418 research outputs found

    Overlapping connectivity patterns during semantic processing of abstract and concrete words revealed with multivariate Granger Causality analysis

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    Unlike concrete, nouns refer to notions beyond our perception. Even though there is no consensus among linguists as to what exactly constitutes a concrete or abstract word, neuroscientists found clear evidence of a "concreteness" effect. This can, for instance, be seen in patients with language impairments due to brain injury or developmental disorder who are capable of perceiving one category better than another. Even though the results are inconclusive, neuroimaging studies on healthy subjects also provide a spatial and temporal account of differences in the processing of abstract versus concrete words. A description of the neural pathways during abstract word reading, the manner in which the connectivity patterns develop over the different stages of lexical and semantic processing compared to that of concrete word processing are still debated. We conducted a high-density EEG study on 24 healthy young volunteers using an implicit categorization task. From this, we obtained high spatio-temporal resolution data and, by means of source reconstruction, reduced the effect of signal mixing observed on scalp level. A multivariate, time-varying and directional method of analyzing connectivity based on the concept of Granger Causality (Partial Directed Coherence) revealed a dynamic network that transfers information from the right superior occipital lobe along the ventral and dorsal streams towards the anterior temporal and orbitofrontal lobes of both hemispheres. Some regions along these pathways appear to be primarily involved in either receiving or sending information. A clear difference in information transfer of abstract and concrete words was observed during the time window of semantic processing, specifically for information transferred towards the left anterior temporal lobe. Further exploratory analysis confirmed a generally stronger connectivity pattern for processing concrete words. We believe our study could guide future research towards a more refined theory of abstract word processing in the brain

    Multimodal Functional Network Connectivity: An EEG-fMRI Fusion in Network Space

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    EEG and fMRI recordings measure the functional activity of multiple coherent networks distributed in the cerebral cortex. Identifying network interaction from the complementary neuroelectric and hemodynamic signals may help to explain the complex relationships between different brain regions. In this paper, multimodal functional network connectivity (mFNC) is proposed for the fusion of EEG and fMRI in network space. First, functional networks (FNs) are extracted using spatial independent component analysis (ICA) in each modality separately. Then the interactions among FNs in each modality are explored by Granger causality analysis (GCA). Finally, fMRI FNs are matched to EEG FNs in the spatial domain using network-based source imaging (NESOI). Investigations of both synthetic and real data demonstrate that mFNC has the potential to reveal the underlying neural networks of each modality separately and in their combination. With mFNC, comprehensive relationships among FNs might be unveiled for the deep exploration of neural activities and metabolic responses in a specific task or neurological state

    Tensor Analysis and Fusion of Multimodal Brain Images

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    Current high-throughput data acquisition technologies probe dynamical systems with different imaging modalities, generating massive data sets at different spatial and temporal resolutions posing challenging problems in multimodal data fusion. A case in point is the attempt to parse out the brain structures and networks that underpin human cognitive processes by analysis of different neuroimaging modalities (functional MRI, EEG, NIRS etc.). We emphasize that the multimodal, multi-scale nature of neuroimaging data is well reflected by a multi-way (tensor) structure where the underlying processes can be summarized by a relatively small number of components or "atoms". We introduce Markov-Penrose diagrams - an integration of Bayesian DAG and tensor network notation in order to analyze these models. These diagrams not only clarify matrix and tensor EEG and fMRI time/frequency analysis and inverse problems, but also help understand multimodal fusion via Multiway Partial Least Squares and Coupled Matrix-Tensor Factorization. We show here, for the first time, that Granger causal analysis of brain networks is a tensor regression problem, thus allowing the atomic decomposition of brain networks. Analysis of EEG and fMRI recordings shows the potential of the methods and suggests their use in other scientific domains.Comment: 23 pages, 15 figures, submitted to Proceedings of the IEE

