复杂随机抽样数据的多重线性回归分析方法及其应用

目的：探讨综合权重在复杂随机抽样数据线性回归分析中的意义和作用。方法基于蒙特卡洛随机模拟思想,采用SAS中REG和SURVEYREG两个不同的多重线性回归分析过程,分别对同一批复杂随机抽样数据（ n＝6756）在不同随机抽样率条件下进行回归建模,对所得结果进行比较。结果在未考虑和考虑观测权重与抽样权重的多重线性回归模型拟合的结果中,自变量的偏回归系数、标准误及P值的大小均有所不同。结论在对基于不同抽样率的复杂随机抽样资料,尤其是分层随机抽样调查资料的回归建模中,采用多重线性回归模型拟合资料时,将调查数据的综合权重纳入统计分析,方能更准确、灵敏地进行回归系数的参数估计和对结果变量的统计预测。     

脑皮质发育畸形的功能MRI研究

目的 探讨脑皮质发育畸形(MCD)并癫痫患者的脑运动及语言功能区激活情况,及其与异常灰质的关系.方法 选取7例不同类型的MCD患者(局灶性皮质发育不良2例、灰质异位2例、脑裂畸形2例、以及多小脑回畸形1例),应用3TMR仪分别进行双侧肢体对指运动、足趾运动、以及动词联想、图片命名任务的BOLD fMRI扫描,采用组块设计方案,利用SPM 5软件的总体线性模型(GLM)进行分析,产生激活数据.将激活阈值(P＜0.001,经校正检验,激活范围≥30个体素)以上数据按照运动及语言任务不同对激活脑区进行归类分析,并分别观察MCD异常灰质与相应功能区的关系.结果 运动任务激活范围及强度以感觉运动皮层、运动前区为主.语言任务中动词联想任务激活范围以语言相关脑区及运动前区为主,激活强度以语言区为主,且Wernicke区激活强度较高.图片命名任务激活范围及强度均以视皮层为主,但在语言相关皮层激活上,强度及范围以Broca区稍著.两种语言任务联合应用语言皮层区激活范围、强度占据较显著优势.MCD患者功能激活皮层区包含发育异常结构,激活范围均可以包括或延伸到畸形区域.6例行手术治疗患者术后临床随访均未出现新的神经功能损伤及认知障碍,并且癫痫发作频率有显著下降.结论 MCD大脑皮层具有运动、语言功能,相应部位的异常灰质均可能参与简单或复杂任务脑功能活动,在外科术前应得到客观正确的功能评估以避免并发症发生.     

简单型书写痉挛患者异常皮质活动的功能磁共振成像研究

目的:利用功能磁共振成像,研究简单型书写痉挛患者的皮质异常功能活动,探讨其病因基础。材料和方法:分别对10名简单型书写痉挛患者和10名健康右利手志愿者进行手指写、执笔写任务期间的功能磁共振成像。使用AFNI软件,对数据经过预处理及反卷积分析等处理,获得患者组和正常组两个任务的平均脑激活图并进行比较。结果:与正常组之间相比较,患者组初级感觉运动区和辅助运动区表现为与执笔写任务相关的更明显的激活,而双侧的后顶叶皮质激活在两个不同任务期间均较正常减弱。结论:简单型书写痉挛患者存在运动和感觉皮质的异常活动,反映了其运动皮质的去抑制和感觉处理过程的障碍,并且感觉处理过程的异常可能在其病因过程中起重要作用。     

线性模型在研究认知任务行为学效应与脑功能磁共振信号关系中的应用

目的 建立联系认知任务行为学效应与功能磁共振信号的线性模型,并验证模型合理性.方法 通过线性模型建模认知任务行为学效应与功能性磁共振成像(fMRI)信号,通过相关参数的假设检验来确定功能区,并利用Stroop范式的实验数据,对本模型与SPM的分析结果进行比较.结果 该模型应用于真实数据,检出的功能区主要有背外侧前额叶(Brodmann 9/46),额上内侧回(Brodmann 8/9)与SPM结果吻合,与文献报道一致.结论 该模型可以定量分析行为学数据与功能信号的关系,为认知功能影像的分析提供了一个思路.     

