Journal of Neuroscience Methods最新文献_第5页

WormTracer: A precise method for worm posture analysis using temporal continuity 蠕虫追踪器：一种利用时间连续性进行蠕虫姿态分析的精确方法。

IF 2.3 4区医学 Q2 BIOCHEMICAL RESEARCH METHODS

Journal of Neuroscience Methods

Pub Date : 2025-11-30 DOI: 10.1016/j.jneumeth.2025.110644

Koyo Kuze, Ukyo T. Tazawa, Karin Suwazono, Chung-Kuan Chen, Yu Toyoshima, Yuichi Iino

Background

Quantifying behavior is essential across diverse fields including ecology, ethology, neuroscience, and human science, where posture information provides particularly rich insights. Caenorhabditis elegans serves as a valuable model organism for elucidating behavior through underlying neural and molecular mechanisms. However, accurately quantifying C. elegans posture from video images remains challenging, as existing methods each have limitations.

New method

To address these issues, we developed WormTracer, a novel algorithm for accurately quantifying the temporal evolution of worm postures represented by its centerlines. Unlike conventional approaches that analyze frames independently, WormTracer estimates worm centerlines simultaneously across image sequences, enabling the resolution of complex postures difficult to assess from single images. It uses sequences of binarized images as input, making it applicable to diverse imaging conditions. No training data are required; the only manual operation is selecting an appropriate binarization threshold, after which the process is fully automated.

Results

WormTracer consistently produced uniformly accurate centerlines from worm movies acquired under different resolutions, frame rates and various lighting conditions: light background, dark background, or time-varying textures. Tests using multiple wild-type and mutant animals demonstrated the extent of its robustness.

Comparison with existing methods

Centerlines generated by WormTracer showed higher accuracy than those obtained with existing methods such as WormPose, EigenWormTracker, and DeepTangleCrawl.

Conclusion

WormTracer is accurate, easy to use and applicable to various imaging conditions, providing a promising tool for high-throughput behavioral analyses of C. elegans and enabling more detailed motion quantification than existing methods.

背景：在生态学、行为学、神经科学和人类科学等不同领域，行为量化是必不可少的，在这些领域，姿势信息提供了特别丰富的见解。秀丽隐杆线虫作为一种有价值的模式生物，通过潜在的神经和分子机制来阐明行为。然而，从视频图像中准确量化秀丽隐杆线虫的姿势仍然具有挑战性，因为现有的方法都有局限性。新方法：为了解决这些问题，我们开发了WormTracer，这是一种新的算法，用于准确量化由其中心线表示的蠕虫姿态的时间演变。与独立分析帧的传统方法不同，WormTracer在图像序列中同时估计蠕虫中心线，从而使复杂姿态的分辨率难以从单个图像中评估。它使用二值化图像序列作为输入，使其适用于各种成像条件。不需要培训数据；唯一的手动操作是选择适当的二值化阈值，之后的过程是完全自动化的。结果：在不同分辨率、帧率和各种光照条件下（浅色背景、深色背景或时变纹理）获取的蠕虫电影中，WormTracer始终如一地产生一致准确的中心线。使用多种野生型和突变动物的测试证明了其稳健性的程度。与现有方法的比较：与现有方法（如WormPose、EigenWormTracker、DeepTangleCrawl）相比，由WormTracer生成的中心线精度更高。结论：WormTracer具有准确、简便、适用于各种成像条件的特点，为线虫的高通量行为分析提供了一种有前景的工具，比现有的方法可以更详细地进行运动量化。

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引用次数: 0

Empirical multi-scale thresholding for evoked neural activity denoising 诱发神经活动去噪的经验多尺度阈值法。

IF 2.3 4区医学 Q2 BIOCHEMICAL RESEARCH METHODS

Journal of Neuroscience Methods

Pub Date : 2025-11-29 DOI: 10.1016/j.jneumeth.2025.110643

Hamidreza Abbaspour , Harrison C. Walker , Zachary T. Irwin

Background

Evoked potentials (EPs) are responses elicited by stimulation of the nervous system that serve as key biomarkers for assessing neural function, connectivity, and pathophysiology. Reliable EP extraction is challenged by low signal amplitudes, unrelated neural activity, and background noise across overlapping frequency ranges.