    Oscillatory Network Activity in Brain Functions and Dysfunctions

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    Recent experimental studies point to the notion that the brain is a complex dynamical system whose behaviors relating to brain functions and dysfunctions can be described by the physics of network phenomena. The brain consists of anatomical axonal connections among neurons and neuronal populations in various spatial scales. Neuronal interactions and synchrony of neuronal oscillations are central to normal brain functions. Breakdowns in interactions and modifications in synchronization behaviors are usual hallmarks of brain dysfunctions. Here, in this dissertation for PhD degree in physics, we report discoveries of brain oscillatory network activity from two separate studies. These studies investigated the large-scale brain activity during tactile perceptual decision-making and epileptic seizures. In the perceptual decision-making study, using scalp electroencephalography (EEG) recordings of brain potentials, we investigated how oscillatory activity functionally organizes different neocortical regions as a network during a tactile discrimination task. While undergoing EEG recordings, blindfolded healthy participants felt a linear three-dot array presented electromechanically, under computer control, and reported whether the central dot was offset to the left or right. Based on the current dipole modeling in the brain, we found that the source-level peak activity appeared in the left primary somatosensory cortex (SI), right lateral occipital complex (LOC), right posterior intraparietal sulcus (pIPS) and finally left dorsolateral prefrontal cortex (dlPFC) at 45, 130, 160 and 175 ms respectively. Spectral interdependency analysis showed that fine tactile discrimination is mediated by distinct but overlapping ~15 Hz beta and ~80 Hz gamma band large-scale oscillatory networks. The beta-network that included all four nodes was dominantly feedforward, similar to the propagation of peak cortical activity, implying its role in accumulating and maintaining relevant sensory information and mapping to action. The gamma-network activity, occurring in a recurrent loop linked SI, pIPS and dlPFC, likely carrying out attentional selection of task-relevant sensory signals. Behavioral measure of task performance was correlated with the network activity in both bands. In the study of epileptic seizures, we investigated high-frequency (\u3e 50 Hz) oscillatory network activity from intracranial EEG (IEEG) recordings of patients who were the candidates for epilepsy surgery. The traditional approach of identifying brain regions for epilepsy surgery usually referred as seizure onset zones (SOZs) has not always produced clarity on SOZs. Here, we investigated directed network activity in the frequency domain and found that the high frequency (\u3e80 Hz) network activities occur before the onset of any visible ictal activity, andcausal relationships involve the recording electrodes where clinically identifiable seizures later develop. These findings suggest that high-frequency network activities and their causal relationships can assist in precise delineation of SOZs for surgical resection

    Theoretical and experimental study of P300 ERP in the context of Brain-computer interfaces. Part I: Study and analysis of functional connectivity methods.

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    Trabajo Fin de Máster en Ingeniería InformáticaThe analysis of connectivity in brain networks has been widely researched and it has been shown that certain cognitive processes require the integration of distributed brain areas. Functional connectivity attempts to statistically quantify the interdependencies between these brain areas. For this study, an analysis of functional connectivity in an ERP context, more specifically on the P300 component using the Granger Causality metric was proposed. To this end, an analysis method is proposed which consists in quantifying the causality in the P300 signal and the non-P300 signal using the MVCG toolbox to determine if there are differences between the two results obtained. In this respect, a dataset from a Brain-Computer Interface (BCI) based on P300 is analyzed. Causality is determined in overlapping windows calculated from the signals under three aspects: i) Using standard electrodes, ii) Using electrodes selected by Bayesian Linear Discriminant Analysis and exhaustive search by forward selection (BLDA-FS), and iii) Using electrodes selected by the coefficient of determination (r2). Based on this analysis, it is shown that the Granger Causality metric is valid to show the existence of a significant connectivity difference between the P300 signal and the non-P300 signal. This measure shows higher connectivity values for the P300 signal and lower connectivity values for the non-P300 signal. Among the three approaches considered, the standard electrodes and the electrodes selected with BLDA-FS were found to be more discriminative in showing differences between P300 and nonP300 connectivity. Furthermore, through this study, it was possible to differentiate the level of functional connectivity between subjects with cognitive disabilities and nondisabled subjects, observing that the measured functional connectivity was higher in subjects without an underlying cognitive pathology. Studying functional connectivity with Granger Causality may help to incorporate this information as new features that allow better detection of the P300 signal and consequently improve the performance of P300-based BCIs

    Selective attention and speech processing in the cortex

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    In noisy and complex environments, human listeners must segregate the mixture of sound sources arriving at their ears and selectively attend a single source, thereby solving a computationally difficult problem called the cocktail party problem. However, the neural mechanisms underlying these computations are still largely a mystery. Oscillatory synchronization of neuronal activity between cortical areas is thought to provide a crucial role in facilitating information transmission between spatially separated populations of neurons, enabling the formation of functional networks. In this thesis, we seek to analyze and model the functional neuronal networks underlying attention to speech stimuli and find that the Frontal Eye Fields play a central 'hub' role in the auditory spatial attention network in a cocktail party experiment. We use magnetoencephalography (MEG) to measure neural signals with high temporal precision, while sampling from the whole cortex. However, several methodological issues arise when undertaking functional connectivity analysis with MEG data. Specifically, volume conduction of electrical and magnetic fields in the brain complicates interpretation of results. We compare several approaches through simulations, and analyze the trade-offs among various measures of neural phase-locking in the presence of volume conduction. We use these insights to study functional networks in a cocktail party experiment. We then construct a linear dynamical system model of neural responses to ongoing speech. Using this model, we are able to correctly predict which of two speakers is being attended by a listener. We then apply this model to data from a task where people were attending to stories with synchronous and scrambled videos of the speakers' faces to explore how the presence of visual information modifies the underlying neuronal mechanisms of speech perception. This model allows us to probe neural processes as subjects listen to long stimuli, without the need for a trial-based experimental design. We model the neural activity with latent states, and model the neural noise spectrum and functional connectivity with multivariate autoregressive dynamics, along with impulse responses for external stimulus processing. We also develop a new regularized Expectation-Maximization (EM) algorithm to fit this model to electroencephalography (EEG) data
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