Cognitive Behaviors Research Articles

Voluntary wheel exercise improves glymphatic clearance and ameliorates colitis-associated cognitive impairment in aged mice by inhibiting TRPV4-induced astrocytic calcium activity

Background and objectivesChronic colitis exacerbates neuroinflammation, contributing to cognitive impairment during aging, but the mechanism remains unclear. The polarity distribution of astrocytic aquaporin 4 (AQP4) is crucial for the glymphatic system, which is responsible for metabolite clearance in the brain. Physical exercise (PE) improves cognition in the aged. This study aims to investigate the protective mechanism of exercise in colitis-associated cognitive impairment. MethodsTo establish a chronic colitis model, 18-month-old C57BL/6 J female mice received periodic oral administration of 1% wt/vol dextran sodium sulfate (DSS) in drinking water. The mice in the exercise group received four weeks of voluntary wheel exercise. High-throughput sequencing was conducted to screen for differentially expressed genes. Two-photon imaging was performed to investigate the function of the astrocytic calcium activity and in vivo intervention with TRPV4 inhibitor HC-067047. Further, GSK1016790A (GSK1), a TRPV4 agonist, was daily intraperitoneally injected during the exercise period to study the involvement of TRPV4 in PE protection. Colitis pathology was confirmed by histopathology. The novel object recognition (NOR) test, Morris water maze test (MWM), and open field test were performed to measure colitis-induced cognition and anxiety-like behavior. In vivo two-photon imaging and ex vivo imaging of fluorescent CSF tracers to evaluate the function of the glymphatic system. Immunofluorescence staining was used to detect the Aβ deposition, polarity distribution of astrocytic AQP4, and astrocytic phenotype. Serum and brain levels of the inflammatory cytokines were tested by Enzyme-linked immunosorbent assay (ELISA). The brain TUNEL assay was used to assess DNA damage. Expression of critical molecules was detected using Western blotting. ResultsVoluntary exercise alleviates cognitive impairment and anxiety-like behavior in aged mice with chronic colitis, providing neuroprotection against neuronal damage and apoptosis. Additionally, voluntary exercise promotes the brain clearance of Aβ via increased glymphatic clearance. Mechanistically, exercise-induced beneficial effects may be attributed, in part, to the inhibition of TRPV4 expression and TRPV4-related calcium hyperactivity, subsequent promotion of AQP4 polarization, and modulation of astrocyte phenotype. ConclusionThe present study reveals a novel role of voluntary exercise in alleviating colitis-related cognitive impairment and anxiety disorder, which is mediated by the promotion of AQP4 polarization and glymphatic clearance of Aβ via inhibition of TRPV4-induced astrocytic calcium hyperactivity.

Machine Learning Analysis of Artistic Characteristics for Schizophrenia Classification

IntroductionSchizophrenia is affecting multiple functions such as cognition, perception, emotion, and social behaviors, and it has also been shown to influence artistic works created by patients. Among the deviations observed in the art works are distinct characteristics like delusional themes, disordered shorter lines, and unique creativity. Such features, along with altered pictorial perceptions and possibly altered motoric function, suggest that it might be possible to differentiate art made by schizophrenic patients from that of healthy individuals. Given the shortcomings of existing diagnostic methods being very long and with a 25% error rate, we proposed a novel neural network model that leverages these artistic markers for classification to support diagnosis.ObjectivesTo develop and train a neural network model leveraging unique artistic markers for the classification and support of diagnosing schizophrenia.MethodsOur study involved 764 participants, 45% diagnosed with schizophrenia, while the others were either healthy or diagnosed with other mental disorders. The average age of the participants was 38.25 years (SD=13.43), and 43.88% of the participants were females. All participants were instructed to draw eight drawings of human faces. These drawings were digitized and categorized based on participants’ schizophrenia status to form the initial training dataset for our model. This data was processed using Python and converted into a NumPy array, which served as input for our model developed using the Keras library. The structure of the model is presented (Image 1).ResultsWe used area under curve (AUC), specificity, and sensitivity as key evaluation metrics for our model. The model achieved an AUC of 0.90 on a test dataset that was new to the model and was not used in the preceding training phase. It exhibited a sensitivity of 0.84 and a specificity of 0.85, indicating its capacity to identify schizophrenic and non-schizophrenic individuals, respectively (Image 2).Image:Image 2:ConclusionsThe application of machine learning and AI tools to analyze art created by schizophrenia patients, can offer a promising methodology for exploring the differences between schizophrenic and healthy individuals, as well as a possible support for current diagnostic methods. This approach has the potential to provide an additional fast and more accurate diagnosis, enhancing individualized patient care. Future research will focus on refining and validating the model across diverse populations and various art forms.Disclosure of InterestNone Declared

