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Automatic detection of epileptic seizure based on one dimensional cascaded convolutional autoencoder with adaptive window-thresholding. 基于一维级联卷积自动编码器和自适应窗口阈值的癫痫发作自动检测。
Pub Date : 2024-10-29 DOI: 10.1088/1741-2552/ad883a
Sunday Timothy Aboyeji, Xin Wang, Yan Chen, Ijaz Ahmad, Lin Li, Zhenzhen Liu, Chen Yao, Guoru Zhao, Yu Zhang, Guanglin Li, Shixiong Chen

Objective. Identifying the seizure occurrence period (SOP) in extended EEG recordings is crucial for neurologists to diagnose seizures effectively. However, many existing computer-aided diagnosis systems for epileptic seizure detection (ESD) primarily focus on distinguishing between ictal and interictal states in EEG recordings. This focus has limited their application in clinical settings, as these systems typically rely on supervised learning approaches that require labeled data.Approach. To address this, our study introduces an unsupervised learning framework for ESD using a 1D- cascaded convolutional autoencoder (1D-CasCAE). In this approach, EEG recordings from selected patients in the CHB-MIT datasets are first segmented into 5 s epochs. Eight informative channels are chosen based on the correlation coefficient and Shannon entropy. The 1D-CasCAE is designed to autonomously learn the characteristic patterns of interictal (non-seizure) segments through downsampling and upsampling processes. The integration of adaptive thresholding and a moving window significantly enhances the model's robustness, enabling it to accurately identify ictal segments in long EEG recordings.Main results. Experimental results demonstrate that the proposed 1D-CasCAE effectively learns normal EEG signal patterns and efficiently detects anomalies (ictal segments) using reconstruction errors. When compared with other leading methods in anomaly detection, our model exhibits superior performance, as evidenced by its average Gmean, sensitivity, specificity, precision, and false positive rate scores of 98.00% ± 3.51%, 94.94% ± 6.92%, 99.60% ± 0.30%, 79.92% ± 13.56% and 0.0044 ± 0.0030 h-1respectively for a typical patient in CHB-MIT datasets.Significance. The developed model framework can be employed in clinical settings, replacing the manual inspection process of EEG signals by neurologists. Furthermore, the proposed automated system can adapt to each patient's SOP through the use of variable time windows for seizure detection.

目的:在扩展脑电图记录中识别癫痫发作期(SOP)对于神经科医生有效诊断癫痫发作至关重要。然而,现有的许多用于癫痫发作检测(ESD)的计算机辅助诊断系统(CAD)主要侧重于区分脑电图记录中的发作期和发作间期状态。这一重点限制了它们在临床环境中的应用,因为这些系统通常依赖于需要标记数据的超级可视化学习方法:为解决这一问题,我们的研究采用一维级联卷积自动编码器(1D-CasCAE)为 ESD 引入了一种无监督学习框架。在这种方法中,首先将 CHB-MIT 数据集中选定患者的脑电图记录分割成 5 秒的历时。根据相关系数和香农熵选择八个信息通道。1D-CasCAE 的设计目的是通过下采样和上采样过程自主学习发作间期(非发作)片段的特征模式。自适应阈值和移动窗口的整合大大增强了模型的鲁棒性,使其能够在长时间的脑电图记录中准确识别发作节段:实验结果表明,所提出的 1D-CasCAE 能有效学习正常的脑电信号模式,并利用重建误差高效检测异常(发作节段)。与异常检测领域的其他主要方法相比,我们的模型表现出更优越的性能,其在 CHB-MIT 数据集中典型患者的平均平均值、灵敏度、特异性、预判和假阳性率分别为 98.00±3.51%、94.94±6.92%、99.60±0.30%、79.92±13.56% 和 0.0044±0.0030/h:最后,所开发的模型框架可用于临床环境,取代神经科医生对脑电图信号的人工检查过程。通过使用可变时间窗检测癫痫发作,该自动化系统可适应每位患者的 SOP。
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引用次数: 0
Changes in high-frequency neural inputs to muscles during movement cancellation. 运动取消时肌肉高频神经输入的变化。
Pub Date : 2024-10-29 DOI: 10.1088/1741-2552/ad8835
Blanka Zicher, Simon Avrillon, Jaime Ibáñez, Dario Farina

