Electrophysiological Markers in Psychotic Disorders

Electrophysiological approaches, chiefly electroencephalography (EEG) and event-related potentials (ERPs), have emerged as vital tools in the characterisation of psychotic disorders. Their high temporal resolution captures neural oscillations across delta, theta, alpha, beta and gamma bands, revealing abnormalities in both resting-state activity and task-evoked responses. Altered low-frequency rhythms and disrupted synchrony within large-scale networks—such as the default mode and frontoparietal systems—reflect dysfunctional thalamo-cortical connectivity and aberrant salience attribution. Dynamic measures, including connectivity metrics and microstate analysis, map transient patterns of network recruitment that correlate with symptom dimensions and cognitive deficits. Computational modelling links receptor-level pharmacodynamics to macroscopic EEG changes, offering insight into treatment mechanisms. Machine learning classifiers harness spectral and complexity features to distinguish first-episode psychosis from control groups and predict treatment response. Together, these markers hold promise for early diagnosis, stratified prognosis and personalised intervention in schizophrenia and related psychoses.

Research from Nature Portfolio

Recent studies have demonstrated that resting-state power spectral density (PSD) profiles can serve as diagnostic features in first-episode psychosis. By applying multiple machine-learning algorithms—including random forest, support vector machines and Gaussian process classifiers—researchers achieved high specificity in discriminating first-episode patients from healthy controls. In particular, a Gaussian process model based on low-frequency band PSD attained a specificity exceeding 95%, underscoring the diagnostic utility of stimulus-independent EEG. The work emphasises standardised preprocessing pipelines and highlights the potential of resting EEG biomarkers to complement clinical assessment in early psychosis services.

Electrophysiological Markers in Psychotic Disorders publication trend

The graph below shows the total number of articles in electrophysiological markers in psychotic disorders across all publications each year (not limited to Nature Index journals).

Technical terms

Electroencephalography (EEG): A non-invasive technique recording electrical brain activity via scalp electrodes.

Power spectral density (PSD): A measure of signal power distributed across frequency bands, reflecting oscillatory strength.

Event-related potential (ERP): A time-locked brain response to specific sensory, cognitive or motor events.

Dynamic causal modelling (DCM): A computational framework inferring directed connectivity and synaptic kinetics from electrophysiological data.

Multiscale fluctuation dispersion entropy (MFDE): A non-linear complexity metric quantifying variability of EEG fluctuations across temporal scales.

References

1. Neuropharmacological computational analysis of longitudinal electroencephalograms in clozapine-treated patients with schizophrenia using hierarchical dynamic causal modeling. NeuroImage (2023).
2. Multiscale Fluctuation Dispersion Entropy of EEG as a Physiological Biomarker of Schizotypy. IEEE Access (2023).
3. EEG-based Signatures of Schizophrenia, Depression, and Aberrant Aging: A Supervised Machine Learning Investigation. Schizophrenia Bulletin (2024).
4. Power spectral density-based resting-state EEG classification of first-episode psychosis. Scientific Reports (2024).