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功能磁共振脑成像机遇和挑战——中国十年来发展成果及展望
夏明睿 贺永

Cite this article as: Xia MR, He Y. Opportunities and challenges of functional magnetic resonance imaging for human brain research: Achievements and prospects of China in the past ten years[J]. Chin J Magn Reson Imaging, 2022, 13(10): 23-36,65.本文引用格式:夏明睿, 贺永. 功能磁共振脑成像机遇和挑战——中国十年来发展成果及展望[J]. 磁共振成像, 2022, 13(10): 23-36, 65. DOI:10.12015/issn.1674-8034.2022.10.004.


[摘要] 功能磁共振成像(functional magnetic resonance imaging, fMRI)技术由于无创、时空分辨率均衡、可重复性高、可全脑成像等优点,为脑认知、脑发育、脑老化以及脑疾病的机制探索和临床评估提供了关键的技术手段,具有重要的临床价值。最近十年,我国政府布局了大量关于fMRI脑影像的研究项目,在神经科学、神经影像、精神病学等多个学科取得系列原创性成果和突破。多中心脑影像大数据的质控和校正、高场强高时空分辨率数据获取、诊疗评估等临床应用的落地、脑科学的产业化等是今后值得关注的研究方向。本文评述国内学者在fMRI领域十年来取得的重要成果,包括fMRI脑影像计算分析方法及软件平台、基于fMRI脑影像在脑认知、脑发育、脑老化、脑疾病的应用研究,同时对fMRI未来的重要发展方向进行展望。本文梳理和点评了我国fMRI研究领域的重要科研成果,为该领域的研究提供参考。
[Abstract] Due to the advantages of non-invasiveness, balanced spatial and temporal resolution, high repeatability, and whole-brain imaging, functional magnetic resonance imaging (fMRI) technology provides a promising tool for the exploration of the neurobiological mechanisms of brain cognition, brain development, and brain aging, and also exhibits great clinical value for the investigation of the pathology and clinical assessment of brain disorders. In the last decade, the Chinese government has funded a large number of research projects on fMRI brain imaging and has achieved a series of original achievements and breakthroughs in neuroscience, neuroimaging, psychiatry, and other disciplines. Future research directions worthy of attention include quality control and harmonization of multi-center imaging big data, the acquisition of high-spatial-temporal resolution data using high-field-strength scanners, the implementation of clinical applications such as diagnosis and treatment evaluation, and the industrialization of brain science. We reviewed the achievements made by domestic scholars in the field of fMRI in the past decade, including the computational analysis method and software platform for fMRI brain images, the application of fMRI brain images in brain cognition, brain development, brain aging, and brain disorders, and the outlook on the important directions of fMRI in the future in this paper. The purpose of this review is to sort out and comment on the important scientific research achievements in the field of fMRI research in China, and to provide a reference for researchers in this field.
[关键词] 脑认知;认知障碍;精神分裂症;重性抑郁障碍;双相情感障碍;神经发育性疾病;阿尔茨海默病;帕金森病;癫痫;功能磁共振影像;静息态功能磁共振影像;磁共振成像
[Keywords] neurocognition;cognitive impairment;schizophrenia;major depressive disorder;bipolar disorder;neurodevelopmental disease;Alzheimer's disease;Parkinson's disease;epilepsy;functional magnetic resonance imaging;resting-state functional magnetic resonance imaging;magnetic resonance imaging

夏明睿 1, 2, 3   贺永 1, 2, 3, 4*  

1 北京师范大学认知神经科学与学习国家重点实验室,北京 100875

2 北京师范大学神经影像大数据与人脑连接组学北京市重点实验室,北京 100875

3 北京师范大学IDG/麦戈文脑科学研究院,北京 100875

4 北京脑科学与类脑研究中心,北京 102206

贺永,E-mail:yong.he@bnu.edu.cn

作者利益冲突声明:全部作者均声明无利益冲突。


        
        贺永,北京师范大学二级教授,认知神经科学与学习国家重点实验室副主任,神经影像大数据与人脑连接组学北京市重点实验室创始主任,麦戈文脑科学研究院课题组长,国家基金委创新研究群体学术带头人。国家杰出青年科学基金获得者、教育部“长江学者”特聘教授、“国家万人计划”领军人才、北脑学者、科睿唯安全球高被引学者(神经科学与行为科学、连续六年)。主持国家自然科学基金重点项目2项、重点国际合作项目1项和科技部重点研发计划课题。在《Proceedings of the National Academy of Sciences of the United States of America》《Brain》《Molecular Psychiatry》《Biological Psychiatry》等知名期刊发表论文200余篇,受邀在《Trends in Neurosciences》《Biological Psychiatry》等不同领域的国际顶级期刊发表综述。发表论文在谷歌学术上被引用44000余次,H指数为96。担任神经影像学国际期刊《Human Brain Mapping》副主编及《NeuroImage》期刊编委。研究领域为计算神经影像、人脑连接组学,是国际上最早采用多模态脑成像技术和复杂网络计算理论研究人脑连接组的学者之一,其工作对于从脑网络水平理解人脑结构和功能的连接规则、形成发育机制以及探索脑疾病的临床评估具有重要价值。

基金项目: 国家自然科学基金 82021004
收稿日期:2022-10-11
接受日期:2022-10-14
中图分类号:R445.2 
文献标识码:A
DOI: 10.12015/issn.1674-8034.2022.10.004
本文引用格式:夏明睿, 贺永. 功能磁共振脑成像机遇和挑战——中国十年来发展成果及展望[J]. 磁共振成像, 2022, 13(10): 23-36, 65. DOI:10.12015/issn.1674-8034.2022.10.004

