Depression is a complicated disorder, so Generation Scotland (along with other international researchers) are bringing together large samples, brain images (MRI) and new machine learning models (AI) to identify brain markers of people with depression and develop individual treatments. This important project is only in its first stages. This ‘dimensional’ and collaborative approach will be vital to understanding and treating depression as well as other diseases. This study focuses on developing neuroimaging-based biomarkers for major depressive disorder (MDD) at the individual level. Currently, MDD diagnosis relies on symptom-based criteria, and the disorder is seen more as a set of symptoms rather than a disease with a known cause. This makes it challenging to predict treatment response accurately for each patient. Additionally, neuroimaging studies are often complicated by factors like medication use and varying symptom states. To address these challenges, researchers have formed a consortium called COORDINATE-MDD. This consortium aims to identify patterns of brain alterations in MDD using advanced imaging techniques and machine learning algorithms. They're pooling together data from diverse sources, including multi-ethnic community populations, different types of MDD (first episode, recurrent), and individuals both in depressive episodes and in remission. The neuroimaging data include structural MRI, resting-state functional MRI, and PET scans. The researchers are using cutting-edge analytical methods to extract meaningful information from the imaging data. They're focusing on creating a dimensional coordinate system that captures the heterogeneity of MDD at both structural and functional levels. By deeply phenotyping participants and analyzing treatment outcomes, they hope to identify specific neural patterns associated with MDD and predict individual responses to treatment. The consortium's approach involves iterative testing of these neural dimensions in different samples to ensure reliability and specificity. They're also emphasizing the importance of harmonizing data from multiple sites to account for variations in imaging protocols and scanners. Ultimately, the goal is to develop imaging signatures that can improve our understanding of MDD and lead to more personalized treatment strategies. Read the full paper on BMC Psychiatry Image GS volunteers help to develop and use cutting-edge tools to find and treat major depression Depression is a complicated disorder, so Generation Scotland (along with other international researchers) are bringing together large samples, brain images (MRI) and new machine learning models (AI) to identify brain markers of people with depression and develop individual treatments. This important project is only in its first stages. This ‘dimensional’ and collaborative approach will be vital to understanding and treating depression as well as other diseases. Research summary: This study focuses on developing neuroimaging-based biomarkers for major depressive disorder (MDD) at the individual level. Currently, MDD diagnosis relies on symptom-based criteria, and the disorder is seen more as a set of symptoms rather than a disease with a known cause. This makes it challenging to predict treatment response accurately for each patient. Additionally, neuroimaging studies are often complicated by factors like medication use and varying symptom states. To address these challenges, researchers have formed a consortium called COORDINATE-MDD. This consortium aims to identify patterns of brain alterations in MDD using advanced imaging techniques and machine learning algorithms. They're pooling together data from diverse sources, including multi-ethnic community populations, different types of MDD (first episode, recurrent), and individuals both in depressive episodes and in remission. The neuroimaging data include structural MRI, resting-state functional MRI, and PET scans. The researchers are using cutting-edge analytical methods to extract meaningful information from the imaging data. They're focusing on creating a dimensional coordinate system that captures the heterogeneity of MDD at both structural and functional levels. By deeply phenotyping participants and analyzing treatment outcomes, they hope to identify specific neural patterns associated with MDD and predict individual responses to treatment. The consortium's approach involves iterative testing of these neural dimensions in different samples to ensure reliability and specificity. They're also emphasizing the importance of harmonizing data from multiple sites to account for variations in imaging protocols and scanners. Ultimately, the goal is to develop imaging signatures that can improve our understanding of MDD and lead to more personalized treatment strategies. Read the full paper on BMC Psychiatry Image Long-term inflammation may hold a key to understanding depression Researchers, based at the University of Edinburgh, found that longer-term inflammation was related to changes in brain structure, which may help shed light on causes of depression. Research Summary: Depression is the most common mental health condition in adults worldwide, yet we still don’t understand what might lead to people experiencing it. One leading theory is that inflammation in the body adds to depressive symptoms through effects on the brain. This leads to symptoms such as tiredness and low mood. Earlier research measured inflammation in the blood from a single point in time. This approach is not ideal. Inflammation levels can change often, like when you have a cold. It also doesn’t capture longer-term (chronic) inflammation. Long-term inflammation is thought to be more important in understanding changes in brain structure and in depression. In this study, researchers used Generation Scotland data to study changes that reflect longer-term inflammation. They did so by looking at a biological process called DNA methylation. This process adds chemical groups to DNA, which change the activity of genes. We can use these longer-term changes to study the signature that long-term inflammation leaves on DNA. We found that the signature of longer-term inflammation was related to changes in the brain. Changes included differences in grey matter and changes to structural connections. By comparison, the single timepoint blood measure of inflammation was related to current depression symptoms but was not strongly related to brain features. This research shows that having a long term measure or ‘signature’ of chronic inflammation over a person’s lifetime is related to brain structure. Research using single time point measures are not able to show this. However, more research is needed to find out if targeting medical care at inflammation will be a useful treatment for people with depression. This work was led by researchers based at the Division of Psychiatry, University of Edinburgh. The publication can be found in the Brain, Behavior, and Immunity journal, linked below. Structural brain correlates of serum and epigenetic markers of inflammation in major depressive disorder Can DNA methylation research answer important questions about mental health? The recent findings were made after researchers studied data from over 9,000 Generation Scotland volunteers. Research Summary: Our experiences and environment, during early life and childhood, affect how we react to stressful situations in later life. This influences our overall adult mental health. One possible reason for this effect could be small changes in genes, which are turned on and off early in our lives. Generation Scotland is ideal for researching this. We have information about early life, adult mental health, and data for those small genetic changes, known as DNA methylation, for more than 9,000 volunteers. In a recent study, researchers combined all this data together to understand how people's genes and environment might affect mental health. The researchers, based at The University of Edinburgh, found some changes to the genes being turned on and off for people born preterm or with a low birth weight. These changes were in genes involved in the development of two of our senses: sight and hearing. Having depression as an adult influenced DNA methylation at a couple of bits of the genome. However, there was no overlap between these bits and the ones related to the early life experiences. This reveals that DNA methylation is not likely to be responsible for the connection between challenges in early life and adult mental health. Only half of our volunteers have DNA methylation data because the rest of the blood samples haven't had the information taken from them yet. The good news is, that should change soon. Hopefully, with an even bigger sample, the researchers can reveal more insights to improve physical and mental health. Methylome-wide association study of early life stressors and adult mental health This article was published on 2024-05-06
