Journal of Neuroscience Methods最新文献_第7页

Electrode configurations for sensitive and specific detection of compound muscle action potentials to the tibialis anterior muscle after peroneal nerve injury in rats 大鼠腓神经损伤后胫骨前肌复合肌动作电位敏感特异检测的电极配置。

IF 2.7 4区医学 Q2 BIOCHEMICAL RESEARCH METHODS

Journal of Neuroscience Methods

Pub Date : 2024-11-30 DOI: 10.1016/j.jneumeth.2024.110335

JuliAnne Allgood , Sam James , Lillian Laird, Albert Allotey, Jared Bushman

Background

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.

New method

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.

Results

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.

Conclusion

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.

背景：通过电生理学量化周围神经再生是一种常用的技术，但它可能会因虚假的电活动而复杂化。本研究旨在比较测量大鼠胫骨前肌（TA）复合肌动作电位（CMAP）的电极配置，以特异性和敏感地检测腓神经向TA的再生。新方法：10只Sprague-Dawley大鼠腓神经横断直接微缝线修复。采用不同位置和类型的电极进行cmap。术后70天定期收集复合动作电位（CAPs）和步态分析。在第49天（n=4）和第70天（n=6）采集神经切片，用甲苯胺蓝染色评估神经形态学。结果：在CMAPs的测试配置中，同心圆记录/参考电极结合坐骨切迹刺激显示出最低的背景和最高的灵敏度，而一些配置显示出明显的噪声，并且在损伤后70 d的记录期内未检测到CMAPs的变化。CAPs、步态分析、形态测量和肌肉质量支持同心电极收集的cmap所显示的再生程度。结论：侧支神经支配模式可使CMAP记录复杂化，因为邻近肌肉的信号可被检测到并误解为再生。本研究结果表明，结构和电极的差异对TA的CMAP有显著影响。结果确定了使用同心电极的方法，该方法具有高特异性和灵敏度，能够在损伤后早期检测到再生的证据。

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引用次数: 0

Enhancing fMRI quality control 加强功能磁共振成像质量控制。

IF 2.7 4区医学 Q2 BIOCHEMICAL RESEARCH METHODS

Journal of Neuroscience Methods

Pub Date : 2024-11-30 DOI: 10.1016/j.jneumeth.2024.110337

Lennard van den Berg, Nick Ramsey, Mathijs Raemaekers

Background

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.

New method

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.

Results

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.

Conclusions

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.

背景：fMRI在临床环境中面临影响活动图的挑战。模板匹配可以通过提供活动图质量的客观度量来筛选异常结果。本研究测试样本量、年龄或性别特定模板和单边模板如何影响模板匹配结果。新方法：我们使用76名健康受试者执行7项任务的fMRI数据库，评估运动、语言和工作记忆功能。模板是由不同数量的主题、性别和年龄创建的。使用留一交叉验证将个体受试者与模板进行比较。我们还比较了单侧和双侧模板。结果：增加样本量可改善模板匹配，样本量越大回报越小。性别和年龄特定的模板增加了某些任务的相关性，年龄的影响大于性别。一般来说，包含所有受试者的模板提供了最高的相关性，这表明年龄和性别的影响不会超过更大样本量的好处。任务主导半球的单侧模板增加了模板相关性。结论：年龄和性别影响模板，但收益取决于数据库大小。当数据库足够大时，年龄和性别的影响是有益的。单侧模板增强模板匹配，但实际效益取决于患者神经异常的严重程度。

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引用次数: 0

Multi-layer transfer learning algorithm based on improved common spatial pattern for brain–computer interfaces 基于改进公共空间模式的脑机接口多层迁移学习算法。

IF 2.7 4区医学 Q2 BIOCHEMICAL RESEARCH METHODS

Journal of Neuroscience Methods

Pub Date : 2024-11-28 DOI: 10.1016/j.jneumeth.2024.110332

Zhuo Cai , Yunyuan Gao , Feng Fang , Yingchun Zhang , Shunlan Du

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.

