Structural compromise in spiking cortex and connected networks
Bibliographic record
Abstract
ABSTRACT INTRODUCTION Epilepsy is increasingly conceptualized as a network disorder, and advancing methods for its diagnosis and treatment requires characterizing both the epileptic generator and related networks. We combined multimodal magnetic resonance imaging (MRI) and high-density electroencephalography (HD-EEG) to interrogate alterations in cortical microstructure, morphology, and intrinsic local function within and beyond spiking tissue in focal epilepsy. METHODS We studied 25 patients with focal epilepsy (12F, mean ± SD age = 31.28 ± 9.30 years) and 55 age- and sex-matched healthy controls, subdivided into a group of 30 for imaging feature normalization (15F, 31.40 ± 8.74 years) and a group of 25 for replication (12F, 31.04 ± 5.65 years). The 3T MRI acquisition included T1-weighted, diffusion, quantitative T1 relaxometry, and resting-state functional imaging. Open-access MRI processing tools derived cortex-wide maps of morphology and microstructure (cortical thickness, mean diffusivity, and quantitative T1 relaxometry) and intrinsic local function and connectivity (timescales, connectivity distance, and node strength) for all participants. Multivariate approaches generated structural and functional alteration scores for each cortical location. Using HD-EEG electrical source imaging, the most prominent spike type was localized and we quantified MRI alterations within spike sources, as well as in proximal and connected networks. RESULTS Regions harboring spike sources showed increased structural MRI alterations compared to the rest of the brain in patients. Structural compromise extended to all regions with close functional coupling to spike sources, but not to anatomical neighbors of spike sources. This finding was replicated using average control functional and anatomical matrices instead of patient-specific matrices. CONCLUSION Spiking regions contain more marked alterations in microstructure and morphology than the remaining cortex, and combining imaging with neurophysiology techniques may ultimately help identify the epileptogenic zone non-invasively. There are nevertheless broader networks effects, which may relate to a cascading of structural changes to functionally connected cortices. These results underscore the utility of combining high-definition MRI and EEG approaches for characterizing epileptogenic tissue and assessing distributed network effects.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".