这项工作得到了Ray和Dagmar Dolby家庭基金通过加州大学旧金山分校精神病学系（KKS，ANK，NS，JF，VRR，KWS，EFC，ADK）的支持，由美国国立卫生研究院奖号支持。K23NS110962（KWS），大脑与行为研究基金会（KWS）的NARSAD青年研究员资助，以及1907年开拓者奖（KWS）。

该协议已由加州大学旧金山分校机构审查委员会审查和批准。
1. 患者在家录音的设备设置
<ol><li>与设备公司的代表合作，设置四个配置的采集通道，每个植入的引线两个通道。 注：每个通道记录一个双极性记录。配置的通道可以使用相邻（例如，1-2,3-4）或交错（例如，1-3,2-4）电极触点。当植入间距为 10 mm 的引线时，通常使用相邻触点。当植入间距为 3.5 mm 的引线时，使用相邻或交错触点。这是通过检查电极植入物相对于解剖目标的位置重建和检查信号幅度来确定的。如果相邻触点产生低幅度信号，则最好使用交错触点。每个联系人只能在蒙太奇中使用一次。</li>
	<li>确保患者在植入皮质和/或深度导联装置后已从手术中完全恢复（有关植入技术的更多信息，请参见24 ）。</li>
	<li>将遥测棒连接到编程器（临床医生平板电脑），让患者将测量棒放在他们的INS上或将其连接到系留帽（定制，不是设备系统的一部分;见 图1）。</li>
	<li>使用编程器登录到患者数据管理系统 （PDMS;https://pdms.neuropace.com/login.php），导航到正确的患者，然后选择 编程 ，然后选择更改 ECoG 捕获。使用下拉选项将四个已配置通道的 捕获窗口 设置为最大 240 秒。 注意：设备采样率为 250 Hz。</li>
	<li>在同一设置页面上，使用下拉选项将磁体预留 的预留空间 设置为 2，将所有其他触发器类型的预留空间设置为 0。这允许优先保存由磁铁滑动触发的两个记录。 注意：还可以启用计划存储，以便在一天中的设定时间自动和非优先保存神经记录。这些记录不会用于确定生物标志物，但可能对其他目的有用。</li>
	<li>通过选择“ 查看和计划 ”按钮，确认显示的表格中显示的更改，然后选择“确认编程”按钮，将新 编程 的设置与患者的INS同步。 注意： 检测 和 刺激 都应设置为 禁用。</li>
</ol>2. 患者在家录音期间的症状收集
<ol><li>为患者症状报告（例如REDCap 25）准备基于网络的调查，包括 VAS-D、VAS-A、VAS-E 的滑块以及 HAMD-6 每个问题的选择答案。确保记录调查开始和完成的时间。</li>
	<li>向患者提供 REDCap 生成的唯一 URL，以便通过短信或电子邮件 访问 症状调查</li>
</ol>3. 并发家庭症状报告和神经记录的程序
<ol><li>指导患者设置设备，包括远程监视器（患者笔记本电脑）和魔杖、磁铁和用于完成调查的设备（例如智能手机、平板电脑或计算机）（图 1）。步骤3.2-3.8由患者执行。 注意：大多数患者很快学会了这个程序。在植入设备后，当患者仍在医院时进行面对面的培训课程有助于熟悉组件。患者回家后，在患者进行家庭录音时进行视频通话可以作为有用的复习。</li>
	<li>打开远程监视器并使用棒询问设备，将自上次审讯以来发生的皮质电图 （ECoG） 记录下载到远程监视器。</li>
	<li>在 INS 上滑动磁铁以触发磁铁记录。 注意：磁铁轻扫会触发在轻扫比之前：之后为 2：1 的录制。对于编程为 240 秒的 ECoG 捕获，这意味着将存储滑动前 160 秒和滑动后 80 秒的数据。</li>
	<li>启动计时器。使用唯一 URL 完成症状调查。</li>
	<li>4 分钟后或患者完成调查（以较晚发生者为准），再次在 INS 上滑动磁铁以触发另一次记录。</li>
	<li>至少 80 秒后，使用魔杖再次询问设备，将数据从两次磁铁滑动传输到远程监视器。 注意：由于INS的板载内存有限（最多53通道分钟的数据，取决于配置13），因此最好立即将这些ECoG记录传输到远程监视器，以免它们被后续记录覆盖。</li>
	<li>每天至少完成两次步骤 3.1-3.6。</li>
	<li>每天至少一次，通过以太网将远程监视器连接到互联网，然后在远程监视器上选择传输数据和同步以将数据发送到云。</li>
</ol><img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/65177/65177fig01.jpg"> 图 1：用于家庭记录的患者设备。 远程监视器，拴在帽子上的魔杖，磁铁和带有REDCap调查的智能手机。镶嵌图像显示右侧 OFC（蓝色）和右侧 SGC（橙色）电极植入物位置叠加在术前磁共振成像 （MRI） 的白质空 1 mm 各向同性 T1 序列上。所描绘的日冕切片位于最深接触的平面上，因此其他接触可能不以该确切切片为中心（由于电极轨迹不在日冕平面中）。 <a href="https://www.jove.com/files/ftp_upload/65177/65177fig01large.jpg" target="_blank">请点击此处查看此图的大图。</a>
4. 确定个性化生物标志物
<ol><li>创建自我报告的症状调查响应的数据帧（例如 JOVE.PR03_Symptoms.pkl）。</li>
	<li>通过将 ECoG 活动与一系列 Morlet 小波（40 个内核，12 个周期，对数间隔在 1-120 Hz 之间）卷积 ECoG 记录的每个通道计算频谱功率分布，创建一个新的数据帧（例如，JOVE.PR03_NeuralPower.pkl）。</li>
	<li>使用 NeuralPower 数据帧中的trial_id字段，将 ECoG 记录与症状报告启动前 5 分钟至症状报告启动后 5 分钟内发生的症状报告相关联。</li>
	<li>识别症状状态（图2）<ol><li>使用 Python 3.10，将 requirements.txt 文件中列出的要求安装到新环境。这可以使用 pip install -r 要求.txt来完成。</li>
		<li>使用 Jupyter Notebook 打开 JOVE-Symptom_State_and_Biomarker_Analysis.ipynb （补充文件夹 1）。</li>
		<li>验证内核是否设置为安装 .txt要求 并运行 JOVE-Symptom_State_and_Biomarker_Analysis.ipynb 的环境。 注意： JOVE-Symptom_State_and_Biomarker_Analysis.ipynb 使用余弦相似性指标计算患者调查分数之间的统计相似性，范围从 0（无相似性）到 1（相同），并通过聚合所有可能的家庭患者症状报告的相似性值来构建症状状态图。每个症状状态的频谱功率特征是通过聚合与每个推断症状状态关联的频谱功率配置文件来计算的;此过程产生与每个症状状态相关的频谱功率分布（图3）。</li>
