Assessing the Precision of Magnetic Resonance Imaging Axon Diameter Inferences using Oscillating Gradient Spin Echo Pulse Sequences in a 15 T System
Bibliographic record
Abstract
Previous research has linked numerous neurological disorders post-mortem to abnormalities in axon distribution and integrity within white matter tracts. Therefore, it is of high interest to investigate methods that will eventually be able to measure axon diameters in white matter tracts in vivo. Diffusion Magnetic Resonance Imaging is a method with the potential to infer microstructure in vivo using temporal diffusion spectroscopy. Temporal diffusion spectroscopy, when used with certain pulse sequences, such as Oscillating Gradient Spin Echo, can be used to infer micron-scale axon diameters. The most common geometric model used to fit the diffusion signals assumes that axons are long, parallel, straight, cylinders, where only the transverse intra-axonal diffusion coefficient is sensitive to the cylinder’s inner diameter. However, previous research has demonstrated that this geometric model tends to overestimate the intra-axonal diameter of axonal fibers. Due to recent advances in hardware, high-gradient strengths can be used to achieve shorter diffusion times and probe smaller restriction sizes than previously possible. To calibrate temporal diffusion spectroscopy with Oscillating Gradient Spin Echo pulse sequences in this project ex vivo mouse brains were imaged, and the genu substructure of the corpus callosum was analyzed. The images were collected using a 15.2 T Bruker NMR system located at the Vanderbilt University of Institute of Imaging Science. Data were collected in two experiments; first an initial calibration experiment was performed to test the proposed parameters. Then a second experiment was conducted to validate the inferred magnetic resonance axon diameters using transmission electron microscopy in a sample of 6 mice (3 male, 3 female).
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".