The findings could eventually help doctors determine whether patients are taking their prescribed medication and whether the drugs are producing their intended effects.

Scientists from the Nencki Institute of Experimental Biology of the Polish Academy of Sciences analysed more than 24,000 anonymised EEG recordings collected over more than 20 years at two Warsaw institutions: the Institute of Psychiatry and Neurology and the Nowowiejski Psychiatric Hospital.

The team examined 14 groups of drugs used to treat conditions including depression, schizophrenia and epilepsy, analysing more than 75,000 variables extracted from the EEG recordings.

The researchers say it is the largest analysis of its kind ever conducted. The findings were published in eBioMedicine, a journal from The Lancet group.

Previous studies have typically focused on the effects of a single medication and involved relatively small groups of patients.

“Typically, these studies included just over 100 participants and examined only one medication. We analyzed 14 groups of drugs used to treat a range of conditions, including depression, schizophrenia, and epilepsy, using more than 24,000 EEG recordings. To our knowledge, no one has ever performed analyses on this scale before,” Professor Jan Kamiński says.

The researchers analysed virtually every resting-state EEG feature described in the scientific literature.

“We included nearly every measurable EEG characteristic that could be used as a variable in this study, which is why the analysis involved more than 75,000 parameters,” Kamiński says.

The analysis also accounted for factors that can influence EEG activity, including sex, age and psychiatric diagnosis. The study groups were carefully matched for these characteristics.

“The data span more than 20 years. The proportion of women and men was similar across all study groups, which were carefully matched for sex, age, and diagnosis. The medicated group and the control group, consisting of patients before treatment, had comparable demographic and clinical characteristics,” Magdalena Szponar, a doctoral candidate and first author of the paper, says.

The analysis confirmed findings from previous studies while also filling gaps left by earlier research.

The researchers confirmed that benzodiazepines increase beta-band power, a feature commonly associated with wakefulness, focused attention and active information processing. They also found that selective serotonin reuptake inhibitors, or SSRIs, increase gamma-band synchronisation between different brain regions.

Gamma synchronisation plays an important role in integrating sensory information and supports processes including conscious perception, working memory and attention.

The researchers identified more than a dozen previously unreported associations between commonly used medications and EEG features. The largest number of new findings concerned antiepileptic drugs.

The analysis found that antiepileptic medications affect the complexity of the EEG signal, while reducing functional connectivity in the alpha frequency band and increasing functional connectivity in the theta band.

The researchers also found that benzodiazepines reduce theta-band power, a feature associated with emotional processing and heightened emotional engagement. They said this change may reflect the drugs’ well-known anxiety-reducing effects.

Antipsychotic medications were found to increase theta-band synchronisation while decreasing beta-band synchronisation. The researchers said this pattern may indicate an overall reduction in cortical arousal and could help explain the neural mechanisms underlying the drugs’ ability to suppress hallucinations.

The most significant new findings concerned antiepileptic drugs.

“We observed that under the influence of these drugs, the EEG signal strength in the theta band increases, while simultaneously decreasing it in the alpha band. This can be interpreted as a slowing of brain activity,” Kamiński says.

He explains that antiepileptic drugs reduce the excitability of nerve cells, making them less likely to generate the rapid, synchronised activity associated with seizures.

The findings provide scientists and clinicians with a reference framework showing how EEG recordings are expected to change in response to specific medications.

In the future, this knowledge could help determine whether patients are taking their prescribed medications and whether those medications are producing their intended effects. It could also provide a basis for using EEG to study treatment response.

“I can imagine a clinician testing a new SSRI, recording the patient's EEG, and examining how the brain's electrical activity changes. If those changes match the patterns identified in our analyses, this would provide evidence that the medication is exerting its expected effects,” Kamiński says.

The results have been made publicly available through BrainwavesRX, an interactive online platform that enables clinicians and researchers to explore how commonly prescribed psychotropic medications influence the brain’s electrical activity.

The findings also highlight the continuing potential of EEG, a technique that has been used to record the brain’s electrical activity for more than a century.

“The concept of EEG - a method for recording the brain's electrical activity—is now more than 100 years old. However, that does not make it an outdated research tool. As the amount of available data and the sophistication of analytical methods continue to grow, so does the value of EEG. It is an exceptionally information-rich signal, but developing algorithms capable of reliably identifying and generalizing patterns requires very large datasets,” Kamiński concludes. (PAP)

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