Simultaneous spatio-temporal matching pursuit decomposition of evoked brain responses in MEG.
Ontology highlight
ABSTRACT:
SUBMITTER: Kordowski P
PROVIDER: S-EPMC5326632 | biostudies-literature | 2017 Feb
REPOSITORIES: biostudies-literature
Publications
Simultaneous spatio-temporal matching pursuit decomposition of evoked brain responses in MEG.
Kordowski Paweł P Matysiak Artur A König Reinhard R Sielużycki Cezary C
Biological cybernetics 20170121 1
We present a novel approach to the spatio-temporal decomposition of evoked brain responses in magnetoencephalography (MEG) aiming at a sparse representation of the underlying brain activity in terms of spatio-temporal atoms. Our approach is characterized by three attributes which constitute significant improvements with respect to existing approaches: (1) the spatial and temporal decomposition is addressed simultaneously rather than sequentially, with the benefit that source loci and correspondi ...[more]
Similar Datasets
Project description:We present an empirical Bayesian scheme for distributed multimodal inversion of electromagnetic forward models of EEG and MEG signals. We used a generative model with common source activity and separate error components for each modality. Under this scheme, the weightings of error for each modality, relative to source components, are estimated automatically from the data, by optimising the model-evidence. This obviates the need for arbitrary user-defined weightings. To evaluate the scheme, we acquired three types of data simultaneously from twelve participants: total magnetic flux (as recorded by 102 magnetometers), orthogonal in-plane gradients of the magnetic field (as recorded by 204 planar gradiometers) and voltage differences in the electrical field (recorded by 70 electrodes). We assessed the relative precision of each sensor-type in terms of signal-to-noise ratio (SNR); using empirical sample variances and optimised estimators from the generative model. We then compared the localisation of face-evoked responses, using each modality separately, with that obtained by their "fusion" under the common generative model. Finally, we quantified the conditional precisions of the source estimates using their posterior covariance, confirming that EEG can improve MEG-based source reconstructions.
| S-EPMC2912501 | biostudies-literature
Project description:Intra-cortical microstimulation (ICMS) is a widely used technique to artificially stimulate cortical tissue. This method revealed functional maps and provided causal links between neuronal activity and cognitive, sensory or motor functions. The effects of ICMS on neural activity depend on stimulation parameters. Past studies investigated the effects of stimulation frequency mainly at the behavioral or motor level. Therefore the direct effect of frequency stimulation on the evoked spatio-temporal patterns of cortical activity is largely unknown. To study this question we used voltage-sensitive dye imaging to measure the population response in the barrel cortex of anesthetized rats evoked by high frequency stimulation (HFS), a lower frequency stimulation (LFS) of the same duration or a single pulse stimulation. We found that single pulse and short trains of ICMS induced cortical activity extending over few mm. HFS evoked a lower population response during the sustained response and showed a smaller activation across time and space compared with LFS. Finally the evoked population response started near the electrode site and spread horizontally at a propagation velocity in accordance with horizontal connections. In summary, HFS was less effective in cortical activation compared to LFS although HFS had 5 fold more energy than LFS.
| S-EPMC6141467 | biostudies-literature
Project description:BackgroundMatching pursuit algorithm (MP), especially with recent multivariate extensions, offers unique advantages in analysis of EEG and MEG.MethodsWe propose a novel construction of an optimal Gabor dictionary, based upon the metrics introduced in this paper. We implement this construction in a freely available software for MP decomposition of multivariate time series, with a user friendly interface via the Svarog package (Signal Viewer, Analyzer and Recorder On GPL, http://braintech.pl/svarog), and provide a hands-on introduction to its application to EEG. Finally, we describe numerical and mathematical optimizations used in this implementation.ResultsOptimal Gabor dictionaries, based on the metric introduced in this paper, for the first time allowed for a priori assessment of maximum one-step error of the MP algorithm. Variants of multivariate MP, implemented in the accompanying software, are organized according to the mathematical properties of the algorithms, relevant in the light of EEG/MEG analysis. Some of these variants have been successfully applied to both multichannel and multitrial EEG and MEG in previous studies, improving preprocessing for EEG/MEG inverse solutions and parameterization of evoked potentials in single trials; we mention also ongoing work and possible novel applications.ConclusionsMathematical results presented in this paper improve our understanding of the basics of the MP algorithm. Simple introduction of its properties and advantages, together with the accompanying stable and user-friendly Open Source software package, pave the way for a widespread and reproducible analysis of multivariate EEG and MEG time series and novel applications, while retaining a high degree of compatibility with the traditional, visual analysis of EEG.
