Rational and Continuous Measurement of the Emotional Decision Making in Visual Recognition of Facial Emotional Expressions with M.A.R.I.E.: First Half

Context: The advent of Artificial Intelligence (AI) requires modeling prior to its implementation in algorithms for most human skills. This observation requires us to have a detailed and precise understanding of the interfaces of verbal and emotional communications. The progress of AI is significant on the verbal level but modest in terms of the recognition of facial emotions even if this functionality is one of the oldest in humans and is omnipresent in our daily lives. Dysfunction in the ability for facial emotional expressions is present in many brain pathologies encountered by psychiatrists, neurologists, psychotherapists, mental health professionals including social workers. It cannot be objectively verified and measured due to a lack of reliable tools that are valid and consistently sensitive. Indeed, the articles in the scientific literature dealing with Visual-Facial-Emotions-Recognition (ViFaEmRe), suffer from the absence of 1) consensual and rational tools for continuous quantified measurement, 2) operational concepts. We have invented a software that can use computer-morphing attempting to respond to these two obstacles. It is identified as the Method of Analysis and Research of the Integration of Emotions (M.A.R.I.E.). Our primary goal is to use M.A.R.I.E. to understand the physiology of ViFaEmRe in normal healthy subjects by standardizing the measurements. Then, it will allow us to focus on subjects manifesting abnormalities in this ability. Our second goal is to make our contribution to the progress of AI hoping to add the dimension of recognition of facial emotional expressions. Objective: To study: 1) categorical vs dimensional aspects of recognition of ViFaEmRe, 2) universality vs idiosyncrasy, 3) immediate vs ambivalent Emotional-Decision-Making, 4) the Emotional-Fingerprint of a face and 5) creation of population references data. Methods: With M.A.R.I.E. enable a rational quantified measurement of Emotional-Visual-Acuity (EVA) of 1) a) an individual observer, b) in a population aged 20 to 70 years old, 2) measure the range and intensity of expressed emotions by 3 Face-Tests, 3) quantify the performance of a sample of 204 observers with hyper normal measures of cognition, “thymia,” (ibid. defined elsewhere) and low levels of anxiety 4) analysis of the 6 primary emotions. Results: We have individualized the following continuous parameters: 1) “Emotional-Visual-Acuity”, 2) “Visual-Emotional-Feeling”, 3) “Emotional-Quotient”, 4) “Emotional-Deci-sion-Making”, 5) “Emotional-Decision-Making Graph” or “Individual-Gun-Trigger”6) “Emotional-Fingerprint” or “Key-graph”, 7) “Emotional-Finger-print-Graph”, 8) detecting “misunderstanding” and 9) detecting “error”. This allowed us a taxonomy with coding of the face-emotion pair. Each face has specific measurements and graphics. The EVA improves from ages of 20 to 55 years, then decreases. It does not depend on the sex of the observer, nor the face studied. In addition, 1% of people endowed with normal intelligence do not recognize emotions. The categorical dimension is a variable for everyone. The range and intensity of ViFaEmRe is idiosyncratic and not universally uniform. The recognition of emotions is purely categorical for a single individual. It is dimensional for a population sample. Conclusions: Firstly, M.A.R.I.E. has made possible to bring out new concepts and new continuous measurements variables. The comparison between healthy and abnormal individuals makes it possible to take into consideration the significance of this line of study. From now on, these new functional parameters will allow us to identify and name “emotional” disorders or illnesses which can give additional dimension to behavioral disorders in all pathologies that affect the brain. Secondly, the ViFaEmRe is idiosyncratic, categorical, and a function of the identity of the observer and of the observed face. These findings stack up against Artificial Intelligence, which cannot have a globalist or regionalist algorithm that can be programmed into a robot, nor can AI compete with human abilities and judgment in this domain. *Here “Emotional disorders” refers to disorders of emotional expressions and recognition.

