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Abstract

Anxiety is one of the most prevalent mental disorders, affecting approximately 5-10% of the adult population worldwide. It can severely impact quality of life, but also place a large burden on the health systems. Despite its omnipresence and impact on mental and physical health, most of the individuals suffering from anxiety do not receive appropriate treatment. Furthermore, while neuroimaging research consistently implicated subcortical structures such as amygdala, hippocampus and prefrontal cortex in anxiety, there is still a lack of consensus on the underlying neurophysiological processes contributing to this condition. Thus, the objective neurophysiological markers for anxiety remain elusive. Methods allowing non-invasive recording and assessment of cortical processing provide an opportunity to help identify anxiety signatures that could be used as intervention targets. In this paper, we tackle this problem by applying a regression spatial filter called Source-Power Comodulation (SPoC) to trait anxiety data of 43 individuals. By maximizing the correlation of alpha band power and the level of trait anxiety in resting state electroencephalography (EEG) we are able to obtain neurophysiologically meaningful patterns that should be helpful in the search of biomarkers for mental disorders.