       基于血氧水平依赖(blood oxygen-level dependent, BOLD)的功能磁共振成像(functional magnetic resonance imaging, fMRI)技术利用血液动力学中BOLD对比度增强原理对脑功能活动进行成像[1],是目前应用最为广泛的脑功能成像技术之一。fMRI具有被试无创、时空分辨率相对均衡、可重复性高、可全脑成像等优点,为脑认知、脑发育老化以及重大神经精神疾病的脑影像研究提供了重要的技术手段。近年来,国内外研究者采用fMRI技术对脑的工作机制、发展规律、异常特征开展了大量的研究。以“brain” [All Fields] and “fMRI” [All Fields]作为关键字在Web of Science核心数据库搜索,可搜索到65272篇与fMRI相关的脑科学研究论文。文章数量从1993年的3篇到2021年的4833篇,以平均每年167篇的速度增长,自2015年后每年论文数保持在4000篇以上(图1),表明fMRI已成为脑科学研究中成熟且重要的技术手段,并仍在不断快速发展。近十年,我国学者在fMRI脑影像领域取得了一系列重要的原创性研究成果和技术突破,论文发表数量国际占比从2012年的8%提升到2021年的26%(图1)。这与我国对fMRI研究领域的重视极为相关,科技部、国家自然科学基金委,以及多个省市等支持了大量fMRI相关研究项目。例如,国家自然科学基金委从2001年到2017年共设立了1352个涉及fMRI脑成像的研究项目,总投入资金超过9亿元人民币,涉及脑网络、脑认知、脑发育、脑疾病等多个脑科学领域(图2A2B),催生了在神经科学、神经影像、精神病学等多学科的重要研究发现(图2C[2]。本文将从fMRI脑影像的计算方法及其软件平台和基于fMRI的应用研究(脑认知、脑发育老化、神经精神疾病)两个角度综述近年来我国大陆学者取得的重要研究成果,并对fMRI研究的发展方向进行展望。本述评旨在梳理和点评我国fMRI研究领域的重要科研成果,为该领域的研究提供参考。

图1  1993年至2021年fMRI脑影像研究论文数量及中国研究者论文数量占比。
图2  2A:国家自然科学基金委资助的功能磁共振成像(fMRI)脑影像项目中出现最多的30个关键字;2B:国家自然科学基金委资助的fMRI脑影像项目中出现超过10次的关键字词云图;2C:国家自然科学基金委资助的fMRI脑影像研究论文学科分布。图片引自Sun等[2]

1 fMRI脑影像计算分析方法及软件平台

1.1 fMRI脑影像计算分析方法

       静息态功能磁共振成像(resting-state fMRI, rs-fMRI),兼有较高的空间分辨率和时间分辨率,数据易获取,容易推广,已成为研究脑功能的不可或缺的技术。rs-fMRI目前已被广泛用于脑功能连接或网络分析,以及局部脑活动研究[3]。Zang等在国际上率先提出局部一致性(regional homogeneity, ReHo)[4]和低频振幅(amplitude of low frequency fluctuation, ALFF)[5]计算方法,后又在ALFF的基础上提出比值ALFF(fractional ALFF, fALFF)[6]。ReHo反映的是相邻体素的脑活动的同步性,ALFF反映的是脑活动的波动幅度。这些方法的重要特点是参数简单,在不同研究之间设置较为一致,便于开展基于坐标的荟萃分析。例如,针对阿尔茨海默病(Alzheimer's disease, AD)和轻度认知障碍(mild cognitive impairment, MCI)的荟萃分析发现扣带回等部位ALFF/fALFF的异常[7],并与淀粉样蛋白沉积、葡萄糖代谢的荟萃分析发现的异常部位高度吻合[8]。一项纳入20个研究中心原始数据的帕金森病(Parkinson's disease, PD)荟萃分析发现患者壳核、运动皮层等部位ALFF减低[9],其中壳核的异常与基于正电子发射断层扫描(positron emission tomography, PET)研究发现的多巴胺减少在空间上非常吻合。

       在传统静态(时间不变)rs-fMRI分析基础上,动态rs-fMRI分析致力于揭示脑区间功能协作模式的时变特性,为理解脑的功能组织原则提供更为丰富的时频信息。我国学者在国外研究的基础上拓展了人脑动态功能连接的计算方法学及其应用的系列研究,揭示人脑功能网络的连接模式[10, 11, 12]、小世界属性[13],以及功能模块架构[14]在短时间内(如秒量级)的自发重组,与涉及感知觉的初级脑区相比高级联合皮层脑区(如外侧额叶)呈现较强的时间变异特性。人脑功能连接的动态重组特性呈现明显个体差异,能够预测个体高级认知行为(如流体智力)[12],并随着发育和老化进程进行调整[11,15]。通过病例-对照设计,研究发现脑功能连接动态特性在包括精神分裂症(schizophrenia, SCZ)、孤独症、注意缺陷多动障碍(attention-deficit/hyperactivity disorder, ADHD)、癫痫等多种精神疾病中呈现异常表征[10, 16, 17, 18, 19],涉及默认网络脑区、皮下核团等脑区,与个体的临床症状以及认知行为关联。通过融合多模态脑影像和人脑基因表达数据,研究者发现人脑功能连接模式的动态重组特性受到白质结构连接的强度以及空间分布等解剖特征的约束[10,13],其空间分布、儿童青少年发育模式以及在孤独症中的异常表征均与微观基因表达谱存在关联[15, 16,20]

       在融合多模态MRI和机器学习方法方面,Sui等[21, 22]基于独立成分分析和典型相关分析开发了多种基于有/无监督学习的多模态融合算法,能够针对特定的认知能力、临床症状和基因表达刻画多模态脑影像特征在不同脑疾病患者中的异常共变模式[23, 24, 25]。结合个体化定量预测技术可进一步发掘认知发育、损伤、老化相关的影像学标志物[26]。针对多模态MRI,该团队还充分挖掘了大脑的时空信息和网络拓扑属性[27, 28],研发了多种先进的深度学习框架,提高了多种精神疾病的分类准确率,并从机器学习算法、样本量等诸多因素方面系统地总结了基于脑功能连接的个体化预测模型的可解释性方法策略[29]

