基于SPM的时间簇分析方法

目的：把时间簇分析方法和SPM这两种方法结合起来，能够提高时间簇分析方法的检测灵敏度，使得基于SPM的时间簇分析方法可以检测出大脑激活的时间曲线。方法：先用SPM得到相应的大脑激活，然后再对激活脑区进行TCA处理，就可以得到激活脑区的时间曲线。结果：用基于SPM的时间簇分析方法对一组视觉的fMRI数据进行处理，能够检测出对应于刺激的四个明显的激活峰。结论：基于SPM的时间簇分析方法的检测灵敏度比以前的时间簇分析方法的检测灵敏度有了很大提高，能够用于检测大脑激活的时间曲线。     

α频率光刺激脑电信号同步化的初步研究

研究α频率(8～13Hz)闪光刺激是否能引起人脑枕区同频率脑电信号的增加.采集15名正常志愿者在平静、光刺激状态下的脑电信号,利用脑地形图、小波分析和功率谱估计的方法对α频率光刺激前后的脑电信号进行处理,将归一化后的数据分为平静-激活组、男女对照组、两个不同电极位置组进行对比分析,对组间信号的差异进行了讨论,并对受试者精神状态的变化进行初步探讨.结果表明:周期性α频率光刺激能引起大脑枕区同频率脑电的显著增加.通过分析实验数据,初步得到了大脑在外部闪光刺激时脑电的变化规律,本实验结果对研究外部刺激对脑电的影响有一定的参考价值.     

基于相关分析的fMRI时间序列图像处理

脑与认知科学研究中,对功能磁共振图像的分析,不仅要求脑功能激活区的准确定位,而且要得到脑激活区的动态变化。提出了通过对激活区体元时间序列的分解,构造序列参考波形,用相关分析法检测识别每个时间周期的激活类型,得到脑功能激活区的动态变化图像、生理信号变化时间及信号强度曲线。经视觉试验实测数据建模并检验,该方法可有效检测fMRI数据中与试验密切相关的脑激活区及其周期性变化。     

人脑对握力刺激响应特征的数值计算分析方法

为研究人脑对握力刺激的响应特征,提出一种新的数值计算分析方法：结合独立成分分析和云模型,对握力刺激脑响应特征进行数值计算。采集10名健康受试者不同握力任务下的功能磁共振（fMRI）数据并进行预处理,应用独立成分分析获取不同握力刺激条件下的脑激活区域位置和大小,然后通过云模型计算脑激活区域内的数据分布特征。结果表明,握力刺激的脑激活区域主要分布在对侧大脑Brodmann 2、3、4、6区和同侧小脑,并且随着握力强度的增加,中央前回、中央后回等激活区域增大(激活簇体素个数分别为4 075、4 218、4 965);在不同握力刺激条件下,激活区域的任务态与非任务态间的期望、熵、超熵（Ex、En、He）均有明显的统计学差异,Ex（P<0001）和En(P<0.005)增大,He(P<0.005)减小;不同握力刺激间三个参数的差异不明显,并且非激活区域内任务状态与非任务状态间的期望、熵、超熵均无统计学差异。该方法可为不同任务下大脑激活区域的数据分布特征研究提供一种新的分析手段。     

α频率光刺激脑电信号同步化的初步研究

研究α频率（8-13Hz）闪光刺激是否能引起人脑枕区同频率脑电信号的增加。采集15名正常志愿者在平静、光刺激状态下的脑电信号,利用脑地形图、小波分析和功率谱估计的方法对α频率光刺激前后的脑电信号进行处理,将归一化后的数据分为平静-激活组、男女对照组、两个不同电极位置组进行对比分析,对组间信号的差异进行了讨论,并对受试者精神状态的变化进行初步探讨。结果表明：周期性α频率光刺激能引起大脑枕区同频率脑电的显著增加。通过分析实验数据,初步得到了大脑在外部闪光刺激时脑电的变化规律,本实验结果对研究外部刺激对脑电的影响有一定的参考价值。     

