Discussion on the choice of separated components in fMRI data analysis by spatial independent component analysis

By measuring the changes of magnetic resonance signals during a stimulation, the functional magnetic resonance imaging (fMRI) is able to localize the neural activation in the brain. In this report, we discuss the fMRI application of the spatial independent component analysis (spatial ICA), which maximizes statistical independence over spatial images. Included simulations show the possibility of the spatial ICA on discriminating asynchronous activations or different response patterns in an fMRI data set. An in vivo visual stimulation fMRI test was conducted, and the result shows a proper sum of the separated components as the final image is better than a single component, using fMRI data analysis by spatial ICA. Our result means that spatial ICA is a useful tool for the detection of different response activations and suggests that a proper sum of the separated independent components should be used for the imaging result of fMRI data processing.     

A method for comparing group fMRI data using independent component analysis: application to visual, motor and visuomotor tasks

Independent component analysis (ICA) is an approach for decomposing fMRI data into spatially independent maps and time courses. We have recently proposed a method for ICA of multisubject data; in the current paper, an extension is proposed for allowing ICA group comparisons. This method is applied to data from experiments designed to stimulate visual cortex, motor cortex or both visual and motor cortices. Several intergroup and intragroup metrics are proposed for assessing the utility of the components for comparisons of group ICA data. The proposed method may prove to be useful in answering questions requiring multigroup comparisons when a flexible modeling approach is desired.     

Detecting functional connectivity in the resting brain: a comparison between ICA and CCA

Independent component analysis (ICA) and cross-correlation analysis (CCA) are general tools for detecting resting-state functional connectivity. In this study, we jointly evaluated these two approaches based on simulated data and in vivo functional magnetic resonance imaging data acquired from 10 resting healthy subjects. The influence of the number of independent components (maps) on the results of ICA was investigated. The influence of the selection of the seeds on the results of CCA was also examined. Our results reveal that significant differences between these two approaches exist. The performance of ICA is superior as compared with that of CCA; in addition, the performance of ICA is not significantly affected by structured noise over a relatively large range. The results of ICA could be affected by the number of independent components if this number is too small, however. Converting the spatially independent maps of ICA into z maps for thresholding tends to overestimate the false-positive rate. However, the overestimation is not very severe and may be acceptable in most cases. The results of CCA are dependent on seeds location. Seeds selected based on different criteria will significantly affect connectivity maps.     

Multivariate analysis applied to particle‐induced X‐ray emission mapping

The use of particle‐induced X‐ray emission (PIXE) for elemental speciation and quantification has gained new attention thanks to mapping capabilities. Microprobes are able to raster a proton beam and produce elemental maps on the micrometre scale. Moreover, recent developments of in‐air PIXE instrumentation have enabled the acquisition of large area elemental maps. However, the amount of data produced is very large, and the data processing is not trivial. In this paper, we propose the use of multivariate analysis to process data of PIXE mapping. First, we apply the non‐negative matrix factorization (NMF), which is a nonsupervised machine‐learning algorithm, to decompose the data into a smaller number of components; then, we use the k‐means algorithm to divide the pixels into categories regarding similarities in the NMF results; finally, we sum the spectra of all pixels in the same category so that the results can be analyzed by standard procedures for PIXE quantification. This last step is important to enable the quantification of the elements found in each component by correctly accounting for matrix self‐absorptions. With the procedure described in this paper, not just, we reduced the number of variables, facilitating the reasoning process on the data by employing the multivariate analysis, but we also increased the counting statistics by summing similar pixels leading to better results concerning the quantification of trace elements. We also propose methods for both, the automatic determination of the optimal number of components to describe the dataset, and for the combined analysis of multiple detectors.     

Sorting algorithms for single‐particle imaging experiments at X‐ray free‐electron lasers

Modern X‐ray free‐electron lasers (XFELs) operating at high repetition rates produce a tremendous amount of data. It is a great challenge to classify this information and reduce the initial data set to a manageable size for further analysis. Here an approach for classification of diffraction patterns measured in prototypical diffract‐and‐destroy single‐particle imaging experiments at XFELs is presented. It is proposed that the data are classified on the basis of a set of parameters that take into account the underlying diffraction physics and specific relations between the real‐space structure of a particle and its reciprocal‐space intensity distribution. The approach is demonstrated by applying principal component analysis and support vector machine algorithms to the simulated and measured X‐ray data sets.     

The many faces of radiation‐induced changes

During diffraction experiments even cryo‐cooled protein crystals can be significantly damaged due to chemical and physical changes induced by absorbed X‐ray photons. The character and scale of the observed effects depend strongly on the temperature and the composition of crystals. The absorption of radiation energy results in incremental regular changes to the crystal structure, making its impact on the process of solving the structure strongly correlated with other experimental variables. An understanding of all the dependencies is still limited and does not allow for a precise prediction of the outcome of a particular diffraction experiment. Results are presented of diffraction experiments performed under different experimental conditions. The influence of temperature and crystal composition on different characteristics of radiation damage is analyzed. The observed effects are discussed in terms of their impact on data processing and phasing procedures.     