| S-EPMC3849619 | biostudies-literature
Project description:The development of the human brain is a complex and precisely regulated process that unfolds over a protracted period of time. Human-specific features of this process, especially the ways in which highly complex neural circuits of the cerebral cortex form, are likely to be important factors in the evolution of human specializations. However, in addition to giving us remarkable cognitive and motor abilities, the formation of intricate neural circuits may have also increased our susceptibility to psychiatric and neurodegenerative disorders. Furthermore, substantial evidence suggests that the symptoms and progression of many brain disorders are dramatically influenced by genetic and developmental processes that define regional cell phenotypes and connectivity. Sex differences also play an important role in brain development and function and are a risk factor for several brain disorders, such as autism spectrum disorders (ASD) and depression. Thus understanding the spatiotemporal dynamics and functional organization of the brain transcriptome is essential to teasing out the keys to human neurodevelopment, sexual dimorphism, and evolution as well as our increased susceptibility to certain brain disorders. Most transcriptome studies of the developing brain have been restricted to rodents, and those performed in humans and nonhuman primates have included relatively small sample sizes and predominantly focused on few regions or developmental time points. Because many prominent features of human brain development significantly diverge from those of well-characterized model organisms, the translation of knowledge across species is difficult, and it is likely that many underlying genetic processes have gone undetected. In this study, we have taken a genome-wide approach to analyze the human transcriptome at single-exon resolution with ~1.4 million exon-level probe sets in 16 brain regions from donors representing both sexes and multiple ethnicities, across pre and postnatal development, including adolescence, and adulthood. We also generated genome-wide genotype data for 2.5 million single nucleotide polymorphisms (SNPs) and copy number variants (CNVs) for each specimen. Our analyses of the data revealed several features of the human brain transcriptome: spatiotemporal expression dynamics of individual and functionally related groups of genes, differential exon usage, sex-specific expression patterns and exon usage, and organization of the transcriptome into functional modules. We also profiled developmental trajectories of genes important for neurobiological themes and genes associated with ASD and schizophrenia. Finally, we present associations between specific SNPs and gene expression levels in different brain regions across development. The dataset presented here provides research opportunities and a wealth of information not previously available to the scientific community. Here we report the generation and analysis of genome-wide exon-level transcriptome data from 16 brain regions comprising the cerebellar cortex, mediodorsal nucleus of the thalamus, striatum, amygdala, hippocampus, and 11 areas of the neocortex. The dataset was generated from 1,340 samples collected from both hemispheres of 57 postmortem human brains, spanning from embryonic development to late adulthood and representing males and females of multiple ethnicities. We also performed genome-wide genotyping of 2.5 million single nucleotide polymorphisms (SNPs) and assessed copy number variations for all donors. We found that approximately 86% of protein-coding genes are expressed above background, and over 90% of these are differentially regulated at the whole transcript or exon level across regions and/or time. The majority of these spatiotemporal differences occur before birth, followed by a progressive increase in the similarity among regional transcriptome during the postnatal lifespan. Distinctive transcriptional patterns were also identified among neocortical areas, including an alternative isoform of ANKRD32 that is transiently upregulated in the late midfetal frontal cortex of human but not mouse. Developmentally and regionally regulated differences in gene expression and exon usage were also identified between males and females, including IGF2, an imprinted gene implicated in brain growth and cognitive function, and NLGN4X, a candidate gene for autism spectrum disorders. We found that the developing transcriptome is organized into functionally distinct large-scale networks of co-regulated transcripts. We also observed marked differences in expression trajectories of genes associated with specific neurodevelopmental processes, neural cell types, neurotransmitter systems, autism spectrum disorders, and schizophrenia. Association analysis of the SNP genotypes and transcriptome revealed potential polymorphic regulators of gene expression in different regions across development. This study provides a comprehensive, publicly available dataset on the spatiotemporal human brain transcriptome and new insights into the transcriptional foundations of brain development and evolution. This submission represents the transcriptome component of the study. Submitter did not submit SNP and CNV data due to privacy issue of patients.
