New Approach to Functional Brain Network Study Helps Diagnose Depression

Researchers at IKBFU have developed a trail-blazing method for studying functional brain networks to aid in the diagnosis of depression. The approach involves examining healthy brain networks and comparing them to data from depressed patients. The technique, known as the «consensus network approach» identified notable distinctions between individuals without depression and those with major depressive disorder, providing valuable insights for diagnosis. The study received support from the Russian Science Foundation and was published in the journal Chaos: An Interdisciplinary Journal of Nonlinear Science.

Major depressive disorder (MDD) is a mental disorder which pervades all aspects of life, negatively affecting productivity, alertness, concentration, sleep, and self-esteem. As of today, 3.8 percent of the world's population struggles with depression, according to WHO statistics. By 2030, MDD may become the leading cause of both tangible and intangible losses (as measured by financial costs, mortality rates, morbidity rates, and other related factors) among diseases.

No laboratory test for MDD is currently available. A qualified medical professional may assess the diagnosis by conducting a mental state examination and reviewing the patient's medical history. It is also known that MDD disrupts the production of certain molecules responsible for facilitating communication between brain cells, therefore, functional magnetic resonance imaging (fMRI) may be utilised for diagnosis. This technique allows for the analysis of functional brain networks and the monitoring of overall brain activity. By using fMRI, researchers can compare the reactions of different brain regions located far apart to external stimuli and observe how various brain structures interact with one another.

Researchers from IKBFU, Technical University of Madrid, and Plovdiv Medical University proposed a new approach to the study of functional brain networks, which enabled them to identify differences in fMRI results between a healthy control group and those diagnosed with MDD. The study involved 85 test subjects who underwent fMRI, and the researchers evaluated the findings using both a standard group-based method and a new consensus network approach.

Both experiments involved the analysis of various properties of functional brain networks: the average node degree (the number of neuron connections), and the distribution of node degrees across the network.

In the standard group-based approach, these characteristics were calculated individually for each person, and statistical analysis was then used to identify similarities and differences. On the other hand, the consensus approach required less effort. It involved constructing a common characteristic functional network for each of the two experimental groups, which included connections that were present in 95% of the test subjects.The characteristics were then calculated for each network and directly compared. Therefore, the standard group-based approach required additional measures, while the consensus approach allowed for immediate identification of differences between healthy and depressed subjects based on the calculated network characteristics.

The combined use of the group-based and consensus approaches could allow for the development of an fMRI-based automatic MDD recognition system.