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A fronto-parietal network, comprised of the posterior parietal cortex (PPC) and the dorsal premotor cortex (PMd) has been proposed to be involved in planning and guiding movement. However, the issue of how the network is expressed across the bilateral cortical area according to the effector's side remains unclear. In this study, we tested these questions using electrocorticographic (ECoG) recordings in non-human primates and using a simple visual guided reaching task that induced a left or right hand response based on relevant cues provided for the task. The findings indicate that right hemisphere lateralized network patterns in which the right PMd was strongly coordinated with bilateral PPC immediately after presentation of the movement cue occurred, while the coherence with the left PMd was not enhanced. No difference was found in the coherence pattern between the effector's side (left hand or right hand), but the strength of coherence was different, in that animals showed a higher coherence in the right hand response compared to the left. Our data support that right lateralization in long-range phase synchrony in the 10-20 Hz low beta band is involved in motor preparation stage, irrespective of the upcoming effector's side.
Pubmed ID: 30333734
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Multi paradigm numerical computing environment and fourth generation programming language developed by MathWorks. Allows matrix manipulations, plotting of functions and data, implementation of algorithms, creation of user interfaces, and interfacing with programs written in other languages, including C, C++, Java, Fortran and Python. Used to explore and visualize ideas and collaborate across disciplines including signal and image processing, communications, control systems, and computational finance.
View all literature mentionsInteractive Matlab toolbox for processing continuous and event-related EEG, MEG and other electrophysiological data incorporating independent component analysis (ICA), time/frequency analysis, artifact rejection, event-related statistics, and several useful modes of visualization of the averaged and single-trial data. First developed on Matlab 5.3 under Linux, EEGLAB runs on Matlab v5 and higher under Linux, Unix, Windows, and Mac OS X (Matlab 7+ recommended). EEGLAB provides an interactive graphic user interface (GUI) allowing users to flexibly and interactively process their high-density EEG and other dynamic brain data using independent component analysis (ICA) and/or time/frequency analysis (TFA), as well as standard averaging methods. EEGLAB also incorporates extensive tutorial and help windows, plus a command history function that eases users'' transition from GUI-based data exploration to building and running batch or custom data analysis scripts. EEGLAB offers a wealth of methods for visualizing and modeling event-related brain dynamics, both at the level of individual EEGLAB ''datasets'' and/or across a collection of datasets brought together in an EEGLAB ''studyset.'' For experienced Matlab users, EEGLAB offers a structured programming environment for storing, accessing, measuring, manipulating and visualizing event-related EEG data. For creative research programmers and methods developers, EEGLAB offers an extensible, open-source platform through which they can share new methods with the world research community by publishing EEGLAB ''plug-in'' functions that appear automatically in the EEGLAB menu of users who download them. For example, novel EEGLAB plug-ins might be built and released to ''pick peaks'' in ERP or time/frequency results, or to perform specialized import/export, data visualization, or inverse source modeling of EEG, MEG, and/or ECOG data. EEGLAB Features * Graphic user interface * Multiformat data importing * High-density data scrolling * Defined EEG data structure * Open source plug-in facility * Interactive plotting functions * Semi-automated artifact removal * ICA & time/frequency transforms * Many advanced plug-in toolboxes * Event & channel location handling * Forward/inverse head/source modeling
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