       在针对脑功能网络的组织模式和形成机制的探索中,Lin等[30]采用fMRI数据,结合多目标演化算法探究了人脑网络的重叠模块化结构,该算法不需要预定义模块数量、模块内和模块间连接密度之间的权衡参数等先验信息。研究发现健康年轻人的脑功能网络存在8个重叠模块,这些重叠脑区在每一个传统的非重叠功能模块中都有涉及。此外,研究发现重叠脑区主要位于高节点中心度、高认知灵活性和对网络稳定性有着关键作用的脑区。Gu等[31]发现人脑功能网络的重叠模块化程度随年龄线性下降,重叠区域的数量随年龄线性增加。关于人脑结构网络的形成机制,一个主流的假说认为,人脑结构网络是布线成本和网络效率之间的最优权衡下形成的[32]。Ma等[33]结合多目标演化算法[34]在人脑结构网络尝试验证这一假说。多目标演化算法能够同时优化布线成本和网络效率,构建一批有着近似最优成本-效率权衡的模拟网络。研究结果支持了成本-效率权衡的重要影响,同时也揭示了仅仅基于成本-效率权衡不足以解释人脑结构网络的全部特征,需要进一步考虑更多因素的影响。

       脑功能存在个体差异是神经科学研究者的共识,但差异大小和分布特点的研究一直以来较为缺乏。2013年,研究者利用rs-fMRI绘制出了脑功能个体差异图谱,对此进行了量化和系统性描述[35]。在此基础上,该团队通过对新生儿和猕猴的脑功能个体差异的刻画,揭示了个体差异发育和进化机制[36, 37]。同时,该团队以脑功能个体差异图谱为基础,基于rs-fMRI开发出一种个性化脑功能区剖分技术(personalized brain functional sectors, pBFS),为每个人的大脑“量身定制”,剖分出边界清晰、定义准确的个体化脑功能网络[38]。该方法突破了fMRI的低可靠性瓶颈,使个体脑功能区定位可靠性达80%以上,为功能影像进入临床奠定了基础,并被《柳叶刀-精神病学》评价为“神经影像的转折点”[39]。基于pBFS的“个体化脑功能连接组”近年来也被用于研究脑功能环路与认知表现或脑功能疾病症状之间的关联,如流体智力[40]、精神分裂阳性症状[26]、强迫症[41]、创伤后应激障碍的分离症状[42]、重性抑郁障碍(major depressive disorder, MDD)治疗后的记忆损伤[43]等。相比于组水平脑功能连接,这种个体化脑功能连接组可以提高脑功能区的功能同源性,更准确地建立与多种临床疾病症状有关的大脑特征。个体化脑功能研究为理解认知表现和脑疾病症状的脑功能机制提供了新的见解,有助于脑功能疾病的个性化诊疗。

1.2 fMRI脑影像计算分析软件平台

       我国学者基于已有的fMRI计算方法,研发了一系列具有重要国际影响力的fMRI脑影像计算分析和可视化软件,被国内外学者广泛应用于脑科学研究。如专门针对rs-fMRI脑局部指标和连接的计算工具REST[44]、RESTplus[45]、REST-GCA[46]。Yan等整合开发了流水线式脑影像数据分析软件平台DPARSF[47]和脑成像数据分析工具包DPABI[48]。DPABI/DPARSF数据分析平台融入了头动噪声去除、多重比较校正、数据标准化等方面的最新研究进展,并强调了重测信度和质量控制在脑成像数据处理中的影响,对MRI的数据处理进行了规范化。用户可以从扫描仪原始数据开始,通过开发的一站式解决方案,计算出最终的rs-fMRI指标。最近,Yan等[49]进一步开发了基于大脑皮层的脑影像数据分析软件DPABISurf,解决了基于体空间分析忽视大脑按皮层延展的特性的问题,提高了脑信号提取的敏感性和特异性。基于神经影像的脑网络构建、计算分析和统计方法较为复杂,相应的计算分析软件也较为匮乏,使得很多研究者在开展脑连接组学研究工作时面临较大的困难。针对这些问题,Wang等[50]开发了具有自主知识产权的神经影像脑连接组分析软件GRETNA,可全自动地进行rs-fMRI数据预处理和脑功能网络构建,并提供丰富的、基于图论的复杂网络指标计算选择,包括全局属性和节点属性计算;支持多种常用的复杂网络属性和网络连接的统计分析模型。软件采用先进的并行计算方式,能够有效地提高计算效率,缩短脑网络分析时间。Liao等[51]开发了动态脑连接组分析软件Dynamic BC能够实现三类刻画动态脑活动或连接分析方法:ALFF、功能连接、效应连接。并在时间动态刻画方式上提供了滑动窗和柔性最小二乘两种技术,该软件工具包已在美国斯坦福大学、德国马格德堡大学、德国蒂宾根大学、上海交通大学、四川大学华西医院、首都医科大学附属友谊医院等国内外10多家机构的临床、科研得到应用。Cui等[52]开发的弥散MRI流程化处理工具包PANDA,可对原始弥散图像进行预处理得到每个被试个体的弥散参数图像及进行体素和图谱水平的分析,并可以基于确定性和概率性纤维追踪构建脑结构脑网络。PANDA可以实现从原始图像到最终统计结果的全自动处理,支持串行和并行计算,同时提供了用户友好的图形界面操作。由于其强大的功能和友好的操作系统,PANDA自发布以来在国内外广泛使用,大大促进了弥散MRI技术的应用研究,为探索脑功能活动的结构基础提供了平台支撑。基于神经影像的脑连接组研究具有高度的复杂性,致使其适用的可视化方法和软件十分匮乏。Xia等[53]发展了适用于复杂脑网络的可视化方法,并在此基础上研发了具有自主知识产权的神经影像脑连接组可视化软件BrainNet Viewer。该软件是一个基于MATLAB的灵活、快速、简便易用的脑网络可视化平台,利用球棍模型、曲面映射、三维渲染等可视化技术,支持多物种(人、猴、鼠等)、多模态脑影像的复杂脑网络连接模式的可视化,被全球59个国家1900多个机构广泛用于脑科学研究,引领了国际脑影像连接组学可视化领域的发展。