2011-10-26 | E-GEOD-25219 | biostudies-arrayexpress
Project description:The hedonic meaning of words affects word recognition, as shown by behavioral, functional imaging, and event-related potential (ERP) studies. However, the spatiotemporal dynamics and cognitive functions behind are elusive, partly due to methodological limitations of previous studies. Here, we account for these difficulties by computing combined electro-magnetoencephalographic (EEG/MEG) source localization techniques. Participants covertly read emotionally high-arousing positive and negative nouns, while EEG and MEG were recorded simultaneously. Combined EEG/MEG current-density reconstructions for the P1 (80-120 ms), P2 (150-190 ms) and EPN component (200-300 ms) were computed using realistic individual head models, with a cortical constraint. Relative to negative words, the P1 to positive words predominantly involved language-related structures (left middle temporal and inferior frontal regions), and posterior structures related to directed attention (occipital and parietal regions). Effects shifted to the right hemisphere in the P2 component. By contrast, negative words received more activation in the P1 time-range only, recruiting prefrontal regions, including the anterior cingulate cortex (ACC). Effects in the EPN were not statistically significant. These findings show that different neuronal networks are active when positive versus negative words are processed. We account for these effects in terms of an "emotional tagging" of word forms during language acquisition. These tags then give rise to different processing strategies, including enhanced lexical processing of positive words and a very fast language-independent alert response to negative words. The valence-specific recruitment of different networks might underlie fast adaptive responses to both approach- and withdrawal-related stimuli, be they acquired or biological.
| S-EPMC3733636 | biostudies-other
Project description:The development of the human brain is a complex and precisely regulated process that unfolds over a protracted period of time. Human-specific features of this process, especially the ways in which highly complex neural circuits of the cerebral cortex form, are likely to be important factors in the evolution of human specializations. However, in addition to giving us remarkable cognitive and motor abilities, the formation of intricate neural circuits may have also increased our susceptibility to psychiatric and neurodegenerative disorders. Furthermore, substantial evidence suggests that the symptoms and progression of many brain disorders are dramatically influenced by genetic and developmental processes that define regional cell phenotypes and connectivity. Sex differences also play an important role in brain development and function and are a risk factor for several brain disorders, such as autism spectrum disorders (ASD) and depression. Thus understanding the spatiotemporal dynamics and functional organization of the brain transcriptome is essential to teasing out the keys to human neurodevelopment, sexual dimorphism, and evolution as well as our increased susceptibility to certain brain disorders. Most transcriptome studies of the developing brain have been restricted to rodents, and those performed in humans and nonhuman primates have included relatively small sample sizes and predominantly focused on few regions or developmental time points. Because many prominent features of human brain development significantly diverge from those of well-characterized model organisms, the translation of knowledge across species is difficult, and it is likely that many underlying genetic processes have gone undetected. In this study, we have taken a genome-wide approach to analyze the human transcriptome at single-exon resolution with ~1.4 million exon-level probe sets in 16 brain regions from donors representing both sexes and multiple ethnicities, across pre and postnatal development, including adolescence, and adulthood. We also generated genome-wide genotype data for 2.5 million single nucleotide polymorphisms (SNPs) and copy number variants (CNVs) for each specimen. Our analyses of the data revealed several features of the human brain transcriptome: spatiotemporal expression dynamics of individual and functionally related groups of genes, differential exon usage, sex-specific expression patterns and exon usage, and organization of the transcriptome into functional modules. We also profiled developmental trajectories of genes important for neurobiological themes and genes associated with ASD and schizophrenia. Finally, we present associations between specific SNPs and gene expression levels in different brain regions across development. The dataset presented here provides research opportunities and a wealth of information not previously available to the scientific community.
