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Utilizing diffusion tensor imaging as an image biomarker in exploring the therapeutic efficacy of forniceal deep brain stimulation in a mice model of Alzheimer's disease. 利用弥散张量成像作为图像生物标志物,探索在阿尔茨海默病小鼠模型中使用穹窿深部脑刺激疗法的疗效。
Pub Date : 2024-09-04 DOI: 10.1088/1741-2552/ad7322
You-Yin Chen, Chih-Ju Chang, Yao-Wen Liang, Hsin-Yi Tseng, Ssu-Ju Li, Ching-Wen Chang, Yen-Ting Wu, Huai-Hsuan Shao, Po-Chun Chen, Ming-Liang Lai, Wen-Chun Deng, RuSiou Hsu, Yu-Chun Lo

Objective.With prolonged life expectancy, the incidence of memory deficits, especially in Alzheimer's disease (AD), has increased. Although multiple treatments have been evaluated, no promising treatment has been found to date. Deep brain stimulation (DBS) of the fornix area was explored as a possible treatment because the fornix is intimately connected to memory-related areas that are vulnerable in AD; however, a proper imaging biomarker for assessing the therapeutic efficiency of forniceal DBS in AD has not been established.Approach.This study assessed the efficacy and safety of DBS by estimating the optimal intersection volume between the volume of tissue activated and the fornix. Utilizing a gold-electroplating process, the microelectrode's surface area on the neural probe was increased, enhancing charge transfer performance within potential water window limits. Bilateral fornix implantation was conducted in triple-transgenic AD mice (3 × Tg-AD) and wild-type mice (strain: B6129SF1/J), with forniceal DBS administered exclusively to 3 × Tg-AD mice in the DBS-on group. Behavioral tasks, diffusion tensor imaging (DTI), and immunohistochemistry (IHC) were performed in all mice to assess the therapeutic efficacy of forniceal DBS.Main results.The results illustrated that memory deficits and increased anxiety-like behavior in 3 × Tg-AD mice were rescued by forniceal DBS. Furthermore, forniceal DBS positively altered DTI indices, such as increasing fractional anisotropy (FA) and decreasing mean diffusivity (MD), together with reducing microglial cell and astrocyte counts, suggesting a potential causal relationship between revised FA/MD and reduced cell counts in the anterior cingulate cortex, hippocampus, fornix, amygdala, and entorhinal cortex of 3 × Tg-AD mice following forniceal DBS.Significance.The efficacy of forniceal DBS in AD can be indicated by alterations in DTI-based biomarkers reflecting the decreased activation of glial cells, suggesting reduced neural inflammation as evidenced by improvements in memory and anxiety-like behavior.

目的:随着预期寿命的延长,记忆障碍的发病率也在增加,尤其是阿尔茨海默病(AD)。虽然已对多种治疗方法进行了评估,但迄今为止尚未发现有前景的治疗方法。由于穹窿与记忆相关区域紧密相连,而记忆相关区域在阿兹海默症(AD)中很脆弱,因此对穹窿区域进行深部脑刺激(DBS)被视为一种可能的治疗方法;然而,评估穹窿DBS对阿兹海默症(AD)治疗效果的适当成像生物标志物尚未确立。利用镀金工艺,增加了神经探针上微电极的表面积,在潜在水窗限制范围内提高了电荷转移性能。在三转基因AD小鼠(3 × Tg-AD)和野生型小鼠(品系:B6129SF1/J)中进行了双侧穹窿植入,DBS开启组中的3 × Tg-AD小鼠只接受穹窿DBS治疗。主要结果:结果表明,3 × Tg-AD 小鼠的记忆缺陷和焦虑样行为的增加通过穹窿DBS得到了挽救。此外,穹窿DBS还能正向改变DTI指数,如增加分数各向异性(FA)和降低平均扩散率(MD),同时减少小胶质细胞和星形胶质细胞的数量,这表明穹窿DBS治疗后,3 × Tg-AD小鼠前扣带回皮层、海马、穹窿、杏仁核和内侧皮层的FA/MD改变和细胞数量减少之间可能存在因果关系。穹窿DBS对AD的疗效可以通过基于DTI的生物标志物的改变来体现,这些生物标志物反映了神经胶质细胞活化的减少,表明神经炎症的减轻,记忆力和焦虑样行为的改善也证明了这一点。
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引用次数: 0
Motor imagery with cues in virtual reality, audio and screen. 通过虚拟现实、音频和屏幕的提示进行运动想象。
Pub Date : 2024-09-04 DOI: 10.1088/1741-2552/ad775e
Sonal Santosh Baberwal, Luz Alejandra Magre, K R Sanjaya D Gunawardhana, Michael Parkinson, Tomas Ward, Shirley Coyle

