The Role of the Temporal Pole in Temporal Lobe Epilepsy: A Diffusion Kurtosis Imaging Study
Bibliographic record
Abstract
ABSTRACT Objective This study aims to evaluate the use of diffusion kurtosis imaging (DKI) to detect microstructural abnormalities within the temporal pole (TP) in temporal lobe epilepsy (TLE) patients. Methods DKI quantitative maps were obtained from fourteen lesional (MRI+) and ten non-lesional (MRI-) TLE patients, along with twenty-one healthy controls. This included mean (MK); radial (RK) and axial kurtosis (AK); mean diffusivity (MD) and axonal water fraction (AWF). Automated fiber quantification (AFQ) was used to quantify DKI measurements along the inferior longitudinal (ILF) and uncinate fasciculus (Unc). ILF and Unc tract profiles were compared between groups and tested for correlation with seizure duration. To characterize temporopolar cortex (TC) microstructure, DKI maps were sampled at varying depths from superficial white matter (WM) towards the pial surface. Each patient group was separated according to side ipsilateral to the epileptogenic temporal lobe and their AFQ results were used as input for statistical analyses. Results Significant differences were observed between MRI+ and controls ( p < 0.005), towards the most anterior of ILF and Unc proximal to the TP of the left (not right) ipsilateral temporal lobe for MK, RK, AWK and MD. Noticeable differences were also observed mostly towards the TP for MK, RK and AWK in the MRI-group. DKI measurements correlated with seizure duration, mostly towards the anterior segments of the WM bundles. Stronger differences in MK, RK and AWF within the TC were observed in the MRI+ and noticeable differences (except for MD) in MRI-groups compared to controls. Significance The study demonstrates that DKI has potential to detect subtle microstructural alterations within the anterior segments of the ILF and Unc and the connected TC in TLE patients including MRI-subjects. This could aid our understanding of the extrahippocampal areas involved in seizure generation in TLE and might inform surgical planning, leading to better seizure outcomes.
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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) | 0.001 | 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".