STAT3 ameliorates truncated tau-induced cognitive deficits

Proteolytic cleavage of tau by asparagine endopeptidase(AEP)creates tau-N368 fragments,which may drive the pathophysiology associated with synaptic dysfunction and memory deterioration in the brain of Alzheimer’s disease patients.Nonetheless,the molecular mechanisms of truncated tau-induced cognitive deficits remain unclear.Evidence suggests that signal transduction and activator of transcription-3(STAT3)is associated with modulating synaptic plasticity,cell apoptosis,and cognitive function.Using luciferase reporter assays,electrophoretic mobility shift assays,western blotting,and immunofluorescence,we found that human tau-N368 accumulation inhibited STAT3 activity by suppressing STAT3 translocation into the nucleus.Overexpression of STAT3 improved tau-N368-induced synaptic deficits and reduced neuronal loss,thereby improving the cognitive deficits in tau-N368 mice.Moreover,in tau-N368 mice,activation of STAT3 increased N-methyl-D-aspartic acid receptor levels,decreased Bcl-2 levels,reversed synaptic damage and neuronal loss,and thereby alleviated cognitive deficits caused by tau-N368.Taken together,STAT3 plays a critical role in truncated tau-related neuropathological changes.This indicates a new mechanism behind the effect of tau-N368 on synapses and memory deficits.STAT3 can be used as a new molecular target to treat tau-N368-induced protein pathology.

Impact of propofol and sevoflurane anesthesia on cognition and emotion in gastric cancer patients undergoing radical resection

BACKGROUND Propofol and sevoflurane are commonly used anesthetic agents for maintenance anesthesia during radical resection of gastric cancer.However,there is a debate concerning their differential effects on cognitive function,anxiety,and depression in patients undergoing this procedure.AIM To compare the effects of propofol and sevoflurane anesthesia on postoperative cognitive function,anxiety,depression,and organ function in patients undergoing radical resection of gastric cancer.METHODS A total of 80 patients were involved in this research.The subjects were divided into two groups:Propofol group and sevoflurane group.The evaluation scale for cognitive function was the Loewenstein occupational therapy cognitive assessment(LOTCA),and anxiety and depression were assessed with the aid of the self-rating anxiety scale(SAS)and self-rating depression scale(SDS).Hemodynamic indicators,oxidative stress levels,and pulmonary function were also measured.RESULTS The LOTCA score at 1 d after surgery was significantly lower in the propofol group than in the sevoflurane group.Additionally,the SAS and SDS scores of the sevoflurane group were significantly lower than those of the propofol group.The sevoflurane group showed greater stability in heart rate as well as the mean arterial pressure compared to the propofol group.Moreover,the sevoflurane group displayed better pulmonary function and less lung injury than the propofol group.CONCLUSION Both propofol and sevoflurane could be utilized as maintenance anesthesia during radical resection of gastric cancer.Propofol anesthesia has a minimal effect on patients'pulmonary function,consequently enhancing their postoperative recovery.Sevoflurane anesthesia causes less impairment on patients'cognitive function and mitigates negative emotions,leading to an improved postoperative mental state.Therefore,the selection of anesthetic agents should be based on the individual patient's specific circumstances.

Faster Region Convolutional Neural Network(FRCNN)Based Facial Emotion Recognition

Facial emotion recognition(FER)has become a focal point of research due to its widespread applications,ranging from human-computer interaction to affective computing.While traditional FER techniques have relied on handcrafted features and classification models trained on image or video datasets,recent strides in artificial intelligence and deep learning(DL)have ushered in more sophisticated approaches.The research aims to develop a FER system using a Faster Region Convolutional Neural Network(FRCNN)and design a specialized FRCNN architecture tailored for facial emotion recognition,leveraging its ability to capture spatial hierarchies within localized regions of facial features.The proposed work enhances the accuracy and efficiency of facial emotion recognition.The proposed work comprises twomajor key components:Inception V3-based feature extraction and FRCNN-based emotion categorization.Extensive experimentation on Kaggle datasets validates the effectiveness of the proposed strategy,showcasing the FRCNN approach’s resilience and accuracy in identifying and categorizing facial expressions.The model’s overall performance metrics are compelling,with an accuracy of 98.4%,precision of 97.2%,and recall of 96.31%.This work introduces a perceptive deep learning-based FER method,contributing to the evolving landscape of emotion recognition technologies.The high accuracy and resilience demonstrated by the FRCNN approach underscore its potential for real-world applications.This research advances the field of FER and presents a compelling case for the practicality and efficacy of deep learning models in automating the understanding of facial emotions.

