Dataset Information

High-density EEG mobile brain/body imaging data recorded during a challenging auditory gait pacing task.

ABSTRACT: 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.

SUBMITTER: Wagner J

PROVIDER: S-EPMC6797727 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

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High-density EEG mobile brain/body imaging data recorded during a challenging auditory gait pacing task.

Wagner Johanna J Martinez-Cancino Ramon R Delorme Arnaud A Makeig Scott S Solis-Escalante Teodoro T Neuper Christa C Mueller-Putz Gernot G

Scientific data 20191017 1

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 ...[more]

PMID: 31624252

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Project description: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.

Project description:PurposeRecent studies in epilepsy, cognition, and brain machine interfaces have shown the utility of recording intracranial electroencephalography (iEEG) with greater spatial resolution. Many of these studies utilize microelectrodes connected to specialized amplifiers that are optimized for such recordings. We recently measured the impedances of several commercial microelectrodes and demonstrated that they will distort iEEG signals if connected to clinical EEG amplifiers commonly used in most centers. In this study we demonstrate the clinical implications of this effect and identify some of the potential difficulties in using microelectrodes.MethodsHuman iEEG data were digitally filtered to simulate the signal recorded by a hybrid grid (two macroelectrodes and eight microelectrodes) connected to a standard EEG amplifier. The filtered iEEG data were read by three trained epileptologists, and high frequency oscillations (HFOs) were detected with a well-known algorithm. The filtering method was verified experimentally by recording an injected EEG signal in a saline bath with the same physical acquisition system used to generate the model. Several electrodes underwent scanning electron microscopy (SEM).Key findingsMacroelectrode recordings were unaltered compared to the source iEEG signal, but microelectrodes attenuated low frequencies. The attenuated signals were difficult to interpret: all three clinicians changed their clinical scoring of slowing and seizures when presented with the same data recorded on different sized electrodes. The HFO detection algorithm was oversensitive with microelectrodes, classifying many more HFOs than when the same data were recorded with macroelectrodes. In addition, during experimental recordings the microelectrodes produced much greater noise as well as large baseline fluctuations, creating sharply contoured transients, and superimposed "false" HFOs. SEM of these microelectrodes demonstrated marked variability in exposed electrode surface area, lead fractures, and sharp edges.SignificanceMicroelectrodes should not be used with low impedance (<1 GΩ) amplifiers due to severe signal attenuation and variability that changes clinical interpretations. The current method of preparing microelectrodes can leave sharp edges and nonuniform amounts of exposed wire. Even when recorded with higher impedance amplifiers, microelectrode data are highly prone to artifacts that are difficult to interpret. Great care must be taken when analyzing iEEG from high impedance microelectrodes.

Dataset Information

High-density EEG mobile brain/body imaging data recorded during a challenging auditory gait pacing task.

Publications

High-density EEG mobile brain/body imaging data recorded during a challenging auditory gait pacing task.

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