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      The dual facet of gamma oscillations: Separate visual and decision making circuits as revealed by simultaneous EEG/fMRI

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          Abstract

          It remains an outstanding question whether gamma‐band oscillations reflect unitary cognitive processes within the same task. EEG/MEG studies do lack the resolution or coverage to address the highly debated question whether single gamma activity patterns are linked with multiple cognitive modules or alternatively each pattern associates with a specific cognitive module, within the same coherent perceptual task. One way to disentangle these issues would be to provide direct identification of their sources, by combining different techniques. Here, we directly examined these questions by performing simultaneous EEG/fMRI using an ambiguous perception paradigm requiring holistic integration. We found that distinct gamma frequency sub‐bands reflect different neural substrates and cognitive mechanisms when comparing object perception states vs. no categorical perception. A low gamma sub‐band (near 40 Hz) activity was tightly related to the decision making network, and in particular the anterior insula. A high gamma sub‐band (∼60 Hz) could be linked to early visual processing regions. The demonstration of a clear functional topography for distinct gamma sub‐bands within the same task shows that distinct gamma‐band modulations underlie sensory processing and perceptual decision mechanisms. Hum Brain Mapp 35:5219–5235, 2014. 2014 Wiley Periodicals, Inc.

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          Electrophysiological signatures of resting state networks in the human brain.

          Functional neuroimaging and electrophysiological studies have documented a dynamic baseline of intrinsic (not stimulus- or task-evoked) brain activity during resting wakefulness. This baseline is characterized by slow (<0.1 Hz) fluctuations of functional imaging signals that are topographically organized in discrete brain networks, and by much faster (1-80 Hz) electrical oscillations. To investigate the relationship between hemodynamic and electrical oscillations, we have adopted a completely data-driven approach that combines information from simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI). Using independent component analysis on the fMRI data, we identified six widely distributed resting state networks. The blood oxygenation level-dependent signal fluctuations associated with each network were correlated with the EEG power variations of delta, theta, alpha, beta, and gamma rhythms. Each functional network was characterized by a specific electrophysiological signature that involved the combination of different brain rhythms. Moreover, the joint EEG/fMRI analysis afforded a finer physiological fractionation of brain networks in the resting human brain. This result supports for the first time in humans the coalescence of several brain rhythms within large-scale brain networks as suggested by biophysical studies.
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            Low resolution electromagnetic tomography: a new method for localizing electrical activity in the brain.

            This paper presents a new method for localizing the electric activity in the brain based on multichannel surface EEG recordings. In contrast to the models presented up to now the new method does not assume a limited number of dipolar point sources nor a distribution on a given known surface, but directly computes a current distribution throughout the full brain volume. In order to find a unique solution for the 3-dimensional distribution among the infinite set of different possible solutions, the method assumes that neighboring neurons are simultaneously and synchronously activated. The basic assumption rests on evidence from single cell recordings in the brain that demonstrates strong synchronization of adjacent neurons. In view of this physiological consideration the computational task is to select the smoothest of all possible 3-dimensional current distributions, a task that is a common procedure in generalized signal processing. The result is a true 3-dimensional tomography with the characteristic that localization is preserved with a certain amount of dispersion, i.e., it has a relatively low spatial resolution. The new method, which we call Low Resolution Electromagnetic Tomography (LORETA) is illustrated with two different sets of evoked potential data, the first showing the tomography of the P100 component to checkerboard stimulation of the left, right, upper and lower hemiretina, and the second showing the results for the auditory N100 component and the two cognitive components CNV and P300. A direct comparison of the tomography results with those obtained from fitting one and two dipoles illustrates that the new method provides physiologically meaningful results while dipolar solutions fail in many situations. In the case of the cognitive components, the method offers new hypotheses on the location of higher cognitive functions in the brain.
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              Measuring phase synchrony in brain signals

              This article presents, for the first time, a practical method for the direct quantification of frequency‐specific synchronization (i.e., transient phase‐locking) between two neuroelectric signals. The motivation for its development is to be able to examine the role of neural synchronies as a putative mechanism for long‐range neural integration during cognitive tasks. The method, called phase‐locking statistics (PLS), measures the significance of the phase covariance between two signals with a reasonable time‐resolution (<100 ms). Unlike the more traditional method of spectral coherence, PLS separates the phase and amplitude components and can be directly interpreted in the framework of neural integration. To validate synchrony values against background fluctuations, PLS uses surrogate data and thus makes no a priori assumptions on the nature of the experimental data. We also apply PLS to investigate intracortical recordings from an epileptic patient performing a visual discrimination task. We find large‐scale synchronies in the gamma band (45 Hz), e.g., between hippocampus and frontal gyrus, and local synchronies, within a limbic region, a few cm apart. We argue that whereas long‐scale effects do reflect cognitive processing, short‐scale synchronies are likely to be due to volume conduction. We discuss ways to separate such conduction effects from true signal synchrony. Hum Brain Mapping 8:194–208, 1999. © 1999 Wiley‐Liss, Inc.
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                Author and article information

                Journal
                Hum Brain Mapp
                Hum Brain Mapp
                10.1002/(ISSN)1097-0193
                HBM
                Human Brain Mapping
                John Wiley and Sons Inc. (Hoboken )
                1065-9471
                1097-0193
                16 May 2014
                October 2014
                : 35
                : 10 ( doiID: 10.1002/hbm.v35.10 )
                : 5219-5235
                Affiliations
                [ 1 ] Visual Neuroscience Laboratory IBILI—Institute for Biomedical Imaging and Life Sciences, Faculty of Medicine, University of Coimbra Coimbra Portugal
                [ 2 ] ICNAS—Institute for Nuclear Sciences Applied to Health, University of Coimbra Coimbra Portugal
                [ 3 ] Brain Imaging Center J.W. Goethe University Frankfurt am Main Germany
                [ 4 ] Escuela de psicologia, Pontificia Universidad Católica de Chile Santiago Chile
                Author notes
                [*] [* ]Correspondence to: Miguel Castelo‐Branco, Visual Neuroscience Laboratory, IBILI‐Faculty of Medicine, University of Coimbra, Azinhaga de Santa Comba, 3000‐548 Coimbra, Portugal. E‐mail: mcbranco@ 123456fmed.uc.pt
                Article
                PMC6869291 PMC6869291 6869291 HBM22545
                10.1002/hbm.22545
                6869291
                24839083
                b158073d-1134-4098-a7a0-3e8d5f121d68
                Copyright © 2014 Wiley Periodicals, Inc.
                History
                : 16 February 2014
                : 01 May 2014
                : 02 May 2014
                Page count
                Pages: 17
                Funding
                Funded by: BIN (the Brain Imaging Network of Portugal)
                Award ID: FP7‐HEALTH‐2013‐INNOVATION—1—602186—BRAINTRAIN
                Funded by: Compete PEST
                Award ID: C/SAU/UI3282/2014‐COMPETE
                Award ID: PTDC/SAU‐ORG/118380/2010
                Funded by: Bial Foundation Projects 132 and 133/12
                Award ID: CENTRO‐07‐ST24‐FEDER‐00205
                Funded by: Contract grant sponsor: FCT Portugal
                Award ID: SFRH/BD/65341/2009
                Categories
                Research Article
                Research Articles
                Custom metadata
                2.0
                October 2014
                Converter:WILEY_ML3GV2_TO_JATSPMC version:5.7.2 mode:remove_FC converted:15.11.2019

                simultaneous EEG/fMRI,source localization,multimodal imaging,gamma‐band oscillations,visual perception,cognitive modules

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