Anatomical segmentation of the human medial prefrontal cortex
Bibliographic record
Abstract
The medial prefrontal areas 32, 24, 14, and 25 (mPFC) form part of the limbic memory system, but little is known about their functional specialization in humans. To add anatomical precision to structural and functional magnetic resonance imaging (MRI) data, we aimed to identify these mPFC subareas in histological preparations of human brain tissue, determine sulci most consistently related with mPFC areal boundaries, and use these sulci to delineate mPFC areas in MRIs. To achieve this, we obtained three-dimensional MRI data from 11 ex vivo hemispheres and processed them for cyto- and myelo-architectonic analysis. The architectonic boundaries of mPFC areas were identified in histology and cortical surface length and volumes were measured. Unfolded maps of histologically determined boundaries were generated to identify the association of mPFC areal boundaries with sulci across cases. This analysis showed that cingulate and superior rostral were the sulci most consistently related to mPFC areal boundaries. Based on presence/absence and anastomosis between such sulci, 6 sulci patterns in the 11 hemispheres were found. A further analysis of 102 hemispheres of in vivo MRI scans (N = 51 males, mean ± SD 24.1 ± 3.1 years of age) showed similar sulci patterns, which allowed us to delineate the mFPC areas in them. The volumes of mPFC areas across histological, ex vivo and in vivo MRI delineations were comparable and probabilistic maps generated from the MRIs of the102 hemispheres. Probabilistic maps of mPFC areas were registered to MNI space and are available for regional analysis of functional magnetic resonance imaging data.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".