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      Non-linear Relationship between BOLD Activation and Amplitude of Beta Oscillations in the Supplementary Motor Area during Rhythmic Finger Tapping and Internal Timing

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          Abstract

          Functional imaging studies using BOLD contrasts have consistently reported activation of the supplementary motor area (SMA) both during motor and internal timing tasks. Opposing findings, however, have been shown for the modulation of beta oscillations in the SMA. While movement suppresses beta oscillations in the SMA, motor and non-motor tasks that rely on internal timing increase the amplitude of beta oscillations in the SMA. These independent observations suggest that the relationship between beta oscillations and BOLD activation is more complex than previously thought. Here we set out to investigate this rapport by examining beta oscillations in the SMA during movement with varying degrees of internal timing demands. In a simultaneous EEG-fMRI experiment, 20 healthy right-handed subjects performed an auditory-paced finger-tapping task. Internal timing was operationalized by including conditions with taps on every fourth auditory beat, which necessitates generation of a slow internal rhythm, while tapping to every auditory beat reflected simple auditory-motor synchronization. In the SMA, BOLD activity increased and power in both the low and the high beta band decreased expectedly during each condition compared to baseline. Internal timing was associated with a reduced desynchronization of low beta oscillations compared to conditions without internal timing demands. In parallel with this relative beta power increase, internal timing activated the SMA more strongly in terms of BOLD. This documents a task-dependent non-linear relationship between BOLD and beta-oscillations in the SMA. We discuss different roles of beta synchronization and desynchronization in active processing within the same cortical region.

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          Most cited references52

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          Dynamic imaging of coherent sources: Studying neural interactions in the human brain.

          Functional connectivity between cortical areas may appear as correlated time behavior of neural activity. It has been suggested that merging of separate features into a single percept ("binding") is associated with coherent gamma band activity across the cortical areas involved. Therefore, it would be of utmost interest to image cortico-cortical coherence in the working human brain. The frequency specificity and transient nature of these interactions requires time-sensitive tools such as magneto- or electroencephalography (MEG/EEG). Coherence between signals of sensors covering different scalp areas is commonly taken as a measure of functional coupling. However, this approach provides vague information on the actual cortical areas involved, owing to the complex relation between the active brain areas and the sensor recordings. We propose a solution to the crucial issue of proceeding beyond the MEG sensor level to estimate coherences between cortical areas. Dynamic imaging of coherent sources (DICS) uses a spatial filter to localize coherent brain regions and provides the time courses of their activity. Reference points for the computation of neural coupling may be based on brain areas of maximum power or other physiologically meaningful information, or they may be estimated starting from sensor coherences. The performance of DICS is evaluated with simulated data and illustrated with recordings of spontaneous activity in a healthy subject and a parkinsonian patient. Methods for estimating functional connectivities between brain areas will facilitate characterization of cortical networks involved in sensory, motor, or cognitive tasks and will allow investigation of pathological connectivities in neurological disorders.
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            EEG-correlated fMRI of human alpha activity.

            Electroencephalography-correlated functional magnetic resonance imaging (EEG/fMRI) can be used to identify blood oxygen level-dependent (BOLD) signal changes associated with both physiological and pathological EEG events. Here, we implemented continuous and simultaneous EEG/fMRI to identify BOLD signal changes related to spontaneous power fluctuations in the alpha rhythm (8-12 Hz), the dominant EEG pattern during relaxed wakefulness. Thirty-two channels of EEG were recorded in 10 subjects during eyes-closed rest inside a 1.5-T magnet resonance (MR) scanner using an MR-compatible EEG recording system. Functional scanning by echoplanar imaging covered almost the entire cerebrum every 4 s. Off-line MRI artifact subtraction software was applied to obtain continuous EEG data during fMRI acquisition. The average alpha power over 1-s epochs was derived at several electrode positions using a Fast Fourier Transform. The power time course was then convolved with a canonical hemodynamic response function, down-sampled, and used for statistical parametric mapping of associated signal changes in the image time series. At all electrode positions studied, a strong negative correlation of parietal and frontal cortical activity with alpha power was found. Conversely, only sparse and nonsystematic positive correlation was detected. The relevance of these findings is discussed in view of the current theories on the generation and significance of the alpha rhythm and the related functional neuroimaging findings.
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              Identification of EEG events in the MR scanner: the problem of pulse artifact and a method for its subtraction.

              Triggering functional MRI (fMRI) image acquisition immediately after an EEG event can provide information on the location of the event generator. However, EEG artifact associated with pulsatile blood flow in a subject inside the scanner may obscure EEG events. This pulse artifact (PA) has been widely recognized as a significant problem, although its characteristics are unpredictable. We have investigated the amplitude, distribution on the scalp, and frequency of occurrence of this artifact. This showed large interindividual variations in amplitude, although PA is normally largest in the frontal region. In five of six subjects, PA was greater than 50 microV in at least one of the temporal, parasagittal, and central channels analyzed. Therefore, we developed and validated a method for removing PA. This subtracts an averaged PA waveform calculated for each electrode during the previous 10 s. Particular attention has been given to reliable ECG peak detection and ensuring that the average PA waveform is free of other EEG artifacts. Comparison of frequency spectra for EEG recorded outside and inside the scanner, with and without PA subtraction, showed a clear reduction in artifact after PA subtraction for all four frequency ranges analyzed. As further validation, lateralized epileptiform spikes were added to recordings from inside and outside the scanner: PA subtraction significantly increased the proportion of these spikes that were correctly identified and decreased the number of false spike detections. We conclude that in some subjects, EEG/fMRI studies will be feasible only using PA subtraction. Copyright 1998 Academic Press.
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                Author and article information

                Contributors
                Journal
                Front Hum Neurosci
                Front Hum Neurosci
                Front. Hum. Neurosci.
                Frontiers in Human Neuroscience
                Frontiers Media S.A.
                1662-5161
                30 November 2017
                2017
                : 11
                : 582
                Affiliations
                [1] 1Cognitive Neuroscience Group, Department of Neurology, Brain Imaging Center, Goethe University Frankfurt , Frankfurt am Main, Germany
                [2] 2Department of Neurology, University Hospital Schleswig-Holstein, Campus Kiel, Christian-Albrechts- Universität zu Kiel , Kiel, Germany
                Author notes

                Edited by: Srikantan S. Nagarajan, University of California, San Francisco, United States

                Reviewed by: Marc Himmelbach, Universität Tübingen, Germany; Veena A. Nair, University of Wisconsin-Madison, United States

                *Correspondence: Christian A. Kell c.kell@ 123456em.uni-frankfurt.de
                Article
                10.3389/fnhum.2017.00582
                5714933
                29249950
                b736fdcb-e584-492d-b201-588efd1c8eb9
                Copyright © 2017 Gompf, Pflug, Laufs and Kell.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 29 June 2017
                : 17 November 2017
                Page count
                Figures: 4, Tables: 0, Equations: 0, References: 77, Pages: 11, Words: 8647
                Funding
                Funded by: Hessisches Ministerium für Wissenschaft und Kunst 10.13039/501100003495
                Award ID: LOEWE NeFF
                Categories
                Neuroscience
                Original Research

                Neurosciences
                eeg-fmri,predictive timing,internal time,supplementary motor area (sma),premotor cortex

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