Multiorientation mapping of white matter fiber microstructures in whole mouse brains using serial optical coherence tomography
Bibliographic record
Abstract
Previous studies have shown that the optical coherence tomography (OCT) signal in white matter (WM) is affected by the WM fiber bundles orientation with respect to the microscope’s optical axis. In this paper, we aim to exploit this contrast mechanism to generate a multi-orientation representation of WM microstructure in whole mouse brains. To achieve this, a serial blockface histology set-up has been developed combined with spectral domain OCT equipped with a long-range 10x magnification objective, achieving a near isotropic resolution of 3 micron laterally (xy) and 3.5 micron axially (z). With this imaging system, a map of WM structures can be generated for an entire agarose embedded mouse brain. To precisely control the mouse brain orientation within the agarose, we designed a multi-part 3D printed mold, which allows us to choose the vibratome’s slicing plane (e.g., coronal, axial, sagittal, etc.). After the serial OCT acquisition, every slice is reconstructed as 2D images and stacked to obtain a 2.5D volume. The reconstruction process uses a nextflow computational pipeline, allowing us to parallelize the calculations. Our proposed imaging method emphasizes different WM structures according to their orientation, which we illustrated in the mouse’s anterior commissure olfactory limb. This structure is very bright when observed in axial slices, whereas it has a darker appearance in the coronal slices. Using this method, we plan to acquire whole mouse brains oriented in multiple directions and to create a multi-orientation mouse brain template, which we believe will prove useful to get a better understanding of complex WM microstructure geometries, such as fiber crossing areas.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".