Elsevier

NeuroImage

Volume 112, 15 May 2015, Pages 318-326
NeuroImage

High and low gamma EEG oscillations in central sensorimotor areas are conversely modulated during the human gait cycle

https://doi.org/10.1016/j.neuroimage.2015.03.045Get rights and content

Highlights

  • We apply EEG source imaging to enable neuroimaging during walking.

  • Muscular artifacts are minimized using a novel spectral decomposition approach.

  • High γ amplitudes are statically increased during walking compared to standing.

  • High γ and low γ amplitudes are conversely modulated in the gait cycle.

  • High γ increase and gait phase modulation are located in central sensorimotor areas.

Abstract

Investigating human brain function is essential to develop models of cortical involvement during walking. Such models could advance the analysis of motor impairments following brain injuries (e.g., stroke) and may lead to novel rehabilitation approaches. In this work, we applied high-density EEG source imaging based on individual anatomy to enable neuroimaging during walking. To minimize the impact of muscular influence on EEG recordings we introduce a novel artifact correction method based on spectral decomposition.

High γ oscillations (> 60 Hz) were previously reported to play an important role in motor control. Here, we investigate high γ amplitudes while focusing on two different aspects of a walking experiment, namely the fact that a person walks and the rhythmicity of walking. We found that high γ amplitudes (60–80 Hz), located focally in central sensorimotor areas, were significantly increased during walking compared to standing. Moreover, high γ (70–90 Hz) amplitudes in the same areas are modulated in relation to the gait cycle. Since the spectral peaks of high γ amplitude increase and modulation do not match, it is plausible that these two high γ elements represent different frequency-specific network interactions. Interestingly, we found high γ (70–90 Hz) amplitudes to be coupled to low γ (24–40 Hz) amplitudes, which both are modulated in relation to the gait cycle but conversely to each other. In summary, our work is a further step towards modeling cortical involvement during human upright walking.

Introduction

The ability to walk safely and independently is important for humans. Cortical injuries (e.g., stroke) can cause motor impairment and lead to limitations in the execution of daily life activities. Thus, great effort is put into restoring walking in people with motor impairments. To get a deeper understanding of cortical involvement during walking it is necessary to develop models, which are capable of describing cortical activities in relation to human walking patterns. Such models could facilitate the development of novel rehabilitation strategies in the future.

Neuroimaging studies using functional magnet resonance imaging (fMRI) restrict subjects to a lying position with fixated heads. Therefore, such setups are not well-suited for studying human brain function during walking. To overcome these methodical limitations electroencephalographic (EEG) source imaging (Baillet et al., 2001, Michel et al., 2004) can be used. Despite its low spatial resolution (centimeters), sophisticated analysis of the EEG offers several advantages. First, the temporal resolution of EEG signals in milliseconds allows analyzing cortical processes in relation to walking patterns. Second, analysis in the frequency domain opens possibilities to investigate different elements of cortical activity (Buzsáki and Draguhn, 2004, Siegel et al., 2012). Third and most important for investigating cortical involvement during walking, EEG source imaging can be done in ambulatory conditions (i.e., mobile brain imaging).

In recent years, several studies have investigated brain activity during walking (Gwin et al., 2011, Gramann et al., 2010, Presacco et al., 2011, Severens et al., 2012, Petersen et al., 2012, Wagner et al., 2012, Wagner et al., 2014, De Sanctis et al., 2014, Ehinger et al., 2014, Lau et al., 2014, Seeber et al., 2014). In agreement with earlier studies of isolated foot movement (Pfurtscheller et al., 1997, Crone et al., 1998, Miller et al., 2007, Müller-Putz et al., 2007), β oscillations in central sensorimotor areas were found to be suppressed (event-related desynchronization, ERD) during walking relative to a non-movement reference (Wagner et al., 2012, Severens et al., 2012, Seeber et al., 2014). Additionally, low γ (25–40 Hz) amplitudes were found to be modulated locked to the gait cycle (Wagner et al., 2012, Wagner et al., 2014, Seeber et al., 2014). The same frequency range was reported by Petersen et al. (2012) for significant coherence between EEG recordings over leg motor areas and the anterior tibialis muscle.

