Search Results (49)

Background and Objectives: Alzheimer’s disease (AD) and frontotemporal dementia (FTD) present overlapping clinical and neuroanatomical features, complicating early diagnosis. Therefore, this study evaluated whether EEG microstate analysis can provide reliable markers to distinguish patients with dementia from healthy controls. Materials and Methods: Resting-state EEG was recorded from 36 AD patients, 23 FTD patients, and 29 healthy controls. Preprocessing and microstate analysis were conducted using the MICROSTATELAB pipeline in EEGLAB. Clustering solutions ranging from four to seven classes were tested, with grand mean fitting and variance thresholds. Temporal parameters (duration, occurrence, and coverage) and their ratio-normalized forms were compared across groups using ANCOVA and nonparametric tests. Associations with Mini-Mental State Examination (MMSE) scores were assessed by regression analyses. Results: The four- and seven-class clustering solutions achieved high variance overlap with published microstate templates. In the four-class solution, temporal parameters of microstates B and D significantly differentiated controls from dementia groups, while in the seven-class solution, microstates C and G were the most informative. Ratio-normalized parameters improved group discrimination and were associated with MMSE scores. Conclusions: EEG microstates capture disease-related alterations in large-scale brain dynamics that differentiate patients with dementia from healthy individuals. Full article

Major depressive disorder (MDD) is one of the most prevalent psychiatric conditions and is characterized by alterations in cortical excitability, network connectivity, and neuroplasticity. Despite significant progress in neuroimaging and neurophysiology, the identification of objective and reliable biomarkers remains a major challenge, limiting diagnostic accuracy and treatment optimization. Transcranial magnetic stimulation combined with electroencephalography (TMS-EEG) has emerged as a powerful methodology to probe causal brain dynamics with high temporal resolution. This review aims to summarize recent advances in the application of TMS-EEG to MDD, highlighting the transition from traditional TMS-evoked potential (TEP) analyses to more advanced, multidimensional approaches. We reviewed original research articles published between 2020 and 2025 that investigated neurophysiological markers and approaches to MDD using TMS-EEG. Traditional TEP measures provide markers of local cortical responses but are limited in capturing distributed network dysfunction. Emerging approaches expand the scope of TMS-EEG, allowing for the characterization of oscillatory activity, connectivity patterns, and large-scale network dynamics. Recent contributions also demonstrate the potential of computational and multivariate techniques to enhance biomarker sensitivity and predictive value. Taken together, recent evidence highlights TMS-EEG as a uniquely positioned methodology to investigate the neurophysiological substrates of MDD. By linking conventional TEP-based indices with innovative analytic strategies, TMS-EEG enables a multidimensional assessment of cortical function and dysfunction that transcends traditional descriptive markers. This integrative perspective not only refines mechanistic models of MDD but also opens new avenues for biomarker discovery, patient stratification, and treatment monitoring. Ultimately, the convergence of advanced TMS-EEG approaches with clinical applications holds promise for translating neurophysiological insights into precision psychiatry interventions aimed at improving outcomes in MDD. Full article

Background: Monitoring brain activity through electroencephalography (EEG) has led to significant advancements in the study of brain microstates and their relationship with cognitive processes, such as memory. Objective: A systematic literature review was conducted following the PRISMA methodology, with the aim of identifying and analyzing potential biomarkers of memory functions derived from EEG microstate analysis. Methods: Searches were performed in five major databases (PubMed, Scopus, Web of Science, Springer, and institutional registers), covering studies published between 2019 and 2024. The initial search retrieved 179 records; after removing duplicates and ineligible works, 18 full-text articles were evaluated. Finally, 10 original studies met the inclusion criteria. Although primarily focused on other pathologies or baseline conditions, these studies reported relevant findings related to memory processes. This allowed for an exploratory synthesis of the potential role of EEG microstates as indirect biomarkers of memory. Results: The findings revealed that microstates, particularly microstates C and D, show significant alterations in their duration, coverage, and occurrence in various pathologies, such as Alzheimer’s disease, schizophrenia, and attention disorders, highlighting their potential as noninvasive biomarkers. Conclusions: Although methodological variability across studies represents a limitation, this review provides a solid foundation for future research aimed at standardizing the use of EEG microstates in clinical applications, improving diagnostic accuracy in memory-related diseases. Overall, EEG microstates hold great promise in both neuroscientific research and clinical practice. Full article

