Independent Component Analysis Analysis Research Articles

Novel approach for ECG separation using adaptive constrained IVABMGGMM

In this paper, we introduce the constrained independent vector analysis integrated with the bounded multivariate generalized Gaussian mixture model (cIVABMGGMM) to tackle the limitations of independent component analysis (ICA) when applied to multivariate data, accompanied by its adaptive version, the acIVABMGGMM, aimed at alleviating associated constraints. The acIVABMGGMM employs a full covariance matrix that considers feature correlations, effectively addressing the challenges posed by ICA and independent vector analysis (IVA) models when analyzing multivariate data. The innovative acIVABMGGMM framework merges the adaptability inherent in data-driven methods with the capability to manage noise and other artifacts often encountered in model-based approaches. This technique effectively employs prior knowledge to guide the solution, avoiding the imposition of inaccurate constraints. To overcome these challenges, our set of two constrained methods incorporates prior source information into the IVA model, effectively mitigating its limitations in data with a high number of sources. We assess the efficacy of our proposed models through three distinct ECG separation experiments, which include heartbeat separation, fetal ECG extraction, and arrhythmia detection. Notably, the performance metrics demonstrate the superiority of our models over the baseline approaches in the conducted experiments.

Differences in resting-state brain networks and gray matter between APOE ε2 and APOE ε4 carriers in non-dementia elderly.

Apolipoprotein E (APOE) ε2 and APOE ε4 are the most distinct alleles among the three APOE alleles, both structurally and functionally. However, differences in cognition, brain function, and brain structure between the two alleles have not been comprehensively reported in the literature, especially in non-demented elderly individuals. A neuropsychological test battery was used to evaluate the differences in cognitive performance in five cognitive domains. Independent component analysis (ICA) and voxel-based morphometry (VBM) were used separately to analyze resting-state functional magnetic resonance imaging (rs-fMRI) data and the structure MRI data between the two groups. Finally, correlations between differential brain regions and neuropsychological tests were calculated. APOE ε2 carriers had better cognitive performance in general cognitive, memory, attention, and executive function than APOE ε4 carriers (all p < 0.05). In ICA analyses of rs-fMRI data, the difference in the resting-state functional connectivity (rsFC) between two groups is shown in 7 brain networks. In addition, VBM analyses of the T1-weighted image revealed that APOE ε2 carriers had a larger thalamus and right postcentral gyrus volume and a smaller bilateral putamen volume than APOE ε4 carriers. Finally, differences in brain function and structure may be might be the reason that APOE ε2 carriers are better than APOE ε4 carriers in cognitive performance. These findings suggest that there are significant differences in brain function and structure between APOE ε2 carriers and APOE ε4 carriers, and these significant differences are closely related to their cognitive performance.

A methodology to characterize 4D post-failure slope instability dynamics using remote sensing measurements: A case study of the Aniangzhai landslide in Sichuan, Southwest China

A massive landslide often causes long-lasting instability dynamics that need to be analyzed in detail for risk management and mitigation. Multiple satellite remote sensing observations, in-situ measurements, and geophysical approaches have been jointly implemented to monitor and interpret the life cycle of landslides and their failure mechanisms from various perspectives. In this work, we propose a framework where satellite optical and synthetic aperture radar (SAR) remote sensing techniques are combined with feature extractions using independent component analysis (ICA) and a mathematical relaxation model to assess the complete four-dimensional (4D) spatiotemporal patterns of post-failure slope evolution. The large, deep-seated Aniangzhai landslide in Southwest China that occurred on 17 June 2020 is comprehensively analyzed and characterized for its post-failure mechanism. Time series of Planet high-resolution optical images are first explored to derive the large horizontal motions for the first six months after the failure. Spatiotemporal dynamics of line-of-sight (LOS) displacement in the landslide body are then derived between November 2020 and February 2022 by combining 40 TerraSAR-X (TSX) High-resolution Spotlight (HS) images and 76 medium-resolution Sentinel-1 (S1) SAR datasets using Multi-temporal InSAR (MTI) method. The InSAR-derived results are subsequently analyzed with ICA to find common deformation components of points between optical and MTI results, indicating the same temporal evolution in the deformation pattern. Finally, the complete 4D deformation field for the whole post-failure period is modeled using a decaying exponential model representing stress relaxation after the failure by integrating multiple remote sensing datasets. Cross-correlation analysis of Planet imagery shows a decaying exponential pattern of post-failure displacements with an approximately 94% reduction in the deformation rate after six months with respect to the co-failure event. MTI analysis suggests a maximum LOS displacement rate of approximately 30 cm/year over the main failure body from November 2020 to February 2022; while the high-resolution TSX datasets show irreplaceable advantages in choosing the number of measurement points in MTI analysis with the number of measurement points being five times larger than those obtained by S1 datasets. The ICA analysis reveals three main types of kinematic patterns in the temporal evolution of post-failure deformation in MTI results, the dominant one being an exponential declining pattern similar to the results from Planet observations. Integrated 4D deformation modeling suggests that the most significant post-failure displacement mainly occurred toward the west, amounting to 28 m during the entire post-failure acquisitions from June 2020 until February 2022. Additionally, maximum displacements of 17 m and 19 m occurred in this period toward the north and downward, respectively.

