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In the wake of the incessant evolution of virtual reality (VR) technology, its pervasive integration into an expanding array of sectors, notably the medical field, has become increasingly conspicuous. Amid these developments, post‐traumatic stress disorder (PTSD), a prevalent and debilitating mental health condition, has garnered heightened attention from the medical community. The contemporary therapeutic landscape predominantly revolves around psychological interventions, exemplified by exposure therapy and positive thinking therapy. However, a groundbreaking advancement, namely virtual reality exposure therapy (VRET), has emerged as a prominent achievement, effectively intertwining VR technology with medical applications, particularly in the nuanced treatment of PTSD. As the realms of human‐computer interaction and brain‐computer interface undergo continuous and dynamic expansion, electroencephalography (EEG) has surfaced as a pivotal convergence point across diverse disciplines. The confluence of VRET technology with EEG signals holds the promise of enhancing the precision and objectivity of patient diagnoses. Furthermore, leveraging EEG signals enables a more nuanced and individualized approach to treatment within the framework of VRET technology. Despite extant studies that offer comprehensive summaries of VRET technology, the integration with EEG signals has thus far been a relatively unexplored terrain. This research endeavors to direct attention toward recent advancements in VRET technology within the sphere of mental health treatment. A meticulous synthesis and exposition of existing techniques and applications are presented, providing a comprehensive overview of the current landscape. The exploration extends to the developmental trajectory and prospective implications arising from the amalgamation of VRET technology and EEG technology. Furthermore, a rigorous analysis of the tangible contributions and practical significance of EEG signals in the domain of mental health treatment is undertaken, underscoring the potential avenues for their future applications and the transformative impact on the trajectory of therapeutic interventions.
In the wake of the incessant evolution of virtual reality (VR) technology, its pervasive integration into an expanding array of sectors, notably the medical field, has become increasingly conspicuous. Amid these developments, post‐traumatic stress disorder (PTSD), a prevalent and debilitating mental health condition, has garnered heightened attention from the medical community. The contemporary therapeutic landscape predominantly revolves around psychological interventions, exemplified by exposure therapy and positive thinking therapy. However, a groundbreaking advancement, namely virtual reality exposure therapy (VRET), has emerged as a prominent achievement, effectively intertwining VR technology with medical applications, particularly in the nuanced treatment of PTSD. As the realms of human‐computer interaction and brain‐computer interface undergo continuous and dynamic expansion, electroencephalography (EEG) has surfaced as a pivotal convergence point across diverse disciplines. The confluence of VRET technology with EEG signals holds the promise of enhancing the precision and objectivity of patient diagnoses. Furthermore, leveraging EEG signals enables a more nuanced and individualized approach to treatment within the framework of VRET technology. Despite extant studies that offer comprehensive summaries of VRET technology, the integration with EEG signals has thus far been a relatively unexplored terrain. This research endeavors to direct attention toward recent advancements in VRET technology within the sphere of mental health treatment. A meticulous synthesis and exposition of existing techniques and applications are presented, providing a comprehensive overview of the current landscape. The exploration extends to the developmental trajectory and prospective implications arising from the amalgamation of VRET technology and EEG technology. Furthermore, a rigorous analysis of the tangible contributions and practical significance of EEG signals in the domain of mental health treatment is undertaken, underscoring the potential avenues for their future applications and the transformative impact on the trajectory of therapeutic interventions.
This case-control study investigated the associations between peripheral inflammation, perceived fatigue, and event-related potentials (ERP) during a sustained attention test (SAT) in depression. Participants included 25 individuals with depressive episodes (DE) and 31 healthy controls (HC). A 15-minute SAT (subtest of the Test Battery for Attention, version 2.3.1) was administered with concurrent EEG recordings. Peripheral inflammation was assessed by measuring IL-6, IL-1β, and TNF-α cytokines. Linear mixed models and generalized linear models were utilized for data analysis. Our results showed that the DE group exhibited lower P300 amplitudes than HC (estimate = -0.98, CI95 [-1.60; -0.35], p = 0.004). Furthermore, P300 amplitudes were inversely associated with IL-6 (estimate = -1.73, CI95 [-3.27; -0.19], p = 0.03), regardless of group status. Surprisingly, higher perceived fatigue correlated with increased P300 amplitudes, irrespective of group status (estimate = 0.009, CI95 [0.0004; 0.02], p = 0.05). Finally, accuracy, measured as the total number of correct answers in the SAT, correlated negatively with TNF-α (OR = 0.44, CI95 [0.27; 0.70]). However, no significant effects were found for P300 latency or reaction time in the SAT when comparing DE and HC. The study highlights the potential role of peripheral inflammation on sustained attention in cognitive performance. Due to the low interaction effect, fatigue and P300 amplitude results must be interpreted cautiously. Although P300 amplitudes were lower in DE, no significant association was observed between DE and inflammation in ERP and cognitive performance. Further research is required to confirm these findings.
Depressive states in both healthy individuals and those with major depressive disorder exhibit differences primarily in symptom severity rather than symptom type, suggesting that there is a spectrum of depressive symptoms. The increasing prevalence of mild depression carries lifelong implications, emphasizing its clinical and social significance, which parallels that of moderate depression. Early intervention and psychotherapy have shown effective outcomes in subthreshold depression. Electroencephalography serves as a non-invasive, powerful tool in depression research, with many studies employing it to discover biomarkers and explore underlying mechanisms for the identification and diagnosis of depression. However, the efficacy of these biomarkers in distinguishing various depressive states in healthy individuals and in understanding the associated mechanisms remains uncertain. In our study, we examined the power spectrum density and the region-based phase-locking value in healthy individuals with various depressive states during their resting state. We found significant differences in neural activity, even among healthy individuals. Participants were categorized into high, middle, and low depressive state groups based on their response to a questionnaire, and eyes-open resting-state electroencephalography was conducted. We observed significant differences among the different depressive state groups in theta- and beta-band power, as well as correlations in the theta–beta ratio in the frontal lobe and phase-locking connections in the frontal, parietal, and temporal lobes. Standardized low-resolution electromagnetic tomography analysis for source localization comparing the differences in resting-state networks among the three depressive state groups showed significant differences in the frontal and temporal lobes. We anticipate that our study will contribute to the development of effective biomarkers for the early detection and prevention of depression.
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