Music Performance Anxiety (MPA) is highly prevalent among musicians and often debilitating, associated with changes in cognitive, emotional, behavioral, and physiological responses to performance situations. Efforts have been made to create simulated performance environments in conservatoires and Virtual Reality (VR) to assess their effectiveness in managing MPA. Despite these advances, results have been mixed, underscoring the need for controlled experimental designs and joint analyses of performance, physiology, and subjective ratings in these settings. Furthermore, the broader application of simulated performance environments for at-home use and laboratory studies on MPA remains limited. We designed VR scenarios to induce MPA in pianists and embedded them within a controlled within-subject experimental design to systematically assess their effects on performance, physiology, and anxiety ratings. Twenty pianists completed a performance task under two conditions: a public 'Audition' and a private 'Studio' rehearsal. Participants experienced VR pre-performance settings before transitioning to live piano performances in the real world. We measured subjective anxiety, performance (MIDI data), and heart rate variability (HRV). Compared to the Studio condition, pianists in the Audition condition reported higher somatic anxiety ratings and demonstrated an increase in performance accuracy over time, with a reduced error rate. Additionally, their performances were faster and featured increased note intensity. No concurrent changes in HRV were observed. These results validate the potential of VR to induce MPA, enhancing pitch accuracy and invigorating tempo and dynamics. We discuss the strengths and limitations of this approach to develop VR-based interventions to mitigate the debilitating effects of MPA.

Anxiety affects approximately 5-10% of the adult population worldwide, placing a large burden on the health systems. Despite its omnipresence and impact on mental and physical health, most of the individuals affected by anxiety do not receive appropriate treatment. Current research in the field of psychiatry emphasizes the need to identify and validate biological markers relevant to this condition. Neurophysiological preclinical studies are a prominent approach to determine brain rhythms that can be reliable markers of key features of anxiety. However, while neuroimaging research consistently implicated prefrontal cortex and subcortical structures, such as amygdala and hippocampus, in anxiety, there is still a lack of consensus on the underlying neurophysiological processes contributing to this condition. Methods allowing non-invasive recording and assessment of cortical processing may provide an opportunity to help identify anxiety signatures that could be used as intervention targets. In this study, we apply Source-Power Comodulation (SPoC) to electroencephalography (EEG) recordings in a sample of participants with different levels of trait anxiety. SPoC was developed to find spatial filters and patterns whose power comodulates with an external variable in individual participants. The obtained patterns can be interpreted neurophysiologically. Here, we extend the use of SPoC to a multi-subject setting and test its validity using simulated data with a realistic head model. Next, we apply our SPoC framework to resting state EEG of 43 human participants for whom trait anxiety scores were available. SPoC inter-subject analysis of narrow frequency band data reveals neurophysiologically meaningful spatial patterns in the theta band (4-7 Hz) that are negatively correlated with anxiety. The outcome is specific to the theta band and not observed in the alpha (8-12 Hz) or beta (13-30 Hz) frequency range. The theta-band spatial pattern is primarily localised to the superior frontal gyrus. We discuss the relevance of our spatial pattern results for the search of biomarkers for anxiety and their application in neurofeedback studies.

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.