Paretic Hand Beta Band Intermuscular Coherence Increases Following non-Paretic Hand Exercise to Task-Failure in Chronic Stroke

Abstract

Intermuscular coherence (IMC), especially in the beta band, has been widely used as a non-invasive approach to estimate the strength of corticospinal connectivity. The corticospinal tract is frequently damaged as a result of stroke, which may impair the strength of corticospinal connectivity, particularly that contributing to manual dexterity. Here we investigated acute adaptations in IMC and manual dexterity in fifteen chronic stroke survivors and seven age-matched healthy controls who performed exercise to task-failure with their non-paretic hand (or dominant hand for healthy controls). Dexterity (measured by Box-and-Blocks Test, BBT) and IMC were tested at baseline, following exercise to task-failure, and every 45 min until 4 h after task-failure (7 times in total). At baseline, paretic hand beta and gamma band IMC were significantly reduced in stroke survivors (P's = 0.006). Additionally, at baseline paretic hand (or non-dominant hand for healthy controls) BBT performance and gamma band IMC revealed significant positive correlations in both stroke survivors (R² = 0.40, P = 0.010) and the whole sample (R² = 0.33, P = 0.005). Paretic hand BBT performance increased immediately and at 225 min after task-failure compared with baseline (P's = 0.017 and 0.014, respectively). Paretic hand beta band IMC increased immediately and remained significantly elevated at 45 min after task-failure (P = 0.045 and 0.005, respectively) while paretic hand gamma band IMC was increased at 135 min after task-failure (P = 0.051). Taken together, our results suggest increased strength of corticospinal connectivity to the paretic hand as an acute adaptation to non-paretic hand exercise to task-failure. Concurrent facilitation of paretic hand manual dexterity and strengthened ipsilesional corticospinal connectivity suggests this approach should be explored as a strategy to promote recovery of corticospinal connectivity and paretic hand motor function in neurorehabilitation.

Keywords

intermuscular coherence dexterity task-failure stroke rehabilitation

Introduction

Stroke is the leading cause of adult disability worldwide, with the most persistent motor impairments affecting the upper-extremity (UE) (Nowak et al., 2008). Among stroke survivors, 73–88% present with sensorimotor impairments affecting UE function (Cirstea & Levin, 2000). Impairment in precise control of hand and finger movements is the most common UE dysfunction following stroke (Xu et al., 2015) which severely impacts the ability to perform common tasks and activities of daily living (Cirstea & Levin, 2000; Xu et al., 2015).

Precise control of hand movements involves both direct and indirect pathways from the motor cortex to the neurons innervating hand muscles (Keenan et al., 2012; Lemon, 2008). Shared oscillatory input to spinal motoneurons is thought to coordinate hand muscle activity efficiently during motor tasks (Farmer, 1998; Keenan et al., 2012). Following stroke, the corticospinal tract is often damaged, which affects corticospinal connectivity (Krauth et al., 2019; Larsen et al., 2017) thus altering coherent oscillations in the synaptic inputs to motoneurons (Larsen et al., 2017). Impaired corticospinal connectivity thus potentially influences motor performance (Farmer, 1998), especially tasks requiring manual dexterity (Larsen et al., 2016; Larsen et al., 2017; Lemon, 2008).

Estimates of corticospinal connectivity have been determined non-invasively using coherence analysis, an approach that quantifies the magnitude of concurrent activity between signals in the frequency domain (Grosse et al., 2002; Larsen et al., 2017; Power et al., 2006). For example, intermuscular coherence (IMC) has been used to characterize the functional coupling in physiological activity of two muscles coactivated in the same task (Grosse et al., 2002; Kilner et al., 2000; Larsen et al., 2017). IMC offers several practical advantages as it requires only surface EMG, which is non-invasive, commonly available, and relatively efficient as testing procedures require only a few brief muscle contractions (Power et al., 2006). However, IMC has limitations, such as EMG electrode placement sensitivity, thus it is important to assure accurate EMG electrode placement for IMC testing. IMC correlates strongly with corticomuscular coherence (CMC), a more direct measurement of corticospinal connectivity (Grosse et al., 2002; Krauth et al., 2019).

Previous studies have established that coherence below 60 Hz is functionally important (Farmer et al., 1993; Grosse et al., 2002; Halliday, Conway, Farmer, & Rosenberg, 1999). The signal is typically separated into four frequency bands: delta (1–4 Hz), alpha (8–12 Hz), beta (15–30 Hz), and gamma (30–60 Hz) (Dai et al., 2017; McManus, Hu, Rymer, Suresh, & Lowery, 2016) argued to arise from different origins. The delta band is often attributed to the common modulation of motor unit firing rates and to be correlated with force variability (McManus et al., 2016). The alpha band depends on the feedback from muscle spindles and reflects rhythmical activities of the spinal reflex loop (Erimaki & Christakos, 2008). Beta band coherence is highest during isometric muscle contraction (S. N. Baker, Olivier, & Lemon, 1997; Kilner, Baker, Salenius, Hari, & Lemon, 2000) and attributed to oscillatory cortical and subcortical processes (Halliday et al., 1999; Lowery, Myers, & Erim, 2007). The gamma band is highest during dynamic movements (Omlor, Patino, Hepp-Reymond, & Kristeva, 2007) and related to attention (Engel, Fries, & Singer, 2001) and perception (Tallon-Baudry & Bertrand, 1999) reflective of cortical activities (Fang et al., 2009; Rossiter et al., 2013).

