Minimizing macrovessel signal in cerebral perfusion imaging using independent component analysis.

The pronounced susceptibility effect of macrovessels in MR bolus-tracking studies induces spots of artificially high blood flow and volume in perfusion parameter images. These high-intensity regions impede the detection of perfusion changes and lead to elevated perfusion parameters in adjacent tissues. The purpose of this work was to explore postprocessing methods to reduce the influence of macrovessel signal in dynamic MRI. After data reduction was performed with the use of a principal component analysis (PCA), an independent component analysis (ICA) was applied to separate signal components of different compartments. Based on this decomposition, the dynamic time series were reconstructed with minimized contributions of macrovessel signal and noise. The influence of the temporal resolution and signal-to-noise ratio (SNR) of the source data were investigated by means of a simulation study. A region-of-interest (ROI)-based analysis of corrected and uncorrected in vivo data demonstrated that the influence of arteries and veins was reduced at least by 50%, while gray matter (GM) and white matter (WM) tissues were nearly unaffected by the correction process. Hemodynamic parameter images of the cerebral blood volume (CBV), cerebral blood flow (CBF), and mean transit time (MTT) were calculated from corrected and uncorrected scans. The corrected parameter images showed a clearly reduced macrovessel signal and an improved perceptibility of microvascular perfusion changes compared to the uncorrected ones.     

噪声中EP信号的提取算法及研究进展

诱发电位的提取是脑电信号处理领域的前沿课题近年来 ,通过少次甚至单次试验提取诱发电位已经成为研究的主流。本文对近年来提取诱发电位的信号处理方法进行了简要的回顾 ,并分别从小波变换、神经网络、高阶累积量、独立分量分析等四个方面对算法进行了介绍     

Independent component analysis and clustering improve signal-to-noise ratio for statistical analysis of event-related potentials

OBJECTIVE: To evaluate the effectiveness of a new method of using Independent Component Analysis (ICA) and k-means clustering to increase the signal-to-noise ratio of Event-Related Potential (ERP) measurements while permitting standard statistical comparisons to be made despite the inter-subject variations characteristic of ICA. METHODS: Per-subject ICA results were used to create a channel pool, with unequal weights, that could be applied consistently across subjects. Signals derived from this and other pooling schemes, and from unpooled electrodes, were subjected to identical statistical analysis of the N170 own-face effect in a Joe/No Joe face recognition paradigm wherein participants monitored for a target face (Joe) presented amongst other unfamiliar faces and their own face. Results between the Joe, unfamiliar face and own face conditions were compared using Cohen's d statistic (square root of signal-to-noise ratio) to measure effect size. RESULTS: When the own-face condition was compared to the Joe and unfamiliar-face conditions, the channel map method increased effect size by a factor ranging from 1.2 to 2.2. These results stand in contrast to previous findings, where conventional pooling schemes failed to reveal an N170 effect to the own-face stimulus (Tanaka JW, Curran T, Porterfield A, Collins D. The activation of pre-existing and acquired face representations: the N250 ERP as an index of face familiarity. J Cogn Neurosci 2006;18:1488-97). Consistent with conventional pooling schemes, the channel map approach showed no reliable differences between the Joe and Unfamiliar face conditions, yielding a decrease in effect size ranging from 0.13 to 0.75. CONCLUSIONS: By increasing the signal-to-noise ratio in the measured waveforms, the channel pool method demonstrated an enhanced sensitivity to the neurophysiological response to own-face relative to other faces. SIGNIFICANCE: By overcoming the characteristic inter-subject variations of ICA, this work allows classic ERP analysis methods to exploit the improved signal-to-noise ratio obtainable with ICA.     

一种用ICA去除瞬态诱发耳声发射伪迹的新方法

如何去除伪迹是瞬态诱发耳声发射检测中一个关键的问题。本研究提出了一种用ICA去除伪迹的新方法。首先用四组线性增长的刺激声在耳道内录音 ,得到的波形是瞬态诱发耳声发射和伪迹的混叠。因为伪迹和瞬态诱发耳声发射是统计独立的 ,而且伪迹随刺激声的变化线性增长 ,而瞬态诱发耳声发射随刺激声的变化非线性增长 ,逐渐趋于饱和 ,所以它们在混叠信号中具有不同的混叠系数。用ICA算法可以将各独立分量及混叠矩阵估计出来 ,伪迹是其中的一个独立分量。然后将伪迹的波形置零后再进行一次混叠 ,便达到了去除伪迹的目的。最后通过与传统的DNLR方法比较 ,证明这种方法是有效的     