New method

This study presents a novel framework to estimate the noise distribution around EPs without relying on prior assumptions. The method uses a multi-scale bootstrap approach to statistically characterize noise and uncertainty, allowing separation of meaningful EP components from unrelated background activity. The core principle of the bootstrap is that the variance of resampled distributions empirically estimates variability, enabling noise characterization around the mean. By applying this strategy across multiple frequency bands, the method effectively captures dynamic neural variations and improves EP detection reliability.

Results

The method is evaluated using electrocorticographic (ECoG) recordings, including synthetic and real EPs. Quantitative analysis showed lower mean square error (MSE) between denoised and true EPs, indicating improved signal-to-noise ratio (SNR). Qualitative evaluation of real EPs demonstrated enhanced visualization and more accurate morphology recovery, with reduced false detections and preserved EP integrity.

Comparison with existing methods

Compared with conventional filtering techniques, the proposed method better adapts to non-stationary noise and dispersed EP energy while maintaining computational efficiency, ease of implementation, and adjustable confidence levels.

Conclusions

This approach offers improved EP detection and visualization in clinical and research contexts, particularly where recordings are time-limited or patient tolerance for extended sessions is low, supporting broader applications in neuroscience and neuro-engineering.

背景：诱发电位（EPs）是由神经系统刺激引起的反应，是评估神经功能、连通性和病理生理学的关键生物标志物。可靠的EP提取受到低信号幅度、不相关的神经活动和重叠频率范围内的背景噪声的挑战。新方法：本研究提出了一种新的框架来估计EPs周围的噪声分布，而不依赖于先前的假设。该方法使用多尺度自举方法对噪声和不确定性进行统计表征，从而从不相关的背景活动中分离出有意义的EP成分。自举的核心原理是，重采样分布的方差根据经验估计可变性，从而实现围绕平均值的噪声表征。通过在多个频带中应用该策略，该方法可以有效地捕获动态神经变化，提高电位检测的可靠性。结果：使用皮质电图（ECoG）记录对该方法进行评估，包括合成EPs和真实EPs。定量分析显示，降噪后EPs与真实EPs的均方误差（MSE）较低，信噪比（SNR）有所提高。定性评价真实EPs显示增强的可视化和更准确的形态恢复，减少假检测和保留EP完整性。与现有方法的比较：与传统滤波技术相比，该方法在保持计算效率、易于实现和置信度可调的同时，更好地适应非平稳噪声和分散的EP能量。结论：该方法在临床和研究环境中提供了改进的心电检测和可视化，特别是在记录时间有限或患者对长时间会话的耐受性较低的情况下，支持在神经科学和神经工程中更广泛的应用。

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引用次数: 0

Integrating spatial and temporal metrics in smartphone-based finger tapping test: Insights into motor performance dynamics 基于智能手机的手指敲击测试中整合空间和时间度量：对运动性能动态的洞察。

IF 2.3 4区医学 Q2 BIOCHEMICAL RESEARCH METHODS

Journal of Neuroscience Methods

Pub Date : 2025-11-29 DOI: 10.1016/j.jneumeth.2025.110645

Lucas de Sousa Miranda , Bruna Rafaela da Silva Sousa , Felipe André da Costa Brito , Luis Carlos Pereira Monteiro , Bianca Callegari , André dos Santos Cabral , Anselmo de Athayde Costa e Silva , Alex Crisp , Cândida Helena Lopes Alves , Eliza Maria da Costa Brito Lacerda , Anderson Belgamo , Flávia Monteiro Farias , Givago Silva Souza

Background

The Finger Tapping Test (FTT) is a classical tool for assessing motor timing. While traditionally limited to temporal measures, smartphone-based versions enable simultaneous recording of both temporal and spatial parameters, opening new opportunities for digital phenotyping of motor behavior. We aimed to characterize the temporal evolution of both temporal and spatial metrics during a 30-second smartphone-based FTT.

New method

Eighty healthy adults performed the FTT with both hands using a custom smartphone application. We quantified number of taps, intertap interval, total displacement, and elliptical area across six consecutive 5-second intervals. Friedman’s ANOVA and Kendall’s W were applied to assess time-dependent changes and effect sizes.