354 Brain Structural Alterations in Metabolically Healthy and Unhealthy Obesity: A Quantitative Comparison Using Coordinate-Based Meta-Analysis

OBJECTIVES/GOALS: The primary research goal was to identify brain alterations reliably associated with obesity using coordinate-based meta-analysis. A secondary goal was to compare brain alterations in metabolically healthy (MHO) and unhealthy (MUO) obesity. METHODS/STUDY POPULATION: Source data were peer-reviewed studies reporting locations of gray-matter alterations in group-average, case-control contrasts (obese vs. non-obese) cohorts, performed in a whole-brain, voxel-wise manner. Both voxel-based morphometry and voxel-based physiology studies were included. Three coordinate-based meta-analyses were performed: Pooled (MUO + MHO), MHO, and MUO. RESULTS/ANTICIPATED RESULTS: Thirty-two studies reporting a total of 50 case-control contrasts (MHO, 23; MUO, 27) met inclusion criteria, representing 3,368 participants (obese, 1,781; non-obese, 1587). The pooled analysis yielded 8 cerebral foci (3 nuclear, 5 cortical) in regions implicated in reward-seeking, cognitive, and interoceptive behaviors. MHO yielded 7 cerebral foci (4 nuclear, 3 cortical), partially overlapping Pooled results, with similar behavioral loadings. The MUO pattern was distinct, with 3 cerebellar and 1 occipital foci. DISCUSSION/SIGNIFICANCE: Brain alterations occurred reliably in obesity. The dominant pattern (Pooled & MHO) involved cerebral reward-system circuits, evident even in metabolically healthy obesity. Cerebellar alterations occurred exclusively in metabolically unhealthy obesity, a pattern previously reported in metabolic syndrome.

549 A Nationwide Pilot Study Testing a Remotely-Delivered Prolonged Nightly Fasting Intervention in Stressed Midlife Adults Living with Obesity and Memory Decline

OBJECTIVES/GOALS: Cognitive decline is associated with obesity, stress, poor sleep, and circadian rhythm misalignment, which are themselves functionally intertwined. Irregular food intake timing exacerbates these all. Prolonged nightly fasting (PNF) aligns food intake with innate circadian rhythms. METHODS/STUDY POPULATION: A nationwide, remotely-delivered, 2-arm randomized controlled trial was conducted to assess feasibility and 8-week outcomes of cognition, stress, sleep, eating behaviors, and general eating habits, after a PNF intervention (14-hr nightly fast, 6 nights/week, no calories after 8pm) compared to a health education control (HEC) condition. Eligible participants were living with obesity, stress (Perceived stress scale-4 (PSS-4) total score ≥5), and memory “not as good as it used to be.” Data were collected via Zoom meetings with participants and trained staff and entered into REDCap. All participants had weekly staff check-in calls to report fasting times (PNF group only) and feedback. RESULTS/ANTICIPATED RESULTS: Eligible participants were enrolled from 37 of 50 US states; N=58, 86% women, 71% white, 93% non-Latinx, mean (SD) 50.1 (5.1) years of age, BMI 35.6 (3.6) kg/m^2. No group differences existed at baseline. Linear mixed-effects models were used to compare group differences across all outcome changes. Compared to the HEC condition, the PNF intervention was associated with improved sleep quality (Pittsburgh Sleep Quality Index; B = -2.52; SE = 0.90; 95% CI-4.30 to -0.74; p=0.006). Stress, everyday cognition, and emotional eating behavior significantly changed over time (p<0.02), but there were no group differences. Analysis of feasibility outcomes are on-going. DISCUSSION/SIGNIFICANCE: Changing food intake timing 6 days per week, to exclude nighttime eating without mandating food quality/quantity change, may benefit many individuals living with obesity, stress and memory decline to improve their sleep. Improved sleep quality may lead to more health benefits over time.

Interpretable Cognitive Ability Prediction: A Comprehensive Gated Graph Transformer Framework for Analyzing Functional Brain Networks.

