利用多层受约束一范数优化检测功能磁共振成像中神经活动

本研究提出利用fMRI中神经信号内在的稀疏性,通过积分器转换,最大期望算法优化对脑fMRI中血流动力学变化建立多层神经信号模型,将检测脑fMRI中神经活动转化为受约束的一范数优化问题.利用空间自适应滤波器,优化结果可以准确地检测出fMRI中神经活动.通过与目前主流检测方法时间聚类分析、最大相关性方法及图模型推理法对比,本文提出的方法能够以较小的计算复杂度得出精确的结果.     

基于提升小波变换的功能MRI数据分析

目的 构建一种快速的fMRI数据小波分析方法.方法 用提升小波变换代替平稳小波变换分解fMRI数据,以分离其实验响应信号和干扰信号,再由频谱分析识别实验响应信号所在的小波尺度,并只对实验响应信号所在的小波尺度进行重构,最后对重构信号进行相关分析来检测激活.结果 分析视觉实验数据显示,在显著性水平为α＜10-6时,本文基于提升小波变换的方法比未去噪的相关分析方法更灵敏,而消耗时间比基于平稳小波变换的方法大幅度减少.同时本文方法只需24个数据点即可进行小波重构,而基于平稳小波变换的方法则需要256个数据点.结论 本文为fMRI数据提出了一种既能快速分析又能有效压缩的小波分析方法.     

多发性硬化功能磁共振的研究进展

常规MR检查可以准确显示多发性硬化(MS)病灶的形态学变化,而fMRI能够显示MS病程演变的病理生理过程,为MS研究提供了新的视角.本文就近年来fMRI在MS研究中的应用进行综述.     

正常老年人静息状态脑功能磁共振的默认网络研究

目的 探讨正常老年人静息状态下的功能磁共振(fMRI)默认网络结构及意义.方法 正常健康老年受试者18人,接受静息fMRI扫描;通过时程相关方法检测fMRI数据低频波动信号中以后扣带回为种子区的默认网络,经SPM2软件单样本t检验,激活脑区周值设为经校正的P<0.001,激活像素范围>5.结果 与后扣带回相关的静息状态网络连接的脑区有:前额叶内侧区、海马、颞极、颞中上回、丘脑枕部、楔叶及楔前叶、部分视皮层、顶下小叶.海马区和顶下小叶呈侧化性激活.结论 正常老年人存在基本完整的静息状态fMRI默认网络结构,其部分呈侧化性可能是与老年期脑功能重塑有关.     

基于离散小波变换的fMRI数据特征提取

目的 设计一种灵敏度高且处理速度快的fMRI数据小波分析方法.方法 先用离散小波变换和频谱分析确定有用信号存在的小波分解尺度,也即特征尺度;再对实验数据进行离散小波分解,重构时将非特征尺度里的小波系数设置为0;再以相关分析对小波重构信号进行激活检测.结果 对视觉数据的分析结果显示,新方法的灵敏度与基于平稳小波变换、SPM2方法相当,而优于基于提升小波变换的方法;新方法的处理速度与基于提升小波变换的方法相当,但较平稳小波变换方法有较大提高.结论 本文为fMRI数据提供了一种更为灵敏且快速的小波分析方法,更为实用.     

ICA-R算法在静息态功能磁共振图像的应用研究

目的：针对传统独立成分分析（ICA）方法在处理功能磁共振（fMRI）数据时存在计算量大、效率低等问题,提出基于ICA-R算法处理数据。方法：将脑默认网络的幅值信息作为源信号的部分先验知识,以参考信号的形式引入到传统ICA算法提取静息态fMRI（rsfMRI）数据的默认网络。结果：ICA—R算法能够有效提取符合大脑在静息状态时的自发活动特征的脑默认网络。结论：ICA-R算法克服了传统ICA算法以分离所有的源信号为目标造成的效率低等缺点,避免了传统ICA算法中需要后续处理的步骤,提高了算法效率,并且能够准确地提取脑脑默认网络。     

