TASS, November 14. Scientists from the Immanuel Kant Baltic Federal University (IKBFU) together with colleagues from Bulgaria and Spain with the support of the Russian Science Foundation have developed a new method of diagnosing depression using magnetic resonance imaging (MRI) data, the press service of the Priority 2030 program of the Russian Ministry of Education and Science said, which is running the research.
"Scientists from Immanuel Kant BFU have presented a new method for analyzing neural connections based on functional MRI data. Within its framework, the authors reconstructed functional brain networks for healthy people and depressed patients, after which they compared the calculated characteristics. Such a method was called "consensus approach" and allowed to identify significant differences between healthy people and patients with major depressive disorder", - the press-service noted.
Major depressive disorder, which causes people to lose interest in life for weeks and months without clear external causes, is reported in 3.8% of the world's population. The difficulty in diagnosing depression is that there are no widely available tests to confirm its presence. Diagnosis is made by a psychiatrist based on examination, interview and questionnaires. Depressive states are known to be associated with changes in chemical signaling in the brain.
Group and consensus analyses
Researchers from BFU, Madrid Polytechnic University and Medical University of Plovdiv were able to identify differences between the scans in healthy people and depressed patients. The 85 subjects were first analyzed using the standard group analysis used in psychiatry and then analyzed using a new consensus approach.
Both experiments involved analyzing different characteristics of associations of neurons, the nodes of the brain's functional network whose activity reflects information processing and decision-making. In the group approach, the characteristics were first calculated for each subject individually, and then statistical analysis was applied to find similarities and differences.
The consensus approach required less effort; one common characteristic functional network was constructed for each of the two experimental groups, with connections common to 95% of the subjects in the group, and then characteristics were calculated for each of the networks and compared directly. The differences between healthy and sick subjects were already directly visible from the calculated network characteristics.
In the near future, the researchers plan to develop an automatic depression recognition system based on functional MRI. The combined use of group and consensus approaches is envisioned.