Search Results (3,041)

The incidence of cognitive disorders is increasing globally, with a reported prevalence of over 50 million individuals affected, and current interventions offer limited efficacy. This study investigates the effects of sunflower oil fortified with sunflower lecithin, vitamin D, and vitamin A on scopolamine-induced cognitive dysfunction in mice and explores the underlying mechanisms. The incidence of cognitive disorders, such as Alzheimer’s disease, is increasing yearly, and current interventions offer limited efficacy. Therefore, this research aims to evaluate the cognitive improvement effects of the three added functional factors on mice with learning and memory impairments, along with the associated molecular mechanisms. Behavioral tests, biochemical assays, and real-time quantitative polymerase chain reaction (RT-qPCR) were utilized to examine the intervention effects of these functional factors on scopolamine-induced cognitive impairment in mice. The results revealed that the groups treated with sunflower lecithin and vitamin D significantly enhanced the mice’s exploratory behavior, working memory, and spatial memory, with increases of 1.6 times and 4.5 times, respectively, in the open field and novel object recognition tests (VD group). Additionally, these treatments reduced levels of inflammatory markers and IL-6, increased antioxidant GSH levels, and decreased oxidative stress marker MDA levels, with all effects showing significant differences (p < 0.01). The effects were further enhanced when vitamin A was combined with these treatments. Transcriptomic analysis demonstrated that the intervention groups had markedly improved learning and memory abilities through upregulation of key gene expression levels in the PI3K-AKT signaling pathway, cholinergic pathway, and folate biosynthesis pathway. These findings provide a theoretical basis for the development of nutritionally fortified edible oils with added sunflower lecithin, vitamin D, and vitamin A, which may help prevent and ameliorate cognitive disorders. Full article

Purpose: Over the past decade, tele-neurorehabilitation (TNR) has emerged as a vital and effective tool for delivering continuous care to stroke patients, playing a key role in enhancing functional recovery and ensuring consistent access to rehabilitation services. In the field of TNR, various protocols are utilized to ensure effective cognitive stimulation at home. Recent preliminary studies highlight the employment of multidomain cognitive interventions, which would seem to induce more stable and relevant cognitive recovery in stroke patients. A randomized controlled trial (RCT) study was conducted to compare the effectiveness of a TNR multidomain cognitive approach to conventional face-to-face cognitive treatment. Methods: A total of 30 patients with stroke were equally enrolled and randomly assigned to the experimental and control groups. In the experimental group, patients received sessions of home-based cognitive virtual reality rehabilitation system (VRRS) training. The control group underwent traditional face-to-face cognitive multidomain treatment at the hospital. The therapy was given for one hour every day for four weeks in both groups. Specific cognitive domains, including memory, praxis skills, executive functions, and speech therapy, were stimulated in the procedure. Neuropsychological evaluations were performed at three timepoints: at baseline (T0), at the end of TNR (T1), and six months later (T2). Results: The TNR group demonstrated significant improvements in working memory and language abilities, as well as in depressive symptoms and caregiver burden, with an average decrease of 2.07. Most of this improvement persisted 6 months after treatment. The group that received face-to-face cognitive treatment showed improvements (not persisting at T2) after treatment in a task measuring constructive apraxia and alternating attention with the cognitive skill of set-shifting. Conclusions: According to our findings, multidomain cognitive TNR may be useful in enhancing cognitive outcomes in stroke populations (even six months after treatment concludes). TNR may also be a viable way to deliver these interventions since it boosts people’s motivation to train and, consequently, their adherence to treatment while also having a positive effect on caregivers’ distress management. Full article

