孤独症磁共振脑功能成像研究

孤独症是起病于婴幼儿期严重的慢性神经精神障碍，涉及语言、认知、社会交往能力等多种障碍。磁共振脑功能成像是通过血氧水平依赖法检测脑实质血管内血氧浓度变化，以显示相应的脑功能区。孤独症的磁共振脑功能成像研究主要集中在注意力、运动、语言和面部识别等方面，发现孤独症患者的脑功能区与正常对照组相比较有部分区别。目前孤独症患者脑功能磁共振检查研究尚处于起步阶段，病例较少，脑功能实验设计较为简单。随着研究的逐步深入，脑功能磁共振检查将会对孤独症的脑功能异常做出综合评价并指导临床。     

人脑视觉皮质功能磁共振成像研究

目的研究人脑视觉皮质血氧水平依赖（BOLD）的功能磁共振成像。方法18名正常健康志愿者，在光刺激和非刺激的两种对比条件下，采用EP1技术，采集视觉皮质血氧水平依赖（BOLD）图像。t检验分析得出光刺激状态和非刺激状态信号对比的脑功能图像。结果fMRI图像显示光刺激下脑功能活动激活区主要位于双侧视觉皮质区。结论fMRI可用于在活体人脑上研究各功能区活动，光刺激下的fMRI可对人脑视觉皮质进行定位。     

功能磁共振研究卒中后肢体运动功能重塑机制

脑卒中造成病灶对侧肢体运动功能障碍的恢复与脑可塑性和功能重组有关。在运动功能恢复过程中,大脑皮质有广泛区域参与神经重塑过程。血氧水平依赖功能磁共振成像对于脑卒中后运动功能恢复与脑功能重组之间关系的研究提供了有效平台。     

肾脏血氧水平依赖磁共振成像研究进展

血氧水平依赖磁共振成像是无创性评价肾内氧代谢功能技术.随着肾脏血氧水平依赖磁共振成像临床研究的持续发展,能监测慢性肾脏病、急性肾损伤、肾小球疾病、肾移植、肾血管疾病以及肾脏肿瘤等多种肾脏疾病的氧合水平变化.笔者综述了关于肾脏血氧水平依赖磁共振成像的基本原理与临床进展.     

血氧水平依赖磁共振成像在慢性肾病中的应用

背景:血氧水平依赖的磁共振成像是目前惟一能无创性地监测肾血氧含量的方法。目的:探讨血氧水平依赖的磁共振成像在评价慢性肾病患者肾血氧水平、反映肾功能状态方面的价值。方法:对20名健康志愿者和24名慢性肾病患者行肾脏血氧水平依赖的磁共振成像,测量各组肾皮质及髓质的R2*值,并进行统计学分析。结果与结论:正常肾髓质的R2*值高于皮质(P<0.05),双肾比较差异无显著性意义(P>0.05)。慢性肾病患者皮质及髓质的R2*值均高于正常人(P<0.05),均与血清肌酐水平呈正相关(r=0.564,P=0.004;r=0.588,P=0.003)。提示血氧水平依赖的磁共振成像可以反映肾脏血氧水平,在评价肾脏的功能状态方面有一定价值。     

血氧水平依赖功能磁共振成像研究进展

血氧水平依赖功能磁共振成像（blood oxygenation level dependent-functional magnetic resonance imaging，BOLD-fMRI）是目前应用最广泛的脑功能成像方法，它使抽象的大脑活动得以用图像的方式显示。现就BOLD-fMRI的研究进展综述如下。     

血氧水平依赖磁共振成像在慢性肾病中的应用

背景：血氧水平依赖的磁共振成像是目前惟一能无创性地监测肾血氧含量的方法。目的：探讨血氧水平依赖的磁共振成像在评价慢性肾病患者肾血氧水平、反映肾功能状态方面的价值。方法：对20名健康志愿者和24名慢性肾病患者行肾脏血氧水平依赖的磁共振成像,测量各组肾皮质及髓质的R2＊值,并进行统计学分析。结果与结论：正常肾髓质的R2＊值高于皮质（P〈0.05）,双肾比较差异无显著性意义（P〉0.05）。慢性肾病患者皮质及髓质的R2＊值均高于正常人（P〈0.05）,均与血清肌酐水平呈正相关（r=0.564,P=0.004;r=0.588,P=0.003）。提示血氧水平依赖的磁共振成像可以反映肾脏血氧水平,在评价肾脏的功能状态方面有一定价值。     

BOLD-fMRI成像技术在脑梗死后脑功能重组及运动功能康复机制中的应用

<正>1血氧水平依赖(blood oxygenation level dependent,BOLD)成像机制及原理狭义的脑功能磁共振检查(f MRI)仅指血氧水平依赖-功能磁共振(blood oxygenation level dependent-functional MRI,BOLD-f MRI),其成像机制由Ogawa S等~([2])首次发现并提出,它是一种以脱氧合血红蛋白的磁敏感效应为基础的磁共振成像技术。局部脑区兴奋会使该脑区的代谢水平及局部血液流量增加。脑区代谢水平的提高会产生更多的脱氧合血红蛋白,与前者相反,局部血流量的增加又会带来更多的氧合血红蛋白。由于兴奋脑区局部血流量增加的效应大于前者,因此,     

