Ultrastructural Analysis of Human Uncinate Fasciculus with Spectral-Focusing Coherent Anti-Stokes Raman Spectroscopy
Bibliographic record
Abstract
Abstract Characterizing the ultrastructure of myelin in the human brain is key to understanding the neurobiology of both health and disease. In postmortem human brain tissue, electron microscopy is often technically unfeasible due to poorer tissue quality. The uncinate fasciculus (UF) is a long-range white matter association tract that connects the anterior temporal lobe with the orbitofrontal cortex. The UF is not present in rodents yet is highly expanded in humans and non-human primates. As such, its molecular and ultrastructural properties are virtually unknown. Here, we develop and validate a novel spectral-focusing Coherent Anti-Stokes Raman Spectroscopy (sf-CARS) system coupled with a custom AxonDeepSeg segmentation model to characterize UF ultrastructure in the human postmortem brain (n=6). We provide a proof of concept of this new methodological pipeline in the UF temporal segment and observe that the mean axon diameter detected is 0.93 µm ± 0.54 and mean myelin thickness is 0.48 µm ± 0.14. We also observe that the UF axons are thicker than those in the anterior cingulate cortex white matter. We detail and validate the full methodology, including tissue fixation and sectioning, sf-CARS acquisition settings, as well as the AxonDeepSeg deep learning model parameters such that this pipeline can be utilized by others in the field.
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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".