Graph convolutional deep learning has emerged as a promising method to explore the functional organization of the human brain in neuroscience research. This paper presents a novel framework that utilizes the gated graph transformer (GGT) model to predict individuals' cognitive ability based on functional connectivity (FC) derived from fMRI. Our framework incorporates prior spatial knowledge and uses a random-walk diffusion strategy that captures the intricate structural and functional relationships between different brain regions. Specifically, our approach employs learnable structural and positional encodings (LSPE) in conjunction with a gating mechanism to efficiently disentangle the learning of positional encoding (PE) and graph embeddings. Additionally, we utilize the attention mechanism to derive multi-view node feature embeddings and dynamically distribute propagation weights between each node and its neighbors, which facilitates the identification of significant biomarkers from functional brain networks and thus enhances the interpretability of the findings. To evaluate our proposed model in cognitive ability prediction, we conduct experiments on two large-scale brain imaging datasets: the Philadelphia Neurodevelopmental Cohort (PNC) and the Human Connectome Project (HCP). The results show that our approach not only outperforms existing methods in prediction accuracy but also provides superior explainability, which can be used to identify important FCs underlying cognitive behaviors.

The effectiveness of Grewia hirsuta (Vahl.) as a nootropic on scopolamine-induced cognitive deficit in young and aged mice

The dementia and loss of cognitive nature among humans are becoming epidemics and causing a socio-economic burden. Amnesia and cognitive behaviours are natural phenomena due to aging. The studies in young and aged mice were conducted in order to rule out the impact of aging. It has become necessary to find novel nootropic agents that are derived from natural resources. In synthetic drugs, we have a piracetam prototype nootropic. The natural products obtained from plant sources are safe and compatible with physiological conditions. Hence, we hope our scientific research may lead to novel nootropic agents with fewer side effects. Nootropic activity was assessed by elevated plus maze and T-maze activity of plant-based extracts with standard piracetam.

Health literacy in older adults: The newest vital sign and its relation to cognition and healthy lifestyle behaviors

Health literacy tends to decrease with age, and lower health literacy has been associated with lower levels of physical function, mental health, and medication adherence. The present study examined health literacy in relation to cognition in a sample of community-dwelling older adults. The study also examined the impact of health literacy on engagement in healthy aging lifestyle behaviors. Participants included 128 older adults (age: M = 72.07, SD = 6.71; education: M = 16.34, SD = 2.56; 74% female) who completed a health literacy measure (Newest Vital Sign; NVS), a lifestyle behavior questionnaire (Healthy Aging Activity Engagement scale; HAAE), and several neuropsychological tests. The cognitive domains assessed included memory, executive function, and attention/working memory. Two variables were computed from the NVS to represent the health literacy factors of document and numeracy literacy; these factors demonstrated a small correlation (r = .18). Results revealed that attention/working memory, executive function, and memory were all significantly related to numeracy literacy and overall health literacy. Only memory was significantly related to document literacy. After accounting for age, education, and cognition, a hierarchical regression revealed that health literacy significantly predicted engagement in healthy aging lifestyle behaviors. Multiple cognitive abilities are necessary for searching, finding, and processing information to make health-related decisions. Health literacy accounted for a significant amount of variance in older adults’ engagement in everyday lifestyle behaviors. Health literacy skills may be an area of focus for intervention efforts to improve brain health in older adults.

Long‐term and short‐term plasticity independently mimicked in highly reliable Ru‐doped Ge2Sb2Te5 electronic synapses

AbstractIn order to fulfill the complex cognitive behaviors in neuromorphic systems with reduced peripheral circuits, the reliable electronic synapses mimicked by single device that achieves diverse long‐term and short‐term plasticity are essential. Phase change random access memory (PCRAM) is of great potential for artificial synapses, which faces, however, difficulty to realize short‐term plasticity due to the long‐lasting resistance drift. This work reports the ruthenium‐doped Ge2Sb2Te5 (RuGST) based PCRAM, demonstrating a series of synaptic behaviors of short‐term potentiation, pair‐pulse facilitation, long‐term depression, and short‐term plasticity in the same single device. The optimized RuGST electronic synapse with the high transformation temperature of hexagonal phase >380°C, the outstanding endurance >108 cycles, the low resistance drift factor of 0.092, as well as the extremely high linearity with correlation coefficients of 0.999 and 0.976 in parts of potentiation and depression. Further investigations also go insight to mechanisms of Ru doping according to thorough microstructure characterization, revealing that Ru dopant is able to enter GST lattices thus changing and stabilizing atomic arrangement of GST. This leads to the short‐term plasticity realized by RuGST PCRAM. Eventually, the proposed RuGST electronic synapses performs a high accuracy of ~94.1% in a task of image recognition of CIFAR‐100 database using ResNet 101. This work promotes the development of PCRAM platforms for large‐scale neuromorphic systems.

