Brain Sciences最新文献

Background/objectives: There is a need in Korea for research estimating the impact of aging using the Useful Field Of View (UFOV) test, which can evaluate visual function for elderly drivers.

Methods: This observational study involved young people in their twenties and thirties, later-middle-aged people in their fifties or older, and elderly people 65 or older recruited from the Gangwon-do region. UFOV testing was conducted on the participants where the participants completed a questionnaire about general and driving-related characteristics. A one-way analysis of variance (ANOVA) was performed to analyze the mean difference by age group, and a Pearson correlation analysis was carried out to evaluate the correlation between age and visual function. In addition, a simple linear regression analysis was conducted to verify UFOV subdomains that can confirm changes according to age increasing.

Results: Findings after analyzing UFOV subtest differences by age group revealed significant differences in the visual function index of the young, later-middle-aged, and elderly in all three tests, and the difference between the later-middle-aged and old groups was only found in divided attention. The correlation between age and visual function was significant in all three subtests. And all three subtests were confirmed to be indicators that can verify changes according to increasing age.

Conclusions: This study showed that visual function significantly decreases with age. Selective attention was confirmed as a visual function type that changes sensitively according to increasing age.

Background: The segmentation of electroencephalography (EEG) signals into a limited number of microstates is of significant importance in the field of cognitive neuroscience. Currently, the microstate analysis algorithm based on global field power has demonstrated its efficacy in clustering resting-state EEG. The task-related EEG was extensively analyzed in the field of brain-computer interfaces (BCIs); however, its primary objective is classification rather than segmentation.

Methods: We propose an innovative algorithm for analyzing task-related EEG microstates based on spatial patterns, Riemannian distance, and a modified deep autoencoder. The objective of this algorithm is to achieve unsupervised segmentation and clustering of task-related EEG signals.

Results: The proposed algorithm was validated through experiments conducted on simulated EEG data and two publicly available cognitive task datasets. The evaluation results and statistical tests demonstrate its robustness and efficiency in clustering task-related EEG microstates.

Conclusions: The proposed unsupervised algorithm can autonomously discretize EEG signals into a finite number of microstates, thereby facilitating investigations into the temporal structures underlying cognitive processes.

Background/objectives: Cognitive training paradigms rely on the idea that consistent practice can drive neural plasticity, improving not only connectivity within critical brain networks, but also ultimately result in overall enhancement of trained cognitive functions, irrespective of the specific task. Here we opted to investigate the temporal dynamics of neural activity and cognitive performance during a structured cognitive training program.

Methods: A group of 20 middle-aged participants completed 20 training sessions over 10 weeks. Quantitative EEG (qEEG) parameters, including alpha and theta power, alpha/theta ratio, and fronto-parietal coherence, were analyzed at four time points to assess changes in neural activity.

Results: Results revealed significant overall improvements in the trained task (n-back) performance, without an effect on the untrained task (OSPAN). qEEG analyses showed increased change in posterior (and a less robust in frontal) alpha power, particularly during mid-training, suggesting an improved neural efficiency in regions associated with attentional allocation and task engagement. Theta power remained stable across sessions, indicating a limited influence on neural processes underlying working memory and attentional control. The parietal alpha/theta ratio showed weak increases during mid-training, reflecting subtle shifts in the neural efficacy and cognitive engagement. There were no significant changes in functional connectivity between frontal and parietal locations.

Conclusions: Our findings suggest that cognitive training primarily influences localized neural activity, rather than network-level connectivity. This lack of a longer-range network-level effect might also explain the failure of cognitive training paradigms to induce performance enhancements on the untrained tasks.

Background: The spontaneous fluctuations in functional magnetic resonance imaging (fMRI) signals of the brain's gray matter (GM) have been interpreted as representations of neural activity variations. In previous research, white matter (WM) signals, often considered noise, have also been demonstrated to reflect characteristics of functional activity and interactions among different brain regions. Recently, functional gradients have gained significant attention due to their success in characterizing the functional organization of the whole brain. However, previous studies on brain functional gradients have predominantly focused on GM, neglecting valuable functional information within WM.

