Depression, a common yet complex mental health issue, affects millions worldwide. Despite its prevalence and long clinical history, treating depression remains fraught with challenges. A diagnosis of depression relies on the interpretation of subjective reports by a trained clinician — and there are more than 200 ways that a person can have the right constellation of symptoms to be depressed. In fact, two depressed people can share a single self-reported symptom — like difficulty concentrating. This heterogeneity of depression means that symptoms and treatment responses can vary widely among individuals — and can lead to prolonged care journeys for patients seeking treatment.
Today, however, psychiatrists and neuroscientists are shifting this narrative by utilizing well-known and extensively-studied technology and clinical concepts to develop what could be designated a new medical specialty: precision psychiatry. This paradigm shift toward evidence-based, data-driven decision-making in mental health combines elements of both psychiatry and neuroscience to help refine diagnoses so that patients experience improved outcomes sooner and clinicians can better design effective, personalized care plans in less time.
Brain-function measurements guide better decisions
Precision psychiatry involves utilizing neural measures, like electroencephalography (EEG) and Event-Related Potentials (ERPs), to gather functional and interpretable brain measures that are associated with specific brain functions to better understand subtypes or “neurotypes” within a condition, such as depression.
ERPs are electrical changes in the brain that happen in response to particular stimuli or events while a person is engaging in a cognitive task. These changes reflect the activity of the neural networks involved in performing the task — and various ERPs can be used to measure distinct neural functions. ERPs are a safe and noninvasive way to investigate a range of brain functions: specific ERPs can be used to study a diverse array of brain functions, such as attention, emotion, memory, reward sensitivity, and more. And many ERPs have also been associated with mental health conditions, including depression, post-traumatic stress disorder, substance use disorder and others.
ERPs are, therefore, well-suited brain-based biomarkers — they are measures of brain functions that can be used to facilitate precision psychiatry. Using ERPs, clinicians can help reduce heterogeneity within depression – and enable more objective subtyping and personalized strategies. Even though a group of depressed individuals may share few reported symptoms in common, ERPs can be used to identify more homogeneous groups of individuals that share a similar profile of neural function. This approach holds the potential to drive a paradigm shift in the stratification and treatment of depression toward more precise and effective clinical decisions and an accelerated path toward optimal management.
Uncovering heterogeneity within mental health conditions
Traditionally, depression and most other mental health conditions have been managed as a singular condition, with patients receiving standard treatments such as medication, cognitive behavioral therapy, or combinations of therapies. However, mental health conditions are not homogenous disorders; they encompass a wide range of symptoms and severities.
A categorical approach fails to capture this heterogeneity, leading to a one-size-fits-all protocol that does not account for the nuances of individual cases. Being unable to identify specific neurotypes within a condition objectively results in an imprecise trial-and-error approach to treatment that can delay improved outcomes while driving up costs for patients, providers and payers alike.
This process can be time-consuming, frustrating, and potentially harmful if patients experience adverse reactions or intolerable side effects from ineffective medications. The historic approach to treating mental health disorders underscores the need for defining neurotypes within a condition, which could enable clinicians to prescribe targeted therapies and predict outcomes.
ERPs closely align with distinct neurotypes
Enter ERPs, which have been studied for decades in their relation to several mental health conditions, particularly in areas such as attention, reward processing, effort, and emotion. ERP findings are notable for their strong reliability and replicability. This consistency makes ERPs a robust tool for understanding mental health, offering a solid foundation for developing precision psychiatry approaches.
Research has shown that individuals with depression, for example, exhibit blunted brain activity both in anticipating and receiving rewards. This suggests a dysfunction in the brain’s reward system, which could contribute to the anhedonia often experienced by those with depression.
These findings highlight the potential of ERPs to provide objective, quantifiable measurements across a wide variety of conditions. Using functional and interpretable neural measures, clinicians can more easily identify individuals at risk of developing mental health conditions and provide insights into the likely trajectory of the disorder. The resulting neurotypes based on the analysis of ERPs offer clinicians detailed profiles that can help tailor treatment strategies that have been demonstrated in research as effective.
Applying ERPs to standardized patient care and clinical trials
The development of low-cost, accessible EEG systems has made it feasible to incorporate these quantifiable neural measures into clinical decision-making. This accessibility can facilitate the widespread adoption of ERP measures in identifying and treating specific neurotypes within conditions.
Likewise, pharmaceutical companies can utilize brain measures to develop treatments targeted to specific neurotypes. This can result in the creation of medications that are tailored to distinct neurological patterns, rather than broad and heterogeneous mental health conditions. The use of ERPs can help clinicians in their practice, or those involved in clinical trials, visualize and understand the vast heterogeneity within conditions, enabling personalization based on patients’ brain function measurements, accelerating improved patient outcomes.
The integration of ERP measures into patient care delivery and clinical trials represents a call to action for a fundamental change in the way mental health is managed. By moving beyond subjective and heterogeneous criteria and embracing objective brain function measures, the field of psychiatry can achieve greater precision and efficacy in addressing complex disorders.
Through ERPs, making meaningful progress in overcoming our current mental health crisis looks promising. With continued research and collaboration, a more nuanced and effective approach could help millions of patients experience the optimal outcomes they deserve in much less time.
Photo credit: nambitomo, Getty Images
Greg Hajcak, Ph.D., has published more than 350 papers on ERPs and mental health, is the Sheri Sobrato Professor of Child & Adolescent Mental Health at Santa Clara University, and is chief scientific officer of Universal Brain.
This post appears through the MedCity Influencers program. Anyone can publish their perspective on business and innovation in healthcare on MedCity News through MedCity Influencers. Click here to find out how.