人脑运动皮层功能磁共振成像研究

目的采用功能用磁共振成像（fMRI）回波平面（EPI）技术,研究人脑运动皮质血氧水平依赖（BOLD）的功能磁共振成像。方法27名正常健康志愿者,右手挤压橡皮圈,在运动和静止两种对比条件下,采集运动皮层的回波平面图像（BOLD-fMRI）。分析运动状态和非运动状态信号对比的脑功能图像。结果fMRI图像显示运动刺激下脑功能活动激活区主要位于对侧感觉运动皮质区、辅助运动区等。结论fMRI可用于研究活体人脑各功能区的活动,fMRI可对运动刺激下的人脑运动皮质进行初步定位。     

颈部脊髓静息态血氧水平依赖对比功能性磁共振成像信号的频域分析

首先对2例健康受试者进行组块设计的运动任务颈段脊髓血氧水平依赖对比功能性磁共振成像成像,采用SPM5软件包分析,观察运动任务引发的血氧水平依赖信号强度.之后对10例受试者进行静息状态下颈段脊髓血氧水平依赖对比功能性磁共振成像,采用GIFT软件进行独立成分分析,观察不同强度信号(±4%范围之内和±20%范围之外)的频域分布特征.信号变化强度在±4%范围之内的独立成分,频域分布主要集中在约0.1 Hz带宽,在约0.8 Hz带宽也较为集中;信号变化强度超过+20%范围的独立成分,频域分布主要集中在约1.0 Hz带宽,在约0.3 Hz带宽也较为集中.结果提示,静息态脊髓血氧水平依赖成像的高波幅变化信号主要来源于心跳和呼吸造成的运动伪迹:典型血氧水平依赖信号变化波幅范围内的独立成分,部分来源于心跳和呼吸运动交互作用产生的运动伪迹,其他成分的来源需要进一步研究.     

清醒猴功能磁共振成像研究进展

清醒猴功能性磁共振成像(fMRI)对于行为神经科学的研究具有很大的潜能.由于各种技术困难,这一方法 尚未得到广泛采用.本文就清醒猴的fMRI研究方法 及其在行为神经科学领域的应用作一介绍,并简要讨论其优势、局限与发展前景.     

fMRI at 20: has it changed the world?

The prevalence of fMRI in cognitive neuroscience research is clear, but the overall impact of the associated research in the broader scope of our scientific community, and of society, is less obvious. The first reports of fMRI garnered huge interest in many areas, giving rise to a wide range of applications and technical developments over the past 20 years. Using five primary areas, i.e. scientific impact, clinical practice, cognitive neuroscience, mental illness, and society-this essay examines the question: Has fMRI changed the world?     

The future of fMRI in cognitive neuroscience

Over the last 20 years, fMRI has revolutionized cognitive neuroscience. Here I outline a vision for what the next 20 years of fMRI in cognitive neuroscience might look like. Some developments that I hope for include increased methodological rigor, an increasing focus on connectivity and pattern analysis as opposed to "blobology", a greater focus on selective inference powered by open databases, and increased use of ontologies and computational models to describe underlying processes.     

A method for generating reproducible evidence in fMRI studies

Insights into cognitive neuroscience from neuroimaging techniques are now required to go beyond the localisation of well-known cognitive functions. Fundamental to this is the notion of reproducibility of experimental outcomes. This paper addresses the central issue that functional magnetic resonance imaging (fMRI) experiments will produce more desirable information if researchers begin to search for reproducible evidence rather than only p value significance. The study proposes a methodology for investigating reproducible evidence without conducting separate fMRI experiments. The reproducible evidence is gathered from the separate runs within the study. The associated empirical Bayes and ROC extensions of the linear model provide parameter estimates to determine reproducibility. Empirical applications of the methodology suggest that reproducible evidence is robust to small sample sizes and sensitive to both the magnitude and persistency of brain activation. It is demonstrated that research findings in fMRI studies would be more compelling with supporting reproducible evidence in addition to standard hypothesis testing evidence.     

Seeing patterns through the hemodynamic veil--the future of pattern-information fMRI

Pattern-information fMRI (pi-fMRI) has become a popular method in neuroscience. The technique is motivated by the idea that spatial patterns of fMRI activity reflect the neuronal population codes of perception, cognition, and action. In this commentary, we discuss three fundamental outstanding questions: (1) What is the relationship between neuronal patterns and fMRI patterns? (2) Does pattern-information fMRI benefit from hyperacuity, enabling the investigation of columnar-level neuronal information, even at low resolution? (3) Do high-resolution and high-field fMRI increase sensitivity to pattern information? The empirical answers will enable us to optimize pi-fMRI data acquisition and to understand the ultimate potential and appropriate interpretation of pi-fMRI results. Furthermore, considering the relationship between neuronal activity and fMRI at the level of spatiotemporal patterns provides a novel and important perspective on the basis of the fMRI signal.     

