Learning Speed Research Articles

Biopsychosocial Impact of Multiple Sclerosis in Omani Patients: A Multicenter Comparative Study

Background/Objectives: Multiple sclerosis (MS) is a chronic neurological disorder characterized by various clinical presentations and manifestations that include biopsychosocial impediments. This study has three interrelated goals relevant to biopsychosocial functioning: (i) compare reasoning ability, neuropsychological functioning, affective range, and quality of life (QoL) between people with multiple sclerosis (PwMS) and healthy controls; (ii) explore gender differences in reasoning ability and neuropsychological functioning, affective symptoms, and QoL among PwMS; and (iii) examine the relationship between QoL and cognitive performance in PwMS, focusing on those with inadequate vs. adequate QoL. Methods: This multicenter study was carried out among clinically stable PwMS (no relapse in the last two months) at follow-up in two tertiary care units in urban Oman. Healthy controls, matched for age and sex, were also recruited as a comparison group. Data were collected using cognitive batteries sensitive to current reasoning ability and conventional neuropsychological batteries designed to measure verbal learning, visual-spatial ability, and processing speed. The affective range (anxiety and depressive symptoms) and quality of life (QoL) were also evaluated. Results: The PwMS group scored lower on current reasoning ability, verbal learning, visual-spatial ability, and processing speed compared to the control group. The incidence of anxiety was higher in the PwMS group, but there were no statistically significant differences in depressive symptoms. No significant differences were found in cognitive variables between the two sexes, except in visual-spatial ability, where women outperformed men. PwMS with low QoL scored lower on attention and concentration indices than those with adequate QoL. According to QoL, no significant differences were observed in reasoning, verbal learning, or visual-spatial ability. Conclusions: The present sentinel study suggests that the Omani cohort with MS tends to have lower indices of current reasoning ability, visual and spatial memory, and cognitive speed compared to control subjects. Gender differences are minimal, except for visual-spatial abilities, where women outperform men. Quality of life significantly affects cognitive functioning. In general, the biopsychosocial impediment appears to be significant, indicating the need for comprehensive evaluation and care in the management of MS.

Invariant Extended Kalman Filtering for Pedestrian Deep-Inertial Odometry

Abstract. Indoor localization for pedestrians, which relies solely on inertial odometry, has been a topic of great interest. Its significance lies in its ability to provide positioning solutions independently, without the need for external data. Although traditional strap-down inertial navigation shows rapid drift, the introduction of pedestrian dead reckoning (PDR), and artificial intelligence (AI) has enhanced the applicability of inertial odometry for indoor localization. However, inertial odometry continues to be affected by drift, inherent to the nature of dead reckoning. This implies that even a slight error at a given moment can lead to a significant decrease in accuracy after continuous integration operations. In this paper, we propose a novel approach aimed at enhancing the positioning accuracy of inertial odometry. Firstly, we derive a learning-based forward speed using inertial measurements from a smartphone. Unlike mainstream methods where the learned speed is directly used to determine the position, we use the forward speed combined with non-holonomic constraint (NHC) as a measurement to update the state predicted within a strap-down inertial navigation framework. Secondly, we employ an invariant extended Kalman filter (IEKF)-based state estimation to facilitate fusion to cope with the nonlinearity arising from the system and measurement model. Experimental tests are carried out in different scenarios using an iPhone 12, and traditional methods, including PDR, robust neural inertial navigation (RONIN), and the EKF-based method, are compared. The results suggest that the method we propose surpasses these traditional methods in performance.

Learning‐based tracking control of AUV: Mixed policy improvement and game‐based disturbance rejection

AbstractA mixed adaptive dynamic programming (ADP) scheme based on zero‐sum game theory is developed to address optimal control problems of autonomous underwater vehicle (AUV) systems subject to disturbances and safe constraints. By combining prior dynamic knowledge and actual sampled data, the proposed approach effectively mitigates the defect caused by the inaccurate dynamic model and significantly improves the training speed of the ADP algorithm. Initially, the dataset is enriched with sufficient reference data collected based on a nominal model without considering modelling bias. Also, the control object interacts with the real environment and continuously gathers adequate sampled data in the dataset. To comprehensively leverage the advantages of model‐based and model‐free methods during training, an adaptive tuning factor is introduced based on the dataset that possesses model‐referenced information and conforms to the distribution of the real‐world environment, which balances the influence of model‐based control law and data‐driven policy gradient on the direction of policy improvement. As a result, the proposed approach accelerates the learning speed compared to data‐driven methods, concurrently also enhancing the tracking performance in comparison to model‐based control methods. Moreover, the optimal control problem under disturbances is formulated as a zero‐sum game, and the actor‐critic‐disturbance framework is introduced to approximate the optimal control input, cost function, and disturbance policy, respectively. Furthermore, the convergence property of the proposed algorithm based on the value iteration method is analysed. Finally, an example of AUV path following based on the improved line‐of‐sight guidance is presented to demonstrate the effectiveness of the proposed method.