River temperature analysis with a new way of using Independant Component Analysis

In the context of global warming, river management is essential to maintain favourable water temperature ranges for aquatic species. Therefore, understanding the main factors influencing the water temperature becomes a key part in the management process. In this paper, we used Independent Component Analysis (ICA) to identify these main factors and improve water temperature forecasting. The study is caried out on two rivers in Normandy (France) with quite different characteristics. Each river was equipped with several temperature sensors which series range from 2011 to 2021. The ICA analysis of the data series reveals that the thermal regime of these two rivers is mainly controlled by seasonal and daily climatic factors. The Sélune regime also turns out to be influenced by the presence of a dam, dismantled during the monitoring of the river. The temperature of the Odon appears to be clearly controlled by seasonal lightening conditions in connection with the presence of the riparian vegetation. Complementary, an innovative approach called “successive ICA” is used to reconstruct the natural thermal regime of the Sélune without the presence of the dam. Emphasis is therefore placed here on the interest of ICA in hydrology as en elementary method for extracting the main influencing factors and quantifying their importance on the thermal regime of a river. It also allows to remove the influence of a particular factor and reconstruct time series better suited for temperature forecasting. The method used here is not specific to temperature time series and can be applied to any region even with different hydrological characteristics.

Large-scale neural network computations and multivariate representations during approach-avoidance conflict decision-making

Many real-world situations require navigating decisions for both reward and threat. While there has been significant progress in understanding mechanisms of decision-making and mediating neurocircuitry separately for reward and threat, there is limited understanding of situations where reward and threat contingencies compete to create approach-avoidance conflict (AAC). Here, we leverage computational learning models, independent component analysis (ICA), and multivariate pattern analysis (MVPA) approaches to understand decision-making during a novel task that embeds concurrent reward and threat learning and manipulates congruency between reward and threat probabilities. Computational modeling supported a modified reinforcement learning model where participants integrated reward and threat value into a combined total value according to an individually varying policy parameter, which was highly predictive of decisions to approach reward vs avoid threat during trials where the highest reward option was also the highest threat option (i.e., approach-avoidance conflict). ICA analyses demonstrated unique roles for salience, frontoparietal, medial prefrontal, and inferior frontal networks in differential encoding of reward vs threat prediction error and value signals. The left frontoparietal network uniquely encoded degree of conflict between reward and threat value at the time of choice. MVPA demonstrated that delivery of reward and threat could accurately be decoded within salience and inferior frontal networks, respectively, and that decisions to approach reward vs avoid threat were predicted by the relative degree to which these reward vs threat representations were active at the time of choice. This latter result suggests that navigating AAC decisions involves generating mental representations for possible decision outcomes, and relative activation of these representations may bias subsequent decision-making towards approaching reward or avoiding threat accordingly.

Independent Component and Graph Theory Analyses Reveal Normalized Brain Networks on Resting-State Functional MRI After Working Memory Training in People With HIV.