2 基于fMRI的脑认知研究

       fMRI技术被运用于探索视觉通路上各个层级的神经加工机制。Yu等[54]在对被试进行30天的视觉训练后,发现外侧膝状体大细胞层的BOLD信号变化与行为测试的结果显著相关,从而首次证明在视觉通路上在视网膜后的第一站—外侧膝状体,也具有学习引发的神经可塑性。Zhang等[55]采用掩蔽刺激的方式,证明即使是无法被主观意识到的“不可见”注意线索,依然可以引发注意效应。同时,这一效应与V1至V4的BOLD信号强度之间存在显著的相关,其中V1的相关性最强。这一发现意味着过往仅能在较高级皮层观察到的注意显著图,在初级视皮层就已经存在。进而,Mo等[56]借助fMRI的群感受野技术,比较对正立面孔和倒立面孔的加工,证明复杂自然刺激的注意优先图形成于早期视觉皮层,且受到个体关于刺激图像空间结构的知觉经验的调控。这些结果大大拓展了研究者对注意领域的理解。Chen等[57]使用fMRI的多变量模式分析(multi-voxel pattern analysis, MVPA)方法,发现在对进行运动刺激的视觉训练前,颞中回运动区MT和视皮层的V3A分别对有噪声的运动刺激和方向一致的运动刺激有较强的神经表征。随着训练的持续,MT的功能逐渐被V3A所替代,这也为知觉学习的迁移机制提出了一种可能。

       fMRI脑成像技术已广泛应用到人脑语言中枢研究方面。研究发现,大脑主管汉语和西方语言“听”和“说”的区域有重要重叠,左额下回的布罗卡区和左颞中上回的威尔尼克区同时负责这两种不同类型语言的表达和理解。非常重要的是,母语为汉语者在加工声调时右颞叶表现出很强的激活,而这个区域对英语使用者来说并不重要[58, 59]。就书面语言来说,左额中回对汉字阅读非常重要[60]。相对于正常儿童,患有中文阅读障碍者在进行汉字判断任务时左额中回激活非常弱,但这个脑区并不负责母语为西方语言者的阅读[61, 62, 63]。这些研究在理论上严肃挑战了基于西方语言研究而提出的人脑语言中枢统一论,在临床上已用于指导设计神经外科手术方案(以便术中保护国人的大脑语言功能)和适于国人的语言康复技术。最近应用定量磁共振成像技术[64, 65]的研究从微观髓鞘水平探讨了国人大脑结构的偏侧化问题[66],发现额叶的3个亚区呈现显著左侧化,而颞叶的3个亚区呈现右侧化。这一偏侧化“混合”模式与先前的传统磁共振研究发现不一致。因为文献中尚未有应用类似技术对西方语言的偏侧化研究,目前尚不清楚微观髓鞘水平偏侧化的混合模式是否受文化特点影响。

       fMRI的研究对人类语义记忆认知神经基础理论也产生了重要的贡献。人类长时记忆中语义记忆系统中存储大量关于世界的概念知识,包括客体、事件及各种抽象知识与信念,是客体加工、语言、思维等多种高级认知功能的基础。30年来无损功能脑影像研究发现概念知识在大脑中的高度分布式表征,由多个脑区系统参与,相应发展出语义记忆知识以多通道感知觉经验形式表征的主流“具身”理论,但对相对独立于特定感觉与运动经验的抽象概念知识存储机制缺乏有效解释[67]。近年来,国内学者采用多种特殊被试人群模型,结合fMRI的多种神经活动分析思路(单变量激活强度、多变量表征相似性、多变量机器学习等)与其他模态MRI实验,分离感知觉经验信息、解析不同知识表征之间的整合机制。研究发现早期视觉剥夺的个体大脑中视觉相关知识(如视觉性物体“彩虹”、物体的颜色)完全无法通过感知觉经验获得的知识-存储于前颞叶上部。该脑区对特定感觉信号不敏感,并与通常涉及语言处理的区域(包括perisylvian区域和下额叶)有较强功能连接,被推测可能表征基于(语言)符号联系的抽象概念关系。健康被试除此处脑区之外,在视觉皮层相应脑区也有相应表征[68, 69],而且整体语义网络在静息状态下也形成相对分离的感知觉与语言子网络的拓扑结构[70]。基于这些发现,Bi发展出人脑语义记忆的感觉-符号双重编码模型[71]。为了解答fMRI研究所识别的这些分布于不同脑区的知识编码如何加以整合与抽象,她们发展了新分析方法-表征相似性结合机器学习的脑损伤解码行为预测,-发现左脑连接枕叶和颞叶前部脑区的白质连接模式可以解码概念语义空间,从而提出脑连接表征高维语义记忆的机制[72, 73]

       在情景记忆领域,Xue等[74]结合fMRI等脑成像手段、表征相似性分析和人工智能深度学习技术,系统地揭示了人脑记忆表征的交互作用以及动态变化机制。在一项开创性的研究中,他们首次采用表征相似性分析技术,定位了人脑中分布式的记忆痕迹,并发现有效的学习伴随着学习时更高的神经表征相似性[75]。通过后续一系列的研究,他们逐步发展了有效学习的神经激活模式再现假说[76, 77]。此外,他们进一步结合表征相似性分析技术和全局相似性的计算模型,考察了学习材料之间记忆表征的相互作用对记忆强度的影响,并发现这种全局相似性不仅影响记忆的强度,还可解释错误记忆的产生[78, 79]。最近的研究结合表征相似性分析和计算机深度学习技术,揭示了在记忆不同的阶段,大脑神经表征的特点和动态变化特征[80, 81, 82]。这些发现有助于我们更好地理解分散学习[83]、测试效应[84]、记忆术[85]等学习方法的神经机制,以及学习能力的个体差异[86],并为启发新一代人工智能提供了启发。

