Temporal Lobe Epilepsy Perturbs the Brain‐Wide Excitation‐Inhibition Balance: Associations with Microcircuit Organization, Clinical Parameters, and Cognitive Dysfunction
Bibliographic record
Abstract
Excitation-inhibition (E/I) imbalance is theorized as a key mechanism in the pathophysiology of epilepsy, with ample research focusing on elucidating its cellular manifestations. However, few studies investigate E/I imbalance at the macroscale, whole-brain level, and its microcircuit-level mechanisms and clinical significance remain incompletely understood. Here, the Hurst exponent, an index of the E/I ratio, is computed from resting-state fMRI time series, and microcircuit parameters are simulated using biophysical models. A broad decrease in the Hurst exponent is observed in pharmaco-resistant temporal lobe epilepsy (TLE), suggesting more excitable network dynamics. Connectome decoders point to temporolimbic and frontocentral cortices as plausible network epicenters of E/I imbalance. Furthermore, computational simulations reveal that enhancing cortical excitability in TLE reflects atypical increases in recurrent connection strength of local neuronal ensembles. Mixed cross-sectional and longitudinal analyses show stronger E/I ratio elevation in patients with longer disease duration, more frequent electroclinical seizures as well as interictal epileptic spikes, and worse cognitive functioning. Hurst exponent-informed classifiers discriminate patients from healthy controls with high accuracy (72.4% [57.5%-82.5%]). Replicated in an independent dataset, this work provides in vivo evidence of a macroscale shift in E/I balance in TLE patients and points to progressive functional imbalances that relate to cognitive decline.
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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 | 0.000 | 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) | 0.001 | 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".