Background: The excessive accumulation of Aβ plays a critical role in the development of Alzheimer’s disease. However, the therapeutic potential of drugs like curcumin is often limited by low biocompatibility and BBB permeability. In this study, we developed a nanomaterial, BP-PEG-Tar@Cur, which was designed to enhance the biocompatibility of (curcumin) Cur, target Aβ, and augment BBB permeability through near-infrared (NIR) photothermal effects. Methods: Soluble Aβ, ThT fluorescence, and Aβ depolymerization fluorescence experiments were conducted to evaluate the ability of BP-PEG-Tar@Cur to inhibit Aβ aggregation and dissociate Aβ fibrils. Cell uptake assays were performed to confirm the targeting ability of BP-PEG-Tar@Cur towards Aβ. In vitro mitochondrial ROS clearance and in vivo detection of inflammatory factors were used to assess the anti-inflammatory and antioxidant properties of the nanodrug. Water maze behavioral experiments were conducted to evaluate the effect of BP-PEG-Tar@Cur on spatial memory, learning ability, and behavioral disorders in AD mice. Results: The nanodrug effectively inhibited Aβ aggregation and dissociated Aβ fibrils in vitro. BP-PEG-Tar@Cur demonstrated efficiency in curbing ROS overproduction in mitochondria and dampening the activation of microglia and astrocytes triggered by Aβ aggregation. Water maze behavioral experiments revealed that BP-PEG-Tar@Cur enhanced spatial memory, learning ability, and alleviated behavioral disorders in AD mice. Conclusions: Collectively, these findings demonstrate that BP-PEG-Tar@Cur has the potential to be an effective targeted drug for inhibiting Aβ aggregation and improving cognitive impairment in AD mice. Full article

Despite progress in understanding the link between executive functions (EFs) (i.e., a set of skills involved in goal-directed activities crucial for regulating thoughts and actions) and word decoding skills, research has not yet determined the dynamics and extent of this link. This longitudinal study examined whether EF subcomponents (inhibition, working memory, and cognitive flexibility) significantly predict decoding skills in Portuguese, which has a semitransparent orthography. The sample included 81 children (M_age = 5.36 years, SD_age = 0.30; 40 girls) in their final year of kindergarten. EF performance was evaluated during kindergarten using nonverbal performance-based tests, and decoding skills were assessed one year later in first grade through a pseudoword reading task. A three-step regression analysis was used to explore the unique contributions of each EF subcomponent to decoding skills. Findings indicated that inhibitory control skills were significant predictors of first-grade decoding outcomes. However, adding working memory abilities to the regression model did not increase the explained variance. In the final step, including cognitive flexibility skills reduced the significance of inhibitory control and increased the amount of explained variance. These results suggest that, while inhibitory control plays an important role, cognitive flexibility has a more significant impact on word decoding skills, highlighting the importance of early development of specific EFs for decoding abilities. Full article

A primary purpose of sleep for humans is to remove toxins and metabolic wastes from the brain (e.g., Aβ, tau, lactate) that would otherwise build up and compromise brain functionality. There are currently no drugs or devices that have been clinically shown in humans to enhance brain toxin removal, either during sleep or wakefulness. This perspective article focuses on a recently (re)discovered major route of toxin drainage from the human brain through meningeal lymphatic vessels (mLVs) and the primary enhancer of their flow—the cytokine Vascular Endothelial Growth Factor (VEGF). The purpose of this perspective article is to present pre-clinical and clinical evidence relevant to a new bioengineered technology (Transcranial Radiofrequency Treatment; TRFT) that appears to enhance mLV flow to increase brain toxin cleansing in humans during wakefulness. In being both safe and non-invasive, TRFT is administered in-home, presently through a device called “MemorEM”. Two months of daily TRFT during wakefulness increased the typically low plasma/brain levels of VEGF in Alzheimer’s Disease (AD) subjects, which was associated with increased Aβ and tau toxin removal from their brains during wakefulness—ostensibly through VEGF-increased mLV flow. Even irrespective of baseline VEGF levels, brain toxin cleansing was increased by TRFT in AD subjects, who also experienced a notable reversal of their cognitive impairment after TRFT. Additional clinical studies are nonetheless required to firmly establish TRFT’s brain cleansing abilities during wakefulness. In performing a major duty of sleep, TRFT during wakefulness is proposed as a viable intervention to counter the decline in nighttime brain toxin cleansing that occurs with aging and in multiple brain diseases, most notably Alzheimer’s Disease. The implications of TRFT for insomnia and for sleep deprivation are also discussed, as is the potential for TRFT to extend healthy human longevity. Full article

