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      Multimodal Fingerprints of Resting State Networks as assessed by Simultaneous Trimodal MR-PET-EEG Imaging

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          Abstract

          Simultaneous MR-PET-EEG (magnetic resonance imaging - positron emission tomography – electroencephalography), a new tool for the investigation of neuronal networks in the human brain, is presented here for the first time. It enables the assessment of molecular metabolic information with high spatial and temporal resolution in a given brain simultaneously. Here, we characterize the brain’s default mode network (DMN) in healthy male subjects using multimodal fingerprinting by quantifying energy metabolism via 2- [ 18F]fluoro-2-desoxy-D-glucose PET (FDG-PET), the inhibition – excitation balance of neuronal activation via magnetic resonance spectroscopy (MRS), its functional connectivity via fMRI and its electrophysiological signature via EEG. The trimodal approach reveals a complementary fingerprint. Neuronal activation within the DMN as assessed with fMRI is positively correlated with the mean standard uptake value of FDG. Electrical source localization of EEG signals shows a significant difference between the dorsal DMN and sensorimotor network in the frequency range of δ, θ, α and β–1, but not with β–2 and β–3. In addition to basic neuroscience questions addressing neurovascular-metabolic coupling, this new methodology lays the foundation for individual physiological and pathological fingerprints for a wide research field addressing healthy aging, gender effects, plasticity and different psychiatric and neurological diseases.

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          Most cited references25

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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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            Spontaneous low-frequency BOLD signal fluctuations: an fMRI investigation of the resting-state default mode of brain function hypothesis.

            Recent neuroimaging studies have lead to the proposal that rest is characterized by an organized, baseline level of activity, a default mode of brain function that is suspended during specific goal-oriented mental activity. Previous studies have shown that the primary function subserved by the default mode is that of an introspectively oriented, self-referential mode of mental activity. The default mode of brain function hypothesis is readdressed from the perspective of the presence of low-frequency blood oxygenation level-dependent (BOLD) functional magnetic resonance imaging (fMRI) signal changes (0.012-0.1 Hz) in the resting brain. The results show that the brain during rest is not tonically active in a single mode of brain function. Rather, the findings presented here suggest that the brain recurrently toggles between an introspectively oriented mode (default mode) and a state-of-mind that tentatively might be interpreted as an extrospectively oriented mode that involves a readiness and alertness to changes in the external and internal environment.
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              Large-scale cortical correlation structure of spontaneous oscillatory activity.

              Little is known about the brain-wide correlation of electrophysiological signals. We found that spontaneous oscillatory neuronal activity exhibited frequency-specific spatial correlation structure in the human brain. We developed an analysis approach that discounts spurious correlation of signal power caused by the limited spatial resolution of electrophysiological measures. We applied this approach to source estimates of spontaneous neuronal activity reconstructed from magnetoencephalography. Overall, correlation of power across cortical regions was strongest in the alpha to beta frequency range (8–32 Hz) and correlation patterns depended on the underlying oscillation frequency. Global hubs resided in the medial temporal lobe in the theta frequency range (4–6 Hz), in lateral parietal areas in the alpha to beta frequency range (8–23 Hz) and in sensorimotor areas for higher frequencies (32–45 Hz). Our data suggest that interactions in various large-scale cortical networks may be reflected in frequency-specific power envelope correlations.
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                Author and article information

                Contributors
                i.neuner@fz-juelich.de
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                25 July 2017
                25 July 2017
                2017
                : 7
                : 6452
                Affiliations
                [1 ]Institute of Neuroscience and Medicine 4, INM-4, Forschungszentrum Jülich, Germany
                [2 ]Institute of Neuroscience and Medicine 11, INM-11, Forschungszentrum Jülich, Germany
                [3 ]ISNI 0000 0001 0728 696X, GRID grid.1957.a, Department of Psychiatry, Psychotherapy and Psychosomatics, , RWTH Aachen University, ; Aachen, Germany
                [4 ]JARA – BRAIN – Translational Medicine, Aachen, Germany
                [5 ]ISNI 0000 0001 0728 696X, GRID grid.1957.a, Department of Nuclear Medicine, , RWTH Aachen University, ; Aachen, Germany
                [6 ]ISNI 0000 0001 0728 696X, GRID grid.1957.a, Department of Neurology, , RWTH Aachen University, ; Aachen, Germany
                [7 ]ISNI 0000 0004 1936 7857, GRID grid.1002.3, Monash Institute of Medical Engineering, Department of Electrical and Computer Systems Engineering, and Monash Biomedical Imaging, School of Psychological Sciences, , Monash University, ; Melbourne Victoria, Australia
                Author information
                http://orcid.org/0000-0001-5875-5316
                http://orcid.org/0000-0003-1526-6592
                Article
                5484
                10.1038/s41598-017-05484-w
                5527085
                28743861
                cbc1040f-05c2-4c31-b7f5-2db95ac5129f
                © The Author(s) 2017

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 14 October 2016
                : 25 May 2017
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