Objective.Cortical beta (13-30 Hz) and gamma (30-60 Hz) oscillations are prominent in the motor cortex and are known to be transmitted to the muscles despite their limited direct impact on force modulation. However, we currently lack fundamental knowledge about the saliency of these oscillations at spinal level. Here, we developed an experimental approach to examine the modulations in high-frequency inputs to motoneurons under different motor states while maintaining a stable force, thus constraining behaviour.Approach.Specifically, we acquired brain and muscle activity during a 'GO'/'NO-GO' task. In this experiment, the effector muscle for the task (tibialis anterior) was kept tonically active during the trials, while participants (N= 12) reacted to sequences of auditory stimuli by either keeping the contraction unaltered ('NO-GO' trials), or by quickly performing a ballistic contraction ('GO' trials). Motor unit (MU) firing activity was extracted from high-density surface and intramuscular electromyographic signals, and the changes in its spectral contents in the 'NO-GO' trials were analysed.Main results.We observed an increase in beta and low-gamma (30-45 Hz) activity after the 'NO-GO' cue in the MU population activity. These results were in line with the brain activity changes measured with electroencephalography. These increases in power occur without relevant alterations in force, as behaviour was restricted to a stable force contraction.Significance.We show that modulations in motor cortical beta and gamma rhythms are also present in muscles when subjects cancel a prepared ballistic action while holding a stable contraction in a 'GO'/'NO-GO' task. This occurs while force levels produced by the task effector muscle remain largely unaltered. Our results suggest that muscle recordings are informative also about motor states that are not force-control signals. This opens up new potential use cases of peripheral neural interfaces.

目的: 皮质β(13-30赫兹)和γ(30-60赫兹)振荡在运动皮质中非常突出,尽管它们对力量调节的直接影响有限,但已知它们会传递到肌肉。然而,我们目前对这些振荡在脊髓水平的显著性缺乏基本了解。在此,我们开发了一种实验方法来研究在不同运动状态下运动神经元的高频输入调节,同时保持稳定的力量,从而约束行为:具体来说,我们获取了 "GO"/"NO-GO "任务中的大脑和肌肉活动。在该实验中,任务的效应肌肉(胫骨前肌)在试验过程中保持强直性活动,而参与者(12 人)则对听觉刺激序列做出反应,要么保持收缩不改变("NO-GO "试验),要么快速进行弹道收缩("GO "试验)。我们从高密度表面和肌内肌电信号中提取了运动单元(MU)的发射活动,并分析了在 "NO-GO "试验中其频谱内容的变化。这些结果与脑电图测量到的大脑活动变化一致。由于行为仅限于稳定的力量收缩,因此这些力量的增加并没有引起力量的相关改变:我们的研究表明,当受试者在 "GO"/"NO-GO "任务中保持稳定收缩时取消准备好的弹道动作,肌肉中也会出现运动皮层 beta 和 gamma 节律的调节。这种情况发生时,任务效应肌肉产生的力水平基本保持不变。我们的研究结果表明,肌肉记录也能提供非力控制信号的运动状态信息。这为外周神经接口开辟了新的潜在用例。
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引用次数: 0
Neural decoding and feature selection methods for closed-loop control of avoidance behavior. 用于避让行为闭环控制的神经解码和特征选择方法。
Pub Date : 2024-10-29 DOI: 10.1088/1741-2552/ad8839
Jinhan Liu, Rebecca Younk, Lauren M Drahos, Sumedh S Nagrale, Shreya Yadav, Alik S Widge, Mahsa Shoaran

Objective.Many psychiatric disorders involve excessive avoidant or defensive behavior, such as avoidance in anxiety and trauma disorders or defensive rituals in obsessive-compulsive disorders. Developing algorithms to predict these behaviors from local field potentials (LFPs) could serve as the foundational technology for closed-loop control of such disorders. A significant challenge is identifying the LFP features that encode these defensive behaviors.Approach.We analyzed LFP signals from the infralimbic cortex and basolateral amygdala of rats undergoing tone-shock conditioning and extinction, standard for investigating defensive behaviors. We utilized a comprehensive set of neuro-markers across spectral, temporal, and connectivity domains, employing SHapley Additive exPlanations for feature importance evaluation within Light Gradient-Boosting Machine models. Our goal was to decode three commonly studied avoidance/defensive behaviors: freezing, bar-press suppression, and motion (accelerometry), examining the impact of different features on decoding performance.Main results.Band power and band power ratio between channels emerged as optimal features across sessions. High-gamma (80-150 Hz) power, power ratios, and inter-regional correlations were more informative than other bands that are more classically linked to defensive behaviors. Focusing on highly informative features enhanced performance. Across 4 recording sessions with 16 subjects, we achieved an average coefficient of determination of 0.5357 and 0.3476, and Pearson correlation coefficients of 0.7579 and 0.6092 for accelerometry jerk and bar press rate, respectively. Utilizing only the most informative features revealed differential encoding between accelerometry and bar press rate, with the former primarily through local spectral power and the latter via inter-regional connectivity. Our methodology demonstrated remarkably low training/inference time and memory usage, requiring<310 ms for training,<0.051 ms for inference, and 16.6 kB of memory, using a single core of AMD Ryzen Threadripper PRO 5995WX CPU.Significance.Our results demonstrate the feasibility of accurately decoding defensive behaviors with minimal latency, using LFP features from neural circuits strongly linked to these behaviors. This methodology holds promise for real-time decoding to identify physiological targets in closed-loop psychiatric neuromodulation.