Background/Objectives: Developmental language disorders (DLD) are often associated with co-occurring neurodevelopmental difficulties, including attentional or social–emotional problems. Another nonverbal domain, i.e., visual cognition and its relationship to DLD, is virtually unexplored. However, learning visuospatial regularities – a scene-grammar - is crucial for navigating our daily environment. These regularities show certain similarities to the structure of language and there is preliminary evidence for a relationship between scene processing and language competence in preschoolers with and without DLD. This study compared implicit and explicit visuospatial knowledge of everyday indoor scenes in older children, aged 6 to 10 years, of both groups. Methods: We measured ‘dwell times’ on semantic and syntactic object – scene inconsistencies via eye-tracking and performance in an object-placement task, and their associations with children’s language, visual, and cognitive skills. Results: Visual attention towards object-scene inconsistencies was highly comparable between groups, but children with DLD scored lower in a visual perception test and higher language skills were associated with higher visuo-cognitive performance in both tasks. In the explicit scene-grammar measurement, this relationship only existed for children with DLD and disappeared when nonverbal cognitive performance was controlled. Conclusions: Our study suggests the existence of mild problems in visuospatial processing co-occurring with DLD, which is partly influenced by age and nonverbal cognitive ability. The acquisition of visual cognition and linguistic knowledge is an interactive, multimodal process where the perception of objects in scenes might affect how the words for these objects are learned and vice versa. A better understanding of this interplay could eventually have impact on the diagnosis and treatment of DLD. Full article

The present study aims to investigate whether there are different second language trajectories among immigrant children and what influences these trajectories. This longitudinal study included 443 children all learning German as a second language (49.9% girls). On average, at T1, the children were 3;6; at T2, 4;9; at T3, 6;2; and at T4, 7;3 years old. Discontinuous piecewise latent class growth analysis revealed four classes: improvement to low-level proficiency, improvement to medium-level proficiency, improvement to high-level proficiency, and permanent high-level proficiency. Class membership was predicted by early childcare attendance, the duration of the parents’ residence in Switzerland, contact with German speakers, the child’s cognitive abilities, and the parental socioeconomic status. Full article

Background/Objectives: The literature highlights the importance of parental involvement in autism treatment. However, much research has predominantly focused on child outcomes and cognitive dimensions. This study explores the impact of an early intensive intervention with parental involvement, focusing on changes in parents’ affective exchanges. Notably, given the paucity of studies on fathers in the intervention context, this study examines the comparative trajectory of change considering both caregivers. Methods: Twenty autistic preschoolers were monitored for one year during a parental-based intervention. Child–mother and child–father play interactions were coded with the Emotional Availability Scales at baseline and at 12 months. Repeated measures linear mixed-effect models were employed to investigate time and caregiver effects and their interaction. Results: Results highlighted both similarities and differences in change trajectories between caregivers. Parental sensitivity, structuring, and non-intrusiveness significantly increased for both parents with fathers showing more prominent gains in structuring the interaction while being non-intrusive. Child responsiveness and involvement significantly increased, showing similar trajectories with both caregivers. Children were generally more involved while interacting with their fathers. Conclusion: Parent–child interactions with caregivers evolved toward more adaptive exchanges regarding emotional availability for children’s and parents’ dimensions. Fathers appeared to be particularly receptive regarding acquiring structuring abilities and non-intrusive behaviors. Our results underscore the importance of investigating parental features as well as the importance of actively involving caregivers to support distal outcomes and generalization. Full article

Identifying and diagnosing cognitive impairment remains challenging. Some diagnostic procedures are invasive, expensive, and not always accurate. Meanwhile, evidence suggests that cognitive impairment is associated with changes in gait parameters. Certain gait parameters manifesting differences between people with and without cognitive impairment are more pronounced when adding a secondary task (dual-task scenario). In this systematic review, the capability of gait analysis to identify cognitive impairment is investigated. Twenty-three studies published between 2014 and 2024 met the inclusion criteria. A significantly lower gait speed and cadence as well as higher gait variability were found in people with mild cognitive impairment (MCI) and/or dementia, compared with the group with no cognitive impairment. While dual tasks appeared to amplify the differences between the two populations, the type of secondary tasks (e.g., calculations and recalling phone numbers) had an effect on gait changes. The activity and volume of different brain regions were also different between the two populations during walking. In conclusion, while this systematic review supported the potential of using gait analysis to identify cognitive impairment, there are a number of parameters researchers need to consider such as gait variables to be studied, types of dual tasks, and analysis of brain changes while performing the movement tasks. Full article