	</ol></li>
	<li>在识别出两种以上症状状态的情况下，将统计比较对应于最严重症状状态和最不严重症状状态的光谱功率分布。</li>
	<li>使用基于聚类的排列 t 检验来识别严重症状状态的谱功率明显大于最不严重症状状态的谱功率的频谱频率。区分症状状态的连续频谱频率范围被视为单个候选生物标志物。</li>
</ol><img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/65177/65177fig02.jpg"> 图 2：测量症状状态的方法示意图，显示了代表性示例的结果。 获得患者自我报告的调查，并将逐项症状评分标准化为 0 到 1 之间的范围（颜色越深表示症状严重程度越低，颜色越亮表示症状严重程度越高）。（1）每个完成的调查都代表患者症状的时间快照，并表示为高维空间中的一个点（黑色）。（2）时间点在症状调查图中链接在一起，该图将调查报告之间的余弦相似性（点之间的线）联系起来。（3） 图社区检测根据图连接的模式将每个时间点分配给社区或症状状态（彩色点和线）。（4）症状严重程度评分根据状态分配取平均值，并提供每种状态的一般症状表型。（5） 每个状态的发生可以作为栅格图随时间进行跟踪（垂直线反映分配给一个州的症状报告）。 <a href="https://www.jove.com/files/ftp_upload/65177/65177fig02large.jpg" target="_blank">请点击此处查看此图的大图。</a>
5. 设备检测器设置编程
<ol><li>使用编程器登录到 PDMS 并选择正确的患者、 编程和 更改检测。</li>
	<li>根据所选的个性化生物标志物，选择正确的检测活动通道。<ol><li>首次创建 模式时，首先通过单击下面显示的 ECoG、从该 ECoG 中选择的通道以及突出显示的时间段来选择 ECOG。</li>
		<li>从预先存在的模式创建模式时，通过单击 模式 并选择更改通道按钮来更改 通道 。</li>
	</ol></li>
	<li>对于检测，选择节奏活动，这意味着带通检测器。</li>
	<li>单击 更多控件 和 高级设置。选择所需的最小频率和最大频率。确保设置如下： 带通：开; 线长：关闭; 区域：关闭; 反转逻辑：不反转。</li>
	<li>对探测器的最小 振幅 和 最小持续时间 进行编程。从最小振幅 0.8% 和最短持续时间 0.64 s 开始（相当于 带通阈值 为 5， 检测分析窗口大小 为 1280 ms）。 注意： 最小振幅 是检测到的活动必须超过的总信号幅度的百分比。只有高于此阈值的振幅才能用于检测。 最小持续时间 是高振幅信号必须持续多长时间。这被跟踪为固定持续时间时间箱的计数。具体来说，y 128 ms 时间箱中的 x 必须满足这些条件，其中 x 是 带通阈值 ，y 是 [检测分析窗口大小/128]，如技术参数所示。在后续触发发生之前，必须退出事件（即触发检测器）。某些设置可能过于敏感，导致无限期地停留在一集中，从而防止后续触发。</li>
	<li>选择所有检测器设置后，单击 完成 关闭所有编程窗口。 注意：可以对多个探测器进行编程;可以根据这些检测器之间的 AND/OR 逻辑触发检测。从一个检测器开始，了解行为，然后再引入第二个检测器。</li>
	<li>将魔杖放在患者的INS上，选择 “查看和编程 ”，然后单击确认消息以初始化此检测器设置。</li>
</ol>6. 滴定设备检测器设置
<ol><li>在设备上对设备检测器进行编程和初始化后，进行测试记录，以评估是否应调整检测器的灵敏度以达到所需的触发频率。这可以通过 实时ECoG 或审讯报告来实现。检测器灵敏度的调整是根据患者症状报告、副作用和结果的经验确定的。在整个治疗过程中可能需要进行这些调整。每天，30分钟的刺激可以作为评估临床疗效的保守起点，同时还可以保持电池寿命。</li>
	<li>实时 ECoG<ol><li>将魔杖放在患者的INS上，在编程器上选择 实时ECOG 。</li>
		<li>在实时录制期间，计算录制期间发生的检测次数。这提供了探测器触发频率的指示。 注意：某些检测器可能与状态相关，特别是设置为低频率的检测器（例如，在睡眠或嗜睡期间更活跃）。因此，实时记录在估计探测器触发的频率方面存在局限性。现场录音也可能受到电磁干扰（例如线路噪声）或魔杖定位不良的影响。</li>
	</ol></li>
	<li>审讯报告<ol><li>设置探测器后几分钟到几小时，将魔杖放在患者的INS上以询问设备。</li>
		<li>在 PDMS 中，导航到“ 活动”，选择“ 事件列表”，然后单击从录制时间开始的 “初始询问 ”。底部的表提供了所有检测事件的列表，其中包含时间。该文档可以以 pdf 格式导出并解析以进行量化。</li>
	</ol></li>
	<li>根据记录的单位时间内的检测次数与所需的刺激密度，根据需要调整检测器持续时间和幅度参数。请务必在每组更改后单击 “查看和程序 ”，以在患者的INS上初始化这些更改。</li>
</ol>7. 设备刺激设置编程
<ol><li>使用编程器，登录到PDMS并选择正确的患者， 编程和 变化刺激。</li>
	<li>选择所需的引线触点或罐（INS）作为阳极和阴极。选择所需的刺激电流、脉冲宽度、持续时间和频率。 注意：最多可以编程五种刺激疗法;每种疗法包括两次刺激，可以编程为相同或不同。给定的检测器触发可能导致不同数量的刺激疗法，具体取决于发作的持续持续时间。仅编程疗法 1，两个脉冲具有相同的参数，这将导致每次触发检测器时传递一致的刺激持续时间。在此配置中，触发探测器时传递的刺激总持续时间将是 Stim 1 突发 1 和 Stim 1 突发 2 的总和。如果编程并提供了多种疗法，则在给定的发作期间最多可以提供五种疗法。必须退出发作并触发新的发作，以便提供后续刺激。</li>
	<li>如果需要限制每天提供的刺激总量，请选择 “每天的 治疗限制”和“ 治疗限制重置时间”的值。 注意： 治疗限制重置时区 设置为患者的主要中心。如果患者已搬迁，这可能与患者的家乡时区不同。</li>
	<li>具体来说，如果不需要过夜刺激，请设置 每天的 治疗限值和 治疗限值重置时间，以便检测器达到复位时间和患者就寝时间之间的治疗限值。</li>
	<li>将魔杖放在患者的INS上，选择 “查看和程序 ”，然后单击确认消息以初始化刺激设置。</li>
</ol>