Influence of BPPV and Meniere's Disease on Cognitive Abilities: A Questionnaire-Based Study

The vestibular system connects the inner ear to the midbrain and subcortical structures and can affect cognition. Patients with vertigo often experience cognitive symptoms such as attention deficits, memory problems, and spatial perception difficulties. This study aimed to explore the cognitive impairments associated with Benign paroxysmal positional vertigo(BPPV) and Meniere's Disease(MD). A non-experimental group comparison design was used with 107 participants divided into three groups: Group I(clinically normal), Group II(BPPV), and Group III(MD). Participants completed a questionnaire with 10 cognition-related questions, and their responses were scored. The data were found to be non-normally distributed. The analysis revealed a significant difference in scores between Group I and both Group II and Group III. Chi-square tests showed that the responses to cognition-related questions varied among the groups, with Group II exhibiting more cognitive problems. Associated conditions like hypertension, diabetes, and hearing loss did not significantly influence the responses within each group. This study suggests a significant relationship between cognitive problems and patients with BPPV and MD. However, there was no association found between the cognitive problems experienced in BPPV and MD patients. These findings align with previous research indicating that vestibular disorders can lead to deficits in spatial memory, attention, and other cognitive functions. By understanding the link between cognition and vestibular disorders, we can improve diagnosis and rehabilitation services to enhance the quality of life for these patients.

Alexa,let's train now!——A systematic review and classification approach to digital and home-based physical training interventions aiming to support healthy cognitive aging

Background:There is mounting evidence that regular physical activity is an important prerequisite for healthy cognitive aging.Consequently,the finding that almost one-third of the adult population does not reach the recommended level of regular physical activity calls for further public health actions.In this context,digital and home-based physical training interventions might be a promising alternative to center-based intervention programs.Thus,this systematic review aimed to summarize the current state of the literature on the effects of digital and home-based physical training interventions on adult cognitive performance.Methods:In this pre-registered systematic review(PROSPERO;ID:CRD42022320031),5 electronic databases(PubMed,Web of Science,Psyclnfo,SPORTDiscus,and Cochrane Library)were searched by 2 independent researchers(FH and PT)to identify eligible studies investigating the effects of digital and home-based physical training interventions on cognitive performance in adults.The systematic literature search yielded 8258 records(extra17 records from other sources),of which 27 controlled trials were considered relevant.Two reviewers(FH and PT)independently extracted data and assessed the risk of bias using a modified version of the Tool for the assEssment of Study qualiTy and reporting in EXercise(TESTEX scale).Results:Of the 27 reviewed studies,15 reported positive effects on cognitive and motor-cognitive outcomes(i.e.,performance improvements in measures of executive functions,working memory,and choice stepping reaction test),and a considerable heterogeneity concerning study-related,population-related,and intervention-related characteristics was noticed.A more detailed analysis suggests that,in particular,interventions using online classes and technology-based exercise devices(i.e.,step-based exergames)can improve cognitive performance in healthy older adults.Approximately one-half of the reviewed studies were rated as having a high risk of bias with respect to completion adherence(≤85%)and monitoring of the level of regular physical activity in the control group.Conclusion:The current state of evidence concerning the effectiveness of digital and home-based physical training interventions is mixed overall,though there is limited evidence that specific types of digital and home-based physical training interventions(e.g.,online classes and step-based exergames)can be an effective strategy for improving cognitive performance in older adults.However,due to the limited number of available studies,future high-quality studies are needed to buttress this assumption empirically and to allow for more solid and nuanced conclusions.

