey: molecular mechanisms of tangential migration
Bibliographic record
Abstract
Cortical GABAergic interneurons are critical components of neural networks. They provide local and long-range inhibition and help coordinate network activities involved in various brain functions, including signal processing, learning, memory and adaptative responses. Disruption of cortical GABAergic interneuron migration thus induces profound deficits in neural network organization and function, and results in a variety of neurodevelopmental and neuropsychiatric disorders including epilepsy, intellectual disability, autism spectrum disorders and schizophrenia. It is thus of paramount importance to elucidate the specific mechanisms that govern the migration of interneurons to clarify some of the underlying disease mechanisms. GABAergic interneurons destined to populate the cortex arise from multipotent ventral progenitor cells located in the ganglionic eminences and pre-optic area. Post-mitotic interneurons exit their place of origin in the ventral forebrain and migrate dorsally using defined migratory streams to reach the cortical plate, which they enter through radial migration before dispersing to settle in their final laminar allocation. While migrating, cortical interneurons constantly change their morphology through the dynamic remodeling of actomyosin and microtubule cytoskeleton as they detect and integrate extracellular guidance cues generated by neuronal and non-neuronal sources distributed along their migratory routes. These processes ensure proper distribution of GABAergic interneurons across cortical areas and lamina, supporting the development of adequate network connectivity and brain function. This short review summarizes current knowledge on the cellular and molecular mechanisms controlling cortical GABAergic interneuron migration, with a focus on tangential migration, and addresses potential avenues for cell-based interneuron progenitor transplants in the treatment of neurodevelopmental disorders and epilepsy.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".