脑深部刺激治疗重度抑郁症

<ol>
	<li>Kaiser, R. H., Andrews-Hanna, J. R., Wager, T. D., Pizzagalli, D. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Large-scale+network+dysfunction+in+major+depressive+disorder:+A+meta-analysis+of+resting-state+functional+connectivity.">Large-scale network dysfunction in major depressive disorder: A meta-analysis of resting-state functional connectivity.</a> JAMA Psychiatry. 72 (6), 603-611 (2015).</li><li>Goldstein-Piekarski, A. N., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Mapping+neural+circuit+biotypes+to+symptoms+and+behavioral+dimensions+of+depression+and+anxiety.">Mapping neural circuit biotypes to symptoms and behavioral dimensions of depression and anxiety.</a> Biological Psychiatry. 91 (6), 561-571 (2022).</li><li>Williams, L. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Precision+psychiatry:+a+neural+circuit+taxonomy+for+depression+and+anxiety.">Precision psychiatry: a neural circuit taxonomy for depression and anxiety.</a> The Lancet Psychiatry. 3 (5), 472-480 (2016).</li><li>Ionescu, D. F., Rosenbaum, J. F., Alpert, J. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Pharmacological+approaches+to+the+challenge+of+treatment-resistant+depression.">Pharmacological approaches to the challenge of treatment-resistant depression.</a> Dialogues in Clinical Neuroscience. 17 (2), 111-126 (2015).</li><li>Mayberg, H. S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Deep+brain+stimulation+for+treatment-resistant+depression.">Deep brain stimulation for treatment-resistant depression.</a> Neuron. 45 (5), 651-660 (2005).</li><li>Kennedy, S. H., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Deep+brain+stimulation+for+treatment-resistant+depression:+follow-up+after+3+to+6+years.">Deep brain stimulation for treatment-resistant depression: follow-up after 3 to 6 years.</a> The American Journal of Psychiatry. 168 (5), 502-510 (2011).</li><li>Holtzheimer, P. E., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Subcallosal+cingulate+deep+brain+stimulation+for+treatment-resistant+depression:+a+multisite,+randomised,+sham-controlled+trial.">Subcallosal cingulate deep brain stimulation for treatment-resistant depression: a multisite, randomised, sham-controlled trial.</a> The Lancet Psychiatry. 4 (11), 839-849 (2017).</li><li>Dougherty, D. D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+randomized+sham-controlled+trial+of+deep+brain+stimulation+of+the+ventral+capsule/ventral+striatum+for+chronic+treatment-resistant+depression.">A randomized sham-controlled trial of deep brain stimulation of the ventral capsule/ventral striatum for chronic treatment-resistant depression.</a> Biological Psychiatry. 78 (4), 240-248 (2015).</li><li>Morishita, T., Fayad, S. M., Higuchi, M., Nestor, K. A., Foote, K. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Deep+brain+stimulation+for+treatment-resistant+depression:+systematic+review+of+clinical+outcomes.">Deep brain stimulation for treatment-resistant depression: systematic review of clinical outcomes.</a> Neurotherapeutics. 11 (3), 475-484 (2014).</li><li>Lo, M. -. C., Widge, A. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Closed-loop+neuromodulation+systems:+next-generation+treatments+for+psychiatric+illness.">Closed-loop neuromodulation systems: next-generation treatments for psychiatric illness.</a> International Review of Psychiatry. 29 (2), 191-204 (2017).</li><li>Hoang, K. B., Cassar, I. R., Grill, W. M., Turner, D. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Biomarkers+and+stimulation+algorithms+for+adaptive+brain+stimulation.">Biomarkers and stimulation algorithms for adaptive brain stimulation.</a> Frontiers in Neuroscience. 11, 564 (2017).</li><li>Little, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Adaptive+deep+brain+stimulation+in+advanced+Parkinson+disease.">Adaptive deep brain stimulation in advanced Parkinson disease.</a> Annals of Neurology. 74 (3), 449-457 (2013).</li><li>Jarosiewicz, B., Morrell, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+RNS+system:+brain-responsive+neurostimulation+for+the+treatment+of+epilepsy.">The RNS system: brain-responsive neurostimulation for the treatment of epilepsy.</a> Expert Review of Medical Devices. 18 (2), 129-138 (2021).</li><li>Scangos, K. W., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Closed-loop+neuromodulation+in+an+individual+with+treatment-resistant+depression.">Closed-loop neuromodulation in an individual with treatment-resistant depression.</a> Nature Medicine. 27 (10), 1696-1700 (2021).</li><li>Cagle, J. N., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Embedded+human+closed-loop+deep+brain+stimulation+for+Tourette+syndrome:+a+nonrandomized+controlled+trial.">Embedded human closed-loop deep brain stimulation for Tourette syndrome: a nonrandomized controlled trial.</a> JAMA Neurology. 79 (10), 1064-1068 (2022).