Name: using wavelet entropy to demonstrate how mindfulness practice increases coordination between irregular cerebral and cardiac activities
Uploaded: 2017-05-10T13:30:00.000+00:00
Duration: 8 min 8 s
Description: Watch this Scientific Journal Video about Using Wavelet Entropy to Demonstrate how Mindfulness Practice Increases Coordination between Irregular Cerebral and Cardiac Activities at JoVE.com

本研究得到了香港大学SPF-201209176152基金的支持。我们感谢马尔海恩博士教授课程，并招募了MBSR参与者。

本研究由香港大学机构审查委员会批准。所有参与者在参与本研究之前提供书面知情同意书。 实验设计<img alt="表格1" src="/files/ftp_upload/55455/table01.jpg" /> 表1. MBSR课程概述。参加者8周的MBSR训练课程。 <ol><li>招募参加者 注意:来自当地MBSR课程的11名健康成年人（5名女性）参加了这项脑电图研究，年龄为28-52岁。基于Beck抑郁症患者抑郁症的参与者被排除在研究之外。课程遵循标准的MBSR培训（ 表1 ），参与者必须承诺培训结构。 </li><li>脑电录音系统<ol><li>使用128通道EEG在安静的房间内进行脑电图记录ystem由EEG帽，放大器，流浆箱和台式电脑组成。 </li></ol></li></ol> EEG和ECG数据采集注意:EEG数据收集遵循类似于之前描述的方法14的标准程序。要求每个参与者在进入实验地点之前清洗头发和头皮。通知参与者实验程序，主要是他/她将正常休息10分钟，并在记录EEG和ECG设备的同时进行10分钟的呼吸。 <ol><li>用酒精拭子清洁参与者的脸部区域和乳突。 </li><li>使用卷尺，测量参与者的头围，然后选择适当大小的帽子。从nasion进行一次测量，并在耳朵顶部和头皮上进行另一个测量。标记顶点（nasion之间的中间点两只耳朵之间的距离和中距离）用柔软的标记笔。 </li><li>根据10-5电极系统15设置电极位置。定位帽子，使得Cz电极在顶点之上，Nz电极处于nasion，Lz电极处于该位置，RM电极处于右乳突，LM电极处于左乳突。 <ol><li>将ECG电极置于左右锁骨下窝。 </li><li>使用钝点注射器将电极固定器用凝胶填充。 </li><li>将每个电极的阻抗保持在20kΩ以下。通过调节电极位置来减少阻抗，增加与头皮的接触;必要时加入更多的凝胶。 </li><li>拧紧下腹部，请求参与者保持静止。 </li><li>将EEG设备的采样速率设置为1,000 Hz。使用左乳突作为原始参考点。 </li></ol></li><li>记录EEG数据（步骤1.2。1）在MBSR课程开始时（2周内）。 <ol><li>让参与者进行简短的身体扫描以放松身体。参与者呼吸呼吸时要注意呼吸。在EEG数据采集期间，每个参与者进行10分钟的MBSR心灵呼吸（MBSR正念状态）和10分钟的正常休息（控制条件）。这产生了具有两个条件的前MBSR训练数据集。 注意:平衡呼吸暂停的顺序和参与者之间的正常休息。 </li></ol></li><li>参加者完成MBSR课程约1个月后再次记录脑电数据。 <ol><li>请求每位参与者在脑电数据采集期间进行10分钟的正常呼吸和10分钟的正常休息。这产生了具有两个条件的后MBSR训练数据集。 </li></ol></li></ol> EEG和ECG数据分析注意:前四个步骤是用于数据预处理，其余步骤用于小波熵计算和相关分析。在这里，一个名为EEGLAB 16的开源软件用于EEG分析，但是不同软件的操作应该是相似的。以下将以EEGLAB为基础的操作进行说明。有关详细信息，请参阅EEGLAB手册（https://sccn.ucsd.edu/wiki/EEGLAB_Wiki#EEGLAB_Tutorial）。 <ol><li>使用EEG软件通过选择"工具"&gt;"更改采样率"，以250 Hz重新采样数据。使用有限脉冲响应（FIR）滤波器，通过选择工具&gt;滤波数据&gt;基本FIR滤波器，通过0.5 - 100 Hz通带滤波。 <ol><li>为了减少由于国家特定的市电交流电而产生的噪音，请使用短路非线性无限脉冲响应（IIR）滤波器，通过选择"工具&gt;滤波数据&gt;短非线性IIR过滤器。这覆盖了频谱分析中的所有相关频率，同时消除了不必要的噪音。 </li></ol></li><li>使用EEG软件通过选择Plot&gt;通道数据（滚动）来可视地滚动并检查EEG信号。 <ol><li>识别和删除包含明显肌肉噪音和任何其他奇怪事件的脑电图段。左键单击并将鼠标拖放到不良段以突出显示，然后使用EEG软件"删除"或"拒绝"段。 注意:肌肉活动如吞咽或面部表情常常产生不连续和不规则的信号。奇怪的事件通常由头部或身体动作引起，这通常导致EEG信号的显着偏移（低频）。 </li><li>确定是否有任何不良通道。通过选择"工具"&gt;"插值"通道，使用球面插值方法重建每个不良通道。 </li></ol></li><li>使用EEG软件执行In依赖组件分析17 （ICA）对数据的选择工具&gt;运行ICA。有经验丰富的EEG操作员通过选择工具&gt;拒绝数据使用ICA&gt;拒绝组件通过地图，目视识别和丢弃眼睛运动和眨眼，肌肉运动和其他可能的噪音的组成部分。工具&gt;删除组件。 EEG软件将使用保留的组件自动重建数据。 </li><li>在进一步分析之前，使用EEG软件将数据重新引导至所有通道的平均值，方法是选择"工具"&gt;"重新引用"。 </li><li>频谱分析。 <ol><li>使用EEG软件命令行功能"spectopo"使用Welch的功率谱密度估计18计算EEG的光谱，并获得δ（1-4Hz），θ（4-8Hz），α（ 8-12Hz），β（12-30Hz）和γ（30-80Hz）波。 </li><li>计算相对po每种类型的波18的频率（ 即相对于总频谱功率的各个频带的功率比例）。 </li></ol></li><li>使用统计参数映射（SPM）方法进行脑电图源分析。 <ol><li>使用最小范数估计19将头皮电极的脑电信号变换成3D脑源信号。 注意:这里，原来的频道号是122，并被转换为分布在三维脑模型中的8,196个频道。显着性水平设定为p = 0.01。请参阅SPM手册中的源分析（www.fil.ion.ucl.ac.uk/spm/doc/spm8_manual.pdf）。 </li></ol></li><li>使用以下步骤计算每个EEG通道的小波熵（以及估计的三维脑源信号）: <ol><li>给了一个信号<img alt="公式1" src="/files/ftp_upload/55455/55455eq1.jpg" /> ，计算小波系数为<img alt="方程式2" src="/files/ftp_upload/55455/image002.jpg" /> ，哪里<img alt="公式3" src="/files/ftp_upload/55455/image003.jpg" />是Haar小波族的正交基础<img alt="公式4" src="/files/ftp_upload/55455/image004.jpg" />表示分解水平（本研究中N = 10）。 </li><li>将相对能量定义为<img alt="公式5" src="/files/ftp_upload/55455/image005.jpg" /> ，与<img alt="方程式6" src="/files/ftp_upload/55455/image006.jpg" /> 。 </li><li>计算小波熵为<img alt="公式7" src="/files/ftp_upload/55455/image007.jpg" /> 。平均所有信道的小波熵获得每个参与者的EEG小波熵。 </li></ol></li><li>使用软件包对ECG数据进行峰值分析，以获得心跳信号，其由ECG波峰的数量和峰之间的间隔组成。 注意:假设在正常休息和正常呼吸状态下，正常心跳60-100次/分钟，10分钟持续时间的心跳情况N的数量应相对较小，满足600 &lt; N &lt;1,000。 <ol><li>为了从心跳信号获得可靠的小波熵，使用500点的滑动窗口和10点的步进增量来计算一系列小波熵，采用与EEG相同的程序，详见3.7节。使用平均值作为最终的小波熵。 </li></ol></li><li>使用具有Pearson相关函数的任何统计软件，分析心电图的小波熵与头皮每个通道的脑电图脑的脑和心脏相关性。 <ol><li>使用EEG软件命令行功能"topoplot"来构建相关性的统计头皮图。绘制线性相关图与心率熵和平均EEG熵只有那些nnels与心率熵显着相关（见代表性结果部分）。 </li></ol></li></ol>

