The PaleoArchiNeo (PAN) human brain atlas: A dataset on a standard neuroanatomical MRI template following a phylogenetic approach
Bibliographic record
Abstract
Cortical atlases provide consistent divisions of the human cortex into areas that have common structural as well as meaningful and distinctive functional characteristics. They constitute a fundamental tool to study and quantify changes in healthy and pathological states. Historically, the most widely used atlases follow the cytoarchitecture described by Brodmann and/or the myeloarchitectonic characteristics described by Vogt-Vogt. These histological approaches have since been combined to the standard anatomical nomenclature of gyri and sulci, referring to the corresponding cytoarchitectonic area(s) present in a gyrus, when applicable or necessary (e.g. area 4 of Brodmann in the pre-central gyrus). More recently, common functional features depicted by resting state functional MRI have guided the division of the cortex into functional regions of interest. However, to date, there are no human MRI atlases that divide the cortex considering the common evolutionary changes experienced by the mammalian cortex. Hence, the present dataset describes the PaleoArchiNeo (PAN) Human Brain, a voxel-based atlas that divides the human cortex into five regions of interest (ROIs) following a phylogenetic approach: 1- archicortex, 2- paleocortex, 3- peri-archicortex, 4- proisocortex, 5- neocortex, and thirty neocortical sub-ROIs that follow the gyral Terminologia Anatomica.The masks of the ROIs and sub-ROIs were segmented on the T1-weighted MNI ICBM 152 2009c symmetric average brain MRI model, the latest version of the most widely used standard brain template. The segmentations have been performed manually by anatomist experts, following the MRI anatomical landmarks that have been previously described, correlated, and validated with histology by other groups.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.016 |
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".