Journal of Neuroscience Methods最新文献

{"title":"STSimM: A new tool for evaluating neuron model performance and detecting spike trains similarity","authors":"A. Marasco ,&nbsp;C.A. Lupascu ,&nbsp;C. Tribuzi","doi":"10.1016/j.jneumeth.2024.110324","DOIUrl":"10.1016/j.jneumeth.2024.110324","url":null,"abstract":"<div><h3>Background:</h3><div>In computational neuroscience, performance measures are essential for quantitatively assessing the predictive power of neuron models, while similarity measures are used to estimate the level of synchrony between two or more spike trains. Most of the measures proposed in the literature require setting an appropriate time-scale and often neglect silent periods.</div></div><div><h3>New method:</h3><div>Four time-scale adaptive performance and similarity measures are proposed and implemented in the <em>STSimM</em> (Spike Trains Similarity Measures) Python tool. These measures are designed to accurately capture both the precise timing of individual spikes and shared periods of inactivity among spike trains.</div></div><div><h3>Results:</h3><div>The proposed ST-measures demonstrate enhanced sensitivity over <em>Spike-contrast</em> and <em>SPIKE-distance</em> in detecting spike train similarity, aligning closely with <em>SPIKE-synchronization</em>. Correlations among all similarity measures were observed in Poisson datasets, whereas in vivo-like synaptic stimulations showed correlations only between ST-measures and SPIKE-synchronization.</div></div><div><h3>Comparison of existing method:</h3><div>The <em>STSimM</em> measures are compared with <em>SPIKE-distance</em>, SPIKE-synchronization and <em>Spike-contrast</em> using four spike train datasets with varying similarity levels.</div></div><div><h3>Conclusion:</h3><div>ST-measures appear more suitable for detecting both the precise timing of single spikes and shared periods of inactivity among spike trains compared to those considered in this work. Their flexibility originates from two primary factors: firstly, the inclusion of four key measures — ST-Accuracy, ST-Precision, ST-Recall, ST-Fscore — capable of discerning similarity levels across neuronal activity, whether interleaved with silent periods or solely focusing on spike timing accuracy; secondly, the integration of three model parameters that govern both precise spike detection and the weighting of silent periods.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"415 ","pages":"Article 110324"},"PeriodicalIF":2.7,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142791828","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":"Electrode configurations for sensitive and specific detection of compound muscle action potentials to the tibialis anterior muscle after peroneal nerve injury in rats","authors":"JuliAnne Allgood ,&nbsp;Sam James ,&nbsp;Lillian Laird,&nbsp;Albert Allotey,&nbsp;Jared Bushman","doi":"10.1016/j.jneumeth.2024.110335","DOIUrl":"10.1016/j.jneumeth.2024.110335","url":null,"abstract":"<div><h3>Background</h3><div>Quantifying peripheral nerve regeneration via electrophysiology is a commonly used technique, but it can be complicated by spurious electrical activity. This study sought to compare electrode configurations for measuring compound muscle action potential (CMAP) of the tibialis anterior (TA) muscle in a rat model for specific and sensitive detection of regeneration of peroneal nerve to the TA.</div></div><div><h3>New method</h3><div>10 Sprague-Dawley rats underwent a peroneal nerve transection with direct microsuture repair. CMAPs were conducted with different placements and types of electrodes. Compound action potentials (CAPs) and gait analysis were regularly collected up to 70 days (d) post operation (PO). Nerve sections were harvested at 49 d (n = 4) and 70 d (n = 6) PO and stained with toluidine blue to assess nerve morphometry.</div></div><div><h3>Results</h3><div>Of the tested configurations for CMAPs, a concentric recording/reference electrode in combination with stimulation from the sciatic notch showed the least background and highest sensitivity, while some configurations showed significant noise and did not detect changes in CMAPs within the 70 d recording period following injury. CAPs, gait analysis, morphometry, and muscle mass support the extent of regeneration indicated by CMAPs collected with concentric electrodes.</div></div><div><h3>Conclusion</h3><div>Collateral innervation patterns can complicate CMAP recordings as signals from adjacent muscles can be detected and misinterpreted as regeneration. The outcome of this study shows how differences in configurations and electrodes have significant effects on CMAP for the TA. The results identify methods using concentric electrodes that provide high specificity and sensitivity capable of detecting evidence of regeneration early after injury.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"415 ","pages":"Article 110335"},"PeriodicalIF":2.7,"publicationDate":"2024-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142769820","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 fMRI quality control","authors":"Lennard van den Berg,&nbsp;Nick Ramsey,&nbsp;Mathijs Raemaekers","doi":"10.1016/j.jneumeth.2024.110337","DOIUrl":"10.1016/j.jneumeth.2024.110337","url":null,"abstract":"<div><h3>Background</h3><div>fMRI in clinical settings faces challenges affecting activity maps. Template matching can screen for abnormal results by providing an objective metric of activity map quality. This research tests how sample size, age, or gender-specific templates, and unilateral templates affect template matching results.</div></div><div><h3>New method</h3><div>We used an fMRI database of 76 healthy subjects performing 7 tasks assessing motor, language, and working memory functions. Templates were created with varying numbers of subjects, genders, and ages. Individual subjects were compared to templates using leave-one-out cross validation. We also compared unilateral and bilateral templates.