46 research outputs found

    Graph Theoretical Characteristics of EEG-Based Functional Brain Networks in Patients With Epilepsy: The Effect of Reference Choice and Volume Conduction

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    It is well-established that both volume conduction and the choice of recording reference (montage) affect the correlation measures obtained from scalp EEG, both in the time and frequency domains. As a result, a number of correlation measures have been proposed aiming to reduce these effects. In our previous work, we have showed that scalp-EEG based functional brain networks in patients with epilepsy exhibit clear periodic patterns at different time scales and that these patterns are strongly correlated to seizure onset, particularly at shorter time scales (around 3 and 5 h), which has important clinical implications. In the present work, we use the same long-duration clinical scalp EEG data (multiple days) to investigate the extent to which the aforementioned results are affected by the choice of reference choice and correlation measure, by considering several widely used montages as well as correlation metrics that are differentially sensitive to the effects of volume conduction. Specifically, we compare two standard and commonly used linear correlation measures, cross-correlation in the time domain, and coherence in the frequency domain, with measures that account for zero-lag correlations: corrected cross-correlation, imaginary coherence, phase lag index, and weighted phase lag index. We show that the graphs constructed with corrected cross-correlation and WPLI are more stable across different choices of reference. Also, we demonstrate that all the examined correlation measures revealed similar periodic patterns in the obtained graph measures when the bipolar and common reference (Cz) montage were used. This includes circadian-related periodicities (e.g., a clear increase in connectivity during sleep periods as compared to awake periods), as well as periodicities at shorter time scales (around 3 and 5 h). On the other hand, these results were affected to a large degree when the average reference montage was used in combination with standard cross-correlation, coherence, imaginary coherence, and PLI, which is likely due to the low number of electrodes and inadequate electrode coverage of the scalp. Finally, we demonstrate that the correlation between seizure onset and the brain network periodicities is preserved when corrected cross-correlation and WPLI were used for all the examined montages. This suggests that, even in the standard clinical setting of EEG recording in epilepsy where only a limited number of scalp EEG measurements are available, graph-theoretic quantification of periodic patterns using appropriate montage, and correlation measures corrected for volume conduction provides useful insights into seizure onset

    International Federation of Clinical Neurophysiology (IFCN) – EEG research workgroup: Recommendations on frequency and topographic analysis of resting state EEG rhythms. Part 1: Applications in clinical research studies

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    In 1999, the International Federation of Clinical Neurophysiology (IFCN) published “IFCN Guidelines for topographic and frequency analysis of EEGs and EPs” (Nuwer et al., 1999). Here a Workgroup of IFCN experts presents unanimous recommendations on the following procedures relevant for the topographic and frequency analysis of resting state EEGs (rsEEGs) in clinical research defined as neurophysiological experimental studies carried out in neurological and psychiatric patients: (1) recording of rsEEGs (environmental conditions and instructions to participants; montage of the EEG electrodes; recording settings); (2) digital storage of rsEEG and control data; (3) computerized visualization of rsEEGs and control data (identification of artifacts and neuropathological rsEEG waveforms); (4) extraction of “synchronization” features based on frequency analysis (band-pass filtering and computation of rsEEG amplitude/power density spectrum); (5) extraction of “connectivity” features based on frequency analysis (linear and nonlinear measures); (6) extraction of “topographic” features (topographic mapping; cortical source mapping; estimation of scalp current density and dura surface potential; cortical connectivity mapping), and (7) statistical analysis and neurophysiological interpretation of those rsEEG features. As core outcomes, the IFCN Workgroup endorsed the use of the most promising “synchronization” and “connectivity” features for clinical research, carefully considering the limitations discussed in this paper. The Workgroup also encourages more experimental (i.e. simulation studies) and clinical research within international initiatives (i.e., shared software platforms and databases) facing the open controversies about electrode montages and linear vs. nonlinear and electrode vs. source levels of those analyses

    Connectivity Analysis in EEG Data: A Tutorial Review of the State of the Art and Emerging Trends

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    Understanding how different areas of the human brain communicate with each other is a crucial issue in neuroscience. The concepts of structural, functional and effective connectivity have been widely exploited to describe the human connectome, consisting of brain networks, their structural connections and functional interactions. Despite high-spatial-resolution imaging techniques such as functional magnetic resonance imaging (fMRI) being widely used to map this complex network of multiple interactions, electroencephalographic (EEG) recordings claim high temporal resolution and are thus perfectly suitable to describe either spatially distributed and temporally dynamic patterns of neural activation and connectivity. In this work, we provide a technical account and a categorization of the most-used data-driven approaches to assess brain-functional connectivity, intended as the study of the statistical dependencies between the recorded EEG signals. Different pairwise and multivariate, as well as directed and non-directed connectivity metrics are discussed with a pros-cons approach, in the time, frequency, and information-theoretic domains. The establishment of conceptual and mathematical relationships between metrics from these three frameworks, and the discussion of novel methodological approaches, will allow the reader to go deep into the problem of inferring functional connectivity in complex networks. Furthermore, emerging trends for the description of extended forms of connectivity (e.g., high-order interactions) are also discussed, along with graph-theory tools exploring the topological properties of the network of connections provided by the proposed metrics. Applications to EEG data are reviewed. In addition, the importance of source localization, and the impacts of signal acquisition and pre-processing techniques (e.g., filtering, source localization, and artifact rejection) on the connectivity estimates are recognized and discussed. By going through this review, the reader could delve deeply into the entire process of EEG pre-processing and analysis for the study of brain functional connectivity and learning, thereby exploiting novel methodologies and approaches to the problem of inferring connectivity within complex networks