       fMRI脑影像也被用于情绪与情绪障碍的神经表征、调控及其临床应用。以往研究对人类情绪及其相关障碍的研究主要采用传统心理学和精神病学方法,认为大脑是“神秘不可测的黑匣子”。近年来,随着无创脑功能成像技术快速发展,人脑心理行为活动一定程度变得“可视”了。从脑与情绪认知科学角度,解析人脑在各种情绪状态下(如焦虑与应激)的活动规律[87]、情绪与学习记忆相互作用的脑机制[88],以及跟各种情绪问题和精神障碍的关联模式[89],给传统心理学和精神病学对情绪及其精神障碍的认识带来了革命性的推动作用。综合利用脑影像、情感与认知行为实验以及生理心理等技术方法,在解析人类情感认知(如情绪感知、表达和调控)和焦虑/抑郁等情绪障碍的神经表征机制[90, 91, 92, 93],为基于脑的情绪功能及相关障碍的综合评估、神经调控及其临床应用提供科学依据[89,94]。同时,发展心理学、情绪神经科学和行为遗传学等技术方法有机融合,为解析个体情绪与认知发展规律及其背后的认知神经生物机制[95, 96],以及遗传与环境交互作用带来新的变革[97, 98]。这方面研究不仅有利于揭示情绪发生发展的认知神经机制,而且为理解情绪相关障碍产生机理提供了新途径,有望为情绪问题及其相关障碍的早期鉴别,诊断与干预提供科学依据。

       在社会决策领域,神经科学、认知心理学与计算科学的方法与技术的融合从“单脑-多脑-干预”三个层面系统揭示了社会决策的认知神经机制。“单脑”层面的研究工作结合计算建模与脑成像技术,揭示了个体做出亲社会决策的认知、计算和神经基础,提出了亲社会决策的计算模型——社会参照点模型,并表明利他型、利己型个体在杏仁核和眶额叶皮层神经活动的个体化差异,解释了人类复杂的社会决策及个体差异[99]。“多脑”层面的研究工作利用高生态互动范式创设群体合作与竞争情境,并结合超扫描技术同步记录多人神经信号,发现群体成员在背外侧前额叶和颞顶联合区神经活动锁相的同步特征,并揭示群体成员在背外侧前额叶神经活动的同步抑制是群体冲突的关键神经机制[100]。基于上述工作所揭示的认知神经机制,“干预”层面的研究工作主要从药理调控和认知干预两个角度开展,其研究结果揭示催产素可调控个体对自我-他人利益的主观价值计算和大脑杏仁核活动,提升亲社会行为[99],可优化个体的社会反馈学习[101, 102],可提升群体内部成员的合作[103, 104],可有效促进合作行为在大规模社会网络中的传播[105]。以上研究发现验证了催产素影响社会决策的整合性框架,即“催产素的社会适应模型”[106]。从认知层面,发现了社会安慰剂效应[107],揭示了个体通过实验操作,如:服用惰性药物并获得积极的认知信念后,可以拉近个体社交距离、提升信任与合作行为。

3 基于fMRI的脑发育及脑老化研究

       儿童时期是个体运动、认知、情绪和社会能力发展的关键阶段,同时伴随着脑结构和功能的快速发育,随着fMRI技术及其数据分析方法的发展,fMRI被越来越多地应用于人脑功能发育的研究中。默认网络是人脑最为核心的功能系统之一,参与并支持复杂的认知功能,如自我参照、自传体记忆、社会推断等内在心理过程。探索儿童青少年期脑默认网络的发育规律对于理解个体认知发展的神经机制具有重要启发。Fan等[108]从连接、全局和节点水平系统刻画默认网络功能组织模式在儿童青少年期的发育轨迹。研究发现儿童青少年默认网络功能连接的空间分布模式在连接水平逐渐趋于成人,网络内全局和局部效率随年龄显著增加。默认网络内部在发育进程中呈现出功能分化,可识别出三个分离的子系统。人脑功能组织具有层级化加工的模式,支持了从初级感知觉信息到高级认知的信息编码过程。但初级皮层到高级皮层的核心连接梯度在新生儿脑中并尚未出现,提示了其与脑发育过程的密切关系。最近,基于rs-fMRI数据和脑功能连接梯度分析框架,Xia等[109]基于两个独立研究中心的6~12岁的大样本儿童青少年rs-fMRI脑影像数据系统揭示了儿童青少年期人脑功能网络连接梯度的发育规律,及其与认知发展及基因表达的关联,发现初级皮层-联合皮层梯度模式在儿童早期已经出现,并在8~11岁期间从次级梯度上升为首要梯度,初级皮层-联合区域梯度的全局指标以及区域分数都具有显著的年龄效应,梯度分数变化主要位于默认网络、感觉运动区域以及视觉皮层,并与个体的工作记忆任务表现显著正相关。最后,通过连接组-转录组联合分析,发现核心连接梯度的发育模式与化学突触传递调控等生物过程相关的基因表达水平具有紧密的联系。同期,Dong等[110, 111]基于“彩巢计划”项目纵向加速队列,也研究了个体从儿童到青少年的发育过程中大脑功能梯度的变化趋势[112],发现儿童时期个体主梯度的两端主要位于感觉运动皮层及视觉皮层,随着年龄的增加,主梯度转变为初级感觉运动区域-默认网络的类成年分布模式,其中13~14岁为这一转变发生的关键时期。此外,儿童青少年时期个体运动、认知和社交能力的发展极大依赖于脑网络动态特性的发育。探索儿童青少年期脑功能动态网络的发育规律,揭示其与微观基因表达谱的关联,对于理解个体认知发展的生物学机制具有重要价值。Lei等[15]基于多层脑网络模型研究儿童青少年期脑功能网络模块化组织的动态特性,系统揭示了脑功能网络动态特性的发育模式及其与认知发展和微观基因表达的关系。研究发现,儿童青少年脑功能网络的模块化结构呈现时变特性,在不同模块间频繁切换的节点主要位于联合皮层;随着年龄的增长,脑节点在模块间切换特性随年龄显著下降,主要位于默认网络、额顶网络以及感觉运动网络,涉及自我参照、社会认知以及运动等功能。基于脑连接组-转录组联合分析,发现脑区动态特性发育速率的空间分布与离子转运和含碱基化合物转运相关的基因表达谱有显著关联。