Results

Temporal parameters were sensitive to fatigue: tap count and total displacement decreased, while intertap interval increased across blocks. In contrast, the elliptical area remained stable, indicating preserved spatial control despite temporal deterioration. The largest effect size was found for intertap interval, particularly among female participants (Kendall’s W = 0.8).

Comparison with existing methods: Unlike traditional FTT implementations that capture only timing, our smartphone-based approach integrates spatial metrics, revealing a dissociation between temporal fatigue and spatial stability during repetitive tapping.

Conclusion

These findings demonstrate the added value of spatial metrics in digital motor assessments, highlighting the potential of smartphone-based FTT as a scalable and low-cost tool for behavioral neuroscience. By bridging temporal and spatial domains, this approach advances methodological innovation in motor performance analysis and paves the way for applications in both research and clinical populations.

背景：手指敲击测试（FTT）是评估运动时间的经典工具。虽然传统上仅限于时间测量，但基于智能手机的版本可以同时记录时间和空间参数，为运动行为的数字表型提供了新的机会。我们的目标是在30秒基于智能手机的FTT中描述时间和空间度量的时间演变。新方法：80名健康成年人使用定制的智能手机应用程序用双手进行FTT。我们量化了连续6个5秒间隔的攻丝次数、攻丝间隔、总位移和椭圆面积。采用Friedman's ANOVA和Kendall's W来评估随时间变化和效应大小。结果：时间参数对疲劳敏感：各区块的攻丝次数和总位移减少，攻丝间隔增加。相比之下，椭圆区域保持稳定，表明尽管时间退化，但仍保留了空间控制。最大的效应量出现在间隔时间，尤其是在女性参与者中（Kendall’s W = 0.8）。与现有方法的比较：与传统的FTT实现只捕获时间不同，我们基于智能手机的方法集成了空间指标，揭示了重复敲击过程中时间疲劳和空间稳定性之间的分离。结论：这些发现证明了空间指标在数字运动评估中的附加价值，突出了基于智能手机的FTT作为一种可扩展且低成本的行为神经科学工具的潜力。通过连接时间和空间域，这种方法推进了运动性能分析的方法创新，并为研究和临床人群的应用铺平了道路。

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引用次数: 0

Parkinson's disease diagnostic support based on voxel fusion of resting BOLD signals and DTI features using multimodal pretraining 基于多模态预训练的静息BOLD信号和DTI特征体素融合的帕金森病诊断支持

IF 2.3 4区医学 Q2 BIOCHEMICAL RESEARCH METHODS

Journal of Neuroscience Methods

Pub Date : 2025-11-29 DOI: 10.1016/j.jneumeth.2025.110646

Qingchuan Zhang , Zhenqiao Liu , Zexi Song , Shaoyi Song , Xuan Li , Zihan Li , Min Zuo

Background

Parkinson’s disease (PD) involves concurrent changes in brain functional activity and white matter microstructure, yet single-modality analyses often fail to capture these complex alterations.

New Methods

We propose a voxel-level dual-stream Swin Transformer fusion framework (DSTFP) to investigate multimodal structure–function relationships in PD. DSTFP employs parallel transformer branches to extract temporal dynamics from resting-state functional MRI (rs-fMRI) and topological features from diffusion tensor imaging (DTI) fractional anisotropy maps. A cross-modal attention fusion module establishes voxel-wise correspondence between functional and structural features.

Results

Applied to the publicly available Parkinson’s Progression Markers Initiative (PPMI) dataset, DSTFP discriminates PD, prodromal, and control groups with high robustness. Structural decoupling index (SDI) and structure–function coupling (SFC) analyses of fused features reveal distributed brain regions with characteristic alterations in functional–structural interactions.

Comparison with Existing Methods

DSTFP outperforms conventional single-modality and baseline multimodal models in both classification accuracy and interpretability, providing more detailed insight into voxel-level structure–function relationships.

Conclusions

The proposed framework offers a robust, interpretable approach for multimodal neuroimaging analysis in PD. All source code is publicly available to support reproducibility (https://github.com/MAOmgg/DSTFP).

