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眼球运动检查在阿尔茨海默病诊断的研究进展

Research progress on eye movement examination in the diagnosis of Alzheimer’s disease

来源期刊: 眼科学报 | 2021年1月 第36卷 第1期 66-73 发布时间:2020–06–30 收稿时间:2022/12/6 14:38:38 阅读量:3971
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关键词:
眼球运动阿尔茨海默病筛查人工智能
eye movement Alzheimer’s disease screening artificial intelligence
DOI:
10.3978/j.issn.1000-4432.2021.01.15
阿尔茨海默病(Alzheimer’s disease,AD)是发生于老年期或老年前期的中枢神经系统退行性病变,以进行性认知功能障碍为特征。随着社会老龄化加剧,AD已成为全球公共卫生问题,亟需研发更敏感、便捷和经济的筛查技术进行早期防控。眼球运动与认知功能密切相关,且眼球运动检查有非侵入性、成本低、检查时间短等优点。研究眼球运动异常和认知功能障碍之间的相关性,有助于研发更简便易操作的认知功能障碍筛查工具。随着人工智能技术的发展,机器学习算法强大的特征提取和计算能力对处理眼球运动检查结果有显著优势。本文对既往AD患者与眼球运动异常之间的相关性研究进行综述,并对机器学习算法模型辅助下,基于眼球运动异常模式进行认知功能障碍早期筛查技术开发的研究前景予以展望。
Alzheimer’s disease (AD) is a degenerative disease of the central nervous system that occurs in old age or early old age. It is characterized by progressive cognitive dysfunction. With the world population aging, AD has become a global public health problem. The development of a more sensitive, convenient, and economic screening technology for AD is urgently needed. The eye movement function is closely related to cognitive function. Moreover, eye movement examination has advantages including non-invasiveness, low cost, and short examination time. Researches on the correlation between abnormal eye movement and cognitive dysfunction can help to develop a simple and easy-to-use screening tool for cognitive dysfunction. With the development of artificial intelligence technology, the dominant feature extraction and computing capabilities of machine learning algorithms have a significant advantage in processing eye movement inspection results. This article reviews the correlation between AD and eye movement abnormalities aiming to provide the research prospects of early screening technology development for cognitive dysfunction based on abnormal eye movement with the application of machine learning models.
    眼球运动指眼球各种自主或非自主的运动,包括注视、扫视、追随运动等。早在20世纪70年代,已有研究者发现眼球运动异常与阿尔茨海默病(Alzheimer’s disease,AD)的诊断及病情进展密切相关,并可以作为其辅助诊断的生物标志物[1-2]。眼球运动检查有非侵入性、成本低、检查时间短、对检查者专业水平要求低等优点。研究眼球运动异常和AD之间的相关性,有助于研发更简便易行的筛查工具。
    AD的临床上常见表现为记忆障碍、失语、失用、失认、视空间能力损害、抽象思维和计算力损害、人格和行为改变等一项或多项认知域的不同程度受损[3]。根据受损的严重程度,还可将患者进一步分为轻度认知功能障碍(mild cognitive impairment,MCI)和痴呆2种不同的阶段[4-8]。AD是老年人群最常见的痴呆类型[9-11],由于目前尚无明确有效的治疗药物,其早期防控至关重要[12]。AD患者的传统诊断方法主要依靠症状、体征、神经心理学检查量表等检测结果,受检查者经验影响大,诊断可靠性也有待进一步提高[5,13-14]。AD的进一步确诊和分类还需结合磁共振成像等多项检查综合判断[15-18],检查过程复杂、价格不菲,且包括有创操作,进一步增加了早期诊治的难度[14]。寻找更敏感、更便捷的新型AD早期筛查方法,对我国AD防控有重要意义。随着人工智能技术的发展,机器学习算法强大的特征提取功能和计算能力对于眼球运动检查结果的统计和分析具有显著优势,也有助于推动认知障碍相关眼球运动异常的病理生理机制研究。本文对既往MCI和AD患者与眼球运动异常之间的相关性研究进行综述,并展望了在机器学习算法模型辅助下,基于眼球运动异常模式进行认知功能障碍早期筛查技术开发的研究前景。

1 认知功能障碍相关眼球运动简介

    与认知功能障碍相关的眼球运动主要包括扫视运动、反向扫视运动、微扫视运动、平滑追随运动等一系列眼部活动[19]。在实验室条件下,上述眼部活动均可以由红外摄像头和数据分析软件组成的眼球运动测量仪精确捕捉和测量,其中扫视运动、注视运动、视动性眼球震颤等是最为常见的类型[20]