动词联想及图片命名任务激活大脑语言功能区的fMRI研究

目的:利用fMRI探讨语言任务中动词联想及图片命名任务对大脑语言区的激活模式并分析其临床应用可行性。方法:15例健康志愿者中男8例,女7例,平均年龄(35.7±13.6)岁,均为右利手。应用3T MR仪对每例受试者分别行右手对指运动、动词联想及图片命名任务的BOLD fMRI扫描,采用组块设计方案,利用SPM5软件的总体线性模型(GLM)进行分析并产生激活数据,组分析采用单样本t检验,激活阈值经校正检验,P<0.001(个体)及P<0.01(组分析),激活区坐标经Talairach转换并标定出相应Brodmann(BA)脑区。对每例受试者的两种语言任务分别计算两侧大脑半球激活范围,采用经修改的不包含视皮层激活区的计算公式进行语言优势半球偏侧指数(LI)计算。结果:动词联想任务主要激活双侧Broca′s区(BA45)、左前额叶背外侧脑区(BA46)、左Wernicke′s区(BA39)、梭状回(BA37)和辅助运动区(BA6),以左侧前额、颞叶语言相关脑区激活为主。图片命名任务主要激活视皮层(BA18)及左BA37、左顶叶(BA7)、基底节区、左背外侧前额叶(BA9)和双侧BA45,以枕、颞叶后部脑区为主。右手对指运动激活左额叶初级运动区(BA4)和BA6。两种语言任务LI计算,符合率为93.3%(14/15),其中左侧优势12例,右侧优势及双侧分布各1例。结论:动词联想主要激活大脑语义加工区,并反映语言区的偏侧优势。图片命名任务可与动词联想共同激活部分语言相关脑区,但对于分析语言偏侧优势有一定缺陷。两种任务的联合使用能够全面了解整体语言功能状况。运动激活作为语言激活信号标准化处理的一个内在评估指标使激活区统计更趋精确和客观。     

人类反应抑制的磁共振功能成像研究

目的:应用功能磁共振技术研究人类反应抑制的神经中枢.材料和方法:对16名受试者分别进行两种事件相关设计的反应抑制任务,同时通过MR采集其脑部数据.用SPM软件进行数据处理.分别获得两个任务的反应抑制的激活图像并寻找共同激活区.在共同激活区中,进行信号强度与行为学数据的相关性分析以寻找与反应抑制活动最相关的部位.结果:成功的反应抑制在两个任务中分别激活了一系列以右侧大脑半球皮质为主的功能区,共同激活区包括右侧额中、下回皮质,右侧颞叶、枕叶皮质.其中位于右侧额中、下回皮质的激活区(BA9/46)的信号强度和两个任务的行为学数据都呈显著相关.结论:位于右侧额中、下回皮质的功能区(BA9/46)可能是反应抑制的神经中枢.     

手运动组块设计与事件相关设计的fMRI比较研究

目的:探讨组块和事件相关两种fMRI实验设计在方法学上的差异及其适用范围。方法:对13例健康志愿者进行组块和事件相关两种不同任务模式下右手简单对指运动脑fMRI实验。采用GE 1．5T磁共振全身扫描仪,所获数据应用SPM 99软件进行预处理和统计分析,获取两种实验设计下的脑激活图及各激活区的范围和最大激活强度,对比两种实验设计方法之间的异同。结果:组块设计实验中可见对侧初级运动区(M1)、双侧辅助运动区(SMA)及同侧小脑半球 (CER)明显激活。事件相关设计实验可见对侧M1区及双侧SMA明显激活,小脑未见激活;SMA激活区包括前后两部分,即位于前方的辅助运动前区(Pre-SMA)和后方固有区(SMA Proper);M1区和SMA激活强度较弱、范围较小;事件相关设计可反映各激活区的血流动力学反应过程。结论:两种实验设计均可激活对侧M1区及双侧SMA,以组块设计激活作用更强、范围更大,因此有利于功能区的检出和定位;事件相关设计激活作用较弱,但可以反映功能区的活动细节,有利于研究各功能区之间的相互联系。     

