功能磁共振脑影像学
通讯作者:
Online: 2020-12-15
作者简介 About authors
严超赣,中国科学院心理研究所研究员,博士生导师,心理所抑郁症大数据国际研究中心主任,心理所磁共振成像研究中心主任,入选爱思唯尔 2019年中国高被引学者(神经科学)。主要研究领域为静息态功能磁共振方法学、数据分析软件平台、脑自发活动机制及其在抑郁症中的应用。在国际主流学术期刊(如PNAS, Cerebral Cortex, NeuroImage, Human Brain Mapping, Translational Psychiatry, Neuroinformatics等)发表学术论文60余篇,其中30余篇为第一作者和(或)通讯作者,论文总被引1.2万余次,h指数33(Google Scholar)。第一/通讯作者论文中,有5篇入选ESI Top 1%高被引论文,其中更有2篇入选ESI Top 1‰高被引论文。为NeuroImage: Reports创刊副主编,NeuroImage和Journal of Neuroscience Methods编委,国际人脑图谱学会通讯委员会(OHBM Communications Committee)委员。
本文引用格式
严超赣, 李雪莹, 鲁彬.
针对上述阻碍脑影像在精神疾病领域应用与发展的问题,我们从脑影像方法学、脑影像数据分析平台、脑影像大数据在精神疾病中的应用三个方面入手,开展抑郁症脑影像学研究。我们进行了一系列脑影像方法学研究工作,提出了多种脑影像方法学问题的解决方案;在此基础上建立了DPABI/DPABISurf/DPARSF脑影像数据分析平台;并进一步牵头建立了抑郁症脑影像大数据联盟(REST-meta-MDD),以期突破“小样本”瓶颈,开启精神疾病脑影像研究大数据合作共享的新模式。
首先,面对脑影像领域的方法学挑战,我们尝试对困扰脑影像领域的多个问题提出了解决方案,并使之成为国际标准。脑功能影像方法学的一大挑战是被试的头动。被试在磁共振扫描仪中的微小头动(小至0.2 mm)会在功能连接图像中产生伪迹[7,8,9]。这使得研究中发现的脑自发活动组间差异有时很可能仅是由头动伪迹造成的,反映的并非神经活动。我们在头动伪迹问题的研究中,提出并考察了多个头动校正模型对头动伪迹的控制作用,最终发现在个体水平上进行24参数头动回归,且在组分析上进行头动协变量控制,可有效地控制头动噪声、排除头动伪迹[10]。该解决方案有效地缓解了领域内对头动伪迹的焦虑,被认为是未来短期到中期内的领域标准[11],研究入选ESI Top 1‰高被引论文。
脑影像计算方法的另一大挑战是多重比较校正。由于大脑有几万个体素,如果不进行有效的多重比较校正,极易出现假的阳性结果,得到完全不可信的结论。Eklund等人曾在PNAS上发表论文[12],指出当时通行于脑影像领域的多重比较校正方法存在严重缺陷,具有极高的假阳性,甚至认为过去的4万项脑功能影像研究的结果都可能存在问题。由此,我们对31种多重比较校正方法的假阳性进行了比较研究(图1),并提出除假阳性之外,结果的可重复性也是评价多重比较校正方法是否适用的重要指标。最终得出结论,基于无阈限团块增强的置换检验(permutation test with TFCE)方法,能够很好地将假阳性率控制在5%以下,并且具有最高的可重复性,这项工作已于2018年发表[13]。该论文提出的解决方案有效地缓解了领域内对多重比较校正的担忧,得到了国际同行的广泛关注和引用,入选ESI Top 1%高被引论文。
图1
此外,针对来自不同研究医院和扫描仪的数据具有较强异质性的问题,我们提出的通过后处理均值回归和标准差商除的标准化方法有效地控制了站点效应[14],该方案入选ESI Top 1%高被引论文。在静息态功能成像计算方法方面,我们还开发了功能一致性(functional concordance)的新指标,衡量脑影像度量大脑不同功能层面的整合程度[15];提出了功能稳定性新指标,揭示了稳定性指标在大脑不同网络间分布的规律[16],这些指标有望为开发心理状态检查和疾病诊断的神经标记物提供参考。这些脑影像方法学的解决方案和原创性的计算方法,提高了脑影像研究结果的可靠性,已成为被领域内同行广泛接受的国际通用标准,并且为我们后续开发的脑影像计算平台中的标准化数据处理流程提供了可信的理论基础与指导。
在积累了大量脑影像方法学工作成果的基础上,我们对这些方法学研究成果进行了整合,开发了流水线式脑影像数据分析软件平台DPARSF[17]和脑成像数据分析工具包DPABI[18]。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,对磁共振成像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,通过我们开发的一站式解决方案计算出最终的静息态功能指标。最近,我们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。