Cognitive training may partially reverse cognitive deficits in people with HIV (PWH). Previous functional MRI (fMRI) studies demonstrate that working memory training (WMT) alters brain activity during working memory tasks, but its effects on resting brain network organization remain unknown. To test whether WMT affects PWH brain functional connectivity in resting-state fMRI (rsfMRI). Prospective. A total of 53 PWH (ages 50.7 ± 1.5 years, two women) and 53 HIV-seronegative controls (SN, ages 49.5 ± 1.6 years, six women). Axial single-shot gradient-echo echo-planar imaging at 3.0 T was performed at baseline (TL1), at 1-month (TL2), and at 6-months (TL3), after WMT. All participants had rsfMRI and clinical assessments (including neuropsychological tests) at TL1 before randomization to Cogmed WMT (adaptive training, n=58: 28 PWH, 30 SN; nonadaptive training, n=48: 25 PWH, 23 SN), 25 sessions over 5-8 weeks. All assessments were repeated at TL2 and at TL3. The functional connectivity estimated by independent component analysis (ICA) or graph theory (GT) metrics (eigenvector centrality, etc.) for different link densities (LDs) were compared between PWH and SN groups at TL1 and TL2. Two-way analyses of variance (ANOVA) on GT metrics and two-sample t-tests on FC or GT metrics were performed. Cognitive (eg memory) measures were correlated with eigenvector centrality (eCent) using Pearson's correlations. The significance level was set at P < 0.05 after false discovery rate correction. The ventral default mode network (vDMN) eCent differed between PWH and SN groups at TL1 but not at TL2 (P=0.28). In PWH, vDMN eCent changes significantly correlated with changes in the memory ability in PWH (r=-0.62 at LD=50%) and vDMN eCent before training significantly correlated with memory performance changes (r=0.53 at LD=50%). ICA and GT analyses showed that adaptive WMT normalized graph properties of the vDMN in PWH. 1 TECHNICAL EFFICACY: 1.

Resting-state functional MRI for motor cortex mapping in childhood-onset focal epilepsy.

Task-based functional MRI (fMRI) mapping of the motor function prior to epilepsy surgery has limitations in children with epilepsy. We present a data-driven method to automatically delineate the motor cortex using task-free, resting-state fMRI (rsfMRI) data. We used whole-brain rsfMRI for independent component analysis (ICA). A template matching process with Discriminability Index-based Component Identification score was used for each participant to select and combine motor ICA components in their native brain space, resulting in a whole-brain ICA Motor Map (wIMM). We validated wIMM by comparing individual results with bilateral finger-tapping motor task fMRI activation, and evaluated its reproducibility in controls. Data from 64 patients and 12 controls were used to generate group wIMM maps. The hit rate between wIMM and motor task activation ranged from 60% to 79% across all participants. Sensitivity of wIMM for capturing the task activation peak was 87.5% among 32 patients and 100% in 12 controls with available motor task results. We also showed high similarity in repeated runs in controls. Our results show the sensitivity and reproducibility of an automated motor mapping method based on ICA analysis of rsfMRI in children with epilepsy. The ICA maps may provide different, but useful, information than task fMRI. Future studies will expand our method to mapping other brain functions, and may lead to a surgical planning tool for patients who cannot perform task fMRI and help predict their postsurgical function.

Selecting a pre-processing pipeline for the analysis of EEG event-related rhythms modulation.

When deciding how to pre-process EEG data, researchers need to make a choice at each single step of the procedure among different possibilities, equally valid. Therefore, in this work, we illustrate how these decisions may affect the quality of the final cleaned data in an Action Observation/Motor Imagery protocol, using quantitative indices. In particular, we showed the effect of segmenting or not the data in epochs around the stimulus presentation time on the independent component analysis (ICA) used for artifact removal. For ICA analysis, we tested two algorithms (SOBI and Extended Infomax). Finally, three re-reference approaches (Common averaged reference-CAR, robust-CAR and reference electrode standardization technique - REST) were also applied and their effects compared. Results showed that the segmenting method has a prominent effect on the cleaning procedure and consequently on final EEG data quality. Extended Infomax is confirmed as the method of choice for the identification of the artifactual components and, finally, CAR and the REST re-referencing techniques led to similar good results.

EEG Resting-State Functional Networks in Amnestic Mild Cognitive Impairment.