Correlation between CMC and IMC in the 15–30 Hz frequency band (i.e., beta band) (Kilner et al., 1999; Larsen et al., 2016; Perez et al., 2006) contributes to the premise that IMC offers comparable information to CMC regarding descending cortical drive (Baker et al., 1997; Brown et al., 1999; Grosse et al., 2002; Kamper et al., 2014; Kattla & Lowery, 2010) and can therefore be used as an accessible and effective approach to study the strength of corticospinal connectivity (Grosse et al., 2002; Larsen et al., 2017; Perez et al., 2006).

Both CMC and IMC are reduced acutely (3–5 days) following stroke (Larsen et al., 2017; von Carlowitz-Ghori et al., 2014), but begin to normalize in parallel with improved motor function 6 or more months following stroke onset (Krauth et al., 2019; von Carlowitz-Ghori et al., 2014; Zheng et al., 2018). Such observations suggest that increased beta band coherence in stroke survivors may be attributable to motor recovery mechanisms (Belardinelli et al., 2017; Krauth et al., 2019; Zheng et al., 2018). While a number of studies have investigated long-term changes in coherence following stroke (Belardinelli et al., 2017; Krauth et al., 2019; von Carlowitz-Ghori et al., 2014; Zheng et al., 2018), studies investigating acute adaptations in coherence in response to intervention or exercise in stroke survivors are lacking. In healthy individuals acute increases in beta (15–30 Hz) and gamma (30–60 Hz) band IMC have been suggested to reflect increased, or strengthened, corticospinal connectivity; such increases may relate to improved coordination of multiple concurrently active muscles (Kidgell et al., 2006; Kilner et al., 2002; Semmler et al., 2004) and improved efficiency of motor unit recruitment in selected muscles (Baker et al., 1997; Conway et al., 1995; Farmer et al., 1993; Perez et al., 2006) during motor tasks. On this premise, interventions that systematically induce acute increases in beta and gamma band IMC may be effective for inducing neuroplasticity in neurorehabilitation (Patten et al., 2025).

Directly training the paretic hand is often difficult for stroke survivors, especially for those with severe motor impairment including weakness and/or limited joint range of motion (Sun et al., 2018). Previous studies in healthy adults have reported a temporary increase in cortical excitability in the non-exercised hemisphere following unilateral motor training (Carroll et al., 2008; Hinder et al., 2011; Hortobagyi et al., 2011; Lee et al., 2010; Poh et al., 2013). Furthermore, unimanual force production is recognized to activate both hemispheres to some degree (Cramer et al., 1999; Perez & Cohen, 2008; Ruddy & Carson, 2013). Unilateral training of the non-paretic hand may therefore induce acute neural effects in the non-exercised ipsilesional hemisphere (IH) thus offering a means to promote paretic hand motor recovery following stroke. Our preliminary study in chronic stroke survivors (Patten et al., 2013) found an acute increase in IH cortical excitability, accompanied by facilitation of power grip strength in the non-exercised paretic hand after non-paretic hand exercise to task-failure; these effects persisted for at least 2 h following task-failure. However, recovery of grip strength and dexterity are often disassociated after stroke (Xu et al., 2015), thus it is important to understand how ipsilesional corticospinal connectivity and paretic hand manual dexterity are affected after non-paretic hand exercise to task-failure.

Here we used IMC to investigate acute adaptations in corticospinal connectivity in response to non-paretic hand exercise to task-failure in chronic stroke survivors. IMC and box-and-blocks test performance (BBT), an assessment of manual dexterity (Mathiowetz et al., 1985a), were monitored at baseline, immediately following, and at 45 min intervals until 4 h after task-failure. We hypothesized that BBT performance and beta and gamma band IMC in the non-exercised paretic hand would increase concurrently in response to exercise to task-failure reflecting strengthened oscillatory drive from cortical and subcortical processes.

Materials and Methods

Subjects

Fifteen chronic stroke survivors meeting the following criteria participated: (1) individuals who had suffered a single, monohemispheric stroke; (2) at least 6 months prior to enrollment; (3) ability to form and release a power grip with the paretic hand. Study exclusion criteria were: (1) presence of cognitive impairment as defined by inability to comprehend and follow three step commands; (2) corrected vision less than 20/20; (3) severe osteoarthritis or prior pathological fracture; (4) significant cardiovascular impairments contraindicative to exertion. All study procedures were approved by the University of Florida Health Science Center (IRB-01) or University of California, Davis Institutional Review Boards and carried out in conformity with the standards set by the Declaration of Helsinki. Prior to enrollment all participants provided written informed consent.

Clinical Assessments

Upper limb motor impairment was assessed in stroke survivors using the upper extremity component of the Fugl-Meyer Assessment (UE FMA) (Fugl-Meyer et al., 1975) and Modified Ashworth Scale (MAS) (Bohannon & Smith, 1987). The Montreal Cognitive Assessment (MoCA) was administered to screen for impaired cognitive function (Rossetti et al., 2011). The BBT, which assesses manual dexterity, was used as our primary behavioral outcome. Three BBT trials were performed in each hand at baseline, and one BBT trial was performed in the paretic hand at each time point after task-failure. We counted the number of participants showing BBT facilitation (i.e., BBT score post-task-failure greater than baseline) and the extent of facilitation at each time point after task-failure.

We tested maximal voluntary isometric power grip force (MVC) using two custom grip dynamometers, which were instrumented with load cells (LBMU-250, Interface, Scottsdale, AZ, USA). Power grip was performed while the arm was maintained in the standard position (Ding & Patten, 2018; Mathiowetz et al., 1985a). The analog signal was sampled (2000 Hz) and processed online (100 ms moving-window median) using Signal (Version 6.0, Cambridge Electronic Designs, Cambridge, UK). The processed force signal was displayed on a television screen (Samsung, TruSurround HD, Dolby Digital, 48 inches) to provide real-time feedback. At baseline, four MVC trials were performed in each hand; each trial followed by a two-minute rest interval. The maximal value of the filtered force trace was identified as MVC for each trial; these values were averaged to calculate MVC for each hand.