抓握运动对儿童言语流畅影响的脑功能成像研究

目的:比较无手部抓握运动训练儿童和有手部抓握运动训练经历儿童在完成语义流畅性任务时大脑功能的不同,讨论抓握运动是否促进言语生成的表现。方法:将24名儿童按有无手部抓握运动经历,分为有运动经历和无运动经历两组,以汉语语义流畅性测验为言语任务,采用大脑功能磁共振成像(fMRI)方法,应用基于Matlab的统计参数绘图软件(SPM)分析脑区激活信息。结果:完成言语任务时两组被试产词量无显著性差异。有运动训练经历组儿童完成言语任务时大脑激活程度比无运动训练经历组强烈,激活区域范围广,主要体现在左脑岛、双侧额下回、左额叶(中央前回)。结论:抓握训练可能对汉语加工有明显影响。额叶中央前回是抓握技能对言语加工影响的重要脑区。     

"催眠态"与"气功态"脑运动皮质低频活动差异的初步研究

目的:研究催眠态和气功态下脑运动皮质的低频同步活动,探讨催眠态与气功态对脑功能皮质活动的不同效应。材料和方法:研究对象为1例28岁的健康女性志愿者,右利手。采用气功入静和催眠诱导分别进入气功态和催眠态。研究采用GEsigna VH/i3.0T磁共振扫描机,先后进行无任务(静息态、催眠态和气功态)和运动任务的BOLD序列扫描。图像的处理先以运动任务获得的运动皮质定位作为种子对三种状态的功能图像做交互相关分析,获得脑功能连接的激活图,然后对这些激活的体素采用功率谱分析,获得相应的优势频率和能量。结果:脑功能低频连接激活体素的数量在静息态、催眠态、气功态间没有明显差异,但通过功率谱分析,催眠态主要表现为优势频率能量的升高,而气功态则主要表现为优势频率的增加。结论:气功态和催眠态下脑运动皮质的低频活动存在一定的差异,提示两种状态有通过不同的途径产生效应,但其生理基础有待于进一步研究。     

利手和非利手随意运动的全脑功能磁共振成像

目的 利用全脑功能磁共振成像(fMRI)技术，探讨参与利手和非利手简单随意运动的关键脑结构。方法 采用Siemens公司Sonata 1．5T磁共振成像系统，对7名健康右利手志愿者的利手或非利手食指按键运动进行了全脑扫描。数据经头动矫正、空间标准化、空间平滑等预处理后，通过互相关分析分别获得利手和非利手运动的脑激活统计参数图。结果 利手运动主要激活对侧初级运动区(MI)、双侧辅助运动区(SMA)、双侧运动二区(MII)和同侧小脑，而非利手运动除以上区域外还激活了对侧前运动区(PMC)，而且SMA和MII的激活体积大于利手运动。结论 全脑fMRI研究表明，随意运动依赖于大脑皮质和小脑等许多脑结构的参与.与利手运动相比，非利手运动更依赖于SMA和PMC等高级运动控制区.     

Functional MRI in human motor control studies and clinical applications.

Functional magnetic resonance imaging (fMRI) has been a useful tool for the noninvasive mapping of brain function associated with various motor and cognitive tasks. Because fMRI is based on the blood oxygenation level dependent (BOLD) effect, it does not directly record neural activity. With the fMRI technique, distinguishing BOLD signals created by cortical projection neurons from those created by intracortical neurons appears to be difficult. Two major experimental designs are used in fMRI studies: block designs and event-related designs. Block-designed fMRI presupposes the steady state of regional cerebral blood flow and has been applied to examinations of brain activation caused by tasks requiring sustained or repetitive movements. By contrast, the more recently developed event-related fMRI with time resolution of a few seconds allows the mapping of brain activation associated with a single movement according to the transient aspects of the hemodynamic response. Increasing evidence suggests that multiple motor areas are engaged in a networked manner to execute various motor acts. In order to understand functional brain maps, it is important that one understands sequential and parallel organizations of anatomical connections between multiple motor areas. In fMRI studies of complex motor tasks, elementary parameters such as movement length, force, velocity, acceleration and frequency should be controlled, because inconsistency in those parameters may alter the extent and intensity of motor cortical activation, confounding interpretation of the findings obtained. In addition to initiation of movements, termination of movements plays an important role in the successful achievement of complex movements. Brain areas exclusively related to the termination of movements have been, for the first time, uncovered with an event-related fMRI technique. We propose the application of fMRI to the elucidation of the pathophysiology of movement disorders, particularly dystonia, which exhibits involuntary co-contraction of agonist and antagonist muscles and manifests abnormal posture or slow repetition of movements.     

