Research Article: A computational pipeline for a neurotransmitter-centric analysis of the effects of psychiatric medication on EEG spectral power

Abstract:
Traditional pharmaco-electroencephalography (EEG) studies have mainly examined the effects of psychotropic medications at the level of individual drugs or broad drug classes, limiting biological specificity and clinical translation. This study aimed to determine whether modeling EEG spectral power changes according to the engagement of distinct neurotransmitter systems provides a more mechanistic understanding of psychotropic drug effects in a real-world clinical population. We analyzed 4,128 EEG sessions from 2,083 patients in the Temple University Hospital EEG Corpus, a large heterogeneous dataset. EEG data were preprocessed and segmented into canonical frequency bands (delta, theta, alpha, beta, and gamma). Psychotropic medication data were systematically extracted and coded at the receptor level for serotonin, dopamine, norepinephrine, histamine, and acetylcholine systems using the Neuroscience-based Nomenclature framework. Receptor profiles were summarized to represent each patient’s overall neurotransmitter engagement (agonistic, neutral, antagonistic, or mixed). Linear mixed-effects models were applied to assess relationships between neurotransmitter profiles and log-transformed spectral power while controlling for electrode location and patient-level variability. Frequency- and region-specific EEG patterns were identified across neurotransmitter systems. Dopamine antagonists were associated with higher delta and theta power at central electrode locations and lower alpha power at occipital and temporal locations, whereas dopamine agonists were associated with higher delta activity at occipital locations and increased frontal gamma power. Serotonin antagonists showed associations with elevated slow-wave and alpha power, while serotonin agonists were linked to increased frontal alpha, decreased occipital alpha, and enhanced temporal gamma power. Both norepinephrine antagonists and agonists showed positive relationships with delta power, with a broader topographical pattern for antagonists. Theta power was positively associated with norepinephrine antagonists and negatively associated with norepinephrine agonists. Norepinephrine antagonists were related to lower temporal alpha and higher frontal and parietal gamma power. Histamine antagonists and mixed histaminergic agents were associated with lower delta, theta, and alpha power. Acetylcholine antagonists were linked to higher delta, theta, and alpha power across electrode locations. Modeling psychotropic medication effects on EEG at the neurotransmitter receptor level offers a biologically grounded and clinically relevant improvement over traditional drug class-based approaches. This neurotransmitter-centric framework enhances mechanistic interpretability and may support the development of EEG biomarkers for personalized, mechanism-based psychiatric care.

Introduction:
Traditional pharmaco-electroencephalography (EEG) studies have mainly examined the effects of psychotropic medications at the level of individual drugs or broad drug classes, limiting biological specificity and clinical translation. This study aimed to determine whether modeling EEG spectral power changes according to the engagement of distinct neurotransmitter systems provides a more mechanistic understanding of psychotropic drug effects in a real-world clinical population.

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