Diffusion Tensor Imaging Abnormalities in Focal Cortical Dysplasia
Bibliographic record
Abstract
PURPOSE: Focal cortical dysplasia (FCD) is one of the most common underlying pathologic substrates in patients with medically intractable epilepsy. While magnetic resonance imaging (MRI) evidence of FCD is an important predictor of good surgical outcome, conventional MRI is not sensitive enough to detect all lesions. Previous reports of diffusion tensor imaging (DTI) abnormalities in FCD suggest the potential of DTI in the detection of FCD. The purpose of this study was to study subcortical white matter underlying small lesions of FCD using DTI. METHODS: Five patients with medically intractable epilepsy and FCD were investigated. Diffusion tensor imaging images were acquired (20 contiguous 3 mm thick axial slices) with maps of fractional anisotropy (FA), trace apparent diffusion coefficient (trace/3 ADC), and principal eigenvalues (ADC parallel and ADC perpendicular to white matter tracts) being calculated for each slice. Region of interest analysis was used to compare subcortical white matter ipsilateral and contralateral to the lesion. RESULTS: Three subjects with FCD associated with underlying white matter hyperintensities on T2 weighted MRI were observed to have increased trace/3 ADC, reduced fractional anisotropy and increased perpendicular water diffusivity which was greater than the relative increase in the parallel diffusivity. No DTI abnormalities were identified in two patients with FCD without white matter hyperintensities on conventional T2-weighted MRI. CONCLUSIONS: While DTI abnormalities in FCD with obvious white matter involvement are consistent with micro-structural degradation of the underlying subcortical white matter, DTI changes were not identified in FCD lesions with normal appearing white matter.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".