2011-10-27 | GSE25219 | GEO
Project description:Functional MRI (fMRI) studies have shown that low-frequency (<0.1 Hz) spontaneous fluctuations of the blood oxygenation level dependent (BOLD) signal during restful wakefulness are coherent within distributed large-scale cortical and subcortical networks (resting state networks, RSNs). The neuronal mechanisms underlying RSNs remain poorly understood. Here, we describe magnetoencephalographic correspondents of two well-characterized RSNs: the dorsal attention and the default mode networks. Seed-based correlation mapping was performed using time-dependent MEG power reconstructed at each voxel within the brain. The topography of RSNs computed on the basis of extended (5 min) epochs was similar to that observed with fMRI but confined to the same hemisphere as the seed region. Analyses taking into account the nonstationarity of MEG activity showed transient formation of more complete RSNs, including nodes in the contralateral hemisphere. Spectral analysis indicated that RSNs manifest in MEG as synchronous modulation of band-limited power primarily within the theta, alpha, and beta bands-that is, in frequencies slower than those associated with the local electrophysiological correlates of event-related BOLD responses.
| S-EPMC2851876 | biostudies-other
Project description:Brain development and function depend on the precise regulation of gene expression. However, our understanding of the complexity and dynamics of the transcriptome of the human brain is incomplete. Here we report the generation and analysis of exon-level transcriptome and associated genotyping data, representing males and females of different ethnicities, from multiple brain regions and neocortical areas of developing and adult post-mortem human brains. We found that 86 per cent of the genes analysed were expressed, and that 90 per cent of these were differentially regulated at the whole-transcript or exon level across brain regions and/or time. The majority of these spatio-temporal differences were detected before birth, with subsequent increases in the similarity among regional transcriptomes. The transcriptome is organized into distinct co-expression networks, and shows sex-biased gene expression and exon usage. We also profiled trajectories of genes associated with neurobiological categories and diseases, and identified associations between single nucleotide polymorphisms and gene expression. This study provides a comprehensive data set on the human brain transcriptome and insights into the transcriptional foundations of human neurodevelopment.
| S-EPMC3566780 | biostudies-literature
Project description:Understanding spatio-temporal dynamics of biotic communities containing large numbers of species is crucial to guide ecosystem management and conservation efforts. However, traditional approaches usually focus on studying community dynamics either in space or in time, often failing to fully account for interlinked spatio-temporal changes. In this study, we demonstrate and promote the use of tensor decomposition for disentangling spatio-temporal community dynamics in long-term monitoring data. Tensor decomposition builds on traditional multivariate statistics (e.g. Principal Component Analysis) but extends it to multiple dimensions. This extension allows for the synchronized study of multiple ecological variables measured repeatedly in time and space. We applied this comprehensive approach to explore the spatio-temporal dynamics of 65 demersal fish species in the North Sea, a marine ecosystem strongly altered by human activities and climate change. Our case study demonstrates how tensor decomposition can successfully (i) characterize the main spatio-temporal patterns and trends in species abundances, (ii) identify sub-communities of species that share similar spatial distribution and temporal dynamics, and (iii) reveal external drivers of change. Our results revealed a strong spatial structure in fish assemblages persistent over time and linked to differences in depth, primary production and seasonality. Furthermore, we simultaneously characterized important temporal distribution changes related to the low frequency temperature variability inherent in the Atlantic Multidecadal Oscillation. Finally, we identified six major sub-communities composed of species sharing similar spatial distribution patterns and temporal dynamics. Our case study demonstrates the application and benefits of using tensor decomposition for studying complex community data sets usually derived from large-scale monitoring programs.
| S-EPMC5685633 | biostudies-literature
Project description:A new approach for analysis of event-related fMRI (BOLD) signals is proposed. The technique is based on measures from information theory and is used both for spatial localization of task-related activity, as well as for extracting temporal information regarding the task-dependent propagation of activation across different brain regions. This approach enables whole brain visualization of voxels (areas) most involved in coding of a specific task condition, the time at which they are most informative about the condition, as well as their average amplitude at that preferred time. The approach does not require prior assumptions about the shape of the hemodynamic response function (HRF) nor about linear relations between BOLD response and presented stimuli (or task conditions). We show that relative delays between different brain regions can also be computed without prior knowledge of the experimental design, suggesting a general method that could be applied for analysis of differential time delays that occur during natural, uncontrolled conditions. Here we analyze BOLD signals recorded during performance of a motor learning task. We show that, during motor learning, the BOLD response of unimodal motor cortical areas precedes the response in higher-order multimodal association areas, including posterior parietal cortex. Brain areas found to be associated with reduced activity during motor learning, predominantly in prefrontal brain regions, are informative about the task typically at significantly later times.
| S-EPMC4028845 | biostudies-literature