结合小波变换对心电信号中RR间期检测的一种新方法

目的:探讨改善心电信号噪声干扰的方法,提高对心率变异信号中的RR间期检测精度。方法:①采用国际上通用的MIT/BIH数据库作为研究心率变异信号中RR间期的研究对象,选取其中的代表性数据组进行实验分析(受严重基线漂移影响的正常心电信号101.dat,大量室性期前收缩的心电信号105.dat,无噪声干扰的正常心电信号213.dat,心动过速的心电信号217.dat)。②利用小波变化方法结合Mallat算法对含有噪声的心电信号进行多尺度分析和信号重构,并利用R波的自身波形特点,采用相对周期极大值法来进行R波位置检测,进而计算RR间期序列值。结果:利用小波变换对含有噪声的信号进行噪声消除可以达到在很大程度保留原始信号的波形特征的同时又取得良好消噪效果的目的。能够显著减小工频干扰、基线漂移和肌电干扰等噪声对判别的影响。通过MIT/BIH数据库中四组有代表性特征的心电信号进行研究,发现采用相对周期极大值检测法可以显著减少检测中易出现的漏检和误检现象,快速而准确的获得RR间期的序列值。结论:小波变换法能够显著减少噪声对信号的干扰,特别是离散小波的应用使数字信号的处理由理论走向实际,结合Mallat快速算法,使得小波变换完全走向实用化。周期极大值法对心率变异信号中RR间期的检测有较好的精确度和快速性。     

局灶性癫痫功能磁共振的语言重组与网络连接研究

目的:探讨局灶性癫痫患者的语言任务模式fMRI脑激活图,fMRI语言网络连接,分析语言功能与fMRI语言网络连接的联系.方法:选择18例符合诊断标准的局灶性癫痫患者(癫痫组)及18例健康志愿者(对照组),其中癫痫组完成以词语阅读任务为刺激模式的fMRI检查,以实现脑内相应功能区激活.利用SPM99软件进行分析,得出癫痫组与对照组fMRI的激活范围与信号变化,通过计算语言任务状态下fMRI最强激活区域时间过程相关性的信号变化,进行fMRI语言网络连接分析,采用独立样本t检验,比较癫痫组和对照组语言功能、fMRI语言网络连接的差异.结果:(1)局灶性癫痫患者fMRI语言激活图显示,激活强度和体积较健康组明显降低(P＜0.01),(2)在词语阅读任务刺激下,癫痫组与对照组最强激活区域相互间fMRI语言网络连接比较,差异具有非常显著性(P＜0.01).结论:局灶性癫痫损害患者的语言功能,癫痫患者语言fMRI激活强度、体积较对照组明显降低,且在最强激活区内,癫痫患者的fMRI语言网络连接明显低于对照组,同时结果显示癫痫患者之间的语言偏侧化不明显,局灶性癫痫患者语言功能的损害与fMRI语言网络连接减少密切相关.     

抽动秽语综合征的扩散张量成像和功能磁共振成像研究进展

抽动秽语综合征（TS）是一种儿童期发病的神经精神疾患,临床主要表现为多发性运动性和发声性抽动。病理生理学研究结果支持TS患者皮层-纹状体-丘脑-皮层（CSTC）环路可能存在着功能障碍。基于DTI的形态学研究显示,脑白质异常不仅局限于运动通路,躯体感觉通路、边缘系统等同样受累。任务态fMRI研究显示,TS患者存在功能代偿机制。静息态fMRI研究显示,TS患者皮层-基底核网络存在功能障碍。本文对TS的扩散张量成像和fMRI研究进展进行综述。     

脑血氧水平依赖性MR功能成像原理及其在神经外科中手术的应用研究

1研究背景磁共振成像是继CT后利用形态学诊断疾病的一项新技术,从20世纪70年代末应用于临床医学诊断以来发展异常迅速,进入20世纪90年代后,脑功能磁共振成像(fMRI)开始出现:1900年美国贝尔实验室学者Ogawa发现血液中的脱氧血红蛋白可改变血管周围水分子的质子信号,该信号可以通     

Detection and detrending in fMRI data analysis

This article addresses the impact that colored noise, temporal filtering, and temporal detrending have on the fMRI analysis situation. Specifically, it is shown why the detection of event-related designs benefit more from pre-whitening than blocked designs in a colored noise structure. Both theoretical and empirical results are provided. Furthermore, a novel exploratory method for producing drift models that efficiently capture trends and drifts in the fMRI data is introduced. A comparison to currently employed detrending approaches is presented. It is shown that the novel exploratory model is able to remove a major part of the slowly varying drifts that are abundant in fMRI data. The value of such a model lies in its ability to remove drift components that otherwise would have contributed to a colored noise structure in the voxel time series.     

