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      Effects of rhythmic stimulus presentation on oscillatory brain activity: the physiology of cueing in Parkinson’s disease

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          Abstract

          The basal ganglia play an important role in beat perception and patients with Parkinson’s disease (PD) are impaired in perception of beat-based rhythms. Rhythmic cues are nonetheless beneficial in gait rehabilitation, raising the question how rhythm improves movement in PD. We addressed this question with magnetoencephalography recordings during a choice response task with rhythmic and non-rhythmic modes of stimulus presentation. Analyses focused on (i) entrainment of slow oscillations, (ii) the depth of beta power modulation, and (iii) whether a gain in modulation depth of beta power, due to rhythmicity, is of predictive or reactive nature. The results show weaker phase synchronisation of slow oscillations and a relative shift from predictive to reactive movement-related beta suppression in PD. Nonetheless, rhythmic stimulus presentation increased beta modulation depth to the same extent in patients and controls. Critically, this gain selectively increased the predictive and not reactive movement-related beta power suppression. Operation of a predictive mechanism, induced by rhythmic stimulation, was corroborated by a sensory gating effect in the sensorimotor cortex. The predictive mode of cue utilisation points to facilitation of basal ganglia-premotor interactions, contrasting with the popular view that rhythmic stimulation confers a special advantage in PD, based on recruitment of alternative pathways.

          Highlights

          • We investigate how rhythmic cues improve movement in Parkinson’s disease
          • MEG-recorded slow and fast oscillatory activity was analysed
          • Predictive modulation of beta oscillations was reduced in PD patients
          • Yet rhythmicity promoted a predictive mode of cue utilization and beta modulation
          • Results point to a facilitation of basal ganglia-cortical interaction in rhythmic cueing

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          Most cited references 72

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          Nonparametric statistical testing of EEG- and MEG-data.

          In this paper, we show how ElectroEncephaloGraphic (EEG) and MagnetoEncephaloGraphic (MEG) data can be analyzed statistically using nonparametric techniques. Nonparametric statistical tests offer complete freedom to the user with respect to the test statistic by means of which the experimental conditions are compared. This freedom provides a straightforward way to solve the multiple comparisons problem (MCP) and it allows to incorporate biophysically motivated constraints in the test statistic, which may drastically increase the sensitivity of the statistical test. The paper is written for two audiences: (1) empirical neuroscientists looking for the most appropriate data analysis method, and (2) methodologists interested in the theoretical concepts behind nonparametric statistical tests. For the empirical neuroscientist, a large part of the paper is written in a tutorial-like fashion, enabling neuroscientists to construct their own statistical test, maximizing the sensitivity to the expected effect. And for the methodologist, it is explained why the nonparametric test is formally correct. This means that we formulate a null hypothesis (identical probability distribution in the different experimental conditions) and show that the nonparametric test controls the false alarm rate under this null hypothesis.
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            FieldTrip: Open Source Software for Advanced Analysis of MEG, EEG, and Invasive Electrophysiological Data

            This paper describes FieldTrip, an open source software package that we developed for the analysis of MEG, EEG, and other electrophysiological data. The software is implemented as a MATLAB toolbox and includes a complete set of consistent and user-friendly high-level functions that allow experimental neuroscientists to analyze experimental data. It includes algorithms for simple and advanced analysis, such as time-frequency analysis using multitapers, source reconstruction using dipoles, distributed sources and beamformers, connectivity analysis, and nonparametric statistical permutation tests at the channel and source level. The implementation as toolbox allows the user to perform elaborate and structured analyses of large data sets using the MATLAB command line and batch scripting. Furthermore, users and developers can easily extend the functionality and implement new algorithms. The modular design facilitates the reuse in other software packages.
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              Beta-band oscillations--signalling the status quo?

              In this review, we consider the potential functional role of beta-band oscillations, which at present is not yet well understood. We discuss evidence from recent studies on top-down mechanisms involved in cognitive processing, on the motor system and on the pathophysiology of movement disorders that suggest a unifying hypothesis: beta-band activity seems related to the maintenance of the current sensorimotor or cognitive state. We hypothesize that beta oscillations and/or coupling in the beta-band are expressed more strongly if the maintenance of the status quo is intended or predicted, than if a change is expected. Moreover, we suggest that pathological enhancement of beta-band activity is likely to result in an abnormal persistence of the status quo and a deterioration of flexible behavioural and cognitive control. (c) 2010 Elsevier Ltd. All rights reserved.
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                Author and article information

                Journal
                Neuroimage Clin
                Neuroimage Clin
                NeuroImage : Clinical
                Elsevier
                2213-1582
                02 September 2015
                2015
                02 September 2015
                : 9
                : 300-309
                Affiliations
                [a ]Dept. of Neurology, Radboud University Medical Centre, Radboud University, Nijmegen, The Netherlands
                [b ]Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
                Author notes
                [* ]Corresponding author at: Dept. of Neurology, Radboud University Medical Centre, PO Box 9101, Nijmegen, 6500 HB, The Netherlands. Tel.: +31 24 3668254.
                Article
                S2213-1582(15)00156-4
                10.1016/j.nicl.2015.08.018
                4579287
                © 2015 The Authors. Published by Elsevier Inc.

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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