Objective: Training plays a significant role in motor imagery (MI), particularly in applications such as Motor Imagery-based Brain-Computer Interface (MIBCI) systems and rehabilitation systems. Previous studies have investigated the intricate relationship between cues and MI signals. However, the medium of presentation still remains an emerging area to be explored, as possible factors to enhance Motor Imagery signals.. Approach: We hypothesise that the medium used for cue presentation can significantly influence both performance and training outcomes in MI tasks. To test this hypothesis, we designed and executed an experiment implementing no- feedback MI. Our investigation focused on three distinct cue presentation mediums -audio, screen, and virtual reality(VR) headsets-all of which have potential implications for BCI use in the Activities of Daily Lives. Main Results: The results of our study uncovered notable variations in MI signals depending on the medium of cue presentation, where the analysis is based on 3 EEG channels. To substantiate our findings, we employed a comprehensive approach, utilizing various evaluation metrics including Event- Related Synchronisation(ERS)/Desynchronisation(ERD), Feature Extraction (using Recursive Feature Elimination (RFE)), Machine Learning methodologies (using Ensemble Learning), and participant Questionnaires. All the approaches signify that Motor Imagery signals are enhanced when presented in VR, followed by audio, and lastly screen. Applying a Machine Learning approach across all subjects, the mean cross-validation accuracy (Mean ± Std. Error) was 69.24 ± 3.12, 68.69 ± 3.3 and 66.1±2.59 when for the VR, audio-based, and screen-based instructions respectively. Significance: This multi-faceted exploration provides evidence to inform MI- based BCI design and advocates the incorporation of different mediums into the design of MIBCI systems, experimental setups, and user studies. The influence of the medium used for cue presentation may be applied to develop more effective and inclusive MI applications in the realm of human-computer interaction and rehabilitation.

目的:训练在运动想象(MI)中发挥着重要作用,尤其是在基于运动想象的脑机接口(MIBCI)系统和康复系统等应用中。以往的研究已经探究了线索与运动想象信号之间错综复杂的关系。然而,作为增强运动想象信号的可能因素,呈现媒介仍然是一个有待探索的新兴领域:我们假设,提示呈现的媒介会显著影响运动智能任务的表现和训练结果。为了验证这一假设,我们设计并实施了一项无反馈移动迷宫实验。我们的研究侧重于三种不同的提示呈现媒介--音频、屏幕和虚拟现实(VR)头盔--所有这些媒介都对在日常生活活动中使用生物识别(BCI)具有潜在影响:我们的研究结果揭示了 MI 信号的显著差异,这取决于提示呈现的媒介,其中分析基于 3 个脑电图通道。为了证实我们的研究结果,我们采用了一种综合方法,利用各种评估指标,包括事件相关同步(ERS)/不同步(ERD)、特征提取(使用递归特征消除(RFE))、机器学习方法(使用集合学习)和参与者问卷调查。所有方法都表明,运动图像信号在 VR 中呈现时会得到增强,其次是音频,最后是屏幕。在所有受试者中应用机器学习方法,VR、音频和屏幕指令的交叉验证平均准确率(平均值±标准误差)分别为 69.24 ± 3.12、68.69 ± 3.3 和 66.1 ± 2.59:这一多方面的探索为基于 MI 的 BCI 设计提供了依据,并倡导在 MIBCI 系统设计、实验设置和用户研究中采用不同的媒介。在人机交互和康复领域,提示呈现媒介的影响可用于开发更有效、更具包容性的多元智能应用。
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引用次数: 0
Mechanical and thermal stimulation for studying the somatosensory system: a review on devices and methods. 用于研究躯体感觉系统的机械和热刺激:设备和方法综述。
Pub Date : 2024-09-03 DOI: 10.1088/1741-2552/ad716d
M Sperduti, N L Tagliamonte, F Taffoni, E Guglielmelli, L Zollo