Functional organization of the human corpus callosum unveiled with BOLD-fMRI gradients

Abstract Gradients capture the underlying functional organization of the brain. Cortical gradients have been well characterized, however very little is known about the underlying gradient of the white matter. Here, we proposed a functionally gradient mapping of the corpus callosum by using blood-oxygen-level-dependent functional magnetic resonance imaging (BOLD-fMRI), which for the first time uncovered three distinct but stable spatial axes: posterior-anterior, dorsal-ventral, and left-right. The three spatial patterns were replicated in another independent cohort and robust across scanning conditions. We further associated the three gradient maps with brain anatomy, connectome, and task-related brain functions, by using structural magnetic resonance imaging, both resting-state and task fMRI, and diffusion tensor imaging data. The posterior-anterior gradient distribution of the corpus callosum showed a similar pattern with the cerebral cortex, gradually extending from the primary cortex to the transmodal cortex. The dorsal-ventral gradient distribution revealed an N-shaped pattern from the primary cortex to the higher-order cognitive cortex. The posterior-anterior and dorsal-ventral gradient maps were also associated with white-matter microstructures, such as fractional anisotropy and myelin water fraction. The left-right gradient showed an inverted V-shaped pattern, which delineated the inter-hemisphere separation. These findings provide fundamental insight into the functional organization of the human corpus callosum, unveiling potential patterns of functional interaction with the cerebral cortex and their associations with cognitive behaviors.

Adolescent Stress-Induced Ventral Hippocampus Redox Dysregulation Underlies Behavioral Deficits and Excitatory/Inhibitory Imbalance Related to Schizophrenia.

Redox dysregulation has been proposed as a convergent point of childhood trauma and the emergence of psychiatric disorders, such as schizophrenia (SCZ). A critical region particularly vulnerable to environmental insults during adolescence is the ventral hippocampus (vHip). However, the impact of severe stress on vHip redox states and their functional consequences, including behavioral and electrophysiological changes related to SCZ, are not entirely understood. After exposing adolescent animals to physical stress (postnatal day, PND31-40), we explored social and cognitive behaviors (PND47-49), the basal activity of pyramidal glutamate neurons, the number of parvalbumin (PV) interneurons, and the transcriptomic signature of the vHip (PND51). We also evaluated the impact of stress on the redox system, including mitochondrial respiratory function, reactive oxygen species (ROS) production, and glutathione (GSH) levels in the vHip and serum. Adolescent-stressed animals exhibited loss of sociability, cognitive impairment, and vHip excitatory/inhibitory (E/I) imbalance. Genome-wide transcriptional profiling unveiled the impact of stress on redox system- and synaptic-related genes. Stress impacted mitochondrial respiratory function and changes in ROS levels in the vHip. GSH and glutathione disulfide (GSSG) levels were elevated in the serum of stressed animals, while GSSG was also increased in the vHip and negatively correlated with sociability. Additionally, PV interneuron deficits in the vHip caused by adolescent stress were associated with oxidative stress. Our results highlight the negative impact of adolescent stress on vHip redox regulation and mitochondrial function, which are partially associated with E/I imbalance and behavioral abnormalities related to SCZ.

Using Artificial Populations to Study Psychological Phenomena in Neural Models

The recent proliferation of research into transformer based natural language processing has led to a number of studies which attempt to detect the presence of human-like cognitive behavior in the models. We contend that, as is true of human psychology, the investigation of cognitive behavior in language models must be conducted in an appropriate population of an appropriate size for the results to be meaningful. We leverage work in uncertainty estimation in a novel approach to efficiently construct experimental populations. The resultant tool, PopulationLM, has been made open source. We provide theoretical grounding in the uncertainty estimation literature and motivation from current cognitive work regarding language models. We discuss the methodological lessons from other scientific communities and attempt to demonstrate their application to two artificial population studies. Through population based experimentation we find that language models exhibit behavior consistent with typicality effects among categories highly represented in training. However, we find that language models don't tend to exhibit structural priming effects. Generally, our results show that single models tend to over estimate the presence of cognitive behaviors in neural models.

