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      Unidirectional brain to muscle connectivity reveals motor cortex control of leg muscles during stereotyped walking.

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          Abstract

          In lower mammals, locomotion seems to be mainly regulated by subcortical and spinal networks. On the contrary, recent evidence suggests that in humans the motor cortex is also significantly engaged during complex locomotion tasks. However, a detailed understanding of cortical contribution to locomotion is still lacking especially during stereotyped activities. Here, we show that cortical motor areas finely control leg muscle activation during treadmill stereotyped walking. Using a novel technique based on a combination of Reliable Independent Component Analysis, source localization and effective connectivity, and by combining electroencephalographic (EEG) and electromyographic (EMG) recordings in able-bodied adults we were able to examine for the first time cortical activation patterns and cortico-muscular connectivity including information flow direction. Results not only provided evidence of cortical activity associated with locomotion, but demonstrated significant causal unidirectional drive from contralateral motor cortex to muscles in the swing leg. These insights overturn the traditional view that human cortex has a limited role in the control of stereotyped locomotion, and suggest useful hypotheses concerning mechanisms underlying gait under other conditions.

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          Most cited references75

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          LIBSVM: A library for support vector machines

          LIBSVM is a library for Support Vector Machines (SVMs). We have been actively developing this package since the year 2000. The goal is to help users to easily apply SVM to their applications. LIBSVM has gained wide popularity in machine learning and many other areas. In this article, we present all implementation details of LIBSVM. Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.
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            Testing for nonlinearity in time series: the method of surrogate data

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              Identifying true brain interaction from EEG data using the imaginary part of coherency.

              The main obstacle in interpreting EEG/MEG data in terms of brain connectivity is the fact that because of volume conduction, the activity of a single brain source can be observed in many channels. Here, we present an approach which is insensitive to false connectivity arising from volume conduction. We show that the (complex) coherency of non-interacting sources is necessarily real and, hence, the imaginary part of coherency provides an excellent candidate to study brain interactions. Although the usual magnitude and phase of coherency contain the same information as the real and imaginary parts, we argue that the Cartesian representation is far superior for studying brain interactions. The method is demonstrated for EEG measurements of voluntary finger movement. We found: (a) from 5 s before to movement onset a relatively weak interaction around 20 Hz between left and right motor areas where the contralateral side leads the ipsilateral side; and (b) approximately 2-4 s after movement, a stronger interaction also at 20 Hz in the opposite direction. It is possible to reliably detect brain interaction during movement from EEG data. The method allows unambiguous detection of brain interaction from rhythmic EEG/MEG data.
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                Author and article information

                Journal
                Neuroimage
                NeuroImage
                Elsevier BV
                1095-9572
                1053-8119
                Oct 01 2017
                : 159
                Affiliations
                [1 ] The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy; Translational Neural Engineering Laboratory, Center for Neuroprosthetics and Institute of Bioengineering, EPFL, Lausanne, Switzerland. Electronic address: fiorenzo.artoni@santannapisa.it.
                [2 ] The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy; Pisa University Hospital, Pisa, Italy.
                [3 ] Pisa University Hospital, Pisa, Italy.
                [4 ] The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy.
                [5 ] Swartz Center for Computational Neuroscience, University of California, San Diego, La Jolla, CA, United States.
                [6 ] The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy; Translational Neural Engineering Laboratory, Center for Neuroprosthetics and Institute of Bioengineering, EPFL, Lausanne, Switzerland.
                [7 ] Pisa University Hospital, Pisa, Italy. Electronic address: c.chisari@ao-pisa.toscana.it.
                Article
                S1053-8119(17)30581-5
                10.1016/j.neuroimage.2017.07.013
                28782683
                af17ff11-d9a8-4c61-aa7c-69b5cf0d75f0
                History

                Connectivity,Decoding,Electroencephalography (EEG),Electromyography (EMG),Locomotion,Mobile brain imaging (MOBI)

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