我们要感谢 Wanting Li 女士提供的数字技术支持。这项工作得到了内蒙古医学基金 （2022QNWN0010， 2023SGGZ047， 2023SGGZ0010） 的支持。

该研究得到了中国呼和浩特市内蒙古心理健康中心（内蒙古自治区第三医院、内蒙古自治区脑科医院）学术伦理委员会的批准。所有研究参与者都签署了知情同意书。
1. PSG 程序
<ol><li>数据采集<ol><li>包括根据 DSM-V 诊断的 MDD 患者。 注意：本研究包括 22 名根据 DSM-V 诊断的 MDD 患者。由于受教育程度低，2 例 MDD 患者拒绝参加。因为他们都发现，在认知筛选工具中完成 TMT-B 时，阅读汉字并不容易。</li>
		<li>排除被诊断患有严重痴呆、焦虑、谵妄和其他疾病的参与者，这些疾病使他们无法整晚完成 PSG。</li>
		<li>收集参与者的人口统计数据，包括姓名、国籍、性别、年龄、出生日期、结婚、主诉、症状、主要诊断、病史、科室、体温、脉搏、呼吸频率、血压、身高、体重、体重指数、腹围和上胸围。</li>
		<li>每个参与者都被指示完成 Epworth 睡眠量表16。</li>
	</ol></li>
	<li>连接派生<ol><li>收集参与者的信息后，引导参与者进入睡眠监测室。</li>
		<li>逐步收集脑电图 （EEG）、眼电图 （EOG）、下巴肌电图 （EMG） 和心电图 （ECG） 的所有推导。 注意：参与者应保持他/她的面部、皮肤和头发清洁干燥。</li>
		<li>根据国际 10-20 电极放置系统连接 15 个脑电图电极（图 1）17。</li>
		<li>连接 EOG 的两个派生 （E1， E2），如图 2 所示。</li>
		<li>连接下巴肌电图的三个推导（Chin1、Chin2、ChinZ），如图 3 所示。</li>
		<li>连接 ECG 的两个推导 （+， −），如图 4 所示。</li>
	</ol></li>
	<li>启动数据采集系统<ol><li>双击 设备管理器 启动软件。</li>
		<li>双击 Room 选项并添加参与者的个人信息。 注意：个人信息包括姓名、性别、出生日期、文件名、文件路径等。可以根据需要添加更多详细信息（例如，身高、体重、颈围、医生、技术人员、医院、地址、婚姻状况、职业和监护人 [如果参与者是儿童]）。</li>
		<li>点击 OK 按钮。</li>
		<li>点击 开始录制 按钮。</li>
		<li>单击 校准 按钮并指示参与者根据分步校准程序执行（参见 补充图 1）。 注意： 表 1 中列出了校准分步程序。在校准过程中，应打开远程呼叫系统，以便指导参与者按照医生的指示进行操作。</li>
	</ol></li>
	<li>整夜收集数据<ol><li>整晚将导数保持在正确的位置。 注意：这将由值班技术人员完成。值班技术人员应接受过哮喘、不稳定型心绞痛等疾病的应急管理培训。</li>
	</ol></li>
	<li>后校准<ol><li>唤醒参与者并打开睡眠监测室的灯。</li>
		<li>起床时间通常是早上 6：00。如果参与者在早上 6：00 之前醒来并且不想继续，请停止监控。</li>
		<li>如果参与者在上午 6：00 处于快速眼动 （REM） 睡眠期，请继续监测直到 REM 睡眠期结束。</li>
		<li>再次重复步骤 1.3.5 进行后校准。</li>
		<li>点击 关闭 按钮停止录制。</li>
	</ol></li>
	<li>轻轻去除所有衍生物，以避免皮肤损伤。</li>
	<li>得分<ol><li>双击 ProFusion PSG4 按钮以启动评分系统。</li>
		<li>双击参与者的姓名以选择参与者。</li>
		<li>单击 Scoring 按钮，然后选择 Scoring Automatically 选项，以对睡眠阶段和觉醒进行评分。 注意：睡眠阶段和觉醒根据 AASM 睡眠和相关事件评分手册：规则、术语和技术规范18 进行评分。技术人员也可以手动修改评分。</li>
		<li>单击 Create 按钮并布局结果（图 5）。</li>
	</ol></li>
	<li>定义和方程<ol><li>根据 PSG 数据和波形（如 表 2 所示），获取变量，包括卧床时间 （TIB）、总睡眠时间 （TST）、睡眠潜伏期 （SL）、睡前觉醒 （WASO）、睡眠效率（SE，等于 TST/TB）和 REM 潜伏期。</li>