目的:许多精神疾病都涉及过度回避或防御行为,例如焦虑症和创伤症中的回避或强迫症中的防御仪式。从局部场电位(LFP)中开发出预测这些行为的算法,可作为对此类疾病进行闭环控制的基础技术。一个重大挑战是确定编码这些防御行为的 LFP 特征。我们分析了接受音调冲击条件反射和消退的大鼠下边缘皮层和杏仁基底外侧的 LFP 信号,这是研究防御行为的标准。我们使用了一整套跨频谱、时间和连接域的神经标记物,并在光梯度提升机模型中使用 SHapley Additive exPlanations 进行特征重要性评估。我们的目标是解码三种常见的回避/防御行为:冻结、压杠抑制和运动(加速度测量),研究不同特征对解码性能的影响。频带功率和通道之间的频带功率比成为各次会议的最佳特征。高伽马(80-150 Hz)功率、功率比和区域间相关性比其他频段更有信息量,而其他频段与防御行为更有经典联系。专注于信息量大的特征可提高成绩。在对 16 名受试者进行的 4 次记录过程中,我们发现加速度测量挺举和杠铃按压率的平均决定系数分别为 0.5357 和 0.3476,皮尔逊相关系数分别为 0.7579 和 0.6092。仅利用信息量最大的特征就能发现加速度和压杆率之间的编码差异,前者主要通过局部频谱功率,后者则通过区域间连接。我们的方法显示了极低的训练/推理时间和内存使用率,只需要
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引用次数: 0
Robust entropy rate estimation for nonstationary neuronal calcium spike trains based on empirical probabilities. 基于经验概率的非稳态神经元钙离子尖峰列车稳健熵率估计
Pub Date : 2024-10-28 DOI: 10.1088/1741-2552/ad6cf4
Sathish Ande, Srinivas Avasarala, Sarpras Swain, Ajith Karunarathne, Lopamudra Giri, Soumya Jana

Objective. Temporal patterns in neuronal spiking encode stimulus uncertainty, and convey information about high-level functions such as memory and cognition. Estimating the associated information content and understanding how that evolves with time assume significance in the investigation of neuronal coding mechanisms and abnormal signaling. However, existing estimators of the entropy rate, a measure of information content, either ignore the inherent nonstationarity, or employ dictionary-based Lempel-Ziv (LZ) methods that converge too slowly for one to study temporal variations in sufficient detail. Against this backdrop, we seek estimates that handle nonstationarity, are fast converging, and hence allow meaningful temporal investigations.Approach. We proposed a homogeneous Markov model approximation of spike trains within windows of suitably chosen length and an entropy rate estimator based on empirical probabilities that converges quickly.Main results. We constructed mathematical families of nonstationary Markov processes with certain bi/multi-level properties (inspired by neuronal responses) with known entropy rates, and validated the proposed estimator against those. Further statistical validations were presented on data collected from hippocampal (and primary visual cortex) neuron populations in terms of single neuron behavior as well as population heterogeneity. Our estimator appears to be statistically more accurate and converges faster than existing LZ estimators, and hence well suited for temporal studies.Significance. The entropy rate analysis revealed not only informational and process memory heterogeneity among neurons, but distinct statistical patterns in neuronal populations (from two different brain regions) under basal and post-stimulus conditions. Taking inspiration, we envision future large-scale studies of different brain regions enabled by the proposed tool (estimator), potentially contributing to improved functional modeling of the brain and identification of statistical signatures of neurodegenerative diseases.

