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。     

人单核细胞趋化蛋白—1（MCP—1）cDNA的克隆和序列测定

人单核细胞趋化蛋白－１（ＭＣＰ－１）是由多种细胞产生的一种趋化因子。它不但能趋化单核细胞还能激活单核细胞，在机体防御。炎症恢复和抗肿瘤等方面起重要作用。本文利用ＲＴ－ＰＣＲ方法克隆了人ＭＣＰ－１ｃＤＮＡ，经核鞋酸序列测定，除第１２位密码子为ＴＧＴ与国外报道的ＴＧＣ不同，但编码相同的氨基酸半胱氨酸外，其余序列完全相同。     

人单核细胞趋化蛋白－1（MCP－1）cDNA的克隆和序列测定

人单核细胞趋化蛋白－１（ＭＣＰ－１）是由多种细胞产生的一种趋化因子。它不但能趋化单核细胞还能激活单核细胞，在机体防御、炎症恢复和抗肿瘤等方面起重要作用。本文利用ＲＴ－ＰＣＲ方法克隆了人ＭＣＰ－１ｃＤＮＡ，经核苷酸序列测定，除第１２位密码子为ＴＧＴ与国外报道的ＴＧＣ不同、但编码相同的氨基酸半胱氨酸外，其余序列完全相同。     

基于独立成分分析的时间自相关方法在功能磁共振激活区自适应提取中的应用

传统基于ICA的激活区检测手段是将分离后的独立成分与参考信号做相关性分析。实际问题中,不同区域的脑血流动力学响应情况不同,因此往往得不到标准的参考信号。针对此类问题,提出时间自相关方法(TSC)与ICA方法结合,在不需要参考信号的情况下,通过检测体素点各周期的时间序列相关性,对fMRI数据进行激活区提取。应用5 邻域ICA方法对fMRI数据逐点处理,然后应用时间自相关算法检测各时间序列周期间的相关性,选择最大的自相关系数作为该体素点的信号值。再通过Z变换将相关系数分布转换为服从N(0,1)的Z分布,提取出具有显著性差异（a=0.05）的激活区。将自相关算法应用于仿真数据和12组双手握拳运动的真实fMRI数据的处理,结果表明该方法能够准确提取出仿真数据中的激活区。对真实数据的处理,该方法在空间准确性上与GLM方法无显著性差别(0.4653±0.1368 vs 0.4905±0.1341),在时间准确性上显著优于GLM方法 (0.6364±0.0111 vs 0.3692±0.0109),具有良好的脑功能激活区检测及空间定位能力。     

内源性IL-12决定人PBMC产生干扰素γ的水平

目的：IFN－γ是由被有丝分裂原或抗原所激活的T细胞和NK细胞所产生,它具有广泛的免疫调节活性,现认为IL－12（外源性）是诱导IFN－γ产生的强诱导剂,并可促进静息CD4^ T细胞朝向Th1表型分化,即诱导细胞免疫。目的是为了解PBMC产生的内源性IL－12是否在体外可诱导IFN－γ的产生及通过何机制诱导细胞免疫。方法：用抗CD3抗体、PHA、抗CD3抗体加抗CD28抗体和抗原（MLC）来检测被刺激的PBMC细胞的IFN－γ的产生。同时也用IL－12和IL－12Rβ1的中和抗体来抑制IFN－γ的产生。结果：活的人PBMC中IFN－γ分泌依赖于内源性IL－12的产生,而且激活的T细胞可诱导APC细胞产生IL－2,此过程是通过T细胞表面的CD40L和APC的CD40相互作用而实现。结论：这些结果显示,内源性IL－12在正常罕主抗细胞内抗原的感染反应中起重要作用,在某些形式的自身免疫性疾病和移植排斥反应的免疫病理发生中也起中心作用。     

树Qu载脂蛋白CⅡ的cDNA和蛋白质结构分析

以从树Qu肝脏mRNA反转录获得的cDNA一链为模板，应用SMART－RACE技术，获得了树QuapocⅡ的cDNSA序列，并推导出其蛋白质氨基酸序列，应用分子生物学软件对该蛋白的一级、二级结构进行分析和比较。结果显示，树QuapocⅡcDNA序列（在GenBank中的注册号为AF335585）长483bp，其中开放阅读框架306bp，编码101个氨基酸的前原蛋白，其蛋白原由79个氨基酸组成。树QuapocⅡ的氨基酸顺序与人、猴、狗的同源性分别为86％、87％和81％。与人有其它种属动物的相同序列比较显示，树QuapocⅡ分子C－端激活LPL功能区比N－脂质结合区更具保守性。     