Background: Glaucoma is the leading cause of irreversible blindness around the world and is characterized as a group of irreversible optic neuropathies with multiple risk factors such as age, race/ethnicity, sex, and intraocular pressure (IOP), amongst many others that play a role in disease etiology. However, IOP is the only modifiable risk factor, with higher IOP often causing increased damage to the optic nerve, resulting in the vast majority of medical and surgical treatments aiming to reduce IOP. There are a number of interventions available to treat glaucoma including micro-invasive glaucoma surgery (MIGS), whose usage has drastically increased due to its safety and efficacy. Studies also highlight the IOP-reducing effect of cataract surgery, which is the most common procedure performed globally. However, other, more targeted therapies and surgeries have been shown to have a more significant effect on IOP reduction. The objective of this study is to compare the IOP and medication reduction between cataract surgery (CS), MIGS, and MIGS and cataract surgery (MACS) clinical trials. Methods: This analysis consisted of publicly available data on CS, MIGS, and MACS clinical trials from 2005 to 2017 using ClinicalTrials.gov. Data reporting and synthesis adhered to PRISMA guidelines. MIGS interventions studied in this analysis include iStent^®, CyPass^® Micro-Stent, Ex-PRESS^®, Hydrus^®, PRESERFLO^™ MicroShunt, and XEN^® Gel Stent. The main variables of interest are the mean IOP and mean number of glaucoma medications used. The primary outcomes were the baseline, post-procedure, and reduction in IOP and glaucoma medication use. Cohorts were further subdivided by the follow-up period (6, 12, and 24 months), as well as their medicated or unmedicated status for pre-op IOP measurement. PROSPERO CRD42025102892. Results: A total of 21 trials were included in this review, comprising 3330 clinical trial participants: 7 CS trials (N = 570), 13 MIGS trials (N = 1577), and 9 MACS trials (N = 1183). All interventions studied resulted in a decrease in both the IOP and medication usage with varying degrees. At 12 months, the wash-out baseline IOP reduction (mmHg) was 6.9 (27.5%) for CS, 8.8 (34.0%) for MIGS, and 8.2 (32.6%) for MACS. The medication reduction was 0.8 (56.1%) following CS, 1.0 (39.5%) for MIGS, and 1.3 (86.4%) for MACS. At 24 months, the wash-out baseline IOP reduction was 6.3 (25.1%) for CS, 8.4 (33.1%) for MIGS, and 7.6 (30.1%) for MACS. At 24 months, the medication reduction was 0.9 (58.3%) for CS, 1.5 (79.8%) for MIGS, and 1.3 (86.1%) for MACS. Conclusions: The results indicate that CS, MIGS, and MACS all result in a decrease in the IOP and glaucoma medications; however, MIGS and MACS outperform CS in IOP and medication reduction. Adopting MIGS and MACS for patients with ocular hypertension or mild-to-moderate glaucoma will help improve patient outcomes through reducing the IOP and medication burden. Given that glaucoma affects certain populations to a greater degree, future research analyzing racial representation is critical in ensuring the appropriate applicability of clinical trial results toward diverse populations. Full article

Multiple sclerosis (MS) is a chronic neurological disease often resulting in motor and autonomic dysfunction. This case report investigates the acute and subacute effects of the EXOPULSE Mollii Suit (EMS), a wearable device capable of delivering transcutaneous electrical nerve stimulation to multiple anatomical regions, in a 43-year-old woman with MS. The patient underwent a clinical evaluation before the EMS treatment, during which central nervous system (CNS) and autonomic nervous system (ANS) responses were monitored using electroencephalography (EEG), heart rate variability (HRV), and infrared thermography (IRT). Immediately after the first EMS application, the clinical evaluation was repeated. The intervention continued at home for one month, followed by a post-treatment evaluation similar to the pre-intervention assessment. Functional evaluations showed improvements in sit-to-stand performance (from 8 s to 6 s), muscle tone (MAS scale for the right side from 3 to 2 and for the left side from 2 to 1), clonus, and spasticity (from 3 to 2). EEG results revealed decreased θ-band power (on average, from 0.394 to 0.253) and microstates’ reorganization. ANS activity modifications were highlighted by both HRV (e.g., RMSSD from 0.118 to 0.0837) and IRT metrics (e.g., nose tip temperature sample entropy from 0.090 to 0.239). This study provides the first integrated analysis of CNS and ANS responses to EMS in an MS patient, combining functional scales with multimodal instrumental measurements, emphasizing the possible advantages EMS for MS treatment. Although preliminary, these results demonstrated the potentiality of the EMS to deliver effective and personalized rehabilitative interventions for MS patients. Full article

