Factors Affecting Macro-Structural Development in the Cerebral Cortex: The Potential Role of Tissue Removal Through Pruning and Apoptosis
Bibliographic record
Abstract
Several theories have been developed to explain mechanisms of macro-structural development in the cerebral cortex, including external skull constraints, axonal tension, differential proliferation of neural progenitors, differential cortical expansion, and axonal pushing. These theories are not necessarily mutually exclusive, and some combination thereof may be required in order to fully explain and characterize complex folding, sulcal development and thinning in the cortex. This manuscript provides an overview of the leading theories of contributing factors to macro-structural cortical development, and presents additional potential contributing factors, including tissue removal through pruning and apoptosis. Although tissue removal has been proposed as a potentially major factor in microcephaly and megalencephaly-conditions with major deviations from healthy macro-structural cortical development-in this manuscript, it is proposed that tissue removal may be an important factor in healthy neurodevelopment, as well as in additional pathological conditions. This manuscript also presents the theory that tissue removal may be linked to learning. Potential consequences for a variety of pathological conditions, and potential relationships with previously established theories of macro-structural cortical development are discussed.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| 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 | 0.000 |
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".