Electromyography Exposes Heterogeneity in Muscle Co-Contraction following Stroke

Abstract

Walking after stroke is often described as requiring excessive muscle co-contraction, yet, evidence that co-contraction is a ubiquitous motor control strategy for this population remains inconclusive. Co-contraction, the simultaneous activation of agonist and antagonist muscles, can be assessed with electromyography (EMG) but is often described qualitatively. Here, our goal is to determine if co-contraction is associated with gait impairments following stroke. Fifteen individuals with chronic stroke and nine healthy controls walked on an instrumented treadmill at self-selected speed. Surface EMGs were collected from the medial gastrocnemius (MG), soleus (SOL), and tibialis anterior (TA) of each leg. EMG envelope amplitudes were assessed in three ways: (1) no normalization, (2) normalization to the maximum value across the gait cycle, or (3) normalization to maximal M-wave. Three co-contraction indices were calculated across each agonist/antagonist muscle pair (MG/TA and SOL/TA) to assess the effect of using various metrics to quantify co-contraction. Two factor ANOVAs were used to compare effects of group and normalization for each metric. Co-contraction during the terminal stance (TSt) phase of gait is not different between healthy controls and the paretic leg of individuals post-stroke, regardless of the metric used to quantify co-contraction. Interestingly, co-contraction was similar between M-max and non-normalized EMG; however, normalization does not impact the ability to resolve group differences. While a modest correlation is revealed between the amount of TSt co-contraction and walking speed, the relationship is not sufficiently strong to motivate further exploration of a causal link between co-contraction and walking function after stroke. Co-contraction does not appear to be a common strategy employed by individuals after stroke. We recommend exploration of alternative EMG analysis approaches in an effort to learn more about the causal mechanisms of gait impairment following stroke.

Lower Extremity Long-Latency Reflexes Differentiate Walking Function After Stroke

“…To our knowledge, this is the first time that LLRs have been investigated in parallel with walking function in chronic stroke. We chose to assess LLRs during isolated plantarflexion because many individuals with chronic stroke lack the capacity to activate the plantarflexors during walking, limiting their ability to generate enough propulsive force to walk efficiently (Marks and Hirschberg 1958; Jonkers et al 2009; Banks et al 2017). It is also challenging to accurately assess walking after stroke because many individuals cannot walk completely unassisted (i.e., without a brace, cane, or weight support) for long durations, all of which is required for thorough biomechanical analysis (Jorgensen et al 1995).…”

“…With regard to walking mechanics following stroke, it is recognized that many individuals have weakness of the lower extremity musculature which is typically associated with propulsive power deficits (Lamontagne et al 2002; Jonkers et al 2009; Banks et al 2017). The ankle joint power profile during walking typically reveals a large, distinct concentric peak of propulsive power during pre-swing, known as A2 (Winter 2009).…”

A Review of EMG Techniques for Detection of Gait Disorders

“…The shape and timing of EMG patterns were less similar to controls [16]. Among stroke patients, the EMG activity displayed heterogeneity in comparison with healthy individuals [17]. Nieuwboer et al [18] demonstrated that raw EMG and its linear envelopes of Parkinson's patients during freezing episodes displayed abnormal activity of TA and GM.…”

Surface Electromyography Normalization Affects the Interpretation of Muscle Activity and Coactivation in Children With Cerebral Palsy During Walking

“…This may have consequences for the normalized sEMG in the other phases of gait, which in turn could affect the interpretation of muscle activity. Therefore, in people with neurological conditions, it is also suggested to not normalize the sEMG data (13,(19)(20)(21)(22).…”

“…The different ways of normalizing sEMG data are used interchangeably when investigating muscle coactivation. Although the majority of the studies in the literature usually normalize the sEMG data prior to calculating the coactivation index (9,12,13,23,24), using absolute data has been suggested to be beneficial because it prevents unnecessary data transformation (19). Calculations are often done using a ratio, and therefore, normalizing the sEMG data prior may normalize the data twice.…”