Sustained β suppression and low γ modulation were found to be simultaneously present and superimposed in the frequency domain and in spatial location during walking. Nevertheless, the different spectral peaks of β suppression and low γ modulation suggest that these phenomena are different elements of EEG activity during walking. We proposed that altered levels of β suppression during walking signify enhanced cortical excitability in central sensorimotor areas. Furthermore, gait cycle related modulation of low γ amplitudes may reflect sensorimotor processing linked to the motion sequences (Seeber et al., 2014).

In this work, we further develop the electrophysiological model of walking, including data from higher frequency oscillations (> 50 Hz). Previous studies showed high γ oscillations (60–90 Hz) to play an important role in motor execution (Crone et al., 1998, Pfurtscheller et al., 2003, Miller et al., 2007, Cheyne et al., 2008, Ball et al., 2008, Donner et al., 2009, Muthukumaraswamy, 2010, Darvas et al., 2010, Joundi et al., 2012). High γ power increase in electrocorticographic (ECoG) recordings correspond spatially well to fMRI activity (Hermes et al., 2012a) and its superior focal distribution enables the decoding of single finger movement (Kubánek et al., 2009, Miller et al., 2009, Scherer et al., 2009, Hermes et al., 2012b). The feasibility of detecting high γ activity in the motor system from non-invasive recordings was reported for magnetoencephalography (MEG) (Cheyne et al., 2008, Dalal et al., 2008, Donner et al., 2009, Muthukumaraswamy, 2010) and EEG (Ball et al., 2008, Darvas et al., 2010) during isolated limb movements. However, due to muscular [electromyographic (EMG)] and movement artifacts, it is very challenging to detect high γ activity from EEG recordings during walking. EMG artifacts during body movements affect EEG recordings in a wide range of frequencies (~ 20–300 Hz) (Muthukumaraswamy, 2013, Castermans et al., 2014).

Extending the previous findings of our group (Wagner et al., 2012, Seeber et al., 2014) we focus on two different aspects of the walking experiment: the fact that a person walks and the rhythmicity of walking movements. Therefore, we first investigate differences of the amplitude spectra between conditions walking and standing. In these analyses we introduce a novel artifact correction method based on spectral decomposition to minimize the impact of muscular influence on EEG source images. This correction method enables us to analyze high γ activity during walking. Second, we examine amplitude modulations in relation to gait phases reflecting the rhythmicity of walking movements.

Section snippets

Experiment and recordings

Data were taken from a previous study of our group (Wagner et al., 2012). Ten healthy volunteers (5 female, 5 male, 25.6 ± 3.5 years) completed four runs (6 min each) of active walking and three runs of upright standing (3 min each) in a robotic gait orthosis (Lokomat, Hocoma, Switzerland). Walking speed was constant and adjusted for each participant individually ranging from 1.8 to 2.2 km per hour. The Lokomat was operated with 100% guidance force and body weight support was less than 30% in every

Muscular artifact correction

The spatial map of the first PSC (with the largest eigenvalue) showed activity located in lateral and dorsal regions close to the location of head and neck muscles (Figs. 1a, 2c). The eigenvalue of the first PSC was 5–10 times bigger than the eigenvalue of the 2nd one in every subject (Fig. 2b). Moreover, the spectral profile of this component increases from 2–20 Hz and remains at a certain level for higher frequencies (Figs. 1b/c, 2a/c, S1). The spatial location and spectral profile of the

Muscular artifact correction

The spatial location and the frequency spectrum of the first principal spectral component (Figs. 1a–c) suggest this component to represent muscular artifacts. The first PSC can be robustly identified by its eigenvalue, because its magnitude is 5 to 10 times larger than the eigenvalue of the 2nd PSC in every subject (Fig. 2b). Spatially widespread activity and such with high amplitudes lead to large eigenvalues in our approach. Both criteria are given for muscular activities and therefore

Acknowledgments

This work was supported by the European Union research project BETTER (ICT-2009.7.2-247935) (www.car.upm-csic.es/bioingenieria/better/), BioTechMed Graz and the Land Steiermark projects BCI4REHAB (bci.tugraz.at/bci4rehab) and rE(EG)map! (bci.tugraz.at/reegmap). This is the sole opinion of the authors and funding agencies are not liable for any use that may be made of the information contained herein.