目的:神经元尖峰振荡的时间模式编码刺激的不确定性,并传递有关记忆和认知等高级功能的信息。估算相关的信息含量并了解其如何随时间演变,对于研究神经元编码机制和异常信号具有重要意义。然而,现有的熵率估算器(一种信息含量测量方法)要么忽略了固有的非平稳性,要么采用基于字典的 Lempel-Ziv (LZ) 方法,这种方法收敛速度太慢,无法对时间变化进行足够详细的研究。在此背景下,我们寻求能够处理非平稳性、快速收敛、从而进行有意义的时间研究的估算方法:我们提出了在适当长度的窗口内对尖峰列车进行同质马尔可夫模型近似的方法,以及基于经验概率的熵率估计器,该估计器收敛速度很快:我们构建了具有某些双/多级特性(受神经元反应的启发)的非平稳马尔可夫过程数学族,这些数学族具有已知的熵率,并针对这些数学族验证了所提出的估计器。从单个神经元行为和群体异质性的角度,对从海马(和初级视觉皮层)神经元群体收集的数据进行了进一步的统计验证。 我们的估计器在统计上似乎比现有的 LZ 估计器更准确,收敛速度更快,因此非常适合时间研究:熵率分析不仅揭示了神经元之间的信息和过程记忆异质性,还揭示了神经元群(来自两个不同脑区)在基础和刺激后条件下的不同统计模式。受此启发,我们设想未来将利用所提出的工具(估计器)对不同脑区进行大规模研究,从而为改进大脑功能建模和识别神经退行性疾病的统计特征做出潜在贡献。
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引用次数: 0
Deep learning-based spike sorting: a survey. 基于深度学习的尖峰排序:一项调查。
Pub Date : 2024-10-25 DOI: 10.1088/1741-2552/ad8b6c
Luca Meyer, Majid Zamani, János Rokai, Andreas Demosthenous

Objective.Deep learning is increasingly permeating neuroscience, leading to a rise in signal-processing applications for extracellular recordings. These signals capture the activity of small neuronal populations, necessitating 'spike sorting' to assign action potentials (spikes) to their underlying neurons. With the rise in publications delving into new methodologies and techniques for deep learning-based spike sorting, it is crucial to synthesise these findings critically. This survey provides an in-depth evaluation of the approaches, methodologies and outcomes presented in recent articles, shedding light on the current state-of-the-art.Approach.Twenty-four articles published until December 2023 on deep learning-based spike sorting have been examined. The proposed methods are divided into three sub-problems of spike sorting: spike detection, feature extraction and classification. Moreover, integrated systems, i.e., models that detect spikes and extract features or do classification within a single network, are included.Main Results.Although most algorithms have been developed for single-channel recordings, models utilising multi-channel data have already shown promising results, with efficient hardware implementations running quantised models on ASICs and FPGAs. Convolutional neural networks have been used extensively for spike detection and classification as the data can be processed spatiotemporally while maintaining low-parameter models and increasing generalisation and efficiency. Autoencoders have been mainly utilised for dimensionality reduction, enabling subsequent clustering with standard methods. Also, integrated systems have shown great potential in solving the spike sorting problem from end to end.Significance.This survey explores recent articles on deep learning-based spike sorting and highlights the capabilities of deep neural networks in overcoming associated challenges, but also highlights potential biases of certain models. Serving as a resource for both newcomers and seasoned researchers in the field, this work provides insights into the latest advancements and may inspire future model development.

目的:深度学习正日益渗透到神经科学领域,导致细胞外记录信号处理应用的增加。这些信号捕获了小神经元群的活动,需要进行 "尖峰分类",以便将动作电位(尖峰)分配给其下层神经元。随着深入研究基于深度学习的尖峰排序新方法和新技术的论文不断增加,对这些研究成果进行批判性总结至关重要。本调查对近期文章中提出的方法、方法论和结果进行了深入评估,揭示了当前的先进水平。方法:研究了截至 2023 年 12 月发表的 24 篇关于基于深度学习的尖峰排序的文章。所提出的方法分为尖峰分类的三个子问题:尖峰检测、特征提取和分类。主要结果:虽然大多数算法都是针对单通道记录开发的,但利用多通道数据的模型已经显示出良好的效果,在 ASIC 和 FPGA 上运行量化模型的硬件实现效率很高。卷积神经网络已被广泛用于尖峰检测和分类,因为在保持低参数模型、提高泛化和效率的同时,还能对数据进行时空处理。自动编码器主要用于降低维度,以便随后使用标准方法进行聚类。此外,集成系统在从头到尾解决尖峰排序问题方面显示出巨大潜力。意义:本调查探讨了近期有关基于深度学习的尖峰排序的文章,强调了深度神经网络在克服相关挑战方面的能力,同时也强调了某些模型的潜在偏差。作为该领域新人和经验丰富的研究人员的资源,这项工作提供了对最新进展的见解,并可能激励未来的模型开发。
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引用次数: 0
Investigating the synergistic neuromodulation effect of bilateral rTMS and VR brain-computer interfaces training in chronic stroke patients. 研究双侧经颅磁刺激和虚拟现实脑机接口训练对慢性中风患者的协同神经调节作用
Pub Date : 2024-10-24 DOI: 10.1088/1741-2552/ad8836
Monica Afonso, Francisco Sánchez-Cuesta, Yeray González-Zamorano, Juan Pablo Romero, Athanasios Vourvopoulos