The somatosensory system is widely studied to understand its functioning mechanisms. Multiple tests, based on different devices and methods, have been performed not only on humans but also on animals andex-vivomodels. Depending on the nature of the sample under analysis and on the scientific aims of interest, several solutions for experimental stimulation and for investigations on sensation or pain have been adopted. In this review paper, an overview of the available devices and methods has been reported, also analyzing the representative values adopted during literature experiments. Among the various physical stimulations used to study the somatosensory system, we focused only on mechanical and thermal ones. Based on the analysis of their main features and on literature studies, we pointed out the most suitable solution for humans, rodents, andex-vivomodels and investigation aims (sensation and pain).

为了了解躯体感觉系统的运作机制,人们对其进行了广泛的研究。基于不同的设备和方法,不仅对人类,而且对动物和体外模型进行了多种测试。根据所分析样本的性质和感兴趣的科学目标,已经采用了多种实验刺激和感觉或疼痛研究解决方案。本综述报告概述了现有的设备和方法,并分析了文献实验中采用的代表值。在用于研究体感系统的各种物理刺激中,我们只关注机械和热刺激。根据对其主要特征的分析和文献研究,我们指出了最适合人类、啮齿动物和体外模型以及研究目的(感觉和疼痛)的解决方案。
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引用次数: 0
A simplified adversarial architecture for cross-subject silent speech recognition using electromyography. 利用肌电图进行跨主体无声语音识别的简化对抗架构
Pub Date : 2024-09-03 DOI: 10.1088/1741-2552/ad7321
Qiang Cui, Xingyu Zhang, Yakun Zhang, Changyan Zheng, Liang Xie, Ye Yan, Edmond Q Wu, Erwei Yin

Objective. The decline in the performance of electromyography (EMG)-based silent speech recognition is widely attributed to disparities in speech patterns, articulation habits, and individual physiology among speakers. Feature alignment by learning a discriminative network that resolves domain offsets across speakers is an effective method to address this problem. The prevailing adversarial network with a branching discriminator specializing in domain discrimination renders insufficiently direct contribution to categorical predictions of the classifier.Approach. To this end, we propose a simplified discrepancy-based adversarial network with a streamlined end-to-end structure for EMG-based cross-subject silent speech recognition. Highly aligned features across subjects are obtained by introducing a Nuclear-norm Wasserstein discrepancy metric on the back end of the classification network, which could be utilized for both classification and domain discrimination. Given the low-level and implicitly noisy nature of myoelectric signals, we devise a cascaded adaptive rectification network as the front-end feature extraction network, adaptively reshaping the intermediate feature map with automatically learnable channel-wise thresholds. The resulting features effectively filter out domain-specific information between subjects while retaining domain-invariant features critical for cross-subject recognition.Main results. A series of sentence-level classification experiments with 100 Chinese sentences demonstrate the efficacy of our method, achieving an average accuracy of 89.46% tested on 40 new subjects by training with data from 60 subjects. Especially, our method achieves a remarkable 10.07% improvement compared to the state-of-the-art model when tested on 10 new subjects with 20 subjects employed for training, surpassing its result even with three times training subjects.Significance. Our study demonstrates an improved classification performance of the proposed adversarial architecture using cross-subject myoelectric signals, providing a promising prospect for EMG-based speech interactive application.

