Altered White Matter Structural Network in Frontal and Temporal Lobe Epilepsy: A Graph-Theoretical Study
Bibliographic record
Abstract
Temporal lobe epilepsy (TLE) and frontal lobe epilepsy (FLE) are the largest subgroup of partial epilepsy, and focal cortical dysplasias (FCDs) are highly epileptogenic brain lesions and are a frequent cause for antiepileptic drug (AED)-resistant focal epilepsies that mostly occur in the temporal and frontal lobes. We performed a graph-theoretical study based on the diffusion tensor imaging (DTI) data of patients with FLE or TLE caused by FCDs or lesions with high suspicion of FCDs and evaluated their cognitive function by the Chinese version of the Montreal Cognitive Assessment-Basic (MoCA-BC). The construction of the white matter structural network and graph-theoretical analysis was performed by Pipeline for Analysing Brain Diffusion Images (PANDA) and Graph-theoretical Network Analysis (GRETNA). We used the nonparametric covariance analysis to compare the differences in diffusion metrics, network attributes and nodal attributes among FLE, TLE, and healthy control (HC) groups and then performed post hoc pairwise comparisons. Nonparametric Spearman partial correlation analysis was performed to analyse the correlation of network attributes with the age of onset, duration of disease, and MoCA-BC scores in patients with FLE and TLE. The results showed that the white matter structural network in patients with FLE and TLE was impaired in a more extensive set of regions than the FCD location. The similarities in white matter alterations between FLE and TLE suggested that their epileptogenic network might affect the fronto-temporal white matter tracts and thalamo-occipital connections, which might be responsible for the overlapping cognitive deficits in FLE and TLE. The white matter impairments in patients with FLE were more severe than those in patients with TLE, which might be explained by more affected nodes in the areas of DMN in patients with FLE.
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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 | 0.001 |
| 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".