Student perspectives on the flipped classroom concept in secondary math lessons

The flipped classroom concept is the reversal of the traditional teaching activities so that new content is first worked through by the learners independently and then the joint lesson time is used to practise and consolidate this content. This concept can result in didactic advantages. However, it is assumed that the concept also places increased demands on learners. As part of an intervention study with 452 participants in 15 lower secondary schools in Lower Saxony (Germany), pupils were given a questionnaire including the following items “I like this in the flipped classroom” and “I don’t like this in the flipped classroom”. This article analyses the answers to those questions using a qualitative content analysis. The resulting categories are analysed in more detail in a thematic analysis to identify specific aspects of the flipped classroom that the learners like or dislike. The preparation through videos, self-directed learning and adaptability of the learning speed were named particularly frequently in the positively connoted categories. The negative aspects included comprehension problems, the desire for more explanations from the teachers, time pressure, and time management problems. These results contrast with previous research results on the flipped classroom concept in higher education didactics.

Macro- and Microstructural White Matter Differences in Neurologic Postacute Sequelae of SARS-CoV-2 Infection.

Neuropsychiatric complications of SARS-CoV-2 infection, also known as neurologic postacute sequelae of SARS-CoV-2 infection (NeuroPASC), affect 10%-60% of infected individuals. There is growing evidence that NeuroPASC is a multi system immune dysregulation disease affecting the brain. The behavioral manifestations of NeuroPASC, such as impaired processing speed, executive function, memory retrieval, and sustained attention, suggest widespread WM involvement. Although previous work has documented WM damage following acute SARS-CoV-2 infection, its involvement in NeuroPASC is less clear. We hypothesized that macrostructural and microstructural WM differences in NeuroPASC participants would accompany cognitive and immune system differences. In a cross-sectional study, we screened a total of 159 potential participants and enrolled 72 participants, with 41 asymptomatic controls (NoCOVID) and 31 NeuroPASC participants matched for age, sex, and education. Exclusion criteria included neurologic disorders unrelated to SARS-CoV-2 infection. Assessments included clinical symptom questionnaires, psychometric tests, brain MRI measures, and peripheral cytokine levels. Statistical modeling included separate multivariable regression analyses of GM/WM/CSF volume, WM microstructure, cognitive, and cytokine concentration between-group differences. NeuroPASC participants had larger cerebral WM volume than NoCOVID controls (β = 0.229; 95% CI: 0.017-0.441; t = 2.16; P = .035). The most pronounced effects were in the prefrontal and anterior temporal WM. NeuroPASC participants also exhibited higher WM mean kurtosis, consistent with ongoing neuroinflammation. NeuroPASC participants had more self-reported symptoms, including headache, and lower performance on measures of attention, concentration, verbal learning, and processing speed. A multivariate profile analysis of the cytokine panel showed different group cytokine profiles (Wald-type-statistic = 44.6, P = .046), with interferon (IFN)-λ1 and IFN-λ2/3 levels higher in the NeuroPASC group. NeuroPASC participants reported symptoms of lower concentration, higher fatigue, and impaired cognition compatible with WM syndrome. Psychometric testing confirmed these findings. NeuroPASC participants exhibited larger cerebral WM volume and higher WM mean kurtosis than NoCOVID controls. These findings suggest that immune dysregulation could influence WM properties to produce WM volume increases and consequent cognitive effects and headaches. Further work will be needed to establish mechanistic links among these variables.

Enhancing learning experiences: EEG-based passive BCI system adapts learning speed to cognitive load in real-time, with motivation as catalyst.