Purpose: Sex classification is a major benchmark of previous work in learning on the structural connectome, a naturally occurring brain graph that has proven useful for studying cognitive function and impairment. While graph neural networks (GNNs), specifically graph convolutional networks (GCNs), have gained popularity lately for their effectiveness in learning on graph data, achieving strong performance in adult sex classification tasks, their application to pediatric populations remains unexplored. We seek to characterize the capacity for GNN models to learn connectomic patterns on pediatric data through an exploration of training techniques and architectural design choices. Methods: Two datasets comprising an adult BRIGHT dataset (N = 147 Hodgkin’s lymphoma survivors and N = 162 age similar controls) and a pediatric Human Connectome Project in Development (HCP-D) dataset (N = 135 healthy subjects) were utilized. Two GNN models (GCN simple and GCN residual), a deep neural network (multi-layer perceptron), and two standard machine learning models (random forest and support vector machine) were trained. Architecture exploration experiments were conducted to evaluate the impact of network depth, pooling techniques, and skip connections on the ability of GNN models to capture connectomic patterns. Models were assessed across a range of metrics including accuracy, AUC score, and adversarial robustness. Results: GNNs outperformed other models across both populations. Notably, adult GNN models achieved 85.1% accuracy in sex classification on unseen adult participants, consistent with prior studies. The extension of the adult models to the pediatric dataset and training on the smaller pediatric dataset were sub-optimal in their performance. Using adult data to augment pediatric models, the best GNN achieved comparable accuracy across unseen pediatric (83.0%) and adult (81.3%) participants. Adversarial sensitivity experiments showed that the simple GCN remained the most robust to perturbations, followed by the multi-layer perceptron and the residual GCN. Conclusions: These findings underscore the potential of GNNs in advancing our understanding of sex-specific neurological development and disorders and highlight the importance of data augmentation in overcoming challenges associated with small pediatric datasets. Further, they highlight relevant tradeoffs in the design landscape of connectomic GNNs. For example, while the simpler GNN model tested exhibits marginally worse accuracy and AUC scores in comparison to the more complex residual GNN, it demonstrates a higher degree of adversarial robustness. Full article

Pediatric cancers, while rare, pose unique challenges due to the heightened sensitivity of developing tissues and the increased risk of long-term radiation-induced effects. Radiotherapy (RT) is a cornerstone in pediatric oncology, but its application is limited by concerns about toxicity, particularly secondary malignancies, growth abnormalities, and cognitive deficits. CyberKnife (CK), an advanced robotic radiosurgery system, has emerged as a promising alternative due to its precision, non-invasiveness, and ability to deliver hypofractionated, high-dose RT while sparing healthy tissues. This narrative review explores the existing evidence on CK application in pediatric patients, synthesizing data from case reports, small series, and larger cohort studies. All the studies analyzed reported cases of tumors located in the skull or in the head and neck region. Findings suggest CK’s potential for effective tumor control with favorable toxicity profiles, especially for complex or inoperable tumors. However, the evidence remains limited, with the majority of studies involving small sample sizes and short follow-up periods. Moreover, concerns about the “dose-bath” effect and limited long-term data on stochastic risks warrant cautious adoption. Compared to Linac-based RT and proton therapy, CK offers unique advantages in reducing session numbers and enhancing patient comfort, while its real-time tracking provides superior accuracy. Despite these advantages, CK is associated with significant limitations, including a higher potential for low-dose scatter (often referred to as the “dose-bath” effect), extended treatment times in some protocols, and high costs requiring specialized expertise for operation. Emerging modalities like π radiotherapy further underscore the need for comparative studies to identify the optimal technique for specific pediatric cases. Notably, proton therapy remains the benchmark for minimizing long-term toxicity, but its cost and availability limit its accessibility. This review emphasizes the need for balanced evaluations of CK and highlights the importance of planning prospective studies and long-term follow-ups to refine its role in pediatric oncology. A recent German initiative to establish a CK registry for pediatric CNS lesions holds significant promise for advancing evidence-based applications and optimizing treatment strategies in this vulnerable population. Full article