Robust controlled functional MRI in alert monkeys at high magnetic field: effects of jaw and body movements

The use of functional magnetic resonance imaging (fMRI) in alert non-human primates is of great potential for research in systems neuroscience. It can be combined with invasive techniques and afford better understanding of non-invasively acquired brain imaging signals in humans. However, the difficulties in optimal application of alert monkey fMRI are multi-faceted, especially at high magnetic fields where the effects of motion and of changes in B0 are greatly amplified. To overcome these difficulties, strict behavioral controls and elaborate animal-training are needed. Here, we introduce a number of hardware developments, quantify the effect of movements on fMRI data, and present procedures for animal training and scanning for well-controlled and artifact-reduced alert monkey fMRI at high magnetic field. In particular, we describe systems for monitoring jaw and body movements, and for accurately tracking eye movements. A link between body and jaw movement and MRI image artifacts is established, showing that relying on the immobilization of an animal's head is not sufficient for high-quality imaging. Quantitative analysis showed that body and jaw movement events caused large instabilities in fMRI time series. On average, body movement events caused larger instabilities than jaw movement events. Residual baseline brain image position and signal amplitude shifts were observed after the jaw and body movement events ended. Based on these findings, we introduce a novel behavioral paradigm that relies on training the monkeys to stay still during long trials. A corresponding analysis method discards all data that were not obtained during the movement-free periods. The baseline position and amplitude shifts are overcome by motion correction and trial-by-trial signal normalization. The advantages of the presented method over conventional scanning and analysis are demonstrated with data obtained at 7 T. It is anticipated that the techniques presented here will prove useful for alert monkey fMRI at any magnetic field.     

The utility of near-infrared spectroscopy in the regression of low-frequency physiological noise from functional magnetic resonance imaging data

Near-infrared spectroscopy (NIRS) signals have been shown to correlate with resting-state BOLD-fMRI data across the whole brain volume, particularly at frequencies below 0.1 Hz. While the physiological origins of this correlation remain unclear, its existence may have a practical application in minimizing the background physiological noise present in BOLD-fMRI recordings. We performed simultaneous, resting-state fMRI and 28-channel NIRS in seven adult subjects in order to assess the utility of NIRS signals in the regression of physiological noise from fMRI data. We calculated the variance of the residual error in a general linear model of the baseline fMRI signal, and the reduction of this variance achieved by including NIRS signals in the model. In addition, we introduced a sequence of simulated hemodynamic response functions (HRFs) into the resting-state fMRI data of each subject in order to quantify the effectiveness of NIRS signals in optimizing the recovery of that HRF. For comparison, these calculations were also performed using a pulse and respiration RETROICOR model. Our results show that the use of 10 or more NIRS channels can reduce variance in the residual error by as much as 36% on average across the whole cortex. However the same number of low-pass filtered white noise regressors is shown to produce a reduction of 19%. The RETROICOR model obtained a variance reduction of 6.4%. Our HRF simulation showed that the mean-squared error (MSE) between the recovered and true HRFs is reduced by 21% on average when 10 NIRS channels are applied and by introducing an optimized time lag between the NIRS and fMRI time series, a single NIRS channel can provide an average MSE reduction of 14%. The RETROICOR model did not provide a significant change in MSE. By each of the metrics calculated, NIRS recording is shown to be of significant benefit to the regression of low-frequency physiological noise from fMRI data.     

