Scientists at the Okinawa institute of science and technology have used brain imaging to identify three sub-types of depression , including one that is supposedly unresponsive to commonly prescribed serotonin boosting drugs.
Who was studied and how ?
The scientists collected clinical, data from a total of 134 individuals . Half of whom were newly diagnosed with depression. The other half did not have a depression depression diagnosis. Data about the participants sleep patterns, stress and other mental conditions were recorded. Functional MRI was used to map brain activity patterns in different regions. They examined 78 regions covering the entire brain.
What did they find ?
The results of the cluster analysis suggest three subtypes of depressive subjects. 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 traumatic experience. One marked by unusual connectivity patterns in the brain but no significant childhood trauma, one marked by significant childhood trauma but no unusual connectivity patterns in the brain, and one marked by both .
The researchers found that functional connectivity in the angular gyrus, a brain region associated with processing language and numbers, spatial cognition, attention, and other aspects of cognition and a history of child hood trauma played a large role in determining whether SSRIs were effective in treating depression.
Patients who had an increased functional connectivity between the brain’s different regions and those who had also experienced childhood trauma had a sub-type of depression that was unresponsive to treatment by SSRIs.
What are the Implications ?
In time it is hoped that these results will help psychiatrists and therapists improve diagnoses and treat their patients more effectively.
- 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, 2018; 8 (1) DOI: 10.1038/s41598-018-32521-z