Decoding mental health in the UK Biobank

A study of a database containing more than 500,000 participants provides new insights into biomarkers for anxiety and depression.

The effort to identify biomarkers for mental health conditions has intensified over recent decades. Despite this ambition, neuroimaging studies have often presented conflicting findings. For instance, a 2007 study highlighted increased functional connectivity in the Default Mode Network (DMN), associated with major depressive disorder—a contrast to a 2009 study’s findings of decreased connectivity within the same brain regions. These discrepancies are due to the limitations of small sample sizes in neuroimaging studies, which compromise generalizability and amplify variability.

Addressing these challenges, a team from The Personomics Lab at Washington University in St. Louis, led by Dr. Janine Bijsterbosch, has utilized the vast database of the UK Biobank. This endeavor, published in Human Brain Mapping, aims to provide brain biomarkers of mental health and as a result introduced the Recent Depressive Symptoms (RDS-4) score, a novel measure designed to capture the nuances of depression.

The UK Biobank (UKB) stands as one of the largest neuroimaging datasets globally, encompassing detailed health records and MRI data for a significant portion of its participants. This extensive dataset ensures a diverse representation of depression symptoms, offering a more nuanced understanding of mental health than what smaller samples could provide. This diversity is crucial, given that individuals diagnosed with the same disorder, such as depression, may exhibit a wide range of symptoms.

One of the study’s objectives was to develop new tools to facilitate neuroimaging research using the UK Biobank (UKB). To achieve this, the researchers compared individuals with and without a history of mental health issues based on their responses to four self-report questions, answered on the same day the participants underwent an MRI brain scan. These questions, assessing feelings of depressed mood, disinterest, restlessness, and tiredness over the preceding two weeks, form the basis of the RDS-4 score. The RDS-4 score is designed to track acute depression severity or symptom fluctuations within a short timeframe, making it a refined, state-specific indicator. This means it is specifically aimed at capturing temporary states of depressive symptoms that can vary over days or weeks, unlike previous summary scores. For example, the N-12 measures neuroticism, which is a personality trait indicating one’s long-term sensitivity to negative emotion, and the GAD-7 measures general anxiety, not specifically designed to capture short-term changes in mood. They also used online mental health questionnaires to further identify associations with these mental health scores, providing a more comprehensive view of participants’ mental health status.

The analysis confirmed that although the N-12 is commonly used as a marker for overall mental health risk and has a strong association with depression risk, it contrasts with the online mental health questionnaire results, which are more adept at identifying anxiety through their correlation with GAD-7 scores rather than depression. This highlights the specific utility of the N-12 for broader, long-term mental health predispositions, or “trait” mental health. This contrast between the N-12 and the online questionnaire findings highlights the significance of the RDS-4 score. Unlike the N-12, RDS-4 is designed to recognize short-term, fluctuating “state” mental health conditions, such as current depressive episodes, showcasing its distinct and complementary role in mental health assessments within the UK Biobank (UKB).

This study also investigated the complex interplay between psychological symptoms and brain structure via advanced computational techniques. The study employed what is known as Canonical Correlation Analysis (CCA) to link mental health metrics, such as the RDS-4 score, with neuroimaging features. This methodological approach reveals how variations in brain connectivity and structure relate to self-reported symptoms of mental health conditions, offering insights into the biological markers of mental disorders. The inclusion of neuroimaging data enhances the validity of RDS-4 as a reliable measure for capturing the acute manifestations of depression, providing a more comprehensive understanding of mental health that bridges subjective experiences with objective biological markers.

Conducted on such a vast scale, this study illuminates the path forward in identifying mental health brain biomarkers, highlighting the crucial role of computational methods in unraveling the complexities of mental health disorders. By leveraging the UKB dataset, the research addresses the critical issues of small sample sizes and narrow inclusion criteria that have historically constrained neuroimaging studies.