Objective.Stroke is a major cause of adult disability worldwide, resulting in motor impairments. To regain motor function, patients undergo rehabilitation, typically involving repetitive movement training. For those who lack volitional movement, novel technology-based approaches have emerged that directly involve the central nervous system, through neuromodulation techniques such as transcranial magnetic stimulation (TMS), and closed-loop neurofeedback like brain-computer interfaces (BCIs). This, can be augmented through proprioceptive feedback delivered many times by embodied virtual reality (VR). Nonetheless, despite a growing body of research demonstrating the individual efficacy of each technique, there is limited information on their combined effects.Approach.In this study, we analyzed the Electroencephalographic (EEG) signals acquired from 10 patients with more than 4 months since stroke during a longitudinal intervention with repetitive TMS followed by VR-BCI training. From the EEG, the event related desynchronization (ERD) and individual alpha frequency (IAF) were extracted, evaluated over time and correlated with clinical outcome.Main results.Every patient's clinical outcome improved after treatment, and ERD magnitude increased during simultaneous rTMS and VR-BCI. Additionally, IAF values showed a significant correlation with clinical outcome, nonetheless, no relationship was found between differences in ERD pre- post- intervention with the clinical improvement.Significance.This study furnishes empirical evidence supporting the efficacy of the joint action of rTMS and VR-BCI in enhancing patient recovery. It also suggests a relationship between IAF and rehabilitation outcomes, that could potentially serve as a retrievable biomarker for stroke recovery.

目的:脑卒中是全球成年残疾人的主要致残原因,会导致运动障碍。为了恢复运动功能,患者需要接受康复训练,通常包括重复运动训练。对于缺乏自主运动能力的患者,新出现的基于技术的方法通过经颅磁刺激(TMS)等神经调节技术和脑机接口(BCIs)等闭环神经反馈技术,直接参与中枢神经系统。此外,虚拟现实技术(VR)多次提供的本体感觉反馈也可对此进行增强。然而,尽管越来越多的研究证明了每种技术的单独功效,但有关其综合效果的信息却十分有限:在这项研究中,我们分析了 10 名中风超过 4 个月的患者在接受重复 TMS 和 VR-BCI 训练的纵向干预期间获得的脑电图(EEG)信号。从脑电信号中提取了事件相关非同步化(ERD)和个体阿尔法频率(IAF),对其进行了长期评估,并将其与临床结果相关联:结果:治疗后,每位患者的临床疗效都有所改善。此外,IAF 值与临床结果有显著相关性,但干预前和干预后 ERD 的差异与临床改善之间没有关系:本研究为经颅磁刺激和虚拟现实脑干成像联合作用在促进患者康复方面的疗效提供了实证支持。该研究还表明,IAF 与康复效果之间存在关系,有可能成为中风康复的可检索生物标志物。
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引用次数: 0
Modeling electrical impedance in brain tissue with diffusion tensor imaging for functional neurosurgery applications. 利用扩散张量成像建立脑组织电阻抗模型,用于功能神经外科应用。
Pub Date : 2024-10-24 DOI: 10.1088/1741-2552/ad7db2
Niranjan Kumar, Aidan Ahamparam, Charles W Lu, Karlo A Malaga, Parag G Patil