Cognitive performance in bipolar disorder: Comparison between individuals with and without suicide attempts and healthy controls

IntroductionBipolar disorder (BD) presents an elevated risk of suicide. One hypothesis for this is that cognitive impairments contribute to suicidal behavior. However, the relationship between cognition and suicidal behavior in BD remains unclear. ObjectiveTo compare cognitive performance in individuals with BD with previous suicide attempts vs no attempt vs healthy controls (HC). MethodsWe included 306 participants: 101 individuals with BD (49 with previous suicide attempts and 52 without attempts) and 205 HC. Participants underwent a clinical interview and neuropsychological evaluation. We assessed cognitive performance in social cognition, episodic verbal memory, working memory, processing speed, executive functioning, sustained attention, and intelligence. Afterwards, individuals with BD were separated between groups with and without suicide attempts. We then compared cognitive performance and clinical characteritics between groups. ResultsIndividuals with BD who attempted suicide had more hospitalizations, higher prevalence of psychiatric family history, and overall greater functional impairment. Regarding cognition, HC outperformed both BD groups, with the exception of social cognition and Stroop Color-Word and interference weighted score. However, there were no significant differences in cognitive performance among both BD groups. LimitationsCross-sectional design and dichotomization between suicide attempt vs. no attempt ConclusionsBD with previous suicide attempt present worse clinical outcomes and higher illness severity. Nevertheless, there were no cognitive differences among individuals with BD with and without suicide attempts. Both BD groups may share a vulnerability for cognitive impairment, but not for clinical and functional outcomes. Our results point to significant heterogeneity among clinical and functioning areas of BD.

ChildCI framework: Analysis of motor and cognitive development in children-computer interaction for age detection

This article presents a comprehensive analysis of the different tests proposed in the recent ChildCI framework,11https://github.com/BiDAlab/ChildCIdb_v1. proving its potential for generating a better understanding of children’s neuromotor and cognitive development along time, as well as their possible application in other research areas such as e-Health and e-Learning. In particular, we propose a set of over 100 global features related to motor and cognitive aspects of the children interaction with mobile devices, some of them collected and adapted from the literature.Furthermore, we analyse the robustness and discriminative power of the proposed feature set including experimental results for the task of children age group detection based on their motor and cognitive behaviours. Two different scenarios are considered in this study: (i) single-test scenario, and (ii) multiple-test scenario. Results over 93% accuracy are achieved using the publicly available ChildCIdb_v1 database (over 400 children from 18 months to 8 years old), proving the high correlation of children’s age with the way they interact with mobile devices.

Dorsal CA3 overactivation mediates witnessing stress-induced recognition memory deficits in adolescent male mice.

Witnessing violent or traumatic events is common during childhood and adolescence and could cause detrimental effects such as increased risks of psychiatric disorders. This stressor could be modeled in adolescent laboratory animals using the chronic witnessing social defeat (CWSD) paradigm, but the behavioral consequences of CWSD in adolescent animals remain to be validated for cognitive, anxiety-like, and depression-like behaviors and, more importantly, the underlying neural mechanisms remain to be uncovered. In this study, we first established the CWSD model in adolescent male mice and found that CWSD impaired cognitive function and increased anxiety levels and that these behavioral deficits persisted into adulthood. Based on the dorsal-ventral functional division in hippocampus, we employed immediate early gene c-fos immunostaining after behavioral tasks and found that CWSD-induced cognition deficits were associated with dorsal CA3 overactivation and anxiety-like behaviors were associated with ventral CA3 activity reduction. Indeed, chemogenetic activation and inhibition of dorsal CA3 neurons mimicked and reversed CWSD-induced recognition memory deficits (not anxiety-like behaviors), respectively, whereas both inhibition and activation of ventral CA3 neurons increased anxiety-like behaviors in adolescent mice. Finally, chronic administration of vortioxetine (a novel multimodal antidepressant) successfully restored the overactivation of dorsal CA3 neurons and the cognitive deficits in CWSD mice. Together, our findings suggest that dorsal CA3 overactivation mediates CWSD-induced recognition memory deficits in adolescent male mice, shedding light on the pathophysiology of adolescent CWSD-induced adverse effects and providing preclinical evidence for early treatment of stress-induced cognitive deficits.

