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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.

      One sentence summary:

      Motor cortex proactively drives contralateral swing leg muscles during treadmill walking, counter to the traditional view of stereotyped human locomotion.

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      Estimating the Dimension of a Model

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        EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis.

        We have developed a toolbox and graphic user interface, EEGLAB, running under the crossplatform MATLAB environment (The Mathworks, Inc.) for processing collections of single-trial and/or averaged EEG data of any number of channels. Available functions include EEG data, channel and event information importing, data visualization (scrolling, scalp map and dipole model plotting, plus multi-trial ERP-image plots), preprocessing (including artifact rejection, filtering, epoch selection, and averaging), independent component analysis (ICA) and time/frequency decompositions including channel and component cross-coherence supported by bootstrap statistical methods based on data resampling. EEGLAB functions are organized into three layers. Top-layer functions allow users to interact with the data through the graphic interface without needing to use MATLAB syntax. Menu options allow users to tune the behavior of EEGLAB to available memory. Middle-layer functions allow users to customize data processing using command history and interactive 'pop' functions. Experienced MATLAB users can use EEGLAB data structures and stand-alone signal processing functions to write custom and/or batch analysis scripts. Extensive function help and tutorial information are included. A 'plug-in' facility allows easy incorporation of new EEG modules into the main menu. EEGLAB is freely available (http://www.sccn.ucsd.edu/eeglab/) under the GNU public license for noncommercial use and open source development, together with sample data, user tutorial and extensive documentation.
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          Support-vector networks

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            Author and article information

            Affiliations
            [a ]The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy
            [b ]Translational Neural Engineering Laboratory, Center for Neuroprosthetics and Institute of Bioengineering, EPFL, Lausanne, Switzerland
            [c ]Pisa University Hospital, Pisa, Italy
            [d ]Swartz Center for Computational Neuroscience, University of California, San Diego, La Jolla, CA, United States
            Author notes
            [* ]Corresponding author. The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy. fiorenzo.artoni@ 123456santannapisa.it (F. Artoni)
            [** ]Corresponding author. Pisa University Hospital, Pisa, Italy. c.chisari@ 123456ao-pisa.toscana.it (C. Chisari).
            [1]

            Equal senior contributors.

            Author contributions

            FA designed the study, developed the experimental set up, supervised the experiments, analyzed the data, discussed the results and wrote the paper. AP and FA performed the connectivity analysis and wrote the corresponding parts of the paper. FA, CF, FB performed the experiments. SMa supervised the data processing activities. SMi co-designed and supervised the experiments and wrote the paper. CC was responsible for the experimental aspects of the study and supervised the experiments. All authors approved the final manuscript.

            Journal
            9215515
            20498
            Neuroimage
            Neuroimage
            NeuroImage
            1053-8119
            1095-9572
            25 July 2019
            04 August 2017
            01 October 2017
            19 August 2019
            : 159
            : 403-416
            28782683
            6698582
            10.1016/j.neuroimage.2017.07.013
            NIHMS1534574

            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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