       在认知老化与脑老化领域,北京老年脑健康促进计划(Beijing Aging Brain Rejuvenation Initiative, BABRI)结合多模态磁共振脑成像、机器学习等技术,通过大样本社区研究与长期追踪随访,采用加速追踪研究设计,建立了超万人的中国本土老年脑健康数据库,涵盖了以多模态影像数据为核心的八个类别超过四十项指标[113],建立中国人群认知老化常模,构建脑健康体检神经心理测评体系,搭建脑健康体检关键技术平台[114]。基于大样本数据,BABRI探究了正常老化人群的脑结构连接组拓扑效率老化机制[115],公布了中国MCI的社区流行病学数据[116],探讨了MCI不同亚型的脑影像特征[117, 118, 119],系统地揭示了MCI人群风险因素和保护因素的脑影像学机制:阐明了痴呆高危基因如APOEe4[120, 121, 122, 123, 124]、Sorl1等[125]对大脑功能与结构的作用机制;着重研究了社区老年人群常见血管源性慢病的认知减退机制,揭示了2型糖尿病[126, 127, 128]、高血压[129, 130, 131]、隐匿性中风[132, 133]等疾病对大脑功能结构网络的特异性损伤,探究了教育[134]等因素对于大脑的保护路径;对MCI的中医药干预疗效进行了深入系统研究,建立起以神经影像为核心的中医药药效评估研究模式[135, 136],完成为期两年的中医药干预临床研究[137]。这些发现有助于进一步研究中国老年人群认知衰退规律,促进老年脑健康、预防认知障碍及相关重大脑疾病防治,为认知障碍干预改善新方法提供了新视角。

4 基于fMRI的脑疾病研究

       关于神经精神疾病,过去40年内的神经科学研究旨在揭示疾病的发病机理。然而,目前针对疾病的诊断和治疗依然缺乏客观有效的生物标记用于开发新型临床诊治手段以提高患者的诊断准确率和治疗有效率。基于MRI技术,并结合精神病理学、神经生物学以及分子生物学,目前的研究已先后发现SCZ、MDD、双相情感障碍(bipolar disorder, BD)以及神经发育性疾病等多种神经精神疾病的潜在影像学标记。同时,对大脑功能、生理学和生物化学进行活体评估的技术的发展和改进,以及从高维数据中提取关键细节的数据分析方法的进步,使得该领域的研究进展往精准医学进一步靠拢[138]。将神经影像研究发现转换用于临床实践的关键在于利用多模态MRI技术定位并精确测量可以用于疾病诊断[139, 140, 141]、生物学分类[142, 143, 144],以及预测/追踪治疗效果[145, 146]的微小脑影像学标记。国内自1999年起已开展了ADHD的fMRI研究,并在国际上较早发表ADHD的rs-fMRI的相关研究成果[147, 148]。如:采用功能连接的方法发现ADHD青少年的前扣带回功能连接存在异常,使用ReHo分析方法发现ADHD的额叶-纹状体-小脑环路异常。在国际上,首次使用rs-fMRI的数据探讨ADHD的小世界属性的异常[149],以及对ADHD进行诊断的模式判别[150],发现使用“留一法”对ADHD的ReHo数据分类判断ADHD的正确率可以达到85%。此后开展的药物影像学研究发现,ADHD的药物治疗可以使ADHD的静息态脑功能指标趋于正常化[151, 152]。此外,对于弥散张量成像的数据,首次使用基于概率追踪的方法探讨ADHD的小世界属性,发现ADHD存在全局效率的下降以及局部效率增强[153],为揭示ADHD脑功能网络的异常提供了结构基础的解释。

       SCZ是一种机制未明的重性精神疾病,具有高复发性、高致残性和高异质性等特点,给国家和社会带来了沉重的负担。然而,SCZ目前仍然依靠症状诊断和经验治疗,临床亟需精准、稳定且具有生物学意义的客观诊疗生物学标记和治疗靶点。非侵入式多模态脑成像结合人工智能等计算技术,可帮助在体观测SCZ的脑结构与功能异常,为发现客观定量的生物学标记物提供了可能,同时与基因组与多组学技术的融合可能为理解SCZ的病理机制开辟了新的途径。结合多中心的fMRI和人工智能技术,Li等[154]提出了一种多层次的系统研究框架,发现了一个表征纹状体功能异常的全新SCZ影像学标记物,在不同种族、多个独立中心、不同磁共振机型上均验证了该标记物可用于SCZ的精准识别、预后评估和临床分层,进而结合多组学的机制解析帮助理解SCZ的发病机制。在影像学研究的基础上,如何结合基因组等遗传信息理解疾病病理机制及建模是另一重要的科学问题。系列SCZ的影像基因组学研究,发现了多个精神疾病易感基因与特定神经环路的关联[155, 156],基于全基因组风险的研究报道了遗传高风险个体具有类疾病的脑连接模式[157, 158, 159]。Hu等[160]进而通过结合神经影像特征与遗传风险,发展了基于机器学习的分类与预测模型,表明多层次融合模型为SCZ的多中心大样本分类和抗精神病药物治疗结果提供更准确的预测能力。SCZ的多模态神经影像学和影像基因组学研究为疾病的发病机制提供重要科学依据,同时也正为疾病客观诊断和个体化疗效预测提供有临床前景的生物学标记物。然而,如何将相关科学发现真正纳入未来的疾病诊断与分类标准或日常临床实践中,仍需持续深入的研究以及多方面的临床验证。