Analysis of children with type 1 diabetes in Korea: high prevalence of specific anti-islet autoantibodies, immunogenetic similarities to Western populations with "unique" haplotypes, and lack of discrimination by aspartic acid at position 57 of DQB

This study analyzed the expression of anti-islet autoantibodies and HLA-DR and -DQ genotypes in Korean children with type 1 diabetes mellitus (T1DM). The positivity of the anti-ICA512, anti-GAD65, and anti-insulin autoantibodies in the newly onset T1DM patients (n = 15) was 66.7%, 86.7%, and 46.7%, respectively, and all of them had one or more of the autoantibodies. HLA analysis showed higher frequencies of HLA-DRB1*0301, *0405, *09012 and -DQB1*0201, *0401, *03032 alleles in T1DM patients compared to controls (P(c) < 0.05). Because HLA-DQB1*0401, *03032 alleles carry aspartic acid at position 57 of DQB, susceptibility to T1DM in Korean children was not related to the presence of aspartic acid at position 57 of DQB1 locus. We suggest this unique HLA-DR, -DQ allele distribution might be an important factor for the low incidence of T1DM in Korea, and the combined anti-islet autoantibody assays could be valuable screening markers for the early detection of T1DM in Korea.     

一种独立成分分析的鲁棒算法及其在脑磁图数据分析中的应用

独立成分分析是一种新的信号处理统计方法。被广泛用于各个领域。在信号分析中面临的难题是：源信号的不同特性（既包括超高斯信号又包括亚高斯信号）；未知的独立源数目；传感器信号受到较大的加性噪声污染。针对以上难题，本文提出了一种独立成分分析的鲁棒算法。该方法先对观测数据作预处理，将包含噪声的高维传感器观测信号降维分解到信号子空间和噪声子空间。利用交叉验证法估计出独立源的数目（解决了独立成分分析本身不能确定源数目的缺陷）；然后利用快速稳定的FastICA算法分离独立成分。通过人工合成的数据和实际的脑磁图数据分析。验证了这种方法的功效。     

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.     

空间ICA在fMRI数据上的应用与分析

独立成分分析(ICA)技术试图将多维数据分解成若干个相互统计独立的分量。时间ICA和空间ICA都可以用于分析功能核磁共振成像(fMRI)数据。但由于fMRI数据空间维数远远大于时间维数，为计算方便，在分析fMRI数据时。则更多的使用空间ICA方法。本文在单任务激励实验中，利用ICA方法从fMRI数据中分离出若干个与任务相关的独立分量，其中包括与任务相关的恒定分量(CTR)和与任务相关的暂态分量(TTR)；通过将这些独立分量进行空间映射，得到了与任务相关的脑部激活区域。将此结果与SPM的分析比较，得到了一致的结果。在对结果的分析中，我们进一步指出了ICA方法的特点和局限性。     

基于扩展Infomax ICA的ERP少次提取方法研究

事件相关电位(event related potential,ERP)提取是脑电研究的重点之一,目前临床上主要通过相干平均的方法来获取.由于脑电的非平稳性,使其需要大量重复刺激才能获得,对于受试者极不方便,也不利于ERP的实时检测.本文以反映大脑稀少认知事件的相关电位P300为例,采用扩展Infomax(extended informationmaximization)独立分量分析(independent component analysis,ICA)算法,先滤除眼动、工频干扰,再重构脑电数据,最后经少次叠加即可得到与通常需多次相干平均结果相近的比较满意的P300波形.说明ICA算法在ERP的峰值和潜伏期模式识别上具有较为明显的效果,具有潜在的临床工程应用价值.     

ICA在糖尿病诊疗中的应用研究

用免疫酶法分析了糖尿病患者及甲状腺疾病患者血清中胰岛细胞抗体（ＩＣＡ）的水平。结果为经胰岛素吸收后,ＩＤＤＭ组阳性率为４６．７％（最高）,ＮＩＤＤＭ组阳性率为１３．３％,甲状腺疾病组（其中１例Ｇｒａｖｅｓ’病伴糖尿病ＩＣＡ阳性）阳性率为４％,健康组０。统计学分析,Ｐ〈０．０５,具有显著性差异,表明ＩＣＡ是Ⅰ型糖尿病的特异性免疫标志物,ＩＣＡ检测作为对ＩＤＤＭ与ＮＩＤＤＭ的鉴别诊断,特别是对ＬＡＤＡ