不同程度冠脉狭窄对心电及R—R间期疗列Lyapunov？…

目的：已有的研究表明，心脏的活动是混沌的。心电时间序列的非线性动力学数值指标可反映心脏的总体动态活动特征。本文试图探讨不同程度冠脉狭窄对心电及Ｒ－Ｒ间期序列Ｌｙａｐｕｎｏｖ指数的影响。方法：通过对不同程度冠脉狭窄的冠心病患者的正常人的心电和Ｒ－Ｒ间期时间序列的Ｌｙａｐｕｎｏｖ指数计算，以期从医学数据统计中发现有价值的规律性。结果：初步研究表明，正常人和冠脉重度狭窄患者心电序列的Ｌｙａｐｕｎｏｖ指数     

中国人群中一个新的HLA—DRB4等位基因（DRB4＊NC）的发现及？…

目的：对人群进行ＨＬＡ－ＤＲＢ的分型及多态性研究,发现未曾报道过的ＨＬＡ－ＤＲＢ等位基因。方法：用ＨＬＡ－ＤＲＢ通用引物扩增该个体所有ＤＲＢ等位基因的第二外显子,并对扩增产物进行克隆,通过双脱氧法进行ＤＮＡ测序。结果：发现一个新的ＨＬＡ－ＤＲＢ核苷酸序列。结论：通过与已发表的序列及Ｇｅｎｂａｎｋ已收载的序列比较,显示该序列属于ＨＬＡ－ＤＲＢ４家族的新等位基因,我们暂时称它为ＤＲＢ４＊ＮＣ。该序列已     

人神经营养素—4基因的扩增及序列分析

从人基因组DNA获取神经营养素－4（neurotrophin-4,NT-4)基因，并对该目的基因进行序列测定，本实验直接从人脑组织中提取基因组DNA，根据人神经营养素－4的cDNA序列，设计一对寡核苷酸引物，采用PCR，获得人NT－4基因，用DNA序列分析NT－4基因，结果，我们获取的人NT－4基因与献报道有五个碱基不同，我们认为：直接采用PCR来获取目的基因，出现碱基错配的可能性较大；测序仪器和实验条件对结果有一定影响关系。     

激活巨噬细胞的肌动蛋白分布和钙离子水平

目的：研究被LPS和IFN－γ激活的巨噬细胞的肌动蛋白分布和钙离子水平，探讨肌动蛋白构成和钙离子浓度在巨噬细胞游走和吞噬方面的作用。方法：LPS和IFN－γ激活大鼠巨噬细胞后，用Phallacidin和Fura-2标记肌动蛋白和游离子钙郭，共聚焦激光扫描显微镜和钙离子图像分析下观察肌动蛋白分布和分析钙离子水平。结果：在被激活的巨噬细胞，游走细胞和吞噬细胞的数量增加，细胞内F－肌动蛋白增多，钙离子水平升高，游走细胞有头部和尾部，头部伸出板状为足和丝状伪足。板状伪足和丝状伪足内含有丰富的F－肌动蛋白。细胞底部出现肌动蛋白丝构成的应力纤维。在游走细胞，尾部的钙离子水平比头部高，游走细胞转向时，弯曲部的钙离子水平明显 高，特别是弯曲部的外侧区。巨噬细胞吞噬淋巴细胞时，吞噬处的钙离子水平显著升高。结论巨噬细胞被LPS和IFN－γ激活时，游走和吞噬功能明显增强，细胞内F－肌动蛋白分布和钙离子水平发生了特征的形态学变化。     

Model-free functional MRI analysis based on unsupervised clustering

Conventional model-based or statistical analysis methods for functional MRI (fMRI) are easy to implement, and are effective in analyzing data with simple paradigms. However, they are not applicable in situations in which patterns of neural response are complicated and when fMRI response is unknown. In this paper the "neural gas" network is adapted and rigourosly studied for analyzing fMRI data. The algorithm supports spatial connectivity aiding in the identification of activation sites in functional brain imaging. A comparison of this new method with Kohonen's self-organizing map and with a fuzzy clustering scheme based on deterministic annealing is done in a systematic fMRI study showing comparative quantitative evaluations. The most important findings in this paper are: (1) both "neural gas" and the fuzzy clustering technique outperform Kohonen's map in terms of identifying signal components with high correlation to the fMRI stimulus, (2) the "neural gas" outperforms the two other methods with respect to the quantization error, and (3) Kohonen's map outperforms the two other methods in terms of computational expense. The applicability of the new algorithm is demonstrated on experimental data.     