Contribution of Metabolic Imaging in the Exploration of Cognitive Disorders Related to COVID-19

On March 11, 2019, the WHO declared COVID-19 a pandemic disease. It is a respiratory tropism SARS COV 2 infection. In the emergency of the pandemic, in medical imaging, only computed tomography (CT) of the lungs was favored to assess lung lesions. In addition, many cases of post-COVID-19 cognitive disorders have been reported. As the curve dips and services restart correctly, other imaging techniques have been used to better explore the disease. The objective of this presentation is to illustrate the contribution of metabolic imaging in the exploration of post COVID-19 cognitive disorders and to discuss the pathophysiological mechanisms. Hypometabolism brain lesions are objective signs of functional impairment whose pathophysiological mechanism is not yet fully understood. Metabolic imaging with PET-SCAN is a suitable tool for exploring these disorders, both for the severity and extent of the lesions and for the topography of the brain damage.

Use of donepezil for neurocognitive recovery after brain injury in adult and pediatric populations:a scoping review

There are few pharmacologic options for the treatment of cognitive deficits associated with traumatic brain injury in pediatric patients.Acetylcholinesterase inhibitors such as donepezil have been evaluated in adult patients after traumatic brain injury,but relatively less is known about the effect in pediatric populations.The goal of this review is to identify knowledge gaps in the efficacy and safety of acetylcholinesterase inhibito rs as a potential a djuvant treatment fo r neurocognitive decline in pediatric patients with traumatic brain injury.Investigators queried PubMed to identify literature published from database inception thro ugh June 2023 desc ribing the use of donepezil in young adult traumatic brain injury and pediatric patients with predefined conditions.Based on preselected search criteria,340 unique papers we re selected for title and abstra ct screening.Thirty-two reco rds were reviewed in full after eliminating preclinical studies and pape rs outside the scope of the project.In adult traumatic brain injury,we review results from 14 papers detailing 227 subjects where evidence suggests donepezil is well tole rated and shows both objective and patient-reported efficacy for reducing cognitive impairment.In children,3 pape rs report on 5 children recovering from traumatic brain injury,showing limited efficacy.An additional 15 pediatric studies conducted in populations at risk for cognitive dysfunction provide a broader look at safety and efficacy in 210 patients in the pediatric age group.Given its promise for efficacy in adults with traumatic brain injury and tole rability in pediatric patients,we believe further study of donepezil for children and adolescents with traumatic brain injury is warranted.

Risk factors for cognitive impairment in patients with chronic kidney disease

BACKGROUND Chronic kidney disease(CKD)patients have been found to be at risk of concurrent cognitive dysfunction in previous studies,which has now become an important public health issue of widespread concern.AIM To investigate the risk factors for concurrent cognitive dysfunction in patients with CKD.METHODS This is a prospective cohort study conducted among patients with CKD between October 2021 and March 2023.A questionnaire was formulated by literature review and expert consultation and included questions about age,sex,education level,per capita monthly household income,marital status,living condition,payment method,and hypertension.RESULTS Logistic regression analysis showed that patients aged 60-79 years[odds ratio(OR)=1.561,P=0.015]and≥80 years(OR=1.760,P=0.013),participants with middle to high school education(OR=0.820,P=0.027),divorced or widowed individuals(OR=1.37,P=0.032),self-funded patients(OR=2.368,P=0.008),and patients with hypertension(OR=2.011,P=0.041)had a higher risk of cognitive impairment.The risk of cognitive impairment was lower for those with a college degree(OR=0.435,P=0.034)and married individuals.CONCLUSION The risk factors affecting cognitive dysfunction are age,60-79 years and≥80 years;education,primary school education or less;marital status,divorced or widowed;payment method,selffunded;hypertension;and CKD.