    Dynamics and network structure in neuroimaging data

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    Performance comparison of functional and effective brain connectivity methods

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    Functional and effective connectivity estimates based on electroencephalography (EEG) and magnetoencephalography (MEG) are widely used to understand and reveal new insight into the dynamic behaviour of the brain. However, with a large number of different connectivity methods that are currently available, there is a lack of systematic comparative studies including a statistical evaluation of their performance to understand the strengths and shortcomings of competing methods. Here, we present a simulation framework to evaluate and compare the performance of connectivity estimators on simulated, yet realistic electromagnetic recordings. We assess the ability of various methods to reconstruct cortical networks, while systematically varying specific parameters which are of significant importance during the simulation, preprocessing or inverse source reconstruction of realistic EEG recordings. A decisive advantage of this simulation framework, when compared with models utilised in other studies, is the integration of volume conduction artifacts. This is achieved by modelling the propagation of electric or magnetic fields from an electric primary current source through biological tissue towards measurement sensors. Subsequently, inverse source reconstruction approaches are applied to estimate the temporal activity patterns of underlying network nodes. The implementation of these concepts enabled the analysis of parameters involved during forward modelling and source reconstruction which may affect the estimation of connectivity on the source level. The experiments carried out in this work unfold the behaviour of estimators regarding the effect of signal-to-noise ratio (SNR), length of data sets, various phase shifts between correlated signals, the impact of regularization used in inverse source reconstruction, errors in the localization and varying network sizes. For each simulation, strengths and weaknesses of methods are pointed out. Furthermore, pitfalls and obstacles researchers might come across when applying particular estimators on EEG recordings are discussed. Building on the insight gained from simulation studies, the final part of the thesis analyses the performance of connectivity estimators when applied to resting-state EEG recordings. Network reconstructions with priority on the alpha frequency band reveal a default-mode-network (DMN) with dominant posterior-to-anterior information flow. We detected no significant variations in the amount of correctly identified network links between connectivity methods. However, we discuss differences in connectivity spectra that emerged, which affect the interpretability and applicability of methods

    Characterization And Perturbation Of Functional Networks That Support Human Memory

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    Episodic memory is essential to our daily lives, as it attaches meaning to the constant stream of sensory inputs to the brain. However, episodic memory often fails in a number of common neurocognitive disorders. Effective therapies remain elusive, owing to the complexity of brain networks and neural processes that support episodic encoding and retrieval. In particular, it is not understood how inter-regional communication within the brain supports memory function, though such communication may be critical to the highly integrative nature of episodic memory. To uncover the patterns of memory-related functional connectivity, we asked a large cohort of neurosurgical patients with indwelling electrodes to perform a verbal free-recall task, in which patients viewed lists of simple nouns and recalled them a short time later. As patients performed this task, we collected intracranial EEG (iEEG) from electrodes placed on the cortical surface and within the medial temporal lobe (MTL). First, we examined whole-brain functional networks that emerged during the encoding and retrieval phases of this task, using spectral methods to correlate frequency-specific signals between brain regions. We identified a dynamic network of regions that exhibited enhanced theta (3-8 Hz) connectivity during successful memory processing, whereas regions tended to desynchronize at high frequencies (30-100 Hz). Next, using only electrodes placed within the MTL, we asked whether functional coupling was also observed among this mesoscale subnetwork of highly specialized regions that play an outsize role in memory. Recapitulating our earlier findings, we noted broadly enhanced theta connectivity within the MTL, centering on the left entorhinal cortex during successful encoding operations. Finally, to determine whether such low-frequency functional connections reflect correlative or causal relations in the brain, we applied direct electrical stimulation via electrodes placed within the MTL. We found that low-frequency connections (5-13 Hz) predicted the emergence of theta activity at distant regions in the brain – particularly when stimulation occurred near white matter – indicating the potential causal relevance of iEEG-based functional connections. Taken together, these studies underscore the importance of low-frequency functional coupling to memory across spatial scales, and suggest this form of coupling indicates a causal relation between brain regions
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