Distinct β Band Oscillatory Networks Subserving Motor and Cognitive Control during Gait Adaptation

Abstract

Everyday locomotion and obstacle avoidance requires effective gait adaptation in response to sensory cues. Many studies have shown that efficient motor actions are associated with rhythm (8 -13 Hz) and ␤ band (13-35 Hz) local field desynchronizations in sensorimotor and parietal cortex, whereas a number of cognitive task studies have reported higher behavioral accuracy to be associated with increases in ␤ band power in prefrontal and sensory cortex. How these two distinct patterns of ␤ band oscillations interplay during gait adaptation, however, has not been established. Here we recorded 108 channel EEG activity from 18 participants (10 males, 22-35 years old) attempting to walk on a treadmill in synchrony with a series of pacing cue tones, and quickly adapting their step rate and length to sudden shifts in pacing cue tempo. Independent component analysis parsed each participant's EEG data into maximally independent component (IC) source processes, which were then grouped across participants into distinct spatial/spectral clusters. Following cue tempo shifts, mean ␤ band power was suppressed for IC sources in central midline and parietal regions, whereas mean ␤ band power increased in IC sources in or near medial prefrontal and dorsolateral prefrontal cortex. In the right dorsolateral prefrontal cortex IC cluster, the ␤ band power increase was stronger during (more effortful) step shortening than during step lengthening. These results thus show that two distinct patterns of ␤ band activity modulation accompany gait adaptations: one likely serving movement initiation and execution; and the other, motor control and inhibition.

Establishing a Right Frontal Beta Signature for Stopping Action in Scalp EEG: Implications for Testing Inhibitory Control in Other Task Contexts

“…We anticipate that other researchers can deploy this same signature for stopping in scalp EEG to test the recruitment and timing of inhibitory control in other task contexts (c.f. van Gaal, et al, 2008; Wagner, et al, 2016). Accordingly, we provide companion material which shares the data and analysis stream.…”

Understanding Minds in Real-World Environments: Toward a Mobile Cognition Approach

“…By using gait dynamics such as heel strikes to time-lock continuous EEG recordings, Gwin et al (2011) have reported increased power spectral activity in the left and right sensorimotor cortex during contralateral foot suspension in subjects walking at a steady pace on a treadmill, suggesting increased cortical involvement related to visuo-motor integration and error monitoring. More recently, Wagner et al (2016) reported different patterns of power spectral activity reflecting movement initiation and execution (Mu and Beta desynchronization in sensorimotor and parietal cortex) and motor control and inhibition (increased frontal Beta power) during a gait adaptation task. These important early findings demonstrate the feasibility of characterizing modulations of EEG activity in relation to body dynamics through the integration of brain and body measurements.…”

“…Equally impressively, recent study successfully demonstrated the feasibility of parsing non-brain from brain signals in subjects cycling in a natural environment (Zink et al, 2016). Moreover, advanced EEG data analyses can be applied toward signal source localization based on the reconstruction of equivalent dipoles of independent components (Gramann et al, 2010; Wagner et al, 2016). However, EEG source modeling methods are essentially based on computational derivations and therefore require a feed of high-dimensional EEG data (i.e., 120+ channels) in order to reach sufficient approximations of signals' origins.…”

Nonlinear Coupling between Cortical Oscillations and Muscle Activity during Isotonic Wrist Flexion

“…ICs were identified as outliers if their locations in the clustering vector space were larger than five times of standard deviation from the obtained cluster centers. Only clusters including ICs from more than half of the subjects (i.e., at least six subjects) were used for further analysis (Wagner et al, 2016). …”