Laura Marzetti
Publications
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5.74Impact points
Estimating true brain connectivity from EEG/MEG data invariant to linear and static transformations in sensor space.
NeuroImage. 12/2011; 60(1):476-88.
The imaginary part of coherency is a measure to investigate the synchronization of brain sources on the EEG/MEG sensor level, robust to artifacts of volume conduction meaning that independent sources cannot generate a significant result. It does not mean, however, that volume conduction is irrelevan... [more] The imaginary part of coherency is a measure to investigate the synchronization of brain sources on the EEG/MEG sensor level, robust to artifacts of volume conduction meaning that independent sources cannot generate a significant result. It does not mean, however, that volume conduction is irrelevant when true interactions are present. Here, we analyze in detail the possibilities to construct measures of true brain interactions which are strictly invariant to linear spatial transformations of the sensor data. Specifically, such measures can be constructed from maximization of imaginary coherency in virtual channels, bivariate measures as a corrected variate of imaginary coherence, and global measures indicating the total interaction contained within a space or between two spaces. A complete theoretic framework on this question is provided for second order statistical moments. Relations to existing linear and nonlinear approaches are presented. We applied the methods to resting state EEG data, showing clear interactions at all bands, and to a combined measurement of EEG and MEG during rest condition and a finger tapping task. We found that MEG was capable of observing brain interactions which were not observable in the EEG data.
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4.01Impact points
Cortical origin of myoclonus in early stages of corticobasal degeneration.
Movement disorders : official journal of the Movement Disorder Society. 04/2011; 26(8):1567-9.
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A signal-processing pipeline for magnetoencephalography resting-state networks.
Brain connectivity. 01/2011; 1(1):49-59.
Abstract To study functional connectivity using magnetoencephalographic (MEG) data, the high-quality source-level reconstruction of brain activity constitutes a critical element. MEG resting-state networks (RSNs) have been documented by means of a dedicated processing pipeline: MEG recordings are de... [more] Abstract To study functional connectivity using magnetoencephalographic (MEG) data, the high-quality source-level reconstruction of brain activity constitutes a critical element. MEG resting-state networks (RSNs) have been documented by means of a dedicated processing pipeline: MEG recordings are decomposed by independent component analysis (ICA) into artifact and brain components (ICs); next, the channel maps associated with the latter ones are projected into the source space and the resulting voxel-wise weights are used to linearly combine the IC time courses. An extensive description of the proposed pipeline is provided here, along with an assessment of its performances with respect to alternative approaches. The following investigations were carried out: (1) ICA decomposition algorithm. Synthetic data are used to assess the sensitivity of the ICA results to the decomposition algorithm, by testing FastICA, INFOMAX, and SOBI. FastICA with deflation approach, a standard solution, provides the best decomposition. (2) Recombination of brain ICs versus subtraction of artifactual ICs (at the channel level). Both the recombination of the brain ICs in the sensor space and the classical procedure of subtracting the artifactual ICs from the recordings provide a suitable reconstruction, with a lower distortion using the latter approach. (3) Recombination of brain ICs after localization versus localization of artifact-corrected recordings. The brain IC recombination after source localization, as implemented in the proposed pipeline, provides a lower source-level signal distortion. (4) Detection of RSNs. The accuracy in source-level reconstruction by the proposed pipeline is confirmed by an improved specificity in the retrieval of RSNs from experimental data.
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2.08Impact points
Multimodal integration of fMRI and EEG data for high spatial and temporal resolution analysis of brain networks.
Brain topography. 06/2010; 23(2):150-8.
Two major non-invasive brain mapping techniques, electroencephalography (EEG) and functional magnetic resonance imaging (fMRI), have complementary advantages with regard to their spatial and temporal resolution. We propose an approach based on the integration of EEG and fMRI, enabling the EEG tempor... [more] Two major non-invasive brain mapping techniques, electroencephalography (EEG) and functional magnetic resonance imaging (fMRI), have complementary advantages with regard to their spatial and temporal resolution. We propose an approach based on the integration of EEG and fMRI, enabling the EEG temporal dynamics of information processing to be characterized within spatially well-defined fMRI large-scale networks. First, the fMRI data are decomposed into networks by means of spatial independent component analysis (sICA), and those associated with intrinsic activity and/or responding to task performance are selected using information from the related time-courses. Next, the EEG data over all sensors are averaged with respect to event timing, thus calculating event-related potentials (ERPs). The ERPs are subjected to temporal ICA (tICA), and the resulting components are localized with the weighted minimum norm (WMNLS) algorithm using the task-related fMRI networks as priors. Finally, the temporal contribution of each ERP component in the areas belonging to the fMRI large-scale networks is estimated. The proposed approach has been evaluated on visual target detection data. Our results confirm that two different components, commonly observed in EEG when presenting novel and salient stimuli, respectively, are related to the neuronal activation in large-scale networks, operating at different latencies and associated with different functional processes.
