Journal of Neuroscience Methods最新文献

{"title":"Single-channel electroencephalography decomposition by detector-atom network and its pre-trained model","authors":"Hiroshi Higashi","doi":"10.1016/j.jneumeth.2024.110323","DOIUrl":"10.1016/j.jneumeth.2024.110323","url":null,"abstract":"<div><div>Signal decomposition techniques utilizing multi-channel spatial features are critical for analyzing, denoising, and classifying electroencephalography (EEG) signals. To facilitate the decomposition of signals recorded with limited channels, this paper presents a novel single-channel decomposition approach that does not rely on multi-channel features. Our model posits that an EEG signal comprises short, shift-invariant waves, referred to as atoms. We design a decomposer as an artificial neural network aimed at estimating these atoms and detecting their time shifts and amplitude modulations within the input signal. The efficacy of our method was validated across various scenarios in brain–computer interfaces and neuroscience, demonstrating enhanced performance. Additionally, cross-dataset validation indicates the feasibility of a pre-trained model, enabling a plug-and-play signal decomposition module.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"414 ","pages":"Article 110323"},"PeriodicalIF":2.7,"publicationDate":"2024-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142716230","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing detection of SSVEP-based BCIs via a novel temporally local canonical correlation analysis","authors":"Guoxian Xia,&nbsp;Li Wang,&nbsp;Shiming Xiong,&nbsp;Jiaxian Deng","doi":"10.1016/j.jneumeth.2024.110325","DOIUrl":"10.1016/j.jneumeth.2024.110325","url":null,"abstract":"<div><h3>Background</h3><div>In recent years, spatial filter-based frequency recognition methods have become popular in steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) systems. However, these methods are ineffective in suppressing local noise, and they rely on the length of the data. In practical applications, enhancing recognition performance with short data windows is a significant challenge for the BCI systems.</div></div><div><h3>New method</h3><div>With extracting temporal information and eliminating local noise, a temporally local canonical correlation analysis based on training data-driven (TI-tdCCA) method is proposed to enhance the recognition performance of SSVEPs. Based on a novel framework, the filters are derived by incorporating the Laplacian matrix through the use of TI-CCA between the concatenated training data and individual templates. The target frequency is subsequently determined by applying the appropriate spatial filters and Laplacian matrix.</div></div><div><h3>Results</h3><div>The experimental results on two datasets, consisting of 40 classes and recording from 35 and 70 subjects respectively, demonstrate that the proposed method consistently outperforms the eight competing methods in the majority of cases. The proposed method is simultaneously evaluated by an extended version that incorporates artificial reference signals. The extended method demonstrates a significant improvement over the proposed method. Specifically, with a time window of 0.7 s, the average recognition accuracy of the subjects increases by 10.71 % on the Benchmark dataset and by 6.98 % on the BETA dataset, respectively.</div></div><div><h3>Comparison with existing methods</h3><div>Our extended method outperforms the state-of-the-art methods by at least 3 %, and it effectively suppresses local noise and maintains excellent scalability.</div></div><div><h3>Conclusions for research articles</h3><div>The proposed method can effectively combine spatial and temporal filters to improve the recognition performance of SSVEPs.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"414 ","pages":"Article 110325"},"PeriodicalIF":2.7,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142693126","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving computational models of deep brain stimulation through experimental calibration","authors":"Jan Philipp Payonk ,&nbsp;Henning Bathel ,&nbsp;Nils Arbeiter ,&nbsp;Maria Kober ,&nbsp;Mareike Fauser ,&nbsp;Alexander Storch ,&nbsp;Ursula van Rienen ,&nbsp;Julius Zimmermann","doi":"10.1016/j.jneumeth.2024.110320","DOIUrl":"10.1016/j.jneumeth.2024.110320","url":null,"abstract":"<div><h3>Background:</h3><div>Deep brain stimulation has become a well-established clinical tool to treat movement disorders. Nevertheless, the knowledge of processes initiated by the stimulation remains limited. To address this knowledge gap, computational models are developed to gain deeper insight. However, their predictive power remains constrained by model uncertainties and a lack of validation and calibration.