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      Altered physiological brain variation in drug‐resistant epilepsy

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          Abstract

          Introduction

          Functional magnetic resonance imaging ( fMRI) combined with simultaneous electroencephalography ( EEGfMRI) has become a major tool in mapping epilepsy sources. In the absence of detectable epileptiform activity, the resting state fMRI may still detect changes in the blood oxygen level‐dependent signal, suggesting intrinsic alterations in the underlying brain physiology.

          Methods

          In this study, we used coefficient of variation ( CV) of critically sampled 10 Hz ultra‐fast fMRI (magnetoencephalography, MREG) signal to compare physiological variance between healthy controls ( n = 10) and patients ( n = 10) with drug‐resistant epilepsy ( DRE).

          Results

          We showed highly significant voxel‐level ( p < 0.01, TFCE‐corrected) increase in the physiological variance in DRE patients. At individual level, the elevations range over three standard deviations ( σ) above the control mean ( μ) CV MREG values solely in DRE patients, enabling patient‐specific mapping of elevated physiological variance. The most apparent differences in group‐level analysis are found on white matter, brainstem, and cerebellum. Respiratory (0.12–0.4 Hz) and very‐low‐frequency ( VLF = 0.009–0.1 Hz) signal variances were most affected.

          Conclusions

          The CV MREG increase was not explained by head motion or physiological cardiorespiratory activity, that is, it seems to be linked to intrinsic physiological pulsations. We suggest that intrinsic brain pulsations play a role in DRE and that critically sampled fMRI may provide a powerful tool for their identification.

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          Most cited references57

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          Resting-state fMRI: a review of methods and clinical applications.

          Resting-state fMRI measures spontaneous low-frequency fluctuations in the BOLD signal to investigate the functional architecture of the brain. Application of this technique has allowed the identification of various RSNs, or spatially distinct areas of the brain that demonstrate synchronous BOLD fluctuations at rest. Various methods exist for analyzing resting-state data, including seed-based approaches, independent component analysis, graph methods, clustering algorithms, neural networks, and pattern classifiers. Clinical applications of resting-state fMRI are at an early stage of development. However, its use in presurgical planning for patients with brain tumor and epilepsy demonstrates early promise, and the technique may have a future role in providing diagnostic and prognostic information for neurologic and psychiatric diseases.
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            Decoding wakefulness levels from typical fMRI resting-state data reveals reliable drifts between wakefulness and sleep.

            The mining of huge databases of resting-state brain activity recordings represents state of the art in the assessment of endogenous neuronal activity-and may be a promising tool in the search for functional biomarkers. However, the resting state is an uncontrolled condition and its heterogeneity is neither sufficiently understood nor accounted for. We test the hypothesis that subjects exhibit unstable wakefulness, i.e., drift into sleep during typical resting-state experiments. Analyzing 1,147 resting-state functional magnetic resonance data sets, we revealed a reliable loss of wakefulness in a third of subjects within 3 min and demonstrated the dynamic nature of the resting state, with fundamental changes in the associated functional neuroanatomy. Implications include the necessity of wakefulness monitoring and modeling, taking measures to maintain a state of wakefulness, acknowledging the possibility of sleep and exploring its consequences, and especially the critical assessment of possible false-positive or false-negative results. Copyright © 2014 Elsevier Inc. All rights reserved.
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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
                janne.kananen@student.oulu.fi
                Journal
                Brain Behav
                Brain Behav
                10.1002/(ISSN)2157-9032
                BRB3
                Brain and Behavior
                John Wiley and Sons Inc. (Hoboken )
                2162-3279
                15 August 2018
                September 2018
                : 8
                : 9 ( doiID: 10.1002/brb3.2018.8.issue-9 )
                : e01090
                Affiliations
                [ 1 ] Department of Diagnostic Radiology Medical Research Center Oulu University Hospital Oulu Finland
                [ 2 ] Oulu Functional NeuroImaging‐Group Research Unit of Medical Imaging, Physics and Technology University of Oulu Oulu Finland
                [ 3 ] Research Unit of Neuroscience, Neurology University of Oulu Oulu Finland
                [ 4 ] Department of Neurology and Medical Research Center Oulu Oulu University Hospital Oulu Finland
                [ 5 ] Department of Clinical Neurophysiology Medical Research Center Oulu Oulu University Hospital Oulu Finland
                [ 6 ] Faculty of Medicine Department of Radiology – Medical Physics University Medical Center Freiburg University of Freiburg Freiburg Germany
                [ 7 ] Center for Translational Neuromedicine Department of Neurosurgery University of Rochester Rochester New York
                [ 8 ] Faculty of Health and Medical Sciences Center for Basic and Translational Neuroscience University of Copenhagen Copenhagen Denmark
                Author notes
                [*] [* ] Correspondence

                Janne Kananen, Department of Diagnostic Radiology, Medical Research Center, Oulu University Hospital, P. O. Box 50, Oulu 90029, Finland.

                Email: janne.kananen@ 123456student.oulu.fi

                Author information
                http://orcid.org/0000-0001-6831-8056
                http://orcid.org/0000-0001-7079-3673
                http://orcid.org/0000-0002-1216-9984
                Article
                BRB31090
                10.1002/brb3.1090
                6160661
                30112813
                800e09de-9eda-401b-acc7-f69bc0006826
                © 2018 The Authors. Brain and Behavior published by Wiley Periodicals, Inc.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                : 06 March 2018
                : 04 July 2018
                : 08 July 2018
                Page count
                Figures: 7, Tables: 3, Pages: 17, Words: 10354
                Funding
                Funded by: Jane and Aatos Erkkos Foundation
                Funded by: Finnish Academy
                Award ID: 275352
                Award ID: 123772
                Funded by: Finnish Medical Foundation
                Funded by: Finnish Neurological Foundation
                Funded by: Oulu University Hospital
                Funded by: Epilepsy Research Foundation
                Funded by: Finnish Cultural Foundation
                Funded by: North Ostrobothnia Regional Fund
                Funded by: Orion Research Foundation
                Funded by: Tauno Tönning Foundation
                Categories
                Original Research
                Original Research
                Custom metadata
                2.0
                brb31090
                September 2018
                Converter:WILEY_ML3GV2_TO_NLMPMC version:version=5.4.9 mode:remove_FC converted:28.09.2018

                Neurosciences
                brain physiology,coefficient of variation,epilepsy,fmri,mreg,multimodal imaging
                Neurosciences
                brain physiology, coefficient of variation, epilepsy, fmri, mreg, multimodal imaging

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