       尽管关于MDD病理生理学的神经影像学文献越来越多,但缺乏可重复的发现,这可能主要由于分析方法的差异和小样本量引起的样本异质性。为了解决这些问题,国内学者发起了我国的MDD脑影像大数据建设,如疾病影像数据库计划-抑郁症工作组(Disease Imaging Data Archiving project-MDD workgroup, DIDA-MDD)数据库[161, 162]、抑郁症脑影像大数据联盟(Depression Imaging ResearchConsortium,DIRECT)[163],展开MDD脑影像多中心大数据合作研究。基于DIDA-MDD的rs-fMRI多中心大数据库,Xia等[161]系统评价了脑影像预处理方法、统计模型、多中心校正算法等关键分析策略对识别MDD异常脑功能核心节点的影响,发现了初级皮层和联合皮层的内/外侧额顶功能活动异常稳定的可重复性,为MDD脑影像连接组标志物研究提供了可重复性验证框架和关键的方法学指导。进一步,他们构建了MDD患者的高精度脑功能连接网络模型。揭示了MDD患者脑网络首要连接梯度收缩、功能系统连接模式的异质性降低的脑网络异常新机制;通过连接组-转录组联合多维融合分析,发现MDD连接梯度异常与跨突触信号传递、钙离子结合相关基因表达的关联;基于机器学习方法,以患者在基线期时脑功能网络的首要连接梯度为特征,建立了抗抑郁药物(选择性5-羟色胺再摄取抑制剂)治疗8周后临床症状改善的预测模型,揭示了药物对背内侧/外侧前额叶、海马的关键连接通路不同的调控作用[162,164]。DIRECT联盟开展了针对MDD的rs-fMRI多中心数据荟萃分析计划,发现MDD患者默认网络内部的功能连接相对健康对照显著下降,且这种下降效应主要是由复发患者贡献的,并且可能反映了抗抑郁药物的作用[165]。此外,联盟还发现MDD患者脑网络的全局效率和局部效率均异常降低,并且这种降低也主要是由复发患者贡献的,可能与他们长期服用抗抑郁药物有关[166]。这些研究成果为MDD脑网络初级系统和高级系统的紊乱机制提供了新的理解,表明高精度脑连接大数据计算方法及脑网络核心节点和关键通路在MDD等精神疾病的治疗评估中具有重要的临床价值,有望改进MDD脑影像引导的精准治疗,为MDD患者带来福音。

       MRI技术的快速发展推动了精神医学领域寻找客观生物学标志指导临床诊疗。MDD、BD和SCZ作为三种常见的重性精神疾病,其共性的影像学机制尚未清晰。前期大量单一比较研究显示三种重性精神疾病共同存在前额叶和皮层下区域功能的异常升高及后脑(初级感知觉中枢)功能的异常降低;在此基础上,跨疾病的直接比较研究进一步明确了三种重性精神疾病存在共有的核心神经生物学损害[167, 168, 169, 170],呈现共性的脑网络随机化紊乱连接模式,以及默认网络、额顶网络和感知觉网络等系统间的过度连接,特别是背外侧前额叶作为精神疾病神经调控关键靶点在三种精神疾病中存在共性的跨系统过度整合[169,171]。基于不同疾病间的共性异常,研究结合深度学习方法的研究跨诊断地识别出重性精神疾病前额叶-后脑功能不平衡的典型及非典型影像学亚型,并在多模态影像学、遗传及临床特征层面进行验证[172, 173],在MDD中,将典型亚型的主导特征前额叶,和非典型亚型主导特征枕叶分别作为靶点进行早期个体化精准经颅磁刺激治疗后,患者的影像学异常及临床症状均显著改善。研究成果建立起来的前额叶-后脑功能不平衡引导的分型诊断及个体化治疗策略,将有助于精神疾病构建基于客观影像学标志的精准医学框架,具有重要临床及科研价值。

       AD是一种高发的神经退行性疾病,累及多个脑区及其连接构成的脑网络。Li等[174]提出利用单中心统计确定差异、多中心二次统计明确稳定异常特征的方法,明确了AD脑活动异常表征的脑内分布模式,发现AD脑活动的一致性改变和能量谱变化模式可以用来预测个体风险。Liu等[175, 176]引入空间距离约束的效率度量,发现AD脑长距离连接损害越严重,脑网络信息传递效率越低。在此基础上,Jin等[177]从多中心、多维度的角度揭示AD的脑功能网络的全局组织结构紊乱特征和信息传递效率下降模式,进一步明确了这些脑功能异常与AD认知能力、病理特征、代谢异常的关系,并利用多变量分析方法和独立数据集,评估了AD的功能异常模式作为早期识别的影像指标的可靠性和泛化性。Dai等[178]发现AD不仅会有选择性地损坏人脑功能网络核心区域,例如默认网络、突显网络和执行控制网络内功能连接减弱,也可能导致患者脑动态功能连接的异常,主要是额叶和颞叶区域,以及更低的脑区时间变化性[179]。基于多模态MRI数据,Dai等[180]进一步发现AD患者的脑功能和结构网络的耦合增强,这可能意味着患者脑功能网络灵活性的降低。Chen等[181]基于多中心大样本定义了稳定可重复的AD亚型,为AD的异质性研究提供了新视角,有望在未来利用它们为临床决策提供信息。此外,基于结构和功能网络属性可以对AD患者与健康对照组进行有效地区分,分类正确率达到89.47%[182]。上述研究从脑活动、脑连接到脑网络拓扑结构,从单个中心探索性对比研究到多个中心独立交互验证,明确了AD在脑活动、脑连接和脑网络上的异常表征,有望能为AD患者的早期识别提供新线索和新方法,并为AD的精准诊疗和干预奠定理论基础。

       我国学者于2009年在国际上首先发表基于rs-fMRI的PD相关研究,发现PD患者基底节运动环路脑区ReHo降低[183]。此后利用ALFF和功能连接进行了系列rs-fMRI研究,揭示了PD运动、非运动症状相关神经机制[184, 185],结合模式分析建立了PD相关空间协变性模式,主要表现为纹状体、辅助运动区、枕叶、桥脑神经活动降低,及顶叶、丘脑、小脑神经活动升高,该模式判断PD的正确率超过90%[186]。此外,利用任务态fMRI阐释了PD运动自动化、序列效应、协调运动等运动缺陷的神经机制[187, 188, 189],首次明确了小脑在PD病生理机制中具有重要作用[190],为了解PD发病机制和研发有效治疗靶点提供了依据。