Linking visual gamma to task‐related brain networks—a simultaneous EEG‐fMRI study

There is a growing interest in human gamma‐band oscillatory activity due to its direct link to neuronal populations, its associations with many cognitive processes, and its positive relationship with fMRI BOLD signal. Visual gamma has been successfully detected using concurrent EEG‐fMRI recordings and linked to activity in the visual cortex using voxel‐wise regression analysis. As gamma‐band oscillations reflect predominantly feedforward projections between brain regions, its inclusion in functional connectivity analysis is highly recommended; however, very few studies have investigated this line of research. In the current study, we aimed to explore this gap by asking which fMRI brain network is related to gamma activity induced by the color discrimination task. Advanced denoising strategies and multitaper spectral decomposition were applied to EEG data to detect gamma oscillations, and group independent component analysis was performed on fMRI data to identify task‐related neural networks. Despite using only trials without motor response (50% of the trials), the two neural measures were successfully coupled. One of the six task‐related networks, the occipito‐parietal network, exhibited significant trial‐by‐trial covariations with gamma oscillations. In addition to the expected extrastriate visual cortex, the network encompasses extensive brain activations in the precuneus, bilateral intraparietal, and anterior insular cortices. We argue that the visual cortex is the source of gamma, whereas the remaining brain regions exhibit feedforward and feedback connections related to this oscillatory activity. Our findings provide evidence for the electrophysiological basis of the connectivity revealed by BOLD signal and impart novel insights into the neural mechanism of color discrimination.     

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。     

Assessing a signal model and identifying brain activity from fMRI data by a detrending-based fractal analysis

One of the major challenges of functional magnetic resonance imaging (fMRI) data analysis is to develop simple and reliable methods to correlate brain regions with functionality. In this paper, we employ a detrending-based fractal method, called detrended fluctuation analysis (DFA), to identify brain activity from fMRI data. We perform three tasks: (a) Estimating noise level from experimental fMRI data; (b) Assessing a signal model recently introduced by Birn et al.; and (c) Evaluating the effectiveness of DFA for discriminating brain activations from artifacts. By computing the receiver operating characteristic (ROC) curves, we find that the ROC curve for experimental data is similar to the curve for simulated data with similar signal-to-noise ratio (SNR). This suggests that the proposed algorithm for estimating noise level is very effective and that Birn’s model fits our experimental data very well. The brain activation maps for experimental data derived by DFA are similar to maps derived by deconvolution using a widely used software, AFNI. Considering that deconvolution explicitly uses the information about the experimental paradigm to extract the activation patterns whereas DFA does not, it remains to be seen whether one can effectively integrate the two methods to improve accuracy for detecting brain areas related to functional activity.     

Detecting Functional Connectivity in fMRI Using PCA and Regression Analysis

A fMRI connectivity analysis approach combining principal component analysis (PCA) and regression analysis is proposed to detect functional connectivity between the brain regions. By first using PCA to identify clusters within the vectors of fMRI time series, more energy and information features in the signal can be maintained than using averaged values from brain regions of interest. Then, regression analysis can be applied to the extracted principal components in order to further investigate functional connectivity. Finally, t-test is applied and the patterns with t-values lager than a threshold are considered as functional connectivity mappings. The validity and reliability of the presented method were demonstrated with both simulated data and human fMRI data obtained during behavioral task and resting state. Compared to the conventional functional connectivity methods such as average signal based correlation analysis, independent component analysis (ICA) and PCA, the proposed method achieves competitive performance with greater accuracy and true positive rate (TPR). Furthermore, the ‘default mode’ and motor network results of resting-state fMRI data indicate that using PCA may improve upon application of existing regression analysis methods in study of human brain functional connectivity.     

Cortical areas related to performance of WAIS Digit Symbol Test: A functional imaging study

Many neuropsychological studies have shown that the Digit Symbol Test (DST) of the Wechsler Adult Intelligence Scale (WAIS) is useful for screening for dysfunctions of the brain. However, it remains unclear which brain areas are actually involved in the performance of DST and what brain functions are used for executing this test. In this study, we examined the cortical areas related to cognitive aspects of DST using functional magnetic resonance imaging (fMRI) and determined executive brain functions involved in this test on the basis of fMRI results. Eleven healthy young adults (mean = 21.6 years) performed a modified DST (mDST) task and its control task, which required a simple graphomotor response during fMRI data acquisition. The direct comparison of brain activations between the mDST task and the control task revealed greater activations in a fronto-parietal cortical network, including the bilateral inferior frontal sulci, left middle frontal gyrus (close to the frontal eye field) and left posterior parietal cortex. These activations are interpreted as reflecting the visual search process and/or the updating process of working memory during the mDST task execution. Furthermore, we found a positive correlation between the number of correct responses and activations in the bilateral inferior frontal regions, suggesting that these prefrontal areas have a crucial role in the performance of DST in a healthy young adult population.     

