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      Prefrontal, posterior parietal and sensorimotor network activity underlying speed control during walking

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          Abstract

          Accumulating evidence suggests cortical circuits may contribute to control of human locomotion. Here, noninvasive electroencephalography (EEG) recorded from able-bodied volunteers during a novel treadmill walking paradigm was used to assess neural correlates of walking. A systematic processing method, including a recently developed subspace reconstruction algorithm, reduced movement-related EEG artifact prior to independent component analysis and dipole source localization. We quantified cortical activity while participants tracked slow and fast target speeds across two treadmill conditions: an active mode that adjusted belt speed based on user movements and a passive mode reflecting a typical treadmill. Our results reveal frequency specific, multi-focal task related changes in cortical oscillations elicited by active walking. Low γ band power, localized to the prefrontal and posterior parietal cortices, was significantly increased during double support and early swing phases, critical points in the gait cycle since the active controller adjusted speed based on pelvis position and swing foot velocity. These phasic γ band synchronizations provide evidence that prefrontal and posterior parietal networks, previously implicated in visuo-spatial and somotosensory integration, are engaged to enhance lower limb control during gait. Sustained μ and β band desynchronization within sensorimotor cortex, a neural correlate for movement, was observed during walking thereby validating our methods for isolating cortical activity. Our results also demonstrate the utility of EEG recorded during locomotion for probing the multi-regional cortical networks which underpin its execution. For example, the cortical network engagement elicited by the active treadmill suggests that it may enhance neuroplasticity for more effective motor training.

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          The role of executive function and attention in gait.

          Until recently, gait was generally viewed as a largely automated motor task, requiring minimal higher-level cognitive input. Increasing evidence, however, links alterations in executive function and attention to gait disturbances. This review discusses the role of executive function and attention in healthy walking and gait disorders while summarizing the relevant, recent literature. We describe the variety of gait disorders that may be associated with different aspects of executive function, and discuss the changes occurring in executive function as a result of aging and disease as well the potential impact of these changes on gait. The attentional demands of gait are often tested using dual tasking methodologies. Relevant studies in healthy adults and patients are presented, as are the possible mechanisms responsible for the deterioration of gait during dual tasking. Lastly, we suggest how assessments of executive function and attention could be applied in the clinical setting as part of the process of identifying and understanding gait disorders and fall risk. 2007 Movement Disorder Society
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            Dynamic sensorimotor interactions in locomotion.

            Locomotion results from intricate dynamic interactions between a central program and feedback mechanisms. The central program relies fundamentally on a genetically determined spinal circuitry (central pattern generator) capable of generating the basic locomotor pattern and on various descending pathways that can trigger, stop, and steer locomotion. The feedback originates from muscles and skin afferents as well as from special senses (vision, audition, vestibular) and dynamically adapts the locomotor pattern to the requirements of the environment. The dynamic interactions are ensured by modulating transmission in locomotor pathways in a state- and phase-dependent manner. For instance, proprioceptive inputs from extensors can, during stance, adjust the timing and amplitude of muscle activities of the limbs to the speed of locomotion but be silenced during the opposite phase of the cycle. Similarly, skin afferents participate predominantly in the correction of limb and foot placement during stance on uneven terrain, but skin stimuli can evoke different types of responses depending on when they occur within the step cycle. Similarly, stimulation of descending pathways may affect the locomotor pattern in only certain phases of the step cycle. Section ii reviews dynamic sensorimotor interactions mainly through spinal pathways. Section iii describes how similar sensory inputs from the spinal or supraspinal levels can modify locomotion through descending pathways. The sensorimotor interactions occur obviously at several levels of the nervous system. Section iv summarizes presynaptic, interneuronal, and motoneuronal mechanisms that are common at these various levels. Together these mechanisms contribute to the continuous dynamic adjustment of sensorimotor interactions, ensuring that the central program and feedback mechanisms are congruous during locomotion.
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              Independent EEG Sources Are Dipolar

              Independent component analysis (ICA) and blind source separation (BSS) methods are increasingly used to separate individual brain and non-brain source signals mixed by volume conduction in electroencephalographic (EEG) and other electrophysiological recordings. We compared results of decomposing thirteen 71-channel human scalp EEG datasets by 22 ICA and BSS algorithms, assessing the pairwise mutual information (PMI) in scalp channel pairs, the remaining PMI in component pairs, the overall mutual information reduction (MIR) effected by each decomposition, and decomposition ‘dipolarity’ defined as the number of component scalp maps matching the projection of a single equivalent dipole with less than a given residual variance. The least well-performing algorithm was principal component analysis (PCA); best performing were AMICA and other likelihood/mutual information based ICA methods. Though these and other commonly-used decomposition methods returned many similar components, across 18 ICA/BSS algorithms mean dipolarity varied linearly with both MIR and with PMI remaining between the resulting component time courses, a result compatible with an interpretation of many maximally independent EEG components as being volume-conducted projections of partially-synchronous local cortical field activity within single compact cortical domains. To encourage further method comparisons, the data and software used to prepare the results have been made available (http://sccn.ucsd.edu/wiki/BSSComparison).
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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
                12 May 2015
                2015
                : 9
                : 247
                Affiliations
                [1] 1Functional and Applied Biomechanics Section, Rehabilitation Medicine Department, National Institutes of Health Bethesda, MD, USA
                [2] 2Robotics Engineering Department, Daegu Gyeongbuk Institute of Science and Technology Daegu, South Korea
                [3] 3Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology Daejeon, South Korea
                Author notes

                Edited by: Klaus Gramann, Berlin Institute of Technology, Germany

                Reviewed by: Johanna Wagner, Graz University of Technology, Austria; Daniel P. Ferris, University of Michigan, USA

                *Correspondence: Hyung-Soon Park, Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology, 291 Daehakro, Yuseong-gu, Daejeon 305-701, South Korea hyungspark@ 123456kaist.ac.kr
                Article
                10.3389/fnhum.2015.00247
                4429238
                26029077
                be71c7ab-88b5-4496-971f-c80cb3175bd9
                Copyright © 2015 Bulea, Kim, Damiano, Stanley and Park.

                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
                : 28 January 2015
                : 17 April 2015
                Page count
                Figures: 6, Tables: 2, Equations: 0, References: 64, Pages: 13, Words: 9696
                Categories
                Neuroscience
                Original Research

                Neurosciences
                electroencephalography,source localization,motor cortex,gait,motor learning,gamma oscillations,event-related desynchronization,neurorehabilitation

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