Unlocking the Potential of ERPs for Precision Psychiatry

The Search For Reliable Biomarkers

Biological measures are driving the growth of precision psychiatry. Researchers have been studying various biomarkers for decades, including memory, genes, cognitive performance, speech, hormones, and gut microbes. While some associations have been found between these biomarkers and mental health, few have been used to categorize patients in clinical trials. ERPs, on the other hand, have shown high test-retest reliability and interpretability across different mental health conditions. They can be used to measure brain responses to rewards, emotional content, and decision-making, and have been linked to specific subtypes of depression and the prediction of treatment outcomes.

For example, a 2023 study of adolescents with depression found that an ERP associated with the response to emotional stimuli predicted a response to cognitive behavioral therapy. This suggests that ERPs could serve as neural biomarkers for predicting outcomes in adolescent depression. Another study published in the Annual Review of Clinical Psychology in 2019 concluded that ERPs can be used clinically to identify psychopathology and chart the potential development of conditions starting from childhood. The error-related negativity ERP was found to be instrumental in understanding the development of anxiety disorders and treatment opportunities.

The History Of ERPs

ERPs have been studied since the 1960s primarily in psychological and academic research. They have been used to study a wide range of cognitive domains, including memory, attention, vision, and emotion. In recent years, ERP biomarkers have helped uncover insights into mental health, especially in challenging conditions like schizophrenia. Promising ERP biomarkers of depression have focused on brain responses to rewards, emotional content, and decision-making. These ERPs have not only been linked to depression but also accurately predicted individuals' risk for depression and its development over time.

For instance, a 2019 meta-analysis found that ERPs can be used clinically to help identify psychopathology and track the potential development of conditions. Later research showed that ERPs associated with receiving rewards were closely tied to depression and could predict remission. Combining emotion- and reward-related ERPs could help differentiate subtypes of depression and improve treatment precision.

Applying ERPs To Clinical Trials

ERPs measured through EEG have been shown to be instrumental in scientific research. There are significant opportunities for pharmaceutical companies to incorporate ERPs in clinical trials to develop more personalized treatments. Compared to functional magnetic resonance imaging (fMRI), ERP data is easier and less expensive to capture. EEG studies are relatively simple to administer, and electrodes do not require messy conductive gel. ERP measures of brain function are also more reliable than fMRI-based measures, which can help pharmaceutical companies develop novel therapies in less time and with fewer resources.

Investigators can use ERPs to measure brain function at every phase of a clinical trial. At participant recruitment, a quick ERP assessment can identify appropriate participants who would benefit most from a drug. This helps ensure homogeneity among different cohorts and supports more streamlined clinical trials. In addition, ERP data can be used to monitor treatment responses and predict outcomes.

A Data-Driven Future

With advancements in data science techniques like generative AI, ERPs are becoming more accessible and affordable. Technological progress has simplified EEG studies and ERP data analysis, making these biomarkers more practical for clinicians and patients. Given the growing evidence in peer-reviewed journals, we can expect ERPs to become more incorporated in mental health clinical trials in 2025. ERPs are poised to move from academic labs to real-world importance and become a standard method of assessing brain function in precision psychiatry.