背景：帕金森病（PD）涉及脑功能活动和白质微观结构的同时变化，但单模式分析通常无法捕获这些复杂的变化。新方法：我们提出了一个体素级双流Swin Transformer融合框架（DSTFP）来研究PD中的多模态结构-函数关系。DSTFP利用并联变压器支路从静息状态功能MRI （rs-fMRI）中提取时间动态，从扩散张量成像（DTI）分数各向异性图中提取拓扑特征。跨模态注意力融合模块在功能和结构特征之间建立体素对应关系。结果：应用于公开可用的帕金森进展标记计划（PPMI）数据集，DSTFP以高稳健性区分PD，前驱和对照组。结构解耦指数（SDI）和结构-功能耦合（SFC）的融合特征分析揭示了具有功能-结构相互作用特征改变的分布脑区。与现有方法的比较：DSTFP在分类精度和可解释性方面优于传统的单模态和基线多模态模型，可以更详细地了解体素级结构-功能关系。结论：所提出的框架为帕金森病的多模态神经影像学分析提供了一个可靠的、可解释的方法。所有源代码都是公开的，以支持再现性（https://github.com/MAOmgg/DSTFP）。

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引用次数: 0

Automated ladder rung test for evaluating motor coordination in Parkinson’s disease mouse models 评估帕金森病小鼠模型运动协调性的自动梯阶试验。

IF 2.3 4区医学 Q2 BIOCHEMICAL RESEARCH METHODS

Journal of Neuroscience Methods

Pub Date : 2025-11-29 DOI: 10.1016/j.jneumeth.2025.110642

Peng Zhang , Wei Xu , Wei Jiang , Xiaofei Jin , Yajun Lou , Tianyi Yang , Weitao Li , Keqiang Gao , Fan Gao , Zhiyu Qian

Background

The ladder rung walking test assesses fine motor coordination in Parkinson’s disease (PD) mouse models but relies on labor-intensive, subjective manual scoring, necessitating an automated, objective system.

New Method

We developed a cost-effective automated ladder rung test system with a ladder featuring regular and irregular rung patterns, array through-beam optical sensors for foot-error detection, and an Arduino microcontroller. Custom Python software enables intuitive control, real-time visualization, dynamic sensor mapping, adjustable debounce, and CSV data export.

Results

In an MPTP-induced PD mouse model, the system detected increased foot errors on irregular rungs (5.13 ± 1.04 vs. 1.78 ± 0.69 in controls, p < 0.0001) and longer traversal times (18.04 ± 2.64 s vs. 13.38 ± 1.95 s, p = 0.001), corroborated by open field and rotarod tests and a 68.7 % reduction in substantia nigra neurons.

Comparison with Existing Methods

Unlike costly camera-based systems requiring complex algorithms, our system uses simple photoelectric sensors and costs approximately 127 USD for all components, achieving 96.4 % precision and 99.3 % recall, making it accessible and user-friendly.

Conclusions

This automated system offers a reproducible, high-throughput tool for objective motor assessment in PD and neurological models, enhancing preclinical research.

背景：阶梯行走试验评估帕金森病（PD）小鼠模型的精细运动协调能力，但依赖于劳动密集型的主观人工评分，需要一个自动化、客观的系统。新方法：我们开发了一种具有成本效益的自动梯子梯级测试系统，该系统具有规则和不规则梯级模式的梯子，用于脚误差检测的阵列通束光学传感器和Arduino微控制器。自定义Python软件支持直观控制，实时可视化，动态传感器映射，可调debounce和CSV数据导出。结果：在mptp诱导的PD小鼠模型中，该系统检测到不规则台阶上的足部误差增加（5.13±1.04比对照组的1.78±0.69,p < 0.0001），行走时间延长（18.04±2.64s比13.38±1.95s, p = 0.001），开阔视野和旋转杆试验证实了这一点，黑质神经元减少68.7%。与现有方法的比较：与昂贵的基于相机的系统需要复杂的算法不同，我们的系统使用简单的光电传感器，所有组件的成本约为127美元，达到96.4%的精度和99.3%的召回率，易于使用和用户友好。结论：该自动化系统为PD和神经模型的客观运动评估提供了一个可重复的、高通量的工具，加强了临床前研究。

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引用次数: 0

A robust method for primary cerebellar culture and genetic manipulation of Purkinje cells from postnatal mice 出生后小鼠浦肯野细胞的初级小脑培养和遗传操作的可靠方法。