The sustainability of regional development in Europe is deeply influenced by heterogeneous globalization processes, yet the divergent long-term trajectories of these processes remain poorly quantified, hindering the design of targeted policies. This study aims to identify and characterize clusters of European countries with similar patterns of overall globalization development in order to assess implications for sustainable and cohesive growth. A novel clustering algorithm is developed that integrates Dynamic Time Warping with k-means to account for temporal misalignments and capture similarities in development dynamics rather than just static levels. Analysis based on the KOF Globalization Index for 40 countries reveals four distinct clusters: highly globalized and stable Western European economies, converging Central and Eastern European countries, microstates with niche integration models, and a peripheral group of Southeastern European nations facing significant challenges. The results demonstrate a persistent core–periphery divergence in globalization paths across Europe. This divergence presents a major obstacle to achieving territorial cohesion and equitable sustainable development outcomes. Methodologically, this study provides a robust framework for analyzing longitudinal socioeconomic processes. The main conclusion is that a one-size-fits-all EU cohesion policy is insufficient; instead, cluster-specific strategies are necessary in order to mitigate regional inequalities, enhance resilience, and ensure that the benefits of globalization contribute to the goals of sustainable development. The findings offer a quantitative basis for such targeted policy interventions. Full article

Background: In recent years, complexity analysis has attracted considerable attention in the field of neural mechanism exploration due to its nonlinear characteristics, providing a new perspective for revealing the complex information processing mechanisms of the brain. In precision sports such as shooting, complexity analysis can quantify the complexity of activity in different areas of the brain and dynamic changes. Methods: This study extracted multiple complexity indicators based on microstate and traceability analysis and examined brain complexity during the shooting preparation stage and the brain’s reaction mechanisms under audiovisual limitations. Results: Microstate Lempel-Ziv complexity and microstate fluctuation complexity in low-light environment were significantly higher than those in normal environment. The complexity of the brain increases and then decreases during shooting. In low-light conditions, nine brain regions—insula R’, posterior cingulate R’, entorhinal, superior frontal L’, caudal anterior cingulate L’, rostral anterior cingulate L’, posterior cingulate R’, medial orbitofrontal L’ and rostral middle frontal R’—exhibited differential results. SSV-R_PHC-COG and SSV-R_LOF-SCORE showed strong negative correlations with behavioral indicators. Conclusions: First, during shooting, the processing of visual information mainly relies on the secondary cortex and visual connection functions, rather than the primary cortex. Furthermore, there are automated processes based on experience in shooting sports. Second, noise has little effect on shooting, but low light has a multifaceted impact on shooting. This is mainly reflected in difficulties in integrating sensorimotor information, excessive memory retrieval, reduced movement stability, triggering of negative emotions, and changes in shooting strategies. Full article

Background/Objectives: Emerging evidence suggests that individuals with autism spectrum disorder (ASD) exhibit altered neural connectivity and disrupted brain network dynamics, which can be captured through EEG microstate analysis. Most research to date has focused on older children, adolescents, or adults with ASD, while studies focusing on preschool-aged children with ASD remain limited. Given that early brain development is critical for understanding the onset and progression of ASD, more research targeting this age group is essential. Methods: In this study, resting EEG data were collected from 59 preschool-aged children with ASD and 59 typically developing (TD) participants. Results: The results revealed a reduction in global explained variance and coverage of microstate in children with ASD, indicating poorer social performance that was independent of alpha power after the removal of the 1/f-like aperiodic signal. These findings reflect the social symptoms commonly observed in ASD. Additionally, alpha power was found to modulate the occurrence and duration of microstates in both groups. Conclusions: Our findings highlight that atypical microstates can serve as reliable biomarkers for ASD, offering valuable insights into the neurophysiological mechanisms underlying the disorder and paving the way for future research directions. Full article

