Summary: Understanding how changes in the brain are related to changes in well-being is key to developing new targets for treating mental health disorders.
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Associate Professor Miriam Klein-Flügge and colleagues analyzed brain connectivity and mental health data from nearly 500 people. In particular, they looked at the connectivity of the amygdala, a brain region known for its importance in processing emotions and rewards.
The researchers used functional magnetic resonance imaging to consider seven small subdivisions of the amygdala and their associated networks rather than combining the entire region as previous studies had done.
The team also took a more precise approach to data on mental well-being, looking at a large group of healthy people and using questionnaires that captured information on well-being in the social, emotional, sleep, and anger domains.
This generated more precise data than much research that still uses broad diagnoses like depression or anxiety, which involve many different symptoms.
The article, published in Nature Human Behaviorshows how the improved level of detail about brain connectivity and well-being made it possible to characterize the exact brain networks that relate to these different aspects of mental health.
The brain connections that mattered most in discerning whether an individual was struggling with sleep problems, for example, looked very different from those that contained information about their social well-being.
Associate Professor Miriam Klein-Flügge from the Department of Experimental Psychology, based at the Wellcome Center for Integrative Neuroimaging (WIN), said: “Understanding how changes in the brain relate to changes in well-being is an important step in the journey towards greater specific mental health treatments”.
“We look at the brain in much finer subdivisions than previous research, which more closely resembles how the brain is organized, and our findings indicate that it may one day be possible to develop very precise, non-invasive ways to target specific areas of the brain. brain, making future treatments much more refined.”
The researchers also found that the nature of the identified brain networks differed. For example, they found that connectivity in evolutionarily older subcortical circuits was most strongly related to the tendency to experience negative emotions, whereas amygdala connectivity to both newer and older subcortical circuits clearly contributed to well-being. Social.
The findings indicate the potential benefit of considering mental well-being and the brain networks involved on a finer scale than before, a scale that more closely resembles the functional organization of the brain.
Although more research is needed, in the future it may be possible to target treatments to the brain circuits most relevant to an individual’s key symptoms.
This possibility is becoming more tangible with current progress in non-invasive methods of deep brain stimulation like ultrasound, for example.
About this research news in mental health and neuroscience
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Font: Oxford University
Contact: Press Office – University of Oxford
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original research: Closed access.
“Relationship between nuclei-specific amygdala connectivity and dimensions of mental health in humans” by Miriam C. Klein-Flügge et al. Nature Human Behavior
Summary
Relationship between nuclei-specific amygdala connectivity and dimensions of mental health in humans
There has been a growing interest in the use of neuroimaging measures to predict psychiatric disorders. However, predictions are generally based on large brain networks and a large heterogeneity of disorders. Therefore, they lack both anatomical and behavioral specificity, impeding the advancement of targeted interventions.
Here we address both challenges. First, using resting-state fMRI, we partitioned the amygdala, a region implicated in mood disorders, into seven nuclei. Next, a factor analysis of the questionnaire provided frequently altered subclinical mental health dimensions in anxious-depressive individuals, such as negative emotions and sleep problems.
Finally, for each behavioral dimension, we identified the most predictive resting-state functional connectivity between individual amygdala nuclei and highly specific regions of interest, such as the dorsal raphe nucleus in the brainstem or medial frontal cortical regions. Connectivity in circumscribed amygdala networks predicted behaviors in an independent data set.
Our results reveal specific relationships between dimensions of mental health and connectivity in precise subcortical networks.