Background. Alzheimer's cognitive-behavioral syndrome is the result of impaired connectivity between nerve cells, due to misfolded proteins, which accumulate and disrupt specific brain networks. Electroencephalography, because of its excellent temporal resolution, is an optimal approach for assessing the communication between functionally related brain regions. Objective. To detect and compare EEG resting-state networks (RSNs) in patients with amnesic mild cognitive impairment (aMCI), and healthy elderly (HE). Methods. We recruited 125 aMCI patients and 70 healthy elderly subjects. One hundred and twenty seconds of artifact-free EEG data were selected and compared between patients with aMCI and HE. We applied standard low-resolution brain electromagnetic tomography (sLORETA)-independent component analysis (ICA) to assess resting-state networks. Each network consisted of a set of images, one for each frequency (delta, theta, alpha1/2, beta1/2). Results. The functional ICA analysis revealed 17 networks common to groups. The statistical procedure demonstrated that aMCI used some networks differently than HE. The most relevant findings were as follows. Amnesic-MCI had: i) increased delta/beta activity in the superior frontal gyrus and decreased alpha1 activity in the paracentral lobule (ie, default mode network); ii) greater delta/theta/alpha/beta in the superior frontal gyrus (i.e, attention network); iii) lower alpha in the left superior parietal lobe, as well as a lower delta/theta and beta, respectively in post-central, and in superior frontal gyrus(ie, attention network). Conclusions. Our study confirms sLORETA-ICA method is effective in detecting functional resting-state networks, as well as between-groups connectivity differences. The findings provide support to the Alzheimer's network disconnection hypothesis.

Multi-spatial-scale dynamic interactions between functional sources reveal sex-specific changes in schizophrenia.

We introduce an extension of independent component analysis (ICA), called multiscale ICA, and design an approach to capture dynamic functional source interactions within and between multiple spatial scales. Multiscale ICA estimates functional sources at multiple spatial scales without imposing direct constraints on the size of functional sources, overcomes the limitation of using fixed anatomical locations, and eliminates the need for model-order selection in ICA analysis. We leveraged this approach to study sex-specific and sex-common connectivity patterns in schizophrenia. Results show dynamic reconfiguration and interaction within and between multi-spatial scales. Sex-specific differences occur (a) within the subcortical domain, (b) between the somatomotor and cerebellum domains, and (c) between the temporal domain and several others, including the subcortical, visual, and default mode domains. Most of the sex-specific differences belong to between-spatial-scale functional interactions and are associated with a dynamic state with strong functional interactions between the visual, somatomotor, and temporal domains and their anticorrelation patterns with the rest of the brain. We observed significant correlations between multi-spatial-scale functional interactions and symptom scores, highlighting the importance of multiscale analyses to identify potential biomarkers for schizophrenia. As such, we recommend such analyses as an important option for future functional connectivity studies.

Resting state fMRI brain mapping in pediatric supratentorial brain tumors

BackgroundFunctional mapping of eloquent brain areas is crucial for preoperative planning in patients with brain tumors. Resting state functional MRI (rs-fMRI) allows the localization of functional brain areas without the need for task performance, making it well-suited for the pediatric population. In this study the independent component analysis (ICA) rs-fMRI functional mapping results are reported in a group of 22 pediatric patients with supratentorial brain tumors. Additionally, the functional connectivity (FC) maps of the sensori-motor network (SMN) obtained using ICA and seed-based analysis (SBA) are compared.ResultsDifferent resting state networks (RSNs) were extracted using ICA with varying levels of sensitivity, notably, the SMN was identified in 100% of patients, followed by the Default mode network (DMN) (91%) and Language networks (80%). Additionally, FC maps of the SMN extracted by SBA were more extensive (mean volume = 25,288.36 mm3, standard deviation = 13,364.36 mm3) than those found on ICA (mean volume = 13,403.27 mm3, standard deviation = 9755.661 mm3). This was confirmed by statistical analysis using a Wilcoxon signed rank t test at p < 0.01.ConclusionsResults clearly demonstrate the successful applicability of rs-fMRI for localizing different functional brain networks in the preoperative assessment of brain areas, and thus represent a further step in the integration of computational radiology research in a clinical setting.

Aberrant Resting-State Functional Connectivity of the Dorsal Attention Network in Tinnitus.