Objective: Sleep deprivation negatively affects mood and sleepiness across subjective, objective, behavioral, and neuroimaging measures. However, the relationship between abnormal brain dynamics after sleep deprivation and mood or sleepiness impairments, from a temporal perspective, remains underexplored. Electroencephalogram microstate analysis offers a valuable approach for investigating the large-scale dynamics of brain networks. Methods: We implemented a strict sleep deprivation protocol with 71 participants, collecting resting-state electroencephalogram data, subjective sleepiness, objective alertness, and mood assessments after normal sleep and sleep deprivation (SD) conditions. Microstate time parameters were compared between sleep control (SC) and SD conditions. Additionally, we investigated how changes in these parameters correlated with subjective or objective measures of sleepiness and mood changes between SC and SD. Results: SD significantly decreased the mean duration and occurrence of microstate B while increasing those of microstate C. A decrease in microstate B occurrence significantly correlated with a higher Karolinska Sleepiness Scale score, whereas a reduction in microstate B duration indicated an increased response time on the Psychomotor Vigilance Performance. No significant associations were observed between microstate parameters and positive mood decline between SC and SD. Pearson correlation analysis was performed on the positive mood scores in both conditions. The findings demonstrated a significant positive relationship between positive mood scores and the mean duration of microstate B under SD conditions. Conclusions: Using a large SD dataset, this study demonstrated that subjective or objective sleepiness and positive mood were associated with decreased microstate B. These findings suggest that SD disrupts neural dynamics within the visual network. Full article

Background:Working memory (WM) is a core cognitive function closely linked to various cognitive processes including language, decision making, and reasoning. Transcranial alternating current stimulation (tACS), a non-invasive brain stimulation technique, has been shown to modulate cognitive abilities and treat psychiatric disorders. Although gamma tACS ($\gamma$-tACS) has demonstrated positive effects on WM, its underlying neural mechanisms remain unclear. Methods: In this study, we employed electroencephalogram (EEG) microstate analysis to investigate the spatiotemporal dynamics of $\gamma$-tACS effects on WM performance. Healthy participants (N = 104) participated in two-back and three-back WM tasks before and after two types (sine and triangular) of $\gamma$-tACS, with sham stimulation as a control. Results: Our results revealed that $\gamma$-tACS improved performance in both the two-back and three-back tasks, with triangular $\gamma$-tACS showing greater accuracy improvement in the three-back task than the sham group. Furthermore, $\gamma$-tACS significantly modulated EEG microstate dynamics, specifically downregulating microstate Class C and upregulating microstate Classes D and B. These changes were positively correlated with reduced reaction times in the three-back task. Conclusions: Our findings establish microstate analysis as an effective approach for evaluating $\gamma$-tACS-induced changes in global brain activity and advance the understanding of how $\gamma$-tACS influences WM. Full article

Background/Objectives: Precise stepping control is fundamental to human mobility, and impairments increase fall risk in older adults and individuals with neurological conditions. This study investigated the cortical networks underlying stepping accuracy using mobile brain/body imaging with electroencephalography (EEG)-based exact low-resolution electromagnetic tomography-independent component analysis (eLORETA-ICA) and microstate segmentation analysis (MSA). Methods: Sixteen healthy male participants performed a precision stepping task while wearing a mobile EEG system. Step performance was quantified using error distance, measuring deviation between target and heel contact points. Preprocessed EEG data were analyzed using eLORETA-ICA and MSA, with participants categorized into high- and low-performing groups. Results: Seven microstate clusters were identified, with the anterior cingulate cortex (ACC) showing the highest microstate probability (21.15%). The high-performing group exhibited amplified theta-band activity in the ACC, enhanced activity in the precuneus and postcentral gyrus, and suppressed mu- and beta-band activity in the paracentral lobules. Conclusions: Stepping accuracy relies on a distributed neural network, with the ACC playing a central role in performance monitoring. We propose an integrated framework comprising the following systems: error monitoring (ACC), sensorimotor integration (paracentral lobules), and visuospatial processing (precuneus and occipital regions). These findings highlight the importance of neural oscillatory mechanisms in precise motor control and offer insights for rehabilitation strategies and fall prevention programs. Full article

Variations in stratospheric atmospheric circulation significantly impact tropospheric weather and climate. Understanding these variations not only aids in better prediction of tropospheric weather and climate but also provides guidance for the development and flight trajectories of stratospheric aircraft. Our understanding of the stratosphere has made remarkable progress over the past 100 years. However, we still lack a comprehensive perspective on large-scale patterns in stratospheric circulation, as the stratosphere is a typical complex system. To address this gap, we employed the eigen microstate approach (EMA) to revisit the characteristics of zonal wind from 70–10 hPa from 1980 to 2022, based on ERA5 reanalysis data. Our analysis focused on the three leading modes, corresponding to variations in the strength of the quasi-biennial oscillation (QBO) and the stratospheric atmospheric circulations in the Arctic and Antarctic, respectively. After filtering out high-frequency components from the temporal evolutions of these modes, a significant 11-year cycle was observed in the Antarctic stratospheric atmospheric circulation mode, potentially linked to the 11-year solar cycle. In contrast, the Arctic stratospheric atmospheric circulation mode showed a 5–6-year cycle without evidence of an 11-year periodicity. This difference is likely due to the timing of polar vortex breakdowns: the Antarctic polar vortex breaks up later, experiencing its greatest variability in late spring and early summer, making it more susceptible to solar radiation effects, unlike the Arctic polar vortex, which peaks in winter and early spring. The fourth mode exhibits characteristics of a Southern Hemisphere dipole and shows a significant correlation with the Antarctic stratospheric atmospheric circulation mode, leading it by about two months. We designed a linear prediction model that successfully demonstrated its predictive capability for the Antarctic polar vortex. Full article