IF 2.3 4区医学 Q2 BIOCHEMICAL RESEARCH METHODS

Journal of Neuroscience Methods

Pub Date : 2025-11-22 DOI: 10.1016/j.jneumeth.2025.110627

Heiling Lu , Friederike Stephani , Myrna Aulia , Edwin Mientjes , Martijn Schonewille , Catarina Osório

Background

Primary cerebellar cultures are a powerful system for studying genes and pathways involved in development and disease. However, maintaining the survival of Purkinje cells in vitro remains challenging. These neurons represent a small fraction of the cerebellar population and are highly sensitive to culture conditions, making genetic manipulation difficult. Existing protocols often use more than one culture medium, vary in the developmental stage of the cerebellar tissue used, and may require supplementary support cell cultures.

New method

We describe a robust method for dissociated cerebellar cultures that supports Purkinje cell development and enables efficient genetic manipulation using two techniques.

Results

Our protocol supports the development of Purkinje cells from embryonic day 18 and postnatal day 1 mouse cerebellar tissue, with P1-derived neurons more closely resembling their in vivo counterparts. Genetic manipulation was achieved via cell-specific adeno-associated viral transduction and non-cell-specific antisense oligonucleotide transfection. Functional neuronal activity was confirmed using calcium imaging with Fluo-4 AM.

Comparison with existing methods

Our approach unifies key aspects of existing protocols. It uses a single-medium composition, eliminating the need for separate seeding and maintenance media. Culturing each cerebellum individually preserves the intrinsic number of supporting cells, eliminating the need for parallel cultures. Using postnatal tissue also reduces animal usage. Additionally, gene expression can be successfully modulated using antisense oligonucleotides.

Conclusions

This protocol provides an efficient framework for the culture, development, and genetic manipulation of Purkinje cells, facilitating their application in molecular, cellular, and functional studies of cerebellar development and disease models.

背景：小脑原代培养是研究与发育和疾病相关的基因和通路的一个强大系统。然而，在体外维持浦肯野细胞的存活仍然具有挑战性。这些神经元只占小脑总数的一小部分，对培养条件高度敏感，使得基因操作变得困难。现有的方案通常使用一种以上的培养基，在使用的小脑组织的发育阶段有所不同，并且可能需要补充支持细胞培养。新方法：我们描述了一种强大的分离小脑培养方法，支持浦肯野细胞发育，并使用两种技术实现有效的遗传操作。结果：我们的方案支持浦肯野细胞从胚胎18天和出生后1天的小鼠小脑组织中发育，其p1来源的神经元更接近其体内对应物。基因操作是通过细胞特异性腺相关病毒转导和非细胞特异性反义寡核苷酸转染实现的。用Fluo-4 AM钙显像证实功能性神经元活动。与现有方法的比较：我们的方法统一了现有协议的关键方面。它使用单一介质组成，不需要单独的播种和维护介质。单独培养每个小脑保留了支持细胞的固有数量，消除了平行培养的需要。使用产后组织也减少了动物的使用。此外，基因表达可以成功地调节使用反义寡核苷酸。结论：该方案为浦肯野细胞的培养、发育和遗传操作提供了一个有效的框架，促进了它们在小脑发育和疾病模型的分子、细胞和功能研究中的应用。

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引用次数: 0

A high-resolution approach for cerebrospinal fluid cytokine detection using push-pull sampling and nano dot blot: Minute by minute TNF-α dynamics during epileptiform activity 采用推拉取样和纳米点印迹技术检测脑脊液细胞因子的高分辨率方法：癫痫样活动期间逐分钟的TNF-α动态

IF 2.3 4区医学 Q2 BIOCHEMICAL RESEARCH METHODS

Journal of Neuroscience Methods

Pub Date : 2025-11-21 DOI: 10.1016/j.jneumeth.2025.110641

Salvador M. Martinez-Gallegos , Alberto Morales-Villagrán , Laura Medina-Ceja , Kenia Pardo-Peña , Silvia Casillas-Romero

Background

TNF-α is a key proinflammatory cytokine implicated in the initiation and progression of neuroinflammatory responses. However, conventional detection techniques pose sample volume limitations that hinder the temporal resolution that can be achieved using animal models.