	</ol></li>
</ol>2. 使用认知筛查工具
<ol><li>双击 THINC-it 按钮，在计算机上启动软件，输入个人信息，并指导参与者逐步完成每个子测试。<ol><li>单击 Chinese 按钮以选择简体中文版本。</li>
		<li>单击 加 按钮以添加新参与者。</li>
		<li>在相应的空白处输入姓名、年龄、性别、惯用手、教育水平和位置。</li>
		<li>单击 BEGIN TEST 按钮逐步开始子测试（参见 补充图 2）。</li>
	</ol></li>
	<li>完成感知缺陷问卷（PDQ-5，主观认知缺陷）。<ol><li>大声朗读“在过去 7 天内，您多久一次难以组织事情”，并指导参与者选择 5 个回答选项之一：过去 7 天内从不、很少（一次或两次）、有时（3 - 5 次）、经常（大约每天一次）和非常频繁（每天一次以上）。</li>
		<li>大声朗读“在过去 7 天内，您多久难以集中精力阅读的内容”， 并指导参与者从步骤 2.2.1 中描述的 5 个回答选项中选择一个。</li>
		<li>大声朗读“在过去 7 天内，除非您查阅，否则您多久忘记一次日期”， 并指示参与者从步骤 2.2.1 中描述的 5 个回答选项中选择一个。</li>
		<li>大声朗读“在过去 7 天内，您多久忘记一次电话交谈后所说的内容”， 并指示参与者从步骤 2.2.1 中描述的 5 个回答选项中选择一个。</li>
		<li>大声读出：「在过去的 7 天里，你多久觉得自己的大脑完全空白了？」 并指示参与者从步骤 2.2.1 中描述的 5 个响应选项中选择一个。</li>
		<li>单击 OK 按钮开始其他子测试。</li>
	</ol></li>
	<li>完成观察者测试（SPO，注意力反射）。<ol><li>点击 教程 按钮和 下一步 按钮阅读分步说明。</li>
		<li>单击 Start （开始） 按钮开始 SPO 测试。 注意：SPO 测试用于评估注意力;参与者需要尽快触摸突然出现的符号。 注意：该软件将自动在屏幕上显示最佳反应时间（毫秒）、正确响应、最长连胜和索引。</li>
		<li>单击 OK 按钮开始下一个子测试。</li>
	</ol></li>
	<li>完成符号检查测试（SC，工作记忆的反射）。<ol><li>单击 Tutorial 和 Next 按钮阅读分步说明。</li>
		<li>点击 Start 开始 按钮开始 SC 测试。 注意：SC 测试用于评估工作记忆。参与者需要在计时器用完之前识别隐藏的符号，并调用下一个隐藏的符号。该软件将自动在屏幕上显示 最快反应时间（毫秒）、正确答案、最长连胜 和 索引 。</li>
		<li>单击 OK 按钮开始下一个子测试。</li>
	</ol></li>
	<li>完成密码破译器测试（CB，反映信息处理速度）。<ol><li>单击 Tutorial 和 Next 按钮阅读分步说明。</li>
		<li>点击 Start 开始 按钮开始 CB 测试。 注意：CB 测试用于评估信息处理速度。参与者需要点击正确的符号以适合指定的圆圈（每个符号都被标识为圆圈内的某个数字）。该软件将在屏幕上自动显示 最快反应时间（毫秒）、正确的节点、最长的条纹以及 索引 。</li>
		<li>点击 OK 按钮进入下一个子测试。</li>
	</ol></li>
	<li>完成 TMT-B（轨迹，执行功能的反映）。<ol><li>单击 Tutorial 和 Next 按钮阅读分步说明。</li>
		<li>点击 Start 开始 按钮开始 CB 测试。 注意：Trails 测试用于评估执行功能。参与者需要在不抬起手指的情况下从一个字符开始画一条线到数字 1，然后从数字 1 继续到另一个字符，然后从这个字符继续到数字 2，一直持续到数字 9，尽可能快。该软件会自动在屏幕上显示 完成时间（毫秒）、错误、最长条纹和索引。</li>
		<li>单击 OK 按钮完成最后一个子测试。 注意：软件将自动显示参与者在每次测试中的表现;下载这些结果。</li>
	</ol></li>
</ol>3. 统计分析
<ol><li>使用学生 t 检验来评估组差异。执行 Pearson 相关性分析以评估睡眠质量与认知功能之间的关系。将 P < 0.05 视为具有统计显著性。 注：所有统计分析均使用 SPSS 25.0 版进行。</li>
</ol>