基于肌电图的无声语音识别性能下降的主要原因是说话者之间的语音模式、发音习惯和个体生理差异。通过学习能解决不同说话者之间领域偏移的判别网络进行特征对齐是解决这一问题的有效方法。目前流行的对抗网络带有一个专门从事领域分辨的分支分辨器,对分类器的分类预测没有足够的直接贡献。为此,我们提出了一种简化的基于差异的对抗网络,其端到端结构精简,适用于基于肌电图的跨主体无声语音识别。通过在分类网络的后端引入核规范 Wasserstein 差异度量,可获得跨主体的高度一致特征,该特征可用于分类和领域判别。鉴于肌电信号的低水平和隐含噪声特性,我们设计了一个级联自适应整流网络作为前端特征提取网络,利用可自动学习的通道阈值自适应重塑中间特征图。由此产生的特征能有效过滤掉不同受试者之间的特定领域信息,同时保留对跨受试者识别至关重要的领域不变特征。我们使用 100 个中文句子进行了一系列句子级分类实验,证明了我们方法的有效性,通过使用 60 个受试者的数据进行训练,在 40 个新受试者身上测试的平均准确率达到了 89.46%。特别是在使用 20 个受试者进行训练的情况下,在 10 个新受试者上进行测试时,我们的方法比最先进的模型显著提高了 10.07%,甚至超过了其三倍训练受试者的结果。我们的研究表明,利用跨受试者肌电信号的对抗结构提高了分类性能,为基于肌电信号的语音交互应用提供了广阔的前景。
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引用次数: 0
Predicting cognitive load with EEG using Riemannian geometry-based features. 利用基于黎曼几何特征的脑电图预测认知负荷。
Pub Date : 2024-09-03 DOI: 10.1088/1741-2552/ad680b
Iris Kremer, Wissam Halimi, Andy Walshe, Moran Cerf, Pablo Mainar

Objective. We show that electroencephalography (EEG)-based cognitive load (CL) prediction using Riemannian geometry features outperforms existing models. The performance is estimated using Riemannian Procrustes Analysis (RPA) with a test set of subjects unseen during training.Approach. Performance is evaluated by using the Minimum Distance to Riemannian Mean model trained on CL classification. The baseline performance is established using spatial covariance matrices of the signal as features. Various novel features are explored and analyzed in depth, including spatial covariance and correlation matrices computed on the EEG signal and its first-order derivative. Furthermore, each RPA step effect on the performance is investigated, and the generalization performance of RPA is compared against a few different generalization methods.Main results. Performances are greatly improved by using the spatial covariance matrix of the first-order derivative of the signal as features. Furthermore, this work highlights both the importance and efficiency of RPA for CL prediction: it achieves good generalizability with little amounts of calibration data and largely outperforms all the comparison methods.Significance. CL prediction using RPA for generalizability across subjects is an approach worth exploring further, especially for real-world applications where calibration time is limited. Furthermore, the feature exploration uncovers new, promising features that can be used and further experimented within any Riemannian geometry setting.

目的:我们的研究表明,利用黎曼几何特征进行基于脑电图的认知负荷预测优于现有模型。我们使用黎曼 Procrustes 分析法(RPA)估算了该模型的性能,测试集是在训练过程中未见过的受试者:使用认知负荷分类训练的黎曼均值最小距离模型评估性能。基线性能是使用信号的空间协方差矩阵作为特征确定的。对各种新特征进行了深入探讨和分析,包括根据脑电信号及其一阶导数计算的空间协方差和相关矩阵。此外,还研究了每个 RPA 步骤对性能的影响,并将 RPA 的泛化性能与几种不同的泛化方法进行了比较:通过使用信号一阶导数的空间协方差矩阵作为特征,性能得到了极大改善。此外,这项工作还凸显了 RPA 在认知负荷预测中的重要性和效率:它只需少量校准数据就能实现良好的泛化能力,并在很大程度上优于所有比较方法:使用 RPA 进行认知负荷预测以实现跨科目泛化是一种值得进一步探索的方法,尤其是在校准时间有限的实际应用中。此外,特征探索发现了新的、有前途的特征,这些特征可以在任何黎曼几何设置中使用和进一步实验。
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引用次数: 0
Emotion recognition of EEG signals based on contrastive learning graph convolutional model. 基于对比学习图卷积模型的脑电信号情感识别。
Pub Date : 2024-08-29 DOI: 10.1088/1741-2552/ad7060
Yiling Zhang, Yuan Liao, Wei Chen, Xiruo Zhang, Liya Huang