The Impact of Opioid Drugs on Memory and Other Cognitive Functions: A Review

Background and Purpose: Opioids, used for centuries to alleviate pain, have become a double-edged sword. While effective, they come with a host of adverse effects, including memory and cognition impairment. This review delves into the impact of opioid drugs on cognitive functions, explores underlying mechanisms, and investigates their prevalence in both medical care and illicit drug use. The ultimate goal is to find ways to mitigate their potential harm and address the ongoing opioid crisis. Methods: We sourced data from PubMed and Google Scholar, employing search combinations like “opioids,” “memory,” “cognition,” “amnesia,” “cognitive function,” “executive function,” and “inhibition.” Our focus was on English-language articles spanning from the inception of these databases up to the present. Results: The literature consistently reveals that opioid use, particularly at high doses, adversely affects memory and other cognitive functions. Longer deliberation times, impaired decision-making, impulsivity, and behavioral disorders are common consequences. Chronic high-dose opioid use is associated with conditions such as amnesiac syndrome (OAS), post-operative cognitive dysfunction (POCD), neonatal abstinence syndrome (NAS), depression, anxiety, sedation, and addiction. Alarming trends show increased opioid use over recent decades, amplifying the risk of these outcomes. Conclusion: Opioids cast a shadow over memory and cognitive function. These effects range from amnesiac effects, lessened cognitive function, depression, and more. Contributing factors include over-prescription, misuse, misinformation, and prohibition policies. Focusing on correct informational campaigns, removing punitive policies, and focusing on harm reduction strategies have been shown to lessen the abuse and use of opioids and thus helping to mitigate the adverse effects of these drugs. Further research into the impacts of opioids on cognitive abilities is also needed as they are well demonstrated in the literature, but the mechanism is not often completely understood.

The Emotional Intelligence and the Associated Factors among Nursing Students

Introduction: Emotional intelligence, or the capacity to cope one’s emotions, makes it simpler to form good connections with others and do caring duties. Nursing students can enroll a health team in a helpful and beneficial way with the use of emotional intelligence. Nurses who can identify, control, and interpret both their own emotions and those of their patients provide better patient care. The purpose of this study was to assess the emotional intelligence and to investigate the relationship and differences between emotional intelligence and demographic characteristics of nursing students. Methods: A cross-sectional study was carried out on 381 nursing students. Data collection was completed by “Schutte Self Report Emotional Intelligence Test”. Data were analyzed with the Statistical Package for Social Science. An independent t test, ANOVA, and Pearson correlation, multiple linear regression were used. Results: The results revealed that the emotional intelligence mean was 143.1 ± 21.6 (ranging from 33 to 165), which is high. Also, the analysis revealed that most of the participants 348 (91.3%) had higher emotional intelligence level. This finding suggests that nursing students are emotionally intelligent and may be able to notice, analyze, control, manage, and harness emotion in an adaptive manner. Also, academic year of nursing students was a predictor of emotional intelligence. Furthermore, there was positive relationship between the age and emotional intelligence (p < 0.05). The students’ ability to use their EI increased as they rose through the nursing grades. Conclusion: This study confirmed that the emotional intelligence score of the nursing students was high. Also, academic year of nursing students was a predictor of emotional intelligence. In addition, a positive relationship was confirmed between the emotional intelligence and age of nursing students. .

Multi-Objective Equilibrium Optimizer for Feature Selection in High-Dimensional English Speech Emotion Recognition

Speech emotion recognition(SER)uses acoustic analysis to find features for emotion recognition and examines variations in voice that are caused by emotions.The number of features acquired with acoustic analysis is extremely high,so we introduce a hybrid filter-wrapper feature selection algorithm based on an improved equilibrium optimizer for constructing an emotion recognition system.The proposed algorithm implements multi-objective emotion recognition with the minimum number of selected features and maximum accuracy.First,we use the information gain and Fisher Score to sort the features extracted from signals.Then,we employ a multi-objective ranking method to evaluate these features and assign different importance to them.Features with high rankings have a large probability of being selected.Finally,we propose a repair strategy to address the problem of duplicate solutions in multi-objective feature selection,which can improve the diversity of solutions and avoid falling into local traps.Using random forest and K-nearest neighbor classifiers,four English speech emotion datasets are employed to test the proposed algorithm(MBEO)as well as other multi-objective emotion identification techniques.The results illustrate that it performs well in inverted generational distance,hypervolume,Pareto solutions,and execution time,and MBEO is appropriate for high-dimensional English SER.