1.1 扫视

    扫视运动(saccade)是一种眼球快速从一个注视点向另一个注视点移动的运动,根据诱发扫视的不同原因,又分为自发性扫视和反射性扫视2种类型。扫视运动有助于视网膜上视觉敏感度和色觉敏感度均最高的黄斑区域快速捕捉目标,与大脑的注意力机制密切相关,也是对认知功能障碍筛查最敏感的眼球运动指标之一[21]。产生扫视的神经通路复杂,包括大脑皮质和脑干等多个脑区[22]。自发性扫视主要由个体的意向诱发,即主动看向感兴趣的区域,其启动脑区为额叶[23-24]。反射性扫视则由新出现在视野范围内的物体诱发,其启动脑区为枕叶[23-24]。位于背侧额叶皮层的辅助眼球运动区则对扫视运动有控制和监督作用[25-26]。其他与扫视运动相关的脑区还包括小脑、中脑背侧四叠体的上丘区域、黒质网状部和前额叶皮层背外侧等[27]

1.2 反向扫视

    扫视运动过程中,如果眼球扫视的方向和出现的目标物方向相反,则称为反向扫视(anti-saccade)。反向扫视的调控主要由前额叶皮质传导至下丘脑的抑制信号调控,在实验室条件下更易于检测[28]

1.3 微扫视

    眼球运动相对静止时称为注视,即使在注视时眼球也存在的轻微震动(震动幅度小于1°视角),称为微扫视(microsaccades)。微扫视可保证视网膜上的图像持续轻微移动,进而维持入射光线对视网膜的刺激和视觉图像的感知[29]。微扫视的神经控制通路位于脑干,正常人群的微扫视眼球运动方向多为注视点的水平方向[30]

1.4 扫视性侵扰

    在注视过程中,有时会发生扫视性侵扰(saccadic intrusion),即在突然出现离开注视点的扫视后,又重新扫视回到注视点位置[31]。扫视性侵扰在正常人群中也可出现,并非完全为病理表现,但在神经系统疾病患者中更为常见[31-32]。扫视性侵扰的发生可能与控制微扫视的脑干区域功能异常相关[33],也可能是由认知功能下降引起的注意力和工作记忆力下降所致[34]

1.5 平滑追随眼球运动

    平滑追随眼球运动(smooth pursuit)是一种在追随运动目标过程中出现的眼球运动行为,是为保证运动目标始终位于视网膜黄斑中心凹的位置,眼睛追随慢速运动的目标物移动的一种眼动类型,追随的最大速度可达60°/s。在理想的平滑追随眼球运动过程中,眼睛可以始终追随移动的目标物,尽可能减少无法追随目标物后,重新返回目标物位置的追赶性扫视出现的次数。与扫视运动的通路类似,控制平滑追随眼球运动的神经通路为皮质-脑桥-小脑神经通路[35-36],该通路从枕颞叶外侧皮层提取目标物的移动信息,将信号传入至额叶眼球运动区和辅助眼球运动区进一步处理后[35,37],再传递至脑干区,产生扫视信号指令,最终传出命令至眼外肌效应器以调控眼部运动[38]

1.6 瞳孔对光反射

    瞳孔对光反射指在光线刺激下双眼瞳孔直径缩小的反射。其中光照侧瞳孔缩小称为直接对光反射,对侧瞳孔缩小称为间接对光反射。该通路是从视网膜起始,经视神经、视交叉和视束的传入视觉通路,在进入外侧膝状体前离开视束,再经四叠体上丘臂到达中脑顶盖前区。顶盖前区发出的纤维止于两侧的动眼神经副核。动眼神经副核的轴突经动眼神经到睫状神经节更换神经元,节后纤维随睫状短神经走行支配瞳孔括约肌,括约肌收缩引起双侧瞳孔同时缩小[39]。该通路的任何一处功能异常,均有可能导致瞳孔对光反射减弱或消失。

2 MCI患者眼球运动异常表现

    MCI是介于正常衰老和痴呆之间的一种状态,也是一种认知功能障碍综合征。患者的认知能力与年龄和教育程度相匹配的正常人群相比有轻度减退,但是日常生活和工作能力未受明显影响[1,40]。MCI患者属于痴呆发病高危人群,3年发病率可高达46%[41]。对中老年人群进行MCI早期筛查,有助于针对高危人群尽早进行痴呆防控。因此,通过异常眼球运动辅助筛查MCI患者,对早期防治AD有重要临床意义。
    根据是否存在记忆功能受损,MCI患者可以被分为“遗忘型”和“非遗忘型”。其中,遗忘型MCI(amnestic MCI,aMCI)表现为以记忆力损害为主,其他认知域功能相对正常;非遗忘型MCI(non-amnestic MCI,naMCI)则表现为记忆能力相对正常,而其他一个或多个认知域功能受损[40]。遗忘型MCI多进展为AD,非遗忘型MCI可进展为包括AD在内的多种痴呆类型[42]。和AD患者类似,aMCI患者存在扫视潜伏期延长[43]、反向扫视潜伏期延长[44]和正确率下降[45]等异常表现。功能磁共振成像结果[45]显示:与健康老年人群相比,在反向扫视任务中,aMCI患者的额叶眼区活性下降,提示反向扫视任务的错误率升高可能与额叶眼区受损导致眼球运动抑制功能下降有关。与临床确诊的AD患者相比,aMCI患者的眼球运动异常较轻。但MCI患者的眼球运动异常相关研究数量仍较有限,尤其是联合神经影像学检查结果的分析,还有待进一步研究。