Social competence in autism: A structural equation modeling approach.

Autistic individuals present with difficulties in social competence (e.g., navigating social interactions and fostering relationships). Clinical interventions widely target social cognition and social behavior, but there is inconsistent understanding of the underlying components of social competence. The present study used structural equation modeling to examine social cognition and social behavior and explore the relationship between these latent constructs. Autistic youth (ages 10-17; n = 219) and their caregivers participated in this study. Constructs of social cognition and social behavior were captured using caregiver-report and self-report rating scales, as well as observational measures and direct clinical assessments (e.g., NEPSY-II). Measurement models of social cognition and social behavior demonstrated adequate to good fit. Correlational models demonstrated adequate to poor fit, indicating latent constructs of social cognition and social behavior are not closely related in autistic youth. Exploratory examination of a subsample of male youth (n = 157) evidenced improved model fit of social behavior, specifically. Findings tease apart social cognition and social behavior as cohesive and separable constructs; results do not support a structural relationship between social cognition and social behavior. Noted treatment implications include consideration of how targeting social cognition and social behavior together or separately may support autistic youth's progress toward reaching their identified therapeutic goals and supporting their self-directed social development.

Predicting daily cognition and lifestyle behaviors for older adults using smart home data and ecological momentary assessment

Objective Extraction of digital markers from passive sensors placed in homes is a promising method for understanding real-world behaviors. In this study, machine learning (ML) and multilevel modeling (MLM) are used to examine types of digital markers and whether smart home sensors can predict cognitive functioning, lifestyle behaviors, and contextual factors measured through ecological momentary assessment (EMA). Method Smart home sensors were installed in the homes of 44 community-dwelling midlife and older adults for 3–4 months. Sensor data were categorized into eight digital markers. Participants responded to iPad-delivered EMA prompts 4×/day for 2 wk. Prompts included an n-back task and survey on recent (past 2 h) lifestyle and contextual factors. Results ML marker rankings revealed that sensor counts (indicating increased activity) and time outside the home were among the most influential markers for all survey questions. Additionally, MLM revealed for every 1000 sensor counts, mental sharpness, social, physical, and cognitive EMA responses increased by 0.134–0.155 points on a 5-point scale. For every additional 30-minutes spent outside home, social, physical, and cognitive EMA responses increased by 0.596, 0.472, and 0.157 points. Advanced ML joint classification/regression significantly predicted EMA responses from smart home digital markers with error of 0.370 on a 5-point scale, and n-back performance with a normalized error of 0.040. Conclusion Results from ML and MLM were complimentary and comparable, suggesting that machine learning may be used to develop generalized models to predict everyday cognition and track lifestyle behaviors and contextual factors that impact health outcomes using smart home sensor data.

Disrupting PIAS3-mediated SUMOylation of MLK3 ameliorates poststroke neuronal damage and deficits in cognitive and sensorimotor behaviors

Activated small ubiquitin-like modifiers (SUMOs) have been implicated in neuropathological processes following ischemic stroke. However, the target proteins of SUMOylation and their contribution to neuronal injury remain to be elucidated. MLK3 (mixed-lineage kinase 3), a member of the mitogen-activated protein kinase kinase kinase (MAPKKK) family, is a critical regulator of neuronal lesions following cerebral ischemia. Here, we found that SUMOylation of MLK3 increases in both global and focal ischemic rodent models and primary neuronal models of oxygen and glucose deprivation (OGD). SUMO1 conjugation at the Lys401 site of MLK3 promoted its activation, stimulated its downstream p38/c-Jun N-terminal kinase (JNK) cascades, and led to cell apoptosis. The interaction of MLK3 with PIAS3, a SUMO ligase, was elevated following ischemia and reperfusion. The PINIT domain of PIAS3 was involved in direct interactions with MLK3. Overexpression of the PINIT domain of PIAS3 disrupted the MLK3-PIAS3 interaction, inhibited SUMOylation of MLK3, suppressed downstream signaling, and reduced cell apoptosis and neurite damage. In rodent ischemic models, the overexpression of the PINIT domain reduced brain lesions and alleviated deficits in learning, memory, and sensorimotor functions. Our findings demonstrate that brain ischemia-induced MLK3 SUMOylation by PIAS3 is a potential target against poststroke neuronal lesions and behavioral impairments.