References (63)

  • R. Elul

    The genesis of the EEG

    Int. Rev. Neurobiol.

    (1972)
  • A.K. Engel et al.

    Beta-band oscillations: signalling the status quo?

    Curr. Opin. Neurobiol.

    (2010)
  • B. Fischl

    FreeSurfer

    Neuroimage

    (2012)
  • J.T. Gwin et al.

    Electrocortical activity is coupled to gait cycle phase during treadmill walking

    Neuroimage

    (2011)
  • N. Jenkinson et al.

    New insights into the relationship between dopamine, beta oscillations and motor function

    Trends Neurosci.

    (2011)
  • R.A. Joundi et al.

    Driving oscillatory activity in the human cortex enhances motor performance

    Curr. Biol.

    (2012)
  • E. Maris et al.

    Nonparametric statistical testing of EEG and MEG-data

    J. Neurosci. Methods

    (2007)
  • C.M. Michel et al.

    Towards the utilization of EEG as a brain imaging tool

    Neuroimage

    (2012)
  • C.M. Michel et al.

    EEG source imaging

    Clin. Neurophysiol.

    (2004)
  • K.J. Miller et al.

    Broadband changes in the cortical surface potential track activation of functionally diverse neuronal populations

    Neuroimage

    (2014)
  • G.R. Müller-Putz et al.

    Event-related beta EEG-changes during passive and attempted foot movements in paraplegic patients

    Brain Res.

    (2007)
  • R. Oostenveld et al.

    The five percent electrode system for high-resolution EEG and ERP measurements

    Clin. Neurophysiol.

    (2001)
  • G. Pfurtscheller et al.

    Event-related EEG/MEG synchronization and desynchronization: basic principles

    Clin. Neurophysiol.

    (1999)
  • G. Pfurtscheller et al.

    Foot and hand area mu rhythms

    Int. J. Psychophysiol.

    (1997)
  • G. Pfurtscheller et al.

    Spatiotemporal patterns of beta desynchronization and gamma synchronization in corticographic data during self-paced movement

    Clin. Neurophysiol.

    (2003)
  • A. Pogosyan et al.

    Boosting cortical activity at beta-band frequencies slows movement in humans

    Curr. Biol.

    (2009)
  • J. Wagner et al.

    Level of participation in robotic-assisted treadmill walking modulates midline sensorimotor EEG rhythms in able-bodied subjects

    Neuroimage

    (2012)
  • S. Baillet et al.

    Electromagnetic brain mapping

    IEEE Signal Process. Mag.

    (2001)
  • P. Brown

    Oscillatory nature of human basal ganglia activity: relationship to the pathophysiology of Parkinson's disease

    Mov. Disord.

    (2003)
  • G. Buzsáki et al.

    Neuronal oscillations in cortical networks

    Science

    (2004)
  • G. Buzsáki et al.

    The origin of extracellular fields and currents—EEG, ECoG, LFP and spikes

    Nat. Rev. Neurosci.

    (2012)
  • Cited by (142)

    • Decoding locomotion speed and slope from local field potentials of rat motor cortex

      2022, Computer Methods and Programs in Biomedicine
      Citation Excerpt :

      Previous studies have shown that the β power is high during rest and static periods of hand movement, such as holding objects, and decreases during planning and execution of movements [44]. In addition, studies using EEG on humans during walking have shown modulations in β and γ oscillations during the gait cycle [45,46]. Noga et al. [47] showed that the power of LFP oscillations in the mesencephalic locomotor region (MLR) in all frequency bands below 100 Hz is directly related to the speed of locomotion.

    • Stepping in time: Alpha-mu and beta oscillations during a walking synchronization task

      2022, NeuroImage
      Citation Excerpt :

      After AC, a power decrease in the alpha-mu and beta frequency band as well as power increases in the theta (4-7 Hz) and high beta/low gamma (> 30 Hz) can be observed compared to a standing baseline (see Fig. 3B). These frequency band specific changes compared to a resting baseline and sustained over the whole gait cycle have been observed in previous studies (e.g Pizzamiglio et al., 2017; Seeber et al., 2015). Topographies of the alpha-mu and beta band reveal power increases at lateral channels compared to standing baseline in the beta but not alpha-mu band (Fig. 3B topographies).

    View all citing articles on Scopus
    View full text