Objective.Electroencephalogram (EEG) signals offer invaluable insights into the complexities of emotion generation within the brain. Yet, the variability in EEG signals across individuals presents a formidable obstacle for empirical implementations. Our research addresses these challenges innovatively, focusing on the commonalities within distinct subjects' EEG data.Approach.We introduce a novel approach named Contrastive Learning Graph Convolutional Network (CLGCN). This method captures the distinctive features and crucial channel nodes related to individuals' emotional states. Specifically, CLGCN merges the dual benefits of CL's synchronous multisubject data learning and the GCN's proficiency in deciphering brain connectivity matrices. Understanding multifaceted brain functions and their information interchange processes is realized as CLGCN generates a standardized brain network learning matrix during a dataset's learning process.Main results.Our model underwent rigorous testing on the Database for Emotion Analysis using Physiological Signals (DEAP) and SEED datasets. In the five-fold cross-validation used for dependent subject experimental setting, it achieved an accuracy of 97.13% on the DEAP dataset and surpassed 99% on the SEED and SEED_IV datasets. In the incremental learning experiments with the SEED dataset, merely 5% of the data was sufficient to fine-tune the model, resulting in an accuracy of 92.8% for the new subject. These findings validate the model's efficacy.Significance.This work combines CL with GCN, improving the accuracy of decoding emotional states from EEG signals and offering valuable insights into uncovering the underlying mechanisms of emotional processes in the brain.

脑电图(EEG)信号为了解大脑情绪产生的复杂性提供了宝贵的信息。然而,不同个体的脑电信号存在差异,这给经验性实施带来了巨大障碍。我们的研究以创新的方式应对了这些挑战,重点关注不同受试者脑电图数据的共性。这种方法能捕捉与个体情绪状态相关的显著特征和关键通道节点。具体来说,CLGCN 融合了对比学习(Contrastive Learning)的多主体同步数据学习和图卷积网络(Graph Convolutional Network)在解读大脑连接矩阵方面的双重优势。在数据集的学习过程中,CLGCN 会生成标准化的大脑网络学习矩阵,从而实现对多方面大脑功能及其信息交换过程的理解。我们的模型大大简化了新受试者的再训练过程,只需要初始样本量的 5%进行微调,就能达到 92.8% 的惊人准确率。此外,我们的模型还在 DEAP 和 SEED 数据集上进行了广泛测试,证明了我们模型的有效性。
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引用次数: 0
Epileptic network identification: insights from dynamic mode decomposition of sEEG data. 癫痫网络识别:动态模式分解 sEEG 数据的启示。
Pub Date : 2024-08-29 DOI: 10.1088/1741-2552/ad705f
Alejandro Nieto Ramos, Balu Krishnan, Andreas V Alexopoulos, William Bingaman, Imad Najm, Juan C Bulacio, Demitre Serletis

Objective.For medically-refractory epilepsy patients, stereoelectroencephalography (sEEG) is a surgical method using intracranial electrode recordings to identify brain networks participating in early seizure organization and propagation (i.e. the epileptogenic zone, EZ). If identified, surgical EZ treatment via resection, ablation or neuromodulation can lead to seizure-freedom. To date, quantification of sEEG data, including its visualization and interpretation, remains a clinical and computational challenge. Given elusiveness of physical laws or governing equations modelling complex brain dynamics, data science offers unique insight into identifying unknown patterns within high-dimensional sEEG data. We apply here an unsupervised data-driven algorithm, dynamic mode decomposition (DMD), to sEEG recordings from five focal epilepsy patients (three with temporal lobe, and two with cingulate epilepsy), who underwent subsequent resective or ablative surgery and became seizure free.Approach.DMD obtains a linear approximation of nonlinear data dynamics, generating coherent structures ('modes') defining important signal features, used to extract frequencies, growth rates and spatial structures. DMD was adapted to produce dynamic modal maps (DMMs) across frequency sub-bands, capturing onset and evolution of epileptiform dynamics in sEEG data. Additionally, we developed a static estimate of EZ-localized electrode contacts, termed the higher-frequency mode-based norm index (MNI). DMM and MNI maps for representative patient seizures were validated against clinical sEEG results and seizure-free outcomes following surgery.Main results.DMD was most informative at higher frequencies, i.e. gamma (including high-gamma) and beta range, successfully identifying EZ contacts. Combined interpretation of DMM/MNI plots best identified spatiotemporal evolution of mode-specific network changes, with strong concordance to sEEG results and outcomes across all five patients. The method identified network attenuation in other contacts not implicated in the EZ.Significance.This is the first application of DMD to sEEG data analysis, supporting integration of neuroengineering, mathematical and machine learning methods into traditional workflows for sEEG review and epilepsy surgical decision-making.