DPABI/DPABISurf/DPARSF为研究者提供了可靠易行的标准化数据处理流程,使得研究者们可以快速简便地将这些经过验证的脑影像方法学解决方案应用到自己的研究中。DPABI论文于2016年发表于Neuroinformatics,入选ESI Top 1‰高被引论文,并入选由中国科学院文献情报中心发布的2015–2019年中国热点论文榜,在医学领域研究论文中获得被引用数最多的中国学者论文排名的第10名。我们对DPABI软件平台进行持续的更新和维护,定期举办特训营,并发布免费教学课程,建设交流论坛,长期为DPABI软件使用者和领域内研究者提供技术支持。DPABI/DPABISurf/DPARSF的大规模应用推动了领域内大量研究采用经过验证的高标准分析流程,减少了虚假结果和噪声伪迹,从而改善了领域实践,也为进一步采用严谨的多中心方法开展精神疾病的脑影像大数据研究打下坚实的基础。
重性抑郁障碍常被称为抑郁症,是一种具有高复发率、高自杀率和高致残率的精神疾病,在全球有近3亿患者[19]。由于磁共振检查价格高昂(数千元每人每小时)等原因,目前脑影像学研究的样本量都很小(<100),容易出现统计力不足、研究结果相互抵触等问题。为了解决小样本问题,借助在脑影像方法学和软件上的积累,我们联合DPABI/DPARSF的国内临床用户及精神科专家,牵头建立了抑郁症脑影像大数据联盟(REST-meta-MDD)。目前,联盟已成功汇聚了来自17家国内医院和大学的25个抑郁症研究组的1 300例抑郁症患者和1 128例正常对照数据,2020年1月1日起,汇聚的脱敏数据已经向全球研究者开放,是目前世界上最大的可公开获取的抑郁症功能磁共振脑影像数据库,已有包括斯坦福大学(Stanford University)和北卡罗来纳大学(University of North Carolina)在内的100余家研究单位申请并获批使用。通过这种大数据合作模式,我们发现了默认网络功能连接在复发MDD患者中减低[20]。这种减低可由用药治疗导致,为未来发展MDD诊断治疗评估的客观指标奠定了基础(图2)。研究成果发表在PNAS上[20],入选ESI Top 1%高被引论文。
图2
回顾我们在功能磁共振脑影像学方面的研究,我们从磁共振脑影像方法学入手,搭建了广受欢迎的脑影像数据处理平台。现如今,抑郁症发病率越来越高,对社会发展和家庭幸福产生越来越严重的障碍和负担。我们将目光聚焦于抑郁症,希望通过我们在方法学和数据平台上的积累,借助医疗影像大数据,服务于数字医疗,推进功能磁共振脑影像真正走向临床。未来,在大数据的基础上,借助当前方兴未艾的机器学习和深度学习方法,有望通过功能磁共振脑影像技术建立具有临床实用价值的精神疾病影像生物学指标。
参考文献
Sex beyond the genitalia: The human brain mosaic
[J]. ,
What we can do and what we cannot do with fMRI
[J]. , - 1476-4687 (Electronic):
Twenty years of functional MRI: The science and the stories
[J]. ,Since its inception over twenty years ago, the field of functional magnetic resonance imaging (fMRI) has grown in usage, sophistication, range of applications, and impact. After twenty years, it's useful to briefly look back as well as forward - to size up just how far we have come and speculate just how far we may go. This is an introduction to the special issue of
Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging
[J]. ,The majority of functional neuroscience studies have focused on the brain's response to a task or stimulus. However, the brain is very active even in the absence of explicit input or output. In this Article we review recent studies examining spontaneous fluctuations in the blood oxygen level dependent (BOLD) signal of functional magnetic resonance imaging as a potentially important and revealing manifestation of spontaneous neuronal activity. Although several challenges remain, these studies have provided insight into the intrinsic functional architecture of the brain, variability in behaviour and potential physiological correlates of neurological and psychiatric disease.
