AsianScientist (Nov. 5, 2018) – A research group in Japan has identified three subtypes of depression using a combination of computational, neuroimaging and self-reporting methods. Their findings are published in Scientific Reports.
According to the World Health Organization, nearly 300 million people worldwide suffer from depression and the number of diagnoses is on the rise globally. Yet, doctors and scientists have a poor understanding of what causes depression and why some medicines do not work on patients suffering from this debilitating condition.
In the present study, scientists from the Neural Computational Unit at the Okinawa Institute of Science and Technology Graduate University (OIST), Japan, in collaboration with colleagues at the Nara Institute of Science and Technology and Hiroshima University, have identified three distinct subtypes of depression.
The scientists collected clinical, biological and life history data from 134 individuals. Half of their sample population were newly diagnosed with depression, while the other half had not been diagnosed with the disease based on blood tests and questionnaires. The participants were asked about their sleep patterns, whether or not they faced stressful situations, or if they had any other mental health conditions.
The researchers also scanned participants’ brains using magnetic resonance imaging to map brain activity patterns in different regions. This technique allowed them to examine 78 regions of the brain and better understand how the activities of different brain regions were correlated.
With over 3,000 measurable features, including whether or not participants had experienced trauma, the scientists were faced with the dilemma of finding a way to analyze such a large data set accurately.
“The major challenge in this study was to develop a statistical tool that could extract relevant information for clustering similar subjects together,” said Dr. Tomoki Tokuda of OIST.
The researchers therefore designed a novel statistical method that would help perform data clustering and identify key patterns within their data. They subsequently observed a group of closely-placed data clusters, consisting of features essential for assessing the mental health of an individual. Three out of the five data clusters were found to represent different subtypes of depression.
The three distinct subtypes of depression were characterized by two main factors: functional connectivity patterns synchronized between different regions of the brain and childhood trauma. The scientists found that the brain’s functional connectivity in regions that involved the angular gyrus—a brain region associated with processing language and numbers, spatial cognition, attention and other aspects of cognition—played a large role in determining whether selective serotonin reuptake inhibitors (SSRIs) were effective in treating depression.
Serotonin is a neurotransmitter that influences our moods, our interactions with other people, as well as our sleep patterns and memory. SSRIs are thought to alleviate depression by boosting the levels of serotonin in the brain. The researchers reported that patients who had also experienced childhood trauma and had increased functional connectivity between the brain’s different regions were relatively unresponsive to treatment by SSRIs.
“[Our findings] provide scientists studying the neurobiological aspects of depression a promising direction in which to pursue their research,” said study corresponding author Professor Kenji Doya.
The article can be found at: Tokuda et al. (2018) Identification of Depression Subtypes and Relevant Brain Regions Using a Data-driven Approach.
Source: Okinawa Institute of Science and Technology Graduate University; Photo: Pexels.
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