Derivative temporal clustering analysis: detecting prolonged neuronal activity

Temporal clustering analysis (TCA) and independent component analysis (ICA) are promising data-driven techniques in functional magnetic resonance imaging (fMRI) experiments to obtain brain activation maps in conditions with unknown temporal information regarding the neuronal activity. Although comparable to ICA in detecting transient neuronal activities, TCA fails to detect prolonged plateau brain activations. To eliminate this pitfall, a novel derivative TCA (DTCA) method was introduced and its algorithms with different subtraction intervals were tested on simulated data with a pattern of prolonged plateau brain activation. It was found that the best performance of DTCA method in generating functional maps could be obtained if the subtraction interval is equal to or larger than the length of the rising time of the fMRI response. The DTCA method and its theoretical predication were further investigated and validated using in vivo fMRI data sets. By removing the limitations in the previous TCA, DTCA has shown its powerful capability in detecting prolonged plateau neuronal activities.     

基于独立成分分析的高光谱图像异常检测

针对高光谱图像中背景及目标先验知识未知条件下的异常目标检测问题,提出了一种基于独立成分分析(ICA)的异常探测算法.首先估计原始数据的虚拟维(VD)以确定要分离的独立成分个数,在此基础上进行快速独立成分分析(FastICA),然后基于平均局部奇异度选择含异常信息较多的独立成分,最后使用丰度量化算法得到异常目标的丰度图像...     

Cortex-based independent component analysis of fMRI time series

The cerebral cortex is the main target of analysis in many functional magnetic resonance imaging (fMRI) studies. Since only about 20% of the voxels of a typical fMRI data set lie within the cortex, statistical analysis can be restricted to the subset of the voxels obtained after cortex segmentation. While such restriction does not influence conventional univariate statistical tests, it may have a substantial effect on the performance of multivariate methods.

Here, we describe a novel approach for data-driven analysis of single-subject fMRI time series that combines techniques for the segmentation and reconstruction of the cortical surface of the brain and the spatial independent component analysis (sICA) of the functional time courses (TCs). We use the mesh of the white matter/gray matter boundary, automatically reconstructed from high-spatial-resolution anatomical MR images, to limit the sICA decomposition of a coregistered functional time series to those voxels which are within a specified region with respect to the cortical sheet (cortex-based ICA, or cbICA). We illustrate our analysis method in the context of fMRI blocked and event-related experimental designs and in an fMRI experiment with perceptually ambiguous stimulation, in which an a priori specification of the stimulation protocol is not possible.

A comparison between cbICA and conventional hypothesis-driven statistical methods shows that cortical surface maps and component TCs blindly obtained with cbICA reliably reflect task-related spatiotemporal activation patterns. Furthermore, the advantages of using cbICA when the specification of a temporal model of the expected hemodynamic response is not straightforward are illustrated and discussed. A comparison between cbICA and anatomically unconstrained ICA reveals that — beside reducing computational demand — the cortex-based approach improves the fitting of the ICA model in the gray matter voxels, the separation of cortical components and the estimation of their TCs, particularly in the case of fMRI data sets with a complex spatiotemporal statistical structure.     

Optimizing ICA in fMRI using information on spatial regularities of the sources

Spatial independent component analysis (ICA) is a well-established technique for multivariate analysis of functional magnetic resonance imaging (fMRI) data. It blindly extracts spatiotemporal patterns of neural activity from functional measurements by seeking for sources that are maximally independent. Additional information on one or more sources (e.g., spatial regularity) is often available; however, it is not considered while looking for independent components. In the present work, we propose a new ICA algorithm based on the optimization of an objective function that accounts for both independence and other information on the sources or on the mixing model in a very general fashion. In particular, we apply this approach to fMRI data analysis and illustrate, by means of simulations, how inclusion of a spatial regularity term helps to recover the sources more effectively than with conventional ICA. The improvement is especially evident in high noise situations. Furthermore we employ the same approach on data sets from a complex mental imagery experiment, showing that consistency and physiological plausibility of relatively weak components are improved.     