Methods: In this paper, we have elucidated the symmetrical nature of the functional hierarchy in the left and right brain hemispheres in healthy individuals, utilizing the principal functional gradient of the whole-brain WM while also accounting for gender differences.

Results: Interestingly, both males and females exhibit a similar degree of asymmetry in their brain regions, albeit with distinct regional variations. Additionally, we have thoroughly examined and analyzed the distribution of functional gradient values in the spatial structure of the corpus callosum (CC) independently, revealing that a simple one-to-one correspondence between structure and function is absent. This phenomenon may be associated with the intricacy of their internal structural connectivity.

Conclusions: We suggest that the functional gradients within the WM regions offer a fresh perspective for investigating the structural and functional characteristics of WM and may provide insights into the regulation of neural activity between GM and WM.

Parkinson's disease is the second most common neurodegenerative disease worldwide, characterized by bradykinesia, rigidity, tremor, and postural instability. These symptoms often lead to significant postural deformities and an increased risk of falls, severely impacting the quality of life. Conventional rehabilitation methods have shown benefits, but recent advancements suggest that virtual reality (VR) could offer a promising alternative. This scoping review aims to analyze the current literature to evaluate the effectiveness of VR in the postural rehabilitation of patients with PD. A scientific literature search was performed using the following databases: PubMed, PEDro, Cochrane, and Google Scholar, focusing on randomized controlled trials (RCTs) published in English. Our selection criteria included studies that compared VR-based rehabilitation to traditional methods regarding posture-related outcomes. We identified and analyzed nine RCTs that met our inclusion criteria. The results consistently demonstrated that VR-based rehabilitation leads to greater improvements in balance and gait compared to conventional therapy. Key findings include significant enhancements in balance confidence and postural control and a reduction in fall rates. The superior efficacy of VR-based rehabilitation can be attributed to its engaging and immersive nature, which enhances patient motivation and adherence to therapy. VR allows for precise, repeatable training scenarios tailored to individual patient needs, providing a safe environment to practice and improve motor skills. In conclusion, VR-based rehabilitation represents an innovative approach with substantial potential to improve the quality of life for PD patients. However, limitations such as small sample sizes and short intervention durations in existing studies highlight the need for larger multicenter trials with longer follow-up periods to confirm these findings.

The interaction between Alzheimer's disease (AD) and sleep deprivation has recently gained attention in the scientific literature, and recent advances suggest that AD epidemiology management should coincide with the management of sleeping disorders. This review focuses on the aspects of the mechanisms underlying the link between AD and insufficient sleep with progressing age. We also provide information which could serve as evidence for future treatments of AD from the early stages in connection with sleep disorder medication.

Background/objectives: Negative symptoms in schizophrenia spectrum disorders are related to impaired social functioning and lower quality of life, making accurate assessment important. To date, most tools for assessing negative symptoms are observational, which can be influenced by the raters' experience and opinion. Self-rating scales, like the Self-Evaluation of Negative Symptoms (SNS), could complement observer ratings by adding information from the patient's perspective. Here, we aim to evaluate the psychometric properties of the Dutch translation of the SNS and the relationship between the SNS and functional outcomes.

Methods: The SNS was added to the Pharmacotherapy Monitoring Outcome Survey (PHAMOUS)-protocol for adults with a DSM-5 classification of a disorder in the psychosis spectrum. Internal consistency was assessed by Cronbach's alpha. Confirmatory factor analysis (CFA) was used to evaluate the construct validity of the five subscales of the SNS. Correlational analyses were performed between the SNS and the Positive and Negative Syndrome Scale (PANSS), the Health of Nation Outcomes Scales (HoNOS), the Global Assessment of Functioning (GAF), Functional Remission tool (FR) and the Manchester Short Assessment of Quality of Life (ManSA).

Results: A total of 247 patients participated in this study. Internal consistency was good (α = 0.87). CFA confirmed the five-factor structure of the SNS. The SNS was significantly correlated (all p < 0.001) with the PANSS positive (r = 0.31), PANSS negative (r = 0.33), HoNOS (r = 0.37), FR (r = 0.27) and the ManSA (r = -0.40).

