Data-driven Approach Identifies Three Sub-types Of Depression

Three sub-types of depression have been identified by scientists from the Neural Computational Unit at the Okinawa Institute of Science and Technology Graduate University (OIST), in collaboration with their colleagues at Nara Institute of Science and Technology and clinicians at Hiroshima University. They found that one out of these sub-types seems to be untreatable by Selective Serotonin Reuptake Inhibitors (SSRIs), the most commonly prescribed medicines for the condition.

Serotonin is a neurotransmitter that influences our moods, interactions with other people, sleep patterns and memory. SSRIs are thought to take effect by boosting the levels of serotonin in the brain. However, these drugs do not have the same effect on everyone, and in some people, depression does not improve even after taking them.

“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,”

says Prof. Kenji Doya.

Different Brain Regions

For the study, the scientists collected clinical, biological, and life history data from 134 individuals – half of whom were newly diagnosed with depression and the other half who had no depression diagnosis – using questionnaires and blood tests. Participants were asked about their sleep patterns, whether or not they had stressful issues, or other mental health conditions.

Researchers also scanned participants’ brains using magnetic resonance imaging (MRI) to map brain activity patterns in different regions. The technique they used allowed them to examine 78 regions covering the entire brain, to identify how its activities in different regions are correlated.

“This is the first study to identify depression sub-types from life history and MRI data,”

says Prof. Doya.

Structure of functional connectivity in feature clusters F2

Structure of functional connectivity in feature clusters F2 (in bold line) and F4 (in dashed line).
Color corresponds to intrinsic connectivity networks. Relevant brain areas for network nodes are based on Automated Anatomical Labeling (AAL). DMN denotes default mode network; RECN right executive control network; LECN left executive control network.
Credit: Tomoki Tokuda et al. CC-BY

With over 3000 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,”

says Dr. Tomoki Tokuda, a statistician and the lead author of the study. He therefore designed a novel statistical method that would help detect multiple ways of data clustering and the features responsible for it.

Using this method, the researchers identified a group of closely-placed data clusters, which consisted of measurable features essential for accessing mental health of an individual. Three out of the five data clusters were found to represent different sub-types of depression.

Angular Gyrus

The three distinct sub-types of depression were characterized by two main factors: functional connectivity patterns synchronized between different regions of the brain and childhood trauma experience.

They 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 SSRIs were effective in treating depression.

Classification of D1, D2 and D3.

Classification of D1, D2 and D3.
Panel (a): Distribution of subjects in D1, D2, and D3 for AG-related FC and CATS scores. The dashed line denotes a possible threshold of AG-related FC score to discriminate between subjects in D3 and other subjects, while the dot-dashed line denotes a possible threshold of CATS score to discriminate between subjects in D1 and D2, provided that subjects in D3 have already been sorted out.
Panel (b): Classifier of subjects based on AG-related FC (AG-FC) score and CATS score. Blue circles denote classifier’s relevant features, while red ones denote resultant subject clusters.
‘Responsive’ or ‘Resistant’ signs denote whether SSRI treatment may work or not based on after-six-week BDI scores. This classifier is established based on Panel (a).
Credit: Tomoki Tokuda et al. CC-BY

Patients with increased functional connectivity between the brain’s different regions who had also experienced childhood trauma had a sub-type of depression that is unresponsive to treatment by SSRIs drugs, the researchers found. On the other hand, the other two subtypes – where the participants’ brains did not show increased connectivity among its different regions or where participants had not experienced childhood trauma – tended to respond positively to treatments using SSRIs drugs.

This study not only identifies sub-types of depression for the first time, but also identifies some underlying factors and points to the need to explore new treatment techniques.

“It provides scientists studying neurobiological aspects of depression a promising direction in which to pursue their research,”

says Prof. Doya. In time, he and his research team hope that these results will help psychiatrists and therapists improve diagnoses and treat their patients more effectively.

The research was supported by the Strategic Research Program for Brain Sciences from Japan Agency for Medical Research and development, AMED.

Tomoki Tokuda, Junichiro Yoshimoto, Yu Shimizu, Go Okada, Masahiro Takamura, Yasumasa Okamoto, Shigeto Yamawaki & Kenji Doya
Identification of depression subtypes and relevant brain regions using a data-driven approach
Scientific Reports volume 8, Article number: 14082 (2018)