EMG Recordings

EMG data were recorded from the first dorsal interosseous (FDI) and opponens pollicis (OP) using surface electrodes placed according to the Surface EMG for Non-Invasive Assessment of Muscles (SENIAM) guidelines (Hermens et al., 2000). EMG signals were sampled at 2000 Hz using Signal (Version 6.0, Cambridge Electronic Designs, Cambridge, UK) and written to disc for offline analysis.

Intermuscular Coherence

We investigated IMC between FDI and OP. During IMC testing, participants performed 20 trials of unimanual power grip at 10% MVC with each hand. A force feedback trace and colored target zone (10 ± 2% MVC) were presented on a television screen. Participants were instructed to maintain force as stably as possible in the target zone; each trial lasted 5 s. Because coherence strength is enhanced during sustained, tonic contraction (Kilner et al, 1999), we evaluated force stability over the last 2 s of each force trace which is typically the most stable. Trials with a sudden change of force (i.e., > 6 N/s) within the 2 s window were excluded. Coherence strength may be also influenced by the number of trials included in the analysis (Larsen et al., 2017), thus we evaluated coherence across an equal number of trials for all individuals. After excluding trials involving >6 N/s change in force, 11–12 trials remained for two severely impaired stroke survivors. As a result, across all individuals we identifed the 10 trials with the smallest coefficient of variation (CV = SD / mean of grip force) during the two-second window. EMG signals were zeroed and filtered (4th order Butterworth, 10–450 Hz bandpass) before IMC calculation. The process of IMC calculation is illustrated in Figure 1.

Figure 1.

Example illustration of intermuscular coherence (IMC) calculation for a single individual. A) The last 2 s of each force trace (shaded area) were evaluated for the 10 trials with smallest force variability during this window. The squared coherence between FDI B) and OP C) EMG was calculated using the Welch's averaged, modified periodogram method. D) The magnitude of coherence for each band was quantified as illustrated by the shaded area below the curve. Vertical lines indicate the boundaries of the frequency bands of interest (delta, alpha, beta, gamma).

The Welch's averaged, modified periodogram method (Welch, 1967), was performed to calculate the squared coherence C_xy(f) between FDI and OP:

C_{x y} (f) = \frac{{| P_{x y} (f) |}^{2}}{P_{x x} (f) P_{y y} (f)}

(1)

Where P_xy(f) is the cross-spectrum mean of the two muscles, and P_xx(f) and P_yy(f) are, respectively, their autospectrum densities. We used the MATLAB function “mscohere” to calculate coherence-squared using 1024 sample segments tapered by a Hanning window overlapped by 75% to estimate the entire frequency spectrum (Dai et al., 2017).

Coherence was calculated separately in each frequency band: delta (1–4 Hz), alpha (8–12 Hz), beta (15–30 Hz), and gamma (30–60 Hz) (Dai et al., 2017; McManus et al., 2016). Coherence magnitude for each band (MBC_raw) was quantized by its mean band coherence:

M B C_{r a w} = \frac{\int_{f_{1}}^{f_{2}} C_{x y} (f) d f}{B}

(2)

Where Cxy(f) is the magnitude of the squared coherence, B is the width of one frequency band, and f₁ and f₂ are the lower and upper bounds of the corresponding band, respectively.

All MBC_raw data were transformed to Fisher's values before further computation or statistical analysis (Castronovo et al., 2015; Dai et al., 2017), using the following Fisher's z-transformation equation:

M B C = a r c t a n h \sqrt{M B C_{r a w}}

(3)

Non-paretic Hand Task-Failure Protocol

Participants performed intermittent submaximal isometric power grip with the non-paretic hand. Real-time force feedback superimposed on a shaded zone positioned at 30% age-referenced normative MVC (Mathiowetz et al., 1985b) was displayed on a television screen as a target (described above). Participants were instructed to grip, position the force trajectory in the target zone, hold (7 s), rest (3 s), and repeat (Figure 2). Non-paretic hand MVC was retested after every 10 submaximal contractions. This sequence was repeated until task-failure, defined as 30% reduction of MVC from baseline. This approach assured all participants fatigued to the same extent and the exercise was terminated using a consistent, objective criterion.

Figure 2.

Experimental protocol. Participants performed repeated submaximal power grip with the non-paretic hand to a criterion of 30% normative MVC (Mathiowetz et al., 1985b). MVC measurement was repeated after every 10 submaximal contractions until task-failure, defined as two consecutive MVCs ≥30% below baseline. Exercise targeted the non-paretic hand only. Outcome measures (BBT, IMC) were taken at baseline, immediately after task-failure, and every 45 min until 225 min after task-failure. IMC was measured in both hands at each time point. BBT was measured in both hands at baseline and the paretic hand after task-failure.

Experimental Procedures

Each experiment was conducted in a single session. Clinical assessments were performed at baseline, followed by EMG acquisition to determine baseline IMC. Non-paretic hand gripping to task-failure was initiated after IMC testing. Following task-failure, the BBT and IMC were retested immediately, and every 45 min until 4 h after task-failure (7 time points total) (Figure 2).

