Disrupted anatomic white matter network in left mesial temporal lobe epilepsy
Bibliographic record
Abstract
OBJECTIVE: Brain imaging studies have shown widespread structural abnormalities in patients with temporal lobe epilepsy (TLE) within and beyond the affected temporal lobe, suggesting an altered network. Graph theoretical analysis based on white matter tractography has provided a new perspective to evaluate the connectivity of the brain. The alterations in the topologic properties of a whole brain white matter network in patients with TLE remain unknown. The purpose of this study was to examine the white matter network in a cohort of patients with left TLE and mesial temporal sclerosis (mTLE) compared to healthy controls. METHODS: Anatomic brain networks of 16 patients with left mTLE were compared to those of 21 healthy controls. A white matter structural network was constructed from diffusion tensor tractography for each participant, and network parameters were compared between the patient and control groups. RESULTS: Patients with left mTLE exhibited concurrent decreases of global and local efficiencies and widespread reduction of regional efficiency in ipsilateral temporal, bilateral frontal, and bilateral parietal areas. Communication hubs, such as the left precuneus, were also altered in patients with mTLE compared to controls. SIGNIFICANCE: Our results demonstrate white matter network disruption in patients with left mTLE, supporting the notion that mTLE is a systemic brain disorder.
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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) | 0.002 | 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".