Computer-based learning has gained popularity in recent years, providing learners greater flexibility and freedom. However, these learning environments do not consider the learner's mental state in real-time, resulting in less optimized learning experiences. This research aimed to explore the effect on the learning experience of a novel EEG-based Brain-Computer Interface (BCI) that adjusts the speed of information presentation in real-time during a learning task according to the learner's cognitive load. We also explored how motivation moderated these effects. In accordance with three experimental groups (non-adaptive, adaptive, and adaptive with motivation), participants performed a calibration task (n-back), followed by a memory-based learning task concerning astrological constellations. Learning gains were assessed based on performance on the learning task. Self-perceived mental workload, cognitive absorption and satisfaction were assessed using a post-test questionnaire. Between-group analyses using Mann-Whitney tests suggested that combining BCI and motivational factors led to more significant learning gains and an improved learning experience. No significant difference existed between the BCI without motivational factor and regular non-adaptive interface for overall learning gains, self-perceived mental workload, and cognitive absorption. However, participants who undertook the experiment with an imposed learning pace reported higher overall satisfaction with their learning experience and a higher level of temporal stress. Our findings suggest BCI's potential applicability and feasibility in improving memorization-based learning experiences. Further work should seek to optimize the BCI adaptive index and explore generalizability to other learning contexts.

Reinforcement Learning with Decoupled State Representation for Robot Manipulations

Deep reinforcement learning has significantly advanced robot manipulations by providing an alternative solution for designing control strategies using raw images as direct inputs. While images offer additional environmental information, the end-to-end policy training manner (from image to action) requires simultaneous representation and task learning by the agent. This often necessitates a substantial number of interaction samples to achieve satisfactory policy performance. Previous works has attempted to address this challenge by learning a visual representation model that encodes the entire image into a low-dimensional vector before the policy training. However, since this vector contains both robot and object information, it inevitably introduces coupling within the state, which can mislead the policy training process. In this study, a novel method called Reinforcement Learning with Decoupled State Representation is proposed to effectively decouple robot and object information within the state representation. Experimental results demonstrate that the proposed method exhibits faster learning speed and achieves superior performance compared to previous methods across various robot manipulation tasks. Moreover, with only 3096 offline images, the proposed method successfully applies to real-world robot pushing tasks, which demonstrates its high practicability.

Power Quality Disturbance Monitoring in PV Integrated Power System with Mode Decomposition and Ensemble Extreme Learning Machine

Power quality disturbance (PQD) monitoring has become an important issue in modern power system due to integration of several renewable energy sources such as photovoltaic (PV), wind energy system (WES), Fuel cells etc. This research presents a mode decomposed based ensemble extreme learning machine (EELM) to recognise and classify the PQD events with higher accuracy in terms of rapid learning speed and smaller computational burden in a complete PV based power system, The PQD signals are decomposed using a variational mode decomposition (VMD) to obtain effective band limited intrinsic mode functions (BIIMF) which leads to compute robust features and improves classification accuracy. For Power Quality Disturbances detection and classification, an ensemble extreme learning machine is suggested since an ensemble outperforms any single contributing model in terms of performance and prediction. The proposed VMD-EELM approach is validated in a modified IEEE 13 Bus system integrating PV with eleven types of PQDs. The proposed research having 100% classification accuracy for no noise and 99.88%, 99.94% 99.94% for 20dB, 30dB and 40dB noises respectively. It is being demonstrated that the suggested method can reliably identify and track PQD occurrences both with and without noise.

Research on Cooperative Tracking of Multiple Agents on Heterogeneous Ground

Abstract Pursuit-evasion scenarios are typical and significant area of study multi-agent behaviours. While previous research has primarily focused on training pursuit strategies on idealized flat, this paper explores pursuit strategies on heterogeneous ground that rely on ground features from the effects of friction and viscous forces. Since agent interacts with heterogeneous ground and changes its acceleration, we perform multi-agent modelling. Aiming at the multi-agent deep deterministic policy gradient (MADDPG) algorithm’s low training efficiency and slow convergence speed in pursuit learning, we use the prioritized experience selection mechanism of MADDPG algorithm (PES-MADDPG) which improve the experience extraction mechanism based on the policy evaluation function error and the experience extraction training frequency. With this approach, the design of rewards results in faster convergence of reward values. The experimental findings confirm that the suggested method delivers better performance and effectiveness for pursuers and evaders on heterogeneous ground, and both can acquire the relevant movement strategies through training.

