Independent component analysis of high-resolution imaging data identifies distinct functional domains

SCTICA: Sub-packet constrained temporal ICA method for fMRI data analysis

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

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.

The analysis of functional magnetic resonance imaging (fMRI) signals using independent component analysis (ICA) has been a hotspot in the recent years. The main principle of performing the blind source separation (BSS) using spatial independent component analysis (SICA) is described. Since the fMRI signals from the experiments can be seen as a specific problem of BSS, the FastICA can be used to realize the BSS. Current analytical techniques, which are applied to fMRI signals, require a prior knowledge or specific assumptions about the time courses of processes contributing to the measured signals. Without any prior knowledge about the time courses of processes contributing to the measured signals, only the FastICA can perform BSS and be used to separate fMRI signals into task-related independent components, head movement independent components, transient task-related independent components, noisy independent components and other independent component signals.

In functional magnetic resonance imaging (fMRI), spatial smoothing procedure is generally a stable step in the preprocessing stream. Previous research (including ours) suggested dependency of the static functional connectivity on the size of the spatial smoothing kernel size. But its impact on the time-varying patterns of functional connectivity has not been investigated. Here, we sought to identify the effects of spatial smoothing on brain dynamics by performing dynamic functional network connectivity (dFNC) and meta-state analysis, a unique approach capable of examining a higher-dimensional temporal dynamism of whole-brain functional connectivity. Gaussian smoothing kernel with different widths at half of the maximum of the height of the Gaussian (4, 8, and 12 mm FWHM) were used during preprocessing prior to the group independent component analysis (ICA) with a relatively high model order of 75. dFNC was conducted using the sliding-time window approach and k-means clustering algorithm. Meta-state dynamics method was performed by reducing the number of windowed FNC correlations using principal components analysis (PCA), temporal and spatial ICA and k-means. Results revealed robust effects of spatial smoothing on the connectivity dynamics of several network pairs including a variety of cognitive/attention networks in a connectivity state with the highest occurrence (FDR corrected-p < 0.01). Meta-state analyses indicated significant changes in meta-state metrics including the number of meta-states, meta-state changes, meta-state span, and the total distance. These changes were particularly pronounced when we compared resting state data smoothed with 8 vs. 12 mm FWHM. Our preliminary findings give insights into the effects of spatial smoothing kernel size on the dynamics of functional connectivity and its consequences on meta-state parameters. It also provides further indication of the importance of evaluating variance associated with preprocessing steps on analysis outcomes.

Mild traumatic brain injury (mTBI) is a complex syndrome that affects up to 600 per 100,000 individuals, with a particular concentration among military personnel. About half of all mTBI patients experience a diverse array of chronic symptoms which persist long after the acute injury. Hence, there is an urgent need for better understanding of the white matter and gray matter pathologies associated with mTBI to map which specific brain systems are impacted and identify courses of intervention. Previous works have linked mTBI to disruptions in white matter pathways and cortical surface abnormalities. Herein, we examine these hypothesized links in an exploratory study of joint structural connectivity and cortical surface changes associated with mTBI and its chronic symptoms. Briefly, we consider a cohort of 12 mTBI and 26 control subjects. A set of 588 cortical surface metrics and 4,753 structural connectivity metrics were extracted from cortical surface regions and diffusion weighted magnetic resonance imaging in each subject. Principal component analysis (PCA) was used to reduce the dimensionality of each metric set. We then applied independent component analysis (ICA) both to each PCA space individually and together in a joint ICA approach. We identified a stable independent component across the connectivity-only and joint ICAs which presented significant group differences in subject loadings (p<0.05, corrected). Additionally, we found that two mTBI symptoms, slowed thinking and forgetfulness, were significantly correlated (p<0.05, corrected) with mTBI subject loadings in a surface-only ICA. These surface-only loadings captured an increase in bilateral cortical thickness.

Not alike to principal component analysis (PCA) based monitoring statistics (T2 and SPE), the control limits for independent component analysis (ICA) monitoring statistics (I2 , Ie 2 and SPE) cannot be determined directly from a particular approximation distribution due to latent variables do not follow Gaussian distribution. Lee et al. (2004) proposed to use kernel density estimation (KDE) to obtain the control limits. However, the KDE method is very sensitive to the choice of smoothing parameter. Therefore, this study utilizes the support vector machine (SVM) for process fault detection by taking information of ICA extracted statistics as inputs of SVM. The proposed method (named, ICA-SVM) will be implemented in the Tennessee Eastman Process. In which, several multivariate monitoring schemes such as PCA, ICA, modified ICA and ICA- PCA will be also compared to demonstrate the efficiency of proposed method.