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9.43Impact points
Temporal dynamics of spontaneous MEG activity in brain networks.
Proceedings of the National Academy of Sciences of the United States of America. 03/2010; 107(13):6040-5.
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 neuron... [more] 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.
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2.30Impact points
Minimum Overlap Component Analysis (MOCA) of EEG/MEG data for more than two sources.
Journal of neuroscience methods. 08/2009;
In many situations various methods to analyze EEG/MEG data result in subspaces of the sensor space spanned by potentials of a set of sources. We propose a general model free method to decompose such a subspace into contributions from distinct sources. This unique decomposition can be achieved by fir... [more] In many situations various methods to analyze EEG/MEG data result in subspaces of the sensor space spanned by potentials of a set of sources. We propose a general model free method to decompose such a subspace into contributions from distinct sources. This unique decomposition can be achieved by first finding the respective subspace in source space using a linear inverse method and then finding the linear transformation such that the source distributions are mutually orthogonal and have a minimum overlap. The corresponding algorithm is a generalization of the recently presented 'Minimum Overlap Component Analysis' (MOCA) to more than two sources. The computational cost is negligible and the algorithm is almost never trapped in local minima. The method is illustrated with results for alpha rhythm.
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5.74Impact points
Understanding brain connectivity from EEG data by identifying systems composed of interacting sources.
NeuroImage. 09/2008; 42(1):87-98.
In understanding and modeling brain functioning by EEG/MEG, it is not only important to be able to identify active areas but also to understand interference among different areas. The EEG/MEG signals result from the superimposition of underlying brain source activities volume conducted through the h... [more] In understanding and modeling brain functioning by EEG/MEG, it is not only important to be able to identify active areas but also to understand interference among different areas. The EEG/MEG signals result from the superimposition of underlying brain source activities volume conducted through the head. The effects of volume conduction produce spurious interactions in the measured signals. It is fundamental to separate true source interactions from noise and to unmix the contribution of different systems composed by interacting sources in order to understand interference mechanisms. As a prerequisite, we consider the problem of unmixing the contribution of uncorrelated sources to a measured field. This problem is equivalent to the problem of unmixing the contribution of different uncorrelated compound systems composed by interacting sources. To this end, we develop a principal component analysis-based method, namely, the source principal component analysis (sPCA), which exploits the underlying assumption of orthogonality for sources, estimated from linear inverse methods, for the extraction of essential features in signal space. We then consider the problem of demixing the contribution of correlated sources that comprise each of the compound systems identified by using sPCA. While the sPCA orthogonality assumption is sufficient to separate uncorrelated systems, it cannot separate the individual components within each system. To address that problem, we introduce the Minimum Overlap Component Analysis (MOCA), employing a pure spatial criterion to unmix pairs of correlates (or coherent) sources. The proposed methods are tested in simulations and applied to EEG data from human micro and alpha rhythms.
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High-resolution spatio-temporal neuronal activation in the visual oddball task: a simultaneous EEG/fMRI study
Noninvasive Functional Source Imaging of the Brain and Heart and the International Conference on Functional Biomedical Imaging, 2007. NFSI-ICFBI 2007. Joint Meeting of the 6th International Symposium on; 11/2007
The combined use of EEG and fMRI allows for the fusion of electrophysiological and hemodynamic information in the study of human cognitive functions. In order to investigate cerebral activity during a visual oddball task, simultaneous EEG/fMRI recording from 10 healthy subjects was performed. A devo... [more] The combined use of EEG and fMRI allows for the fusion of electrophysiological and hemodynamic information in the study of human cognitive functions. In order to investigate cerebral activity during a visual oddball task, simultaneous EEG/fMRI recording from 10 healthy subjects was performed. A devoted data-analysis method based on trial-by-trial coupling of concurrent EEG and fMRI for the high-resolution spatio-temporal analysis of P300 neuronal activation was developed. Our results obtained from fMRI data showed the involvement of inferior and medial frontal gyrus, cingulated motor area, middle temporal gyrus, and inferior parietal lobule in the oddball task; furthermore, activations were generally right lateralized, in accordance with previous findings. Using the high temporal resolution of EEG, we could separate neuronal activations specifically related to P300 activity, and therefore study the activation timing. We found that the detection of rare targets, that is able to elicit the P300 component, stimulates a limbic-parietofrontal circuit, with latencies ranging between 300 and 400 ms. Our findings suggest that the proposed approach might be extended to other event-related experimental paradigms, and might represent an valuable tool for a clearer understanding of the cerebral mechanisms underlying a wide range of cognitive functions.