评估重度抑郁症患者的睡眠质量和认知功能

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</ol>

作者没有什么可披露的。

这个由两部分组成的协议包含需要注意的关键步骤。对于 PSG 来说，得分是最重要的一步;因此，PSG 技术人员需要获得认证和经验。此外，在 PSG 之前，技术人员和参与者之间的沟通是必要的，因为患者在 PSG 期间可能会感到不舒服并退出。对于认知筛查工具，关键步骤是使用数字化设备评估认知功能。医生需要逐步介绍这些程序，以确保参与者理解它们。此外，如果参与者表现不佳是由于不熟悉说明，则允许重复该任务。最后但并非最不重要的一点是，正如 Hou 等人 13 所提到的，认知筛查工具不适合智商 （IQ） 低的个体。医生应收集患者的病史，以排除先天性低智力个体。
PSG 受到“第一夜效应”的限制，这是由新颖的实验室环境引起的。这通常会导致 PSG 测量的入睡潜伏期增加、REM 潜伏期增加、REM 睡眠百分比降低和 SE 降低，并可能导致临床误解19,20。主观睡眠问卷（如 PSQI、家庭 PSG 和活动记录仪）是评估睡眠质量的替代方法。中文版认知筛查工具的主要局限性是 TMT-B1，一些受教育程度较低的中国参与者对繁体汉字不熟悉，这可能是与该措施相关性不显著的原因之一。因此，建议对中国 MDD 患者使用具有更强有效性的认知筛查工具元素（即 PDQ-5、Spotter 和 Codebreaker）的组合。
在目前的研究中，招募的 MDD 患者是首发和未接受过药物治疗的患者。在临床实践中，选择性 5-羟色胺再摄取抑制剂（例如帕罗西汀）和血清素和去甲肾上腺素再摄取抑制剂（例如度洛西汀）可以通过降低睡眠连续性来影响睡眠结构21。因此，应该开展更多研究来阐明接受抗抑郁治疗的 MDD 患者认知症状与 SD 之间的关系。昼夜节律是临床实践中不容忽视的另一点。已发现昼夜节律与认知功能和睡眠质量有关22。建议在 PSG 之前或之后 1 周内使用活动记录仪检测 MDD 患者的昼夜节律特征。
本研究的不足需要指出。首先，样本量小。其次，据报道会影响睡眠质量的昼夜节律未被评估。因为认知筛查工具包括执行功能评估，所以它可以用于那些患有以此类功能障碍为特征的神经认知障碍的人，例如脑小血管病和精神分裂症。

重度抑郁症 （MDD） 是一种严重的情绪障碍，会导致严重的发病率和死亡率，影响全球超过 3 亿人1。MDD 的特征是情绪低落、驱动力减退、失去兴趣以及自残或自杀的想法2。认知症状和睡眠障碍 （SD） 是 MDD 的两种非情绪相关症状 3,4。先前的研究表明，MDD 患者 SD 的患病率为 60-90%，而中国人群中这些患者的睡眠障碍患病率为 85.22%5。
尽管很少有研究调查认知症状与 SD 之间的因果关系，但初步证据表明 MDD6 个体的睡眠障碍与认知症状之间存在显着关联。例如，Cha 等人 3 发现，主观睡眠质量可以预测主观认知表现和客观认知障碍，这部分是由抑郁严重程度介导的。Biddle 等人 7 还发现，在患有 MDD 的老年男性中，通过活动记录仪测量的睡眠效率 （SE） 不佳与认知功能较差相关，与抑郁严重程度无关。观察到的差异可能是由方法学差异引起的。 
在这些文献中，研究人员使用了各种工具来评估睡眠和认知。匹兹堡睡眠质量指数 （PSQI） 通常被选为主观工具。还使用了体动图设备。然而，PSG 被认为是检测睡眠障碍8 （sleep disorders） 的金标准，是一种敏感、客观的 SD 评估9。据我们所知，在关于 MDD 患者睡眠与认知之间关系的研究中，很少有人使用 PSG 来评估睡眠质量。
MDD 中的认知症状可能是多维的，在某些情况下通过新的药理学策略来解决10,11。近年来出现了几种适用于 MDD 的认知评估工具12。THINC集成工具（THINC-it，以下简称认知筛查工具）是由Hou et al.13开发的经过验证的认知筛查工具，由一份主观问卷（感知缺陷问卷，PDQ-5）和四项客观认知测试（选择反应时间、1-back、数字符号替换和Trail making part-B）组成。它被认为是检测 MDD14 患者认知缺陷的一种省时、可接受的方法。还有另外两种常用工具用于评估 MDD 患者的认知功能：精神病学认知障碍筛查 D （SCIP-D） 和双相情感障碍评定中的认知主诉 （COBRA）12。前者是缺乏自我指导测试的客观工具，后者是缺乏客观版本的主观工具。这些工具之间的构成测试也有所不同。SCIP-D 由五个客观测试组成：语言学习、工作记忆、语言流利度、延迟记忆和处理速度。COBRA 包括执行功能、处理速度和注意力、语言学习和记忆以及视觉记忆12。最近开发的中文版认知筛查工具已被证实是评估中国 MDD15 患者认知缺陷的良好工具。因此，使用认知筛查工具软件和 PSG，目前的研究试图揭示 MDD 患者认知症状与 SD 之间的关系。

<table><tbody><tr><td>Computer</td><td>Surface</td><td>Surface pro2</td><td></td></tr><tr><td>Derivation</td><td>Compumedics</td><td>Grael</td><td></td></tr><tr><td>Electrode</td><td>Compumedics</td><td>Grael</td><td></td></tr><tr><td>Polysomnography</td><td>Compumedics</td><td>Grael</td><td></td></tr><tr><td>Software</td><td>Compumedics</td><td>Compumedics Profusion PSG4</td><td></td></tr></tbody></table>

association between sleep quality and cognitive symptoms in patients with major depressive disorder

