The ability to maintain our body’s balance and stability in space is crucial for performing daily activities. Effective postural control (PC) strategies rely on integrating visual, vestibular, and proprioceptive sensory inputs. While neuroimaging has revealed key areas involved in PC—including brainstem, cerebellum, and cortical networks—the rapid neural mechanisms underlying dynamic postural tasks remain less understood. Therefore, we used EEG microstate analysis within the BioVRSea experiment to explore the temporal brain dynamics that support PC. This complex paradigm simulates maintaining an upright posture on a moving platform, integrated with virtual reality (VR), to replicate the sensation of balancing on a boat. Data were acquired from 266 healthy subjects using a 64-channel EEG system. Using a modified k-means method, five EEG microstate maps were identified to best model the paradigm. Differences in each microstate maps feature (occurrence, duration, and coverage) between experimental phases were analyzed using a linear mixed model, revealing significant differences between microstates within the experiment phases. The temporal parameters of microstate C showed significantly higher levels in all experimental phases compared to other microstate maps, whereas microstate B displayed an opposite pattern, consistently showing lower levels. This study marks the first attempt to use microstate analysis during a dynamic task, demonstrating the decisive role of microstate C and, conversely, microstate B in differentiating the PC phases. These results demonstrate the utility of microstate technique in studying temporal brain dynamics during PC, with potential applications in the early detection of neurodegenerative diseases.

Disrupted postural control (PC), a hallmark of neurological conditions such as Parkinson’s disease (PD) and mild traumatic brain injury (mTBI), has been associated with altered neural oscillatory dynamics measured via electroencephalography (EEG) [1]–[3]. However, few studies have employed ecologically valid paradigms to assess these dynamics [4]. In this study, we utilized BioVRSea, a multimodal virtual reality (VR) platform combining realistic postural perturbations with real-time EEG to examine cortical responses to multisensory stimulation [5]. We tested whether EEG time–frequency representations could differentiate pathological PC responses in PD and mTBI from normative function in matched healthy controls (HC). Motion sickness susceptibility and symptom changes were evaluated using the Motion Sickness Susceptibility Questionnaire (MSSQ) [6] and the Motion Sickness Symptoms Scale. In HC, only the older subgroup exhibited a significant increase in low-frequency (delta–theta, ≈1–7 Hz) power at 50% perturbation intensity and during the post-perturbation phase (cluster-level p < 0.05). Compared with matched controls, PD participants showed increased spectral power in both the delta–theta (≈1–7 Hz) and alpha–beta (≈10–30 Hz) ranges (cluster-level p < 0.05), particularly at higher perturbation intensities, suggesting impaired sensorimotor integration [7], [8]. In contrast, no significant EEG differences were observed between the mTBI group and matched controls (p > 0.05). These findings demonstrate the feasibility of synchronized VR–EEG paradigms for capturing challenge-dependent neural signatures of postural control dysfunction. Further validation is warranted to determine their potential clinical utility in neurological assessment.

Background/Objectives: Cannabis use has a particularly high prevalence in individuals with psychotic disorders. Although cannabis use is generally associated with cognitive impairments in the general population, its impact on cognition in psychosis remains controversial. This study aimed to investigate the association between cannabis use and cognitive performance in a cohort of individuals affected by psychotic disorders. Methods: A total of 105 inpatients with psychotic disorders (mean age: 40.3 years; 34 females) were recruited from the University Hospital Center “Mother Teresa” in Tirana. Data collection included socio-demographic and clinical variables. Cognitive functioning was evaluated using the Montreal Cognitive Assessment (MoCA), while psychopathology was assessed with the Brief Negative Symptom Scale (BNSS), the Calgary Depression Scale for Schizophrenia (CDSS), the Psychotic Symptom Rating Scales (PSYRATS), and the Scale for the Assessment of Thought, Language, and Communication (TLC). Results: Cannabis users (CU) were more frequently male, younger, and exhibited an earlier onset of psychosis compared to non-users (No-CU). Importantly, CU demonstrated higher MoCA scores, with the most favorable outcomes observed among daily users. Conclusions: Contrary to the prevailing assumption that cannabis use exacerbates cognitive decline, our findings indicate an unexpected association between cannabis use and preserved cognitive functioning in psychosis. These results underscore the need to consider dosage, frequency, and cannabinoid composition (THC/CBD ratio) when interpreting cannabis-related cognitive outcomes in psychotic disorders.