Biomarker studies and the future of personalized treatment for depression
[J]. ,
Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion
[J]. ,
Impact of in-scanner head motion on multiple measures of functional connectivity: relevance for studies of neurodevelopment in youth
[J]. ,It has recently been reported (Van Dijk et al., 2011) that in-scanner head motion can have a substantial impact on MRI measurements of resting-state functional connectivity. This finding may be of particular relevance for studies of neurodevelopment in youth, confounding analyses to the extent that motion and subject age are related. Furthermore, while Van Dijk et al. demonstrated the effect of motion on seed-based connectivity analyses, it is not known how motion impacts other common measures of connectivity. Here we expand on the findings of Van Dijk et al. by examining the effect of motion on multiple types of resting-state connectivity analyses in a large sample of children and adolescents (n=456). Following replication of the effect of motion on seed-based analyses, we examine the influence of motion on graphical measures of network modularity, dual-regression of independent component analysis, as well as the amplitude and fractional amplitude of low frequency fluctuation. In the entire sample, subject age was highly related to motion. Using a subsample where age and motion were unrelated, we demonstrate that motion has marked effects on connectivity in every analysis examined. While subject age was associated with increased within-network connectivity even when motion was accounted for, controlling for motion substantially attenuated the strength of this relationship. The results demonstrate the pervasive influence of motion on multiple types functional connectivity analysis, and underline the importance of accounting for motion in studies of neurodevelopment.
The influence of head motion on intrinsic functional connectivity MRI
[J]. ,Functional connectivity MRI (fcMRI) has been widely applied to explore group and individual differences. A confounding factor is head motion. Children move more than adults, older adults more than younger adults, and patients more than controls. Head motion varies considerably among individuals within the same population. Here we explored the influence of head motion on fcMRI estimates. Mean head displacement, maximum head displacement, the number of micro movements (>0.1 mm), and head rotation were estimated in 1000 healthy, young adult subjects each scanned for two resting-state runs on matched 3T scanners. The majority of fcMRI variation across subjects was not linked to head motion. However, head motion had significant, systematic effects on fcMRI network measures. Head motion was associated with decreased functional coupling in the default and frontoparietal control networks--two networks characterized by coupling among distributed regions of association cortex. Other network measures increased with motion including estimates of local functional coupling and coupling between left and right motor regions--a region pair sometimes used as a control in studies to establish specificity. Comparisons between groups of individuals with subtly different levels of head motion yielded difference maps that could be mistaken for neuronal effects in other contexts. These effects are important to consider when interpreting variation between groups and across individuals.
A comprehensive assessment of regional variation in the impact of head micromovements on functional connectomics
[J]. ,Functional connectomics is one of the most rapidly expanding areas of neuroimaging research. Yet, concerns remain regarding the use of resting-state fMRI (R-fMRI) to characterize inter-individual variation in the functional connectome. In particular, recent findings that
Methods to detect, characterize, and remove motion artifact in resting state fMRI
[J]. ,
Cluster failure: Why fMRI inferences for spatial extent have inflated false-positive rates
[J]. ,
Reproducibility of R-fMRI metrics on the impact of different strategies for multiple comparison correction and sample sizes
[J]. ,Concerns regarding reproducibility of resting-state functional magnetic resonance imaging (R-fMRI) findings have been raised. Little is known about how to operationally define R-fMRI reproducibility and to what extent it is affected by multiple comparison correction strategies and sample size. We comprehensively assessed two aspects of reproducibility, test-retest reliability and replicability, on widely used R-fMRI metrics in both between-subject contrasts of sex differences and within-subject comparisons of eyes-open and eyes-closed (EOEC) conditions. We noted permutation test with Threshold-Free Cluster Enhancement (TFCE), a strict multiple comparison correction strategy, reached the best balance between family-wise error rate (under 5%) and test-retest reliability/replicability (e.g., 0.68 for test-retest reliability and 0.25 for replicability of amplitude of low-frequency fluctuations (ALFF) for between-subject sex differences, 0.49 for replicability of ALFF for within-subject EOEC differences). Although R-fMRI indices attained moderate reliabilities, they replicated poorly in distinct datasets (replicability < 0.3 for between-subject sex differences, < 0.5 for within-subject EOEC differences). By randomly drawing different sample sizes from a single site, we found reliability, sensitivity and positive predictive value (PPV) rose as sample size increased. Small sample sizes (e.g., < 80 [40 per group]) not only minimized power (sensitivity < 2%), but also decreased the likelihood that significant results reflect