New method

In this study, we developed a novel system designed to quantify

cytokines via continuous sampling of cerebrospinal fluid (CSF) via low-flow pushpull perfusion coupled with immunodetection via nanodot blotting. The system was tested in a 4-aminopyridine (4-AP) model of acute epileptiform activity, and the time course of TNF-α was characterized with a 1-min temporal resolution.

Results

We observed rapid release of TNF-α within minutes of 4-AP administration. The peak of this release coincided temporally with the onset of epileptiform discharge trains, highlighting the relevance of TNF-α in the early phase of seizurelike activity. TNF-α levels returned to baseline within the first hour, a dynamic that would likely be missed by conventional low temporal resolution methodologies.

Comparison with existing methods and conclusions

Our findings demonstrate the utility of this minute-by-minute approach in capturing transient neuroinflammatory events and suggest that TNF-α may play a role in the initiation or modulation of acute epileptiform activity.

背景：TNF-α是一种关键的促炎细胞因子，与神经炎症反应的发生和进展有关。然而，传统的检测技术存在样本量限制，阻碍了使用动物模型可以实现的时间分辨率。新方法：在这项研究中，我们开发了一种新的系统，通过低流量推拉灌注和纳米点印迹免疫检测，通过脑脊液（CSF）的连续采样来量化细胞因子。该系统在急性癫痫样活性的4-氨基吡啶（4-AP）模型中进行了测试，TNF-α的时间过程以1 min的时间分辨率为特征。结果：我们观察到4-AP给药后几分钟内TNF-α快速释放。这种释放的高峰在时间上与癫痫样放电序列的发作相吻合，突出了TNF-α在癫痫样活动早期的相关性。TNF-α水平在第一个小时内恢复到基线水平，这一动态很可能被传统的低时间分辨率方法所忽略。与现有方法和结论的比较：我们的研究结果证明了这种逐分钟方法在捕获短暂性神经炎症事件中的实用性，并表明TNF-α可能在急性癫痫样活动的启动或调节中发挥作用。

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引用次数: 0

Reconstruction and dynamic analysis of corticomuscular subnetworks reveal task-specific neural coordination in visuomotor control 皮质肌肉子网络的重建和动态分析揭示了视觉运动控制中特定任务的神经协调。

IF 2.3 4区医学 Q2 BIOCHEMICAL RESEARCH METHODS

Journal of Neuroscience Methods

Pub Date : 2025-11-20 DOI: 10.1016/j.jneumeth.2025.110640

Yugen You , Jianeng Lin , Xinyuan Zhang , Jiewei Lu , Ningbo Yu , Jianda Han

Background:

Understanding visuomotor coordination is critical for advancing behavioral neuroscience, particularly in elucidating fine hand control in daily activities. However, its dynamic organization in corticomuscular networks remains unclear.

New method:

This study proposes a novel framework for reconstructing and analyzing corticomuscular subnetworks from simultaneously recorded electroencephalography (EEG) and electromyography (EMG) signals during visuomotor tasks. Fifteen right-handed healthy subjects (9 males; mean age:

22.80 \pm 1.01

years) performed a grip-force tracking task under conditions with and without visual feedback. Dynamic corticomuscular networks were constructed with partial transfer entropy to capture evolving bidirectional information flow. Task-specific subnetworks were identified through non-negative matrix factorization and reconstructed based on activation-profile clustering with k-means. Subnetwork features were extracted, and correlation with behavioral performance was analyzed.

Results:

Eight activation profiles were identified without visual feedback, whereas three of these, involving more connections, were found with visual feedback. Moreover, subnetwork features involving efferent connections were correlated with tracking accuracy with visual feedback, while subnetworks involving afferent proprioceptive feedback were correlated with tracking accuracy without visual feedback.

Comparison with existing methods:

A subnetwork reconstruction technique was integrated with conventional network decomposition to deal with the over-segmentation problem, improving interpretability. To the best of our knowledge, this is the first study to decompose task-state dynamic cortico-muscular-cortical networks. Compared to full network analysis, the subnetworks showed stronger correlations with task performance.

Conclusion:

This study proposed a novel framework for analyzing dynamics of corticomuscular subnetworks and revealed distinct neural coordination strategies for visually guided and proprioception-driven motor control.