基于组独立成分分析方法的情绪刺激对脑部激活区域的研究

目的观察积极和消极情绪状态下的脑激活差异情况,探究脑部对正性和负性情绪信息的处理机制。方法采用组独立成分分析技术处理情绪刺激下的功能磁共振图像数据。结果在积极情绪刺激下,杏仁核周围、顶叶和右扣带回区域的连接性十分显著;而在消极的情绪刺激下,杏仁核和丘脑附近区域激活程度明显,并且这些区域与楔叶右侧、左侧颞中回区域产生高度相关性。结论大脑处理消极情绪时,激活脑区与皮层-丘脑枕核-杏仁核通路大致相符,并且左侧楔前叶和右侧后扣带回区域的连接性存在显著下降。     

音调与语音Oddball实验的脑电流源模型分析

采用语音和音调Oddball实验范式,研究大脑离散电流源在靶刺激响应过程中的电流时间过程。采集了健康受试者的64导联脑电数据,建立并比较音调和语音靶刺激ERPs数据的区域源电流模型。结果显示:听觉靶刺激识别的主要神经源包括双侧颞上回和双侧脑岛。音调靶刺激识别引起右侧区域源的N2/P3幅度大于左侧,呈现右偏侧化,其中脑岛的右偏侧化较明显,而语音靶刺激识别引起左侧区域源N2/P3幅度大于右侧,呈现左偏侧化,双侧脑岛和双侧颞上回都出现明显的左偏侧化。研究表明:靶刺激识别这一脑处理过程随刺激模式的不同存在差异,音调靶刺激识别以右侧脑岛和右侧颞上回的激活更强,而语音靶刺激识别以左侧颞上回的激活更强。     

抑郁症的ERPs电流源模型与特征分析

采用3-刺激视觉oddball实验范式,研究干扰子刺激和靶刺激条件下抑郁症患者的大脑电流源的异常电流响应过程.分别采集了抑郁症和健康受试者的64导联脑电,以该实验任务的功能磁共振激活簇空间坐标为约束条件,建立了干扰子刺激和靶刺激条件下事件相关电位的脑区域源模型,通过脑源重建法计算得到14个区域源的源电流波形,经非配对t检验和置换检验后发现:在靶刺激条件下,抑郁症患者的右侧脑岛、右侧中央前沟在P300晚期产生的源电流幅度与正常人有显著差异(P＜0.05),它引起右侧头皮前额区的异常负电位.在干扰子刺激条件下,患者的左侧中央前沟在P300晚期产生的源电流与正常人有明显差异,差异显著性具有趋势意义(P=0.087),它引起左侧前额区的异常近零电位.结果表明:通过源电流计算发现了抑郁症患者的中央前沟和脑岛的P300活动发生显著异常.     

视听刺激脑电信号的相位同步分析

脑电(EEG)同步被认为是脑功能区域整合的表现。高级脑功能需要具有特定功能的多区域神经系统间进行不同层次的整合和协调来完成。本文提出了一种新的相位同步分析方法—互近似熵。采用分段频率,用同步指数、互信息熵与互近似熵方法对视听刺激EEG导联数据进行了相位同步的比较分析,三种分析得到了一致的结果,说明互近似熵方法也能很好反映出两导联的相位同步。文章同时通过相位同步分析结果进行了大脑反应区域的探索分析。此研究为脑机接口的设计奠定了基础。     

基于DCM的减法运算脑功能效应连通性方法研究

动态因果模型(Dynamic causal modeling,DCM)是一种时空上可再生的网络模型,用来研究功能核磁共振中功能整合的因果关系,是效应连通性的分析方法,该方法是将实验设计中得到的激活区域时间序列加入到DCM模型中,实验任务的刺激响应作为对模型的扰动,利用DCM和贝叶斯估计计算出各神经元或者神经系统之间前后影响的因果关系,以及大范围的内在连接,然后利用贝叶斯因子对所设计各模型的参数做最优化选择,从中选择出符合生理的最佳模型。本文主要研究心算借位减法任务激活的左侧大脑区域,左侧顶上小叶、左侧顶下小叶和左侧额中回之间的效应连接,并得到符合生理意义的连接网络。     