Standardizing the intrinsic brain: towards robust measurement of inter-individual variation in 1000 functional connectomes
[J]. ,As researchers increase their efforts to characterize variations in the functional connectome across studies and individuals, concerns about the many sources of nuisance variation present and their impact on resting state fMRI (R-fMRI) measures continue to grow. Although substantial within-site variation can exist efforts to aggregate data across multiple sites such as the 1000 Functional Connectomes Project (FCP) and International Neuroimaging Data-sharing Initiative (INDI) datasets amplify these concerns. The present work draws upon standardization approaches commonly used in the microarray gene expression literature, and to a lesser extent recent imaging studies, and compares them with respect to their impact on relationships between common R-fMRI measures and nuisance variables (e.g., imaging site, motion), as well as phenotypic variables of interest (age, sex). Standardization approaches differed with regard to whether they were applied post-hoc vs. during pre-processing, and at the individual vs. group level; additionally they varied in whether they addressed additive effects vs. additive + multiplicative effects, and were parametric vs. non-parametric. While all standardization approaches were effective at reducing undesirable relationships with nuisance variables, post-hoc approaches were generally more effective than global signal regression (GSR). Across approaches, correction for additive effects (global mean) appeared to be more important than for multiplicative effects (global SD) for all R-fMRI measures, with the exception of amplitude of low frequency fluctuations (ALFF). Group-level post-hoc standardizations for mean-centering and variance-standardization were found to be advantageous in their ability to avoid the introduction of artifactual relationships with standardization parameters; though results between individual and group-level post-hoc approaches were highly similar overall. While post-hoc standardization procedures drastically increased test-retest (TRT) reliability for ALFF, modest reductions were observed for other measures after post-hoc standardizations a phenomena likely attributable to the separation of voxel-wise from global differences among subjects (global mean and SD demonstrated moderate TRT reliability for these measures). Finally, the present work calls into question previous observations of increased anatomical specificity for GSR over mean centering, and draws attention to the near equivalence of global and gray matter signal regression. (C) 2013 Elsevier Inc.
Concordance among indices of intrinsic brain function: insights from inter-individual variation and temporal dynamics
[J]. ,
Stability of dynamic functional architecture differs between brain networks and states
[J]. ,
DPARSF: A MATLAB toolbox for "pipeline" data analysis of resting-state fMRI
[J]. ,Resting-state functional magnetic resonance imaging (fMRI) has attracted more and more attention because of its effectiveness, simplicity and non-invasiveness in exploration of the intrinsic functional architecture of the human brain. However, user-friendly toolbox for
DPABI: Data processing & analysis for (resting-state) brain imaging
[J]. ,Brain imaging efforts are being increasingly devoted to decode the functioning of the human brain. Among neuroimaging techniques, resting-state fMRI (R-fMRI) is currently expanding exponentially. Beyond the general neuroimaging analysis packages (e.g., SPM, AFNI and FSL), REST and DPARSF were developed to meet the increasing need of user-friendly toolboxes for R-fMRI data processing. To address recently identified methodological challenges of R-fMRI, we introduce the newly developed toolbox, DPABI, which was evolved from REST and DPARSF. DPABI incorporates recent research advances on head motion control and measurement standardization, thus allowing users to evaluate results using stringent control strategies. DPABI also emphasizes test-retest reliability and quality control of data processing. Furthermore, DPABI provides a user-friendly pipeline analysis toolkit for rat/monkey R-fMRI data analysis to reflect the rapid advances in animal imaging. In addition, DPABI includes preprocessing modules for task-based fMRI, voxel-based morphometry analysis, statistical analysis and results viewing. DPABI is designed to make data analysis require fewer manual operations, be less time-consuming, have a lower skill requirement, a smaller risk of inadvertent mistakes, and be more comparable across studies. We anticipate this open-source toolbox will assist novices and expert users alike and continue to support advancing R-fMRI methodology and its application to clinical translational studies.
Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017
[J]. ,BACKGROUND: The Global Burden of Diseases, Injuries, and Risk Factors Study 2017 (GBD 2017) includes a comprehensive assessment of incidence, prevalence, and years lived with disability (YLDs) for 354 causes in 195 countries and territories from 1990 to 2017. Previous GBD studies have shown how the decline of mortality rates from 1990 to 2016 has led to an increase in life expectancy, an ageing global population, and an expansion of the non-fatal burden of disease and injury. These studies have also shown how a substantial portion of the world's population experiences non-fatal health loss with considerable heterogeneity among different causes, locations, ages, and sexes. Ongoing objectives of the GBD study include increasing the level of estimation detail, improving analytical strategies, and increasing the amount of high-quality data. METHODS: We estimated incidence and prevalence for 354 diseases and injuries and 3484 sequelae. We used an updated and extensive body of literature studies, survey data, surveillance data, inpatient admission records, outpatient visit records, and health insurance claims, and additionally used results from cause of death models to inform estimates using a total of 68 781 data sources. Newly available clinical data from India, Iran, Japan, Jordan, Nepal, China, Brazil, Norway, and Italy were incorporated, as well as updated claims data from the USA and new claims data from Taiwan (province of China) and Singapore. We used DisMod-MR 2.1, a Bayesian meta-regression tool, as the main method of estimation, ensuring consistency between rates of incidence, prevalence, remission, and cause of death for each condition. YLDs were estimated as the product of a prevalence estimate and a disability weight for health states of each mutually exclusive sequela, adjusted for comorbidity. We updated the Socio-demographic Index (SDI), a summary development indicator of income per capita, years of schooling, and total fertility rate. Additionally, we calculated differences between male and female YLDs to identify divergent trends across sexes. GBD 2017 complies with the Guidelines for Accurate and Transparent Health Estimates Reporting. FINDINGS: Globally, for females, the causes with the greatest age-standardised prevalence were oral disorders, headache disorders, and haemoglobinopathies and haemolytic anaemias in both 1990 and 2017. For males, the causes with the greatest age-standardised prevalence were oral disorders, headache disorders, and tuberculosis including latent tuberculosis infection in both 1990 and 2017. In terms of YLDs, low back pain, headache disorders, and dietary iron deficiency were the leading Level 3 causes of YLD counts in 1990, whereas low back pain, headache disorders, and depressive disorders were the leading causes in 2017 for both sexes combined. All-cause age-standardised YLD rates decreased by 3.9% (95% uncertainty interval [UI] 3.1-4.6) from 1990 to 2017; however, the all-age YLD rate increased by 7.2% (6.0-8.4) while the total sum of global YLDs increased from 562 million (421-723) to 853 million (642-1100). The increases for males and females were similar, with increases in all-age YLD rates of 7.9% (6.6-9.2) for males and 6.5% (5.4-7.7) for females. We found significant differences between males and females in terms of age-standardised prevalence estimates for multiple causes. The causes with the greatest relative differences between sexes in 2017 included substance use disorders (3018 cases [95% UI 2782-3252] per 100 000 in males vs s1400 [1279-1524] per 100 000 in females), transport injuries (3322 [3082-3583] vs 2336 [2154-2535]), and self-harm and interpersonal violence (3265 [2943-3630] vs 5643 [5057-6302]). INTERPRETATION: Global all-cause age-standardised YLD rates have improved only slightly over a period spanning nearly three decades. However, the magnitude of the non-fatal disease burden has expanded globally, with increasing numbers of people who have a wide spectrum of conditions. A subset of conditions has remained globally pervasive since 1990, whereas other conditions have displayed more dynamic trends, with different ages, sexes, and geographies across the globe experiencing varying burdens and trends of health loss. This study emphasises how global improvements in premature mortality for select conditions have led to older populations with complex and potentially expensive diseases, yet also highlights global achievements in certain domains of disease and injury. FUNDING: Bill & Melinda Gates Foundation.
Reduced default mode network functional connectivity in patients with recurrent major depressive disorder
[J]. ,
Assessment of the impact of shared brain imaging data on the scientific literature
[J]. ,Data sharing is increasingly recommended as a means of accelerating science by facilitating collaboration, transparency, and reproducibility. While few oppose data sharing philosophically, a range of barriers deter most researchers from implementing it in practice. To justify the significant effort required for sharing data, funding agencies, institutions, and investigators need clear evidence of benefit. Here, using the International Neuroimaging Data-sharing Initiative, we present a case study that provides direct evidence of the impact of open sharing on brain imaging data use and resulting peer-reviewed publications. We demonstrate that openly shared data can increase the scale of scientific studies conducted by data contributors, and can recruit scientists from a broader range of disciplines. These findings dispel the myth that scientific findings using shared data cannot be published in high-impact journals, suggest the transformative power of data sharing for accelerating science, and underscore the need for implementing data sharing universally.
A brain imaging-based deep learning classifier for sex and Alzheimer' s Disease
[J]. ,Neutralizing antibodies (nAbs) hold promise as effective therapeutics against COVID-19. Here, we describe protein engineering and modular design principles that have led to the development of synthetic bivalent and tetravalent nAbs against SARS-CoV-2. The best nAb targets the host receptor binding site of the viral S-protein and its tetravalent versions can block entry with a potency that exceeds the bivalent nAbs by an order of magnitude. Structural studies show that both the bivalent and tetravalent nAbs can make multivalent interactions with a single S-protein trimer, observations consistent with the avidity and potency of these molecules. Significantly, we show that the tetravalent nAbs show much increased tolerance to potential virus escape mutants. Bivalent and tetravalent nAbs can be produced at large-scale and are as stable and specific as approved antibody drugs. Our results provide a general framework for developing potent antiviral therapies against COVID-19 and related viral threats, and our strategy can be readily applied to any antibody drug currently in development.
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