A Unified Filtering Method for Estimating Asymmetric Orientation Distribution Functions: Where and How Asymmetry Occurs in the Brain
Bibliographic record
Abstract
Abstract Numerous filtering methods have been proposed for estimating asymmetric orientation distribution functions (ODFs) for diffusion magnetic resonance imaging (dMRI). It can be hard to make sense of all these different methods, which share similar features and result in similar outputs. The objectives of this work are two-fold: to disentangle the various filtering methods proposed in the past for estimating asymmetric ODFs, and to study the occurrence of asymmetric patterns in dMRI brain acquisitions. Hence, we describe a new filtering equation for estimating asymmetric ODFs resulting from the unification of these previously proposed filtering methods. Our method is distributed as an open-source GPU-accelerated python software to facilitate its integration into any existing dMRI processing pipeline. Following its validation on toy datasets, we apply our method to multi-shell multi-tissue fiber ODF reconstructions for 21 subjects from the Human Connectome Project in test-retest acquisitions. Our results show that our method estimates branching, fanning, bending, ending and other complex asymmetric fiber configurations in less than 2 minutes. Also, our novel number of fiber directions (NuFiD) index reveals that the filtering reduces the number of peak directions in the resulting asymmetric representation. Finally, our MNI-aligned template of asymmetries, describing the degree of asymmetry of each voxel, suggests that at least 60% of brain voxels in a dMRI acquisition contain asymmetric fiber configurations.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".