Analysis of fMRI data by blind separation of data in a tiny spatial domain into independent temporal component

Independent Component Analysis (ICA) is a promising tool for the analysis of functional magnetic resonance imaging (fMRI) time series. In these studies, mostly assumed is a spatially independent component map of fMRI data (spatial ICA). In this paper, we assume that the temporal courses of the signal and noises are independent within a Tiny spatial domain (temporal ICA). Then with fast-ICA algorithm, spatially neighboring fMRI data were blindly separated into several temporal courses and were preassumed to be formed by a signal time course and several noise time courses where the signal has the largest correlation coefficient with the reference signal. The final functional imaging was completed for the signals obtained from each voxel. Simulations showed that compared with the spatial ICA method, the new temporal ICA method is more effective than the spatial ICA in detecting weak signal in a fMRI dataset. As background noise, the simulations include simulated Gaussian noise and fMRI data without stimulation. Finally, vivo fMRI tests showed that the excited areas evoked by a visual stimuli are mainly in the region of the primary visual cortex and that evoked by auditory stimuli are mainly in the region of the primary temporal cortex.     

An improved multi-objective optimization-based CICA method with data-driver temporal reference for group fMRI data analysis

Group independent component analysis (GICA) has been successfully applied to study multi-subject functional magnetic resonance imaging (fMRI) data, and the group independent component (GIC) represents the commonality of all subjects in the group. However, some studies show that the performance of GICA can be improved by incorporating a priori information, which is not always considered when looking for GICs in existing GICA methods. In this paper, we propose an improved multi-objective optimization-based constrained independent component analysis (CICA) method to take advantage of the temporal a priori information extracted from all subjects in the group by incorporating it into the computational process of GICA for group fMRI data analysis. The experimental results of simulated and real data show that the activated regions and the time course detected by the improved CICA method are more accurate in some sense. Moreover, the GIC computed by the improved CICA method has a higher correlation with the corresponding independent component of each subject in the group, which means that the improved CICA method with the temporal a priori information extracted from the group can better reflect the commonality of the subjects. These results demonstrate that the improved CICA method has its own advantages in fMRI data analysis.     

Fuzzy cluster analysis of high-field functional MRI data

Functional magnetic resonance imaging (fMRI) based on blood-oxygen level dependent (BOLD) contrast today is an established brain research method and quickly gains acceptance for complementary clinical diagnosis. However, neither the basic mechanisms like coupling between neuronal activation and haemodynamic response are known exactly, nor can the various artifacts be predicted or controlled. Thus, modeling functional signal changes is non-trivial and exploratory data analysis (EDA) may be rather useful. In particular, identification and separation of artifacts as well as quantification of expected, i.e. stimulus correlated, and novel information on brain activity is important for both, new insights in neuroscience and future developments in functional MRI of the human brain. After an introduction on fuzzy clustering and very high-field fMRI we present several examples where fuzzy cluster analysis (FCA) of fMRI time series helps to identify and locally separate various artifacts. We also present and discuss applications and limitations of fuzzy cluster analysis in very high-field functional MRI: differentiate temporal patterns in MRI using (a) a test object with static and dynamic parts, (b) artifacts due to gross head motion artifacts. Using a synthetic fMRI data set we quantitatively examine the influences of relevant FCA parameters on clustering results in terms of receiver-operator characteristics (ROC) and compare them with a commonly used model-based correlation analysis (CA) approach. The application of FCA in analyzing in vivo fMRI data is shown for (a) a motor paradigm, (b) data from multi-echo imaging, and (c) a fMRI study using mental rotation of three-dimensional cubes. We found that differentiation of true "neural" from false "vascular" activation is possible based on echo time dependence and specific activation levels, as well as based on their signal time-course. Exploratory data analysis methods in general and fuzzy cluster analysis in particular may help to identify artifacts and add novel and unexpected information valuable for interpretation, classification and characterization of functional MRI data which can be used to design new data acquisition schemes, stimulus presentations, neuro(physio)logical paradigms, as well as to improve quantitative biophysical models.     