</div></div><div><h3>New method:</h3><div>Exemplified with rodent microelectrodes, we present a workflow for validating electrode model geometry using microscopy and impedance spectroscopy <em>in vitro</em> before implantation. We address uncertainties in the tissue distribution and dielectric properties and outline a concept for calibrating the computational model based on <em>in vivo</em> impedance spectroscopy measurements.</div></div><div><h3>Results:</h3><div>The standard deviation of the volume of tissue activated across the 18 characterized electrodes was approximately 32.93%, underscoring the importance of electrode characterization. Thus, the workflow significantly enhances the model predictions’ credibility of neural activation exemplified in a rodent model.</div></div><div><h3>Comparison with existing methods:</h3><div>Computational models are frequently employed without validation or calibration, relying instead on manufacturers’ specifications. Our approach provides an accessible method to obtain a validated and calibrated electrode geometry, which significantly enhances the reliability of the computational model that relies on this electrode.</div></div><div><h3>Conclusion:</h3><div>By reducing the uncertainties of the model, the accuracy in predicting neural activation is increased. The entire workflow is realized in open-source software, making it adaptable for other use cases, such as deep brain stimulation in humans. Additionally, the framework allows for the integration of further experiments, enabling live updates and refinements to computational models.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"414 ","pages":"Article 110320"},"PeriodicalIF":2.7,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142644419","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ST-SHAP: A hierarchical and explainable attention network for emotional EEG representation learning and decoding","authors":"Minmin Miao ,&nbsp;Jin Liang ,&nbsp;Zhenzhen Sheng ,&nbsp;Wenzhe Liu ,&nbsp;Baoguo Xu ,&nbsp;Wenjun Hu","doi":"10.1016/j.jneumeth.2024.110317","DOIUrl":"10.1016/j.jneumeth.2024.110317","url":null,"abstract":"<div><h3>Background:</h3><div>Emotion recognition using electroencephalogram (EEG) has become a research hotspot in the field of human–computer interaction, how to sufficiently learn complex spatial–temporal representations of emotional EEG data and obtain explainable model prediction results are still great challenges.</div></div><div><h3>New method</h3><div>In this study, a novel hierarchical and explainable attention network ST-SHAP which combines the Swin Transformer (ST) and SHapley Additive exPlanations (SHAP) technique is proposed for automatic emotional EEG classification. Firstly, a 3D spatial–temporal feature of emotional EEG data is generated via frequency band filtering, temporal segmentation, spatial mapping, and interpolation to fully preserve important spatial–temporal-frequency characteristics. Secondly, a hierarchical attention network is devised to sufficiently learn an abstract spatial–temporal representation of emotional EEG and perform classification. Concretely, in this decoding model, the W-MSA module is used for modeling correlations within local windows, the SW-MSA module allows for information interactions between different local windows, and the patch merging module further facilitates local-to-global multiscale modeling. Finally, the SHAP method is utilized to discover important brain regions for emotion processing and improve the explainability of the Swin Transformer model.</div></div><div><h3>Results:</h3><div>Two benchmark datasets, namely SEED and DREAMER, are used for classification performance evaluation. In the subject-dependent experiments, for SEED dataset, ST-SHAP achieves an average accuracy of 97.18%, while for DREAMER dataset, the average accuracy is 96.06% and 95.98% on arousal and valence dimension respectively. In addition, important brain regions that conform to prior knowledge of neurophysiology are discovered via a data-driven approach for both datasets.</div></div><div><h3>Comparison with existing methods:</h3><div>In terms of subject-dependent and subject-independent emotional EEG decoding accuracies, our method outperforms several closely related existing methods.</div></div><div><h3>Conclusion:</h3><div>These experimental results fully prove the effectiveness and superiority of our proposed algorithm.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"414 ","pages":"Article 110317"},"PeriodicalIF":2.7,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142622220","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"New approach to control ischemic severity ex vivo","authors":"Bindu Modi ,&nbsp;Kaejaren C.N. Caldwell ,&nbsp;Colby E. Witt ,&nbsp;Moriah E. Weese-Myers ,&nbsp;Ashley E. Ross","doi":"10.1016/j.jneumeth.2024.110321","DOIUrl":"10.1016/j.jneumeth.2024.110321","url":null,"abstract":"<div><h3>Background</h3><div>It is advantageous to be able to both control and define a metric for ischemia severity in ex <em>vivo</em> models to enable more precise comparisons to <em>in vivo</em> models and to facilitate more sophisticated mechanistic studies. Currently, the primary method to induce and study ischemia <em>ex vivo</em> is to completely deplete oxygen and glucose in the culture media; however, <em>in vivo</em> ischemia often involves varying degrees of severities.