Mapping Cortical Laminar Structure in the 3D BigBrain
Bibliographic record
Abstract
Histological sections offer high spatial resolution to examine laminar architecture of the human cerebral cortex; however, they are restricted by being 2D, hence only regions with sufficiently optimal cutting planes can be analyzed. Conversely, noninvasive neuroimaging approaches are whole brain but have relatively low resolution. Consequently, correct 3D cross-cortical patterns of laminar architecture have never been mapped in histological sections. We developed an automated technique to identify and analyze laminar structure within the high-resolution 3D histological BigBrain. We extracted white matter and pial surfaces, from which we derived histologically verified surfaces at the layer I/II boundary and within layer IV. Layer IV depth was strongly predicted by cortical curvature but varied between areas. This fully automated 3D laminar analysis is an important requirement for bridging high-resolution 2D cytoarchitecture and in vivo 3D neuroimaging. It lays the foundation for in-depth, whole-brain analyses of cortical layering.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".