Glyphosate as a direct or indirect activator of pro-inflammatory signaling and cognitive impairment

Glyphosate-based herbicides are widely used around the world, making it likely that most humans have significant exposure. Because of habitual exposure, there are concerns about toxicity including neurotoxicity that could result in neurological, psychiatric, or cognitive impairment. We recently found that a single injection of glyphosate inhibits long-term potentiation, a cellular model of learning and memory, in rat hippocampal slices dissected 1 day after injection, indicating that glyphosate-based herbicides can alter cognitive function. Glyphosate-based herbicides could adversely affect cognitive function either indirectly and/or directly. Indirectly, glyphosate could affect gut microbiota, and if dysbiosis results in endotoxemia(leaky gut), infiltrated bacterial by-products such as lipopolysaccharides could activate pro-inflammatory cascades. Glyphosate can also directly trigger pro-inflammatory cascades. Indeed, we observed that acute glyphosate exposure inhibits long-term potentiation in rat hippocampal slices. Interestingly, direct inhibition of long-term potentiation by glyphosate appears to be similar to that of lipopolysaccharides. There are several possible measures to control dysbiosis and neuroinflammation caused by glyphosate. Dietary intake of polyphenols, such as quercetin, which overcome the inhibitory effect of glyphosate on long-term potentiation, could be one effective strategy. The aim of this narrative review is to discuss possible mechanisms underlying neurotoxicity following glyphosate exposure as a means to identify potential treatments.

E2E-MFERC:AMulti-Face Expression Recognition Model for Group Emotion Assessment

In smart classrooms, conducting multi-face expression recognition based on existing hardware devices to assessstudents’ group emotions can provide educators with more comprehensive and intuitive classroom effect analysis,thereby continuouslypromotingthe improvementof teaching quality.However,most existingmulti-face expressionrecognition methods adopt a multi-stage approach, with an overall complex process, poor real-time performance,and insufficient generalization ability. In addition, the existing facial expression datasets are mostly single faceimages, which are of low quality and lack specificity, also restricting the development of this research. This paperaims to propose an end-to-end high-performance multi-face expression recognition algorithm model suitable forsmart classrooms, construct a high-quality multi-face expression dataset to support algorithm research, and applythe model to group emotion assessment to expand its application value. To this end, we propose an end-to-endmulti-face expression recognition algorithm model for smart classrooms (E2E-MFERC). In order to provide highqualityand highly targeted data support for model research, we constructed a multi-face expression dataset inreal classrooms (MFED), containing 2,385 images and a total of 18,712 expression labels, collected from smartclassrooms. In constructing E2E-MFERC, by introducing Re-parameterization visual geometry group (RepVGG)block and symmetric positive definite convolution (SPD-Conv) modules to enhance representational capability;combined with the cross stage partial network fusion module optimized by attention mechanism (C2f_Attention),it strengthens the ability to extract key information;adopts asymptotic feature pyramid network (AFPN) featurefusion tailored to classroomscenes and optimizes the head prediction output size;achieves high-performance endto-end multi-face expression detection. Finally, we apply the model to smart classroom group emotion assessmentand provide design references for classroom effect analysis evaluation metrics. Experiments based on MFED showthat the mAP and F1-score of E2E-MFERC on classroom evaluation data reach 83.6% and 0.77, respectively,improving the mAP of same-scale You Only Look Once version 5 (YOLOv5) and You Only Look Once version8 (YOLOv8) by 6.8% and 2.5%, respectively, and the F1-score by 0.06 and 0.04, respectively. E2E-MFERC modelhas obvious advantages in both detection speed and accuracy, which can meet the practical needs of real-timemulti-face expression analysis in classrooms, and serve the application of teaching effect assessment very well.

