ArticleEBioMedicine2026
Brainwaves under medication: revealing class-specific neural signatures of psychotropic medication from 24,000 EEGs.
Article in EBioMedicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundPsychotropic medications remain foundational in psychiatric care, yet the neurophysiological mechanisms through which they exert therapeutic and adverse effects are still poorly characterised, limiting the field's ability to optimise treatment selection and monitoring. Electroencephalography (EEG) offers a non-invasive, real-time window into brain function that could support more precise, mechanism-informed prescribing; however, progress has been constrained by the absence of sufficiently large and systematically analysed pharmaco-EEG datasets.
methodsIn this cross-sectional observational study, we analysed over 24,000 clinical EEG recordings (∼6000 h of data) obtained across a wide range of psychiatric diagnoses and medication regimens. We compared more than 75,000 spectral, connectivity, and nonlinear EEG features across major drug classes, including benzodiazepines, SSRIs, antipsychotics, and anticonvulsants.
findingsDimensionality-reduced analyses revealed robust, class-specific neurophysiological signatures that can be linked to psychotropic drugs' mechanisms of action: benzodiazepines increased beta and decreased theta-alpha power; SSRIs enhanced gamma-band coherence; and antipsychotics and anticonvulsants produced marked slow-wave amplification and reductions in signal complexity. All results are made publicly accessible through an interactive resource (BrainwavesRX), enabling clinicians and researchers to explore medication-specific EEG effects at multiple levels of granularity.
interpretationBy establishing a population-level reference atlas of psychotropic medication effects on human neural dynamics, this study provides an important foundation for future studies leveraging EEG to predict treatment response, detect insufficient or excessive pharmacological effects, and ultimately advance the development of individualised, data-driven psychiatric care.
fundingThe publication was prepared as part of Foundation of Polish Science's Proof of Concept (FENG.02.01-IP.05-0010/24) and BRAINCITY IRAP (FENG.02.07-IP.05-0179/23) projects.
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