认知症状和睡眠障碍 （SD） 是重度抑郁症 （MDD） 的常见非情绪相关症状。在临床实践中，认知症状和 SD 都与 MDD 进展有关。然而，只有少数研究调查了 MDD 患者认知症状与 SD 之间的联系，并且只有初步证据表明情绪障碍患者的认知症状与 SD 之间存在显着关联。本研究调查了重度抑郁症患者的认知症状与睡眠质量之间的关系。纳入 MDD 患者 （n = 20）;他们的平均汉密尔顿抑郁量表 17 评分为 21.95 （±2.76）。使用金标准多导睡眠图 （PSG） 评估睡眠质量，并使用经过验证的 THINC 集成工具 （认知筛查工具） 评估 MDD 患者的认知功能。总体而言，结果显示认知筛查工具的总分与睡眠潜伏期、睡后觉醒和睡眠效率之间存在显着相关性。这些发现表明，认知症状与 MDD 患者的睡眠质量差有关。

参与者的男女比例为 11/9。所有个体的年龄在 35.35 ± 6.83 岁之间，受教育程度在 14.80 ± 3.29 岁之间，汉密尔顿抑郁量表-17 （HAMD-17） 评分为 21.95 分± 2.76 分，SE 为 76.71 ± 8.57%。关于认知筛查工具得分，PDQ-5、SPO、SC、CB、TMT-B 和总分分别为 1756.35 ± 395.49、2046.75 ± 684.45、852.15 ± 651.81、845.75 ± 641.15、911.45 ± 619.13 和 4140.80 ± 1298.14 分（表 2）。
图 5 显示了两个具有代表性的参与者。一名 49 岁的 MDD 女性患者（图 5A）的 THNIC-it 评分较低，包括 SPO 评分 = 0 、 SC 评分 = 0、CB 评分 = 0 和 TMT-B 评分 = 680。她的 SL （40.5 min） 、 WASO （60.5 min） 和较低的 SE （82.2%）。一名 48 岁的男性 MDD 患者（图 5B）评分较高，包括 SPO 评分 = 1856 、 SC 评分 = 1250、CB 评分 = 163 和 TMT-B 评分 = 1484。他的 SL （7.0 min） 较高，WASO （5.5 min） 和较低的 SE （88.1%）。总分与 SL （r = -0.0856，p < 0.001）、WASO （r = -0.687，p = 0.001） 和 SE （r = 0.081，p < 0.001） 显著相关。
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/66274/66274fig01.jpg"> 图 1：国际 10-20 电极放置系统。 说明多导睡眠图期间脑电图 （EEG） 的推荐 （A） 和可接受 （B） 推导中使用的电极位置的图像。该数字经 AASM 睡眠和相关事件评分手册18 许可改编。 <a href="https://www.jove.com/files/ftp_upload/66274/66274fig01large.jpg" target="_blank">请单击此处查看此图的较大版本。</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/66274/66274fig02.jpg"> 图 2：EOG 的两个推导的位置。 （A） 眼电图 （EOG） 的推荐和 （B） 可接受的推导。该数字经 AASM 睡眠和相关事件评分手册18 许可改编。 <a href="https://www.jove.com/files/ftp_upload/66274/66274fig02large.jpg" target="_blank">请单击此处查看此图的较大版本。</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/66274/66274fig03.jpg"> 图 3：下巴 EMG 的三个推导的位置。 在下巴上放置电极以进行肌电图 （EMG） 记录。该数字经 AASM 睡眠和相关事件评分手册18 许可改编。 <a href="https://www.jove.com/files/ftp_upload/66274/66274fig03large.jpg" target="_blank">请单击此处查看此图的较大版本。</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/66274/66274fig04.jpg"> 图 4：心电图的两个推导的位置。 该图显示了心脏记录期间导联 II 放置在躯干上的位置。该数字经 AASM 睡眠和相关事件评分手册18 许可改编。 <a href="https://www.jove.com/files/ftp_upload/66274/66274fig04large.jpg" target="_blank">请单击此处查看此图的较大版本。</a>
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/66274/66274fig05.jpg"> 图 5.代表参与者。 （A） 得分较低的 49 岁参与者的 SL （40.5 min） 、WASO （60.5 min） 和较低的 SE （82.2%）也较高。（B） 一名 THNIC-it 评分较高的 49 岁参与者的 SL （7.0 min） 、WASO （5.5 min） 和较高的 SE （88.1%）也较低。 <a href="https://www.jove.com/files/ftp_upload/66274/66274fig05large.jpg" target="_blank">请单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>记录适当系统响应的校准步骤。</td>
		</tr><tr><td>1. 执行并记录 EEG 和 EMG 电极的阻抗检查。</td>
		</tr><tr><td>2. 记录至少 30 秒的脑电图，患者醒来时安静地躺着，睁着眼睛。</td>
		</tr><tr><td>3. 记录至少 30 秒的脑电图，患者醒来时闭着眼睛安静地躺着。</td>
		</tr><tr><td>4. 让患者上下看，不要移动头部 （5x）。</td>
		</tr><tr><td>5. 让患者左右看，不要移动头部 （5x）。</td>
		</tr><tr><td>6. 让患者眨眼 （5 次）。</td>
		</tr><tr><td>7. 让患者咬牙和/或咀嚼（5 秒）。</td>
		</tr><tr><td>8. 让患者模拟打鼾或嗡嗡声（5 秒）。</td>
		</tr><tr><td>9. 要求患者正常呼吸，并确保气流和努力通道信号同步。</td>
		</tr><tr><td>10. 让患者进行屏气（10 秒）。</td>
		</tr><tr><td>11. 要求患者仅通过鼻子呼吸（10 秒）。</td>
		</tr><tr><td>12. 要求患者仅用嘴呼吸（10 秒）。</td>
		</tr><tr><td>13. 让患者弯曲左脚/左脚的脚趾 （5x）。</td>
		</tr><tr><td>14. 让患者弯曲右脚/rase 脚趾在右脚上 （5x）。</td>
		</tr><tr><td>15. 调整心电图信号以提供清晰的波形 - R 波应向上偏转。</td>
		</tr><tr><td>16. 在 PSG 记录结束时执行并记录 EEG、EOG 和 EMG 电极的重复阻抗检查。</td>
		</tr><tr><td>17. 在 PSG 记录结束时重复阻抗测量和生理校准。</td>
		</tr></tbody></table>表 1：PSG 校准程序。 缩写： EEG： 脑电图;EMG：肌电图;EOG ： 眼电图;PSG：多导睡眠图。
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always"><tbody><tr><td>性别 （男/女）</td>
			<td>11/9</td>
		</tr><tr><td>年龄 （岁）</td>
			<td>35,35 ± 6,83</td>
		</tr><tr><td>教育水平（年）</td>
			<td>14.80 ± 3.29</td>
		</tr><tr><td>HAMD-17 （分数）</td>
			<td>21.95 ± 2.76</td>
		</tr><tr><td>标准误差 （%）</td>
			<td>76,71 ± 8,57</td>
		</tr><tr><td>PDQ-5 （分数）</td>
			<td>1756.35 ± 395.49</td>
		</tr><tr><td>SPO（分数）</td>
			<td>2046.75 ± 684.45</td>
		</tr><tr><td>SC （分数）</td>
			<td>852.15 ± 651.81</td>
		</tr><tr><td>CB （分数）</td>
			<td>845.75 ± 641.15</td>
		</tr><tr><td>TMT-B （分数）</td>
			<td>911.45 ± 619.13</td>
		</tr><tr><td>THINC-it 总分</td>
			<td>4140.80 ± 1298.14</td>
		</tr></tbody></table>表 2：患者人口统计数据和评分。 HAMD：汉密尔顿抑郁量表;SE：睡眠效率;PDQ-5：感知缺陷问卷;SPO： 点样试验;SC： 符号检查测试;CB：密码破译器测试;TMT-B： 轨迹制作测试-B;THINC-it：THINC 集成工具。
补充图 1：显示校准程序的屏幕截图。<a href="https://www.jove.com/files/ftp_upload/66274/Supplementary%20Figure%201.zip" target="_blank">请点击此处下载此文件。</a>
补充图 2：显示认知筛选工具子测试的屏幕截图。<a href="https://www.jove.com/files/ftp_upload/66274/Supplementary%20Figure%202.zip" target="_blank">请点击此处下载此文件。</a>