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作者宣称他们没有竞争的经济利益。

这项EEG研究提供了具体的证据，与正常休息相比，MBSR心脏呼吸增强了正面和枕叶的α波。这与大多数关于冥想中增加α波的报道一致，特别是初学者21 。阿尔法波浪通常表现出唤醒和警惕的状态，不受任何具体任务的束缚。这样的精神状态是冥想的目的之一;也就是说，保持清醒和清晰，无任何幻想的航班或入睡。在MBSR实践中增强的α波同步也可以增强心灵与身体22之间的协调。 大脑中大量神经元的电子活动形成非线性分层系统，其不规则性可以通过熵8很好地测量。我们发现在MBSR实践中，脑电的不规则大部分头皮脑电图，尤其是额叶和双侧枕叶脑区域的活动减少。小波熵的进一步来源分析表明，MBSR实践中枕骨和脑脊骨活动的不规则性降低。涉及的其他领域包括右中扣带和上颞叶。这符合另一项研究，表明在MBSR培训23之后，灰质增加。高级冥想从业者通常在非快速眼动睡眠期间具有较高的枕头脑电图伽马波1 。 MBSR训练的一个重要特征是不考虑脑/脑信息流。这可能潜在地减少大脑活动的不规则。因为大脑中的主要信息来源是视觉皮质，视觉相关的大脑区域比其他区域更受MBSR实践的影响是合理的纳秒。这使得心灵对不相关信息的反应较少，并且更注意注意呼吸。 小心熵分析显示，与正常休息状态相比，注意呼吸期间心脏心脏的不规则性较少，这意味着由于副交感神经和交感神经紧张之间的相对不稳定的相互作用，自主神经系统的精确平衡。注意呼吸也可能影响心脏收缩，因为肺感觉传入对中脑中的迷走神经运动神经元发出抑制性投射，其通过迷走神经24抑制心动过速。心脑血管相互作用由脑干中的脑干和边缘系统介导25 。鉴于中枢神经系统与心脏活动之间的联系，似乎合理的是，通过训练心灵，MBSR或其他冥想练习也可以影响其他身体功能，如重置压力反射灵敏度，提高肺部气体交换效率，平衡自主神经系统26 。 许多东方传统都强调了心灵的联系。在这方面，我们的研究提供了熵的第一个客观证据。在MBSR实践中，脑和心脏活动的不规则变得更加协调，双边体感区域的协调最大。这个地区直接负责人体的感官输入和运动，这种增加的协调意味着在MBSR实践中更多的身体意识。发现正念训练在功能MRI研究中增加了内脏区域的活动，其具有比EEG 27更好的空间分辨率。我们的研究结果表明，在MBSR实践中，大脑和心脏可能变得更加夹带，从而在系统中卸载不必要的熵<s<html> up class ="xref"&gt; 28。因为心跳是由自主神经系统调节的，其中自主神经调节由神经节之间的相互作用调节;中耳节迷走神经和高阶脑结构，如延髓，胰岛和边缘系统，自主神经和中枢神经系统在MBSR实践中可能变得更加协调。 在我们这样的研究中的一个重要步骤是选择正确的熵指数来测量脑电图和心电图的不规则性。与使用近似熵29的 Renu Madhavi 等人不同，我们选择使用小波熵来集中在不同尺度的能量之间的复杂性。由于脑电子活动源自于不同尺度和频谱具有不同特征的大量神经元，所以小波熵应更适合于测量脑电图的不规则性&gt; 30。类似地，瞬时心率在不同的时间尺度下也具有不同的特征，因此对心电图31应用小波熵是合理的。此外，香农的熵和近似熵对采样频率敏感，近似熵需要大量的计算时间，与数据长度呈指数增长，除了源空间中的计算外。 目前研究的几个限制值得注意。首先，头皮脑电图的空间分辨率相对较差，尽管其时间分辨率高。为了解决这个问题，我们的研究中采用了源分析。 EEG源重建是一个不成问题，另一种方法是从以前的功能磁共振成像（fMRI）研究中获得空间先验，这有助于提高重构精度32,33 。另一个限制是t帽子之间的脑和心脏活动之间的协调被评估受试者之间;课程内协调将留给未来的研究。另外，心电图的熵不是直接从ECG信号中得到的，而是来自心跳间隔，因为心电图的振幅可能受到电极位置和皮肤阻抗的很大影响，因此在受试者之间是不可比的。 以前的研究通常对EEG信号进行频谱分析，将EEG中的不规则视为噪声。相比之下，我们建议使用小波熵作为更敏感的指标来测量大脑的混沌电子活动，作为思维状态的反映。所提出的方法提供了客观证据，表明思维和心灵在冥想中更加协调的传统假设。 因为小波熵可以很敏感地监视冥想状态，所以可以用于心理训练正如在临床实践中使用熵来测量手术期间的麻醉水平一样。使用类似于小波熵的索引，可以使用无线EEG设备来方便地监视个人的日常冥想练习或用于教育目的的其他心理训练。然而，由于无线EEG通常只能稳定地测量前额中的通道，因此我们需要探讨这些通道是否足够敏感以监测心理状态。 该过程的关键步骤包括将每个电极的皮肤阻抗保持为低，以获得更好的信号质量和更低的噪声;适当进行MBSR培训;在实验之前确保一个好的，不疲劳的精神状态，因为冥想需要很大的精力;选择适当的熵指数（小波熵）;并熟悉EEG数据的SPM源分析。