目的:对于药物难治性癫痫患者,立体脑电图(sEEG)是一种外科手术方法,通过颅内记录来识别参与早期癫痫组织和传播的大脑网络(即致痫区,EZ)。如果确定了致痫区,通过切除、消融或神经调控对其进行手术治疗,就能使癫痫发作痊愈。迄今为止,sEEG 数据的量化,包括其可视化和解释,仍然是临床和计算方面的挑战。鉴于模拟复杂脑动力学的物理定律或管理方程难以捉摸,数据科学为识别高维 sEEG 数据中的未知模式提供了独特的见解。在此,我们将一种无监督的数据驱动算法--动态模式分解(DMD)应用于五名局灶性癫痫患者(三名颞叶癫痫患者和两名扣带回癫痫患者)的 sEEG 记录,这些患者随后接受了切除或消融手术,癫痫不再发作:方法:DMD 获取非线性数据动态的线性近似值,生成定义重要信号特征的相干结构("模式"),用于提取频率、增长率和空间结构。我们对 DMD 进行了调整,以生成跨频率子带的动态模态图 (DMM),捕捉 sEEG 数据中癫痫样动态的开始和演变。此外,我们还开发了 EZ 定位电极接触的静态估计值,称为基于高频模式的规范指数(MNI)。针对代表性患者癫痫发作的 DMM 和 MNI 图与临床 sEEG 结果和术后无癫痫发作结果进行了验证:主要结果:DMD 在较高频率,即伽马(包括高伽马)和贝塔范围内信息量最大,可成功识别 EZ 接触点。对 DMM/MNI 图的综合解释最能确定特定模式网络变化的时空演变,与所有五名患者的 sEEG 结果和预后非常吻合。该方法还能识别与 EZ 无关的其他接触点的网络衰减:这是 DMD 在 sEEG 数据分析中的首次应用,支持将神经工程、数学和机器学习方法整合到传统的 sEEG 检查和癫痫手术决策工作流程中。
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引用次数: 0
Neural subspaces of imagined movements in parietal cortex remain stable over several years in humans. 人类顶叶皮层中想象运动的神经子空间在数年内保持稳定。
Pub Date : 2024-08-28 DOI: 10.1088/1741-2552/ad6e19
L Bashford, I A Rosenthal, S Kellis, D Bjånes, K Pejsa, B W Brunton, R A Andersen

Objective.A crucial goal in brain-machine interfacing is the long-term stability of neural decoding performance, ideally without regular retraining. Long-term stability has only been previously demonstrated in non-human primate experiments and only in primary sensorimotor cortices. Here we extend previous methods to determine long-term stability in humans by identifying and aligning low-dimensional structures in neural data.Approach.Over a period of 1106 and 871 d respectively, two participants completed an imagined center-out reaching task. The longitudinal accuracy between all day pairs was assessed by latent subspace alignment using principal components analysis and canonical correlations analysis of multi-unit intracortical recordings in different brain regions (Brodmann Area 5, Anterior Intraparietal Area and the junction of the postcentral and intraparietal sulcus).Main results.We show the long-term stable representation of neural activity in subspaces of intracortical recordings from higher-order association areas in humans.Significance.These results can be practically applied to significantly expand the longevity and generalizability of brain-computer interfaces.Clinical TrialsNCT01849822, NCT01958086, NCT01964261.