Read this Scientific Article Protocol about Author Spotlight: Unveiling the Connection Between Sleep Disorders and Cognitive Symptoms in Depression at JoVE.com

作者聚焦：揭示睡眠障碍与抑郁症认知症状之间的联系

在这里，我们提出了一个方案，用于调查重度抑郁症患者认知症状与睡眠质量之间的关系。使用 THINC 集成工具和多导睡眠图评估认知症状和睡眠质量。

重度抑郁症患者睡眠质量与认知症状的相关性

Cognitive symptoms and sleep disturbance (SD) are common non-mood-related symptoms of major depressive disorder (MDD). In clinical practice, both ...

在这项研究中，金标准多导睡眠图用于评估睡眠质量，经过验证的认知筛查工具用于评估重度抑郁症患者的认知功能。思考最近开发的中文版连接筛查工具已被证实是评估中国重度抑郁症患者结缔缺陷的良好工具。目前的研究揭示了重度抑郁症患者的认知症状与睡眠障碍之间的密切关系。

association-between-sleep-quality-cognitive-symptoms-patients-with

Nanyang Technological University

Research

JoVE Journal

Medicine

Association Between Sleep Quality and Cognitive Symptoms in Patients with Major Depressive Disorder

607 次观看.  Sleep Medicine Center of Inner Mongolia Mental Health Center (The Third Hospital of Inner Mongolia Autonomous Region, Brain Hospital of Inner Mongolia Autonomous Region). 在这项研究中，金标准多导睡眠图用于评估睡眠质量，经过验证的认知筛查工具用于评估重度抑郁症患者的认知功能。思考最近开发的中文版连接筛查工具已被证实是评估中国重度抑郁症患者结缔缺陷的良好工具。目前的研究揭示了重度抑郁症患者的认知症状与睡眠障碍之间的密切关系。

