Magnetic resonance imaging and histology correlation in the neocortex in temporal lobe epilepsy
Bibliographic record
Abstract
OBJECTIVE: To investigate the histopathological correlates of quantitative relaxometry and diffusion tensor imaging (DTI) and to determine their efficacy in epileptogenic lesion detection for preoperative evaluation of focal epilepsy. METHODS: We correlated quantitative relaxometry and DTI with histological features of neuronal density and morphology in 55 regions of the temporal lobe neocortex, selected from 13 patients who underwent epilepsy surgery. We made use of a validated nonrigid image registration protocol to obtain accurate correspondences between in vivo magnetic resonance imaging and histology images. RESULTS: We found T1 to be a predictor of neuronal density in the neocortical gray matter (GM) using linear mixed effects models with random effects for subjects. Fractional anisotropy (FA) was a predictor of neuronal density of large-caliber neurons only (pyramidal cells, layers 3 and 5). Comparing multivariate to univariate mixed effects models with nested variables demonstrated that employing T1 and FA together provided a significantly better fit than T1 or FA alone in predicting density of large-caliber neurons. Correlations with clinical variables revealed significant positive correlations between neuronal density and age (rs = 0.726, pfwe = 0.021). This study is the first to relate in vivo T1 and FA values to the proportion of neurons in GM. INTERPRETATION: Our results suggest that quantitative T1 mapping and DTI may have a role in preoperative evaluation of focal epilepsy and can be extended to identify GM pathology in a variety of neurological disorders.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".