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Concept: Independent component analysis


Alzheimer’s disease (AD) is a neurodegenerative disease, usually diagnosed by neuropsychological tests, and excluded from other cerebral diseases by brain images. An electroencephalogram (EEG) provides a means of disclosing the reduced functional couplings between brain regions that occurs with AD. In the present study, 16 probable AD patients and 15 age-matched, gender-matched normal subjects were enrolled. Spectral coherence and cross mutual information (CMI) were used to analyze EEGs during intermittent photic stimulation (PS). Ocular- and heartbeat-related source components (SCs) obtained from multi-channel EEGs by the independent component analysis were discarded, and the photic-related SCs were reduced using a comb filter. The undisturbed SCs and photic-related SCs before and after photic reduction were used to reconstruct photic-preserved EEGs and photic-reduced EEGs, from which harmonic coherences (direct photic-driving response) and rhythmic coherences and CMI (indirect photic affection) were computed, respectively. Our results indicate that the rhythmic coherences (particularly in the alpha and beta bands) and CMI variables as well as the harmonic coherences (particularly related to 3-Hz PS) were significantly lower in the probable AD than in normal subjects, whereas the variables derived from the resting EEGs were not statistically significant. This finding implied that the variables obtained during PS could be used to disclose impaired intra-brain associations in probable AD.

Concepts: Statistics, Neuroscience, Cerebral cortex, Neurology, Neurodegenerative disorders, Electroencephalography, Probability theory, Independent component analysis


In an effort to identify non-intentionally added substances (NIAS), which is still a challenging task for analytical chemists, PET pellets, preforms and bottles were analyzed by an optimized headspace solid phase microextraction coupled to gas chromatography-mass spectrometry (HS-SPME/GC-MS). Fingerprints obtained by the proposed method were analyzed by three chemometric tools: Principal Components Analysis (PCA), Independent Components Analysis (ICA) and a multi-block method (Common Components and Specific Weights Analysis CCSWA) in order to extract pertinent variations in NIAS concentrations. Total ion current (TIC) chromatograms were used for PCA and ICA while extracted ion chromatograms (EIC) were used for CCSWA, each ion corresponding to a block. PCA managed to discriminate pellets and preforms from bottles due to several NIAS. Volatiles like 2-methyl-1,3-dioxolane, ethylene glycol, ethylbenzene and xylene were responsible for the discrimination of pellets and preforms. Less volatile compounds like linear aldehydes and phthalates were responsible for the discrimination of bottles. ICA showed more specific discriminations especially for bottles and pellets while CCSWA managed to discriminate preforms. The proposed methodology, combining HS-SPME/GC-MS with chemometric tools proved its efficiency in highlighting NIAS in PET samples in a relatively simple and fast approach compared to classical techniques.

Concepts: Chromatography, Principal component analysis, Ethylene glycol, Volatile, Independent component analysis, Nonlinear dimensionality reduction, Terephthalic acid, Polyethylene terephthalate


Blood oxygen-level-dependent functional magnetic resonance imaging (BOLD fMRI) has rapidly become a popular technique for the investigation of brain function in healthy individuals, patients as well as in animal studies. However, the BOLD signal arises from a complex mixture of neuronal, metabolic and vascular processes, being therefore an indirect measure of neuronal activity, which is further severely corrupted by multiple non-neuronal fluctuations of instrumental, physiological or subject-specific origin. This review aims to provide a comprehensive summary of existing methods for cleaning the BOLD fMRI signal. The description is given from a methodological point of view, focusing on the operation of the different techniques in addition to pointing out the advantages and limitations in their application. Since motion-related and physiological noise fluctuations are two of the main noise components of the signal, techniques targeting their removal are primarily addressed, including both data-driven approaches and using external recordings. Data-driven approaches, which are less specific in the assumed model and can simultaneously reduce multiple noise fluctuations, are mainly based on data decomposition techniques such as principal and independent component analysis. Importantly, the usefulness of strategies that benefit from the information available in the phase component of the signal, or in multiple signal echoes is also highlighted. The use of global signal regression for denoising is also addressed. Finally, practical recommendations regarding the optimization of the preprocessing pipeline for the purpose of denoising and future venues of research are indicated. Through the review, we summarize the importance of signal denoising as an essential step in the analysis pipeline of task-based and resting state fMRI studies.