Principal components analysis (PCA) has been widely used in many applications, particularly, data compression. Independent component analysis (ICA) has been also developed for blind source separation along with many other applications such as channel equalization, speech processing. Recently, it has been shown that the ICA can be also used for hyperspectral data compression. This paper investigates these two transforms in hyperspectral data compression and further evaluates their strengths and weaknesses in applications of target detection, mixed pixel classification and abundance quantification. In order to take advantage of the strengths of both transform, a new transform, called mixed PCA/ICA transform is developed in this paper. The idea of the proposed mixed PCA/ICA transform is derived from the fact that it can integrate different levels of information captured by the PCA and ICA. In doing so, it combines m principal components (PCs) resulting from the PCA and n independent components (ICs) generated by the ICA to form a new set of (m+n) mixed components used for hyperspectral data compression. The resulting transform is referred to as mixed (m,n)-PCA/ICA transform. In order to determine the total number of components, p needed to be generated for the mixed (m,n)-PCA/ICA transform, a recently developed virtual dimensionality (VD) is introduced to estimate the p where p = m + n. If m = p and n = 0, then mixed (m,n)-PCA/ICA transform is reduced to PCA transform. On the other hand, if m = 0 and n = p, then mixed (m,n)-PCA/ICA transform is reduced to ICA. Since various combinations of m and n have different impacts on the performance of the mixed PCA/ICA spectral/spatial compression in applications, experiments based on subpixel detection and mixed pixel quantification are conducted for performance evaluation.

Data compression on the illumination adjustable images by PCA and ICA

Background: Renal involvement is a frequent manifestation of primary mitochondrial disorders (PMD), either as a presenting feature or during the disease course. Simultaneously, the metabolomic profile of chronic kidney disease (CKD) is often associated with underlying mitochondrial dysfunction. This study aimed to characterize urinary metabolic signatures in genetically confirmed paediatric PMD without chronic kidney disease, comparing them to healthy controls, suspected (unconfirmed) mitochondrial disease (SMD), and non-mitochondrial CKD. Methods: We performed untargeted 1H-NMR metabolomic profiling of 76 urine samples from 61 paediatric patients. Outlier samples and patients undergoing acute decompensation were excluded from the main analyses. Final comparisons included genetically confirmed PMD without CKD (n = 13), SMD (n = 10), non-mitochondrial CKD (n = 28; 17 at stages 1–2 and 9 at stages 3–5), and healthy controls (n = 10). Spectral data were analyzed using multivariate techniques—including principal component analysis (PCA) and partial least squares–discriminant analysis (PLS-DA)—as well as univariate methods following normalization to urinary creatinine or total spectral area. Results: Urinary metabolic profiles of PMD patients differed markedly from those of healthy controls and CKD patients. Multivariate analysis (PLS-DA) revealed a high discriminative ability for separating PMD from controls (Q² = 0.52) and CKD (Q² = 0.69). Several metabolites were significantly elevated in PMD, including 4-aminohippurate, homovanillic acid (HVA), cis-aconitate, and fumarate. These remained discriminative when comparing PMD to both CKD and control groups. A multimetabolite panel comprising 4-aminohippurate, HVA, histidine, and cis-aconitate achieved high diagnostic performance in distinguishing PMD from advanced CKD (stage 3–5), with an area under the curve (AUC) of 0.944. This biosignature integrated metabolites involved in distinct functional domains - energy metabolism, amino acid handling, and renal tubular function. Correlations between normalization strategies (creatinine vs. total spectral area) varied across metabolites (Pearson r ranging from –0.32 to 0.98), highlighting the potential impact of normalization choice on quantitative interpretation and biomarker reproducibility. Conclusion: Urinary metabolomic profiling by 1H-NMR revealed a distinct biosignature in pediatric PMD patients without renal involvement, characterized by elevated levels of 4-aminohippurate, HVA, and tricarboxylic cycle intermediates. The consistent increase in 4-aminohippurate and HVA—both reliant on energy-dependent proximal tubular excretion—points to impaired mitochondrial modulation of tubular function, even in the absence of overt renal disease. These findings support the use of urinary 1H-NMR metabolomics as a non-invasive tool for biomarker discovery in PMD and highlight the potential of integrated, multiparametric metabolic fingerprints for diagnostic refinement and patient stratification.