视频: 作者聚焦：揭示睡眠障碍与抑郁症认知症状之间的联系

Eye Tracking, Cortisol, and a Sleep vs. Wake Consolidation Delay: Combining Methods to Uncover an Interactive Effect of Sleep and Cortisol on Memory

Although rises in cortisol can benefit memory consolidation, as can sleep soon after encoding, there is currently a paucity of literature as to how these two factors may interact to influence consolidation. Here we present a protocol to examine the interactive influence of cortisol and sleep on memory consolidation, by combining three methods: eye tracking, salivary cortisol analysis, and behavioral memory testing across sleep and wake delays. To assess resting cortisol levels, participants gave a saliva sample before viewing negative and neutral objects within scenes. To measure overt attention, participants&#8217; eye gaze was tracked during encoding. To manipulate whether sleep occurred during the consolidation window, participants either encoded scenes in the evening, slept overnight, and took a recognition test the next morning, or encoded scenes in the morning and remained awake during a comparably long retention interval. Additional control groups were tested after a 20&#160;min&#160;delay in the morning or evening, to control for time-of-day effects. Together, results showed that there is a direct relation between resting cortisol at encoding and subsequent memory, only following a period of sleep. Through eye tracking, it was further determined that for negative stimuli, this beneficial effect of cortisol on subsequent memory may be due to cortisol strengthening the relation between where participants look during encoding and what they are later able to remember. Overall, results obtained by a combination of these methods uncovered an interactive effect of sleep and cortisol on memory consolidation.

We present a protocol used to discover an interactive effect between sleep and cortisol on memory consolidation, particularly for negative arousing images. Specifically, the experimental design utilizes eye tracking, salivary cortisol analysis, and behavioral memory testing &#8211; methods that can be used with both healthy and clinical participants.

眼睛跟踪，皮质醇和睡眠与唤醒延迟合并：合并方法来揭开睡眠和皮质醇的交互式影响记忆

Although rises in cortisol can benefit memory consolidation, as can sleep soon after encoding, there is currently a paucity of literature as to how these ...

科研

行为学

A High-performance Liquid Chromatography Measurement of Kynurenine and Kynurenic Acid: Relating Biochemistry to Cognition and Sleep in Rats

The kynurenine pathway (KP) of tryptophan degradation has been implicated in psychiatric disorders. Specifically, the astrocyte-derived metabolite kynurenic acid (KYNA), an antagonist at both N-methyl-d-aspartate (NMDA) and &#945;7 nicotinic acetylcholine (&#945;7nACh) receptors, has been implicated in cognitive processes in health and disease. As KYNA levels are elevated in the brains of patients with schizophrenia, a malfunction at the glutamatergic and cholinergic receptors is believed to be causally related to cognitive dysfunction, a core domain of the psychopathology of the illness. KYNA may play a pathophysiologically significant role in individuals with schizophrenia. It is possible to elevate endogenous KYNA in the rodent brain by treating animals with the direct bioprecursor kynurenine, and preclinical studies in rats have demonstrated that acute elevations in KYNA may impact their learning and memory processes. The current protocol describes this experimental approach in detail and combines a) a biochemical analysis of blood kynurenine levels and brain KYNA formation (using high-performance liquid chromatography), b) behavioral testing to probe the hippocampal-dependent contextual memory (passive avoidance paradigm), and c) an assessment of sleep-wake behavior [telemetric recordings combining electroencephalogram (EEG) and electromyogram (EMG) signals] in rats. Taken together, a relationship between elevated KYNA, sleep, and cognition is studied, and this protocol describes in detail an experimental approach to understanding function outcomes of kynurenine elevation and KYNA formation in vivo in rats. Results obtained through variations of this protocol will test the hypothesis that the KP and KYNA serve pivotal roles in modulating sleep and cognition in health and disease states.

Alterations in the kynurenine pathway (KP) neuroactive metabolites are implicated in psychiatric illnesses. Investigating the functional outcomes of an altered kynurenine pathway metabolism in vivo in rodents may help elucidate novel therapeutic approaches. The current protocol combines biochemical and behavioral approaches to investigate the impact of an acute kynurenine challenge in rats.

高效液相色谱法测定犬尿氨和 Kynurenic 酸: 对大鼠认知和睡眠的相关生物化学研究

The kynurenine pathway (KP) of tryptophan degradation has been implicated in psychiatric disorders. Specifically, the astrocyte-derived metabolite ...

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Mild cognitive impairment (MCI) is the first sign of dementia among elderly populations and its early detection is crucial in our aging societies. Common MCI tests are time-consuming such that indiscriminate massive screening would not be cost-effective. Here, we describe a protocol that uses machine learning techniques to rapidly select candidates for further screening via a question-based MCI test. This minimizes the number of resources required for screening because only patients who are potentially MCI positive are tested further.
This methodology was applied in an initial MCI research study that formed the starting point for the design of a selective screening decision tree. The initial study collected many demographic and lifestyle variables as well as details about patient medications. The Short Portable Mental Status Questionnaire (SPMSQ) and the Mini-Mental State Examination (MMSE) were used to detect possible cases of MCI. Finally, we used this method to design an efficient process for classifying individuals at risk of MCI. This work also provides insights into lifestyle-related factors associated with MCI that could be leveraged in the prevention and early detection of MCI among elderly populations.

