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综述
静息态功能磁共振对阿尔茨海默病早期诊断的研究进展
何雨洁 闫少珍 卢洁

Cite this article as: HE Y J, YAN S Z, LU J. Research advance on resting-state functional magnetic resonance imaging in the early diagnosis of Alzheimer's disease[J]. Chin J Magn Reson Imaging, 2024, 15(1): 173-178.本文引用格式:何雨洁, 闫少珍, 卢洁. 静息态功能磁共振对阿尔茨海默病早期诊断的研究进展[J]. 磁共振成像, 2024, 15(1): 173-178. DOI:10.12015/issn.1674-8034.2024.01.029.


[摘要] 阿尔茨海默病(Alzheimer's disease, AD)是以认知障碍为特征的中枢神经系统退行性疾病,出现临床症状前有长达约20年的临床前期,是AD干预的最佳时间窗,因此早期诊断对延缓病情和改善预后至关重要。静息态功能磁共振成像(resting-state functional magnetic resonance imaging, rs-fMRI)具有无创性和高时空分辨率等优势,是研究AD脑功能活动异常最广泛使用的神经成像技术之一,为寻找早期AD非侵入性标记物提供了可能。本文就rs-fMRI技术在AD早期诊断的应用价值进行综述,以期找到早期监测AD的非侵入性影像学标记物。
[Abstract] Alzheimer's disease (AD) is a degenerative disease of the central nervous system characterized by cognitive impairment. There is a preclinical period of approximately 20 years before the onset of clinical symptoms, making it the optional time window for disease intervention. Therefore, the early diagnosis of AD is essential for disease delay and prognosis improvement. Resting-state functional magnetic resonance imaging (rs-fMRI) has the advantages of non-invasive and high spatial-temporal resolution. It is one of the most widely used neuroimaging techniques to study brain functional activity abnormalities in AD, which provides the possibility to find non-invasive markers of early AD. Based on rs-fMRI techniques, we reviewed the application value in the early diagnosis of AD in this article, in order to find non-invasive imaging markers for early monitoring of AD.
[关键词] 阿尔茨海默病;轻度认知障碍;主观认知下降;静息态功能磁共振成像;磁共振成像;早期诊断
[Keywords] Alzheimer's disease;mild cognitive impairment;subjective cognitive decline;resting-state functional magnetic resonance imaging;magnetic resonance imaging;early diagnosis

何雨洁    闫少珍    卢洁 *  

首都医科大学宣武医院放射与核医学科,磁共振成像脑信息学北京市重点实验室,神经变性病教育部重点实验室,北京 100053

通信作者:卢洁,E-mail:imaginglu@hotmail.com

作者贡献声明::卢洁设计和构思本综述的方案,对稿件重要内容进行了修改,对文章的知识性内容作批判性审阅,对最终稿件版本进行了全面的审阅和把关,获得了“十四五”国家重点研发计划项目基金联合北京市科技计划项目的资助;何雨洁设计和构思本综述的方案,起草和撰写稿件,获取、分析或解释本综述的数据/文献;闫少珍设计和构思本综述的方案,对稿件重要内容进行了修改,对文章的知识性内容作批判性审阅,获得了国家自然科学基金的资助;全体作者都同意发表最后的修改稿,同意对本研究的所有方面负责,确保本研究的准确度和诚信。


基金项目: “十四五”国家重点研发计划项目 2022YFC2406900 国家自然科学基金项目 82102010 北京市科技计划项目 Z201100005520018
收稿日期:2023-09-07
接受日期:2023-12-07
中图分类号:R445.2  R749.16 
文献标识码:A
DOI: 10.12015/issn.1674-8034.2024.01.029
本文引用格式:何雨洁, 闫少珍, 卢洁. 静息态功能磁共振对阿尔茨海默病早期诊断的研究进展[J]. 磁共振成像, 2024, 15(1): 173-178. DOI:10.12015/issn.1674-8034.2024.01.029.

