Utilising MRI Data and VOI-Based analysis for enhanced epilepsy prediction: a translational approach from bench to bedside
Bibliographic record
Abstract
Background: Epilepsy is one of the most prevalent neurological disorders globally, profoundly impacting patient’s quality of life and stretching healthcare resources. Despite technological advances in neuroimaging, early and accurate detection of epileptic foci remains elusive, especially when standard MRI scans appear structurally normal. For clinicians, radiologists and neurologists, the limitations of subjective interpretation underscore an urgent need for objective diagnostic methods. This study addresses that gap by introducing Volume of Interest (VOI)-based analysis as an innovative tool to detect microstructural brain abnormalities associated with epilepsy. Methods: This study involves a retrospective analysis of 30 brain MRI images (T1-Weighted MPRAGE), comprising of 12 epilepsy patients and 18 matched controls (11 males, 19 females; mean age 42.7 ± 17.2 years). The images were converted from the DICOM format into the Brain Imaging Data Structures (BIDS) standard using the fMRIPrep platform, which were then normalised following the standard Montreal Neurological Institute (MNI) template. The images were segmented into Grey Matter, White Matter and Cerebrospinal Fluid. The segmented Grey Matter regions have then been analysed using VOI-based analysis from the Harvard-Oxford Cortical Structural Atlas. The VOI-based analysis results were then statistically tested using ANCOVA with False Detection Rate (FDR) correction. Results: Three regions show statistically significant ANCOVA results, which were Superior Frontal Gyrus (p=0.041), Superior Parietal Lobule (p=0.026) and Lingual Gyrus (p=0.036). However, all three regions fail the FDR correction (q=0.36). Conclusion: This work shows that combining MRI data with VOI-based analysis can reveal subtle structural patterns in the superior frontal gyrus, superior parietal lobule and lingual gyrus that may contribute to the understanding of epilepsy risk. Although these patterns were not sustained after FDR correction, they offer a promising direction for more objective predictive tools. Continued research with larger cohorts is essential to confirm these early signals and strengthen their relevance for epilepsy prediction.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 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".