</li><li>He, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Closed-loop+deep+brain+stimulation+for+essential+tremor+based+on+thalamic+local+field+potentials.">Closed-loop deep brain stimulation for essential tremor based on thalamic local field potentials.</a> Movement Disorders. 36 (4), 863-873 (2021).</li><li>Drysdale, A. T., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Resting-state+connectivity+biomarkers+define+neurophysiological+subtypes+of+depression.">Resting-state connectivity biomarkers define neurophysiological subtypes of depression.</a> Nature Medicine. 23 (1), 28-38 (2017).</li><li>Siddiqi, S. H., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Brain+stimulation+and+brain+lesions+converge+on+common+causal+circuits+in+neuropsychiatric+disease.">Brain stimulation and brain lesions converge on common causal circuits in neuropsychiatric disease.</a> Nature Human Behaviour. 5 (12), 1707-1716 (2021).</li><li>Ahearn, E. P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+use+of+visual+analog+scales+in+mood+disorders:+A+critical+review.">The use of visual analog scales in mood disorders: A critical review.</a> Journal of Psychiatric Research. 31 (5), 569-579 (1997).</li><li>Bech, P., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+Hamilton+depression+scale.+Evaluation+of+objectivity+using+logistic+models.">The Hamilton depression scale. Evaluation of objectivity using logistic models.</a> Acta Psychiatrica Scandinavica. 63 (3), 290-299 (1981).</li><li>Khambhati, A. N., Sizemore, A. E., Betzel, R. F., Bassett, D. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Modeling+and+interpreting+mesoscale+network+dynamics.">Modeling and interpreting mesoscale network dynamics.</a> NeuroImage. 180, 337-349 (2018).</li><li>Zamani Esfahlani, F., Bertolero, M. A., Bassett, D. S., Betzel, R. F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Space-independent+community+and+hub+structure+of+functional+brain+networks.">Space-independent community and hub structure of functional brain networks.</a> NeuroImage. 211, 116612 (2020).</li><li>Kleen, J. K., Rao, V. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Managing+neurostimulation+for+epilepsy.">Managing neurostimulation for epilepsy.</a> Deep Brain Stimulation Management. , 177-197 (2022).</li><li>Krucoff, M. O., Wozny, T. A., Lee, A. T., Rao, V. R., Chang, E. F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Operative+technique+and+lessons+learned+from+surgical+implantation+of+the+NeuroPace+Responsive+Neurostimulation&#174;+system+in+57+consecutive+patients.">Operative technique and lessons learned from surgical implantation of the NeuroPace Responsive Neurostimulation&#174; system in 57 consecutive patients.</a> Operative Neurosurgery. 20 (2), E98-E109 (2021).</li><li>Harris, P. A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+REDCap+consortium:+Building+an+international+community+of+software+platform+partners.">The REDCap consortium: Building an international community of software platform partners.</a> Journal of Biomedical Informatics. 95, 103208 (2019).</li><li>Krauss, J. K., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Technology+of+deep+brain+stimulation:+current+status+and+future+directions.">Technology of deep brain stimulation: current status and future directions.</a> Nature Reviews Neurology. 17 (2), 75-87 (2021).</li><li>Dougherty, D. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Deep+brain+stimulation:+clinical+applications.">Deep brain stimulation: clinical applications.</a> The Psychiatric Clinics of North America. 41 (3), 385-394 (2018).</li><li>Drobisz, D., Damborsk&#225;, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Deep+brain+stimulation+targets+for+treating+depression.">Deep brain stimulation targets for treating depression.</a> Behavioural Brain Research. 359, 266-273 (2019).</li><li>Lee, D. J., Lozano, C. S., Dallapiazza, R. F., Lozano, A. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Current+and+future+directions+of+deep+brain+stimulation+for+neurological+and+psychiatric+disorders.">Current and future directions of deep brain stimulation for neurological and psychiatric disorders.</a> Journal of Neurosurgery. 131 (2), 333-342 (2019).</li><li>Sun, F. T., Morrell, M. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Closed-loop+neurostimulation:+the+clinical+experience.">Closed-loop neurostimulation: the clinical experience.</a> Neurotherapeutics. 11 (3), 553-563 (2014).</li><li>Malone, D. A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Deep+brain+stimulation+of+the+ventral+capsule/ventral+striatum+for+treatment-resistant+depression.">Deep brain stimulation of the ventral capsule/ventral striatum for treatment-resistant depression.</a> Biological Psychiatry. 65 (4), 267-275 (2009).</li><li>Zuo, X. -. N., Xu, T., Milham, M. P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Harnessing+reliability+for+neuroscience+research.">Harnessing reliability for neuroscience research.</a> Nature Human Behaviour. 3 (8), 768-771 (2019).</li></ol>