Fractional Gradient Optimized Explainable Convolutional Neural Network for Alzheimer's Disease Diagnosis

Alzheimer’s is one of the brain syndromes that steadily effects the brain memory. The early stage of Alzheimer’s disease referred as mild-cognitive-impairment (MCI) and the growth of Alzheimer’s is not certain in the patients with MCI. The premature Alzheimer’s diagnosis detection is crucial for the healthy brains functionality by avoiding memory loss. Different multi-neural network architectures have been suggested by the researchers for efficient and accurate Alzheimer’s disease (AD) detection. The absence of improved feature extraction mechanisms and unexplored efficient optimizers in complex benchmark architectures lead to an inefficient and inaccurate AD classification. Moreover, the standard Convolutional Neural Network (CNN)-based architectures for Alzheimer’s diagnosis lack interpretability in their predictions. An interpretable, simplified yet effective deep learning model is required for accurate classification of Alzheimer’s disease. In this study, a generalized fractional order-based CNN classifier with explainable artificial intelligence (XAI) capabilities is proposed for accurate, efficient and interpretable classification of AD diagnosis. The proposed study (a) classifies AD accurately by incorporating unexplored pooling technique with enhanced feature extraction mechanism (b) provides fractional order-based optimization approach for adaptive learning and fast convergence speed (c) suggests an interpretable method for proving the transparency of the model. The proposed model outclasses complex benchmark architectures with regard to accuracy using standard ADNI dataset. The suggested fractional order-based CNN classifier achieves an improved accuracy of 99% as compared to the state-of-the-art models.

Expectation to rewards modulates learning emotional words: Evidence from a hierarchical Bayesian model.

In language acquisition, individuals learn the emotional value of words through external feedback. Previous studies have used emotional words as experimental materials to explore the cognitive mechanisms underlying emotional language processing, but have failed to recognize that languages are acquired in changing environments. To this end, this study aims to combine reinforcement learning with emotional word learning, using a probabilistic reversal learning task to explore how individuals acquire the valence of emotional words in a dynamically changing environment. Computational modeling on both behavioral and event-related potential (ERP) data revealed that individuals' expectations to rewards modulated the learning speed and temporal processing of emotional words, demonstrating a clear negative bias. Specifically, as the expected value increased, individuals responded faster and exhibited higher amplitudes for negative emotional words. These findings shed light on the neural mechanisms of emotional word learning in a volatile environment, highlighting the crucial role of expectations in this process and a preference for learning negative information.

Improving I-ELM structure through optimal addition of hidden nodes: Compact I-ELM

Incremental extreme learning machines (I-ELMs) can automatically determine the structure of neural networks and achieve high learning speeds. However, during the process of adding hidden nodes, unnecessary hidden nodes that have little relevance to the target may be added. Several studies have proposed methods to overcome this problem by measuring the relevance between hidden nodes and outputs and adding or removing hidden nodes accordingly. Random hidden nodes have the advantage of creating diverse patterns, but they encounter a problem in which hidden nodes that generate patterns with little or no relevance to the target can be added, thereby increasing the number of hidden nodes. Unlike in existing I-ELMs, which use random hidden nodes, we propose a compact I-ELM algorithm that initially adds linear regression nodes and subsequently applies a method to ensure that the hidden nodes have patterns differing from the existing ones. Based on benchmark data, we confirmed that the proposed method constructs a compact neural network structure with fewer hidden nodes compared to the existing I-ELM systems.

ARTIFICIAL INTELLIGENCE IN HIGHER STUDIES: USE OF AI-BASED TOOLS BY UNIVERSITY STUDENTS AND ITS CHALLENGES

The students of the 21st century study in exclusive ways. While some students rely on experiments, facts, and data, others use techniques and tools of artificial intelligence.AI-based tools are transforming higher education by improvising learning experiences, personalizing instruction, and administrative efficiency. Adaptive learning platforms, for example, are AI-driven systems that personalize instructional content to each students needs, accommodating a wide range of learning styles and speeds. Intelligent tutoring solutions help studentsgain a deeper understanding of difficult subjects by offering them specific guidance and feedback. It undertakes several artificial intelligence methodologies and learning style models to ascertain the mastering styles of university students. Artificial intelligence approaches in higher education generate effective learning environments for university students development.To create an empirical basis for this, a survey of students was carried out to analyze the use of AI-based tools that are important to students. This quantitative paper aims to highlight the various AI tools used by university students and the challenges they face while utilizing such tools. For data collection, 60 students were interviewed by the investigator. The simple random technique of sampling will be used and the resultsof the study reveal that university students are facingvarious challenges while using AI tools.

