It is estimated that nearly one in five people will experience major depressive disorder (MDD) at some point in their lifetime, but despite its ubiquity, and great progress made in the study of mental health, psychiatrists and psychologists still struggle to diagnosis this multifaceted and highly variable condition. As a team of researchers from the Neural Computational Unit at the Okinawa Institute of Science and Technology explain, clinicians currently rely on patient’s responses to standardized questionnaires – a practice that is both subjective and uninformative about the underlying biochemistry and genetics.
Moreover, the wide range of depression symptoms and presentations and the finding that medications in the most popular anti-depressive medication class – selective serotonin reuptake inhibitors (SSRIs) – fail to help some patients has led scientists in the field to speculate that there are several distinct types of depression. Everyone agrees that identifying and characterizing these subtypes, if they exist, is essential for creating better, tailored treatment options, but as of yet, no one has done so convincingly.
"It has always been speculated that different types of depression exist, and they influence the effectiveness of the drug. But there has been no consensus," Professor Kenji Doya said in a statement.
Hoping to clarify the matter and provide objective markers for MDD diagnosis as a whole, Doya and his colleagues decided to compile as much as data as they could about depression and use a statistical analysis to look for previously unseen patterns. Their study, published in Scientific Reports, gathered questionnaire answers, medical information (sequence and transcription analyses of key genes, cortisol levels, brain-derived neurotrophic factor), and functional MRI scans of 78 brain regions from 67 patients with MDD and 67 healthy control subjects. In total, each patient was assessed for over 3,000 measurable features.
The computer-based examination of this vast set of data, using a novel statistical tool developed by the team, revealed clusters corresponding to three depression subtypes. The subtypes were found to differ based on the presence or absence of childhood trauma and patterns of functional connectivity between the right angular gyrus – a brain region involved in visual processing, spatial cognition, memory, attention, and some aspects of self-awareness – and other areas of the default mode network.
Patients with increased angular gyrus function who had also experienced childhood trauma appear to manifest the sub-type of depression that is unresponsive to treatment by SSRI drugs. The other two subtypes – those without increased connectivity and those that had not experienced childhood trauma – tended to respond positively to treatments using SSRIs drugs. It is unclear where patients with childhood trauma but without increased connectivity would fall.
"This is the first study to identify depression sub-types from life history and MRI data," Doya said. "It provides scientists studying neurobiological aspects of depression a promising direction in which to pursue their research."
Earlier this year, a team from Hiroshima University proposed the existence of a new type of depression that is mediated by abnormal functioning of a hormone receptor called MCHR1. Their study was conducted in mice, but follow-up investigations will test if the pathway applies to humans. If confirmed, their discovery would explain why a subset of people with depression do not respond to existing medications, which act on the neurotransmitters serotonin and/or norepinephrine.