Lydia Wang ’26

When referencing common medical conditions, heart disease and high blood pressure are often grouped together, as one usually implies the other. Such groupings—the simultaneous presence of two or more medical conditions—are known as comorbidities. Comorbidities in mental health are common; more than half of individuals with mental disorders have more than one. Their occurrence has been dismissed as coincidence and ignored in meta-analysis of neural correlates (measurements of brain activity); however, dismissing them carries the risk of obtaining inaccurate results. A team of researchers led by Lydia Fortea from the August Pi i Sunyer Biomedical Research Institute (IDIBAPS) sought to address this issue by developing a new neuroimaging meta-analysis method. 

In this study, three disorders that had been studied at least 10 times in other peer-reviewed papers were selected for analysis; the disorders were not specified, so were labeled A, B, and C. For each disorder, 64 studies were simulated with varying levels of comorbidity, with eight simulated levels of comorbidity for each disorder. These were further separated into both adult and pediatric groups. The researchers altered a previously existing method, Seed-based d Mapping with Permutation of Subject Images (SDM-PSI); the altered method can directly test between patient and control brain images. Using this method, gray matter (GM) images of the brain were analyzed and an MRI-based atlas was created that categorized mental disorders and their comorbidities. Upon analysis of the new method, a slight loss of accuracy was observed, as the system would occasionally mistake one simulated comorbidity for A for group B. Overall, however, the researchers succeeded in developing a method that can better disentangle comorbid disorders and their abnormalities through the analysis of brain scan images.

The results of this study present a new neuroimaging analytic approach to mental disorders and their comorbidities. This improved understanding can potentially aid in developing MRI-based tools that can diagnose an individual earlier and provide more targeted treatment. This information can also aid understanding physio-pathological processes and contribute to the improvement of existing therapies. In the future, evaluating demographics such as medication, age of onset, and severity of the conditions may be beneficial in further developing the intricacies of this tool.

Works Cited:

[1] L. Fortea, et al., Focusing on comorbidity—a novel meta-analytic approach and protocol to disentangle the specific neuroanatomy of co-occurring mental disorders. Frontiers in Psychiatry 12, 807839 (2022). doi: 10.3389/fpsyt.2021.807839

[2] Image retrieved from: https://unsplash.com/photos/58Z17lnVS4U