Conclusions: The Dutch SNS shows good psychometric properties and is related to functional outcomes and quality of life. The SNS can be valuable in complementing current observational-based instruments, and future research may investigate whether the SNS can be used as a standalone measurement tool for the assessment of negative symptoms.

Background/Objectives: The Sound Sensitivity Symptoms Questionnaire version 2 (SSSQ2) is a brief clinical tool with six items designed to be used (1) as a measure for severity of sound sensitivity symptoms in general (based on its total score) and (2) as a checklist to screen different forms of sound sensitivity. The objective of this study was to assess the psychometric properties of the SSSQ2. Method: This was a cross-sectional study. A total of 451 people completed the online survey. A total of 154 people completed the survey twice with a two-week interval to establish test-retest reliability. The average age of the participants was 36.5 years (range 18 to 86 years). Results: Confirmatory factor analysis showed that the SSSQ2 is a one-factor questionnaire. Cronbach's α was 0.80. The test-retest reliability was good for the total SSSQ2 score and was moderate for the sum of items 1 and 3 (indicating loudness hyperacusis), item 2 (for pain hyperacusis), item 4 (for misophonia), item 5 (for fear hyperacusis), and item 6 (for noise sensitivity). The minimum amount of change that constitutes a true change in the total SSSQ2 score is ≥5 points. Conclusions: The SSSQ2 can be used in clinical practice or research setting to measure the severity of general sound sensitivity as a one-factor questionnaire with acceptable internal consistency and good reliability. In addition, the individual items in the SSSQ2 can be used as a checklist to screen for various forms of sound sensitivity.

Brain connectivity analysis plays a crucial role in unraveling the complex network dynamics of the human brain, providing insights into cognitive functions, behaviors, and neurological disorders. Traditional graph-theoretical methods, while foundational, often fall short in capturing the high-dimensional and dynamic nature of brain connectivity. Graph Neural Networks (GNNs) have recently emerged as a powerful approach for this purpose, with the potential to improve diagnostics, prognostics, and personalized interventions. This review examines recent studies leveraging GNNs in brain connectivity analysis, focusing on key methodological advancements in multimodal data integration, dynamic connectivity, and interpretability across various imaging modalities, including fMRI, MRI, DTI, PET, and EEG. Findings reveal that GNNs excel in modeling complex, non-linear connectivity patterns and enable the integration of multiple neuroimaging modalities to provide richer insights into both healthy and pathological brain networks. However, challenges remain, particularly in interpretability, data scarcity, and multimodal integration, limiting the full clinical utility of GNNs. Addressing these limitations through enhanced interpretability, optimized multimodal techniques, and expanded labeled datasets is crucial to fully harness the potential of GNNs for neuroscience research and clinical applications.

Background: Spatial working memory is crucial for processing visual and spatial information, serving as a foundation for complex cognitive tasks. However, the effects of prolonged sleep deprivation on its dynamics and underlying neural mechanisms remain unclear. This study aims to investigate the specific trends and neural mechanisms underlying spatial working memory alterations during 36 h of acute sleep deprivation.

Methods: Twenty participants underwent a 36 h protocol of acute sleep deprivation. Utilizing the spatial 2-back task for assessing spatial working memory, combined with event-related potential (ERP) technology, we compared behavioral and neural responses at four critical time points-before deprivation, and after 12, 24, and 36 h of sleep deprivation-to uncover dynamic cognitive changes.

Results: The findings indicate that the impact of sleep deprivation on spatial working memory exhibits significant temporal dependence. After 24 h of deprivation, both behavioral performance and ERP component amplitudes showed significant declines. During the period from 24 to 36 h, the amplitudes of the P2, N2, and P3 components exhibited a recovery trend, potentially reflecting neural compensatory mechanisms.

Conclusions: The impact of 36 h acute sleep deprivation on spatial working memory is characterized by time-dependent and phase-specific effects. Initially, sleep deprivation leads to severe cognitive depletion, followed by an adaptive compensatory phase where neural mechanisms may partially restore function. These findings highlight the non-linear nature of cognitive impairment due to sleep deprivation, involving complex self-regulatory and compensatory mechanisms, with implications for understanding cognitive resilience and adaptive processes.