Statistical Analysis

Statistical analyses were performed in IBM SPSS Statistics 22 (SPSS Inc., Chicago, IL, USA). Data were tested using the Kolmogorov-Smirnov test and found to be normally distributed. Statistical significance was established at P < 0.05. Mixed design [Hand (2) × Group (2)] ANOVA, with repeated measures on Hand, was used to analyze IMC at baseline. Two-way repeated-measures [Hand (2) × Time (7)] ANOVA was used to analyze IMC, and one-way repeated-measures [Time (7)] ANOVA was used to analyze paretic or non-dominant hand BBT. Sphericity was tested using Mauchly's test and the Greenhouse-Geisser correction was applied when non-sphericity occurred. Based on suggestions of current statistical literature, a non-significant ANOVA could still produce a significant pairwise difference in a post-hoc test (i.e., Bonferroni test), making it valid to perform post-hoc analysis in the presence of a non-significant F-test (Howell, 2010; Hsu, 1996; Midway et al., 2020; Ruxton & Beauchamp, 2008; Ryan, 1959; Wilcox, 1987). Therefore, we performed post-hoc comparisons of IMC and BBT at each time point after task-failure compared to baseline regardless of F-test results. Following Bonferroni adjustment, statistical significance was established at P < 0.0083. Pearson correlation was used to investigate the relationships between motor impairment, BBT, and baseline IMC. Raw P values are presented.

Results

Fifteen individuals (11 male), mean age 68.4 (SD 8.3, range 53–83) years, with chronic (mean 6.0, SD 4.0, range 0.6–14.7 years) stroke and 7 age-matched (4 male) healthy controls, mean age 66.2 (SD 5.3, range 59–75 years) participated. Stroke lesion locations included: cortical (3), subcortical (7), and mixed cortical and subcortical (5). Upper-extremity motor impairment was moderately severe [UE FMA 47.5 / 66 (SD 13.4, range 21–64) and MAS 3.5 / 28 (SD 6.4, range 0–17)]. All participants revealed normal cognitive functioning for their age based on the MoCA [mean 26.8 / 30 (SD 1.9, range 22–30)].

Behavioral Adaptations

In stroke survivors, the ANOVA model testing BBT did not show a significant main effect of Time (F_3.4,47.2 = 1.887, P = 0.138). However, post-hoc analysis revealed that paretic hand BBT performance increased immediately and at 225 min after task-failure compared to baseline (P's = 0.017 and 0.014, respectively) (Figure 3A). In detail, 11 stroke survivors showed BBT facilitation: 11/15 immediately (range: 0.3–8.3 blocks); 8/15 at 45 min (range: 0.3–3.3 blocks); 7/15 at 90 min (range: 1.3–10.3 blocks); 6/15 at 135 min (range: 2.3–8.3 blocks); 8/15 at 180 min (range: 0.7–6.0 blocks); and 11/15 at 225 min (range: 0.3–6.3 blocks) after task-failure. In heathy controls, there was no significant change in BBT after task-failure (P's > 0.05) (Figure 3B).

Figure 3.

Non-exercised hand BBT facilitation after task-failure. Data presented are group mean ± SEM. * indicates significant differences compared with baseline (P < 0.05). At the group level, BBT in the paretic hand of stroke survivors: A) tended to be increased immediately and at 225 min, while BBT performance remained stable in the non-exercised hand of healthy controls B) after task-failure.

Intermuscular Coherence

Baseline: In the beta band, the ANOVA model revealed a significant main effect of Hand (F_{1, 20} = 8.33, P = 0.009). Post-hoc analysis revealed that, IMC was significantly greater in the non-paretic, compared with the paretic, hand (P = 0.021) in stroke survivors but not significantly different between hands in healthy controls (P > 0.05) (Figure 4). In the gamma band, the ANOVA model did not reveal a significant main effect of Hand (F_{1, 20} = 1.76, P = 0.200) or a Hand × Band Interaction (F_{1, 20} = 1.86, P = 0.188). However, post-hoc analysis revealed that in stroke survivors, IMC was significantly greater in the non-paretic, compared with the paretic, hand (P = 0.027), but in healthy controls was not significantly different between hands (P > 0.05). In the delta and alpha bands, no significant differences were detected between Hand or Group (P's > 0.05). No significant differences in IMC were detected between stroke and healthy controls in either hand in any band (P's > 0.05).

Figure 4.

Baseline IMC. * indicates significant differences (P < 0.05). In stroke survivors A), beta and gamma band IMC were significantly lower in the paretic, compared with the non-paretic, hand while delta and alpha band IMC revealed no significant differences between hands. In healthy controls, B) IMC was similar between hands across all bands.

After task-failure: For delta band IMC in stroke survivors, the ANOVA model revealed no significant main effects (Hand: F_1,15 = 3.86, P = 0.068; Time: F_3.9,58.2 = 1.60, P = 0.189) or Hand ×Time Interaction (F_3.5,52.6 = 1.00, P = 0.407). However, post-hoc analysis revealed that non-paretic hand IMC increased at 45 and 135 min after task-failure (P's = 0.026 and 0.014, respectively) (Figure 5A). In the alpha band, the ANOVA model revealed a significant main effect of Time (F_3.3,46.6 = 2.76, P = 0.047). Post-hoc analysis revealed that non-paretic hand IMC was significantly increased immediately, at 45 and 225 min after task-failure (P's = 0.001, 0.005 and 0.004, respectively), and appeared to be increased at 90, 135 and 180 min after task-failure (P's = 0.015, 0.012 and 0.049, respectively) (Figure 5B). In the beta band, the ANOVA model revealed significant main effects of Hand (F_1,14 = 10.41, P = 0.006) and Time (F_6,84 = 2.52, P = 0.027). Post-hoc analysis revealed significantly greater non-paretic hand IMC compared with the paretic hand across all timepoints (P = 0.006). Furthermore, non-paretic hand IMC was significantly increased at 45 and 225 min after task-failure (P's = 0.004 and 0.007, respectively) and appeared to be increased at 135 min after task-failure (P = 0.046). In the non-exercised paretic hand, IMC appeared to increase immediately after task-failure (P = 0.045), and significantly increased at 45 min after task-failure (P = 0.005) (Figure 5C). In the gamma band, the ANOVA model revealed significant main effects of Hand (F_1,14 = 10.71, P = 0.006). Post-hoc analysis revealed significantly greater non-paretic, compared with paretic, hand IMC regardless of timepoint (P = 0.006). Non-paretic hand IMC appeared to be increased at 45 and 225 min after task-failure (P's = 0.049 and 0.031, respectively). In the non-exercised paretic hand, IMC appeared to be increased at 135 min after task-failure (P = 0.051) (Figure 5D). In healthy controls, no significant differences in IMC were detected between hands or across time points after task-failure (P's > 0.05) (Figure 6).