Relationship between saccadic eye movements and cortical activity as measured by fMRI: quantitative and qualitative aspects

We investigated the quantitative relationship between saccadic activity (as reflected in frequency of occurrence and amplitude of saccades) and blood oxygenation level dependent (BOLD) changes in the cerebral cortex using functional magnetic resonance imaging (fMRI). Furthermore, we investigated quantitative changes in cortical activity associated with qualitative changes in the saccade task for comparable levels of saccadic activity. All experiments required the simultaneous acquisition of eye movement and fMRI data. For this purpose we used a new high-resolution limbus-tracking technique for recording eye movements in the magnetic resonance tomograph. In the first two experimental series we varied both frequency and amplitude of saccade stimuli (target jumps). In the third series we varied task difficulty; subjects performed either pro-saccades or anti-saccades. The brain volume investigated comprised the frontal and supplementary eye fields, parietal as well as striate cortex, and the motion sensitive area of the parieto-occipital cortex. All these regions showed saccade-related BOLD responses. The responses in these regions were highly correlated with saccade frequency, indicating that repeated processing of saccades is integrated over time in the BOLD response. In contrast, there was no comparable BOLD change with variation of saccade amplitude. This finding speaks for a topological rather than activity-dependent coding of saccade amplitudes in most cortical regions. In the experiments comparing pro- vs anti-saccades we found higher BOLD activation in the "anti" task than in the "pro" task. A comparison of saccade parameters revealed that saccade frequency and cumulative amplitude were comparable between the two tasks, whereas reaction times were longer in the "anti" task than the pro task. The latter finding is taken to indicate a more demanding cortical processing in the "anti" task than the "pro" task, which could explain the observed difference in BOLD activation. We hold that a quantitative analysis of saccade parameters (especially saccade frequency and latency) is important for the interpretation of the BOLD changes observed with visual stimuli in fMRI.     

Brain mapping and detection of functional patterns in fMRI using wavelet transform; application in detection of dyslexia

Background

Functional Magnetic Resonance Imaging (fMRI) has been proven to be useful for studying brain functions. However, due to the existence of noise and distortion, mapping between the fMRI signal and the actual neural activity is difficult. Because of the difficulty, differential pattern analysis of fMRI brain images for healthy and diseased cases is regarded as an important research topic. From fMRI scans, increased blood ows can be identified as activated brain regions. Also, based on the multi-sliced images of the volume data, fMRI provides the functional information for detecting and analyzing different parts of the brain.

Methods

In this paper, the capability of a hierarchical method that performed an optimization algorithm based on modified maximum model (MCM) in our previous study is evaluated. The optimization algorithm is designed by adopting modified maximum correlation model (MCM) to detect active regions that contain significant responses. Specifically, in the study, the optimization algorithm is examined based on two groups of datasets, dyslexia and healthy subjects to verify the ability of the algorithm that enhances the quality of signal activities in the interested regions of the brain. After verifying the algorithm, discrete wavelet transform (DWT) is applied to identify the difference between healthy and dyslexia subjects.

Results

We successfully showed that our optimization algorithm improves the fMRI signal activity for both healthy and dyslexia subjects. In addition, we found that DWT based features can identify the difference between healthy and dyslexia subjects.

Conclusion

The results of this study provide insights of associations of functional abnormalities in dyslexic subjects that may be helpful for neurobiological identification from healthy subject.

     

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.     

Localization of Broca's area using verb generation tasks in the MEG: Validation against fMRI

Functional MRI (fMRI) is routinely used to non-invasively localize language areas. Magnetoencephalography (MEG) is being explored as an alternative technique. MEG tasks to localize receptive language are well established although there are no standardized tasks to localize expressive language areas. We developed two expressive language tasks for MEG and validated their localizations against fMRI data. Ten right-handed adolescents (μ = 17.5 years) were tested with fMRI and MEG on two tasks: verb generation to pictures and verb generation to words. MEG and fMRI data were normalized and overlaid. The number of overlapping voxels activated in fMRI and MEG were counted for each subject, for each task, at different thresholding levels. For picture verb generation, there was 100% concordance between MEG and fMRI lateralization, and for word verb generation, there was 75% concordance. A count showed 79.6% overlap of voxels activated by both MEG and fMRI for picture verb generation and 50.2% overlap for word verb generation. The percentage overlap decreased with increasingly stringent activation thresholds. Our novel MEG expressive language tasks successfully identified neural regions involved in language production and showed high concordance with fMRI laterality. Percentage overlap of activated voxels was also high when validated against fMRI, but showed task-specific and threshold-related effects. The high concordance and high percentage overlap between fMRI and MEG activations confirm the validity of our new MEG task. Furthermore, the higher concordance from the picture verb generation task suggests that this is a promising task for use in the young clinical population.