The dimensionality of L2 teacher self-regulation strategies and its interactions with teacher self-efficacy: Exploratory structural equation modeling

Teacher self-regulation plays a critical role in effective teaching and professional success. It is not only deemed to be a path to professional development but also a prerequisite to cultivating students' self-regulatory capacity. However, our understanding of in-service second/foreign language (L2) teachers' self-regulation strategies is insufficient. This study aimed to address gaps in the literature by exploring the factorial structure of self-regulation strategies for teaching among L2 teachers in China. Grounded in sociocognitive theory, we proposed a multidimensional structure of self-regulation strategies for teaching and made methodological innovations by using rigorous statistical methods including exploratory structural equation modeling (ESEM) and bifactor ESEM to measure the construct-relevant multidimensionality of teacher self-regulation strategies. The study collected data from a sample of 242 in-service L2 teachers across 18 universities in various regions of China. Model comparisons confirmed the ESEM model with the best fit, accounting for 12 specific teaching strategies (i.e., organization, elaboration, critical thinking, goal setting, self-evaluation, self-monitoring, motivational self-talk, emotional regulation, interest enhancement, collective teacher commitment, seeking help and resources, and time and environment management). These specific teaching strategies with moderate correlations confirmed the multidimensional structure of teacher self-regulation entailing cognition, metacognition, motivation, and social behavior. In addition, L2 teachers’ self-regulation strategies were closely connected to their sense of efficacy in classroom management, student engagement, and instructional strategies, supporting the criterion validity.

Neural Field Dynamics: The Interplay of Local and Global Activity in Brain Function

Abstract: Recent advancements in neuroscience have spurred a paradigm shift away from strict localizationism towards an appreciation for the brain as a dynamic, interconnected network. This paper discusses the intricacies of neural field dynamics, emphasizing the role of local and global brain activities and their interplay in shaping cognitive processes and behaviors. We explore the concept of neural microfields and their collective influence at a distance, considering how even subthreshold electric currents generated by neural activity can modulate neuronal excitability and network states. The discussion extends to the brain's operation at the edge of criticality—a state theorized to optimize information processing and adaptability. We delve into how fluctuations in neural activity, potentially stemming from strong emotions or pathological states, can precipitate bifurcations in neuronal behavior, leading to various brain states or phase transitions. The implications of external electric fields, such as those used in neuromodulatory therapies, are also considered, highlighting their capacity to influence neuronal activity and alter systemic dynamics. This review integrates perspectives from computational neuroscience, dynamical systems theory, and electrophysiology to construct a holistic understanding of neural field dynamics and their significance in the functioning of the brain. The paper aims to theoretically contribute to the ongoing dialogue in neuroscience regarding the non-linear and emergent properties of neural networks, paving the way for future research and clinical innovations.

Influencing Factors on Career Preparation Behavior of Nursing Students in the Post COVID-19 Era.

This study aims to determine the factors influencing the career preparation behavior of nursing students in the post-COVID-19 era and to provide a basis for preparation strategies to enhance nursing students' nursing professionalism and career preparation behaviors. This is a descriptive cross-sectional study that measures major satisfaction, self-efficacy, nursing image, nursing professionalism, nursing image and intuition, and career preparation to identify factors influencing nursing students' career preparation behavior in the post-COVID-19 era. An online survey was conducted to collect the data. The data were analyzed using descriptive statistics, Pearson's correlation, and multiple regression analysis using the SPSS/WIN 25.0 program. A total of 240 students were included; most of them were female (86.3%) and between 21 and 25 years old (80%). The level of motivation to pursue nursing (F = 12.34, p < 0.001) and clinical practice satisfaction (F = 11.37, p < 0.001) showed statistically significant differences in career preparation behavior. Self-efficacy (r = 0.32), major satisfaction (r = 0.32), nursing image (r = 0.32), and nursing professionalism (r = 0.32) were positively correlated with career preparation behavior and significant (p < 0.001). According to the findings, nursing professionalism and image can be enhanced by providing career planning and counseling based on the student's degree of comprehension and cognitive behaviors to nurture the professional and positive attitudes that are essential for a successful nursing career. Nursing schools need to incorporate a job portal, facilities, and a mentorship program to help nursing students prepare for their careers.