Figure 5.

Intermuscular coherence (IMC) before and after task-failure, stroke survivors. Data presented are group mean ± SEM. * indicates significant differences compared with baseline (P < 0.008). Φ indicates differences compared with baseline approaching statistical significance (P < 0.05). A) Delta band IMC appeared to be increased in the exercised non-paretic hand at 45 and 135 min after TF. B) Alpha band IMC increased significantly in exercised non-paretic hand immediately, at 45 and 225 min after TF, and appeared to be increased at other timepoints after TF. C) In the non-paretic hand, beta band IMC increased significantly at 45 and 225 min after TF and appeared to be increased at 135 min. In the paretic hand, beta band IMC increased progressively after TF, appeared to be increased at 45 min and reached statistical significance at 90 min after TF, before returning gradually towards baseline. D) In the non-paretic hand, gamma band IMC appeared to be increased at 45 and 225 min after TF. In the paretic hand, IMC appeared to be increased at 135 min after TF.

Figure 6.

Intermuscular coherence (IMC) before and after task-failure, healthy controls. Data presented are group mean ± SEM. Following TF no significant differences in IMC were detected in any band in either hand of healthy controls.

Correlation Analysis

Significant positive correlations were revealed between baseline paretic hand BBT performance and gamma band IMC (R² = 0.40, P = 0.01) in stroke survivors (Figure 7A) and in the whole sample (combined stroke and healthy controls) (R² = 0.33, P = 0.005) (Figure 7B).

Figure 7.

Baseline IMC and BBT performance were correlated. There was significant positive correlation between Gamma band IMC and BBT revealed a significant positive correlation in both stroke (A) and the whole sample (B), suggesting behavioral and clinical relevance of IMC.

Discussion

The current study investigated acute adaptations in corticospinal connectivity using inter-muscular coherence. In response to non-paretic hand exercise to task-failure, we observed acutely increased beta and gamma band IMC accompanied by improved BBT performance in the non-exercised paretic hand of stroke survivors. At baseline, beta and gamma band IMC was reduced in the paretic hand of stroke survivors and the magnitude of gamma band IMC correlated with BBT performance. To our knowledge, this is the first study to investigate acute adaptations in IMC in the non-exercised limb following unilateral motor training in stroke survivors. These findings offer potential clinical utility.

Reduced Baseline Paretic Hand Beta and Gamma Band IMC in Stroke Survivors

IMC is considered to reflect the common oscillatory drive to motor neurons from last-order branches of corticospinal tract fibers (Farmer et al., 1993; Hansen and Nielsen, 2004; Larsen et al., 2016; Larsen et al., 2017). Beta and gamma band IMC arise, at least partially, from cortical origin, increasing the likelihood of impairment due to stroke (Farmer et al., 1993; Halliday et al., 1999; Lowery et al., 2007; Fang et al., 2009; Rossiter et al., 2013). Consistent with previous studies (Farmer et al., 1993; Mima et al., 2001; Fang et al., 2009; Rossiter et al., 2013), at baseline we observed reduced beta and gamma band IMC in the paretic hand of chronic stroke survivors. Damaged corticospinal connections following stroke may impair functional connectivity between the motor cortex and effector muscles (Krauth et al., 2019), or between direct and indirect pathways within the corticospinal tract (Lemon, 2008; Larsen et al., 2017). Discharge properties of corticospinal projection neurons, local interneurons in the motor cortex (Hansen and Nielsen, 2004), and spinal motor neurons (Thomas et al., 2002) would thus be altered affecting the oscillatory network properties responsible for generating IMC resulting in reduced paretic hand IMC (Larsen et al., 2017).

Different from beta and gamma bands, our results did not reveal significant between-hand differences in delta or alpha band IMC. This finding aligns with previous reports that delta and alpha band IMC are preserved in stroke survivors (Farmer et al., 1993; Fang et al., 2009; Yamanaka et al., 2023). Activity in the delta band is attributed to the common modulation of motor unit firing rates (McManus et al., 2016) and may be influenced by recruitment via feedback from muscle spindles and Golgi tendon organs (De Luca et al., 2009) while alpha band coherence depends on the feedback from muscle spindles and may result from rhythmical activities of the spinal reflex loop (Erimaki and Christakos, 2008). Delta and alpha band IMC are therefore unlikely to have cortical involvement (Erimaki and Christakos, 2008; De Luca et al., 2009; McManus et al., 2016) offering explanation for preserved paretic hand delta and alpha band IMC after stroke.