在脑机接口的应用中，被试之间成像方式和脑结构的差异影响了解码算法在不同被试上的有效性。迁移学习就是为了解决这个问题而设计的。迁移学习在运动图像（MI）中有很多应用，但由于域对齐不一致、缺乏突出的数据特征和轨迹权重分配等问题，迁移学习的有效性仍然受到限制。本文提出了一种基于改进公共空间模式（MTICSP）的多层迁移学习算法。首先，采用目标对齐（target Alignment， TA）方法对源域数据和目标域数据进行对齐，减小受试者之间的分布差异；其次，通过计算源域和目标域各试验的协方差矩阵之间的距离，对两类的均值协方差矩阵进行重新加权；第三，通过引入正则化系数，提出改进的公共空间模式（Common Spatial Patterns， CSP），进一步减小源域与目标域之间的差异，提取特征；最后，采用联合分布自适应（JDA）方法对源域和目标域的特征块进行重新对齐。在多源到单目标（MTS）和单源到单目标（STS）两种迁移模式下的两个公共数据集上的实验验证了我们所提出方法的有效性。5人数据集的MTS和STS分别为80.21%和77.58%，9人数据集的MTS和STS分别为80.10%和73.91%。实验结果表明，该算法优于其他先进算法。在自行采集的疲劳脑电数据集上验证了算法MTICSP的泛化能力，在MTS和STS实验中分别获得了94.83%和87.41%的准确率。该方法将改进的CSP与迁移学习相结合，有效地提取了源域和目标域的特征，为迁移学习与运动想象相结合提供了一种新的方法。

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引用次数: 0

Multimodal predictive modeling: Scalable imaging informed approaches to predict future brain health 多模态预测建模：预测未来大脑健康的可扩展成像知情方法

IF 2.7 4区医学 Q2 BIOCHEMICAL RESEARCH METHODS

Journal of Neuroscience Methods

Pub Date : 2024-11-26 DOI: 10.1016/j.jneumeth.2024.110322

Meenu Ajith , Jeffrey S. Spence , Sandra B. Chapman , Vince D. Calhoun

Background:

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.

New Method:

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.

Results:

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.

Comparison with Existing Methods:

The proposed image-assisted method outperforms traditional assessment-only and neuroimaging-only approaches by effectively integrating neuroimaging data with assessment factors.

Conclusion:

This study underscores the potential of neuroimaging-informed predictive modeling to advance our comprehension of the complex relationships between cognitive performance and neural connectivity.

背景：预测未来大脑健康是一项复杂的工作，通常需要整合不同的数据来源。通过神经影像学识别的神经模式和相互作用是可观察到的行为或心理状态表现之前的基本基础和早期指标。新方法：在这项工作中，我们引入了一种多模态预测建模方法，该方法利用成像信息方法来深入了解未来的行为结果。我们采用了三种方法进行评估：仅使用支持向量回归（SVR）的评估方法，仅使用随机森林（RF）的神经成像方法，以及将静息状态功能磁共振成像（rs-fMRI）的静态功能网络连接（sFNC）矩阵与评估相结合的图像辅助方法。图像辅助方法利用部分条件变分自编码器（PCVAE）仅从行为数据预测未来就诊时的大脑健康结构。结果：我们的性能评估表明，图像辅助方法在处理条件信息以预测后续就诊的大脑健康结构及其纵向变化方面表现出色。这些结果表明，在训练阶段，PCVAE模型有效地从神经成像数据中捕获相关信息，从而有可能提高仅使用评估数据进行未来预测的准确性。与现有方法的比较：本文提出的图像辅助方法通过有效地将神经影像学数据与评估因素相结合，优于传统的仅评估和仅神经影像学方法。结论：这项研究强调了神经成像预测模型的潜力，以促进我们对认知表现和神经连通性之间复杂关系的理解。

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引用次数: 0

Single-channel electroencephalography decomposition by detector-atom network and its pre-trained model 利用检测器-原子网络及其预训练模型进行单通道脑电图分解。

IF 2.7 4区医学 Q2 BIOCHEMICAL RESEARCH METHODS

Journal of Neuroscience Methods

Pub Date : 2024-11-23 DOI: 10.1016/j.jneumeth.2024.110323

Hiroshi Higashi

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.

