Advanced multimodal imaging in epileptogenic malformations of cortical development
Bibliographic record
Abstract
Background.Malformations of cortical development (MCD) are a group of congenital anomalies characterized by variable brain deformations and high epileptogenicity.Magnetic resonance imaging (MRI) has revolutionized the clinical management of this disorder because of its unmatched ability to visualize pathological substrates.Yet, the current sensitivity to identify the primary lesion has a limit in covering the full spectrum of MCD, especially those with mild anomalies, substantially challenging a reliable clinical diagnosis.Notably, sporadic histological and MRI studies have indicated that structural anomalies in the primary lesion may extend to remote cortical areas.While these findings, together with functional evidence of widespread epileptogenic networks, suggest distributed pathological substrates, the anatomical patterns and topological principles underlying MCD remain poorly understood.Purpose.To develop advanced multimodal imaging and computational frameworks for the characterization and identification of MCD, and phenotype their whole-brain structure and network organization. Methods.We carried out following projects: 1) Implementation of an automated machine-learning classifier relying on surface-based MRI features to detect subtle malformations; 2) Evaluation of whole-brain morphology using cortical thickness and folding complexity; 3) Development of a novel framework to characterize morphology, intensity, diffusion, and function of the primary lesion; 4) Statistical and graph-theoretical analysis of structural-functional brain network across the MCD spectrum.In projects 1-3, we targeted patients with focal cortical dysplasia (FCD), while project 4 included multiple representative MCD subtypes. Results.In project 1, our classifier accurately identified subtle FCD initially overlooked on routine radiological assessment.The algorithm showed an excellent sensitivity (74%), while achieving a perfect specificity (100%; no false positive) in controls.The performance revealed generalizability
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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 | 0.000 | 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) | 0.005 | 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".