随着神经科学和医学的进步1 ，我们在了解正念和冥想对精神和身体疾病的非药理学益处方面处于更好的地位。许多研究表明，基于心灵的压力减轻（MBSR）训练是一种精神锻炼，可以有效减少心灵和身体的不必要的压力。在冥想的实践中，身体和心灵可能会变得更加夹带。这些问题吸引了学术界和大众的兴趣，并促使我们设计出一种在不同条件下探索心灵与身体（或更具体地，大脑与心脏之间）潜在协调的方法来评估效果的MBSR培训和实践。 脑电图不均匀性（EEG）在常规EEG谱分析中通常被认为是噪声，b它可能包含有用信息，可以通过信号熵来测量。 EEG熵被用作癫痫，阿尔茨海默病和麻醉等临床病症的指标，一些证据表明，脑电图的熵可能比传统的EEG频谱分析更为敏感。熵长期以来被用于热力学，作为描述系统混沌状态的指标。由脑电测量的脑电波由于大脑网络在秩序和混沌之间的运作而闻名于其动态活动。在各种熵中，小波熵提供了一种在不同观测尺度下计算能量熵的方法，非常适合于分析不同尺度的具有高度变化特征的脑电信号。对于完全随机的信号，不同分辨率级别的相对能量将是相同，熵将达到最大值。因此，小波熵作为脑电混沌活动的量度，可以作为区分不同心态的指标。 以前的心电图（ECG）研究提供了与冥想状态相比心悸变异性的冥想相关变化的一致证据7 。心跳由自主神经（包括交感神经和副交感神经）神经系统调节。自主神经调节由神经节之间的相互作用调节;中耳节迷走神经和高阶脑结构，如延髓，岛内和边缘系统。这些结构形成一个分层系统，其非线性活动可以通过熵8进行测量。例如，高阶脑结构中的神经突发可以影响心跳节律9 。在临床情况下，脑紊乱如癫痫发作会扰乱心律10 。神经心脏学研究还表明心脏和大脑之间的紧密相互作用11 。 检查脑和心脏信号之间的关联的一个困难在于它们在信号内容和时间尺度上是完全不同的。因此，一个主要的挑战是确定一个不仅适用于脑和心脏信号的常见措施，而且对于身心协调的背景下的解释也是有意义的。在本文中，我们提出使用小波熵来检测脑和心脏活动不规则之间的可能协调关系，并将脑电信号与心跳信号相关联，然后通过MBSR实验进行评估。 因为MBSR涉及身体和心灵的正念实践，我们假设MBSR正念呼吸动作可能不仅影响脑活动，还可能影响心跳。以前的研究主要根据不同方面分别研究正念训练和/或实践对脑或心脏的影响;这两个紧密相关的系统的同时变化仍然在很大程度上未被探索。只有少数研究报告，正念训练后身体和心灵的协调性更好12 。使用提出的方法，代表性的结果和以前的研究13显示，MBSR正念训练可能会增加身体和心灵在其混沌活动方面的协调，这可能会提供新的洞察心理训练对中央和外围环境的影响神经系统。