ADK为卫材、Evecxia Therapeutics、Ferring Pharmaceuticals、Galderma、Harmony Biosciences、Idorsia、Jazz Pharmaceuticals、Janssen Pharmaceuticals、Merck、Neurocrine Biosciences、Pernix Pharma、Sage Therapeutics、Takeda Pharmaceutical Company、Big Health、Millennium Pharmaceuticals、Otsuka Pharmaceutical和Neurawell Therapeutics提供咨询服务。ADK感谢Janssen Pharmaceuticals，Jazz Pharmaceuticals，Axsome Therapeutics的支持（没有。AXS-05-301），并揭示生物传感器。KWS是Nesos的顾问委员会成员。加州大学旧金山分校和EFC拥有与治疗神经精神疾病的脑刺激相关的专利。其他作者声明没有竞争利益。

深部脑刺激已成为帕金森病，特发性震颤，肌张力障碍和癫痫的既定治疗方法，并且正在许多其他神经精神疾病中积极研究26,27,28,29。绝大多数DBS以开环模式提供，其中刺激是连续传递的。对于阵发性症状，连续刺激可能导致不良副作用或降低治疗效果30。虽然随着抑郁症状的消退，...

重度抑郁症 （MDD） 是一种神经精神疾病，其特征是网络级异常活动和连接1。该疾病表现出各种症状，这些症状因人而异，随时间波动，并且可能源于不同的神经回路2,3。大约 30% 的 MDD 患者对标准护理治疗无效4，这突出表明需要新的方法。
深部脑刺激（DBS）是一种神经调控形式，其中电流被输送到大脑的目标区域，目的是调节活动。DBS治疗MDD在某些应用中非常成功5,6，但在更大的研究中也未能复制7,8。所有引用的研究都采用了开环刺激9，其中推定的治疗刺激的传递是连续的，参数固定。相反，闭环刺激基于与症状状态相关的程序生物标志物或神经活动模式10提供刺激。闭环刺激有两种主要实现方式：响应性刺激和适应性刺激11。当满足编程标准时，响应式刺激以恒定的参数（例如，频率、振幅、脉冲宽度）提供刺激脉冲。通过自适应刺激，刺激参数根据算法作为测量生物标志物的函数动态变化，该算法可能具有多个固定点或自动连续调整。刺激可以是连续的，也可以是间歇性的，伴有适应性刺激。适应性刺激在控制帕金森病症状方面显示出优于开环刺激的功效12。癫痫13 的反应性神经刺激已获得美国食品和药物管理局 （FDA） 的批准，而对 MDD 14 的反应性刺激和对图雷特综合征15 和特发性震颤16 的适应性刺激的早期研究也显示出治疗益处。
为了实现闭环刺激，必须选择并跟踪生理信号，以告知何时应提供刺激。这种反馈是开环和闭环刺激之间的关键区别，通过选择生物标志物来实现。该协议提供了一种根据给定个体经历的症状组合确定个性化生物标志物的程序。未来对患者的荟萃分析将揭示个体之间是否存在共同的生物标志物，或者MDD症状和潜在回路的异质性表现是否需要个性化方法17,18。使用能够感知神经活动和提供电刺激的DBS设备可以发现这种生物标志物并随后实施闭环神经调控。这种方法预先假定神经活动与特定症状状态之间存在密切的时间关系，可能不适用于所有适应症或症状。
虽然帕金森病和特发性震颤等适应症的症状可以使用外围传感器进行测量（例如震颤、强直），但 MDD 的症状通常由患者报告或由临床医生使用标准化问题和观察进行评估。在积累足够数据以计算个性化生物标志物的背景下，临床医生评估是不切实际的，因此使用通过评定量表报告患者症状。这些量表包括抑郁（VAS-D）、焦虑（VAS-A）和能量（VAS-E）的视觉模拟量表19，以及汉密尔顿抑郁评定量表（HAMD-6）20的六个问题形式。并发记录神经活动并完成这些自我报告的症状评级提供了一个配对数据集，可用于查看与高症状状态相关或预测高症状状态的神经信号光谱特征之间的关系。
计算方法（如状态空间建模）可用于揭示症状状态和神经特征之间的关系。图论方法对于表征状态空间21 很有吸引力，因为它们可以通过显式建模测量值22之间的时间接近性来发现不同时间尺度上的状态。症状状态空间模型可识别患者症状存在共同表型的时间段，并可能查明症状子状态，其中患者抑郁症特定维度的评级因环境或背景而异。闭环方法依赖于基于潜在大脑活动的症状状态检测。机器学习分类是最后一步，有助于识别从大脑活动信号中获得的统计特征组合，这些特征最能区分两种或多种症状状态14。这种两阶段方法解释了患者症状随时间的变化，并将症状变异的系统模式与大脑活动联系起来。
本协议利用NeuroPace响应性神经刺激系统（RNS）13,23。确定最佳刺激部位和参数的程序超出了本协议的范围。然而，在设计闭环神经刺激时，给定设备的刺激能力很重要。对于本协议中使用的设备，刺激是电流控制的，并在阳极和阴极之间传递。可以选择一个或多个电极触点或罐（植入式神经刺激器[INS]）作为阳极或阴极。刺激频率 （1-333.3 Hz）、振幅 （0-12 mA）、脉冲宽度（每相 40-1000 μs）和持续时间（每刺激 10-5000 ms）都是预先编程的。可以独立设置多达五种刺激疗法的先前参数;如果继续满足检测标准，则按顺序进行这些治疗。不可能同时提供多个刺激波形（例如，一个不能同时提供两个不同频率的刺激）。刺激波形是对称的双相矩形波，无法更改。