Objective.Decades ago, neurosurgeons used electrical impedance measurements in the brain for coarse intraoperative tissue differentiation. Over time, these techniques were largely replaced by more refined imaging and electrophysiological localization. Today, advanced methods of diffusion tensor imaging (DTI) and finite element method (FEM) modeling may permit non-invasive, high-resolution intracerebral impedance prediction. However, expectations for tissue-impedance relationships and experimentally verified parameters for impedance modeling in human brains are lacking. This study seeks to address this need.Approach.We used FEM to simulate high-resolution single- and dual-electrode impedance measurements along linear electrode trajectories through (1) canonical gray and white matter tissue models, and (2) selected anatomic structures within whole-brain patient DTI-based models. We then compared intraoperative impedance measurements taken at known locations along deep brain stimulation (DBS) surgical trajectories with model predictions to evaluate model accuracy and refine model parameters.Main results.In DTI-FEM models, single- and dual-electrode configurations performed similarly. While only dual-electrode configurations were sensitive to white matter fiber orientation, other influences on impedance, such as white matter density, enabled single-electrode impedance measurements to display significant spatial variation even within purely white matter structures. We compared 308 intraoperative single-electrode impedance measurements in five DBS patients to DTI-FEM predictions at one-to-one corresponding locations. After calibration of model coefficients to these data, predicted impedances reliably estimated intraoperative measurements in all patients (R=0.784±0.116,n=5). Through this study, we derived an updated value for the slope coefficient of the DTI conductance model published by Tuchet al,k=0.0649 S⋅smm-3 (originalk=0.844), for use specifically in humans at physiological frequencies.Significance.This is the first study to compare impedance estimates from imaging-based models of human brain tissue to experimental measurements at the same locationsin vivo. Accurate, non-invasive, imaging-based impedance prediction has numerous applications in functional neurosurgery, including tissue mapping, intraoperative electrode localization, and DBS.

目的:几十年前,神经外科医生使用脑部电阻抗测量来进行粗略的术中组织分辨。随着时间的推移,这些技术在很大程度上被更精细的成像和电生理定位所取代。如今,先进的弥散张量成像(DTI)和有限元法(FEM)建模方法可实现无创、高分辨率的脑内阻抗预测。然而,目前还缺乏对人脑组织阻抗关系的预期和经过实验验证的阻抗建模参数。方法:我们使用有限元模拟高分辨率单电极和双电极阻抗测量,沿线性电极轨迹通过(1)典型灰质和白质组织模型,以及(2)基于全脑患者 DTI 模型的选定解剖结构。然后,我们将在已知位置沿脑深部刺激(DBS)手术轨迹进行的术中阻抗测量结果与模型预测结果进行比较,以评估模型的准确性并完善模型参数。虽然只有双电极配置对白质纤维方向敏感,但阻抗的其他影响因素,如白质密度,使得单电极阻抗测量即使在纯白质结构中也能显示出显著的空间变化。我们将五名 DBS 患者的 308 次术中单电极阻抗测量结果与一一对应位置的 DTI-FEM 预测结果进行了比较。根据这些数据校准模型系数后,所有患者的预测阻抗都能可靠地估计术中测量值(R=0.784±0.116,n=5)。通过这项研究,我们得出了 Tuch 等人发表的 DTI 传导模型斜率系数的最新值,即 k=0.0649S-s/mm3(原始 k=0.844),专门用于生理频率下的人体。意义:这是第一项将基于成像的人体脑组织模型的阻抗估计值与体内相同位置的实验测量值进行比较的研究。准确、无创、基于成像的阻抗预测在功能神经外科领域有很多应用,包括组织绘图、术中电极定位和 DBS。
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引用次数: 0
TMS-induced phase resets depend on TMS intensity and EEG phase. TMS 诱导的相位复位取决于 TMS 强度和脑电图相位。
Pub Date : 2024-10-24 DOI: 10.1088/1741-2552/ad7f87
Brian Erickson, Brian Kim, Philip Sabes, Ryan Rich, Abigail Hatcher, Guadalupe Fernandez-Nuñez, Georgios Mentzelopoulos, Flavia Vitale, John Medaglia

Objective. The phase of the electroencephalographic (EEG) signal predicts performance in motor, somatosensory, and cognitive functions. Studies suggest that brain phase resets align neural oscillations with external stimuli, or couple oscillations across frequency bands and brain regions. Transcranial Magnetic Stimulation (TMS) can cause phase resets noninvasively in the cortex, thus providing the potential to control phase-sensitive cognitive functions. However, the relationship between TMS parameters and phase resetting is not fully understood. This is especially true of TMS intensity, which may be crucial to enabling precise control over the amount of phase resetting that is induced. Additionally, TMS phase resetting may interact with the instantaneous phase of the brain. Understanding these relationships is crucial to the development of more powerful and controllable stimulation protocols.Approach.To test these relationships, we conducted a TMS-EEG study. We applied single-pulse TMS at varying degrees of stimulation intensity to the motor area in an open loop. Offline, we used an autoregressive algorithm to estimate the phase of the intrinsicµ-Alpha rhythm of the motor cortex at the moment each TMS pulse was delivered.Main results. We identified post-stimulation epochs whereµ-Alpha phase resetting and N100 amplitude depend parametrically on TMS intensity and are significantversusperipheral auditory sham stimulation. We observedµ-Alpha phase inversion after stimulations near peaks but not troughs in the endogenousµ-Alpha rhythm.Significance. These data suggest that low-intensity TMS primarily resets existing oscillations, while at higher intensities TMS may activate previously silent neurons, but only when endogenous oscillations are near the peak phase. These data can guide future studies that seek to induce phase resetting, and point to a way to manipulate the phase resetting effect of TMS by varying only the timing of the pulse with respect to ongoing brain activity.