<table><tbody><tr><td>Synamps 2/RT 128-channel EEG/EP/ERP</td><td>Compumedics NeuroScan</td><td>Model 8050</td><td>Amplifiers and&lt;br/&gt; EEG acquisition</td></tr><tr><td>Quik-CapEEG 128 electrodes</td><td>Compumedics NeuroScan</td><td></td><td>EEG caps</td></tr><tr><td>Quik-Gel</td><td>Compumedics NeuroScan</td><td></td><td>conductive electrode gel</td></tr><tr><td>EEGLAB</td><td>Swartz Center for Computational Neuroscience</td><td></td><td>EEG analysis software</td></tr><tr><td>Matlab R2011a</td><td>MathWorks</td><td></td><td>EEGLAB is based on Matlab</td></tr><tr><td>SPM</td><td>Wellcome Trust Centre for Neuroimaging</td><td></td><td>EEG source analysis software</td></tr></tbody></table>

using wavelet entropy to demonstrate how mindfulness practice increases coordination between irregular cerebral and cardiac activities

在东西方，传统教义说，心灵与心灵有着密切的联系，特别是在精神实践中。客观地证明这一点的一个难点在于脑和心脏活动的本质是完全不同的。在本文中，我们提出了一种使用小波熵来测量脑电图（EEG）和心电图（ECG）数据的混沌水平的方法，并展示了如何在不同的实验条件下探索心灵与心脏之间的潜在协调。此外，使用统计参数映射（SPM）来鉴定EEG​​小波熵受实验条件影响最大的脑区域。作为一个例证，在8周标准的基于正念的减轻压力（MBSR）训练课程（预测试）开始时，脑电图和心电图记录在两种不同的条件（正常休息和心灵呼吸）下，之后c我们（后测）。使用所提出的方法，结果一致地表明，与封闭眼睛静止状态期间相比，在脑脊髓脑缺血呼吸状态期间，脑EEG的小波熵降低。类似地，在MBSR心灵呼吸期间发现较低的心率的小波熵。然而，在预测试和后测试之间发现了MBSR注意呼吸期间小波熵的差异。在所有参与者的正常休息期间，脑波熵与心率的熵之间没有观察到相关性，而在MBSR心灵呼吸期间观察到显着的相关性。此外，最相关的脑区域位于大脑的中心区域。这项研究提供了建立正义实践（ 即注意呼吸）可能增加心灵和心脏活动之间协调的证据的方法。

