618 Widespread, depth-dependent microstructural alterations in the cortex of children with drug-resistant focal epilepsy: a quantitative T1 and T2 mapping study
Bibliographic record
Abstract
Objectives We assessed cortical changes in children with drug-resistant focal epilepsy using surface-based T1 and T2 relaxometry (qT1 and qT2), to probe alterations in tissue-microstructure, and their relationship to clinical parameters. Methods Data Acquisition 89 children were scanned unsedated on a 3T Achieva-TX scanner (Philips Healthcare) – 43 with drug-resistant focal epilepsy [mean age=12yrs] and 46 healthy controls [mean age=11.5yrs] (See table 1 for acquisition parameters). All images were motion-corrected.¹ Analysis Surface-reconstruction: FLAIR and T1w images were analysed to reconstruct white/grey matter (WM/GM) and pial surfaces.² These surfaces were used to compute equi-volume cortical surface depths by sampling the surface vertices in steps of 20% of cortical volume (0%: WM/GM, 100%: pial surface). qT1 and qT2 surface-mapping: qT1 and qT2 images³ were rigidly co-registered to their corresponding MPRAGE volume, smoothed, and projected to each depth. Group differences in qT1 and qT2: Surface outputs from the HCP structural pipeline are left-right symmetrical, therefore we flipped qT1 and qT2 surface maps of patients with right hemispheric focus and analysed them with left focus patients. Group-wise alterations at each cortical depth were tested.4 Additionally, vertex-wise qT1 and qT2 values at 20% depth were subtracted from those at 80% depth, and group-differences in cortical gradients were tested as an index of intracortical organisation. Associations between qT1 and qT2 changes in patients and disease duration/number of seizures per year were assessed. Age, sex, cortical thickness and curvature were included as covariates.5 6 TFCE was employed as test statistic, and FWE-correction was applied across modalities and contrasts. Results Figure 2A displays depth-wise group differences in qT1 and qT2. Bilateral qT2 increases and ipsilateral qT1 increases were detected in patients in the outermost cortical depths. The detected changes were not associated with clinical variables. Figure 2B displays group differences in qT1 and qT2 cortical gradients. We detected steeper gradients in patients, with increasingly high qT1 and qT2 in the outermost cortical depths bilaterally. The detected changes were not associated with clinical variables. Conclusions We report the presence of widespread, depth-mediated qT1 and qT2 increases in children with focal epilepsy. Changes appear unrelated to focus laterality, and likely represent gliosis, myelin and iron changes, oedema-associated free-water increases, or a combination of these.7 Based on the typically shorter disease duration in children, and on the lack of associations with disease-severity measures, such changes may represent antecedent neurobiological alterations, rather than the cumulative effect of seizure-activity or medication side-effects. References Cordero-Grande L, et al. Motion-corrected MRI with DISORDER: Distributed and incoherent sample orders for reconstruction deblurring using encoding redundancy. Magnetic Resonance in Medicine 2020;84:713–726. Glasser MF, et al. The minimal preprocessing pipelines for the Human Connectome Project. Neuroimage 2013;80:105–124. Teixeira RPAG, Malik SJ, Hajnal JV. Joint system relaxometry (JSR) and Crámer-Rao lower bound optimization of sequence parameters: A framework for enhanced precision of DESPOT T1 and T2 estimation. Magn Reson Med 2018;79:234–245. Winkler AM, Webster MA, Brooks JC, Tracey I, Smith SM, Nichols TE. Non-parametric combination and related permutation tests for neuroimaging. Human Brain Mapping. 2016;37(4):1486–1511. doi:10.1002/hbm.23115 Galovic M, et al. Resective surgery prevents progressive cortical thinning in temporal lobe epilepsy. Brain 2020;143:3262–3272. Annese J, Pitiot A, Dinov ID, Toga AW. A myelo-architectonic method for the structural classification of cortical areas. NeuroImage 2004;21:15–26. Cercignani M, Dowell NG, Tofts PS. Quantitative MRI of the Brain: Principles of Physical Measurement, Second edition. (CRC Press, 2018).
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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 | 0.000 |
| 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) | 0.002 | 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".