Concepts: Brain, Brain tumor, Nuclear magnetic resonance, Magnetic resonance imaging, Multiple sclerosis, The Signal, Functional magnetic resonance imaging, Independent component analysis


Recent neurobiological models of ADHD suggest that deficits in different neurobiological pathways may independently lead to symptoms of this disorder. At least three independent pathways may be involved: a dorsal frontostriatal pathway involved in cognitive control, a ventral frontostriatal pathway involved in reward processing and a frontocerebellar pathway related to temporal processing. Importantly, we and others have suggested that disruptions in these three pathways should lead to separable deficits at the cognitive level. Furthermore, if these truly represent separate biological pathways to ADHD, these cognitive deficits should segregate between individuals with ADHD. The present study tests these hypotheses in a sample of children, adolescents and young adults with ADHD and controls. 149 Subjects participated in a short computerized battery assessing cognitive control, timing and reward sensitivity. We used Principal Component Analysis to find independent components underlying the variance in the data. The segregation of deficits between individuals was tested using Loglinear Analysis. We found four components, three of which were predicted by the model: Cognitive control, reward sensitivity and timing. Furthermore, 80% of subjects with ADHD that had a deficit were deficient on only one component. Loglinear Analysis statistically confirmed the independent segregation of deficits between individuals. We therefore conclude that cognitive control, timing and reward sensitivity were separable at a cognitive level and that deficits on these components segregated between individuals with ADHD. These results support a neurobiological framework of separate biological pathways to ADHD with separable cognitive deficits.

Concepts: Scientific method, Multivariate statistics, Cognitive science, Principal component analysis, Independent component analysis, Racial segregation, Nonlinear dimensionality reduction, Segregation


Sensorimotor processing is a critical function of the human brain with multiple cortical areas specialised for sensory recognition or motor execution. Although there has been considerable research into sensorimotor control in humans, the steps between sensory recognition and motor execution are not fully understood. To provide insight into brain areas responsible for sensorimotor computation, we used complex categorization-response tasks (variations of a Stroop task requiring recognition, decision-making, and motor responses) to test the hypothesis that some functional modules are participating in both sensory as well as motor processing. We operationalize functional modules as independent components (ICs) yielded by an independent component analysis (ICA) of EEG data and measured event-related responses by means of inter-trial coherence (ITC). Our results consistently found ICs with event-related ITC responses related to both sensory stimulation and motor response onsets (on average 5.8 ICs per session). These findings reveal EEG correlates of tightly coupled sensorimotor processing in the human brain, and support frameworks like embodied cognition, common coding, and sensorimotor contingency that do not sequentially separate sensory and motor brain processes.

Concepts: Brain, Human brain, Cognition, Cerebral cortex, Perception, Principal component analysis, Independent component analysis, Magnetoencephalography


A novel sequence has been introduced that combines multiband imaging with a multi-echo acquisition for simultaneous high spatial resolution pseudo-continuous arterial spin labeling (ASL) and blood-oxygenation-level dependent (BOLD) echo-planar imaging (MBME ASL/BOLD). Resting-state connectivity in healthy adult subjects was assessed using this sequence. Four echoes were acquired with a multiband acceleration of four, in order to increase spatial resolution, shorten repetition time, and reduce slice-timing effects on the ASL signal. In addition, by acquiring four echoes, advanced multi-echo independent component analysis (ME-ICA) denoising could be employed to increase the signal-to-noise ratio (SNR) and BOLD sensitivity. Seed-based and dual-regression approaches were utilized to analyze functional connectivity. Cerebral blood flow (CBF) and BOLD coupling was also evaluated by correlating the perfusion-weighted timeseries with the BOLD timeseries. These metrics were compared between single echo (E2), multi-echo combined (MEC), multi-echo combined and denoised (MECDN), and perfusion-weighted (PW) timeseries. Temporal SNR increased for the MECDN data compared to the MEC and E2 data. Connectivity also increased, in terms of correlation strength and network size, for the MECDN compared to the MEC and E2 datasets. CBF and BOLD coupling was increased in major resting-state networks, and that correlation was strongest for the MECDN datasets. These results indicate our novel MBME ASL/BOLD sequence, which collects simultaneous high-resolution ASL/BOLD data, could be a powerful tool for detecting functional connectivity and dynamic neurovascular coupling during the resting state. The collection of more than two echoes facilitates the use of ME-ICA denoising to greatly improve the quality of resting state functional connectivity MRI.

Concepts: Optics, Principal component analysis, Signal-to-noise ratio, Image processing, Cerebral blood flow, Image resolution, Independent component analysis


Resting-state functional magnetic resonance imaging (rs-fMRI) is widely used to investigate functional brain network connectivity during rest or when the subject is not performing an explicit task. In the standard procedure, subjects are instructed to “let your mind wander” or “think of nothing.” While these instructions appear appropriate to induce a “resting-state,” they could induce distinct psychological and physiological states during the scan. In this study, we investigated whether different instructions affect mental state and functional connectivity (FC) (i.e., induce distinct “resting states”) during rs-fMRI scanning. Thirty healthy subjects were subjected to two rs-fMRI scans differing only in pre-scan instructions: think of nothing (TN) and mind wandering (MW) conditions. Self-reports confirmed that subjects spent the majority of the scanning time in the appropriate mental state. Independent component analysis extracted 19 independent components (ICs) of interest and functional network connectivity analyses indicated several conditional differences of FCs among those ICs, especially characterized by stronger FC in the MW condition than in the TN condition, between default mode network and salience/visual/frontal network. Complementary correlation analysis indicated that some of the network FCs were significantly correlated with their self-reported data on how often they had the TN condition during the scans. The present results provide evidence that the pre-scan instruction has a significant influence on resting-state FC and its relationship with mental activities. This article is protected by copyright. All rights reserved.