利用多通道空间特征的信号分解技术对于脑电图（EEG）信号的分析、去噪和分类至关重要。为了便于对有限信道记录的信号进行分解，本文提出了一种不依赖多信道特征的新型单信道分解方法。我们的模型假设脑电信号由短的、移位不变的波组成，这些波被称为 "原子"。我们将分解器设计为人工神经网络，旨在估计这些原子，并检测输入信号中的时移和振幅调制。我们的方法在脑机接口和神经科学的各种应用场景中都得到了验证，显示出更高的性能。此外，跨数据集验证表明了预训练模型的可行性，使即插即用信号分解模块成为可能。

引用次数: 0

Enhancing detection of SSVEP-based BCIs via a novel temporally local canonical correlation analysis 通过新颖的时间局部典型相关性分析，增强基于 SSVEP 的 BCI 检测。

IF 2.7 4区医学 Q2 BIOCHEMICAL RESEARCH METHODS

Journal of Neuroscience Methods

Pub Date : 2024-11-20 DOI: 10.1016/j.jneumeth.2024.110325

Guoxian Xia, Li Wang, Shiming Xiong, Jiaxian Deng

Background

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.

New method

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.

Results

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.

Comparison with existing methods

Our extended method outperforms the state-of-the-art methods by at least 3 %, and it effectively suppresses local noise and maintains excellent scalability.

Conclusions for research articles

The proposed method can effectively combine spatial and temporal filters to improve the recognition performance of SSVEPs.

背景：近年来，基于空间滤波器的频率识别方法在基于稳态视觉诱发电位（SSVEP）的脑机接口（BCI）系统中大受欢迎。然而，这些方法在抑制局部噪声方面效果不佳，而且依赖于数据的长度。在实际应用中，利用短数据窗口提高识别性能是 BCI 系统面临的一个重大挑战：新方法：通过提取时间信息和消除局部噪声，提出了一种基于训练数据驱动的时间局部典型相关分析（TI-tdCCA）方法，以提高 SSVEPs 的识别性能。基于新颖的框架，滤波器是通过将拉普拉斯矩阵纳入串联训练数据和单个模板之间的 TI-CCA 得出的。随后，通过应用适当的空间滤波器和拉普拉斯矩阵确定目标频率：两个数据集分别包含 40 个类别和来自 35 和 70 个受试者的记录，实验结果表明，在大多数情况下，所提出的方法始终优于八种竞争方法。同时，还对包含人工参考信号的扩展版方法进行了评估。扩展版方法比建议的方法有显著改进。具体来说，在时间窗口为 0.7s 的情况下，受试者在基准数据集上的平均识别准确率提高了 10.71%，在 BETA 数据集上的平均识别准确率提高了 6.98%：与现有方法的比较：我们的扩展方法比最先进的方法优越至少 3%，它有效地抑制了局部噪声，并保持了出色的可扩展性：提出的方法可以有效地结合空间和时间滤波器，提高 SSVEPs 的识别性能。

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引用次数: 0

Improving computational models of deep brain stimulation through experimental calibration 通过实验校准改进脑深部刺激的计算模型。

IF 2.7 4区医学 Q2 BIOCHEMICAL RESEARCH METHODS

Journal of Neuroscience Methods

Pub Date : 2024-11-15 DOI: 10.1016/j.jneumeth.2024.110320

Jan Philipp Payonk , Henning Bathel , Nils Arbeiter , Maria Kober , Mareike Fauser , Alexander Storch , Ursula van Rienen , Julius Zimmermann

Background:

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.

New method:

Exemplified with rodent microelectrodes, we present a workflow for validating electrode model geometry using microscopy and impedance spectroscopy in vitro before implantation. We address uncertainties in the tissue distribution and dielectric properties and outline a concept for calibrating the computational model based on in vivo impedance spectroscopy measurements.

Results:

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.

Comparison with existing methods:

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.

Conclusion:

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.