</div></div><div><h3>New Method</h3><div>In this work, we have successfully developed an approach to both control and characterize three different ischemic severities <em>ex vivo</em> and we define these standard condition metrics <em>via</em> an oxygen sensor: normoxia (control), mild ischemia (partial oxygen-glucose deprivation), and severe ischemia (complete oxygen-glucose deprivation).</div></div><div><h3>Results</h3><div>To validate the extent to which controlling oxygen and glucose concentration <em>ex vivo</em> impacts cell expression, recruitment, and cell damage, we demonstrate changes in cytokine and HIF-1ɑ, an increase in glucose transporter expression level, changes in caspase-3, and rapid microglia recruitment to neurons within only 30 minutes.</div></div><div><h3>Comparison to Existing Methods</h3><div>To the best of our knowledge, this is the first time ischemic severity was controlled and shown to have a measurable effect on protein expression and cell movement within only 30 minutes <em>ex vivo</em>. Our new approach matches with existing literature for controlling ischemic severity <em>in vivo</em>.</div></div><div><h3>Conclusions</h3><div>Overall, this new approach will significantly impact our ability to expand <em>ex vivo</em> platforms for assessing ischemic damage and will provide a new experimental approach for investigating the molecular mechanisms involved in ischemia.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"413 ","pages":"Article 110321"},"PeriodicalIF":2.7,"publicationDate":"2024-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142622217","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adaptive wheel exercise for mouse models of Parkinson’s Disease","authors":"Henry Skelton ,&nbsp;Dayton Grogan ,&nbsp;Amrutha Kotlure ,&nbsp;Ken Berglund ,&nbsp;Claire-Anne Gutekunst ,&nbsp;Robert Gross","doi":"10.1016/j.jneumeth.2024.110314","DOIUrl":"10.1016/j.jneumeth.2024.110314","url":null,"abstract":"<div><h3>Background</h3><div>Physical exercise has been extensively studied for its therapeutic properties in neurological disease, particularly Parkinson’s Disease (PD). However, the established techniques for exercise in mice are not well suited to motor-deficient disease-model animals, rely on spontaneous activity or force exercise with aversive stimuli, and do not facilitate active measurement of neurophysiology with tethered assays. Motorized wheel exercise may overcome these limitations, but has not been shown to reliably induce running in mice.</div></div><div><h3>New method</h3><div>We developed an apparatus and technique for inducing exercise in mice without aversive stimuli, using a motorized wheel that dynamically responds to subject performance.</div></div><div><h3>Results</h3><div>A commercially available motorized wheel system did not satisfactorily provide for exercise, as mice tended to avoid running at higher speeds. Our adaptive wheel exercise platform allowed for effective exercise induction in the 6-hydroxydopamine mouse model of PD, including with precise behavioral measurements and synchronized single-unit electrophysiology.</div></div><div><h3>Comparison with existing methods</h3><div>Our approach provides a superior physical platform and programming strategy compared to previously described techniques for motorized wheel exercise. Unlike voluntary exercise, this allows for controlled experimental induction of running, without the use of aversive stimuli that is typical of treadmill-based techniques.</div></div><div><h3>Conclusions</h3><div>Adaptive wheel exercise should allow for physical exercise to be better studied as a dynamic, physiological intervention in parkinsonian mice, as well as other neurological disease models.