In the context of managing museums, historical, artistic, and archaeological heritage, an advanced decision support system (DSS) can serve as the engine for a business game platform, optimizing decision paths and management strategies. In complex, multi-parameter scenarios, the final decision is often only part of the process; it is equally essential to follow the decision-making path, that is, the sequence of actions necessary to reach the objective. The DSS presented here simplifies the problem by transforming the initial n-dimensional space, defined by the critical success factors (CSFs) selected by experts, into a two-dimensional space. Indeed, thanks to this approach, the computational complexity is reduced to the point that the technological solution can be used even on standard desktop computers and not only on high-performance computing systems. Moreover, the user does not necessarily need to be an IT expert but rather a specialist in the cultural domain. Through grid-based motion algorithms and a hierarchy of CSF priorities, the system quickly identifies optimal solutions in the 2D plane and then maps them back to the n-dimensional space to maintain consistency with the original context. Since the correspondence between n-dimensional micro-states and two-dimensional macro-states is not one-to-one, the DSS returns the specific micro-state of interest from the optimal macro-state, selecting the most effective path. This research aims to develop algorithms that by minimizing entropy and optimizing the system’s dynamics, build optimal paths in the 2D plane, with algorithms capable of restoring the solution in the initial space. Several use cases in the form of business games have been conducted, demonstrating the value of the proposed solution. The result of this work is a simulation environment useful for museum experts to analyze the impact of their management strategies. Thanks to the ability to assign weights to each of the critical success factors (CSFs), the system can display both qualitative and quantitative simulations of museum dynamics as the weights associated with different CSFs vary. Given the system’s generality, it is applicable to various fields where complex business games are required, such as cultural heritage management, logistics, transportation, healthcare systems, and, more broadly, any context where strategic business analysis is needed for the economic enhancement of resources and their optimization. Full article

Food addiction is associated with attention bias and response inhibition deficits, while the relationship between these two domains is unclear. Participants with body mass index (BMI) ≥ 25 and exhibiting food addiction behaviors (FA group, n = 20) were compared with healthy controls (HC group, n = 23). We examined attention-inhibition mechanisms using resting EEG microstate analysis, food-cue-evoked event-related potentials (ERPs), and non-food Go/No-Go tasks. Overweight individuals with food addiction behaviors demonstrated attentional deficits, as indicated by abnormalities in microstate D and the P100 component. Importantly, both microstate D and the P100 component significantly predicted No-Go performance, linking neurophysiological markers to behavioral inhibition. This study suggests that attention bias may be an important interaction factor of response inhibition, providing novel mechanistic insights into food addiction. Full article

The functional significance of RSNs is examined via simultaneous EEG-fMRI studies on the basis of the relation of RSNs with different frequency bands of EEG and EEG-based microstate analysis. In this study, we try to identify RSNs from microstates of cortical surface maps of the BOLD signal. In addition, the scale-free dynamics of these map sequences were also examined. The structural and resting state functional MRI images were acquired on a 3T scanner with three different fMRI acquisition protocols from seven subjects. Microstate segmentations from EEG, fMRI, and simulated data were evaluated. Wavelet-based fractal analysis was performed on map sequence time series and the Hurst exponent (H) was calculated. By using HRF-deconvolved fMRI time series, the effect of the HRF (hemodynamic response function) on fMRI-derived microstates was tested. The fMRI map sequence has a system with a memory system smaller than 16 s. When the HRF was deconvolved, the duration of the memory of the system was reduced to 4 s. On the other hand, the results of simulation data indicated that these systems are specific to the resting state BOLD signal. Similar to EEG microstates, fMRI also has microstates and both of them have scale-free dynamics. fMRI microstate dynamics have two different components, one is related to the HRF and the other is independent of the HRF. The significance of fMRI microstates and their relation with RSNs need to be further studied. Full article