Summary: During moments of acute stress, different brain networks alter their communication over the course of the stressful situation.
Source: University Hospital Tübingen
Did math problems make you stressed at school? That’s what happened to participants in a study of the brain’s reaction to stress.
For the first time, researchers looked at the entire duration of such a situation. They found not only changes in the communication of brain regions but also a dynamic process: Different networks behaved differently during acute stress.
From this, the scientists were able to determine how susceptible a person is to negative mood and how much this increased their risk of mental illness.
Until now, experts knew little about the dynamic processes in the brain during acute stress. Research has usually focused on the brain areas that are active at a given time. Now, however, scientists from the Max Planck Institute of Psychiatry (MPI) and the Department of Psychiatry and Psychotherapy at Tübingen University Hospital have observed what happens in the brain over the entire period of a stressful situation, such as while solving a tricky math problem.
“Our study shows not only where changes occur, but how different brain regions interact and how their communication changes over the course of the situation,” summarizes first author Anne Kühnel from the MPI.
The results of the study were recently published in the journal Biological Psychiatry.
The participants were asked to solve math problems under time pressure while inside a magnetic resonance imaging scanner. No matter how well they did, they only received negative feedback—a stressful situation. The dynamic response of the brain’s networks differed in the study participants.
The scientists were able to relate the responses to how anxious or depressed the participants were. It is known that the more negative a person’s basic temperament is, the higher their risk of mental illness.
“The altered communication between brain regions supports the theory that mental disorders are network diseases in which the interaction of neural units is disturbed,” says MPI Director Elisabeth Binder and continues, “The new findings are important for developing more individualized diagnoses and personalized therapies.”
In addition to magnetic resonance imaging, they measured levels of the stress hormone cortisol and heart rate. Image is in the public domain
Nils Kroemer, who heads the Computational Psychiatry group in Tübingen, sees great potential in the new findings, especially for individualized approaches in the treatment of stress-related diseases: “We were able to show for the first time how important individual patterns of the stress response in the brain are to better understand the experience of stress—including the unfavorable after-effects of stress. In the future, we could use our dynamic models of brain response to study, for example, the targeted effects of drugs that might improve the stress response in high-risk individuals.”
In the study, the scientists included people with and without affective disorders such as depression and anxiety disorders. In addition to magnetic resonance imaging, they measured levels of the stress hormone cortisol and heart rate. The volunteers were taking part in the BeCOME study, in which MPI researchers are searching for biomarkers as objective measurements that provide important information about mental illnesses.
Spatio-temporal dynamics of stress-induced network reconfigurations reflect negative affectivity
Maladaptive stress responses are important risk factors in the etiology of mood and anxiety disorders, but exact pathomechanisms remain to be understood. Mapping individual differences of acute stress-induced neurophysiological changes, especially on the level of neural activation and functional connectivity (FC), could provide important insights in how variation in the individual stress response is linked to disease risk.
Using an established psycho-social stress task flanked by two resting-states, we measured subjective, physiological, and brain responses to acute stress and recovery in 217 participants with and without mood and anxiety disorders. To estimate block-wise changes in stress-induced activation and FC, we used hierarchical mixed-effects models based on denoised timeseries within predefined stress-related regions. We predicted inter- and intra-individual differences in stress phases (anticipation vs. stress vs. recovery) and transdiagnostic dimensions of stress reactivity using elastic net and support vector machines.
We identified four subnetworks showing distinct changes in FC over time. FC but not activation trajectories predicted the stress phase (accuracy: 70%, pperm<.001) and increases in heart rate (R2=.075, pperm<.001). Critically, individual spatio-temporal trajectories of changes across networks also predicted negative affectivity (ΔR2=.075, pperm=.030), but not the presence or absence of a mood and anxiety disorder.
Spatio-temporal dynamics of brain network reconfiguration induced by stress reflect individual differences in the psychopathology dimension negative affectivity. These results support the idea that vulnerability for mood and anxiety disorders can be conceptualized best at the level of network dynamics, which may pave the way for improved prediction of individual risk.
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