背景：理解视觉运动协调对于促进行为神经科学的发展至关重要，特别是在阐明日常活动中的精细手部控制方面。然而，其在皮质肌肉网络中的动态组织尚不清楚。新方法：本研究提出了一种新的框架，用于从视觉运动任务中同时记录的脑电图（EEG）和肌电（EMG）信号中重建和分析皮质肌肉子网络。15名健康右撇子受试者（男性9名，平均年龄22.80±1.01岁）分别在有视觉反馈和无视觉反馈的情况下完成握力跟踪任务。利用部分传递熵构建动态皮质肌肉网络，捕捉不断变化的双向信息流。通过非负矩阵分解识别特定于任务的子网络，并基于k-means的激活轮廓聚类重构。提取子网特征，并分析其与行为性能的相关性。结果：在没有视觉反馈的情况下发现了8个激活谱，而在有视觉反馈的情况下发现了3个涉及更多连接的激活谱。此外，涉及传出连接的子网络特征与有视觉反馈的跟踪精度相关，而涉及传入本体感觉反馈的子网络特征与无视觉反馈的跟踪精度相关。与现有方法的比较：将子网络重构技术与传统的网络分解相结合，解决过分割问题，提高了可解释性。据我们所知，这是第一个分解任务状态动态皮质-肌肉-皮质网络的研究。与全网络分析相比，子网络与任务性能表现出更强的相关性。结论：本研究提出了一个分析皮质肌肉子网络动力学的新框架，揭示了视觉引导和本体感觉驱动运动控制的不同神经协调策略。

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引用次数: 0

Comparing methods for mass univariate analyses of human EEG: Empirical data and simulations 人类脑电图海量单变量分析的比较方法：经验数据与仿真。

IF 2.3 4区医学 Q2 BIOCHEMICAL RESEARCH METHODS

Journal of Neuroscience Methods

Pub Date : 2025-11-20 DOI: 10.1016/j.jneumeth.2025.110630

Anna-Lena Tebbe , Christian Panitz , Andreas Keil

Background

Electroencephalography (EEG) is a widely used method for investigating human brain dynamics. However, EEG analyses are frequently conducted with limited a priori knowledge regarding locations or latencies of meaningful statistical effects. This makes it difficult for researchers to form regions of interest (ROIs), which are then analyzed using traditional statistical models such as analysis of variance. To address this, mass univariate analyses have become a valuable complement to ROI-based approaches. These methods attempt to correct for multiple comparisons while mitigating the risk of false positives and false negatives, thus enabling statistical inference in high-dimensional EEG data.

New method

Here, we review and evaluate different approaches for delineating spatial and temporal effect boundaries in three datasets. Specifically, we focus on permutation-based approaches and their Bayesian alternatives to address within-subjects condition differences in i) steady-state evoked responses, ii) event-related potentials, and iii) time-frequency data.

Results

Overall, simulation results showed variability in the number of datapoints indicating statistical condition differences.

Comparison with existing methods

Cluster-based permutation tests provide a relatively liberal approach to correct for multiple comparisons across domains, with high sensitivity for detecting large effects. In contrast, the permutation-based t_max procedure yields the most conservative method across datasets. Bayesian approaches are continuous in nature and thus strongly depend on the selection of thresholds for when support for a hypothesis is considered meaningful.

Conclusions

These findings provide insights into the strengths and limitations of current mass univariate approaches with the goal of supporting more informed decision-making in the analysis of EEG data.

背景：脑电图（EEG）是一种广泛使用的研究人脑动力学的方法。然而，脑电图分析经常在有限的先验知识下进行，关于有意义的统计效应的位置或潜伏期。这使得研究人员很难形成兴趣区域（roi），然后使用传统的统计模型（如方差分析）对其进行分析。为了解决这个问题，大规模单变量分析已经成为基于roi的方法的一个有价值的补充。这些方法试图纠正多重比较，同时降低假阳性和假阴性的风险，从而实现高维脑电图数据的统计推断。新方法：在此，我们回顾和评估了在三个数据集中描述时空效应边界的不同方法。具体来说，我们专注于基于排列的方法及其贝叶斯替代方案，以解决受试者在i)稳态诱发反应，ii)事件相关电位和iii)时频数据方面的条件差异。结果：总体而言，模拟结果显示数据点数量的可变性表明统计条件的差异。与现有方法的比较：基于聚类的排列测试提供了一种相对自由的方法来纠正跨域的多个比较，对检测大影响具有高灵敏度。相反，基于排列的tmax过程产生跨数据集最保守的方法。贝叶斯方法本质上是连续的，因此强烈依赖于阈值的选择，当支持假设被认为是有意义的。结论：这些发现为当前大规模单变量方法的优势和局限性提供了见解，目的是在EEG数据分析中支持更明智的决策。

