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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 teacher head, 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".