Thalamocortical Connections and Executive Function in Pediatric Temporal and Frontal Lobe Epilepsy
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Largely accepted in the literature is the role the interconnections between the thalamus and cortex play in generalized epilepsy. However, thalamocortical involvement is less understood in focal epilepsy in terms of the effect of seizures on thalamocortical circuitry in the developing brain and subsequent cognitive outcome. We investigated thalamocortical pathway microstructure in pediatric frontal lobe epilepsy and temporal lobe epilepsy and examined the associations between pathway microstructure and measures of executive function. MATERIALS AND METHODS: We examined thalamocortical connections in 24 children with frontal lobe epilepsy, 17 patients with temporal lobe epilepsy, and 25 healthy children using DTI. We investigated several executive function measures in patients and controls, which were distilled into latent executive function components to compare among groups, and the associations between measures of thalamocortical microstructure and executive function. RESULTS: We found no differences in thalamocortical pathway microstructure between the groups, but aspects of executive function (mental flexibility/inhibition/shifting) were impaired in the frontal lobe epilepsy group compared with controls. In patients with frontal lobe epilepsy, younger age at seizure onset and a greater number of antiepileptic drugs were associated with DTI indices indicative of damaged/less developed thalamocortical pathways. In patients with temporal lobe epilepsy, poorer performance on all measures of executive function was associated with DTI indices reflective of damaged/less developed pathways. CONCLUSIONS: Our results give insight into vulnerable neural networks in pediatric focal epilepsy and suggest thalamocortical pathway damage as a potential mechanism of executive function impairment in temporal lobe epilepsy but not frontal lobe epilepsy. Identifying structure-function relations can help inform how we measure functional and cognitive/behavioral outcomes in these populations.
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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 | 0.001 | 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) | 0.001 | 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".