频谱分析在EEG数据的频谱分析中，与正常休息相比，在MBSR心灵呼吸期间，增强了α（8-12Hz）和β（12-30Hz）和减小的δ（1-4Hz）波。 α波的增加在全球有显着性，特别是在额叶和枕叶，而β波的增量主要在额叶。在中部顶部地区注意到减少的三角洲波动（ 图1 ）。然而，我们在MBSR培训之前和之后没有发现重大变化。 <img alt="图1" src="/files/ftp_upload/55455/55455fig1.jpg" /> 图1. MBSR意识呼吸和正常休息条件的频谱分析。频谱分析表明，MBSR实践可以改变大脑活动，如不同之处所示脑电图谱。原始来源:参考文献13 。 <a href="http://ecsource.jove.com/files/ftp_upload/55455/55455fig1large.jpg" target="_blank">请点击此处查看此图的较大版本。</a> 图2示出了所定义的感兴趣区域:枕叶（通道N19-21，N41-46，N67-72，N96-100和N119），中额叶（通道N53 -61和N79-83），以及中间顶叶（通道N48-50，N64-66和N74-776）。 表2显示了每个频谱的功率和MBSR心灵呼吸和正常休息条件下的熵，以及每个感兴趣区域的两个条件之间的差异的t检验的p值。我们结合了前后MBSR训练EEG数据，因为两个阶段之间没有差异。 图2.定义的兴趣区域 EEG通道的定义区域代表中额叶，中叶和枕叶。 <a href="http://ecsource.jove.com/files/ftp_upload/55455/55455fig2large.jpg" target="_blank">请点击此处查看此图的较大版本。</a> <table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always"><tbody><tr><td></td><td></td><td> 三角洲 </td><td> 西塔 </td><td> Α </td><td> Beta版 </td><td> 伽玛 </td><td> 熵 </td></tr><tr><td>中前额叶</td><td> MBSR </td><td> 18.96±3.10 </td><td> 22.29±5.86 </td><td> 30.90±7.76 </td><td> 0.12±43.77 </td><td> -467.25±79.48 </td><td> 0.753±0.060 </td> &lt;/ TR&gt; <tr><td></td><td>休息</td><td> 22.40±6.56 </td><td> 22.76±5.98 </td><td> 26.91±7.14 </td><td> -7.11±42.27 </td><td> -449.76±102.92 </td><td> 0.785±0.066 </td></tr><tr><td></td><td> p值</td><td> 0.0243 </td><td> 0.6555 </td><td> 0.0085 </td><td> 0.0114 </td><td> 0.4419 </td><td> 0.0084 </td></tr><tr><td>枕叶</td><td> MBSR </td><td> 15.39±3.30 </td><td> 17.91±6.53 </td><td> 33.38±6.50 </td><td> -8.03±40.72 </td><td> -466.23±61.46 </td><td> 0.719±0.048 </td></tr><tr><td></td><td>休息</td><td> 19.82±6.32 </td><td> 20.38±8.11 </td><td> 29.73±5.30 </td><td> -11.62±40.22 </td><td> -439.03±102.81 </td><td> 0.763±0.055 </td></tr><tr><td></td><td> p值</td><td> 0.0134 </td><td> 0.1213 </tD&gt; <td> 0.0125 </td><td> 0.0796 </td><td> 0.8936 </td><td> 0.0098 </td></tr><tr><td>顶叶</td><td> MBSR </td><td> 17.95±3.60 </td><td> 19.46±6.54 </td><td> 32.84±6.68 </td><td> 2.68±38.23 </td><td> -487.72±104.13 </td><td> 0.738±0.072 </td></tr><tr><td></td><td>休息</td><td> 21.24±6.37 </td><td> 21.16±8.00 </td><td> 29.79±6.60 </td><td> -1.27±34.42 </td><td> -490.01±123.83 </td><td> 0.764±0.075 </td></tr><tr><td></td><td> p值</td><td> 0.0157 </td><td> 0.0963 </td><td> 0.0177 </td><td> 0.1507 </td><td> 0.2878 </td><td> 0.0368 </td></tr></tbody></table> 表2.脑区的频谱和熵。 MBSR注意呼吸和正常休息条件在三个预定义的感兴趣区域（ROI）中进行了比较。 小波熵分析脑电图的小波熵分析显示，与正常休息相比，MBSR心灵呼吸期间的脑电熵减少与MBSR训练前和后期均有关。因为两个阶段之间没有显着差异，所以它们被合并以产生一个平均的图（ 如图3的第三行所示 ）。颅骨下降的主要区域为额叶和顶叶。 <img alt="图3" src="/files/ftp_upload/55455/55455fig3.jpg" /> 图3.小波熵分析。休息1和MBSR 1表示MBSR前培训，而休息2和MBSR 2表示MBSR后培训。小波熵分析表明，MBSR实践可以减少不规则性脑电子活动。原始来源:参考文献13 。 <a href="http://ecsource.jove.com/files/ftp_upload/55455/55455fig3large.jpg" target="_blank">请点击此处查看此图的较大版本。</a> 小波熵的源分析 EEG信号的源分析可以通过将头皮脑电图解卷积到皮质表面20上的电活动来提高空间分辨率。分析表明，受MBSR正念训练影响的主要脑区位于左中枕叶，前叶，上颞叶和左梭梭（ 图4 ）。在表3中 ，对于从源分析获得的四个解剖标签，我们提供了两个不同状态的熵差的t检验结果，即MBSR正念状态与正常休息。 <img alt="图4" src="/files/ftp_upload/55455/55455fig4.jpg" /> 图4.源分析。来源分析显示，在MBSR心灵呼吸状态期间，各种脑区域（红色突出显示）的熵减少。 <a href="http://ecsource.jove.com/files/ftp_upload/55455/55455fig4large.jpg" target="_blank">请点击此处查看此图的较大版本。</a> <table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always"><tbody><tr><td> 解剖标签 </td><td> 素 </td><td> x，y，z </td><td> t值 </td></tr><tr><td>中间颞回</td><td> 1728 </td><td> -44 -60 16 </td><td> 3.77 </td></tr><tr><td>原生动物</td><td> 1324 </td><td> 2 -56 28 </td><td> 3.63 </td></tr><tr><td>枕骨L </td><td> 749 </td><td> -2 -102-12 </td><td> 3.61 </td></tr><tr><td>梭形L </td><td> 142 </td><td> -102 </td><td> 3.51 </td></tr></tbody></table> 表3.源分析报告。解剖脑区域与MBSR正气呼吸与正常休息条件之间的熵差异显着。 L，大脑左侧。 R，大脑右侧（ p &lt;0.001， t检验，未校正）。 心率小波熵分析显示，平均心率没有显着性差异，但MBSR注意呼吸（MBSR训练前后）的心率熵较低（ 表4 ）。 <table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always"><tbody><tr><td></td><td> Rest1 </td><td> MBSR1 </td><td> Rest2 </td><td> MBSR2 </td></tr><tr><td>心率（每分钟节拍） </td><td> 68.2±9.5 </td><td> 67.7±9.3 </td><td> 71.8±8.1 </td><td> 70.7±8.4 </td></tr><tr><td>心率小波熵</td><td> 0.89±0.05 </td><td> 0.79±0.11 * </td><td> 0.89±0.07 </td><td> 0.80±0.12 ＃ </td></tr></tbody></table> 表4. MBSR心灵呼吸和正常休息条件的心率和心率小波熵。在MBSR训练前和后期比较两个条件。 *休息1和MBSR 1之间的显着差异（ p &lt;0.05）。＃2与MBSR 2之间的显着性差异（ p &lt;0.05）。 关联心与脑活动之间鉴于大脑和心脏之间的潜在联系，我们分析了全脑脑电图熵（ 即单个受试者的所有脑电信道上EEG熵的平均值）与MBSR心灵呼吸和正常休息状态之间的心跳熵的相关性。大脑和心脏的熵在MBSR心脏呼吸期间显着相关，但在正常休息期间并不显着。 如图5的上半部分所示 ，每个通道的脑电图熵与心率熵之间的相关性的意义在脑中央部位最突出。下图显示了中心区域（与心率熵显着相关的虚线通道的平均值）和心率熵之间的EEG熵的相关性。 图5.心与脑电子活动的小波熵之间的相关性。大脑活动的小波熵是中心区域重要通道的平均值，如右上图所示。心脏的小波熵是基于心跳间隔。在所有受试者之间计算MBSR训练前和后期训练结果的相关性（下图）。原始来源:参考文献13 。 <a href="http://ecsource.jove.com/files/ftp_upload/55455/55455fig5large.jpg" target="_blank">请点击此处查看此图的较大版本。</a>

Watch this Scientific Journal Video about Using Wavelet Entropy to Demonstrate how Mindfulness Practice Increases Coordination between Irregular Cerebral and Cardiac Activities at JoVE.com

Using Wavelet Entropy to Demonstrate how Mindfulness Practice Increases Coordination between Irregular Cerebral and Cardiac Activities

该手稿描述了如何使用小波熵指数来分析高密度脑电图（EEG）和心电图（ECG）数据。我们表明，在基于正念的减压实践中，脑和心脏活动的不规则性变得更加协调。

使用小波熵来证明正念练习如何增加不规则脑与心脏活动之间的协调

In both the East and West, traditional teachings say that the mind and heart are somehow closely correlated, especially during spiritual practice. One ...