0 引言

       阿尔茨海默病(Alzheimer's disease, AD)是一种以进行性认知功能障碍和记忆损害为主要特征的中枢神经系统退行性疾病。2023年阿尔茨海默病协会国际大会提出AD最新诊断标准,根据生物标志物异常情况将 AD分为 a、b、c、d四个生物阶段,并根据患者认知障碍程度将其划分为0期至6期共7个临床阶段。其中前4期是AD早期阶段。主观认知下降(subjective cognitive decline, SCD)相当于临床2期,指主观认为存在认知或记忆下降但客观的神经心理测验正常[1],患者仅存在轻度神经病理损伤,仍具有较大的认知储备[2]。轻度认知障碍(mild cognitive impairment, MCI)即临床3期,患者具有客观认知损害但未达痴呆诊断标准,每年约有15.4%~33.4%转化为AD[3]。AD早期阶段是防止个体发展为AD的关键干预窗口期[4]。目前,AD尚无治愈方法,因此寻找早期诊断AD的标志物并及时干预是延缓疾病进展的关键。

       脑内β-淀粉样蛋白(β-amyloid, Aβ)沉积是AD最典型的病理特征之一,AD患者在认知障碍出现前15~20年就已存在Aβ沉积,而Aβ清除障碍被认为是AD发生、发展的驱动因素[5]。类淋巴系统是清除Aβ的主要途径之一,该系统中蛛网膜下腔的脑脊液进入动脉周围间隙,并通过血管壁搏动深入大脑,随后经扩散和对流运动与间质液和Aβ混合后,从静脉周围间隙流出[6]。神经病理学研究[7]表明静息态低频(<0.1 Hz)全脑活动可能驱动脑脊液流量和血管张力,从而影响类淋巴系统清除Aβ的过程。静息态功能磁共振成像(resting-state functional magnetic resonance imaging, rs-fMRI)具有无创性和高时空分辨率等优势,成为研究AD脑功能活动最广泛使用的神经成像技术之一[8],且与病理生物标志物存在显著相关性[9],能够反映大脑病理学特征,有望成为早期诊断AD的非侵入性标记物。因此,本文就rs-fMRI技术在AD早期诊断中的应用及最新研究进展进行综述,包括SCD、MCI和AD患者的脑功能活动改变、功能活动改变与疾病病理和临床量表的关系及各种rs-fMRI技术的鉴别效能,从而探究AD患者神经活动的影像学特征,以期发现非侵入性早期影像标记物、降低疾病进展风险、指导临床预防和治疗。

1 rs-fMRI技术在AD应用的方法学

       fMRI是基于血氧水平依赖(blood oxygenation level dependent, BOLD)信号测量大脑活动时产生的血流动力学改变以间接表征大脑神经元活动。BOLD-fMRI包括rs-fMRI和任务态fMRI,尽管任务态fMRI已广泛用于识别与特定任务对应的大脑区域,但AD患者常常难以长时间配合完成复杂的认知任务,临床应用受限。rs-fMRI在患者保持静息状态时采集脑区信号,不须配合额外复杂任务,具有配合度高、易于测量以及可提供全脑信息的优势,在AD研究中广泛应用。因此,本文主要探讨rs-fMRI在AD早期诊断中的应用价值。

       rs-fMRI的常用指标包括局部一致性(regional homogeneity, ReHo)、低频振幅(amplitude of low frequency fluctuation, ALFF)、低频振幅分数(fractional ALFF, fALFF)和功能连接(functional connectivity, FC)等。ReHo是指通过利用肯德尔一致性系数测量特定体素及其相邻体素在静止时间序列中的一致性[10],从而反映大脑局部体素与周围体素自发性活动的同步性。ALFF为0.01~0.10 Hz的低频振幅BOLD信号值[11],用于反映局部神经自发活动的强弱程度。fALFF是低频范围内的功率除以整个可检测频率范围内的总功率[12],相对于ALFF可有效避免生理噪声的干扰。FC即为两个不同脑区BOLD序列在时间维度上的相关程度[13],是研究不同脑区的有向脑网络或者网络信息的动态变化和因果关系的主要手段,又可分为静态功能连接(static FC, sFC)和动态功能连接(dynamic FC, dFC),sFC默认BOLD信号在短时间内测量的FC相对稳定,dFC能准确反映FC随时间动态变化模式,在预测大脑状态和疾病方面比sFC更具优势[14]