Previous functional magnetic resonance imaging (fMRI) analyses have shown that the dorsal attention network (DAN) is involved in the pathophysiological changes of tinnitus, but few relevant studies have been conducted, and the conclusions to date are not uniform. The purpose of this research was to test whether there is a change in intrinsic functional connectivity (FC) patterns between the DAN and other brain regions in tinnitus patients. Thirty-one patients with persistent tinnitus and thirty-three healthy controls were enrolled in this study. A group independent component analysis (ICA), degree centrality (DC) analysis, and seed-based FC analysis were conducted. In the group ICA, the tinnitus patients showed increased connectivity in the left superior parietal gyrus in the DAN compared to the healthy controls. Compared with the healthy controls, the tinnitus patients showed increased DC in the left inferior parietal gyrus and decreased DC in the left precuneus within the DAN. The clusters within the DAN with significant differences in the ICA or DC analysis between the tinnitus patients and the healthy controls were selected as regions of interest (ROIs) for seeds. The tinnitus patients exhibited significantly increased FC from the left superior parietal gyrus to several brain regions, including the left inferior parietal gyrus, the left superior marginal gyrus, and the right superior frontal gyrus, and decreased FC to the right anterior cingulate cortex. The tinnitus patients exhibited decreased FC from the left precuneus to the left inferior occipital gyrus, left calcarine cortex, and left superior frontal gyrus compared with the healthy controls. The findings of this study show that compared with healthy controls, tinnitus patients have altered functional connections not only within the DAN but also between the DAN and other brain regions. These results suggest that it may be possible to improve the disturbance and influence of tinnitus by regulating the DAN.

Independent component analysis combined with Laplace inversion of spectrally resolved spin-alignment echo/T 1 3D 7Li NMR of superionic Li10GeP2S12

Abstract The use of independent component analysis (ICA) for the analysis of two-dimensional (2D) spin-alignment echo–T 1 7Li NMR correlation data with transient echo detection as a third dimension is demonstrated for the superionic conductor Li10GeP2S12 (LGPS). ICA was combined with Laplace inversion, or discrete inverse Laplace transform (ILT), to obtain spectrally resolved 2D correlation maps. Robust results were obtained with the spectra as well as the vectorized correlation maps as independent components. It was also shown that the order of ICA and ILT steps can be swapped. While performing the ILT step before ICA provided better contrast, a substantial data compression can be achieved if ICA is executed first. Thereby the overall computation time could be reduced by one to two orders of magnitude, since the number of computationally expensive ILT steps is limited to the number of retained independent components. For LGPS, it was demonstrated that physically meaningful independent components and mixing matrices are obtained, which could be correlated with previously investigated material properties yet provided a clearer, better separation of features in the data. LGPS from two different batches was investigated, which showed substantial differences in their spectral and relaxation behavior. While in both cases this could be attributed to ionic mobility, the presented analysis may also clear the way for a more in-depth theoretical analysis based on numerical simulations. The presented method appears to be particularly suitable for samples with at least partially resolved static quadrupolar spectra, such as alkali metal ions in superionic conductors. The good stability of the ICA analysis makes this a prospect algorithm for preprocessing of data for a subsequent automatized analysis using machine learning concepts.

Comparison of dimension reduction methods on fatty acids food source study

Serum fatty acids (FAs) exist in the four lipid fractions of triglycerides (TGs), phospholipids (PLs), cholesteryl esters (CEs) and free fatty acids (FFAs). Total fatty acids (TFAs) indicate the sum of FAs in them. In this study, four statistical analysis methods, which are independent component analysis (ICA), factor analysis, common principal component analysis (CPCA) and principal component analysis (PCA), were conducted to uncover food sources of FAs among the four lipid fractions (CE, FFA, and TG + PL). Among the methods, ICA provided the most suggestive results. To distinguish the animal fat intake from endogenous fatty acids, FFA variables in ICA and factor analysis were studied. ICA provided more distinct suggestions of FA food sources (endogenous, plant oil intake, animal fat intake, and fish oil intake) than factor analysis. Moreover, ICA was discovered as a new approach to distinguish animal FAs from endogenous FAs, which will have an impact on epidemiological studies. In addition, the correlation coefficients between a published dataset of food FA compositions and the loading values obtained in the present ICA study suggested specific foods as serum FA sources. In conclusion, we found that ICA is a useful tool to uncover food sources of serum FAs.