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14.6K 次观看.  The University of Hong Kong. 该手稿描述了如何使用小波熵指数来分析高密度脑电图（EEG）和心电图（ECG）数据。我们表明，在基于正念的减压实践中，脑和心脏活动的不规则性变得更加协调。 

视频: Using Wavelet Entropy to Demonstrate how Mindfulness Practice Increases Coordination between Irregular Cerebral and Cardiac Activities

A Community-based Stress Management Program: Using Wearable Devices to Assess Whole Body Physiological Responses in Non-laboratory Settings

A pragmatic breath-based intervention to benefit human performance and stress management is timely and valuable to individuals seeking holistic approaches for emotional regulation and optimizing compensatory reserve mechanisms. This protocol is designed to not only teach mind-body awareness but also to provide feedback utilizing physiological data and survey results. The primary findings of this study showed that heart coherence and alpha variables were significantly correlated after four weeks of the breath-based meditation stress protocol. Meditation and rhythmic breathing produced significant increases in alpha brain activity. These brain physiological responses conformed to the Pleth Variability Index (PVI) changes, suggesting the ability of the human body to enter into a meditative state and effectively manage stress. When assessed after four weeks of daily practicing the techniques employed in the stress management protocol, based on the Five Facet Mindfulness Questionnaire, subjects improved in applying mindfulness skills. The overall mindfulness score, Pleth variability index (PVI), and perfusion index (PI) increased after the 4-week intervention period. Results from electroencephalography (brain waves) were consistent with a meditative state during the post-study follow-up session. This provides evidence that wearable devices are feasible for data collection during a breath-based stress management intervention. This protocol can be easily and efficiently implemented into any study design in which physiological data are desired in a non-laboratory-based setting.

Stress is an unavoidable and persistent component of life and holistic approaches for its management are being considered. A standardized methodology was created to demonstrate the feasibility of a breath-based stress management protocol that can be used with commercially available portable technology.

基于社区的压力管理计划: 在非实验室环境中使用可穿戴设备评估全身生理反应

A pragmatic breath-based intervention to benefit human performance and stress management is timely and valuable to individuals seeking holistic approaches ...

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Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

As Parkinson&#8217;s disease (PD) is a heterogeneous disorder, personalized medicine is truly required to optimize care. In their current form, standard scores from paper and pencil symptom- measures traditionally used to track disease progression are too coarse (discrete) to capture the granularity of the clinical phenomena under consideration, in the face of tremendous symptom diversity. For this reason, sensors, wearables, and mobile devices are increasingly incorporated into PD research and routine care. These digital measures, while more precise, yield data that are less standardized and interpretable than traditional measures, and consequently, the two types of data remain largely siloed. Both of these issues present barriers to the broad clinical application of the field&#8217;s most precise assessment tools. This protocol addresses both problems. Using traditional tasks to measure cognition and motor control, we test the participant, while co-registering biophysical signals unobtrusively using wearables. We then integrate the scores from traditional paper-and-pencil methods with the digital data that we continuously register. We offer a new standardized data type and unifying statistical platform that enables the dynamic tracking of change in the person&#8217;s stochastic signatures under different conditions that probe different functional levels of neuromotor control, ranging from voluntary to autonomic. The protocol and standardized statistical framework offer dynamic digital biomarkers of physical and cognitive function in PD that correspond to validated clinical scales, while significantly improving their precision.&#160;

This protocol offers a digitization of portions of traditional clinical tasks commonly used to measure cognition and motor control in Parkinson&#8217;s disease. Clinical tasks are digitized while biophysical rhythms are co-registered from different functional levels of the nervous systems, ranging from voluntary, spontaneous, automatic to autonomic.

帕金森病中运动与认知功能的动态数字生物标志物

As Parkinson&#8217;s disease (PD) is a heterogeneous disorder, personalized medicine is truly required to optimize care. In their current form, standard ...

Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography

Microstate and omega complexity are two reference-free electroencephalography (EEG) measures that can represent the temporal and spatial complexities of EEG data and have been widely used to investigate the neural mechanisms in some brain disorders. The goal of this article is to describe the protocol underlying EEG microstate and omega complexity analyses step by step. The main advantage of these two measures is that they could eliminate the reference-dependent problem inherent to traditional spectrum analysis. In addition, microstate analysis makes good use of high time resolution of resting-state EEG, and the four obtained microstate classes could match the corresponding resting-state networks respectively. The omega complexity characterizes the spatial complexity of the whole brain or specific brain regions, which has obvious advantage compared with traditional complexity measures focusing on the signal complexity in a single channel. These two EEG measures could complement each other to investigate the brain complexity from the temporal and spatial domain respectively.

This article describes the protocol underlying electroencephalography (EEG) microstate analysis and omega complexity analysis, which are two reference-free EEG measures and highly valuable to explore the neural mechanisms of brain disorders.

静止状态脑电图的微状态和欧米茄复杂性分析

Microstate and omega complexity are two reference-free electroencephalography (EEG) measures that can represent the temporal and spatial complexities of ...