Relationship of Retinal Nerve Fiber Layer Thickness and Retinal Vessel Calibers with Cognitive Impairment in the Asymptomatic Polyvascular Abnormalities Population

Objective Cognitive impairment(CI)in older individuals has a high morbidity rate worldwide,with poor diagnostic methods and susceptible population identification.This study aimed to investigate the relationship between different retinal metrics and CI in a particular population,emphasizing polyvascular status.Methods We collected information from the Asymptomatic Polyvascular Abnormalities Community Study on retinal vessel calibers,retinal nerve fiber layer(RNFL)thickness,and cognitive function of 3,785participants,aged 40 years or older.Logistic regression was used to analyze the relationship between retinal metrics and cognitive function.Subgroups stratified by different vascular statuses were also analyzed.Results RNFL thickness was significantly thinner in the CI group(odds ratio:0.973,95%confidence interval:0.953–0.994).In the subgroup analysis,the difference still existed in the non-intracranial arterial stenosis,non-extracranial carotid arterial stenosis,and peripheral arterial disease subgroups(P<0.05).Conclusion A thin RNFL is associated with CI,especially in people with non-large vessel stenosis.The underlying small vessel change in RNFL and CI should be investigated in the future.

Resilience to structural and molecular changes in excitatory synapses in the hippocampus contributes to cognitive function recovery in Tg2576 mice

Plaques of amyloid-β(Aβ)and neurofibrillary tangles are the main pathological characteristics of Alzheimer’s disease(AD).However,some older adult people with AD pathological hallmarks can retain cognitive function.Unraveling the factors that lead to this cognitive resilience to AD offers promising prospects for identifying new therapeutic targets.Our hypothesis focuses on the contribution of resilience to changes in excitatory synapses at the structural and molecular levels,which may underlie healthy cognitive performance in aged AD animals.Utilizing the Morris Water Maze test,we selected resilient(asymptomatic)and cognitively impaired aged Tg2576 mice.While the enzyme-linked immunosorbent assay showed similar levels of Aβ42 in both experimental groups,western blot analysis revealed differences in tau pathology in the pre-synaptic supernatant fraction.To further investigate the density of synapses in the hippocampus of 16-18 month-old Tg2576 mice,we employed stereological and electron microscopic methods.Our findings indicated a decrease in the density of excitatory synapses in the stratum radiatum of the hippocampal CA1 in cognitively impaired Tg2576 mice compared with age-matched resilient Tg2576 and non-transgenic controls.Intriguingly,through quantitative immunoelectron microscopy in the hippocampus of impaired and resilient Tg2576 transgenic AD mice,we uncovered differences in the subcellular localization of glutamate receptors.Specifically,the density of GluA1,GluA2/3,and mGlu5 in spines and dendritic shafts of CA1 pyramidal cells in impaired Tg2576 mice was significantly reduced compared with age-matched resilient Tg2576 and non-transgenic controls.Notably,the density of GluA2/3 in resilient Tg2576 mice was significantly increased in spines but not in dendritic shafts compared with impaired Tg2576 and non-transgenic mice.These subcellular findings strongly support the hypothesis that dendritic spine plasticity and synaptic machinery in the hippocampus play crucial roles in the mechanisms of cognitive resilience in Tg2576 mice.

Effect of cognitive training on brain dynamics

The human brain is highly plastic.Cognitive training is usually used to modify functional connectivity of brain networks.Moreover,the structures of brain networks may determine its dynamic behavior which is related to human cognitive abilities.To study the effect of functional connectivity on the brain dynamics,the dynamic model based on functional connections of the brain and the Hindmarsh–Rose model is utilized in this work.The resting-state fMRI data from the experimental group undergoing abacus-based mental calculation(AMC)training and from the control group are used to construct the functional brain networks.The dynamic behavior of brain at the resting and task states for the AMC group and the control group are simulated with the above-mentioned dynamic model.In the resting state,there are the differences of brain activation between the AMC group and the control group,and more brain regions are inspired in the AMC group.A stimulus with sinusoidal signals to brain networks is introduced to simulate the brain dynamics in the task states.The dynamic characteristics are extracted by the excitation rates,the response intensities and the state distributions.The change in the functional connectivity of brain networks with the AMC training would in turn improve the brain response to external stimulus,and make the brain more efficient in processing tasks.