Effects of hypoxia, hyperoxia, and hypercapnia on baseline and stimulus-evoked BOLD, CBF, and CMRO2 in spontaneously breathing animals

Functional magnetic resonance imaging (fMRI) was used to investigate the effects of inspired hypoxic, hyperoxic, and hypercapnic gases on baseline and stimulus-evoked changes in blood oxygenation level-dependent (BOLD) signals, cerebral blood flow (CBF), and the cerebral metabolic rate of oxygen (CMRO2) in spontaneously breathing rats under isoflurane anesthesia. Each animal was subjected to a baseline period of six inspired gas conditions (9% O2, 12% O2, 21% O2, 100% O2, 5% CO2, and 10% CO2) followed by a superimposed period of forepaw stimulation. Significant stimulus-evoked fMRI responses were found in the primary somatosensory cortices. Relative fMRI responses to forepaw stimulation varied across gas conditions and were dependent on baseline physiology, whereas absolute fMRI responses were similar across moderate gas conditions (12% O2, 21% O2 100% O2, and 5% CO2) and were relatively independent of baseline physiology. Consistent with data obtained using well-established techniques, baseline and stimulus-evoked CMRO2 were invariant across moderate physiological perturbations thereby supporting a CMRO2-fMRI technique for non-invasive CMRO2 measurement. However, under 9% O2 and 10% CO2, stimulus-evoked CBF and BOLD were substantially reduced and the CMRO2 formalism appeared invalid, likely due to attenuated neurovascular coupling and/or a failure of the model under extreme physiological perturbations. These findings demonstrate that absolute fMRI measurements help distinguish neural from non-neural contributions to the fMRI signals and may lend a more accurate measure of brain activity during states of altered basal physiology. Moreover, since numerous pharmacologic agents, pathophysiological states, and psychiatric conditions alter baseline physiology independent of neural activity, these results have implications for neuroimaging studies using relative fMRI changes to map brain activity.     

Caffeine reduces the activation extent and contrast-to-noise ratio of the functional cerebral blood flow response but not the BOLD response

Measures of the spatial extent of functional activation are important for a number of functional magnetic resonance imaging (fMRI) applications, such as pre-surgical planning and longitudinal tracking of changes in brain activation with disease progression and drug treatment. The interpretation of the data from these applications can be complicated by inter-subject or inter-session variability in the measured fMRI signals. Prior studies have shown that modulation of baseline cerebral blood flow (CBF) can directly alter the functional CBF and blood oxygenation level dependent (BOLD) responses, suggesting that the spatial extents of functional activation maps based on these signals may also depend on baseline CBF. In this study, we used a caffeine dose (200 mg) to decrease baseline CBF and found significant (p<0.05) reductions in both the CBF activation extent and contrast-to-noise ratio (CNR) but no significant changes in the BOLD activation extent and CNR. In contrast, caffeine significantly changed the temporal dynamics of the BOLD response but not the CBF response. The decreases in the CBF activation extent and CNR were consistent with a significant caffeine-induced decrease in the absolute CBF change accompanied by no significant change in the residual noise. Measures of baseline CBF also accounted for a significant portion of the inter-subject variability in the CBF activation map area and CNR. Factors that can modulate baseline CBF, such as age, medication, and disease, should therefore be carefully considered in the interpretation of studies that use functional CBF activation maps.     

利用独立成分分析实现成组的fMRI信号的盲分离

独立成分分析(ICA)作为盲源分离的一种有效方法已经被成功的用于处理功能磁共振成像(fMRI)数据,但是通常人们只是考虑处理单个被试的数据,对于多个被试的情况却很少有人考虑,本文中分析了目前国际上比较流行的三种用ICA来处理多个被试的fMRI数据的方法,并且利用其中最好的一种方法对我们实验中获得的fMRI数据进行处理,结果表明这种方法可以快速有效地处理多个被试的fMRI数据.     

A method for removal of global effects from fMRI time series

We present a technique for removing global effects from functional magnetic resonance imaging (fMRI) images, using a voxel-level linear model of the global signal (LMGS). The procedure does not assume low-frequency global effects and is based on the assumption that the global signal (the time course of the average intensity per volume) is replicated in the same pattern throughout the brain, although not necessarily at the same magnitude. A second assumption is that all effects that match the global signal are of no interest and can be removed. The method involves modeling the time course of each voxel to the global signal and removing any such global component from the voxel's time course. A challenge that elicits a large change in the global blood oxygenation level-dependent (BOLD) signal, inspired hypercapnia (5% CO(2)/95% O(2)), was administered to 14 subjects during a 144-s, 24-scan fMRI procedure; baseline series were also collected. The method was applied to these data and compared to intensity normalization and low-frequency spline detrending. A large global BOLD signal increase emerged to the hypercapnic challenge. Intensity normalization failed to remove global components due to regional variability. Both LMGS and spline detrending effectively removed low-frequency components, but unlike spline detrending (which is designed to remove only low frequency trends), the LMGS removed higher-frequency global fluctuations throughout the challenge and baseline series. LMGS removes all effects correlated with the global signal, and may be especially useful for fMRI data that include large global effects and for generating detrended images to use with subsequent volume-of-interest (VOI) analyses.     

