A recent study conducted by the Institute of Psychiatry, Psychology & Neuroscience (IoPPN) at King’s College London, the University of East London (UEL), and the University of Pennsylvania has utilized artificial intelligence to analyze brain images of individuals living with major depressive disorder (MDD). The research, titled “Neuroanatomical dimensions in medication-free individuals with major depressive disorder and treatment response to SSRI antidepressant medications or placebo”, published in Nature Mental Health, has revealed that the amount of gray and white matter in the brain can predict the response to treatment with traditional antidepressants (SSRIs) as well as placebo medication.
Gray matter in the brain is responsible for various functions, including sensation processing, perception, voluntary movement, learning, speech, and cognition. On the other hand, white matter facilitates communication between different areas of gray matter in both the brain and the rest of the body.
Although more than 320 million people worldwide suffer from MDD, researchers have yet to identify biomarkers that can predict treatment response. In this study, the researchers aimed to explore if there are distinct brain mechanisms underlying the manifestation of this illness.
The study analyzed brain scans of 685 participants diagnosed with MDD, who were currently experiencing a moderate to severe depressive episode and were not taking any medication at the time of the scan. These scans were compared to those of 699 healthy individuals.
The research team identified two distinct dimensions. Dimension 1 (D1) was characterized by preserved gray and white matter, similar to levels found in the healthy control group. Conversely, individuals in Dimension 2 (D2) exhibited widespread decreases in gray and white matter.
Depression significantly impacts an individual’s daily life, being the leading cause of disability and a major risk factor for suicide. However, there are currently no biomarkers available to identify depression or predict treatment response on an individual level. Professor Cynthia Fu, one of the joint first authors of the study from King’s IoPPN and UEL, highlighted the significance of their findings, stating, “Our findings are a vital first step in defining the biomarkers that make up depression. In this study, we used machine learning to analyze MRI scans in depression.”
The researchers also investigated the relationship between these dimensions and the clinical response to antidepressant use. They discovered that individuals in Dimension 1 showed a significantly greater response to SSRI medication compared to placebo. Conversely, those in Dimension 2 did not exhibit any significant differences in the effectiveness of SSRIs or placebos. The research team suggests that this could serve as a biomarker for identifying the likelihood of treatment resistance at an earlier stage.
Dr. Mathilde Antoniades, another joint first author of the study, highlighted the collaborative effort in gathering data from numerous MDD participants who were not taking any medications. Furthermore, Professor Christos Davatzikos from the University of Pennsylvania emphasized the utilization of state-of-the-art artificial intelligence techniques in this unique dataset.
Moving forward, the researchers aim to define the dimensions specific to depression as well as those shared with other mental health disorders. Their hope is that this further understanding will contribute to improved diagnosis and personalized treatment for individuals with MDD.
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