The traditional procedure for increasing the signal-tonoise ratio of NMR echo trains is based on stacking and then averaging the data acquired at different depths up to a certain depth interval. Although this works efficiently in increasing the signal-to-noise ratio, it also reduces the vertical resolution of the acquired signal. Therefore a trade-off has to be made between cancelling the noise level without deteriorating the targeted signal but yet still achieving acceptable vertical and spectral resolutions. In NMR logging technology, vertical resolution, usually assessed by NMR-total porosity compared to neutrondensity porosity, is important. However, noise can also affect the spectral distribution of the inverted signal that generates the T1 or T 2 distribution and its components like: clay-bound water, irreducible water, and free fluid. In the area of machine learning applied to denoising, relatively new algorithm developments are using approaches like ICA (Independent Component Analysis), which are based on exploring the statistical independency of the signal components. ICA in fact, puts a stronger mathematical condition on the data-independency—rather than perpendicularity (which traditional Principal Component Analysis (PCA) is based on). However, since ICA is a kind of blind source separation algorithm, the subspace of the ICA solution is usually determined by the relevant numbers of principal components obtained after running PCA. There are several ICA approaches proposed in the literature. One of the most popular is the ‘FastIca’ algorithm. We choose the FastIca algorithm for our test due to its ease of implementation in the MATLAB computational environment. The results from simulated data show that ICA-denoising can be more effective than stacking, if the numbers of the blindly defined ICA components are properly selected. The tradeoff between preserving a significant amount of signal and filtering the right amount of noise is a sensitive aspect in implementing ICA for denoising when rock-fluid characteristics are being explored. Results from real data show that ICA can be readily adapted for post-processing in log analysis, but the selection of ICA-subspace poses a major challenge.

In functional magnetic resonance imaging (fMRI) coherent oscillations of the blood oxygen level-dependent (BOLD) signal can be detected. These arise when brain regions respond to external stimuli or are activated by tasks. The same networks have been characterized during wakeful rest when functional connectivity of the human brain is organized in generic resting-state networks (RSN). Alterations of RSN emerge as neurobiological markers of pathological conditions such as altered mental state. In single-subject fMRI data the coherent components can be identified by blind source separation of the pre-processed BOLD data using spatial independent component analysis (ICA) and related approaches. The resulting maps may represent physiological RSNs or may be due to various artifacts. In this methodological study, we propose a conceptually simple and fully automatic time course based filtering procedure to detect obvious artifacts in the ICA output for resting-state fMRI. The filter is trained on six and tested on 29 healthy subjects, yielding mean filter accuracy, sensitivity and specificity of 0.80, 0.82, and 0.75 in out-of-sample tests. To estimate the impact of clearly artifactual single-subject components on group resting-state studies we analyze unfiltered and filtered output with a second level ICA procedure. Although the automated filter does not reach performance values of visual analysis by human raters, we propose that resting-state compatible analysis of ICA time courses could be very useful to complement the existing map or task/event oriented artifact classification algorithms.

The literature on independent component analysis (ICA)-based face recognition generally evaluates its performance using standard principal component analysis (PCA) within two architectures, ICA Architecture I and ICA Architecture II. In this correspondence, we analyze these two ICA architectures and find that ICA Architecture I involves a vertically centered PCA process (PCA I), while ICA Architecture II involves a whitened horizontally centered PCA process (PCA II). Thus, it makes sense to use these two PCA versions as baselines to reevaluate the performance of ICA-based face-recognition systems. Experiments on the FERET, AR, and AT&T face-image databases showed no significant differences between ICA Architecture I (II) and PCA I (II), although ICA Architecture I (or II) may, in some cases, significantly outperform standard PCA. It can be concluded that the performance of ICA strongly depends on the PCA process that it involves. Pure ICA projection has only a trivial effect on performance in face recognition.

Statistical analysis of wind-induced pressure fields and PIV measurements on two buildings

The authors consider the roles of PCA (principal component analysis) and ICA (independent component analysis) in the fusion of several data sources in remote sensing. With high dimensional data from each source they may be tempted to use PCA or ICA to obtain a small subset of most significant components. Smaller feature sets not only can reduce the computation requirements but also can mitigate the Hughes phenomenon. However how they use the resulting feature sets from several data sources can make significant difference in the overall classification performance. Also fusion can lead to image enhancement. Several experiments have been performed to illustrate the conditions that PCA and ICA can be most useful in data fusion.