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Depth Lead</td>
			<td>Neuropace</td>
			<td>DL-330-3.5</td>
			<td>30 cm length, 3.5 mm contact spacing</td>
		</tr>
		<tr>
			<td>Depth Lead</td>
			<td>Neuropace</td>
			<td>DL-330-10</td>
			<td>30 cm length, 10 mm contact spacing</td>
		</tr>
		<tr>
			<td>Depth Lead</td>
			<td>Neuropace</td>
			<td>DL-344-3.5</td>
			<td>44 cm length, 3.5 mm contact spacing</td>
		</tr>
		<tr>
			<td>Depth Lead</td>
			<td>Neuropace</td>
			<td>DL-344-10</td>
			<td>44 cm length, 10 mm contact spacing</td>
		</tr>
		<tr>
			<td>Hat with velcro</td>
			<td>Self-assembled</td>
			<td>NA</td>
			<td>Optional</td>
		</tr>
		<tr>
			<td>Jupyter Notebook</td>
			<td>Project Jupyter</td>
			<td>NA</td>
			<td></td>
		</tr>
		<tr>
			<td>Magnet</td>
			<td>Neuropace</td>
			<td>M-01</td>
			<td></td>
		</tr>
		<tr>
			<td>Programmer</td>
			<td>Neuropace</td>
			<td>PGM-300</td>
			<td>Clinician tablet</td>
		</tr>
		<tr>
			<td>Python 3.10</td>
			<td>Python</td>
			<td>NA</td>
			<td></td>
		</tr>
		<tr>
			<td>Remote Monitor</td>
			<td>Neuropace</td>
			<td>5000</td>
			<td>Patient laptop&#160;</td>
		</tr>
		<tr>
			<td>Responsive Neurostimulation System (RNS)&#160;</td>
			<td>Neuropace</td>
			<td>RNS-320</td>
			<td></td>
		</tr>
		<tr>
			<td>Wand</td>
			<td>Neuropace</td>
			<td>W-02</td>
			<td></td>
		</tr>
	</tbody>
</table>

closed-loop neurostimulation for biomarker-driven, personalized treatment of major depressive disorder

深部脑刺激涉及对目标大脑区域进行电刺激以获得治疗益处。在重度抑郁症（MDD）的背景下，迄今为止的大多数研究都进行了连续或开环刺激，结果有希望但好坏参半。导致这些混合结果的一个因素可能源于何时施加刺激。与连续刺激相比，以个性化和反应性的方式对高症状状态进行刺激给药可能更有效地减轻症状，并且可以避免与习惯化相关的治疗效果减弱。此外，每天较低的刺激总持续时间有利于降低设备能耗。该协议描述了使用长期植入的神经刺激装置的实验工作流程，以实现难治性MDD患者的闭环刺激。这种范式取决于确定与高症状状态相关的患者特异性神经生物标志物，并对设备检测器进行编程，以便通过这种症状状态的读出触发刺激。所描述的程序包括如何获得与患者症状报告同时进行的神经记录，如何在状态空间模型方法中使用这些数据来区分低症状和高症状状态以及相应的神经特征，以及如何随后编程和调整设备以提供闭环刺激治疗。