Energy dependence of site‐specific radiation damage in protein crystals

It is important to consider radiation damage to crystals caused by data collection when solving structures and critical when determining protein function, which can often depend on very subtle structural characteristics. In this study the rate of damage to specific sites in protein crystals cooled at 100 K is found to depend on the energy of the incident X‐ray beam. Several lysozyme crystals were each subjected to 3–26 MGy of cumulative X‐ray exposure by collecting multiple data sets from each crystal at either 9 keV or 14 keV. The integrated electron density surrounding each S atom in the structure was calculated for each data set and the change in electron density was evaluated as a function of dose at the two energies. The rate of electron density decrease per cubic Å per MGy was determined to be greater at 14 keV than at 9 keV for cysteine sulfurs involved in disulphide bridges; no statistically significant differences in the decay rates were found for methionine sulfurs. These preliminary results imply that it might be possible to minimize certain types of specific radiation damage by an appropriate choice of energy. Further experiments studying a variety of photolabile sites over a wider range of energies are needed to confirm this conclusion.     

Classification of iron‐based inks by means of micro‐Raman spectroscopy and multivariate data analysis

In this work, multivariate data analysis methods were applied to the analysis and interpretation of micro‐Raman spectra, collected from a broad set of historical iron‐based ink samples, previously characterised for the content of organic acids (gallic acid, ellagic acid and protocatechuic acid). The proposed method relies on principal component analysis of the noisy spectra typically obtained on original, degraded, organic samples, where fluorescence could affect the Raman signal. The signal components could be distinguished from the noise components and then used to build a linear discriminant analysis (LDA) model, achieving separation of the spectra into three classes. Selection of pure signal factors also improved effectiveness and performances of partial least square regression (PLS) algorithms, allowing quantification of condensed tannic acid residuals. Application of multivariate methods to discriminate signal from noise removes the need for spectral data manipulation (filtering, smoothing and differentiating). The obtained classification method for discrimination of historic inks and the regression method for determination of condensed tannic acid residuals supports the use of Raman analysis of fluorescing organic materials, and may provide information to scholars on ink composition and potentially on its provenance. Copyright © 2013 John Wiley & Sons, Ltd.     

Robust and discriminating method for face recognition based on correlation technique and independent component analysis model

We demonstrate a novel technique for face recognition. Our approach relies on the performances of a strongly discriminating optical correlation method along with the robustness of the independent component analysis (ICA) model. Simulations were performed to illustrate how this algorithm can identify a face with images from the Pointing Head Pose Image Database. While maintaining algorithmic simplicity, this approach based on ICA representation significantly increases the true recognition rate compared to that obtained using our previously developed all-numerical ICA identity recognition method and another method based on optical correlation and a standard composite filter.     

Coherent X‐ray diffractive imaging of protein crystals

The technique of coherent X‐ray diffraction imaging (CXDI) has recently shown great promise for the study of inorganic nanocrystals. In this work the CXDI method has been applied to the study of micrometer‐size protein crystals. Finely sampled diffraction patterns of single crystals were measured and iterative phase‐retrieval algorithms were used to reconstruct the two‐dimensional shape of the crystal. The density maps have limited reproducibility because of radiation damage, but show clear evidence for crystal facets. Qualitative analysis of a number of single‐crystal diffraction peaks indicates the presence of inward surface contraction on 2 µm size crystals. A survey of several hundred diffraction patterns yielded a number of examples with dramatic single‐sided streaks, for which a plausible model is constructed.     

基于核独立成分分析的人脸识别

研究一种基于核独立成分分析的人脸识别方法。利用支持向量机的核函数思想,将原始人脸图像向量映射到高维特征空间,然后在高维特征空间中进行独立成分分析(ICA),提取非线性独立成分作为特征向量进行分类识别。实验结果表明该方法要比常规的基于ICA和PCA的人脸识别算法的识别率要高。     

基于独立主成分和BP神经网络的干红葡萄酒品种的鉴别

为了实现葡萄酒品种的快速无损鉴别,选用五种干红葡萄酒,进行可见和近红外光谱实验,提出了一种用可见和近红外光谱技术快速鉴别葡萄酒品种的新方法.采用独立主成分分析进行模式特征分析,经过选用不同的独立主成分数进行建模和预测,确定最佳独立主成分数为20.将这20个主成分作为神经网络的输入变量,建立三层BP神经网络,实现类别预测的同时也完成了数学建模与优化分析工作.5个品种的葡萄酒样本数均为35,共计175个样本.在神经网络学习中,将其分成训练集样本150个和预测集样本25个.对25个未知样本进行预测,准确率为100%.该研究在独立主成分分析的基础之上,根据干红葡萄酒各独立主成分的混合矩阵向量载荷图,选取了两个波段(400～430 nm与512～532 nm)作为葡萄酒的独立主成分分析的特征波段.说明该文提出的基于光谱技术和模式识别的方法不仅对葡萄酒具有很好的分类和鉴别能力,并且可以提取出葡萄酒的指纹特征,可用于葡萄酒的检测与技术开发.     