目的:脑电图(EEG)信号的相位可预测运动、体感和认知功能的表现。研究表明,大脑相位重置可使神经振荡与外部刺激相一致,或将不同频段和脑区的振荡耦合在一起。经颅磁刺激(TMS)能以非侵入性方式在大脑皮层引起相位重置,从而为控制相位敏感的认知功能提供了可能。然而,TMS 参数与相位重置之间的关系尚未完全明了。TMS 强度尤其如此,它可能是精确控制相位复位诱导量的关键。此外,TMS 相位重置可能与大脑的瞬时相位相互作用。了解这些关系对于开发更强大、更可控的刺激方案至关重要:为了测试这些关系,我们进行了一项 TMS-EEG 研究。我们在开环中对运动区施加不同刺激强度的单脉冲 TMS。在离线状态下,我们使用自回归算法来估算每个 TMS 脉冲发出时运动皮层固有 µ-Alpha 节律的相位:我们确定了µ-Alpha相位重置和N100振幅与TMS强度成参数关系的刺激后时间段,与外周听觉假刺激相比,这些时间段的µ-Alpha相位重置和N100振幅显著。我们在内源性 µ-Alpha 节律的峰值附近而非谷值附近观察到刺激后的µ-Alpha 相位反转:这些数据表明,低强度的 TMS 主要是重置现有的振荡,而在较高强度下,TMS 可能会激活之前沉默的神经元,但只有当内源性振荡接近峰值阶段时才会激活。这些数据可为今后试图诱导相位重置的研究提供指导,并指出了一种方法,即通过改变脉冲与正在进行的大脑活动之间的时间关系来操纵 TMS 的相位重置效应。
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引用次数: 0
An ANN models cortical-subcortical interaction during post-stroke recovery of finger dexterity. 在中风后手指灵活性恢复过程中,大脑皮层与皮层下部之间的相互作用是一个 ANN 模型。
Pub Date : 2024-10-21 DOI: 10.1088/1741-2552/ad8961
Ashraf Kadry, Deborah Solomonow-Avnon, Sumner Lee Norman, Jing Xu, Firas Mawase

Objective: Finger dexterity, and finger individuation in particular, is crucial for human movement, and disruptions due to brain injury can significantly impact quality of life. Understanding the neurological mechanisms responsible for recovery is vital for effective neurorehabilitation. This study explores the role of two key pathways in finger individuation: the corticospinal tract (CST) from the primary motor cortex and premotor areas, and the subcortical reticulospinal tract (RST) from the brainstem. We aimed to investigate how the cortical-reticular network reorganizes to aid recovery of finger dexterity following lesions in these areas.

Approach: To provide a potential biologically plausible answer to this question, we developed an artificial neural network (ANN) to model the interaction between a premotor planning layer, a cortical layer with excitatory and inhibitory corticospinal outputs, and reticulospinal outputs controlling finger movements. The ANN was trained to simulate normal finger individuation and strength. A simulated stroke was then applied to the corticospinal (CS) area, reticulospinal (RS) area, or both, and the recovery of finger dexterity was analyzed.

Main results: In the intact model, the ANN demonstrated a near-linear relationship between the forces of instructed and uninstructed fingers, resembling human individuation patterns. Post-stroke simulations revealed that lesions in both CS and RS regions led to increased unintended force in uninstructed fingers, immediate weakening of instructed fingers, improved control during early recovery, and increased neural plasticity. Lesions in the CS region alone significantly impaired individuation, while RS lesions affected strength and to a lesser extent, individuation. The model also predicted the impact of stroke severity on finger individuation, highlighting the combined effects of CS and RS lesions.

Significance: This model provides insights into the interactive role of cortical and subcortical regions in finger individuation. It suggests that recovery mechanisms involve reorganization of these networks, which may inform neurorehabilitation strategies.