Concepts: Psychology, Brain, Magnetic resonance imaging, Mind, Principal component analysis, Scan, Copyright, Independent component analysis


Principal component analysis (PCA) discovers patterns in multivariate data that include spectra, microscopy, and other biophysical measurements. Direct application of PCA to crowded spectra, images, and movies (without selecting peaks or features) was shown recently to identify their equilibrium or temporal changes. To enable the community to utilize these capabilities with a wide range of measurements, we have developed multiplatform software named TREND to Track Equilibrium and Nonequilibrium population shifts among two-dimensional Data frames. TREND can also carry this out by independent component analysis. We highlight a few examples of finding concurrent processes. TREND extracts dual phases of binding to two sites directly from the NMR spectra of the titrations. In a cardiac movie from magnetic resonance imaging, TREND resolves principal components (PCs) representing breathing and the cardiac cycle. TREND can also reconstruct the series of measurements from selected PCs, as illustrated for a biphasic, NMR-detected titration and the cardiac MRI movie. Fidelity of reconstruction of series of NMR spectra or images requires more PCs than needed to plot the largest population shifts. TREND reads spectra from many spectroscopies in the most common formats (JCAMP-DX and NMR) and multiple movie formats. The TREND package thus provides convenient tools to resolve the processes recorded by diverse biophysical methods.

Concepts: Nuclear magnetic resonance, Magnetic resonance imaging, Multivariate statistics, Principal component analysis, Singular value decomposition, Linear discriminant analysis, Independent component analysis, Nonlinear dimensionality reduction


Resting state fMRI (rfMRI) is gaining in popularity, being easy to acquire, and with promising clinical applications. However, rfMRI studies, especially those involving clinical groups, still lack reproducibility, largely due to the different analysis settings. This is particularly important for the development of imaging biomarkers. The aim of this work was to evaluate the reproducibility of our recent study regarding the functional connectivity of the basal ganglia network in early Parkinson’s disease (PD) (Szewczyk-Krolikowski et al., 2014). In particular, we systematically analysed the influence of two rfMRI analysis steps on the results: the individual cleaning (artefact removal) of fMRI data, and the choice of the set of independent components (template) used for dual regression. Our experience suggests that the use of a cleaning approach based on single-subject independent component analysis, which removes non neural-related sources of inter-individual variability, can help to increase the reproducibility of clinical findings. A template generated using an independent set of healthy controls is recommended for studies where the aim is to detect differences from a “healthy” brain, rather than an “average” template, derived from an equal number of patients and controls. Whilst, exploratory analyses (e.g. testing multiple resting state networks) should be used to formulate new hypotheses, careful validation is necessary before promising findings can be translated into useful biomarkers.

Concepts: Epidemiology, Parkinson's disease, Subthalamic nucleus, Basal ganglia, Substantia nigra, Principal component analysis, Dopamine, Independent component analysis


We developed a biologically plausible unsupervised learning algorithm, error-gated Hebbian rule (EGHR)-β, that performs principal component analysis (PCA) and independent component analysis (ICA) in a single-layer feedforward neural network. If parameter β = 1, it can extract the subspace that major principal components span similarly to Oja’s subspace rule for PCA. If β = 0, it can separate independent sources similarly to Bell-Sejnowski’s ICA rule but without requiring the same number of input and output neurons. Unlike these engineering rules, the EGHR-β can be easily implemented in a biological or neuromorphic circuit because it only uses local information available at each synapse. We analytically and numerically demonstrate the reliability of the EGHR-β in extracting and separating major sources given high-dimensional input. By adjusting β, the EGHR-β can extract sources that are missed by the conventional engineering approach that first applies PCA and then ICA. Namely, the proposed rule can successfully extract hidden natural images even in the presence of dominant or non-Gaussian noise components. The results highlight the reliability and utility of the EGHR-β for large-scale parallel computation of PCA and ICA and its future implementation in a neuromorphic hardware.

Concepts: Principal component analysis, Machine learning, Neural network, Artificial neural network, Singular value decomposition, Independent component analysis, Nonlinear dimensionality reduction, Unsupervised learning