Evidence map›Paper›PMID 42274503›Full record

ReviewBiology2026

State-Dependent Modulation of Neurotransmitter Systems in Epilepsy: A Mechanistic Framework for Seizure Dynamics and Biomarker Variability.

Ekaterina Andreevna Narodova

Abstract readReview
In one paragraph

Review in Biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author.

Ekaterina Andreevna NarodovaDepartment of Neurology, Prof. V.F. Voyno-Yasenetsky Krasnoyarsk State Medical University, 660022 Krasnoyarsk, Russia.ORCID 0000-0002-6184-9206

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Epilepsy is increasingly conceptualized as a disorder of dynamic network instability rather than a static imbalance between excitation and inhibition. However, substantial variability in seizure occurrence, clinical expression, and treatment response remains insufficiently explained by existing models. This narrative review examines how neurotransmitter systems contribute to seizure dynamics within a state-dependent framework, in which factors such as sleep-wake cycles, stress, inflammation, and metabolic conditions modulate network excitability. The review identified four key findings: neurotransmitter function in epilepsy is state-dependent rather than fixed; multiple physiological state modifiers shape seizure susceptibility; seizure termination is an active state-sensitive process; and biomarker performance depends on the prevailing brain state. Evidence from experimental and clinical studies indicates that neurotransmitter function is context-sensitive and interacts with molecular pathways, including ion channel function, synaptic plasticity, and neuromodulatory signaling. These interactions influence key stages of seizure dynamics, including initiation, propagation, and termination, and may differ across etiological categories of epilepsy. This perspective also helps explain the limited performance of static biomarkers, as they do not capture temporal variability in network states. Instead, state-sensitive markers and context-aware interpretations of electrophysiological and clinical data may provide more informative insights. Overall, integrating neurotransmitter mechanisms with dynamic brain states offers a more precise perspective on seizure variability and may support the development of individualized, state-aware approaches to epilepsy management.

Indexed as

biomarkersepilepsyion channelsnetwork excitabilityneurotransmitter systemsseizure dynamicsstate-dependent modulationsynaptic plasticity

Identifiers

PMID42274503
PMCPMC13256034

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.