背景：深部脑刺激已成为治疗运动障碍的成熟临床工具。然而，人们对刺激过程的了解仍然有限。为了填补这一知识空白，人们开发了计算模型以获得更深入的了解。然而，这些模型的预测能力仍然受到模型不确定性以及缺乏验证和校准的限制：新方法：以啮齿类动物微电极为例，我们介绍了一种在植入前利用显微镜和阻抗谱在体外验证电极模型几何形状的工作流程。我们讨论了组织分布和介电特性的不确定性，并概述了根据体内阻抗谱测量结果校准计算模型的概念：结果：18 个表征电极上被激活的组织体积的标准偏差约为 32.93%，凸显了电极表征的重要性。因此，该工作流程大大提高了以啮齿动物模型为例的神经激活模型预测的可信度：与现有方法的比较：计算模型通常未经验证或校准，而是依赖于制造商的规格。我们的方法提供了一种获得经过验证和校准的电极几何形状的简便方法，从而大大提高了依赖该电极的计算模型的可靠性：结论：通过减少模型的不确定性，提高了预测神经激活的准确性。整个工作流程是在开源软件中实现的，因此可适用于其他用例，如人类深部脑刺激。此外，该框架还可以整合更多实验，实现计算模型的实时更新和完善。

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引用次数: 0

ST-SHAP: A hierarchical and explainable attention network for emotional EEG representation learning and decoding ST-SHAP：用于情绪脑电图表征学习和解码的分层可解释注意力网络

IF 2.7 4区医学 Q2 BIOCHEMICAL RESEARCH METHODS

Journal of Neuroscience Methods

Pub Date : 2024-11-12 DOI: 10.1016/j.jneumeth.2024.110317

Minmin Miao , Jin Liang , Zhenzhen Sheng , Wenzhe Liu , Baoguo Xu , Wenjun Hu

Background:

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.

New method

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.

Results:

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.

Comparison with existing methods:

In terms of subject-dependent and subject-independent emotional EEG decoding accuracies, our method outperforms several closely related existing methods.

Conclusion:

These experimental results fully prove the effectiveness and superiority of our proposed algorithm.

研究背景利用脑电图（EEG）进行情绪识别已成为人机交互领域的研究热点，如何充分学习情绪EEG数据的复杂时空表征并获得可解释的模型预测结果仍是巨大挑战：本研究提出了一种新颖的分层可解释注意力网络 ST-SHAP，它结合了 Swin Transformer（ST）和 SHapley Additive exPlanations（SHAP）技术，用于自动情绪脑电图分类。首先，通过频带滤波、时间分割、空间映射和插值生成情绪脑电数据的三维时空特征，以充分保留重要的时空频率特性。其次，设计分层注意力网络，以充分学习情绪脑电图的抽象时空表征并进行分类。具体来说，在该解码模型中，W-MSA 模块用于对局部窗口内的相关性建模，SW-MSA 模块允许不同局部窗口之间的信息交互，而补丁合并模块则进一步促进了局部到全局的多尺度建模。最后，SHAP方法用于发现情绪处理的重要脑区，并提高Swin Transformer模型的可解释性：结果：两个基准数据集（即 SEED 和 DREAMER）用于分类性能评估。在受试者依赖性实验中，ST-SHAP 在 SEED 数据集上的平均准确率为 97.18%，而在 DREAMER 数据集上，ST-SHAP 在唤醒维度和价值维度上的平均准确率分别为 96.06% 和 95.98%。此外，在这两个数据集上，还通过数据驱动方法发现了符合神经生理学先验知识的导入脑区：与现有方法的比较：在与主体相关和与主体无关的情绪脑电图解码准确度方面，我们的方法优于几种密切相关的现有方法：这些实验结果充分证明了我们提出的算法的有效性和优越性。

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引用次数: 0

New approach to control ischemic severity ex vivo 控制体内缺血严重程度的新方法

IF 2.7 4区医学 Q2 BIOCHEMICAL RESEARCH METHODS

Journal of Neuroscience Methods

Pub Date : 2024-11-10 DOI: 10.1016/j.jneumeth.2024.110321

Bindu Modi , Kaejaren C.N. Caldwell , Colby E. Witt , Moriah E. Weese-Myers , Ashley E. Ross

Background

It is advantageous to be able to both control and define a metric for ischemia severity in ex vivo models to enable more precise comparisons to in vivo models and to facilitate more sophisticated mechanistic studies. Currently, the primary method to induce and study ischemia ex vivo is to completely deplete oxygen and glucose in the culture media; however, in vivo ischemia often involves varying degrees of severities.