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Unbiased large-scale coherence mapping for simultaneously acquired EEG and fMRI data
Noninvasive Functional Source Imaging of the Brain and Heart and the International Conference on Functional Biomedical Imaging, 2007. NFSI-ICFBI 2007. Joint Meeting of the 6th International Symposium on; 11/2007
The study of large scale interactions in the brain from EEG signals is carried out in the EEG community since years. However, the validity of a large scale parameter is limited by two factors: the use of a non-neutral reference for the EEG recordings and the artifactual self-interaction between meas... [more] The study of large scale interactions in the brain from EEG signals is carried out in the EEG community since years. However, the validity of a large scale parameter is limited by two factors: the use of a non-neutral reference for the EEG recordings and the artifactual self-interaction between measured EEG signals introduced by volume conduction spread. In this paper, a novel approach for the study of large scale EEG coherency is proposed in which these biasing factors are eliminated. The artifactual self-interaction by volume conduction is eliminated by mapping interactions by means of the imaginary part of the complex coherency; the bias introduced by the choice of an active reference site is eliminated by applying the reference electrode standardization technique (REST) to scalp EEG recordings in order to approximately standardize the reference to a point at infinity that acts like a neutral virtual reference. The method is here applied to map coherency in the alpha band in the case of spontaneous activity EEG data acquired simultaneously to fMRI.
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5.74Impact points
The use of standardized infinity reference in EEG coherency studies.
NeuroImage. 06/2007; 36(1):48-63.
The study of large scale interactions in the brain from EEG signals is a promising method for the identification of functional networks. However, the validity of a large scale parameter is limited by two factors: the use of a non-neutral reference and the artifactual self-interactions between the me... [more] The study of large scale interactions in the brain from EEG signals is a promising method for the identification of functional networks. However, the validity of a large scale parameter is limited by two factors: the use of a non-neutral reference and the artifactual self-interactions between the measured EEG signals introduced by volume conduction. In this paper, we propose an approach to study large scale EEG coherency in which these factors are eliminated. Artifactual self-interaction by volume conduction is eliminated by using the imaginary part of the complex coherency as a measure of interaction and the Reference Electrode Standardization Technique (REST) is used for the approximate standardization of the reference of scalp EEG recordings to a point at infinity that, being far from all possible neural sources, acts like a neutral virtual reference. The application of our approach to simulated and real EEG data shows that the detection of interaction, as opposed to artifacts due to reference and volume conduction, is a goal that can be achieved from the study of a large scale parameter.
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2.08Impact points
A cartesian time--frequency approach to reveal brain interaction dynamics.
Brain topography. 02/2007; 19(3):147-54.
The study of large-scale interactions from magnetoencephalographic data based on the magnitude of the complex coherence computed at channel level is a widely used method to track the coupling between neural signals. Traditionally, a measure based on the magnitude of the complex coherence estimated b... [more] The study of large-scale interactions from magnetoencephalographic data based on the magnitude of the complex coherence computed at channel level is a widely used method to track the coupling between neural signals. Traditionally, a measure based on the magnitude of the complex coherence estimated by Fourier analysis, has been used under the assumption that the neural signals are stationary. Here, we split the complex coherence in its real and imaginary parts and focus on the latter with the advantage that the imaginary part is insensitive to spurious connectivity resulting from volume conducted "self interaction". Furthermore, interacting sources alone contribute to a non-vanishing imaginary part of the complex coherence whereas the contribute of non-interacting sources is also mapped from the magnitude of the complex coherence. Since it has been extensively shown that non-stationary stochastic processes contribute to the generation of neural signals, it is fundamental to be able to define interaction measures that are able to follow the temporal variations in the coupling between neural signals. To this purpose time-frequency domain techniques to estimate the magnitude of the complex coherence have been developed in the past decades. Similarly, we extend the analysis of the imaginary part of complex coherence to the time-frequency domain, by using the short-time Fourier transform to analyze the complex coherence as a function of time. In this way, it is possible to get an indication about the dynamic of the underlying source interaction pattern by looking at channel level interactions without the bias introduced by artifactual self-interaction by volume conduction or by the contribute of non-interacting sources. Furthermore, the corresponding imaginary part of the cross-spectrogram can be used to estimate interactions on a source level by localizing pools of sources interacting at a given frequency and by characterizing their dynamics. The method has been applied to magnetoencephalographic data from a cross-modal visual auditory stimulation and provided evidence for the involvement of temporal and occipital areas in the integrated information processing for simultaneous audio-visual stimulation. Furthermore, the source interaction pattern shows a variation in time that reflects a dynamical synchronization of the involved brain sources in the frequency bands of interest.