Baseline Gamma Band IMC is Related to BBT Performance

At baseline we observed significant correlations between paretic hand gamma band IMC and BBT performance both in stroke survivors and across all participants. The relationship between IMC or CMC and UE motor function in stroke survivors has been extensively studied indicating that reduced coherence and motor function tend to recover six, or more, months post-stroke (von Carlowitz-Ghori et al., 2014; Belardinelli et al., 2017; Zheng et al., 2018; Krauth et al., 2019). Correlation between IMC or CMC and paretic arm motor function is noted in some (Chen et al., 2018; Krauth et al., 2019), but not all (Halliday et al., 2003; Graziadio et al., 2012; Rossiter et al., 2013; Belardinelli et al., 2017; Dai et al., 2017; Larsen et al., 2017), studies. These inconsistent results may reflect heterogeneous characteristics of stroke survivors (e.g., lesion location, chronicity) and/or varied outcomes used to measure UE motor function (e.g., dexterity or muscle strength).

Differences in strength of association with BBT performance likely reflect the specific nature of beta and gamma band IMC. Gamma band IMC is associated with higher cortical processes (Engel et al., 2001) and is relevant to dynamic dexterous movement (Omlor et al., 2007), while beta band IMC is suggested to reflect activity important to force steadiness during isometric muscle contraction (S. N. Baker et al., 1997; Kilner et al., 2000). The BBT involves grasping, lifting, transferring, and releasing small objects (i.e., dynamic, dexterous hand and arm movements) (Mathiowetz et al., 1985a; Santisteban et al., 2016), thus significant association with gamma, but not beta, band IMC follows. This strong association across both stroke survivors and healthy individuals suggests a role for gamma band coherence as a potential biomarker for motor impairment and recovery, especially dexterity, following stroke.

Concurrent Acute Increases in Beta and Gamma Band IMC and BBT Performance in the non-Exercised Paretic Hand After Task-Failure

We observed acute increases in IMC after exercise to task-failure with the non-paretic hand. This finding is aligned with previous studies in healthy adults (Danna-Dos Santos et al., 2010; Kattla and Lowery, 2010; McManus et al., 2016), reporting increased coherent oscillations to the hand muscles arising from cortical and subcortical inputs onto the motoneuron pool in response to fatiguing exercise (Danna-Dos Santos et al., 2010). More interesting, however, is our finding of increased beta and gamma band IMC in the non-exercised paretic hand of stroke survivors accompanied by improved paretic hand BBT performance. Both neural and behavioral adaptations were observed at multiple time points after non-paretic hand exercise to task- failure. These findings may stem from a general facilitation in the non-exercised side after non-paretic hand exercise to task-failure in stroke survivors.

No published study has investigated acute adaptations in coherence in the non-exercised side after unilateral exercise in stroke survivors. We note Perez et al. (Perez et al., 2006) studied healthy subjects following a 32-minute unilateral ankle visuo-motor skill training and reported no resulting acute changes in CMC or IMC in the untrained leg. Similarly, in the current study, we did not observe changes in IMC after task-failure in healthy individuals. Absence of IMC facilitation of the non-exercised side of healthy individuals after unilateral exercise argues against generalized facilitation as explanation for the non-exercised paretic side improvements we observed in stroke survivors. Instead, acute increases in paretic hand beta and gamma band IMC after task-failure point to improved corticospinal connectivity which could have positive effects on motor recovery post-stroke.

The specific mechanisms underlying the neural and behavioral facilitation in the non-exercised paretic hand following task-failure remain unclear. These adaptations could possibly be related to activity of inhibitory and facilitatory brain circuits in the non-exercised hemisphere (Baker and Baker, 2003; Hansen and Nielsen, 2004; Power et al., 2006). Several transcranial magnetic stimulation studies have investigated acute adaptations in the non-exercised hemisphere following unilateral exercise (Carroll et al., 2008; Takahashi et al., 2009; Lee et al., 2010; Hinder et al., 2011; Hortobagyi et al., 2011; Patten et al., 2013; Poh et al., 2013) reporting increased cortical excitability in the non-exercised hemisphere in both healthy adults (Carroll et al., 2008; Lee et al., 2010; Hinder et al., 2011; Hortobagyi et al., 2011; Poh et al., 2013) and chronic stroke survivors (Patten et al., 2013). Additionally, reduced short intracortical inhibition in the non-exercised hemisphere (Takahashi et al., 2009) and reduced interhemispheric inhibition from exercised to non-exercised hemisphere (Hortobagyi et al., 2011) have also been observed acutely in healthy adults following unilateral motor training. As beta and gamma band IMC are attributed to cortical origins (Farmer et al., 1993; Halliday et al., 1999; Lowery et al., 2007; Fang et al., 2009; Rossiter et al., 2013), they are likely to be influenced by such cortical adaptations. This premise is supported by observation of similar responses of cortical excitability and beta band IMC in response to transcranial direct current stimulation in healthy adults (Power et al., 2006).

Greater increases observed in beta vs. gamma band IMC after task-failure could be related to the specific nature of activity producing beta and gamma band coherence and the specific exercise protocol investigated. Beta band IMC is highest during isometric muscle contraction (S. N. Baker et al., 1997; Kilner et al., 2000), while gamma band IMC is highest during dynamic movement (Omlor et al., 2007). Our task-failure exercise protocol involved isometric grip, thus more pronounced adaptations in beta band IMC can be expected. Based on the specificity principle of experience-dependent plasticity (i.e., the nature of the training experience dictates the nature of the plasticity) (Kleim and Jones, 2008), it is not surprising that increased IMC favored the beta band, relative to the gamma band, following exercise to task-failure.

