Frequency‐dependent modulation of neural oscillations across the gait cycle

Abstract

Balance and walking are fundamental to support common daily activities. Relatively accurate characterizations of normal and impaired gait features were attained at the kinematic and muscular levels. Conversely, the neural processes underlying gait dynamics still need to be elucidated. To shed light on gait‐related modulations of neural activity, we collected high‐density electroencephalography (hdEEG) signals and ankle acceleration data in young healthy participants during treadmill walking. We used the ankle acceleration data to segment each gait cycle in four phases: initial double support, right leg swing, final double support, left leg swing. Then, we processed hdEEG signals to extract neural oscillations in alpha, beta, and gamma bands, and examined event‐related desynchronization/synchronization (ERD/ERS) across gait phases. Our results showed that ERD/ERS modulations for alpha, beta, and gamma bands were strongest in the primary sensorimotor cortex (M1), but were also found in premotor cortex, thalamus and cerebellum. We observed a modulation of neural oscillations across gait phases in M1 and cerebellum, and an interaction between frequency band and gait phase in premotor cortex and thalamus. Furthermore, an ERD/ERS lateralization effect was present in M1 for the alpha and beta bands, and in the cerebellum for the beta and gamma bands. Overall, our findings demonstrate that an electrophysiological source imaging approach based on hdEEG can be used to investigate dynamic neural processes of gait control. Future work on the development of mobile hdEEG‐based brain–body imaging platforms may enable overground walking investigations, with potential applications in the study of gait disorders.

Assessing Neurokinematic and Neuromuscular Connectivity During Walking Using Mobile Brain-Body Imaging

“…Reliable connectivity images were generally obtained with neural oscillations in the alpha and beta bands. This is consistent with several studies that reported modulations of alpha and beta oscillations during movement performance, both in seated conditions (Pfurtscheller et al, 1996;Tan et al, 2016;Porcaro et al, 2018Porcaro et al, , 2021Zhao et al, 2019) and walking conditions (Wagner et al, 2016;Zhao et al, 2022). Strongest brain-body connectivity values were located over or close to the bilateral M1, the portion of cortex that is primarily associated with movement execution (Todorov, 2000;Lemon, 2008).…”

“…The ANOVA on NMC maps revealed no significant difference across frequency bands, and no interaction between frequency bands and sensors. Accordingly, alpha and beta neural oscillations, and to some extent also gamma neuronal oscillations, support the coordinated muscular actions of gait performance (Zhao et al, 2022), but no clear functional dissociation between them could be detected in terms of brainbody connectivity at the whole-brain level.…”

“…Strongest brain-body connectivity values were located over or close to the bilateral M1, the portion of cortex that is primarily associated with movement execution (Todorov, 2000;Lemon, 2008). Brain regions that are typically associated with motor planning and coordination, as for instance PMC, PPC, and CER (Battaglia-Mayer and Caminiti, 2019;Zhao et al, 2022), showed less strong but still significant NMC values. Furthermore, the voxel-wise ANOVA that we conducted in our study revealed significant differences in brain-body connectivity across body sensors over the bilateral primary sensorimotor cortex.…”

“…Accordingly, we detected the following gait events: left heel strike (LHS), right heel strike (RHS), left toe off (LTO), and right toe off (RTO) (Gwin et al, 2011;Seeber et al, 2014;Wagner et al, 2016). A full gait cycle was defined as the period between two adjacent left heel strikes (Zhao et al, 2022).…”

“…Particularly, it has been shown that the application of high-density EEG (hdEEG) montages with more than hundred electrodes yields a fine spatial sampling of scalp potentials (Liu et al, 2015;Song et al, 2015;Seeck et al, 2017); furthermore, the implementation of advanced artifact attenuation approaches (Zhao et al, 2021) and head modeling solutions (Taberna et al, 2019a(Taberna et al, ,b, 2021) permits a more accurate characterization of neural oscillations in the source space from hdEEG recordings. In a recent study, we have demonstrated that the application of these advanced solutions on hdEEG data collected in walking participants can support a finer characterization of neural oscillations in different phases of the gait cycle (Zhao et al, 2022). Yet, it remains to be evaluated if the increased spatial specificity brought by hdEEG in neuronal signal reconstruction can support the reliable assessment of gait-related NKC and NMC in the source space, and if this approach provides additional information as compared to previous studies that quantified NKC and NMC at the sensor level (Petersen et al, 2012;Roeder et al, 2018).…”