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1.08Impact points
Open magnetic and electric graphic analysis
Engineering in Medicine and Biology Magazine, IEEE. 06/2005;
In this article, a novel analysis technique, open magnetic and electric graphic analysis (OMEGA), is described and is applied to combine MEG and fMRI measurements in a motor task. The study was intended to demonstrate how, within OMEGA, the localization of brain activation can be complemented by int... [more] In this article, a novel analysis technique, open magnetic and electric graphic analysis (OMEGA), is described and is applied to combine MEG and fMRI measurements in a motor task. The study was intended to demonstrate how, within OMEGA, the localization of brain activation can be complemented by integrated analysis of human multimodal functional (MEG, fMRI) and anatomical (MRI) measurements. The OMEGA software provides an analysis platform for user-independent, fast, and reproducible multimodal analysis in one single software environment. The implementation of OMEGA allows the analyst to receive comprehensive MEG/fMRI results in a matter of minutes after the measurements have been completed. With OMEGA, the clinical researcher gets comprehensive information in a quick and standardized approach about the sites and the time course of neurological activation, which is useful for clinical applications and diagnostics.
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Argos 500: operation of a helmet vector-MEG.
Neurology & clinical neurophysiology : NCN. 02/2004; 2004:97.
We here describe the MEG system recently installed at the University of Ulm; it is specifically designed for clinical application and routine use, to allow investigation of a large number of patients per day. To reach this goal, the system design meets the requirements of reliability, high field sen... [more] We here describe the MEG system recently installed at the University of Ulm; it is specifically designed for clinical application and routine use, to allow investigation of a large number of patients per day. To reach this goal, the system design meets the requirements of reliability, high field sensitivity, minimal set-up time before each measurement and an easy-to-handle user interface. The sensor system consists of a 163 vector-magnetometers array oriented and located in a suitable way to cover the whole head of the patient. Four additional triplets are available as references to arrange software gradiometers. The helmet shaped sensor system is positioned to accommodate the patient in a supine position. Simultaneously to the MEG, there are 64 EEG channels. Other relevant patient information can be recorded up to a total number of 660 acquisition channels. Noise level of a single magnetometer is about 5 fT/square root of Hz. Maximum sampling rate is 4200 Hz.
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Calibration of a vector-MEG helmet system.
Neurology & clinical neurophysiology : NCN. 02/2004; 2004:94.
The MEG system Argos 500, recently installed at the University of Ulm, is designed for clinical application and routine use, to allow investigation of a large number of patients per day. To reach this goal, the system design meets the requirements of reliability, high field sensitivity, minimal set-... [more] The MEG system Argos 500, recently installed at the University of Ulm, is designed for clinical application and routine use, to allow investigation of a large number of patients per day. To reach this goal, the system design meets the requirements of reliability, high field sensitivity, minimal set-up overhead before each measurement and an easy-to-handle user interface.The sensor system consists of a 163 vector-magnetometer array oriented and located in a suitable way to cover the whole head of the patient. Four additional triplets are available as references to build software gradiometers. To use this system at a high performance level, it must be properly calibrated, with these goals: to determine the actual geometry of the sensors array, which can deviate from the design specifications, and to determine the actual sensitivity of each sensor. The calibrating source consists of 31 coils placed at the corners of a head-size dodecahedron. Various details of the calibration system and process are presented here.
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[POSTPARTUM HEMORRHAGIC SYNDROME DUE TO COAGULATION INHIBITORS OF THE HEPARINIC TYPE.]