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"414 ","pages":"Article 110314"},"PeriodicalIF":2.7,"publicationDate":"2024-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142622209","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Early detection of autism spectrum disorder using explainable AI and optimized teaching strategies","authors":"Sarah A. Alzakari ,&nbsp;Arwa Allinjawi ,&nbsp;Asma Aldrees ,&nbsp;Nuha Zamzami ,&nbsp;Muhammad Umer ,&nbsp;Nisreen Innab ,&nbsp;Imran Ashraf","doi":"10.1016/j.jneumeth.2024.110315","DOIUrl":"10.1016/j.jneumeth.2024.110315","url":null,"abstract":"<div><div>Autism spectrum disorder (ASD) is defined by the deficits of social relating, language, object use and understanding, intelligence and learning, and verbal and nonverbal communication. Most of the individuals with ASD have genetic conditions; however, early identification and intervention reduce the use of health services and other diagnostic procedures. The varied nature of ASD is widely acknowledged, with each affected individual displaying distinct traits. The variability among autistic children underscores the challenge of identifying effective teaching strategies, as what works for one child may not be suitable for another. In this study, we merge two ASD screening datasets focusing on toddlers. We employ three feature engineering techniques to extract significant features from the dataset to enhance model performance. This study presents an innovative two-phase method where initially, we employ diverse machine learning models, such as a combination of logistic regression and support vector machine classifiers. The focus of the second phase is on identifying tailored educational methods for children with ASD through the assessment of their behavioral, verbal, and physical responses. The main goal of this study is to develop personalized educational strategies for individuals with ASD. This will be achieved by employing machine learning techniques to enhance precision and better meet their unique needs. Experimental results achieve a classification accuracy of 94% in ASD identification using Chi-square extracted features. Concerning the choice of the best teaching approach for ASD children, the proposed approach shows 99.29% accuracy. Performance comparison with existing studies shows the superior performance of the proposed LR-SVM ensemble coupled with Chi-square features. In conclusion, the proposed approach provides a two-phase strategy for identifying ASD children and offering a suitable teaching strategy with respect to the severity of the ASD, thereby potentially contributing to the development of tailored solutions for children with varying needs.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"413 ","pages":"Article 110315"},"PeriodicalIF":2.7,"publicationDate":"2024-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142622230","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unveiling the decision making process in Alzheimer’s disease diagnosis: A case-based counterfactual methodology for explainable deep learning","authors":"Adarsh Valoor ,&nbsp;G.R. Gangadharan","doi":"10.1016/j.jneumeth.2024.110318","DOIUrl":"10.1016/j.jneumeth.2024.110318","url":null,"abstract":"<div><h3>Background</h3><div>The field of Alzheimer's disease (AD) diagnosis is undergoing significant transformation due to the application of deep learning (DL) models. While DL surpasses traditional machine learning in disease prediction from structural magnetic resonance imaging (sMRI), the lack of explainability limits clinical adoption. Counterfactual inference offers a way to integrate causal explanations into these models, enhancing their robustness and transparency.</div></div><div><h3>New method</h3><div>This study develops a novel methodology combining U-Net and generative adversarial network (GAN) models to create comprehensive counterfactual diagnostic maps for AD. The proposed methodology uses case-based counterfactual reasoning for robust decision classification for counterfactual maps to understand how changes in specific features affect the model's predictions.</div></div><div><h3>Comparison with existing methods</h3><div>The proposed methodology is compared with state-of-the-art visual explanation techniques across the ADNI dataset. The proposed methodology is also benchmarked against other gradient-based and generative models for its ability to generate comprehensive counterfactual maps.</div></div><div><h3>Results</h3><div>The results demonstrate that the proposed methodology significantly outperforms existing methods in accuracy, sensitivity, and specificity while providing detailed counterfactual maps that visualize how slight changes in brain morphology could lead to different diagnostic outcomes. The proposed methodology achieves an accuracy of 95 % and an AUC of 0.97, illustrating its superiority in capturing subtle yet crucial anatomical features.</div></div><div><h3>Conclusions</h3><div>By generating intuitive visual explanations, the proposed methodology improves the interpretability and robustness of AD diagnostic models, making them more reliable and accountable. The use of counterfactual inference enhances clinicians' understanding of disease progression and the impact of different interventions, fostering precision medicine in AD care.