神经科学

Combining Behavior and EEG to Study the Effects of Mindfulness Meditation on Episodic Memory

Although there has been recent interest in how mindfulness meditation can affect episodic memory as well as brain structure and function, no study has examined the behavioral and neural effects of mindfulness meditation on episodic memory. Here we present a protocol that combines mindfulness meditation training, an episodic memory task, and EEG to examine how mindfulness meditation changes behavioral performance and the neural correlates of episodic memory. Subjects in a mindfulness meditation experimental group were compared to a waitlist control group. Subjects in the mindfulness meditation experimental group spent four weeks training and practicing mindfulness meditation. Mindfulness was measured before and after training using the Five Facet Mindfulness Questionnaire (FFMQ). Episodic memory was measured before and after training using a source recognition task. During the retrieval phase of the source recognition task, EEG was recorded. The results showed that mindfulness, source recognition behavioral performance, and EEG theta power in right frontal and left parietal channels increased following mindfulness meditation training. In addition, increases in mindfulness correlated with increases in theta power in right frontal channels. Therefore, results obtained from combining mindfulness meditation training, an episodic memory task, and EEG reveal the behavioral and neural effects of mindfulness meditation on episodic memory.

Here we present a protocol for combining mindfulness meditation training, an episodic memory task, and EEG to understand the behavioral and neural effects of mindfulness meditation on episodic memory.

结合行为和脑电图研究正念冥想对情景记忆的影响

Although there has been recent interest in how mindfulness meditation can affect episodic memory as well as brain structure and function, no study has ...

Measurement of the Directional Information Flow in fNIRS-Hyperscanning Data using the Partial Wavelet Transform Coherence Method

Social interaction is of vital importance for human beings. While the hyperscanning approach has been extensively used to study interpersonal neural synchronization (INS) during social interactions, functional near-infrared spectroscopy (fNIRS) is one of the most popular techniques for hyperscanning naturalistic social interactions because of its relatively high spatial resolution, sound anatomical localization, and exceptionally high tolerance of motion artifacts. Previous fNIRS-based hyperscanning studies usually calculate a time-lagged INS using wavelet transform coherence (WTC) to describe the direction and temporal pattern of information flow between individuals. However, the results of this method might be confounded by the autocorrelation effect of the fNIRS signal of each individual. For addressing this issue, a method termed partial wavelet transform coherence (pWTC) was introduced, which aimed to remove the autocorrelation effect and maintain the high temporal-spectrum resolution of the fNIRS signal. In this study, a simulation experiment was performed first to show the effectiveness of the pWTC in removing the impact of autocorrelation on INS. Then, step-by-step guidance was offered on the operation of the pWTC based on the fNIRS dataset from a social interaction experiment. Additionally, a comparison between the pWTC method and the traditional WTC method and that between the pWTC method and the Granger causality (GC) method was drawn. The results showed that pWTC could be used to determine the INS difference between different experimental conditions and INS's directional and temporal pattern between individuals during naturalistic social interactions. Moreover, it provides better temporal and frequency resolution than the traditional WTC and better flexibility than the GC method. Thus, pWTC is a strong candidate for inferring the direction and temporal pattern of information flow between individuals during naturalistic social interactions.

This protocol describes partial wavelet transform coherence (pWTC) for calculating the time-lagged pattern of interpersonal neural synchronization (INS) to infer the direction and temporal pattern of information flow during social interaction. The effectiveness of pWTC in removing the confounds of signal autocorrelation on INS was proved by two experiments.

使用偏小波变换相干法测量 fNIRS-超扫描数据中的定向信息流

Social interaction is of vital importance for human beings. While the hyperscanning approach has been extensively used to study interpersonal neural ...

A Visuospatial Planning Task Coupled with Eye-Tracker and Electroencephalogram Systems

The planning process, characterized by the capability to formulate an organized plan to reach a goal, is essential for human goal-directed behavior. Since planning is compromised in several neuropsychiatric disorders, the implementation of proper clinical and experimental tests to examine planning is critical. Due to the nature of the deployment of planning, in which several cognitive domains participate, the assessment of planning and the design of behavioral paradigms coupled with neuroimaging methods are current challenges in cognitive neuroscience. A planning task was evaluated in combination with an electroencephalogram (EEG) system and eye movement recordings in 27 healthy adult participants. Planning can be separated into two stages: a mental planning stage in which a sequence of steps is internally represented and an execution stage in which motor action is used to achieve a previously planned goal. Our protocol included a planning task and a control task. The planning task involved solving 36 maze trials, each representing a zoo map. The task had four periods: i) planning, where the subjects were instructed to plan a path to visit the locations of four animals according to a set of rules; ii) maintenance, where the subjects had to retain the planned path in their working memory; iii) execution, where the subjects used eye movements to trace the previously planned path as indicated by the eye-tracker system; and iv) response, where the subjects reported the order of the visited animals. The control task had a similar structure, but the cognitive planning component was removed by modifying the task goal. The spatial and temporal patterns of the EEG revealed that planning induces a gradual and lasting rise in frontal-midline theta activity (FM&#952;) over time. The source of this activity was identified within the prefrontal cortex by source analyses. Our results suggested that the experimental paradigm combining EEG and eye-tracker systems was optimal for evaluating cognitive planning.

The study of cognitive planning combining EEG and eye-tracking systems provides a multimodal approach to investigate the neural mechanisms that mediate cognitive control and goal-directed behavior in humans. Here, we describe a protocol for investigating the role of brain oscillations and eye movements in planning performance.

The planning process, characterized by the capability to formulate an organized plan to reach a goal, is essential for human goal-directed behavior. Since ...