3 AD患者眼球运动异常表现

    AD患者存在多种异常眼球运动类型。和正常对照相比,AD患者扫视潜伏期延长,扫视速率下降、幅度降低[46-48],但上述各指标在不同AD患者间变异度较大[43,49]。其中扫视潜伏期和扫视速率的异常程度和简易精神状态评价(Mini-Mental State Examination,MMSE)量表评分呈正相关,提示扫视测试或许可以和MMSE评分一同作为AD诊断和严重程度分级的依据之一[50]。神经影像学研究结果[51]显示:扫视潜伏期延长可能与AD患者双侧顶叶和枕叶及右侧颞叶容积减少相关。其中顶叶容积的减少可导致视觉注意力下降,进而影响正常扫视功能[51-52]。在反向扫视测试中,AD患者扫视潜伏期延长,扫视方向错误率升高,且校正率低[47,53-54]。虽然类似的发现也存在于非AD类型的痴呆患者中[55],但AD患者的反向扫视的错误率与包括MMSE、阿尔茨海默病评定量表、彩色表格排序测试等神经心理学评分量表的结果相关性更强[12,54-57]。在微扫视和扫视性侵扰检查中,AD患者的眼球运动方向并非与注视点相平行,而是存在显著倾斜[30],且扫视性侵扰的发生频率明显升高,也与MMSE评分相关[58]。因此,微扫视和扫视侵扰也是区分被检查者是否存在认知功能障碍的眼球运动指标之一。AD患者平滑追随眼球运动异常表现和扫视异常类似。在观察目标移动时,AD患者进行平滑眼球运动追随的潜伏期延长,移动速率下降,移动速率的加速度下降,正确捕捉移动物体的时间占比也下降[51,59-61]。由于眼球运动速度常落后于目标物的移动速度,AD患者可频繁出现补偿性扫视以重新捕捉目标物。有研究[62]认为补偿性扫视出现的频率与MMSE评分呈负相关,但也有研究[63]结果显示AD组受试者的平滑追随功能依然位于正常范围。平滑追随眼球运动的异常程度也可能与AD患者病情进展严重程度相关,但目前尚缺乏被学界广泛认可的研究结果,相关病理生理机制也有待进一步研究。扫视性侵扰出现的频率也与MMSE评分呈负相关。对AD患者瞳孔对光反射功能研究相对较少,已有研究[64-65]提示:与正常对照相比,AD患者瞳孔对光反射变化的幅度和速率均有降低。
    研究[46,48-49,59,66]提示:AD患者中最常见的眼球运动异常类型包括未矫正的反向扫视错误率增加、注视不稳定、反射性扫视潜伏期延长、视觉捕捉反射功能障碍及反射性和预测性扫视幅度降低。其中,与其他神经心理学评分量表存在严重程度相关性的眼球运动指标包括扫视潜伏期、扫视速率、反向扫视错误率、扫视性侵扰发生频率及补偿性扫视的出现频率。上述眼球运动指标有望成为AD患者早期筛查和严重程度分级诊断的主要生物标志物。

