Universal Brain Announces Publication of Groundbreaking Depression Research Using Multiple Event-Related Potentials

New findings support the company's neuroscience-driven approach to precision psychiatry, offering actionable insights for personalized depression treatment

PALO ALTO, Calif., Jan. 10, 2025 /PRNewswire/ -- Universal Brain, a pioneering technology company focused on transforming mental healthcare through neuroscience, today announced the publication of its latest peer-reviewed study, "Neurotyping depression using multiple event-related potentials (ERPs): Leveraging task-based variation to predict remission in depression," the paper introduces a novel framework for detecting unique neurophysiological patterns that can help predict which depressed patients are most likely to achieve remission from depression.

"By combining several EEG paradigms with novel statistical analyses, we're moving closer to a future where depression therapies can be precisely tailored to each individual," said Kazu Okuda, Chief Executive Officer of Universal Brain. "In particular, we are poised to leverage a battery of EEG paradigms to better subtype depression – which will pave the way for more precision in treatment development and selection.  These functional measures of brain activity (i.e., neurotypes) will be used to determine what treatment works best for whom."

Key Highlights of the Study

• Multi-Task ERP Analyses
  The study leveraged multiple cognitive and emotional tasks to capture a broader spectrum of brain responses, thereby enabling more robust classification of distinct depression subtypes.
• Predictive Neurotyping
  Through comprehensive ERP assessment, researchers identified patterns linked to a higher likelihood of remission in depression, paving the way for more targeted clinical strategies. Combining ERPs from multiple tasks outperformed classifications based on more traditional single-task analyses.
• Implications for Precision Psychiatry
  These findings underscore the potential of individualized treatment pathways—an approach backed by emerging research in the field of precision psychiatry, which calls for data-driven, personalized solutions to improve outcomes in mental health.

"This work is especially exciting because it bridges the gap between laboratory-based assessments and potential real-world clinical practice. We believe our new method can help physicians potentially select treatment but also help clinicians fine-tune therapeutic interventions to an individual's unique neural signature," commented Greg Hajcak, Chief Scientific Advisor of Universal Brain. "By leveraging multiple ERPs in varying tasks, we can aim to better predict who will respond to specific treatments, a key step in transforming how we approach depression management."

Championing Precision Psychiatry

Universal Brain's overarching mission is to integrate cutting-edge neuroscience with clinical insights to advance precision psychiatry—a transformative model of care that personalizes interventions based on individual biological, behavioral, and environmental factors. This focus resonates with emerging trends in psychiatric research to enhance treatment outcomes.

In close collaboration with academic institutions, research hospitals, and clinical partners, Universal Brain continues to drive neuroscience-based innovation that bridges the gap between academic discoveries and real-world patient care.

For more information on Universal Brain's research and the company's commitment to developing neuroscience-driven solutions for mental health, please visit universal-brain.com or email us at [email protected].

About Universal Brain

Universal Brain is a science-driven technology company dedicated to revolutionizing mental healthcare by unlocking the power of precision psychiatry. Drawing on advances in neuroscience, data analytics, and engineering, Universal Brain develops innovative tools and platforms that personalize psychiatric treatment and improve patient outcomes. To learn more, visit www.universal-brain.com.

SOURCE Universal Brain