目标 脑机接口的一个重要目标是神经解码性能的长期稳定性,理想情况下无需定期重新训练。长期稳定性以前只在非人灵长类实验中得到过证明,而且只在初级感觉运动皮层中得到过证明。在这里,我们扩展了之前的方法,通过识别和排列神经数据中的低维结构来确定人类的长期稳定性。 两名参与者分别在1106天和871天内完成了一项想象中的中心向外伸手任务。通过对不同脑区(布罗德曼第5区、顶内前区以及中央后沟和顶内沟交界处)的多单元皮层内记录进行主成分分析和典型相关分析,采用潜在子空间配准法评估了所有天数对之间的纵向准确性。 主要结果 我们展示了神经活动在人类高阶联想区皮层内记录子空间中的长期稳定表征。 意义 这些结果可实际应用于大幅提高脑机接口的寿命和通用性。
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引用次数: 0
Iterative alignment discovery of speech-associated neural activity. 语音相关神经活动的迭代排列发现。
Pub Date : 2024-08-28 DOI: 10.1088/1741-2552/ad663c
Qinwan Rabbani, Samyak Shah, Griffin Milsap, Matthew Fifer, Hynek Hermansky, Nathan Crone

Objective. Brain-computer interfaces (BCIs) have the potential to preserve or restore speech in patients with neurological disorders that weaken the muscles involved in speech production. However, successful training of low-latency speech synthesis and recognition models requires alignment of neural activity with intended phonetic or acoustic output with high temporal precision. This is particularly challenging in patients who cannot produce audible speech, as ground truth with which to pinpoint neural activity synchronized with speech is not available.Approach. In this study, we present a new iterative algorithm for neural voice activity detection (nVAD) called iterative alignment discovery dynamic time warping (IAD-DTW) that integrates DTW into the loss function of a deep neural network (DNN). The algorithm is designed to discover the alignment between a patient's electrocorticographic (ECoG) neural responses and their attempts to speak during collection of data for training BCI decoders for speech synthesis and recognition.Main results. To demonstrate the effectiveness of the algorithm, we tested its accuracy in predicting the onset and duration of acoustic signals produced by able-bodied patients with intact speech undergoing short-term diagnostic ECoG recordings for epilepsy surgery. We simulated a lack of ground truth by randomly perturbing the temporal correspondence between neural activity and an initial single estimate for all speech onsets and durations. We examined the model's ability to overcome these perturbations to estimate ground truth. IAD-DTW showed no notable degradation (<1% absolute decrease in accuracy) in performance in these simulations, even in the case of maximal misalignments between speech and silence.Significance. IAD-DTW is computationally inexpensive and can be easily integrated into existing DNN-based nVAD approaches, as it pertains only to the final loss computation. This approach makes it possible to train speech BCI algorithms using ECoG data from patients who are unable to produce audible speech, including those with Locked-In Syndrome.

目的。脑机接口(BCI)有可能保护或恢复因神经系统疾病而导致语言生成肌肉功能减弱的患者的语言能力。然而,要成功训练低延迟语音合成和识别模型,需要将神经活动与预期的语音或声学输出进行高时间精度的对齐。这对于无法发出可听语音的患者来说尤其具有挑战性,因为他们无法获得与语音同步的神经活动的基本事实。在这项研究中,我们提出了一种新的神经语音活动检测(nVAD)迭代算法,称为迭代对齐发现动态时间扭曲(IAD-DTW),它将 DTW 集成到深度神经网络(DNN)的损失函数中。该算法旨在发现患者的皮层电图(ECoG)神经反应与他们在收集数据期间试图说话之间的一致性,以训练用于语音合成和识别的 BCI 解码器。为了证明该算法的有效性,我们测试了该算法在预测因癫痫手术而接受短期诊断性心电图记录的具有完整语言能力的健全患者所发出的声音信号的起始和持续时间方面的准确性。我们通过随机扰动神经活动与所有语音起始和持续时间的初始单一估计值之间的时间对应关系,模拟了缺乏基本事实的情况。我们检验了模型克服这些扰动以估计基本事实的能力。结果显示,IAD-DTW 的性能没有明显下降(意义重大。IAD-DTW 计算成本低廉,可轻松集成到现有的基于 DNN 的 nVAD 方法中,因为它只涉及最终损失计算。这种方法使得使用无法发出可听语音的患者(包括锁定综合症患者)的心电图数据训练语音 BCI 算法成为可能。
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引用次数: 0
A preliminary study exploring the effects of transcutaneous spinal cord stimulation on spinal excitability and phantom limb pain in people with a transtibial amputation. 一项初步研究,探索经皮脊髓刺激对截肢者脊髓兴奋性和幻肢痛的影响。
Pub Date : 2024-08-22 DOI: 10.1088/1741-2552/ad6a8d
Ashley N Dalrymple, Lee E Fisher, Douglas J Weber

