Characterizing neuronal cell bodies in human postmortem cerebral white matter tracts
Bibliographic record
Abstract
Until the discovery of white matter neurons (WMN) in the 19th century, white matter (WM) was considered to be completely devoid of neuronal cell bodies. Despite evidence consistently showing evident neuronal soma within cortical WM and their purported implication in neuropsychiatric disorders, these neurons are understudied and have not been characterized in human long-range WM tracts. Using postmortem human brain tissue, we investigated the presence, densities and proportions of excitatory/inhibitory neurons in the uncinate fasciculus (UF) and corpus callosum (CC). We also investigated the ventromedial prefrontal cortex (vmPFC) to validate our methods by comparing our results with previously reported densities of neurons in cortical WM. To identify WMN, we employed fluorescence in situ hybridization with excitatory (SLC17A7) and inhibitory (GAD1) neuronal markers and subsequently validated these neurons at the protein level with NeuN immunohistochemistry. We found that the density of WMN in the vmPFC corresponded with previous independent estimates. The UF displayed a similar, though slightly lower density of WMN compared to the vmPFC, while the CC had a far lower density of WMN than both of these regions. Due to the higher-than-expected density of WMN in the UF, we validated the findings at a second location along the UF temporal segment and confirmed the presence of substantial numbers of WMN in this tract. This research constitutes the first ever validated observation of WMN in human long-range WM tracts, laying the foundation for future research on the phenotype and function of these neurons, and how they may be affected in brain disorders.
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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 | 0.001 | 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".