       以上rs-fMRI分析方法广泛用于AD研究,并发现脑内存在显著变化,这些差异将对诊断AD早期阶段有着重要意义。

2 rs-fMRI技术在AD早期诊断的应用

2.1 rs-fMRI在SCD诊断中的应用

       一般认为SCD阶段仅发生局部神经病理改变,而SCD的rs-fMRI指标与经典病理生物标志物之间的相关性表明rs-fMRI可能是鉴别SCD的潜在影像标志物[9]。跨种族大型队列研究[15]显示,中国和德国SCD患者均存在海马与右侧岛叶间的sFC增加,且与SCD-plus评分呈正相关,认为SCD海马sFC增加可作为反映其认知能力下降的可靠依据。另一项ReHo研究[16]也显示SCD患者海马和海马旁回较健康对照组(health control, HC)增高,且与SCD记忆、语言、空间和注意力等多项认知功能呈负相关,提示海马和海马旁回功能降低可能是痴呆发展的高危因素。此外,有研究证实Aβ最早沉积于楔前叶、内侧额叶皮层和后扣带皮层等默认模式网络(default mode network, DMN)核心区域[17],联合应用ReHo和FC的功能影像学研究[18]同样显示SCD患者主要是DMN脑区异常,后扣带回-楔前叶可能是最早发生的区域。从Aβ产生的角度解释,传统认为DMN的易感性主要是由于其高水平的脑神经活动和代谢应激导致Aβ分泌和沉积增加[19, 20];从Aβ清除障碍的角度解释,Aβ沉积区域的静息态全局BOLD信号减少可能会影响类淋巴回路的清除功能,造成Aβ在DMN区域的传播受阻[21]

       对于rs-fMRI技术在SCD的鉴别效能,内在功能网络研究[22]发现SCD岛叶亚网络sFC的最佳拟合模型对SCD与HC分类的准确度达83.9%,其受试者工作特征曲线的曲线下面积(area under the curve, AUC)为0.876,敏感度为81.6%,特异度为81.8%,提示岛叶sFC可有效区分SCD与HC。YANG等[23]提取116个脑区的标准化ALFF值和fALFF值对44例SCD与57例HC进行分类,结果显示ALFF的准确度和AUC分别为71.38%、0.67,fALFF的准确度和AUC稍低,为63.81%、0.59,进一步联合ALFF和fALFF后其准确度和AUC提升至76.44%、0.69。基于人工智能-多核支持向量机(support vector machine, SVM)的脑功能连接(FC和图论指标)信息组合实现了识别SCD的较高分类性能,结合sFC和图论两种模态的网络特征区分66例SCD和64例HC的准确度达79.23%[24]。因此,rs-fMRI能够有效反映SCD阶段易损脑区(如默认网络、海马等)的局部神经活动和功能连接情况,与病理蛋白Aβ的沉积存在密切联系,并在鉴别SCD和HC方面具有较好的分类性能,能够将诊断窗口前移。此外,人工智能可助力rs-fMRI实现AD早期精准诊断。

2.2 rs-fMRI在MCI诊断中的应用

       MCI患者脑功能活动异常的常见脑区包括楔前叶、后扣带皮层、舌回、海马旁回和颞叶等[25, 26, 27, 28]。与SCD关键脑区一致,楔前叶、后扣带皮层和顶下小叶是MCI患者Aβ沉积的关键脑区,与认知衰退和病程进展密切相关[29, 30]。值得注意的是,PASQUINI等[31]将DMN进一步分为前部DMN和后部DMN,发现后部DMN的FC与全脑皮层Aβ沉积水平呈正相关,即后部DMN FC越高的脑区Aβ沉积越多,并随着疾病进展,在MCI早期阶段Aβ沉积达到高峰,表明Aβ更倾向沉积于高FC的脑区。这一发现丰富了关于Aβ早期沉积于DMN的影像学信息,说明rs-fMRI有助于理解AD病理生理机制,可以作为识别AD不同阶段的有力工具。为了将MCI脑功能活动与Aβ的关系研究进一步扩展到全脑,SCHEEL等[32]发现MCI全脑ALFF越高的区域(DMN和视觉网络:楔前叶、角回、舌回和梭状回)Aβ沉积越少,去除与脑Aβ清除有关的血管效应后这种负相关仍存在,表明DMN和视觉网络的局部神经元活动可能会阻止Aβ沉积,与Aβ最早沉积于DMN的原因相似。