This methodology produces decision trees that target population groups more prone to suffering from mild cognitive impairment and are useful for cost-effective selective screening of the disease.

设计轻度认知障碍有效选择性筛查的机器学习方法

Mild cognitive impairment (MCI) is the first sign of dementia among elderly populations and its early detection is crucial in our aging societies. Common ...

A Chronic Sleep Fragmentation Model using Vibrating Orbital Rotor to Induce Cognitive Deficit and Anxiety-Like Behavior in Young Wild-Type Mice

Sleep disturbance is generally common in populations as a chronic disease or a complained event. Chronic sleep disturbance is proposed to be closely linked to the pathogenesis of diseases, especially neurodegenerative diseases. We recently found that 2 months of sleep fragmentation initiated Alzheimer&#8217;s disease (AD)-like behavioral and pathological changes in young wild-type mice. Herein, we present a standardized protocol to achieve chronic sleep fragmentation (CSF). Briefly, CSF was induced by an orbital rotor vibrating at 110 rpm and operating with a repetitive cycle of 10 s-on, 110 s-off, during light-ON phase (8:00 AM&#8211;8:00 PM) continuously for up to 2 months. Impairments of spatial learning and memory, anxiety-like but not depression-like behavior in mice as consequences of CSF modeling, were evaluated with Morris water maze (MWM), Novel object recognition (NOR), Open field test (OFT) and Forced swimming test (FST). In comparison with other sleep manipulations, this protocol minimizes the handling labors and maximizes the modeling efficiency. It produces stable phenotypes in young wild-type mice and can be potentially generated for a variety of research purposes.

Presented here is a protocol for chronic sleep fragmentation (CSF) model achieved by an electrically controlled orbital rotor, which could induce confirmed cognitive deficit and anxiety-like behavior in young wild-type mice. This model can be applied to explore the pathogenesis of chronic sleep disturbance and related disorders.

使用振动轨道转子诱导年轻野生型小鼠认知缺陷和焦虑样行为的慢性睡眠碎片模型

Sleep disturbance is generally common in populations as a chronic disease or a complained event. Chronic sleep disturbance is proposed to be closely ...

推进睡眠障碍治疗:一种基于果 蝇的筛查方法

High-Throughput Small Molecule Drug Screening For Age-Related Sleep Disorders Using Drosophila melanogaster

Sleep, an essential component of health and overall well-being, often presents challenges for older individuals who frequently experience sleep disorders characterized by shortened sleep duration and fragmented patterns. These sleep disruptions also correlate with an increased risk of various illnesses in the elderly, including diabetes, cardiovascular diseases, and psychological disorders. Unfortunately, existing drugs for sleep disorders are associated with significant side effects such as cognitive impairment and addiction. Consequently, the development of new, safer, and more effective sleep disorder medications is urgently needed. However, the high cost and lengthy experimental duration of current drug screening methods remain limiting factors.
This protocol describes a cost-effective and high-throughput screening method that utilizes Drosophila melanogaster, a species with a highly conserved sleep regulation mechanism compared to mammals, making it an ideal model for studying sleep disorders in the elderly. By administering various small compounds to aged flies, we can assess their effects on sleep disorders. The sleep behaviors of these flies are recorded using an infrared monitoring device and analyzed with the open-source data package Sleep and Circadian Analysis MATLAB Program 2020 (SCAMP2020). This protocol offers a low-cost, reproducible, and efficient screening approach for sleep regulation. Fruit flies, due to their short life cycle, low husbandry cost, and ease of handling, serve as excellent subjects for this method. As an illustration, Reserpine, one of the tested drugs, demonstrated the ability to promote sleep duration in elderly flies, highlighting the effectiveness of this protocol.

作者聚焦:使用 DAM 系统克服果蝇睡眠测量的挑战 

Presented is a protocol for high-throughput drug screening to improve sleep by monitoring the sleep behavior of fruit flies in an elderly Drosophila model.

使用黑腹果蝇对年龄相关睡眠障碍进行高通量小分子药物筛选

Sleep, an essential component of health and overall well-being, often presents challenges for older individuals who frequently experience sleep disorders ...