A Long-Term Target Search Method for Unmanned Aerial Vehicles Based on Reinforcement Learning

Unmanned aerial vehicles (UAVs) are increasingly being employed in search operations. Deep reinforcement learning (DRL), owing to its robust self-learning and adaptive capabilities, has been extensively applied to drone search tasks. However, traditional DRL approaches often suffer from long training times, especially in long-term search missions for UAVs, where the interaction cycles between the agent and the environment are extended. This paper addresses this critical issue by introducing a novel method—temporally asynchronous grouped environment reinforcement learning (TAGRL). Our key innovation lies in recognizing that as the number of training environments increases, agents can learn knowledge from discontinuous trajectories. This insight leads to the design of grouped environments, allowing agents to explore only a limited number of steps within each interaction cycle rather than completing full sequences. Consequently, TAGRL demonstrates faster learning speeds and lower memory consumption compared to existing parallel environment learning methods. The results indicate that this framework enhances the efficiency of UAV search tasks, paving the way for more scalable and effective applications of RL in complex scenarios.

Causal involvement of dorsomedial prefrontal cortex in learning the predictability of observable actions

Social learning is well established across species. While recent neuroimaging studies show that dorsomedial prefrontal cortex (DMPFC/preSMA) activation correlates with observational learning signals, the precise computations that are implemented by DMPFC/preSMA have remained unclear. To identify whether DMPFC/preSMA supports learning from observed outcomes or observed actions, or possibly encodes even a higher order factor (such as the reliability of the demonstrator), we downregulate DMPFC/preSMA excitability with continuous theta burst stimulation (cTBS) and assess different forms of observational learning. Relative to a vertex-cTBS control condition, DMPFC/preSMA downregulation decreases performance during action-based learning but has no effect on outcome-based learning. Computational modeling reveals that DMPFC/preSMA cTBS disrupts learning the predictability, a proxy of reliability, of the demonstrator and modulates the rate of learning from observed actions. Thus, our results suggest that the DMPFC is causally involved in observational action learning, mainly by adjusting the speed of learning about the predictability of the demonstrator.

AGENT Q-LEARNING SPEED DEPENDENCY ON THE FACTORS IN TIC-TAC-TOE

Reinforcement learning of program agents has widespread usage today. Especially, Q-learning, a model-free reinforcement learning technique has shown great results in various applications, like games, self-driving car and robot control. Regarding turn-based games many scientists successfully applied it to train artificial intelligence and to make competitive opponent for human player. While algorithms are well-known there is a room for parametrical optimization to achieve maximum learning speed considering specific problem like turn-based board game. As results of this research show the speed can vary significantly. Tic-Tac-Toe is a very simple and old game that gives an opportunity to try Q-learning without excessive efforts. The algorithm is universal and can be applied to more complex games. It worth noting that core of learning algorithm is the same for any similar game – only rules and board size are changed, which is one of the important properties of Q-learning. This paper investigates the impact of learning rate and discount factor on the speed of learning of the Q-learning program agent in Tic-Tac-Toe board game. It is conducted a series of experiments using developed computer implementation of the algorithm to analyze the correlation between learning rate, discount factor, and the convergence rate of Q-learning in the specified game. So the experimental field consists of two factors, each of which has three levels, and full factorial experiment has nine combinations of these factors. The learning speed dependency on the each factor is presented. The findings reveal strong relationships between these parameters and convergence speed. For example, the speed is increased proportionally to the both factors, but in the case of discount factor the increase is about 1.4 times lower. Practical significance of the research is in the optimization of factors to achieve effective training of the software agent in order to save processing time, the payment of which is one of the main expenses of enterprises in the field of information technologies. In addition, the research contributes to a better understanding of how Q-learning performs in different game scenarios and provides guidelines for parameter selection in similar applications

Classifying Learning Speed Using Brain Networks and Psychological States: Unraveling the Interdependence Between Learning Performance, Psychological States, and Brain Functions.