Abnormalities of electroencephalography microstates in patients with depression and their association with cognitive function

BACKGROUND A growing number of recent studies have explored underlying activity in the brain by measuring electroencephalography(EEG)in people with depression.However,the consistency of findings on EEG microstates in patients with depression is poor,and few studies have reported the relationship between EEG microstates,cognitive scales,and depression severity scales.AIM To investigate the EEG microstate characteristics of patients with depression and their association with cognitive functions.METHODS A total of 24 patients diagnosed with depression and 32 healthy controls were included in this study using the Structured Clinical Interview for Disease for The Diagnostic and Statistical Manual of Mental Disorders,Fifth Edition.We collected information relating to demographic and clinical characteristics,as well as data from the Repeatable Battery for the Assessment of Neuropsychological Status(RBANS;Chinese version)and EEG.RESULTS Compared with the controls,the duration,occurrence,and contribution of microstate C were significantly higher[depression(DEP):Duration 84.58±24.35,occurrence 3.72±0.56,contribution 30.39±8.59;CON:Duration 72.77±10.23,occurrence 3.41±0.36,contribution 24.46±4.66;Duration F=6.02,P=0.049;Occurrence F=6.19,P=0.049;Contribution F=10.82,P=0.011]while the duration,occurrence,and contribution of microstate D were significantly lower(DEP:Duration 70.00±15.92,occurrence 3.18±0.71,contribution 22.48±8.12;CON:Duration 85.46±10.23,occurrence 3.54±0.41,contribution 28.25±5.85;Duration F=19.18,P<0.001;Occurrence F=5.79,P=0.050;Contribution F=9.41,P=0.013)in patients with depression.A positive correlation was observed between the visuospatial/constructional scores of the RBANS scale and the transition probability of microstate class C to B(r=0.405,P=0.049).CONCLUSION EEG microstate,especially C and D,is a possible biomarker in depression.Patients with depression had a more frequent transition from microstate C to B,which may relate to more negative rumination and visual processing.

Cognitive Disorders, Depression and Anxiety in Temporal Lobe Epilepsy: An Overview

Partial epilepsies, originating in a specific brain region, affect about 60% of adults with epilepsy. Temporal lobe epilepsy (TLE) is the most prevalent subtype within this category, often necessitating surgical intervention due to its refractoriness to antiepileptic drugs (AEDs). Hippocampal sclerosis, a common underlying pathology, often exacerbates the severity by introducing cognitive and emotional challenges. This review delves deeper into the cognitive profile of TLE, along with the risk factors for cognitive disorders, depression, and anxiety in this population.

Cognitive interference decision method for air defense missile fuze based on reinforcement learning

To solve the problem of the low interference success rate of air defense missile radio fuzes due to the unified interference form of the traditional fuze interference system,an interference decision method based Q-learning algorithm is proposed.First,dividing the distance between the missile and the target into multiple states to increase the quantity of state spaces.Second,a multidimensional motion space is utilized,and the search range of which changes with the distance of the projectile,to select parameters and minimize the amount of ineffective interference parameters.The interference effect is determined by detecting whether the fuze signal disappears.Finally,a weighted reward function is used to determine the reward value based on the range state,output power,and parameter quantity information of the interference form.The effectiveness of the proposed method in selecting the range of motion space parameters and designing the discrimination degree of the reward function has been verified through offline experiments involving full-range missile rendezvous.The optimal interference form for each distance state has been obtained.Compared with the single-interference decision method,the proposed decision method can effectively improve the success rate of interference.