Of note, all previously reported neural effects in the non-exercised hemisphere were observed immediately after unilateral exercise (Carroll et al., 2008; Takahashi et al., 2009; Lee et al., 2010; Hinder et al., 2011; Hortobagyi et al., 2011; Patten et al., 2013; Poh et al., 2013). By contrast, we observed increased paretic hand beta band IMC at 45-90 minutes, and gamma band IMC at 135 minutes, after task-failure. Following motor training, changes in cortical excitability and synaptic plasticity are known to consolidate gradually and emerge over time before influencing observable motor output. This temporal decoupling is consistent with gradual consolidation of changes in cortical excitability and synaptic plasticity known to emerge over time after either contralateral (Classen et al., 1998; Muellbacher et al., 2002) or ipsilateral (Hinder et al., 2011; Poh et al., 2013) motor training.

Clinical Implications

Non-paretic hand exercise to task-failure may have clinical implications for promoting motor recovery following stroke. Directly training the paretic hand is difficult for stroke survivors, especially for severely impaired individuals. Although the adaptations we observed were temporary, non-paretic hand exercise to task-failure induced a period of 45 - 90 minutes during which increased strength of corticospinal connectivity to paretic hand muscles could potentially influence the efficacy of paretic hand motor training suggesting this approach could be used as a primer to traditional motor rehabilitation. In clinical practice, the patient/client could perform the task-failure protocol with minimal supervision or oversight of collaborative clinical support staff (e.g., PT or OT Assistant, rehabilitation aide) before transitioning to their regular rehabilitation session which could be timed to occur during the sustained temporal window of enhanced common oscillatory drive. Importantly, non-paretic hand exercise to task-failure is a straightforward paradigm that does not require high-technology equipment. The current study serves as a proof-of-concept and offers possible new directions for development of rehabilitation interventions for stroke survivors. Further studies are needed to test whether pairing the non-paretic hand task-failure paradigm with regular rehabilitation sessions will augment functional outcomes.

Limitations

Our study sample included only chronic stroke survivors, limiting generalizability to the population of individuals actively pursuing rehabilitation. Accordingly, we recommend future studies involving a larger number of stroke survivors, in various phases of stroke recovery to confirm and extend these findings. Because coherence is enhanced during tonic contraction (Kilner et al, 1999), we excluded trials involving >6N/s change during the last two seconds of sustained isometric force production. Following this screening step, we selected the ten trials with the lowest force variability applying this criterion to both stroke and control participants. While this may be a particularly stringent data inclusion criterion, our methodological process is objective and reproducible. A consideration for future research evaluating coherence in response to task-failure will be to perform iterative analyses of a larger number of high versus low variable force trials to determine the effect of these differences on coherence strength.

Conclusions

Here we investigated acute adaptations in IMC and motor behavior in response to non-paretic hand exercise to task-failure in chronic stroke survivors. At baseline, paretic hand beta and gamma band IMC were reduced compared to the non-paretic hand in stroke survivors and healthy controls. Furthermore, at baseline gamma band IMC and manual dexterity were positively correlated. Following non-paretic hand exercise to task-failure, beta and gamma band IMC increased in the non-exercised paretic hand with concurrent improvements in paretic hand manual dexterity. Our results suggest that this temporary increase in the strength of corticospinal connectivity to the paretic hand could be leveraged to promote paretic hand motor recovery following stroke.

Footnotes

Acknowledgements

We thank Laurent Kyle Garay for assistance of data collection and Dhaval Jivanji for assistance with data reduction.

Authors’ Contribution

CP and QD designed the experiment. QD and EP collected the data. QD and TM reduced and analysed the data. QD and CP interpreted the data. QD, CP, and EP wrote the manuscript.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Rehabilitation Research and Development Service, (grant number 1 IK6 RX003543-01, 1 IR21 RX001759-01A1).

Grants

This work was supported by VA Rehabilitation R&D N1759P and N9274S (Patten, PI) and University of Florida Graduate School Fellowship (Ding).

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

ORCID iDs

Qian Ding

Theresa E McGuirk

Carolynn Patten

References

Baker

M. R.

Baker

S. N.

(2003). The effect of diazepam on motor cortical oscillations and corticomuscular coherence studied in man. The Journal of Physiology, 546, 931–942. https://doi.org/10.1113/jphysiol.2002.029553

Baker

S. N.

Olivier

Lemon

R. N.

(1997). Coherent oscillations in monkey motor cortex and hand muscle EMG show task-dependent modulation. The Journal of Physiology, 501(Pt 1), 225–241. https://doi.org/10.1111/j.1469-7793.1997.225bo.x

Belardinelli

Laer

Ortiz

Braun

Gharabaghi

(2017). Plasticity of premotor cortico-muscular coherence in severely impaired stroke patients with hand paralysis. NeuroImage: Clinical, 14, 726–733. https://doi.org/10.1016/j.nicl.2017.03.005

Bohannon

R. W.

Smith

M. B.

(1987). Interrater reliability of a modified Ashworth scale of muscle spasticity. Physical Therapy, 67, 206–207. https://doi.org/10.1093/ptj/67.2.206

Brown

Farmer

S. F.

Halliday

D. M.

Marsden

Rosenberg

J. R.

(1999). Coherent cortical and muscle discharge in cortical myoclonus. Brain, 122(Pt 3), 461–472. https://doi.org/10.1093/brain/122.3.461

Carroll

T. J.

Lee

Hsu

Sayde

(2008). Unilateral practice of a ballistic movement causes bilateral increases in performance and corticospinal excitability. Journal of Applied Physiology, 104, 1656–1664. https://doi.org/10.1152/japplphysiol.01351.2007

Castronovo

A. M.

Negro

Conforto

Farina

(2015). The proportion of common synaptic input to motor neurons increases with an increase in net excitatory input. Journal of Applied Physiology, 119, 1337–1346. https://doi.org/10.1152/japplphysiol.00255.2015

Chen

Xie

Zhang

Chen

Cheng

Zhang

(2018). Abnormal functional corticomuscular coupling after stroke. NeuroImage: Clinical, 19, 147–159. https://doi.org/10.1016/j.nicl.2018.04.004

Cirstea

M. C.

Levin

M. F.

(2000). Compensatory strategies for reaching in stroke. Brain, 123(Pt 5), 940–953. https://doi.org/10.1093/brain/123.5.940

10.