Introduction The progression of performance learning (PL) may have complex relationships beyond mere concurrent occurrences and may influence each other. This study aimed to classify the speed of PL using a random forest based on brain network and stress state information and to identify the factors necessary for PL. In addition, this study also aimed to clarify the complex interdependent relationships between PL, psychological state, and brain function through these factors, using covariance structure analysis. Methods A total of 20 healthy individuals participated in a choice reaction time task, and brain function was measured using near-infrared spectroscopy (NIRS). Participants were divided into high-PL and low-PL groups based on the median difference in correct responses. Results Random forest analysis identified the left orbitofrontal area, right premotor cortex, right frontal pole, left frontal pole, left dorsolateral prefrontal cortex, and depression and anxiety as key factors. Covariance structure analysis revealed that depression and anxiety affected PL through the frontal pole and prefrontal cortex, suggesting a complex interplay between psychological state, brain function, and learning. Conclusions These findings suggest that psychological states influence brain networks, thereby affecting learning performance. Tailoring rehabilitation programs to address psychological states and providing targeted feedback may improve learning outcomes. The study provides insights into the theoretical and practical applications of understanding the brain's role in PL, as well as the importance of addressing psychological factors to optimize learning and rehabilitation strategies.

Choosing the machine learning algorithm for detecting intrusions into IoT

Aim. The paper aims to improve the security of IoT devices by applying machine learning algorithms to detect attacks against IoT networks. The relevance of the goal is defined by the ever-growing number of such attacks around the world and the widespread use of IoT systems. The paper provides relevant statistical data. An analysis of the available papers showed that various methods were examined individually and were not compared to each other, so the aim of this paper that consists in identifying the most promising machine learning algorithm for detecting attacks against IoT networks is of relevance. Methods. The paper used the following machine learning methods to detect attacks against IoT networks: logistic regression, SVC, random forest, K-nearest neighbour method, k-means method, naive Bayes classifier, and variants of gradient boosting (XGBoost, AdaBoost, and CatBoost). The novelty consists in the comparison of the outputs of the supervised algorithms with the unsupervised K-means in the context of detection of attacks against IoT networks. The attack detection systems under development were trained using the UNSWNB15 dataset that contains data on nine types of attacks. The number of entries is more than 80 thousand. More than half of the entries deal with attacks. The methods were compared using a number of metrics. Results. An intrusion detection system was structurally defined and implemented. The stages of its operation include the analysis of input data and the output of final statistical data. The results show that the random forest algorithm is the best one out of those examined. The method also performs well in terms of learning speed. That means that the algorithm can be deployed and applied with the greatest success. Conclusions. This paper presents the results of comparing various machine learning algorithms in the context of IoT device intrusion detection. The accuracy and the ROC-AUC curve are used to evaluate the efficiency of the employed models. Having compared the models of the employed algorithms we found that the RandomForestClassifier model has the highest accuracy and a high AUC, which means that this algorithm is the most efficient in terms of IoT network intrusion detection. Further research will be dedicated to distinguishing between the types of attack.

Efficacy and safety of esketamine versus propofol in electroconvulsive therapy for treatment-resistant depression: A randomized, double-blind, controlled, non-inferiority trial

BackgroundElectroconvulsive therapy (ECT) is a commonly used alternative for treatment-resistant depression (TRD). Although esketamine has a rapid pharmacological antidepressant action, it has not been studied as an ECT anesthetic. The objective of this study was to compare the efficacy and safety of esketamine with propofol when both are used as ECT anesthetic agents. MethodsForty patients with TRD were assigned to one of two arms in a double-blind, randomized controlled trial: esketamine or propofol anesthesia for a series of eight ECT sessions. Using a non-inferiority design, the primary outcome was the reduction in HAMD-17 depressive symptoms. The other outcomes were: rates of response and remission, anxiety, suicidal ideation, cognitive function, and adverse events. These were compared in an intention-to-treat analysis. ResultsEsketamine-ECT was non-inferior to propofol-ECT for reducing TRD symptoms after 8 sessions (adjusted Δ = 2.0, 95 % CI: −1.2–5.1). Compared to propofol-ECT, esketamine-ECT also had higher depression response (80 % vs. 70 %; p = .06) and remission (65 % vs. 55 %; p = .11) rates but non-inferiority was not established. In four components of cognitive function (speed of processing, working memory, visual learning, and verbal learning) esketamine-ECT was non-inferior to propofol-ECT. The results for anxiety, suicidal ideation, and adverse events (all p's > .05) were inconclusive. ConclusionEsketamine was non-inferior to propofol when both are used as anesthetics for TRD patients undergoing ECT. Replication studies with larger samples are needed to examine the inconclusive results. Registration numberChiCTR2000033715.

Effects of sodium benzoate on cognitive function in neuropsychiatric disorders: a systematic review and meta-analysis.

PROSPERO, identifier CRD42023457462.