Waveform feature extraction and signal recovery in single-channel TVEP based on Fitzhugh–Nagumo stochastic resonance

Objective. Transient visual evoked potential (TVEP) can reflect the condition of the visual pathway and has been widely used in brain–computer interface. TVEP signals are typically obtained by averaging the time-locked brain responses across dozens or even hundreds of stimulations, in order to remove different kinds of interferences. However, this procedure increases the time needed to detect the brain status in realistic applications. Meanwhile, long repeated stimuli can vary the evoked potentials and discomfort the subjects. Therefore, a novel unsupervised framework was developed in this study to realize the fast extraction of single-channel TVEP signals with a high signal-to-noise ratio. Approach. Using the principle of nonlinear aperiodic FitzHugh–Nagumo (FHN) model, a fast extraction and signal restoration technology of TVEP waveform based on FHN stochastic resonance is proposed to achieve high-quality acquisition of signal features with less average times. Results: A synergistic effect produced by noise, aperiodic signal and nonlinear system can force the energy of noise to be transferred into TVEP and hence amplifying the useful P100 feature while suppressing multi-scale noise. Significance. Compared with the conventional average and average-singular spectrum analysis-independent component analysis(average-SSA-ICA) method, the average-FHN method has a shorter stimulation time which can greatly improve the comfort of patients in clinical TVEP detection and a better performance of TVEP waveform i.e. a higher accuracy of P100 latency. The FHN recovery method is not only highly correlated with the original signal, but also can better highlight the P100 amplitude, which has high clinical application value.

Research Note: Functional Connectivity Between a Corticostriatal Network and the Cerebellum.

Basal ganglia and cerebellum are structurally and functionally connected in animals. In humans, tractography and seed-based functional connectivity have confirmed this cerebellar-striatal relation. Independent component analysis (ICA) showed that both cerebellum and basal ganglia take part in distinct intrinsic networks. Probabilistic ICA analysis was applied to the brain images of 15 healthy volunteers during the resting state and using a 3T MRI. A spatial map corresponding to dorsal and ventral basal ganglia circuits was also found to be in functional coherence with crus 2, especially with its vermal region. It is speculated that such cerebellar-basal ganglionic rsFC could reflect structural interconnections traced in animals and explain reward-based activity detected in the cerebellum.

Functional MRI Correlates of Sleep Quality in HIV.

ObjectiveTo examine resting-state functional MRI (rs-fMRI) networks related to sleep in the context of HIV infection.Methodsrs-fMRI data were collected in 40 HIV-infected (HIV+) individuals at baseline (treatment-naive), 12 week (post-treatment) and one year timepoints. A group of 50 age-matched HIV-negative (HIV-) individuals were also imaged at baseline and one year timepoints. The Pittsburgh Sleep Quality Index (PSQI) questionnaire was administered at all timepoints. Using group independent component analysis (ICA), maps of functional networks were generated from fMRI data; from these, sleep-related networks were selected. A generalized linear model (GLM) was used to analyze if these networks were significantly associated with the PSQI score, and if this relationship was influenced by HIV status/treatment or timepoint.ResultsHIV+ individuals had significantly lower PSQI score after treatment (p=0.022). Networks extracted from group ICA analysis included the anterior and posterior default mode network (DMN), central executive network (CEN), bilateral frontoparietal networks (FPNs), and the anterior cingulate cortex salience network (ACC SN). We found the posterior DMN, right FPN, and ACC SN GLMs showed significantly higher goodness-of-fit after incorporating PSQI data (p = 0.0204, 0.044, 0.044, respectively). Furthermore, the correlation between ACC SN and posterior DMN connectivity was significantly decreased in the HIV+ cohort.ConclusionFunctional networks such as the DMN, FPN, CEN, and ACC SN are altered in poor sleep, as measured by the PSQI score. Furthermore, the relationship between these networks and PSQI is different in the HIV+ and HIV- populations.