       癫痫是临床第二多发的神经系统疾病,是一种异常自发神经电活动导致的反复发作的脑功能障碍性。针对癫痫临床诊断及病生机制理论研究需求,fMRI在以下几个方面得到应用。首先是辅助癫痫活动的检测和定位。fMRI可以检测间期痫样发放引起的BOLD信号的改变。最经典的是采用同步脑电图(electroencephalogram, EEG)采集痫样发放活动,通过广义线性模型或独立成分分析,对癫痫活动进行定位检出。并且,多种rs-fMRI分析指标,如ALFF、ReHo、功能连接密度等,都可以反映癫痫活动发放引起的fMRI信号改变而达到定位效能[191, 192]。相比同步EEG-fMRI方法,rs-fMRI具有简便易行的优点,指标丰富且通过多个指标组合提高检出效能。整体上,fMRI辅助常规结构影像阴性的局灶性癫痫定位诊断,并增进全面性癫痫起源机制的理解[193]。其次是对反映癫痫活动传播的癫痫网络进行描绘。相比电生理测量,fMRI具有真实的空间信息和适宜的时空分辨率。借助多种脑连接分析技术,研究发现颞叶癫痫[193]、原发全面强直阵挛癫痫等成人癫痫以及失神性癫痫[194]、Rolandic癫痫等儿童癫痫各自的特异的癫痫网络模式,揭示不同癫痫发作类型时特异脑功能损害的特征[17,195],并指出丘脑等皮层下结构在癫痫传播过程中的关键作用[19,196]。利用以上fMRI描绘的癫痫局域及网络活动的特征,结合深度学习分析技术,可以构建用于不同癫痫类型鉴别诊断的模型[196, 197]。并且,也可以利用fMRI定量反映癫痫活动以及正常脑功能的强度,用来观察抗癫痫药物对癫痫抑制和脑功能活动的影响效果。

       fMRI对脑肿瘤外科手术亦具有重大价值。术前定位和识别大脑功能皮质和皮质下通路对于在累及功能区的神经外科手术至关重要。由于目前临床上对脑功能的保护主要侧重于运动和语言,因此国内外神经外科医生过去的二十年间一直围绕运动区、语言区的定位以及运动通路和语言通路的构建识别开展相关工作。2005年吴劲松等[198]在国内最早进行了任务态fMRI运动定位与术中直接电刺激的前瞻对照研究,发现运动BOLD与运动诱发电位的吻合率为92.3%。同期,郎黎琴等[199]采用词汇联想任务比较了语言BOLD与电刺激的准确性,发现语言BOLD的敏感度仅为48.1%。国外研究者在5年后进行的综述研究印证了这些发现。德国的Metwali等[200]2019年进行了一项系统分析比较了fMRI和术中电刺激在脑肿瘤手术中的应用。他们的结果显示运动区定位的平均敏感度和特异度分别为92%(95%置信区间87.5%~100%)和76%(95%置信区间68.1%~87.1%)。意大利的Giussani等[201]进行的一项综述纳入了5项相关研究报道脑肿瘤语言定位的敏感度从59%~100%,特异度从0%~97%。这意味着fMRI在语言区定位中的变异很大,这可能受到任务范式不一致、语言网络复杂等因素的影响。法国的Kuchcinski等[202]通过比较40例脑胶质瘤患者直接皮层刺激(direct cortical stimulation, DCS)和fMRI的结果,发现fMRI在语言区定位中的敏感度仅为37.1%,特异度为83.4%。Metwali等[202]进行的系统分析纳入6项研究发现语言区定位的平均敏感度和特异度分别为80%(95%置信区间64%~100%)和71.5%(95%置信区间50%~89%)。显然,fMRI在语言区中的定位有效性低于运动区。除此之外,rs-fMRI也在神经外科的临床中得到应用。Zhang等[203]基于rs-fMRI数据,发现胶质瘤患者在小脑的语言功能区具有显著的局部活动和功能连接的异常。Qiu等[204]先后采用术中电刺激验证了静息态功能连接用于运动和语言区定位的有效性,发现对于手结运动区,静息态功能连接的敏感度和特异度分别为90.91%和89.41%;对于语言皮质定位,静息态功能连接的敏感度仅为47.8%[205]。此后,Lu等[206]报道静息态独立成分分析法进行语言定位的敏感度也仅为60.9%。这意味着不论任务态fMRI还是rs-fMRI,在运动定位中其定位准确性均较高,但对于语言区定位,两种方式的临床应用仍需要进一步研究验证。

5 小结和展望

       我国学者在fMRI脑影像及其分析技术和平台,以及fMRI脑影像在脑认知、脑发育、脑老化、脑重大疾病等脑科学领域取得了诸多重要进展,考虑到论文篇幅限制不能进一步阐述。未来,脑功能成像领域仍存在几个值得关注的发展方向:首先,随着国内外越来越多的大型脑计划项目的实施和数据库的开放共享,fMRI脑影像研究已进入大数据时代[207],多中心脑影像数据的质量控制、校正问题对研究结论的准确性和可重复性起到关键的影响作用。最近一些基于多元成分分析、深度学习校正方法为这些问题的解决提供了重要研究思路[208, 209]。其次,现有研究采用的磁共振机型场强大多为3 T,在fMRI数据获取的时空分辨率、信噪比之间已基本达到平衡极限,高场强(如7 T)机型已开始用于获取更高时空分辨率的高质量fMRI影像[210],能够提供更丰富的脑局部解剖信息和更接近真实脑活动的功能信号,推进更细粒度的脑功能研究。再次,虽然基于fMRI的脑疾病研究已经揭示了很多群体水平的脑异常机制,然而考虑到患者具有较大的个体差异,个体化fMRI分析已开始为脑疾病临床定准诊断及定位治疗提供方案,如指导深部脑刺激治疗、精准定位的经颅磁刺激治疗等[211]。未来的临床应用亟需进一步开展脑疾病中的个体化图谱绘制、基于标准模型的个体偏移定准,以及脑疾病生物学亚型的研究。最后,fMRI脑功能影像研究也孵化了一批具有脑科学特色的科技公司,在脑影像数据分析、脑疾病治疗等细分领域积极引领了脑科学产业化的进步。这些高科技公司的出现吸引了民间资本的投入和公众的关注,将会大力促进fMRI脑影像领域产学研用全链条的发展。

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