Microscopic fractional anisotropy asymmetry in unilateral temporal lobe epilepsy
Bibliographic record
Abstract
Abstract Objectives Surgical resection is the method of choice for treating medically refractory unilateral temporal lobe epilepsy (TLE), but postsurgical prognosis depends on magnetic resonance imaging (MRI) findings. Seizure freedom is more often achieved after resection in MRI-positive patients (those with MRI abnormalities such as mesial temporal sclerosis) than in MRI-negative patients. Diffusion MRI shows promise as a marker of neuronal abnormalities due to its sensitivity to cellular changes such as axon damage, indexed by fractional anisotropy. However, fractional anisotropy is not specific to axon integrity in grey matter where axon orientation is not uniform. In contrast, microscopic fractional anisotropy is a recently introduced dMRI technique that is sensitive to axon integrity regardless of axon orientation. This work investigated whether microscopic fractional anisotropy may be sensitive to hippocampal abnormalities in unilateral TLE. Methods Diffusion MRI was performed on a 3T scanner in 9 patients (age = 33 +/- 12 years) with unilateral TLE and 9 healthy volunteers (age = 26 +/- 6). A deep learning method was employed to segment the hippocampus into smaller subfields corresponding to the subiculum, cornu ammonis (CA) 1, CA2/3, and CA4 plus dentate gyrus (DG). Mean ipsilateral and contralateral measurements of subregion volume, diffusivity, fractional anisotropy, and microscopic fractional anisotropy were compared to investigate asymmetry in each subfield. Results Microscopic fractional anisotropy was reduced, and diffusivity was elevated in the ipsilateral CA4/DG region relative to the contralateral side in all 9 patients. Asymmetries in diffusion metrics between the left and right sides of the hippocampus subfields were not observed in the healthy volunteers. Significance Diffusion MRI may complement standard imaging procedures by detecting abnormalities in MRI-negative patients. Due to its insensitivity to axon orientation, microscopic fractional anisotropy may yield a more robust measurement than fractional anisotropy and may improve epileptic focus localization in surgical candidates.
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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 | 0.001 |
| 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) | 0.002 | 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".