Structural and developmental principles of neuropil assembly in <i>C. elegans</i>
Bibliographic record
Abstract
Summary Neuropil is a fundamental form of tissue organization within brains 1 . In neuropils, densely packed neurons synaptically interconnect into precise circuit architecture 2,3 , yet the structural and developmental principles governing nanoscale precision in bundled neuropil assembly remain largely unknown 4–6 . Here we use diffusion condensation, a coarse-graining clustering algorithm 7 , to identify nested circuit structures within the C. elegans cerebral neuropil (called the nerve ring). We determine that the nerve ring neuropil is organized into four tightly bundled strata composed of related behavioral circuits. We demonstrate that the stratified architecture of the neuropil is a geometrical representation of the functional segregation of sensory information and motor outputs, with specific sensory organs and muscle quadrants mapping onto particular neuropil strata. We identify groups of neurons with unique morphologies that integrate information across strata and that create a sophisticated honeycomb-shaped scaffold that encases the strata within the nerve ring. We resolve the developmental sequence leading to stratified neuropil organization through the integration of lineaging and cell tracking algorithms with high resolution light-sheet microscopy, and reveal principles of cell position, migration and hierarchical outgrowth that guide neuropil organization. Our results uncover conserved design principles underlying nerve ring neuropil architecture and function, and a pioneer neuron-based, temporal progression of outgrowth that guides the hierarchical development of the layered neuropil. Our findings provide a blueprint for using structural and developmental approaches to systematically understand neuropil organization within brains.
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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 | 0.001 |
| Scholarly communication | 0.001 | 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".