Major depressive disorder (MDD) is a neuropsychiatric disease characterized by network-level aberrant activity and connectivity1. The disease manifests a variety of symptoms that vary across individuals, fluctuate over time, and may stem from different neural circuits2,3. Approximately 30% of individuals with MDD are refractory to standard-of-care treatments4, highlighting a need for new approaches.
Deep brain stimulation (DBS) is a form of neuromodulation in which electrical current is delivered to targeted areas of the brain with the goal of modulating the activity. DBS for the treatment of MDD has been very successful in some applications5,6, but has also failed to replicate in larger studies7,8. All of the cited studies employed open-loop stimulation9, in which the delivery of putative therapeutic stimulation was continuous with fixed parameters. In contrast, closed-loop stimulation delivers stimulation based on a programmed biomarker or neural activity pattern associated with the symptom state10. There are two main implementations of closed-loop stimulation: responsive stimulation and adaptive stimulation11. Responsive stimulation delivers bursts of stimulation with constant parameters (e.g., frequency, amplitude, pulse width) when the programmed criteria are met. With adaptive stimulation, stimulation parameters dynamically change as a function of the measured biomarker, according to the algorithm, which may have multiple fix points or automated continuous adjustment. Stimulation can be continuous or intermittent with adaptive stimulation. Adaptive stimulation has shown superior efficacy to open-loop stimulation in controlling symptoms of Parkinson&#39;s disease12. Responsive neurostimulation for epilepsy13 is Food and Drug Administration (FDA)-approved, while early investigations of responsive stimulation for MDD14 and adaptive stimulation for Tourette syndrome15 and essential tremor16 also show therapeutic benefit.
To implement closed-loop stimulation, a physiological signal must be selected and tracked to inform when stimulation should be delivered. This feedback is the key difference between open-loop and closed-loop stimulation and is realized by selecting a biomarker. This protocol provides a procedure for determining a personalized biomarker according to the constellation of symptoms experienced by a given individual. Future meta-analyses across patients will reveal if there are common biomarkers across individuals or if the heterogeneous presentation of MDD symptoms and the underlying circuitry necessitates a personalized approach17,18. Using DBS devices capable of both sensing neural activity and delivering electrical stimulation allows for both the discovery of this biomarker and the subsequent implementation of closed-loop neuromodulation. This approach presupposes a close temporal relationship between neural activity and specific symptom states and may not be applicable for all indications or symptoms.
While indications such as Parkinson&#39;s disease and essential tremor have symptoms that can be measured using peripheral sensors (e.g., tremor, rigidity), symptoms of MDD are typically reported by the patient or assessed by a clinician using standardized questions and observation. In the context of amassing sufficient data to calculate a personalized biomarker, clinician assessments are not practical, and thus patient reports of symptoms through rating scales are used. Such scales include visual analog scales of depression (VAS-D), anxiety (VAS-A), and energy (VAS-E)19, and the six-question form of the Hamilton Depression Rating Scale (HAMD-6)20. Concurrent recordings of neural activity and completion of these self-report symptom ratings provide a paired dataset that can be used to look at relationships between spectral features of the neural signal related to or predictive of high-symptom states.
Computational approaches, such as state-space modeling, can be used to uncover relationships between symptom states and neural features. Graph theoretic methods are attractive for characterizing a state-space21 because they enable the discovery of states over different timescales by explicitly modeling the temporal proximity between measurements22. A symptom state-space model identifies periods of time in which there is a common phenotype of the patient&#39;s symptoms and may pinpoint symptom sub-states in which the ratings on specific dimensions of the patient&#39;s depression differ based on the environment or context. A closed-loop approach relies on the detection of symptom states based on underlying brain activity. Machine learning classification is a final step that helps identify a combination of statistical features derived from brain activity signals that best distinguishes two or more symptom states14. This two-stage approach explains the variability in a patient&#39;s symptoms over time and links systematic patterns of symptom variation to brain activity.
The present protocol utilizes the NeuroPace Responsive Neurostimulation System (RNS)13,23. Procedures to determine the optimal stimulation site(s) and parameters are outside the scope of this protocol. However, the stimulation capabilities of a given device are important to consider when designing closed-loop neurostimulation. For the device used in this protocol, stimulation is current-controlled and delivered between the anode(s) and cathode(s). One or more electrode contacts or the Can (implantable neurostimulator [INS]) can be selected as the anode(s) or cathode(s). Stimulation frequency (1-333.3 Hz), amplitude (0-12 mA), pulse width (40-1000 &#181;s per phase), and duration (10-5000 ms, per stim) are all pre-programmed. The prior parameters can be set independently for up to five stimulation therapies; these therapies are delivered sequentially if the detection criteria continue to be met. It is not possible to deliver multiple stimulation waveforms simultaneously (e.g., one cannot deliver two different frequencies of stimulation concurrently). The stimulation waveform is a symmetric biphasic rectangular wave and cannot be changed.

作者聚焦：闭环深部脑刺激在抑郁症治疗中的治疗益处 

闭环神经刺激用于重度抑郁症的生物标志物驱动型个性化治疗

We are assessing the therapeutic benefit of closed-loop personalized deep brain stimulation for the treatment of major depressive disorder. This protocol outlines how to identify a personalized neural biomarker that is correlated with symptom severity, and then program an implanted neurostimulator to administer stimulation using that biomarker. Prior applications of deep brain stimulation for depression used continuous stimulation, which was always on, regardless of symptom status.This protocol describes using closed-loop stimulation in which we only deliver stimulation during high symptom states. Continued work to identify biomarkers for control of closed-loop stimulation will facilitate an approach where we can target the unique time courses of different symptoms.

此处收集和呈现的数据来自一名患者，其中四通道导联植入右眶额叶皮层（OFC）和右侧颌下扣带（SGC）（图1）。OFC 使用中心到中心间距为 10 mm 的引线，以针对内侧和外侧，而 SGC 使用间距为 3.5 mm 的引线，以实现更集中的空间覆盖。使用相邻触点对四个双极性记录通道进行编程：OFC1-OFC2、OFC3-OFC4、SGC1-SGC2 和 SGC3-SGC4。选择这些触点是为了帮助解释记录信号的位置，并且因为E...

Watch this Scientific Journal Video about Therapeutic Benefit of Closed-Loop Deep Brain Stimulation in Depression Treatment at JoVE.com

与连续的开环刺激相比，由高症状状态的患者特异性神经生物标志物触发的深部脑刺激可以更好地控制重度抑郁症的症状。该协议提供了一种工作流程，用于识别患者特异性神经生物标志物并控制基于已识别生物标志物的治疗刺激的传递。

Deep brain stimulation involves the administration of electrical stimulation to targeted brain regions for therapeutic benefit. In the context of major ...