Characterizing the Relationship Between Eye Movement Parameters and Cognitive Functions in Non-demented Parkinson's Disease Patients with Eye Tracking

Cognitive impairment is a common phenomenon in Parkinson&#8217;s disease that has implications on the prognosis. A simple, noninvasive and objective proxy measurement of cognitive function in Parkinson&#8217;s disease will be helpful in detecting early cognitive decline. As a physiological metric, eye movement parameter is not confounded by the subject&#39;s attributes and intelligence and can function as a proxy marker if it correlates with cognitive functions. To this end, this study explored the relationship between the eye movement parameters and performance in cognitive tests in multiple domains. In the experiment, a visual search task with eye tracking was set up, where subjects were asked to look for a number embedded in an array of alphabets scattered randomly on a computer screen. The differentiation between the number and the alphabet is an overlearned task such that the confounding effect of cognitive ability on the eye movement parameters is minimized. The average saccadic amplitude and fixation duration were captured and calculated during the visual search task. The cognitive assessment battery covered domains of frontal-executive functions, attention, verbal and visual memory. It was found that prolonged fixation duration was associated with poorer performance in verbal fluency, visual and verbal memory, allowing further exploration on the use of eye movement parameters as proxy markers for cognitive function in Parkinson&#8217;s disease patients. The experimental paradigm has been found to be highly tolerable in our group of Parkinson&#39;s disease patients and could be applied transdiagnostically to other disease entities for similar research questions.

Here, we present a protocol to study the relationship between the eye movement parameters and cognitive functions in non-demented Parkinson&#39;s disease patients. The experiment used an eye tracker to measure the saccadic amplitude and fixation duration in a visual search task. The correlation with performance in multi-domain cognitive tasks was subsequently measured.

描述眼动参数与无视力帕金森病患者眼动功能的关系

Cognitive impairment is a common phenomenon in Parkinson&#8217;s disease that has implications on the prognosis. A simple, noninvasive and objective proxy ...

神经科学

Measuring Neural Mechanisms Underlying Sleep-Dependent Memory Consolidation During Naps in Early Childhood

Sleep is critical for daily functioning. One important function of sleep is the consolidation of memories, a process that makes them stronger and less vulnerable to interference. The neural mechanisms underlying the benefit of sleep for memory can be investigated using polysomnography (PSG). PSG is a combination of physiological recordings including signals from the brain (EEG), eyes (EOG), and muscles (EMG) that are used to classify sleep stages. In this protocol, we describe how PSG can be used in conjunction with behavioral memory assessments, actigraphy, and parent-report to examine sleep-dependent memory consolidation. The focus of this protocol is on early childhood, a period of significance as children transition from biphasic sleep (consisting of a nap and overnight sleep) to monophasic sleep (overnight sleep only). The effects of sleep on memory performance are measured using a visuospatial memory assessment across periods of sleep and wakeful-rest. A combination of actigraphy and parent report is used to assess sleep rhythms (i.e., characterizing children as habitual or non-habitual nappers). Finally, PSG is used to characterize sleep stages and qualities of those stages (such as frequencies and the presence of spindles) during naps. The advantage of using PSG is that it is the only tool currently available to assess sleep quality and sleep architecture, pointing to the relevant brain state that supports memory consolidation. The main limitations of PSG are the length of time it takes to prepare the recording montage and that recordings are typically taken over one sleep bought. These limitations can be overcome by engaging young participants in distracting tasks during application and combining PSG with actigraphy and self/parent-report measures to characterize sleep cycles. Together, this unique combination of methods allows for investigations into how naps support learning in preschool children.

This protocol describes methods used to examine neural mechanisms underlying sleep-dependent memory consolidation during naps in early childhood. It includes procedures for examining the effect of sleep on behavioral memory performance, as well as the application and recording of both polysomnography and actigraphy.

测量幼儿期午睡期间睡眠依赖记忆巩固背后的神经机制

Sleep is critical for daily functioning. One important function of sleep is the consolidation of memories, a process that makes them stronger and less ...

Multi-Modal Home Sleep Monitoring in Older Adults

The gold standard for sleep monitoring is attended in-lab polysomnography; however, this method may be cost-prohibitive and inconvenient for patients and research participants. Home sleep testing has gained momentum in the field of sleep medicine due to its convenience and lower cost, as well as being more naturalistic. The accuracy and quality of home sleep testing, however, may be variable because studies are not monitored by sleep technologists. There has been some success in improving the accuracy of home sleep studies by having trained sleep technicians assist participants inside their homes with putting on the devices, but this can be intrusive and time-consuming for those involved. In this protocol, participants undergo at-home sleep monitoring with multiple devices: 1) a single-channel EEG device; 2) a home sleep test for sleep-disordered breathing and periodic limb movements; 3) actigraphy; and 4) sleep logs. A major challenge of this study is obtaining high-quality sleep monitoring data on the first attempt in order to minimize participant burden. This protocol describes the implementation of educational manuals with step-by-step instructions and photos. The goal is to improve the quality of home sleep testing.

Here, we present a protocol to improve the quality of data from home sleep testing by providing a method to enhance instructions through a structured participant visit. This protocol includes the implementation of a step-by-step educational manual with photos to ensure proper placement of equipment.

老年人多式家庭睡眠监测

The gold standard for sleep monitoring is attended in-lab polysomnography; however, this method may be cost-prohibitive and inconvenient for patients and ...

重度抑郁症患者睡眠质量与认知症状的相关性

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描述眼动参数与无视力帕金森病患者眼动功能的关系

测量幼儿期午睡期间睡眠依赖记忆巩固背后的神经机制

老年人多式家庭睡眠监测