健康成人及脑肿瘤患者利手和非利手简单与复杂运动fMRI研究

目的:应用脑血氧水平依赖性功能MRI(BOLD-f MRI)研究健康成年人及脑肿瘤患者运动功能皮层定位并探讨其对脑肿瘤的临床应用价值。方法:10例健康志愿者和32例脑肿瘤患者(术前25例,术后7例)共42例受试者,行利手、非利手的单手握拳(简单运动)或单手对指(复杂运动)运动的脑BOLD-f MRI检查,分析脑肿瘤对运动皮层位置和功能的影响。结果:健康成人运动皮层主要位于对侧躯体感觉运动皮层(SMC),单或双侧辅助运动区(SMA)、运动前区(PMA)和双侧小脑半球。复杂运动或非利手运动时脑功能激活区范围和程度较简单运动或利手运动时增多。累及功能皮层的脑肿瘤患者,可见患侧部分脑功能区激活,但激活区移位、分布弥散。术后脑肿瘤患者功能皮层的位置基本恢复正常。结论:BOLD-f MRI是一种有效而无创的脑功能皮层定位方法,有利于脑肿瘤的精确定位诊断并指导临床治疗。     

Detection of eye movements from fMRI data.

Awake humans make eye movements with amplitudes and frequencies that depend on behavioral state and task. This poses two problems for functional magnetic resonance imaging (fMRI) studies that compare brain activity across tasks. First, motion of the eye in the orbit increases the variance of the MR signal in adjacent regions of the orbitofrontal cortex, hampering activation detection. Second, eye movements are associated with activity in a distributed network of brain areas, confounding comparisons of task activation. Direct measurement of eye movements in the scanner bore is possible with expensive and technically demanding equipment. A method is described that detects eye movements directly from MR data without the use of additional equipment. Changes in the MR time series from the vitreous of the eye were observed that correlated with eye movements, as measured directly with an infrared pupil tracking system. In each of 10 subjects, the variance of the MR time series from the eye vitreous was greater when the subject made eye movements than when the subject fixated centrally (average standard deviation (SD) 99.7 vs. 75.6, P = 0.001). The assessment of eye movements directly from fMRI data may be especially useful for retrospective and meta-analyses.     

Mapping functionally related regions of brain with functional connectivity MR imaging

BACKGROUND AND PURPOSE: In subjects who are performing no prescribed cognitive task, functional connectivity mapped with MR imaging (fcMRI) shows regions with synchronous fluctuations of cerebral blood flow. When specific tasks are performed, functional MR imaging (fMRI) can map locations in which regional cerebral blood flow increases synchronously with the performance of the task. We tested the hypothesis that fcMRI maps, based on the synchrony of low-frequency blood flow fluctuations, identify brain regions that show activation on fMRI maps of sensorimotor, visual, language, and auditory tasks. METHODS: In four volunteers, task-activation fMRI and functional connectivity (resting-state) fcMRI data were acquired. A small region of interest (in an area that showed maximal task activation) was chosen, and the correlation coefficient of the corresponding resting-state signal with the signal of all other voxels in the resting data set was calculated. The correlation coefficient was decomposed into frequency components and its distribution determined for each fcMRI map. The fcMRI maps were compared with the fMRI maps. RESULTS: For each task, fcMRI maps based on one to four seed voxel(s) produced clusters of voxels in regions of eloquent cortex. For each fMRI map a closely corresponding fcMRI map was obtained. The frequencies that predominated in the cross-correlation coefficients for the functionally related regions were below 0.1 Hz. CONCLUSION: Functionally related brain regions can be identified by means of their synchronous slow fluctuations in signal intensity. Such blood flow synchrony can be detected in sensorimotor areas, expressive and receptive language regions, and the visual cortex by fcMRI. Regions identified by the slow synchronous fluctuations are similar to those activated by motor, language, or visual tasks.     