New Method

In this work, we have successfully developed an approach to both control and characterize three different ischemic severities ex vivo and we define these standard condition metrics via an oxygen sensor: normoxia (control), mild ischemia (partial oxygen-glucose deprivation), and severe ischemia (complete oxygen-glucose deprivation).

Results

To validate the extent to which controlling oxygen and glucose concentration ex vivo 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.

Comparison to Existing Methods

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 ex vivo. Our new approach matches with existing literature for controlling ischemic severity in vivo.

Conclusions

Overall, this new approach will significantly impact our ability to expand ex vivo platforms for assessing ischemic damage and will provide a new experimental approach for investigating the molecular mechanisms involved in ischemia.

背景：在体外模型中控制和确定缺血严重程度的指标是非常有利的，这样可以与体内模型进行更精确的比较，并促进更复杂的机理研究。目前，诱导和研究体内外缺血的主要方法是完全耗尽培养基中的氧气和葡萄糖；然而，体内缺血往往涉及不同程度的严重性：在这项工作中，我们成功开发了一种方法，既能控制又能表征体内三种不同的缺血严重程度，我们通过氧传感器定义了这些标准条件指标：常氧（控制）、轻度缺血（部分氧气-葡萄糖剥夺）和重度缺血（完全氧气-葡萄糖剥夺）：为了验证控制体内氧气和葡萄糖浓度对细胞表达、招募和细胞损伤的影响程度，我们在短短30分钟内就证明了细胞因子和HIF-1ɑ的变化、葡萄糖转运体表达水平的增加、caspase-3的变化以及小胶质细胞向神经元的快速招募：据我们所知，这是第一次控制缺血的严重程度，并证明在体内短短 30 分钟内就能对蛋白质表达和细胞运动产生可测量的影响。我们的新方法与现有文献中控制体内缺血严重程度的方法相吻合：总之，这一新方法将极大地影响我们扩展体内外缺血损伤评估平台的能力，并将为研究缺血的分子机制提供一种新的实验方法。

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引用次数: 0

Adaptive wheel exercise for mouse models of Parkinson’s Disease 帕金森病小鼠模型的自适应车轮运动

IF 2.7 4区医学 Q2 BIOCHEMICAL RESEARCH METHODS

Journal of Neuroscience Methods

Pub Date : 2024-11-10 DOI: 10.1016/j.jneumeth.2024.110314

Henry Skelton , Dayton Grogan , Amrutha Kotlure , Ken Berglund , Claire-Anne Gutekunst , Robert Gross

Background

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.

New method

We developed an apparatus and technique for inducing exercise in mice without aversive stimuli, using a motorized wheel that dynamically responds to subject performance.

Results

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.

Comparison with existing methods

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.

Conclusions

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.

背景：体育锻炼对神经系统疾病，尤其是帕金森病（PD）的治疗作用已得到广泛研究。然而，现有的小鼠运动技术并不适合运动缺陷的疾病模型动物，这些技术依赖于自发活动或在厌恶刺激下的强制运动，而且不便于通过系留试验对神经生理学进行主动测量。电动轮运动可以克服这些限制，但尚未证明能可靠地诱导小鼠奔跑：新方法：我们开发了一种仪器和技术，可以在没有厌恶刺激的情况下诱导小鼠运动，使用的电动轮可以动态响应受试者的表现：结果：市场上销售的电动轮系统并不能提供令人满意的运动效果，因为小鼠倾向于避免以较高的速度奔跑。我们的自适应车轮运动平台可在6-羟基多巴胺多发性硬化症小鼠模型中进行有效的运动诱导，包括精确的行为测量和同步单体电生理学：与现有方法比较：与之前描述的电动轮运动技术相比，我们的方法提供了一个更好的物理平台和编程策略。与自主运动不同的是，我们的方法可以对跑步进行可控的实验性诱导，而无需使用基于跑步机的技术中常见的厌恶性刺激：结论：自适应车轮运动可以更好地研究体育锻炼对帕金森病小鼠以及其他神经疾病模型的动态生理干预作用。

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