La Clinica ostetrica e ginecologica. 08/1964; 66:399-410.
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Understanding brain connectivity from EEG data by identifying systems composed of interacting sources
NeuroImage.
In understanding and modeling brain functioning by EEG/MEG, it is not only important to be able to identify active areas but also to understand interference among different areas. The EEG/MEG signals result from the superimposition of underlying brain source activities volume conducted through the h... [more] In understanding and modeling brain functioning by EEG/MEG, it is not only important to be able to identify active areas but also to understand interference among different areas. The EEG/MEG signals result from the superimposition of underlying brain source activities volume conducted through the head. The effects of volume conduction produce spurious interactions in the measured signals. It is fundamental to separate true source interactions from noise and to unmix the contribution of different systems composed by interacting sources in order to understand interference mechanisms.As a prerequisite, we consider the problem of unmixing the contribution of uncorrelated sources to a measured field. This problem is equivalent to the problem of unmixing the contribution of different uncorrelated compound systems composed by interacting sources. To this end, we develop a principal component analysis-based method, namely, the source principal component analysis (sPCA), which exploits the underlying assumption of orthogonality for sources, estimated from linear inverse methods, for the extraction of essential features in signal space.We then consider the problem of demixing the contribution of correlated sources that comprise each of the compound systems identified by using sPCA. While the sPCA orthogonality assumption is sufficient to separate uncorrelated systems, it cannot separate the individual components within each system. To address that problem, we introduce the Minimum Overlap Component Analysis (MOCA), employing a pure spatial criterion to unmix pairs of correlates (or coherent) sources. The proposed methods are tested in simulations and applied to EEG data from human µ and α rhythms.
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The use of standardized infinity reference in EEG coherency studies
NeuroImage.
The study of large scale interactions in the brain from EEG signals is a promising method for the identification of functional networks. However, the validity of a large scale parameter is limited by two factors: the use of a non-neutral reference and the artifactual self-interactions between the me... [more] The study of large scale interactions in the brain from EEG signals is a promising method for the identification of functional networks. However, the validity of a large scale parameter is limited by two factors: the use of a non-neutral reference and the artifactual self-interactions between the measured EEG signals introduced by volume conduction. In this paper, we propose an approach to study large scale EEG coherency in which these factors are eliminated. Artifactual self-interaction by volume conduction is eliminated by using the imaginary part of the complex coherency as a measure of interaction and the Reference Electrode Standardization Technique (REST) is used for the approximate standardization of the reference of scalp EEG recordings to a point at infinity that, being far from all possible neural sources, acts like a neutral virtual reference.The application of our approach to simulated and real EEG data shows that the detection of interaction, as opposed to artifacts due to reference and volume conduction, is a goal that can be achieved from the study of a large scale parameter.
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1.08Impact points
Open magnetic and electric graphic analysis.
IEEE engineering in medicine and biology magazine : the quarterly magazine of the Engineering in Medicine & Biology Society. 24(3):109-16.
The OMEGA software provides an analysis platform for user-independent, fast, and reproducible multimodal data analysis in one single software environment. Synergetic interactions pursued between the two functional imaging techniques fMRI and MEG use the morphological MRI recording as a basis for a c... [more] The OMEGA software provides an analysis platform for user-independent, fast, and reproducible multimodal data analysis in one single software environment. Synergetic interactions pursued between the two functional imaging techniques fMRI and MEG use the morphological MRI recording as a basis for a common coordinate frame. In this way, direct interchange, comparison, and integration among the results of the different modalities have become feasible. The fMRI data analysis provides information about the localization of functional activity with low temporal resolution, whereas the MEG recording complements the corresponding time evolution with a high temporal resolution. The implementation of OMEGA allows the analyst to receive comprehensive MEG/fMRI results in a matter of minutes after the measurements have been completed. With OMEGA, the clinical researcher gets comprehensive information in a quick and standardized approach about the sites and the time course of neurological activation, which is useful for clinical applications and diagnostics.
Following (5)
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Guido Nolte
Fraunhofer -
Scott Makeig
University of California at San Diego -
Mauro Gianni Perrucci
Università degli Studi G. d'Annunzio Chieti e Pescara -
Alessandra Lugaresi
Università degli Studi G. d'Annunzio Chieti e Pescara -
Claudio Robazza
Università degli Studi G. d'Annunzio Chieti e Pescara