Quantitative functional MRI: concepts, issues and future challenges

Since its inception 20 years ago, functional magnetic resonance imaging (fMRI) of the human brain based on the blood oxygenation level dependent (BOLD) contrast phenomenon has proliferated and matured. Today it is the predominant functional brain imaging modality with the majority of applications being in basic cognitive neuroscience where it has primarily been used as a tool to localize brain activity. While the magnitude of the BOLD response is often used in these studies as a surrogate for the level of neuronal activity, the link between the two is, in fact, quite indirect. The BOLD response is dependent upon hemodynamic (blood flow and volume) and metabolic (oxygen consumption) responses as well as acquisition details. Furthermore, the relationship between neuronal activity and the hemodynamic response, termed neurovascular coupling, is itself complex and incompletely understood. Quantitative fMRI techniques have therefore been developed to measure the hemodynamic and metabolic responses to modulations in brain activity. These methods have not only helped clarify the behaviour and origins of the BOLD signal under normal physiological conditions but they have also provided a potentially valuable set of tools for exploring pathophysiological conditions. Such quantitative methods will be critical to realize the potential of fMRI in a clinical context, where simple BOLD measurements cannot be uniquely interpreted, and to enhance the power of fMRI in basic neuroscience research. In this article, recent advances in human quantitative fMRI methods are reviewed, outstanding issues discussed and future challenges and opportunities highlighted.     

The future of functional MRI in clinical medicine

In the last 20 years or so, functional MRI has matured very rapidly from being an experimental imaging method in the hands of a few labs to being a very widely available and widely used workhorse of cognitive neuroscience and clinical neuroscience research internationally. FMRI studies have had a considerable impact on our understanding of brain system phenotypes of neurological and psychiatric disorders; and some impact already on development of new therapeutics. However, the direct benefit of fMRI to individual patients with brain disorders has so far been minimal. Here I provide a personal perspective on what has already been achieved, and imagine how the further development of fMRI over the medium term might lead to even greater engagement with clinical medicine.     

How many subjects constitute a study?

In fMRI there are two classes of inference: one aims to make a comment about the "typical" characteristics of a population, and the other about "average" characteristics. The first pertains to studies of normal subjects that try to identify some qualitative aspect of normal functional anatomy. The second class necessarily applies to clinical neuroscience studies that want to make an inference about quantitative differences of a regionally specific nature. The first class of inferences is adequately serviced by conjunction analyses and fixed-effects models with relatively small numbers of subjects. The second requires random-effect analyses and larger cohorts.     

A comparison of fMRI adaptation and multivariate pattern classification analysis in visual cortex

Functional magnetic resonance imaging (fMRI) has become a ubiquitous tool in cognitive neuroscience. The technique allows noninvasive measurements of cortical responses in the human brain, but only on the millimeter scale. Because a typical voxel contains many thousands of neurons with varied properties, establishing the selectivity of their responses directly is impossible. In recent years, two methods using fMRI aimed at studying the selectivity of neuronal populations on a ‘subvoxel’ scale have been heavily used. The first technique, fMRI adaptation, relies on the observation that the blood oxygen level-dependent (BOLD) response in a given voxel is reduced after prolonged presentation of a stimulus, and that this reduction is selective to the characteristics of the repeated stimuli (adapters). The second technique, multivariate pattern analysis (MVPA), makes use of multivariate statistics to recover small biases in individual voxels in their responses to different stimuli. It is thought that these biases arise due to the uneven distribution of neurons (with different properties) sampled by the many voxels in the imaged volume. These two techniques have not been compared explicitly, however, and little is known about their relative sensitivities. Here, we compared fMRI results from orientation-specific visual adaptation and orientation–classification by MVPA, using optimized experimental designs for each, and found that the multivariate pattern classification approach was more sensitive to small differences in stimulus orientation than the adaptation paradigm. Estimates of orientation selectivity obtained with the two methods were, however, very highly correlated across visual areas.     

Voxel-level functional connectivity using spatial regularization

Discovering functional connectivity between and within brain regions is a key concern in neuroscience. Due to the noise inherent in fMRI data, it is challenging to characterize the properties of individual voxels, and current methods are unable to flexibly analyze voxel-level connectivity differences. We propose a new functional connectivity method which incorporates a spatial smoothness constraint using regularized optimization, enabling the discovery of voxel-level interactions between brain regions from the small datasets characteristic of fMRI experiments. We validate our method in two separate experiments, demonstrating that we can learn coherent connectivity maps that are consistent with known results. First, we examine the functional connectivity between early visual areas V1 and VP, confirming that this connectivity structure preserves retinotopic mapping. Then, we show that two category-selective regions in ventral cortex - the Parahippocampal Place Area (PPA) and the Fusiform Face Area (FFA) - exhibit an expected peripheral versus foveal bias in their connectivity with visual area hV4. These results show that our approach is powerful, widely applicable, and capable of uncovering complex connectivity patterns with only a small amount of input data.