基于多目标优化约束独立成分分析方法的fMRI数据分析研究

约束独立成分分析(CICA)通过加入先验信息,可极大地提高独立成分分析(ICA)的盲源信号分析性能,但还存在先验信息难以获取、先验信息约束条件阈值参数难以选择以及先验信息难以被有效利用等问题,需要进一步研究和解决。在多目标优化框架的基础上,建立一种同时融合时空先验信息的CICA模型,可有效规避CICA中阈值参数选择的问题。此外,提出一种从多被试fMRI数据中提取本真先验信息来指导fMRI组分析的自适应挖掘算法,从而为CICA获取先验信息提供一种新途径。最后,利用10例模拟数据、5例任务态和23例静息态fMRI数据,验证所提方法的有效性。结果表明:基于多目标优化的CICA(MOPCICA)获得的时空源信号总体上优于ICA、包含时间信息的CICA(CICA-tR)和包含空间信息的CICA(CICA-sR)(P<0.05)(如在模拟数据中,对应的空间AUC和时间相关系数分别0.75±0.05、0.62±0.02、0.72±0.03、0.71±0.06和0.81±0.13、0.67±0.04、0.74±0.09、0.77±0.13),而空间独立性则优于CICA-tR和CICA-sR(P<0.05)(如在任务态数据中,对应的峭度和负熵分别为69.20±23.36、17.60±13.22、36.71±13.43和0.031 2±0.007 7、0.003 7±0.002 1、0.018 4±0.004 5),从而说明它具有更好的源信号恢复性能。同时,在静息态数据中利用fMRI本真先验信息,MOPCICA获得的组成分与每个被试相应成分之间的相关系数平均高于ICA、基于牛顿迭代法的CICA(CICA-nR)和基于不动点迭代法的CICA(CICA-fR)(P<0.05)(分别为0.46±0.08、0.44±0.08、0.45±0.08和0.44±0.08),从而更能代表组中被试的共性。研究表明,所提出的方法对fMRI脑功能连通性检测具有重要意义。     

Temporal clustering analysis of cerebral blood flow activation maps measured by laser speckle contrast imaging

Temporal and spatial orchestration of neurovascular coupling in brain neuronal activity is crucial for comprehending the mechanism of functional cerebral metabolism and pathophysiology. Laser speckle contrast imaging (LSCI) through a thinned skull over the somatosensory cortex is utilized to map the spatiotemporal characteristics of local cerebral blood flow (CBF) in anesthetized rats during sciatic nerve stimulation. The time course of signals from all spatial loci among the massive dataset is hard to analyze, especially for the thousands of images, each of which composes millions of pixels. We introduce a temporal clustering analysis (TCA) method, which is proven as an efficient method to analyze functional magnetic resonance imaging (fMRI) data in the temporal domain. The timing and location of CBF activation shows that contralateral hindlimb sensory cortical microflow is activated to increase promptly in less than 1 s after the onset of 2-s electrical stimulation and is evolved in different discrete regions. This pattern is similar but slightly elaborated from the results obtained from laser Doppler flowmetry (LDF) and fMRI. We present this combination to investigate interacting brain regions, which might lead to a better understanding of the nature of brain parcellation and effective connectivity.     

Multimodal Integration of fMRI and EEG Data for High Spatial and Temporal Resolution Analysis of Brain Networks

Two major non-invasive brain mapping techniques, electroencephalography (EEG) and functional magnetic resonance imaging (fMRI), have complementary advantages with regard to their spatial and temporal resolution. We propose an approach based on the integration of EEG and fMRI, enabling the EEG temporal dynamics of information processing to be characterized within spatially well-defined fMRI large-scale networks. First, the fMRI data are decomposed into networks by means of spatial independent component analysis (sICA), and those associated with intrinsic activity and/or responding to task performance are selected using information from the related time-courses. Next, the EEG data over all sensors are averaged with respect to event timing, thus calculating event-related potentials (ERPs). The ERPs are subjected to temporal ICA (tICA), and the resulting components are localized with the weighted minimum norm (WMNLS) algorithm using the task-related fMRI networks as priors. Finally, the temporal contribution of each ERP component in the areas belonging to the fMRI large-scale networks is estimated. The proposed approach has been evaluated on visual target detection data. Our results confirm that two different components, commonly observed in EEG when presenting novel and salient stimuli, respectively, are related to the neuronal activation in large-scale networks, operating at different latencies and associated with different functional processes.     