数字记忆广度与数字工作记忆的功能磁共振比较研究

目的比较数字记忆广度与数字工作记忆的脑区激活特点与差异。方法利用Siemens 1.5T MR机对12名右利手志愿者进行7位数数字记忆广度与2位数数字工作记忆实验,实验采用组块设计,2组任务均设相应对照任务,将记忆任务与对照任务比较,数据采用SPM99软件进行分析和脑功能区定位。结果进行2组任务时,志愿者额叶的Brodmann6区、9区和47区,顶叶的7区和40区,扣带回,海马结构,纹状体以及小脑均有激活。进行数字记忆广度测试时,双侧枕叶Brodmann18区、19区的激活尤其显著,无明显的半球优势,双侧颞叶Brodmann37区也有激活,而进行数字工作记忆时,则在额叶的激活最为显著,额叶和顶叶的激活都表现为左侧半球优势。结论脑区在进行不同要求的短时数字记忆任务时所参与的阶段和所起的作用不同,通过fMRI的研究对推断大脑进行数字信息的处理过程有一定的帮助。     

Improved temporal clustering analysis method for detecting multiple response peaks in fMRI

PURPOSE: To develop an improved temporal clustering analysis (TCA) method for detecting multiple active peaks by running the method once. MATERIALS AND METHODS: Two cases of simulation data and a set of actual fMRI data from nine subjects were used to compare the traditional TCA method with the new method, termed extremum TCA (ETCA). The first case of simulation data simulated event-related activation and block activation in one cerebral area, and the second case simulated event-related activation and block activation in two cerebral areas. An in vivo visual stimulating experiment was performed on a 1.5T MR scanner. All imaging data were processed using both traditional TCA and the new method. RESULTS: The results of both the simulated and actual fMRI data show that the new method is more sensitive and exact than traditional TCA in detecting multiple response peaks. CONCLUSION: The new method is effective in detecting multiple activations even when the timing and location of the brain activation are completely unknown.     

Functional magnetic resonance imaging activation detection: fuzzy cluster analysis in wavelet and multiwavelet domains

PURPOSE: To present novel feature spaces, based on multiscale decompositions obtained by scalar wavelet and multiwavelet transforms, to remedy problems associated with high dimension of functional magnetic resonance imaging (fMRI) time series (when they are used directly in clustering algorithms) and their poor signal-to-noise ratio (SNR) that limits accurate classification of fMRI time series according to their activation contents. MATERIALS AND METHODS: Using randomization, the proposed method finds wavelet/multiwavelet coefficients that represent the activation content of fMRI time series and combines them to define new feature spaces. Using simulated and experimental fMRI data sets, the proposed feature spaces are compared to the cross-correlation (CC) feature space and their performances are evaluated. In these studies, the false positive detection rate is controlled using randomization. To compare different methods, several points of the receiver operating characteristics (ROC) curves, using simulated data, are estimated and compared. RESULTS: The proposed features suppress the effects of confounding signals and improve activation detection sensitivity. Experimental results show improved sensitivity and robustness of the proposed method compared to the conventional CC analysis. CONCLUSION: More accurate and sensitive activation detection can be achieved using the proposed feature spaces compared to CC feature space. Multiwavelet features show superior detection sensitivity compared to the scalar wavelet features.     