       此外,近年来联合rs-fMRI与SVM鉴别MCI的研究取得重要进展。基于ReHo的SVM模型,右侧尾状核ReHo值诊断MCI与HC的准确度为68.6%[33];基于sFC的SVM模型,以海马为种子区的sFC区分MCI和HC的准确度为76%[34],以左侧顶下小叶为种子区的sFC区分遗忘型MCI和SCD的AUC为76.3%[26];基于dFC的SVM模型,区分早期MCI与HC的准确度为82%,AUC为90%[10]。为了避免单一影像模态的不足,联合结构MRI(海马体亚区和杏仁核的体积)和rs-fMRI(节点度、节点路径长度和中介中心性等脑网络特征和ReHo、ALFF、fALFF等体素特征)的SVM模型,区分MCI与HC的准确度达87.18%,AUC达93.71%,敏感度为92.45%,特异度为90.14%[35]。以上研究表明rs-fMRI技术可以揭示AD整个阶段病理蛋白沉积特点,并能在一定程度上识别MCI和HC,合适的多模态成像较单一成像来说诊断效能更佳。

2.3 rs-fMRI在AD诊断中的应用

       DMN是AD最易受损的脑网络之一,表现为DMN的FC降低,其中后扣带皮层/楔前叶是受累严重的脑区[36]。研究发现[37],与HC相比,AD患者ReHo减低主要集中在后扣带回、楔前叶、颞中回、枕中回、角回等后部脑区,ReHo增高区域主要位于前额叶、前-中扣带回、尾状核等,表明AD通过增加前-中部脑区局部功能活动以代偿后部脑区活动减低所导致的记忆功能减退。ZHENG等[38]发现AD患者ALFF降低区域(左侧后扣带回、楔前叶、顶下小叶、枕中回和右侧颞中回)伴随局部脑血流量下降,推测这些区域局部脑血流量下降使得血管清除能力受损,进而加剧Aβ沉积、神经元活动减少,导致神经功能障碍和脑萎缩,这表明BOLD-fMRI信号和脑血流变化的关联可能是AD发病的重要因素。

       此外,ZHAO等[39]基于dFC的SVM模型,发现左侧楔前叶dFC区分AD和HC的准确度为71%,敏感度和特异度分别为72%、71%。后扣带回/楔前叶ALFF组区分AD和HC的AUC达0.734,敏感度和特异度分别为65.7%、73.1%;rCBF和ALFF联合区分AD和HC的AUC达0.921,敏感度和特异度分别为85.3%、88.5%[38],两者联合ReHo可进一步提高AUC至0.978[40],提示后扣带回/楔前叶多模态神经影像学组合可用作区分AD和HC的有效标志物。对于DMN的主要脑区,包括后扣带皮层、左颞顶交界处、右颞顶交界处和内侧前额叶皮质等两两之间的sFC中,左颞顶交界处-内侧前额叶皮质的sFC是区分AD与HC最具鉴别力的特征,其准确度为64%,敏感度为61%,特异度为68%[41]。另外,AD患者的结构MRI通常显示皮层厚度减少,结构和功能信息的有效组合可以提高诊断AD的准确度。PARK等[42]发现内侧前额叶皮质和后扣带皮层的FC和内侧颞叶的皮层厚度区分AD和HC的准确度为77.1%,而与左侧大脑半球颞上回和缘上回的大脑皮层厚度联合可将准确度提高至91.7%,说明颞顶联合皮层对于诊断AD具有更高的准确度。