Sensorimotor integration in S2, PV, and parietal rostroventral areas of the human sylvian fissure

We explored cortical fields on the upper bank of the Sylvian fissure using functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG) to measure responses to two stimulus conditions: a tactile stimulus applied to the right hand and a tactile stimulus with an additional movement component. fMRI data revealed bilateral activation in S2/PV in response to tactile stimulation alone and source localization of MEG data identified a peak latency of 122 ms in a similar location. During the tactile and movement condition, fMRI revealed bilateral activation of S2/PV and an anterior field, while MEG data contained one source at a location identical to the tactile-only condition with a latency of 96 ms and a second rostral source with a longer latency (136 ms). Furthermore, Region-of-interest analysis of fMRI data identified increased bilateral activation in S2/PV and the rostral area in the tactile and movement condition compared with the tactile only condition. An area of cortex immediately rostral to S2/PV in monkeys has been called the parietal rostroventral area (PR). Based on location, latency, and conditions under which this field was active, we have termed the rostral area of human cortex PR as well. These findings indicate that humans, like non-human primates, have a cortical field rostral to PV that processes proprioceptive inputs, both S2/PV and PR play a role in somatomotor integration necessary for manual exploration and object discrimination, and there is a temporal hierarchy of processing with S2/PV active prior to PR.     

A site directed fMRI approach for evaluating functional status in the anterolateral temporal lobes

Functional magnetic resonance imaging (fMRI) is increasingly being used for neurosurgical planning. One potential application relates to identifying eloquent cortex in regions immediately adjacent to epileptogenic foci in temporal lobe epilepsy (TLE). While medial temporal structures, such as the hippocampus and amygdala, are typically removed during surgery, it is often difficult to determine whether nearby cortical regions in the anterolateral temporal lobe should be spared. An essential first step is to identify a method of activating these regions in healthy individuals. The purpose of this study was to develop a site directed fMRI approach for evaluating functional status in the anterolateral temporal lobes. A picture-word matching task, with object category and level of abstraction factors, was used to characterize temporal lobe activation. Whole brain analysis at the group level confirmed the involvement of the temporal poles as well as adjacent superior, middle and inferior temporal gyri within a larger object recognition network. A region-of-interest analysis on the anterolateral temporal lobe demonstrated that activation varied across conditions and regions for individuals. Importantly, it was possible to detect activation in one or more conditions and/or regions for all individuals--demonstrating that it is possible to evaluate functional status. The findings provide the foundation for a novel fMRI approach in neurosurgical planning for TLE.     

Temporal comparison of functional brain imaging with diffuse optical tomography and fMRI during rat forepaw stimulation

The time courses of oxyhaemoglobin ([HbO2]), deoxyhaemoglobin ([HbR]) and total haemoglobin ([HbT]) concentration changes following cortical activation in rats by electrical forepaw stimulation were measured using diffuse optical tomography (DOT) and compared to similar measurements performed previously with fMRI at 2.0 T and 4.7 T. We also explored the qualitative effects of varying stimulus parameters on the temporal evolution of the hemodynamic response. DOT images were reconstructed at a depth of 1.5 mm over a 1 cm square area from 2 mm anterior to bregma to 8 mm posterior to bregma. The measurement set included 9 sources and 16 detectors with an imaging frame rate of 10 Hz. Both DOT [HbR] and [HbO2] time courses were compared to the fMRI BOLD time course during stimulation, and the DOT [HbT] time course was compared to the fMRI cerebral plasma volume (CPV) time course. We believe that DOT and fMRI can provide similar temporal information for both blood volume and deoxyhaemoglobin changes, which helps to cross-validate these two techniques and to demonstrate that DOT can be useful as a complementary modality to fMRI for investigating the hemodynamic response to neuronal activity.