4 人工智能技术的发展对MCI和AD的辅助诊断功能展望

    近十年来,随着机器学习算法的突破,人工智能技术可以使计算机在极短时间习得人类的既得知识和经验并进行相应推理判断。在海量存储空间和强大计算能力的硬件基础上,深度学习算法在图像识别领域发展尤为迅猛。由于人类专家的受训水平有波动性,且存在应对任务的疲劳曲线,在某些定性定量的图像识别场景下,优秀调制的深度学习算法模型可以达到人类专家水平,甚至可以准确判断某些人眼无法识别的特征[67-68],例如Poplin等[68]采用深度学习技术开发的心血管风险预测模型,仅通过受试者的眼底彩照进行5年内心血管不良事件发病风险预测,其接收者操作特征曲线下面积(area under curve,AUC)可达到0.70(95%CI:0.65~0.74),该表现不亚于业内公认的欧洲心血管风险预测计算公式的表现(AUC 0.72,95%CI:0.67~0.76,且该模型通过眼底彩照判断受试者性别的AUC可高达0.98(95%CI:0.97~0.99),超出了人眼可以判断的特征范围。
    眼球运动检查过程无创,耗时短、成本低,可行性高,是理想的认知功能障碍筛查方法。但其也存在检查类型和参数多样,计算过程复杂,人群变异度大的特点。机器学习算法的强大运算和数据处理能力对眼球运动检查结果和眼球运动轨迹视频的分析具备其特有的优势。在视频数据方面,Long等[69]通过使用深度学习技术中的时序分割网络模型,分析了4 196例婴幼儿的眼部行为学表型视频,建立了可用于客观评估包括斜视、眼球震颤、代偿头位等13个行为特征的发生频率及严重程度的人工智能系统,其中识别双眼运动不协调、注视不良等8个行为特征的AUC可达85%以上(86.9%~96.5%)。在眼球运动检查和机器学习的跨学科融合方面,Lagun等[70]通过视觉配对比较测试记录眼球运动发现:机器学习中的支持向量机算法筛查MCI患者准确率可达87%,但该研究由研究者手动提取眼球运动特征,存在损失重要眼球运动特征信息的可能性,且纳入对象数量较少,缺乏外部验证,故模型表现仍有较大提升空间。Pereira等[71]通过比较正常对照组、MCI组和AD组在视觉目标搜索任务中的表现,采用特征序列选择器、遗传算法、Las Vegas算法分别进行自动眼球运动特征提取,再与随机森林、支持向量机和神经网络3种分类模型结合,最终三组受试者的分类准确度最高可达80%。早发型AD患者的发病年龄更早(65岁前),表型异质性强,早期临床诊断难度更大。眼球运动检查结合机器学习算法,也有助于这一类人群的筛查诊断。Pavisic等[61]基于对平滑追随眼球运动模式的记录,采用贝叶斯逻辑回归分类模型,对早发型AD和正常对照患者的分类准确率可高达96%。然而该研究也存在样本量有限(57例),正常对照组与患者组临床表现差异显著易区分等局限性。现将上述研究中最佳模型表现的准确率、敏感度、特异度和AUC结果总结于表1。

        表1 既往研究中基于眼球运动的机器学习相关认知功能分类模型表现总结
Table 1 Summary of the performance of the classification model of cognitive function related to machine learning based on eye movement in previous studies

20230307162941_0640.png
NP:注视新出现图片时间比率;SO:扫视方向;RF:再注视次数;FD:注视时间;TFF:首次注视时间;FB:感兴趣区前注视次数;FC:感兴趣区内注视次数;DF:感兴趣区内总注视时间;FS:注视稳定性;PS:预扫视特征;SP:平    滑追随特征;NA:文献原文未提及。
NP: novelty preference, the fraction of the total looking time spent gazing at the novel image region; SO: saccade orientation, the
corresponding endpoints of the fixations; RF: re-fixations, the times when the gaze position re-visits (re-fixates) on previously seen parts of the stimuli; FD: fixation duration, the duration of fixations during the test phase; TFF: time to first fixation; FB: fixations before, number of fixations before the first fixation on any regions of interest for the first time; FC: fixation count, number of fixations made in a specific region of interest; DF: duration of fixations, total duration of fixations within an ROI; FS: fixation stability; PS: pro-saccade; SP: smooth pursuit; NA: not available in the original literature.

    尽管上述研究所报道的准确率或AUC可达到0.8,但每项研究所涉及的眼球运动检查类型较为单一,未囊括既往全部报道与MCI和AD患者诊断相关的眼球运动类型。且受诊断复杂性的限制,研究纳入受试者人数相对有限,同时包含正常对照、MCI和AD组的研究更少,模型表现稳定程度还有很大提升空间。因此在结合认知功能测试的特定场景下,通过进行更大规模认知障碍人群的眼球运动视频标准化采集,纳入多种类、多模态的眼球运动相关数据,包括但不限于眼球运动检测过程中获得的总注视时间、次数,感兴趣区注视时间、注视次数,扫视潜伏期,反向扫视潜伏期、正确率,扫视速度,记忆性扫视的时间、正确率,平滑追随运动中眼球移动与目标物移动速度比值、追随时间等数值数据,以及眼动视频等图像数据,使用深度学习技术对眼球运动数值数据和图像数据进行特征提取和定量分析。并采用权重分析和热图绘制法,将机器学习模型分类图像特征可视化,消除模型分类过程中的黑箱效应,这有助于探索眼球运动异常与认知功能障碍的关联,辅助认知功能障碍早期筛查,也为认知功能障碍相关异常眼球运动模式发病机制的探索提供新的思路和选择。

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2、康细珍,杨洋,孙伟铭等.非人灵长类动物意识水平量表评估工具现状分析[J].华西医学,2023,38(11):1755-1759.
1、广州市科技计划项目基础研究计划市重点实验室建设项目(202002010006)。This work was supported by Guangzhou Key Laboratory Project, China (202002010006).()
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