革新 fNIRS 研究：一种神经耦合的新方法

New Framework for Understanding Cross-Brain Coherence in Functional Near-Infrared Spectroscopy (fNIRS) Hyperscanning Studies

Despite the growing body of functional near-infrared spectroscopy (fNIRS) hyperscanning studies, the assessment of coupling between two neural signals using wavelet transform coherence (WTC) seems to ignore the directionality of the interaction. The field is currently lacking a framework that allows researchers to determine whether a high coherence value obtained using a WTC function reflects in-phase synchronization (i.e., neural activation is seen in both members of the dyad at the same time), lagged synchronization (i.e., neural activation is seen in one member of the dyad prior to the other member), or anti-phase synchronization (i.e., neural activation is increased in one member of the dyad and decreased in the other). To address this need, a complementary and more sensitive approach for analyzing the phase coherence of two neural signals is proposed in this work. The toolbox allows investigators to estimate the coupling directionality by classifying the phase angle values obtained using traditional WTC into in-phase synchronization, lagged synchronization, and anti-phase synchronization. The toolbox also allows researchers to assess how the dynamics of interactions develop and change throughout the task. Using this novel WTC approach and the toolbox will advance our understanding of complex social interactions through their uses in fNIRS hyperscanning studies.

作者聚焦：解锁 fNIRS 研究的新见解 - 脑间同步分析的新框架 

Wavelet transform coherence (WTC) is a common methodology for assessing the coupling between signals that is used in functional near-infrared spectroscopy (fNIRS) hyperscanning studies. A toolbox for assessing the directionality of the signal interaction is presented in this work.

理解功能性近红外光谱 （fNIRS） 超扫描研究中跨脑相干性的新框架

Despite the growing body of functional near-infrared spectroscopy (fNIRS) hyperscanning studies, the assessment of coupling between two neural signals ...

揭示脑心相互作用与 EEG 和心脏信号

BrainBeats as an Open-Source EEGLAB Plugin to Jointly Analyze EEG and Cardiovascular Signals

The interplay between the brain and the cardiovascular systems is garnering increased attention for its potential to advance our understanding of human physiology and improve health outcomes. However, the multimodal analysis of these signals is challenging due to the lack of guidelines, standardized signal processing and statistical tools, graphical user interfaces (GUIs), and automation for processing large datasets or increasing reproducibility. A further void exists in standardized EEG and heart-rate variability (HRV) feature extraction methods, undermining clinical diagnostics or the robustness of machine learning (ML) models. In response to these limitations, we introduce the BrainBeats toolbox. Implemented as an open-source EEGLAB plugin, BrainBeats integrates three main protocols: 1) Heartbeat-evoked potentials (HEP) and oscillations (HEO) for assessing time-locked brain-heart interplay at the millisecond accuracy; 2) EEG and HRV feature extraction for examining associations/differences between various brain and heart metrics or for building robust feature-based ML models; 3) Automated extraction of heart artifacts from EEG signals to remove any potential cardiovascular contamination while conducting EEG analysis. We provide a step-by-step tutorial for applying these three methods to an open-source dataset containing simultaneous 64-channel EEG, ECG, and PPG signals. Users can easily fine-tune parameters to tailor their unique research needs using the graphical user interface (GUI) or the command line. BrainBeats should make brain-heart interplay research more accessible and reproducible.

作者聚焦：使用用于多模态信号分析的综合 EEGLAB 插件推进脑心相互作用的研究

The BrainBeats toolbox is an open-source EEGLAB plugin designed to jointly analyze EEG and cardiovascular (ECG/PPG) signals. It includes heartbeat-evoked potentials (HEP) assessment, feature-based analysis, and heart artifact extraction from EEG signals. The protocol will aid in studying brain-heart interplay through two lenses (HEP and features), enhancing reproducibility and accessibility.

BrainBeats 作为开源 EEGLAB 插件，用于联合分析 EEG 和心血管信号

The interplay between the brain and the cardiovascular systems is garnering increased attention for its potential to advance our understanding of human ...

使用小波熵来证明正念练习如何增加不规则脑与心脏活动之间的协调

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基于社区的压力管理计划: 在非实验室环境中使用可穿戴设备评估全身生理反应

帕金森病中运动与认知功能的动态数字生物标志物

静止状态脑电图的微状态和欧米茄复杂性分析

结合行为和脑电图研究正念冥想对情景记忆的影响

使用偏小波变换相干法测量 fNIRS-超扫描数据中的定向信息流

A Visuospatial Planning Task Coupled with Eye-Tracker and Electroencephalogram Systems

理解功能性近红外光谱（fNIRS）超扫描研究中跨脑相干性的新框架

理解功能性近红外光谱（fNIRS）超扫描研究中跨脑相干性的新框架

理解功能性近红外光谱（fNIRS）超扫描研究中跨脑相干性的新框架

BrainBeats 作为开源 EEGLAB 插件，用于联合分析 EEG 和心血管信号

使用小波熵来证明正念练习如何增加不规则脑与心脏活动之间的协调

摘要

探索更多视频

此视频中的章节

基于社区的压力管理计划: 在非实验室环境中使用可穿戴设备评估全身生理反应

帕金森病中运动与认知功能的动态数字生物标志物

静止状态脑电图的微状态和欧米茄复杂性分析

结合行为和脑电图研究正念冥想对情景记忆的影响

使用偏小波变换相干法测量 fNIRS-超扫描数据中的定向信息流

A Visuospatial Planning Task Coupled with Eye-Tracker and Electroencephalogram Systems

理解功能性近红外光谱 （fNIRS） 超扫描研究中跨脑相干性的新框架

理解功能性近红外光谱 （fNIRS） 超扫描研究中跨脑相干性的新框架

理解功能性近红外光谱 （fNIRS） 超扫描研究中跨脑相干性的新框架

BrainBeats 作为开源 EEGLAB 插件，用于联合分析 EEG 和心血管信号

理解功能性近红外光谱（fNIRS）超扫描研究中跨脑相干性的新框架

理解功能性近红外光谱（fNIRS）超扫描研究中跨脑相干性的新框架

理解功能性近红外光谱（fNIRS）超扫描研究中跨脑相干性的新框架