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引用次数: 0

CUSP: Complex spike sorting from multi-electrode array recordings with U-net sequence-to-sequence prediction CUSP：复杂尖峰排序从多电极阵列记录与U-net序列到序列预测。

IF 2.3 4区医学 Q2 BIOCHEMICAL RESEARCH METHODS

Journal of Neuroscience Methods

Pub Date : 2025-11-19 DOI: 10.1016/j.jneumeth.2025.110631

Chenhao Bao , Robyn L. Mildren , Adam S. Charles , Kathleen E. Cullen

Background

Complex spikes (CSs) in cerebellar Purkinje cells convey unique signals complementary to Simple spike (SS) action potentials, but are infrequent and variable in waveform. Their variability and low spike counts, combined with recording artifacts such as electrode drift, make automated detection challenging.

New Method

We introduce CUSP (CS sorting via U-net Sequence Prediction), a fully automated deep learning framework for CS sorting in high-density multi-electrode array recordings. CUSP uses a U-Net architecture with hybrid self-attention inception blocks to integrate local field potential and action potential signals and outputs CS event probabilities in a sequence-to-sequence manner. Detected events are clustered and paired with concurrently detected SSs to reconstruct the complete Purkinje cell activity.

Results

Trained on cerebellar neuropixels recordings in rhesus macaques, CUSP achieves human-expert performance (F1 = 0.83 ± 0.03) and even captures valid CS events overlooked during manual annotation.

Comparison with Existing Methods

CUSP outperforms traditional and state-of-the-art CS and SS sorting algorithms on CS detection. It remains robust to waveform variability, spikelet composition, and electrode drift, enabling accurate CS tracking in long-term recordings. In contrast, existing methods often show false-positive biases or degrade under drift.

Conclusions

CUSP provides a scalable, robust framework for analyzing burst-like or dynamically complex spike patterns. Its generalizability makes it valuable for large-scale cerebellar datasets and other neural systems, such as hippocampal pyramidal cells, where complex bursts are critical for computation. By combining expert-level accuracy with automation, CUSP offers a broadly applicable solution for studying information coding across circuits.

背景：小脑浦肯野细胞中的复杂尖峰（CSs）传递与简单尖峰（SS）动作电位互补的独特信号，但波形不频繁且多变。它们的可变性和低尖峰计数，加上电极漂移等记录伪影，使自动检测具有挑战性。新方法：我们引入CUSP （CS排序通过U-net序列预测），这是一个用于高密度多电极阵列记录CS排序的全自动深度学习框架。CUSP使用U-Net架构和混合自关注初始块来集成本地场电位和动作电位信号，并以序列对序列的方式输出CS事件概率。检测到的事件聚集并与并发检测到的ss配对，以重建完整的浦肯野细胞活性。结果：在恒河猴小脑神经像素记录的训练下，CUSP达到了人类专家的表现（F1 = 0.83±0.03），甚至捕获了手动注释中忽略的有效CS事件。与现有方法的比较：CUSP在CS检测上优于传统的和最先进的CS和SS排序算法。它对波形可变性，小穗组成和电极漂移保持鲁棒性，能够在长期记录中进行准确的CS跟踪。相比之下，现有的方法往往显示假阳性偏差或在漂移下退化。结论：CUSP为分析突发或动态复杂的尖峰模式提供了一个可扩展的、健壮的框架。它的通用性使得它对大规模的小脑数据集和其他神经系统（如海马体锥体细胞）很有价值，在这些系统中，复杂的脉冲对计算至关重要。通过将专家级的准确性与自动化相结合，CUSP为研究跨电路的信息编码提供了广泛适用的解决方案。

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引用次数: 0