3 rs-fMRI技术在预测MCI进展的应用

       楔前叶和后扣带回的代谢低下具有预测认知未受损和MCI向AD痴呆转化的价值[30, 43]。左侧楔前叶ReHo介导了Aβ沉积与认知障碍之间的联系[44],提示后扣带回/楔前叶的局部活动降低可能是反映AD病理和临床认知及预测疾病进展的影像标志物。神经心理表现与ReHo的相关性研究[40]显示,情景记忆评分随着疾病的严重程度而降低,即AD<MCI<SCD<HC,而左侧楔前叶的ReHo与情景记忆评分呈正相关,提示楔前叶是预测MCI进展、识别功能障碍和评估疾病严重程度的重要脑区。随着病程进展,部分MCI转化为AD,部分MCI可维持于现阶段,另有少数可逆转为认知正常。已有研究证实Aβ不对称沉积是MCI向AD进展的早期征象,且随着疾病进展,患者双侧大脑皮层呈弥漫性Aβ浸润,这种不对称性最终似乎会消失[45]。大脑左半球在AD病理中表现出潜在的选择脆弱性[46],功能影像研究也发现类似现象,右利手的MCI逆转者和MCI未进展者ReHo变化只出现在大脑左侧半球,MCI进展者则累及双侧大脑半球[47],这说明双侧大脑半球ReHo改变可能具有提示MCI患者病情恶化的作用。然而,这种Aβ左半球偏侧化有时并不显著[48],不同Aβ小鼠模型存在左或右半球偏侧化[49],具体机制需要进一步研究证实。HU等[50]发现MCI逆转者左侧额中回ALFF增加与听觉语言学习记忆呈正相关,这说明额叶局部神经活动增加或许可以改善MCI患者的记忆功能,可作为预测MCI向认知正常转化的影像标记物。aMCI患者认知障碍的严重程度增加与楔叶/楔前叶皮质ALFF减少有关[40, 51],提示楔叶/楔前叶皮质ALFF可以作为监测aMCI进展的成像标志物。此外,有研究对比MCI进展者与MCI稳定者脑dFC的特征及差异,结果显示MCI进展者比MCI稳定者弱连接状态增多,这与AD患者在弱连接状态下停留时间较长的dFC特点类似[52],这一发现为rs-fMRI技术预测MCI进展者向AD转化提供了影像学依据。ZHANG等[53]基于结构MRI和rs-fMRI的SVM模型,区别MCI进展者与AD的准确度为89.8%,与单独使用结构MRI相比,准确度提高了32.66%。以上研究表明,rs-fMRI可以作为一种预测MCI向AD转化的有效工具。采用机器学习方法对rs-fMRI数据进行分析和处理,可以提高预测准确度,为AD早期诊断提供了新的方向和思路。

4 总结

       总的来说,rs-fMRI特征变化提示某些重要脑区在AD早期就已受损,主要位于楔前叶/后扣带回、顶下小叶和海马等,病理蛋白Aβ早期沉积于DMN区域的原因可能与静息态全脑活动影响类淋巴清除途径有关,提示rs-fMRI技术具有作为早期识别AD、指导临床预防和治疗的影像学指标的潜力。在样本量大致相同的rs-fMRI研究中,楔前叶和楔叶可能存在结果相互矛盾的情况,这与AD早期还存在一定的代偿机制有关,扩大样本量或许能更准确地探究该脑区的影像学特征,从而判断其是否可作为早期识别AD的非侵入性影像标志物。

       虽然rs-fMRI在临床研究方面显示出较强的适用性,但这一技术对数据后处理方法存在一定程度的依赖,其研究结果可能因选择后处理方式不同而不尽相同。ReHo具有较高的重测可靠性,但0.01~0.10 Hz不同频带内ReHo生理意义不明,导致其在临床研究中受限。ALFF易受到生理噪声的影响,建议同时使用ALFF和fALFF参数以减小生理噪声。由于建立在先验知识基础上,基于种子点的sFC对研究结果具有较强的解释性,但种子区的选择不当会导致部分结果存在差异。

       后处理技术的新近发展对于提高rs-fMRI早期诊断AD的效能具有巨大前景,未来可能有以下几个方向:(1)使用7 T超高场强MRI进行扫描,有助于准确检测尚未被识别的脑区活动和连接。目前美国食品和药物管理局已批准7 T超高场MR磁体用于临床应用,并有学者提出一种超时空分辨率线扫描fMRI皮层定位的选择和靶向框架以量化功能精度[54]。无论是否进行空间平滑,7 T高场MRI可带来良好的时间相关性和空间特异度[55];(2)将fMRI与正电子发射断层显像、神经心理学评分、生物标志物和遗传因素等综合信息,结合机器学习和人工智能算法,以筛选出最佳的早期诊断影像指标。如GONNEAUD等[56]利用rs-fMRI数据、生物标志物Aβ、AD高风险因素APOEε4和人工智能算法等构建出大脑年龄的预测模型,并发现一种在AD症状出现之前加速脑老化相关的脑功能改变的特征模式;(3)开展多中心、大样本的纵向随访和队列研究,有利于全面了解rs-fMRI指标与AD病理之间的因果关系,以提高AD早期诊断的准确度。

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