我们正在评估闭环个性化深部脑刺激治疗重度抑郁症的治疗益处。该协议概述了如何识别与症状严重程度相关的个性化神经生物标志物，然后对植入的神经刺激器进行编程以使用该生物标志物进行刺激。以前对抑郁症的深部脑刺激的应用使用连续刺激，无论症状状态如何，这种刺激始终处于开启状态。该协议描述了使用闭环刺激，其中我们仅在高症状状态下提供刺激。继续确定用于控制闭环刺激的生物标志物的工作将有助于一种方法，我们可以针对不同症状的独特时间过程。

closed-loop-neurostimulation-for-biomarker-driven-personalized

Research

JoVE Journal

Neuroscience

Closed-Loop Neurostimulation for Biomarker-Driven, Personalized Treatment of Major Depressive Disorder

1.7K 次观看.  University of California, San Francisco. 我们正在评估闭环个性化深部脑刺激治疗重度抑郁症的治疗益处。该协议概述了如何识别与症状严重程度相关的个性化神经生物标志物，然后对植入的神经刺激器进行编程以使用该生物标志物进行刺激。以前对抑郁症的深部脑刺激的应用使用连续刺激，无论症状状态如何，这种刺激始终处于开启状态。该协议描述了使用闭环刺激，其中我们仅在高症状状态下提供刺激。?...

视频: 作者聚焦：闭环深部脑刺激在抑郁症治疗中的治疗益处 

Deep brain stimulation involves the administration of electrical stimulation to targeted brain regions for therapeutic benefit. In the context of major depressive disorder (MDD), most studies to date have administered continuous or open-loop stimulation with promising but mixed results. One factor contributing to these mixed results may stem from when the stimulation is applied. Stimulation administration specific to high-symptom states in a personalized and responsive manner may be more effective at reducing symptoms compared to continuous stimulation and may avoid diminished therapeutic effects related to habituation. Additionally, a lower total duration of stimulation per day is advantageous for reducing device energy consumption. This protocol describes an experimental workflow using a chronically implanted neurostimulation device to achieve closed-loop stimulation for individuals with treatment-refractory MDD. This paradigm hinges on determining a patient-specific neural biomarker that is related to states of high symptoms and programming the device detectors, such that stimulation is triggered by this read-out of symptom state. The described procedures include how to obtain neural recordings concurrent with patient symptom reports, how to use these data in a state-space model approach to differentiate low- and high-symptom states and corresponding neural features, and how to subsequently program and tune the device to deliver closed-loop stimulation therapy.

Deep brain stimulation triggered by a patient-specific neural biomarker of a high-symptom state may better control symptoms of major depressive disorder compared to continuous, open-loop stimulation. This protocol provides a workflow for identifying a patient-specific neural biomarker and controlling the delivery of therapeutic stimulation based on the identified biomarker.

科研

神经科学

Neurostimulation System Setup and Symptom Collection for Patient At-Home Recordings

To begin, ensure the patient has fully recovered from surgery following the implantation of the device with cortical in depth leads. Connect the telemetry wand to the programmer and instruct the patient to hold it over the implantable neurostimulator to attach it to a tethering hat. Using the programmer, log onto the patient data management system, navigate to the correct patient, select Programming, and then choose the Change ECOG Capture option.Proceed to set the capture window to a maximum of 240 seconds for the four configured channels using the dropdown selection. Using the dropdown selections, set the reserve space for magnet reservations to two and the value to zero for any other trigger types. Select the Review and Program button to synchronize the newly programmed settings with the patient's implantable neurostimulator.Verify any changes in the table and select the Confirm Programming button. To prepare for the Patient Symptom Report Survey, use a web-based survey tool such as REDCap, including sliders for VAS D, VAS A, VAS E, and selection responses for each question of the HAM-D 6. Ensure to log the time of survey start and completion.Provide the patient with the unique URL generated by REDCap for assessing the symptom surveys through either email or text message.

用于患者家庭记录的神经刺激系统设置和症状收集

Procedure for Concurrent At-Home Symptom Reports and Neural Recordings

Instruct the patient to set up the equipment, including the remote monitor and wand, magnet, and device for completing the survey. Turn on the remote monitor and, using the wand, interrogate the device. Then download the electrocorticography recordings since the last interrogation to the remote monitor.Swipe the magnet over the implantable neurostimulator to trigger a magnet recording. Start a timer and use the previously generated, unique REDCap URL to complete the symptom survey. After four minutes have elapsed or the patient completes the survey, whichever is longer, the patient will swipe the magnet over the implantable neurostimulator again to trigger another recording After at least 80 seconds, use the wand to interrogate the device again and transfer the data from the two magnet swipes to the remote monitor At least once a day, connect the remote monitor to the internet using ethernet and select Transfer Data and Synchronize on the remote monitor to send the collected data to the cloud.

并发家庭症状报告和神经记录的程序

Programming Device Detector Settings for Closed-Loop Neurostimulation

Begin by logging onto the patient Data Management System, using the programmer. Next, select the correct Patient, Programming, and Change Detection. Based on the selected personalized biomarker, select the correct channel for detecting the activity.For activity detection, select Rhythmic Activity, which connotes a band pass detector. Click More Controls in Advanced Settings. Select the desired minimum and maximum frequency.Ensure to turn on the band pass and turn off the line length in Area Settings. Additionally, set the inversion logic to Not Inverted. Program the detector by setting the minimum amplitude to 0.8%and the minimum duration to 0.64 seconds.After selecting all the detector settings, click the Done button to close all programming windows. Place the wand over the patient's implantable neurostimulator, and select Review All Changes in Program RNS. The biomarker discovery of symptoms was performed based on the neurospectral power between the low depression and the high depression states.It was found that the best choice for a frequency-specific biomarker in this example is 13 to 30 hertz of OFC, three to four.