Under the trend of increasing global aging, this study is based on the theory of mental model to conduct a study on the design of living environments for demented elderly, which is dedicated to meeting the special needs of this special group of demented elderly, improving their quality of life and self-care ability, and reducing the dependence on social medical and nursing resources to achieve the goal of social sustainability. The field research method was used to observe and record the behavior of elderly people with dementia in their homes, which included 20 elderly people aged 60–94 years with mild dementia; the ratio of male to female is 1:1. The Kawakita Jiro (KJ) method is used to extract a mental model of elderly people with dementia, obtain their subjective needs for the home environment, and propose related design principles. The research indicates that the mental space of elderly people with dementia regarding the home environment is mainly composed of three parts: information perception, environmental cognition, and environmental shaping. Based on this mental model, design principles of the home environment of elderly people with dementia are designed, aiming to improve their living experience via environmental information perception design, the enhancement of environmental cognition, and the reduction in the difficulty of shaping the environment. Full article

Figurative language is a complex construct related to intelligence. Psychology and psycholinguistics are trying to understand it from an interdisciplinary perspective, but studies are still scarce, methodologies are heterogeneous, and results are difficult to integrate. Some studies suggest that understanding the cognitive processes underlying figurative language and its forms could provide a new approach to understanding intellectual differences, such as high intellectual ability (HIA), and new instruments to assess it. The language of HIA children develops earlier and includes the use of irony, which involves metalinguistic skills. In this context, the present study aims to offer an instrument, called the verbal irony questionnaire (or VIrQ), to test the comprehension of verbal irony in students with HIA. A convenience sample of n = 169 students with HIA, aged between 7 and 15 years, responded to the VIrQ. An exploratory factor analysis was conducted. The results revealed that 33 items were retained and categorized into four factors. F1, ironic irony (14 items); F2, ironic attitude (8 items); F3, ironic constructions (7 items); and F4, reinforced irony echo (4 items). All factors have adequate reliability indices above 0.70 and below 0.95. Finally, new perspectives are also discussed. Full article

Humanity stands at a pivotal moment of technological revolution, with artificial intelligence (AI) reshaping fields traditionally reliant on human cognitive abilities. This transition, driven by advancements in artificial neural networks, has transformed data processing and evaluation, creating opportunities for addressing complex and time-consuming tasks with AI solutions. Convolutional networks (CNNs) and the adoption of GPU technology have already revolutionized image recognition by enhancing computational efficiency and accuracy. In radiology, AI applications are particularly valuable for tasks involving pattern detection and classification; for example, AI tools have enhanced diagnostic accuracy and efficiency in detecting abnormalities across imaging modalities through automated feature extraction. Our analysis reveals that neuroimaging and chest imaging, as well as CT and MRI modalities, are the primary focus areas for AI products, reflecting their high clinical demand and complexity. AI tools are also used to target high-prevalence diseases, such as lung cancer, stroke, and breast cancer, underscoring AI’s alignment with impactful diagnostic needs. The regulatory landscape is a critical factor in AI product development, with the majority of products certified under the Medical Device Directive (MDD) and Medical Device Regulation (MDR) in Class IIa or Class I categories, indicating compliance with moderate-risk standards. A rapid increase in AI product development from 2017 to 2020, peaking in 2020 and followed by recent stabilization and saturation, was identified. In this work, the authors review the advancements in AI-based imaging applications, underscoring AI’s transformative potential for enhanced diagnostic support and focusing on the critical role of CNNs, regulatory challenges, and potential threats to human labor in the field of diagnostic imaging. Full article

Traditional screening methods for Mild Cognitive Impairment (MCI) face limitations in accessibility and scalability. To address this, we developed and validated a speech-based automatic screening app implementing three speech–language tasks with user-centered design and server–client architecture. The app integrates automated speech processing and SVM classifiers for MCI detection. Functionality validation included comparison with manual assessment and testing in real-world settings (n = 12), with user engagement evaluated separately (n = 22). The app showed comparable performance with manual assessment (F1 = 0.93 vs. 0.95) and maintained reliability in real-world settings (F1 = 0.86). Task engagement significantly influenced speech patterns: users rating tasks as “most interesting” produced more speech content (p < 0.05), though behavioral observations showed consistent cognitive processing across perception groups. User engagement analysis revealed high technology acceptance (86%) across educational backgrounds, with daily cognitive exercise habits significantly predicting task benefit perception (H = 9.385, p < 0.01). Notably, perceived task difficulty showed no significant correlation with cognitive performance (p = 0.119), suggesting the system’s accessibility to users of varying abilities. While preliminary, the mobile app demonstrated both robust assessment capabilities and sustained user engagement, suggesting the potential viability of widespread cognitive screening in the geriatric population. Full article