Sensitivity and specificity of high-resolution balanced steady-state free precession fMRI at high field of 9.4T

Balanced steady-state free precession (bSSFP) is an attractive fMRI method at high fields due to minimal spatial distortion. To examine sensitivity and specificity of bSSFP fMRI at ultrahigh magnetic field of 9.4T, we performed high-resolution pass-band high flip-angle (16°) bSSFP fMRI with four phase cycling (PC) angles at two repetition times (TR) of 10ms and 20ms and conventional gradient-recalled-echo (GRE) fMRI with TR of 20ms on rat brain during forepaw stimulation. The sensitivity of bSSFP fMRI with TR of 20ms was higher than that of GRE fMRI regardless of PC angle. Because of magnetic field inhomogeneity, fMRI foci were changed with PC angle in bSSFP fMRI, which was more prominent when TR was shorter. Within a middle cortical layer region where magnetic field inhomogeneity was relatively small, the homogeneity of bSSFP fMRI signals was higher at shorter TR. Acquisition of baseline transition-band bSSFP images helped to identify pass- and transition-band regions and to understand corresponding bSSFP fMRI signals. Fourier analysis of the multiple PC bSSFP datasets provided echoes of multiple pathways separately, and the main echo component showed lower sensitivity and better homogeneity than the free induction decay component. In summary, pass-band bSSFP techniques would have advantages over GRE-based fMRI in terms of sensitivity, and may be a good choice for fMRI at ultrahigh fields.     

Task-driven ICA feature generation for accurate and interpretable prediction using fMRI

Functional Magnetic Resonance Imaging (fMRI) shows significant potential as a tool for predicting clinically important information such as future disease progression or drug effect from brain activity. Multivariate techniques have been developed that combine fMRI signals from across the brain to produce more robust predictive capabilities than can be obtained from single regions. However, the high dimensionality of fMRI data makes overfitting a significant problem. Reliable methods are needed for transforming fMRI data to a set of signals reflecting the underlying spatially extended patterns of neural dynamics. This paper demonstrates a task-specific Independent Component Analysis (ICA) procedure which identifies signals associated with coherent functional brain networks, and shows that these signals can be used for accurate and interpretable prediction. The task-specific ICA parcellations outperformed other feature generation methods in two separate datasets including parcellations based on resting-state data and anatomy. The pattern of response of the task-specific ICA parcellations to particular feature selection strategies indicates that they identify important functional networks associated with the discriminative task. We show ICA parcellations to be robust and informative with respect to non-neural artefacts affecting the fMRI series. Together, these results suggest that task-specific ICA parcellation is a powerful technique for producing predictive and informative signals from fMRI time series. The results presented in this paper also contribute evidence for the general functional validity of the parcellations produced by ICA approaches.     

Dynamic retrospective filtering of physiological noise in BOLD fMRI: DRIFTER

In this article we introduce the DRIFTER algorithm, which is a new model based Bayesian method for retrospective elimination of physiological noise from functional magnetic resonance imaging (fMRI) data. In the method, we first estimate the frequency trajectories of the physiological signals with the interacting multiple models (IMM) filter algorithm. The frequency trajectories can be estimated from external reference signals, or if the temporal resolution is high enough, from the fMRI data. The estimated frequency trajectories are then used in a state space model in combination of a Kalman filter (KF) and Rauch-Tung-Striebel (RTS) smoother, which separates the signal into an activation related cleaned signal, physiological noise, and white measurement noise components. Using experimental data, we show that the method outperforms the RETROICOR algorithm if the shape and amplitude of the physiological signals change over time.