Walking reduces sensorimotor network connectivity compared to standing

Abstract

BackgroundConsiderable effort has been devoted to mapping the functional and effective connectivity of the human brain, but these efforts have largely been limited to tasks involving stationary subjects. Recent advances with high-density electroencephalography (EEG) and Independent Components Analysis (ICA) have enabled study of electrocortical activity during human locomotion. The goal of this work was to measure the effective connectivity of cortical activity during human standing and walking.MethodsWe recorded 248-channels of EEG as eight young healthy subjects stood and walked on a treadmill both while performing a visual oddball discrimination task and not performing the task. ICA parsed underlying electrocortical, electromyographic, and artifact sources from the EEG signals. Inverse source modeling methods and clustering algorithms localized posterior, anterior, prefrontal, left sensorimotor, and right sensorimotor clusters of electrocortical sources across subjects. We applied a directional measure of connectivity, conditional Granger causality, to determine the effective connectivity between electrocortical sources.ResultsConnections involving sensorimotor clusters were weaker for walking than standing regardless of whether the subject was performing the simultaneous cognitive task or not. This finding supports the idea that cortical involvement during standing is greater than during walking, possibly because spinal neural networks play a greater role in locomotor control than standing control. Conversely, effective connectivity involving non-sensorimotor areas was stronger for walking than standing when subjects were engaged in the simultaneous cognitive task.ConclusionsOur results suggest that standing results in greater functional connectivity between sensorimotor cortical areas than walking does. Greater cognitive attention to standing posture than to walking control could be one interpretation of that finding. These techniques could be applied to clinical populations during gait to better investigate neural substrates involved in mobility disorders.

Brain activity during walking: A systematic review

“…Furthermore, weaker interhemispheric communication of sensorimotor cortex was observed (Lau et al, 2014). However, as mentioned in Table 6, a decrease in multisensory vestibular cortices was also reported (La Fougere et al, 2010).…”

“…However, Sanctis et al (2014), Holtzer et al (2011) and Atsumori et al (2010) registered increased activity in prefrontal areas for young adults during dual task walking, while Hill et al (2013) observed an increased activation of this cortex area only for relative difficult tasks (see Table 8). Also, a stronger communication of non-sensorimotor areas during dual-task walking was observed (Lau et al, 2014).…”

Use of probabilistic weights to enhance linear regression myoelectric control

“…Others have successfully demonstrated that users can learn abstract mappings between EMG activity and prosthesis output (Radhakrishnan et al, 2008, Pistohl et al, 2013, Antuvan et al, 2014). Both linear (Hahne et al, 2014, Jiang et al, 2014b, Smith et al, 2015a) and nonlinear (Jiang et al, 2012, Hahne et al, 2014, Kamavuako et al, 2012, Ameri et al, 2014, Ngeo et al, 2014, Muceli and Farina, 2012) methods of mapping EMG activity to prosthesis movement have been evaluated, though a large emphasis of real-time evaluation has focused on linear methods and is commonly motivated by the motor control concept of muscle synergies (Jiang et al, 2009, d’Avella et al, 2006). EMG amplitude estimates (such as root-mean-square or mean absolute value (MAV)) are typically the primary signal feature used as inputs into these systems (Hahne et al, 2014, Jiang et al, 2014b, Smith et al, 2015a), as they positively correlate with contraction intensity (De Luca, 1997).…”

Mapping the Spatial and Temporal Dynamics of Sensorimotor Integration During the Perception and Performance of Wallowing

“…In studies of cued movement performance, beta band suppression has been measured in the PMC prior to the onset of movement, and is thought to reflect the efference motor copy of movements (Kilavik et al, 2013;Pineda et al, 2013;Wagner et al, 2014). Beta suppression within the primary sensory and motor cortices has been shown during the performance of finger and thumb (Tsai, Jung, Chien, Savostyanov, & Makeig, 2014), hand and foot (Hermes et al, 2010;Neuper, Wortz, & Pfurtscheller, 2006;Yuan et al, 2009), walking and running (Lau et al, 2014;Wagner et al, 2014), tongue (Morash, Bai, Furlani, Lin, & Hallett, 2008), and swallowing movements (Morash et al, 2008;Suntrup et al, 2014). Therefore, it seems reasonable to predict that EEG measured patterns of mu rhythm activity will provide cortical measures of SMI during swallowing.…”

“…However, when using ICA to collect neural and muscle activity during the performance of movement tasks, it has been noted that muscle components represent a majority of the total signal variance, resulting in a reduction of the spectral power exhibited by the neural components (McMenamin et al, 2011;Vos et al, 2010). Despite these possible limitations, a number of studies have reported successful use of an ICA for identifying neural activity during arm movements (Alomari, Samaha, & AlKamha, 2013;Hausser, 2006), finger tapping and hand movements (Lv, Li, Gu, 2010), walking and running movements (Gwin et al, 2010;Lau et al, 2014), and postural adjustments (Slobounov, Cao, Jaiswal, & Newell, 2010), providing evidence that ICA can indeed effectively separate neural from non-neural signals and supporting its use in this study. Taking it a step further, recently Jenson and colleagues used ICA to measure concurrently "in head" mu rhythm activity and "out of head" EMG muscle activity associated with lip movements, which provided a physiological reference to mark the onset and offset of bilabial syllable and multisyllabic speech productions (Jenson et al, 2014).…”