</div></div><div><h3>Results</h3><div>Increasing sample size improved template matches, with diminishing returns for larger sample sizes. Gender and age-specific templates increased correlations for some tasks, with age having a larger effect than gender. Generally, templates including all subjects provided the highest correlations, indicating that age and gender effects did not outweigh the benefits of larger sample sizes. Unilateral templates of the task-dominant hemisphere increased template correlations.</div></div><div><h3>Conclusions</h3><div>Age and gender affect templates, but the benefits depend on the database size. When the database is large enough, age and gender effects are beneficial. Unilateral templates enhance template matching, but practical benefits depend on the severity of neurological abnormalities in patients.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"415 ","pages":"Article 110337"},"PeriodicalIF":2.7,"publicationDate":"2024-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142769821","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":"Multi-layer transfer learning algorithm based on improved common spatial pattern for brain–computer interfaces","authors":"Zhuo Cai ,&nbsp;Yunyuan Gao ,&nbsp;Feng Fang ,&nbsp;Yingchun Zhang ,&nbsp;Shunlan Du","doi":"10.1016/j.jneumeth.2024.110332","DOIUrl":"10.1016/j.jneumeth.2024.110332","url":null,"abstract":"<div><div>In the application of brain–computer interface, the differences in imaging methods and brain structure between subjects hinder the effectiveness of decoding algorithms when applied on different subjects. Transfer learning has been designed to solve this problem. There have been many applications of transfer learning in motor imagery (MI), however the effectiveness is still limited due to the inconsistent domain alignment, lack of prominent data features and allocation of weights in trails. In this paper, a Multi-layer transfer learning algorithm based on improved Common Spatial Patterns (MTICSP) was proposed to solve these problems. Firstly, the source domain data and target domain data were aligned by Target Alignment (TA)method to reduce distribution differences between subjects. Secondly, the mean covariance matrix of the two classes was re-weighted by calculating the distance between the covariance matrix of each trial in the source domain and the target domain. Thirdly, the improved Common Spatial Patterns (CSP) by introducing regularization coefficient was proposed to further reduce the difference between source domain and target domain to extract features. Finally, the feature blocks of the source domain and target domain were aligned again by Joint Distribution Adaptation (JDA) method. Experiments on two public datasets in two transfer paradigms multi-source to single-target (MTS) and single-source to single-target (STS) verified the effectiveness of our proposed method. The MTS and STS in the 5-person dataset were 80.21% and 77.58%, respectively, and 80.10% and 73.91%, respectively, in the 9-person dataset. Experimental results also showed that the proposed algorithm was superior to other state-of-the-art algorithms. In addition, the generalization ability of our algorithm MTICSP was validated on the fatigue EEG dataset collected by ourselves, and obtained 94.83% and 87.41% accuracy in MTS and STS experiments respectively. The proposed method combines improved CSP with transfer learning to extract the features of source and target domains effectively, providing a new method for combining transfer learning with motor imagination.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"415 ","pages":"Article 110332"},"PeriodicalIF":2.7,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142769822","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":"Multimodal predictive modeling: Scalable imaging informed approaches to predict future brain health","authors":"Meenu Ajith ,&nbsp;Jeffrey S. Spence ,&nbsp;Sandra B. Chapman ,&nbsp;Vince D. Calhoun","doi":"10.1016/j.jneumeth.2024.110322","DOIUrl":"10.1016/j.jneumeth.2024.110322","url":null,"abstract":"<div><h3>Background:</h3><div>Predicting future brain health is a complex endeavor that often requires integrating diverse data sources. The neural patterns and interactions identified through neuroimaging serve as the fundamental basis and early indicators that precede the manifestation of observable behaviors or psychological states.</div></div><div><h3>New Method:</h3><div>In this work, we introduce a multimodal predictive modeling approach that leverages an imaging-informed methodology to gain insights into future behavioral outcomes. We employed three methodologies for evaluation: an assessment-only approach using support vector regression (SVR), a neuroimaging-only approach using random forest (RF), and an image-assisted method integrating the static functional network connectivity (sFNC) matrix from resting-state functional magnetic resonance imaging (rs-fMRI) alongside assessments. The image-assisted approach utilized a partially conditional variational autoencoder (PCVAE) to predict brain health constructs in future visits from the behavioral data alone.</div></div><div><h3>Results:</h3><div>Our performance evaluation indicates that the image-assisted method excels in handling conditional information to predict brain health constructs in subsequent visits and their longitudinal changes. These results suggest that during the training stage, the PCVAE model effectively captures relevant information from neuroimaging data, thereby potentially improving accuracy in making future predictions using only assessment data.</div></div><div><h3>Comparison with Existing Methods:</h3><div>The proposed image-assisted method outperforms traditional assessment-only and neuroimaging-only approaches by effectively integrating neuroimaging data with assessment factors.</div></div><div><h3>Conclusion:</h3><div>This study underscores the potential of neuroimaging-informed predictive modeling to advance our comprehension of the complex relationships between cognitive performance and neural connectivity.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"414 ","pages":"Article 110322"},"PeriodicalIF":2.7,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142747373","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":"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}