In modern realities, approximately 31% of people are affected by obsessive anxious thoughts. While many do not consider this issue to be significant, it is unfortunately much more serious than it appears. This symptom can lead to a range of unpleasant consequences, including emotional burnout, neurosis, and severe depression. A team of international scientists led by Kim Chong from the Center for Neurocultural Sciences in South Korea, along with colleagues from the University of Arizona and Dartmut College in the United States, conducted a study to develop a predictive model of rumination using machine learning. The results have been published in the journal Nature Communications [source].
Rumination, also known as negative thinking, refers to a tendency to constantly dwell on the past or future, often related to errors, doubts, or internal conflicts. It was crucial for researchers to understand the brain mechanisms responsible for these disturbing thoughts and how they could be prevented.
Previous studies have shown that rumination is associated with a complex of brain areas known as the default network, or the Default Mode Network (DMN). This network is active when individuals are not engaged in specific tasks and are immersed in their thoughts. However, not all areas of the DMN have the same impact on rumination. It remained unclear which area plays a key role.
In order to answer this question, Kim Chong’s team used functional magnetic resonance imaging (MRI), a method for measuring brain activity by examining changes in blood flow in different regions of the brain. MRI is an advanced neuroimaging technology that allows for safe, painless, and non-invasive research. The researchers scanned both healthy participants and patients with depression during rest, comparing the collected data to their self-reported levels of rumination. This process provided detailed information about the functioning and activity of various sections of the DMN.
Using the MRI data, the team developed a machine learning model to simulate brain activity and automatically predict levels of rumination. The model focused on the dorsal medial prefrontal cortex (DMPFC), one particular region within the DMN, which was found to be responsible for the formation and maintenance of negative thoughts. The intensity of DMPFC activity and its connections with other brain regions proved to be important factors in predicting rumination levels.
The program was tested with individuals who had already received a diagnosis of depression. Remarkably, the model was able to accurately predict the level