A comparison of SAD and two‐wavelength MAD phasing for radiation‐damaged Se‐MET crystals

Although a case has been made that single‐wavelength anomalous dispersion (SAD) is the optimal strategy for data collection in the presence of radiation damage, two‐wavelength MAD experiments at the inflection and a high‐energy remote point of the absorption edge have been shown to be a potentially successful alternative method. In order to further investigate the performance of both data collection strategies, a comparison of SAD and MAD phasing was carried out for increasingly damaged data sets from three different seleno‐methionine protein samples collected under similar experimental conditions. In all but one example the MAD phases appeared to be less affected than SAD phases with increasing exposure to X‐rays, and had a better overall success rate, indicating that this method should be given serious consideration when dealing with radiation‐sensitive crystals. Simultaneous data collection in wedges at all wavelengths seems to be a very important factor in the success of MAD experiments; the decreased absorbed dose resulting from eschewing data collection at the maximum f ′′ wavelength may play a less important role. Specific radiation damage to the selenium atoms is found to be a minor effect compared with the effect on the anomalous dispersion signal, although potentially large enough to be a useful contribution to phasing in both SAD and MAD experiments.     

Effects of soft X‐ray radiation damage on paraffin‐embedded rat tissues supported on ultralene: a chemical perspective

Radiation damage is an important aspect to be considered when analysing biological samples with X‐ray techniques as it can induce chemical and structural changes in the specimens. This work aims to provide new insights into the soft X‐ray induced radiation damage of the complete sample, including not only the biological tissue itself but also the substrate and embedding medium, and the tissue fixation procedure. Sample preparation and handling involves an unavoidable interaction with the sample matrix and could play an important role in the radiation‐damage mechanism. To understand the influence of sample preparation and handling on radiation damage, the effects of soft X‐ray exposure at different doses on ultralene, paraffin and on paraffin‐embedded rat tissues were studied using Fourier‐transform infrared (FTIR) microspectroscopy and X‐ray microscopy. Tissues were preserved with three different commonly used fixatives: formalin, glutaraldehyde and Karnovsky. FTIR results showed that ultralene and paraffin undergo a dose‐dependent degradation of their vibrational profiles, consistent with radiation‐induced oxidative damage. In addition, formalin fixative has been shown to improve the preservation of the secondary structure of proteins in tissues compared with both glutaraldehyde and Karnovsky fixation. However, conclusive considerations cannot be drawn on the optimal fixation protocol because of the interference introduced by both substrate and embedding medium in the spectral regions specific to tissue lipids, nucleic acids and carbohydrates. Notably, despite the detected alterations affecting the chemical architecture of the sample as a whole, composed of tissue, substrate and embedding medium, the structural morphology of the tissues at the micrometre scale is essentially preserved even at the highest exposure dose.     

In situ X‐ray data collection and structure phasing of protein crystals at Structural Biology Center 19‐ID

A prototype of a 96‐well plate scanner for in situ data collection has been developed at the Structural Biology Center (SBC) beamline 19‐ID, located at the Advanced Photon Source, USA. The applicability of this instrument for protein crystal diffraction screening and data collection at ambient temperature has been demonstrated. Several different protein crystals, including selenium‐labeled, were used for data collection and successful SAD phasing. Without the common procedure of crystal handling and subsequent cryo‐cooling for data collection at T = 100 K, crystals in a crystallization buffer show remarkably low mosaicity (<0.1°) until deterioration by radiation damage occurs. Data presented here show that cryo‐cooling can cause some unexpected structural changes. Based on the results of this study, the integration of the plate scanner into the 19‐ID end‐station with automated controls is being prepared. With improvement of hardware and software, in situ data collection will become available for the SBC user program including remote access.     

Phase‐specific Raman analysis of n‐alkane melting by moving‐window two‐dimensional correlation spectroscopy

We use moving‐window two‐dimensional correlation spectroscopy (MW‐2DCOS) for phase‐specific Raman analysis of the n‐alkane (C₂₁H₄₄) during melting from the crystalline solid phase to the intermediate rotator phase and to the amorphous molten phase. In MW‐2DCOS, individual peak‐to‐peak correlation analysis within a small subset of spectra provides both temperature‐resolved and spectrally disentangled Raman assignments conducive to understanding phase‐specific molecular interactions and chain configurations. We demonstrate that autocorrelation MW‐2DCOS can determine the phase transition temperatures with a higher resolving power than commonly used analysis methods including individual peak intensity analysis or principal component analysis. Besides the enhanced temperature resolving power, we demonstrate that asynchronous 2DCOS near the orthorhombic‐to‐rotator transition temperature can spectrally resolve the two overlapping peaks embedded in the Raman CH₂ twisting band in the orthorhombic phase, which had been only predicted but not observed because of thermal broadening near the melting temperature. Published 2016. This article is a U.S. Government work and is in the public domain in the USA.