Slow feature analysis-independent component analysis based integrated monitoring approach for industrial processes incorporating dynamic and static characteristics

Considering dynamic and static characteristics in industrial processes, this paper proposed an integrated monitoring approach based on slow feature analysis and independent component analysis (SFA-ICA), which can fully take advantage of SFA and ICA in extracting dynamic features and static non-Gaussian features. A sequential correlation-based matrix for each variable is first calculated to evaluate the dynamics of the process variable, in which, the variables with weak autocorrelation and cross-correlation are considered as static variables, while the others are dynamic variables. Then, the ICA and SFA algorithms are built for the static and dynamic subspaces. The statistics from each of the subspaces are combined using Bayesian inference to give a final comprehensive statistic. The proposed SFA-ICA monitoring approach is applied to a numerical example, the Tennessee Eastman (TE) process and the continuous stirred tank reactor (CSTR) process. Results show that the SFA-ICA achieves the better fault detection rates for the numerical example, the CSTR process, and several typical faults for TE process.

Altered intra- and inter-network functional connectivity in patients with persistent postural-perceptual dizziness.

Persistent postural-perceptual dizziness (PPPD) is a functional vestibular disorder characterized by persistent dizziness, unsteadiness, and non-spinning vertigo. It is the most common cause of chronic vestibular syndrome, but its pathogenesis is currently unclear. Recent studies have indicated that sensory integration may be altered in PPPD patients. Using independent component analysis (ICA) combined with seed-based functional connectivity analysis, we aimed to analyze changes in brain network functional connectivity (FC) in PPPD patients during the resting state and to explore the underlying pathogenesis of PPPD, particularly the abnormal integration of multiple sensations. Study subjects included 12 PPPD patients and 12 healthy controls and were recruited from January to August 2018. Detailed medical data were collected from all participants. Vestibular function, neurological and medical examinations were conducted to exclude other diseases associated with chronic dizziness. Functional MRI was performed on all subjects. ICA and seed-based functional connectivity analysis were performed to examine changes in intra- and inter-network FC in PPPD patients. In total, 13 independent components were identified using ICA. Compared with healthy controls, PPPD patients showed decreased intra-network FC in the right precuneus within the posterior default mode network. Moreover, seed-based functional connectivity analysis showed decreased intra-network FC between the right precuneus and the bilateral precuneus and left premotor cortex, and enhanced FC between the right precuneus and bilateral corpus callosum. With respect to the inter-network, FC in PPPD patients was increased between the occipital pole visual network and auditory, sensorimotor networks, as well as the lateral visual and auditory networks. Additional analyses showed that FC changes were negatively correlated with dizziness handicap inventory functional scores. In PPPD patients, dysfunction in the precuneus may cause abnormalities in external environment monitoring and in posture and movement regulation. Compensatory strategies may then be adopted to maintain balance. At the local level, information exchange between the two cerebral hemispheres is enhanced via the corpus callosum. At the whole brain level, through enhancement of functional activities of the visual network, the integration of multiple sensations and the regulation of posture and movement are primarily driven by visual information.

A Comparative Study of Different EEG Reference Choices for Event-Related Potentials Extracted by Independent Component Analysis.

In the event-related potential (ERP) of scalp electroencephalography (EEG) studies, the vertex reference (Cz), linked mastoids or ears (LM), and average reference (AVG) are popular reference methods, and the reference electrode standardization technique (REST) is increasingly applied. Because scalp EEG recordings are considered as spatially degraded signals, independent component analysis (ICA) is a widely used data-driven method for obtaining ERPs by decomposing EEG data. However, the accurate estimation of the differences in ERP components extracted by ICA with different references remains unclear. In this study, we first provided formal descriptions of the above reference methods (Cz, LM, AVG, and REST) and ICA decomposition in ERP and then investigated the influences of different reference techniques on simulation and real EEG datasets. The results revealed that (1) the reference method did not change the peak amplitudes and latencies of relative ERPs corresponding to some IC time courses; (2) there were non-negligible effects of different reference methods on both temporal ERPs and spatial topographies of some ICs; and (3) compared to Cz, LM, and AR, considering both the performances of temporal ERPs and spatial topographies, the REST reference had overall superiority. These findings provide a recommended choice of REST for ICA analysis at the trial level and contribute to empirical investigations regarding the use of reference methods in ERP domains with ICA analysis.