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"413 ","pages":"Article 110318"},"PeriodicalIF":2.7,"publicationDate":"2024-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142622224","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"WS-BiTM: Integrating White Shark Optimization with Bi-LSTM for enhanced autism spectrum disorder diagnosis","authors":"Kainat Khan,&nbsp;Rahul Katarya","doi":"10.1016/j.jneumeth.2024.110319","DOIUrl":"10.1016/j.jneumeth.2024.110319","url":null,"abstract":"<div><div>Autism Spectrum Disorder (ASD) is a multifaceted neurodevelopmental condition marked by challenges in social communication, sensory processing, and behavioral regulation. The delayed diagnosis of ASD significantly impedes timely interventions, which can exacerbate symptom severity. With approximately 62 million individuals affected worldwide, the demand for efficient diagnostic tools is critical. This study introduces a novel framework that combines a White Shark Optimization (WSO)-based feature selection method with a Bidirectional Long Short-Term Memory (Bi-LSTM) classifier for enhanced autism classification. Utilizing the WSO technique, we identify key features from autism screening datasets, which markedly improves the model's predictive capabilities. The optimized feature set is then processed by the Bi-LSTM classifier, enhancing its efficiency in handling sequential data. We comprehensively address methodological challenges, including overfitting, generalization, interpretability, and computational efficiency. Furthermore, we conduct a comparative analysis against baseline algorithms such as Neural Networks, Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM) networks, while also employing Particle Swarm Optimization (PSO) for feature selection validation. We evaluate performance metrics, including accuracy, F1-score, specificity, precision, and sensitivity across three ASD datasets: Toddlers, Adults, and Children. Our results demonstrate that the WS-BiTM model significantly outperforms baseline methods, achieving accuracies of 97.6 %, 96.2 %, and 96.4 % on the respective datasets. Additionally, we implemented leave-one-dataset cross-validation and confirmed the statistical significance of our findings through a paired t-test, supplemented by an ablation study to detail the contributions of individual model components. These findings highlight the potential of the WS-BiTM model as a robust tool for ASD classification.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"413 ","pages":"Article 110319"},"PeriodicalIF":2.7,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142622227","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cognition enhances cognition: A comprehensive analysis on cognitive stimulation protocols and their effects on cognitive functions in animal models","authors":"Eugenia Landolfo ,&nbsp;Erica Berretta ,&nbsp;Francesca Balsamo ,&nbsp;Laura Petrosini ,&nbsp;Francesca Gelfo","doi":"10.1016/j.jneumeth.2024.110316","DOIUrl":"10.1016/j.jneumeth.2024.110316","url":null,"abstract":"<div><div>Brain plasticity is involved in the regulation of neural differentiation as well as in functional processes related to memory consolidation, learning, and cognition during healthy life and brain pathology. Modifications in lifestyle, like poor diet, insufficient physical exercise and cognitive stimulation are associated with an increased risk of neurodegeneration; however, there is a paucity of research regarding the impact of individual factors on dementia risk or progression. Cognitive stimulation is a group of techniques and strategies, including cognitive enrichment (CE) and cognitive training (CT), aimed to maintain or improve the functionality of cognitive abilities, such as memory, learning, cognitive flexibility, and attention. The present scoping review focuses on cognitive stimulation by investigating its neuroprotective and therapeutic role on these cognitive functions in rodents. A methodical bibliographic search of experimental studies on rats and mice was conducted on PubMed and Scopus databases up to June 3, 2024. A pool of 29 original research articles was considered as relevant to the topic of the present work. Evidence shows that CE but above all CT influence cognitive performance and brain structure in rodents with specific differences with respect to the quality and quantity of stimulation. There would appear to be greater effects in restoring damage than in preserving or improving a functioning condition. These results provide a theoretical basis to be considered in the therapeutic setting, although further systematic studies would be necessary to identify and characterize the cognitive stimulation protocols which hold the greatest and task-transferable impact on cognitive functioning and maintenance.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"413 ","pages":"Article 110316"},"PeriodicalIF":2.7,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142622212","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}