Objective. Phantom limb pain (PLP) is debilitating and affects over 70% of people with lower-limb amputation. Other neuropathic pain conditions correspond with increased spinal excitability, which can be measured using reflexes andF-waves. Spinal cord neuromodulation can be used to reduce neuropathic pain in a variety of conditions and may affect spinal excitability, but has not been extensively used for treating PLP. Here, we propose using a non-invasive neuromodulation method, transcutaneous spinal cord stimulation (tSCS), to reduce PLP and modulate spinal excitability after transtibial amputation.Approach. We recruited three participants, two males (5- and 9-years post-amputation, traumatic and alcohol-induced neuropathy) and one female (3 months post-amputation, diabetic neuropathy) for this 5 d study. We measured pain using the McGill Pain Questionnaire (MPQ), visual analog scale (VAS), and pain pressure threshold (PPT) test. We measured spinal reflex and motoneuron excitability using posterior root-muscle (PRM) reflexes andF-waves, respectively. We delivered tSCS for 30 min d-1for 5 d.Main Results. After 5 d of tSCS, MPQ scores decreased by clinically-meaningful amounts for all participants from 34.0 ± 7.0-18.3 ± 6.8; however, there were no clinically-significant decreases in VAS scores. Two participants had increased PPTs across the residual limb (Day 1: 5.4 ± 1.6 lbf; Day 5: 11.4 ± 1.0 lbf).F-waves had normal latencies but small amplitudes. PRM reflexes had high thresholds (59.5 ± 6.1μC) and low amplitudes, suggesting that in PLP, the spinal cord is hypoexcitable. After 5 d of tSCS, reflex thresholds decreased significantly (38.6 ± 12.2μC;p< 0.001).Significance. These preliminary results in this non-placebo-controlled study suggest that, overall, limb amputation and PLP may be associated with reduced spinal excitability and tSCS can increase spinal excitability and reduce PLP.

目的:幻肢痛(PLP)会使人衰弱,影响 70% 以上的下肢截肢者。其他神经病理性疼痛症状与脊髓兴奋性增高相对应,脊髓兴奋性可通过反射和 F 波来测量。脊髓神经调控可用于减轻多种情况下的神经病理性疼痛,并可影响脊髓兴奋性,但尚未广泛用于治疗幻肢痛。在此,我们提议使用一种非侵入性神经调节方法--经皮脊髓刺激(tSCS)来减轻幻肢痛,并调节经胫骨截肢后的脊髓兴奋性:我们招募了三名参与者,其中两名男性(截肢后 5 年和 9 年,外伤和酒精引起的神经病变)和一名女性(截肢后 3 个月,糖尿病神经病变)参加这项为期 5 天的研究。我们使用麦吉尔疼痛问卷、视觉模拟量表(VAS)和疼痛压力阈值测试测量疼痛。我们使用后根肌(PRM)反射和 F 波分别测量脊髓反射和运动神经元的兴奋性。我们对患者进行了为期 5 天、每天 30 分钟的 tSCS 治疗:经过 5 天的 tSCS 治疗后,所有参与者的麦吉尔疼痛问卷评分均有临床意义的下降,从 34.0±7.0 降至 18.3±6.8;但 VAS 评分没有临床意义的下降。两名参与者的残肢疼痛压力阈值升高(第 1 天:5.4±1.6 磅;第 5 天:11.4±1.0 磅)。F 波的潜伏期正常,但振幅较小。PRM反射的阈值较高(59.5±6.1 µC),振幅较低,这表明PLP患者的脊髓兴奋性较低。在 tSCS 5 天后,反射阈值显著下降(38.6±12.2 µC;p
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Journal of neural engineering
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