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      Effect of Zolpidem in the Aftermath of Traumatic Brain Injury: An MEG Study

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          Abstract

          In the past two decades, many studies have shown the paradoxical efficacy of zolpidem, a hypnotic used to induce sleep, in transiently alleviating various disorders of consciousness such as traumatic brain injury (TBI), dystonia, and Parkinson's disease. The mechanism of action of this effect of zolpidem is of great research interest. In this case study, we use magnetoencephalography (MEG) to investigate a fully conscious, ex-coma patient who suffered from neurological difficulties for a few years due to traumatic brain injury. For a few years after injury, the patient was under medication with zolpidem that drastically improved his symptoms. MEG recordings taken before and after zolpidem showed a reduction in power in the theta-alpha (4–12 Hz) and lower beta (15–20 Hz) frequency bands. An increase in power after zolpidem intake was found in the higher beta/lower gamma (20–43 Hz) frequency band. Source level functional connectivity measured using weighted-phase lag index showed changes after zolpidem intake. Stronger connectivity between left frontal and temporal brain regions was observed. We report that zolpidem induces a change in MEG resting power and functional connectivity in the patient. MEG is an informative and sensitive tool to detect changes in brain activity for TBI.

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          Most cited references38

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          Investigating the electrophysiological basis of resting state networks using magnetoencephalography.

          In recent years the study of resting state brain networks (RSNs) has become an important area of neuroimaging. The majority of studies have used functional magnetic resonance imaging (fMRI) to measure temporal correlation between blood-oxygenation-level-dependent (BOLD) signals from different brain areas. However, BOLD is an indirect measure related to hemodynamics, and the electrophysiological basis of connectivity between spatially separate network nodes cannot be comprehensively assessed using this technique. In this paper we describe a means to characterize resting state brain networks independently using magnetoencephalography (MEG), a neuroimaging modality that bypasses the hemodynamic response and measures the magnetic fields associated with electrophysiological brain activity. The MEG data are analyzed using a unique combination of beamformer spatial filtering and independent component analysis (ICA) and require no prior assumptions about the spatial locations or patterns of the networks. This method results in RSNs with significant similarity in their spatial structure compared with RSNs derived independently using fMRI. This outcome confirms the neural basis of hemodynamic networks and demonstrates the potential of MEG as a tool for understanding the mechanisms that underlie RSNs and the nature of connectivity that binds network nodes.
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            Falls efficacy as a measure of fear of falling.

            We developed the Falls Efficacy Scale (FES), an instrument to measure fear of falling, based on the operational definition of this fear as "low perceived self-efficacy at avoiding falls during essential, nonhazardous activities of daily living." The reliability and validity of the FES were assessed in two samples of community-living elderly persons. The FES showed good test-retest reliability (Pearson's correlation 0.71). Subjects who reported avoiding activities because of fear of falling had higher FES scores, representing lower self-efficacy or confidence, than subjects not reporting fear of falling. The independent predictors of FES score were usual walking pace (a measure of physical ability), anxiety, and depression. The FES appears to be a reliable and valid method for measuring fear of falling. This instrument may be useful in assessing the independent contribution of fear of falling to functional decline among elderly people.
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              Recovery of consciousness after brain injury: a mesocircuit hypothesis.

              Recovery of consciousness following severe brain injuries can occur over long time intervals. Importantly, evolving cognitive recovery can be strongly dissociated from motor recovery in some individuals, resulting in underestimation of cognitive capacities. Common mechanisms of cerebral dysfunction that arise at the neuronal population level may explain slow functional recoveries from severe brain injuries. This review proposes a "mesocircuit" model that predicts specific roles for different structural and dynamic changes that may occur gradually during recovery. Recent functional neuroimaging studies that operationally identify varying levels of awareness, memory and other higher brain functions in patients with no behavioral evidence of these cognitive capacities are discussed. Measuring evolving changes in underlying brain function and dynamics post-injury and post-treatment frames future investigative work. Copyright 2009 Elsevier Ltd. All rights reserved.
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                Author and article information

                Contributors
                Journal
                Case Rep Neurol Med
                Case Rep Neurol Med
                CRINM
                Case Reports in Neurological Medicine
                Hindawi
                2090-6668
                2090-6676
                2020
                20 March 2020
                : 2020
                : 8597062
                Affiliations
                1Institute of Neuroscience and Medicine (INM-4), Medical Imaging Physics, Forschungszentrum Jülich GmbH, 52425 Jülich, Germany
                2Institute of Neuroscience and Medicine (INM-11), JARA, Forschungszentrum Jülich GmbH, 52425 Jülich, Germany
                3Department of Neurology, RWTH Aachen University, Aachen, Germany
                4Department of Nuclear Medicine, RWTH Aachen University, Aachen, Germany
                5Institute of Neuroscience and Medicine (INM-3), Forschungszentrum Jülich, Jülich GmbH, 52425 Jülich, Germany
                6Department of Neurology, University of Cologne, Cologne, Germany
                7Center of Integrated Oncology (CIO), Universities of Cologne and Bonn, Cologne, Germany
                8Nuclear Medicine Department, Royal Surrey County Hospital, Guildford, Surrey GU2 7XX, UK
                9JARA - BRAIN - Translational Medicine, Aachen, Germany
                Author notes

                Academic Editor: Dennis J. Rivet

                Author information
                https://orcid.org/0000-0001-6361-2571
                https://orcid.org/0000-0003-2688-9410
                https://orcid.org/0000-0003-1526-6592
                Article
                10.1155/2020/8597062
                7109561
                32257474
                c0818d02-e8af-4a45-8fb8-e0bc87f292ce
                Copyright © 2020 Praveen Sripad et al.

                This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 29 August 2019
                : 2 January 2020
                Categories
                Case Report

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