目的:手指的灵活性,尤其是手指的单独活动能力,对人类的运动至关重要,而脑损伤导致的手指灵活性中断会严重影响生活质量。了解恢复的神经机制对于有效的神经康复至关重要。本研究探讨了两条关键通路在手指分离中的作用:来自初级运动皮层和前运动区的皮质脊髓束(CST),以及来自脑干的皮质下网状脊髓束(RST)。我们的目的是研究在这些区域发生病变后,皮质-脊髓网络如何重组以帮助手指灵活性的恢复:为了从生物学角度为这一问题提供一个潜在的合理答案,我们开发了一个人工神经网络(ANN)来模拟前运动规划层、具有兴奋和抑制皮质脊髓输出的皮质层以及控制手指运动的网状脊髓输出之间的相互作用。对 ANN 进行了训练,以模拟正常的手指分离和力量。然后对皮质脊髓(CS)区、网状脊髓(RS)区或两者进行模拟中风,并分析手指灵活性的恢复情况:主要结果:在完好的模型中,方差网络显示指令手指和非指令手指的力量之间存在近乎线性的关系,类似于人类的个体化模式。中风后模拟显示,CS和RS区域的病变导致非指令手指的非预期力量增加,指令手指的力量立即减弱,在早期恢复过程中控制力得到改善,神经可塑性增强。仅 CS 区的病变就会严重影响个体化,而 RS 区的病变会影响力量,但对个体化的影响较小。该模型还预测了中风严重程度对手指个性化的影响,突出了CS和RS病变的综合效应:该模型深入揭示了皮层和皮层下区域在手指个性化中的交互作用。意义:该模型深入揭示了皮层和皮层下区域在手指个体化中的交互作用,表明恢复机制涉及这些网络的重组,可为神经康复策略提供参考。
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引用次数: 0
Global motor dynamics - Invariant neural representations of motor behavior in distributed brain-wide recordings. 全局运动动力学--分布式全脑记录中运动行为的不变神经表征。
Pub Date : 2024-10-21 DOI: 10.1088/1741-2552/ad851c
Maarten C Ottenhoff, Maxime Verwoert, Sophocles Goulis, Louis Wagner, Johannes P van Dijk, Pieter L Kubben, Christian Herff

Objective.Motor-related neural activity is more widespread than previously thought, as pervasive brain-wide neural correlates of motor behavior have been reported in various animal species. Brain-wide movement-related neural activity have been observed in individual brain areas in humans as well, but it is unknown to what extent global patterns exist.Approach.Here, we use a decoding approach to capture and characterize brain-wide neural correlates of movement. We recorded invasive electrophysiological data from stereotactic electroencephalographic electrodes implanted in eight epilepsy patients who performed both an executed and imagined grasping task. Combined, these electrodes cover the whole brain, including deeper structures such as the hippocampus, insula and basal ganglia. We extract a low-dimensional representation and classify movement from rest trials using a Riemannian decoder.Main results.We reveal global neural dynamics that are predictive across tasks and participants. Using an ablation analysis, we demonstrate that these dynamics remain remarkably stable under loss of information. Similarly, the dynamics remain stable across participants, as we were able to predict movement across participants using transfer learning.Significance.Our results show that decodable global motor-related neural dynamics exist within a low-dimensional space. The dynamics are predictive of movement, nearly brain-wide and present in all our participants. The results broaden the scope to brain-wide investigations, and may allow combining datasets of multiple participants with varying electrode locations or calibrationless neural decoder.

目的:与运动相关的神经活动比以前认为的更为广泛,因为在各种动物物种中都有关于运动行为的全脑神经相关性的报道。在人类的个别脑区也观察到了与运动相关的全脑神经活动,但还不清楚在多大程度上存在全球性模式:在这里,我们使用一种解码方法来捕捉和描述运动的全脑神经相关性。我们从植入八名癫痫患者体内的立体定向脑电图电极上记录了有创电生理数据,这些患者同时执行了执行和想象中的抓握任务。这些电极覆盖了整个大脑,包括海马、岛叶和基底节等深层结构。我们使用黎曼解码器从静止试验中提取低维表征并对运动进行分类:主要结果:我们揭示了可预测不同任务和参与者的全局神经动态。通过消融分析,我们证明了在信息丢失的情况下,这些动态变化仍然非常稳定。同样,这些动力学在不同参与者之间也保持稳定,因为我们能够利用迁移学习预测不同参与者的运动:我们的研究结果表明,可解码的全局运动相关神经动力学存在于一个低维空间中。这些动力学对运动具有预测作用,几乎覆盖整个大脑,并且存在于所有参与者中。这些结果拓宽了全脑研究的范围,可将多个参与者的数据集与不同的电极位置或无校准神经解码器结合起来。
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引用次数: 0
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Journal of neural engineering
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