Spinal fMRI during proprioceptive and tactile tasks in healthy subjects: activity detected using cross-correlation, general linear model and independent component analysis

Introduction  Functional MRI (fMRI) of the spinal cord is able to provide maps of neuronal activity. Spinal fMRI data have been analyzed in previous studies by calculating the cross-correlation (CC) between the stimulus and the time course of every voxel and, more recently, by using the general linear model (GLM). The aim of this study was to compare three different approaches (CC analysis, GLM and independent component analysis (ICA)) for analyzing fMRI scans of the cervical spinal cord. Methods  We analyzed spinal fMRI data from healthy subjects during a proprioceptive and a tactile stimulation by using two model-based approaches, i.e., CC analysis between the stimulus shape and the time course of every voxel, and the GLM. Moreover, we applied independent component analysis, a model-free approach which decomposes the data in a set of source signals. Results  All methods were able to detect cervical cord areas of activity corresponding to the expected regions of neuronal activations. Model-based approaches (CC and GLM) revealed similar patterns of activity. ICA could identify a component correlated to fMRI stimulation, although with a lower statistical threshold than model-based approaches, and many components, consistent across subjects, which are likely to be secondary to noise present in the data. Conclusions  Model-based approaches seem to be more robust for estimating task-related activity, whereas ICA seems to be useful for eliminating noise components from the data. Combined use of ICA and GLM might improve the reliability of spinal fMRI results.     

Dual-echo spiral in/in acquisition method for reducing magnetic susceptibility artifacts in blood-oxygen-level-dependent functional magnetic resonance imaging.

MRI signal dropout in gradient recalled echo acquisitions limits the capability of blood-oxygen-level-dependent functional magnetic resonance imaging (fMRI) to study activation tasks that involve the orbitofrontal, temporal, and basal areas of the brain where significant macroscopic magnetic susceptibility differences exist. Among the various approaches aimed to address this issue, the acquisition method based on spiral in/out trajectories is one of the most time-efficient and effective techniques. In this study, we extended further the spiral in/out approach into 3D acquisition and compared the effectiveness of the different spiral in/out trajectory combinations in reducing signal dropout. The activation results from whole brain fMRI studies using complex finger tapping and breath-holding tasks demonstrate that the acquisition method based on dual-echo spiral in/in (DSPIN) trajectories is the most favorable. The DSPIN acquisition method has the following advantages: (1) It reduces most effectively signal dropout in the brain where magnetic susceptibility inhomogeneity is problematic and significantly improves the sensitivity to detect functional activations in those regions. (2) It significantly improves SNR in the whole brain by dual echo averaging without compromising functional contrast. (3) There is no reduction in time-efficiency and spatial resolution.     

Isolation and minimization of head motion-induced signal variations in fMRI data using independent component analysis.

Task-related head movement during acquisition of fMRI data represents a serious confound for both motion correction and estimates of task-related activation. Cost functions implemented in most conventional motion-correction algorithms compare two volumes for similarity but fail to account for signal variability that is not due to motion (e.g., brain activation). We therefore recently proposed the theoretical basis for a novel method for fMRI motion correction, termed motion-corrected independent component analysis (MCICA), that allows for brain activation present in an fMRI time-series to be implicitly modeled and mitigates motion-induced signal changes without having to directly estimate the motion parameters (Liao et al., IEEE Transactions on Medical Imaging 2005;25:29-44). To explore the effects of non-movement-related signal changes on registration error, we performed several previously proposed test simulations (Freire et al., IEEE Transactions on Medical Imaging 2002;21:470-484) to evaluate the performance of MCICA and compare it with the conventional square-of-difference-based measures such as LS-SPM and LS-AIR. We demonstrate that for both simulated data and real fMRI images, the proposed MCICA method performs favorably. Specifically, in simulations MCICA was more robust to the addition of simulated activation, and did not lead to the detection of false activations after correction for simulated task-correlated motion. With actual data from a motor fMRI experiment, the time course of the derived continually task-related ICA component became more correlated with the underlying behavioral task after preprocessing with MCICA compared to other methods, and the associated activation map was more clustered in the primary motor and supplementary motor cortices without spurious activation at the brain edge. We conclude that assessing the statistical properties of a motion-corrupted volume in relation to other volumes in the series, as is done with MCICA, is an accurate means of differentiating between motion-induced signal changes and other sources of variability in fMRI data.