A growing body of evidence indicates a pivotal role of cognition and in particular executive function in gait control and fall prevention. In a recent gait study using electroencephalographic (EEG) imaging, we provided direct proof for cortical top-down inhibitory control in step adaptation. A crucial part of motor inhibition is recognizing stimuli that signal the need to inhibit or adjust motor actions such as steps during walking. One of the EEG signatures of performance monitoring in response to events signaling the need to adjust motor responses, are error-related potential (error-ERP) features. To examine whether error-ERP features may index executive control during gait adaptation, we analyzed high-density (108-channel) EEG data from an auditory gait pacing study. Participants (N = 18) walking on a steadily moving treadmill were asked to step in time to an auditory cue tone sequence, and then to quickly adapt their step length and rate, to regain step-cue synchrony following occasional unexpected shifts in the pacing cue train to a faster or slower cue tempo. Decomposition of the continuous EEG data by independent component analysis revealed a negative deflection in the source-resolved event-related potential (ERP) time locked to 'late' cue tones marking a shift to a slower cue tempo. This vertex-negative ERP feature, localized primarily to posterior medial frontal cortex (pMFC) and peaking 250 ms after the onset of the tempo-shift cue, we here refer to as the step-cue delay negativity (SDN). SDN source, timing, and polarity resemble other error-related ERP features, e.g., the Error-Related Negativity (ERN) and Feedback-Related Negativity (FRN) in (seated) button press response tasks. In single trials, SDN amplitude varied with the magnitude of the cue latency deviation (the time interval between the expected and actual cue onsets). Regression analysis also identified linear coupling between SDN amplitude and the subsequent speed of gait tempo adaptation (as measured by the increase in length of the ensuing adaptation step). The SDN in this paradigm thus seems both to index the perceived need for and the subsequent magnitude of the immediate gait adjustment, consistent with performance-monitoring models. Future research might investigate relationships of these control processes to the impairment of gait adjustment in motor disorders and cognitive decline, for example to develop a biomarker for fall risk prediction in early-stage Parkinson's.

Reproducibility is a cornerstone of scientific communication without which one cannot build upon each other's work. Because modern human brain imaging relies on many integrated steps with a variety of possible algorithms, it has, however, become impossible to report every detail of a data processing workflow. In response to this analytical complexity, community recommendations are to share data analysis pipelines (scripts that implement workflows). Here we show that this can easily be done using EEGLAB and tools built around it. BIDS tools allow importing all the necessary information and create a study from electroencephalography (EEG)-Brain Imaging Data Structure compliant data. From there preprocessing can be carried out in only a few steps using EEGLAB and statistical analyses performed using the LIMO EEG plug-in. Using Wakeman and Henson (2015) face dataset, we illustrate how to prepare data and build different statistical models, a standard factorial design (faces ^∗ repetition), and a more modern trial-based regression approach for the stimulus repetition effect, all in a few reproducible command lines.

Modulation of the amplitude of high-frequency cortical field activity locked to changes in the phase of a slower brain rhythm is known as phase-amplitude coupling (PAC). The study of this phenomenon has been gaining traction in neuroscience because of several reports on its appearance in normal and pathological brain processes in humans as well as across different mammalian species. This has led to the suggestion that PAC may be an intrinsic brain process that facilitates brain inter-area communication across different spatiotemporal scales. Several methods have been proposed to measure the PAC process, but few of these enable detailed study of its time course. It appears that no studies have reported details of PAC dynamics including its possible directional delay characteristic. Here, we study and characterize the use of a novel information theoretic measure that may address this limitation: local transfer entropy. We use both simulated and actual intracranial electroencephalographic data. In both cases, we observe initial indications that local transfer entropy can be used to detect the onset and offset of modulation process periods revealed by mutual information estimated phase-amplitude coupling (MIPAC). We review our results in the context of current theories about PAC in brain electrical activity, and discuss technical issues that must be addressed to see local transfer entropy more widely applied to PAC analysis. The current work sets the foundations for further use of local transfer entropy for estimating PAC process dynamics, and extends and complements our previous work on using local mutual information to compute PAC (MIPAC).

In this report we present a mobile brain/body imaging (MoBI) dataset that allows study of source-resolved cortical dynamics supporting coordinated gait movements in a rhythmic auditory cueing paradigm. Use of an auditory pacing stimulus stream has been recommended to identify deficits and treat gait impairments in neurologic populations. Here, the rhythmic cueing paradigm required healthy young participants to walk on a treadmill (constant speed) while attempting to maintain step synchrony with an auditory pacing stream and to adapt their step length and rate to unanticipated shifts in tempo of the pacing stimuli (e.g., sudden shifts to a faster or slower tempo). High-density electroencephalography (EEG, 108 channels), surface electromyography (EMG, bilateral tibialis anterior), pressure sensors on the heel (to register timing of heel strikes), and goniometers (knee, hip, and ankle joint angles) were concurrently recorded in 20 participants. The data is provided in the Brain Imaging Data Structure (BIDS) format to promote data sharing and reuse, and allow the inclusion of the data into fully automated data analysis workflows.

EEGLAB signal processing environment is currently the leading open-source software for processing electroencephalographic (EEG) data. The Neuroscience Gateway (NSG, nsgportal.org) is a web and API-based portal allowing users to easily run a variety of neuroscience-related software on high-performance computing (HPC) resources in the U.S. XSEDE network. We have reported recently (Delorme et al., 2019) on the Open EEGLAB Portal expansion of the free NSG services to allow the neuroscience community to build and run MATLAB pipelines using the EEGLAB tool environment. We are now releasing an EEGLAB plug-in, nsgportal, that interfaces EEGLAB with NSG directly from within EEGLAB running on MATLAB on any personal lab computer. The plug-in features a flexible MATLAB graphical user interface (GUI) that allows users to easily submit, interact with, and manage NSG jobs, and to retrieve and examine their results. Command line nsgportal tools supporting these GUI functionalities allow EEGLAB users and plug-in tool developers to build largely automated functions and workflows that include optional NSG job submission and processing. Here we present details on nsgportal implementation and documentation, provide user tutorials on example applications, and show sample test results comparing computation times using HPC versus laptop processing.