Classen

Liepert

Wise

S. P.

Hallett

Cohen

L. G.

(1998). Rapid plasticity of human cortical movement representation induced by practice. Journal of Neurophysiology, 79, 1117–1123. https://doi.org/10.1152/jn.1998.79.2.1117

11.

Conway

B. A.

Halliday

D. M.

Farmer

S. F.

Shahani

Maas

Weir

A. I.

Rosenberg

J. R.

(1995). Synchronization between motor cortex and spinal motoneuronal pool during the performance of a maintained motor task in man. The Journal of Physiology, 489, 917–924. https://doi.org/10.1113/jphysiol.1995.sp021104

12.

Cramer

S. C.

Finklestein

S. P.

Schaechter

J. D.

Bush

Rosen

B. R.

(1999). Activation of distinct motor cortex regions during ipsilateral and contralateral finger movements. Journal of Neurophysiology, 81, 383–387. https://doi.org/10.1152/jn.1999.81.1.383

13.

Dai

Suresh

N. L.

Suresh

A. K.

Rymer

W. Z.

(2017). Altered motor unit discharge coherence in paretic muscles of stroke survivors. Frontiers in Neurology, 8, 202. https://doi.org/10.3389/fneur.2017.00202

14.

De Luca

C. J.

Gonzalez-Cueto

J. A.

Bonato

Adam

(2009). Motor unit recruitment and proprioceptive feedback decrease the common drive. Journal of Neurophysiology, 101, 1620–1628. https://doi.org/10.1152/jn.90245.2008

15.

Ding

Patten

(2018). External biomechanical constraints impair maximal voluntary grip force stability post-stroke. Clinical Biomechanics, 57, 26–34. https://doi.org/10.1016/j.clinbiomech.2018.06.001

16.

Engel

A. K.

Fries

Singer

(2001). Dynamic predictions: Oscillations and synchrony in top-down processing. Nature Reviews Neuroscience, 2, 704–716. https://doi.org/10.1038/35094565

17.

Erimaki

Christakos

C. N.

(2008). Coherent motor unit rhythms in the 6–10 hz range during time-varying voluntary muscle contractions: Neural mechanism and relation to rhythmical motor control. Journal of Neurophysiology, 99, 473–483. https://doi.org/10.1152/jn.00341.2007

18.

Fang

Daly

J. J.

Sun

J. Y.

Hvorat

Fredrickson

Pundik

Sahgal

Yue

G. H.

(2009). Functional corticomuscular connection during reaching is weakened following stroke. Clinical Neurophysiology, 120, 994–1002. https://doi.org/10.1016/j.clinph.2009.02.173

19.

Farmer

S. F.

(1998). Rhythmicity, synchronization and binding in human and primate motor systems. The Journal of Physiology, 509, 3–14. https://doi.org/10.1111/j.1469-7793.1998.003bo.x

20.

Farmer

S. F.

Bremner

F. D.

Halliday

D. M.

Rosenberg

J. R.

Stephens

J. A.

(1993). The frequency content of common synaptic inputs to motoneurones studied during voluntary isometric contraction in man. The Journal of Physiology, 470, 127–155. https://doi.org/10.1113/jphysiol.1993.sp019851

21.

Fugl-Meyer

A. R.

Jaasko

Leyman

Olsson

Steglind

(1975). The post-stroke hemiplegic patient. 1. a method for evaluation of physical performance. Scandinavian Journal of Rehabilitation Medicine, 7, 13–31. https://doi.org/10.2340/1650197771331

22.

Graziadio

Tomasevic

Assenza

Tecchio

Eyre

J. A.

(2012). The myth of the ‘unaffected’ side after unilateral stroke: Is reorganisation of the non-infarcted corticospinal system to re-establish balance the price for recovery? Experimental Neurology, 238, 168–175. https://doi.org/10.1016/j.expneurol.2012.08.031

23.

Grosse

Cassidy

M. J.

Brown

(2002). EEG-EMG, MEG-EMG and EMG-EMG frequency analysis: Physiological principles and clinical applications. Clinical Neurophysiology, 113, 1523–1531. https://doi.org/10.1016/S1388-2457(02)00223-7

24.

Halliday

D. M.

Conway

B. A.

Christensen

L. O. D.

Hansen

N. L.

Petersen

N. P.

Nielsen

J. B.

(2003). Functional coupling of motor units is modulated during walking in human subjects. Journal of Neurophysiology, 89, 960–968. https://doi.org/10.1152/jn.00844.2002

25.

Halliday

D. M.

Conway

B. A.

Farmer

S. F.

Rosenberg

J. R.

(1999). Load-independent contributions from motor-unit synchronization to human physiological tremor. Journal of Neurophysiology, 82, 664–675. https://doi.org/10.1152/jn.1999.82.2.664

26.

Hansen

N. L.

Nielsen

J. B.

(2004). The effect of transcranial magnetic stimulation and peripheral nerve stimulation on corticomuscular coherence in humans. The Journal of Physiology, 561, 295–306. https://doi.org/10.1113/jphysiol.2004.071910

27.

Hermens

H. J.

Freriks

Disselhorst-Klug

Rau

(2000). Development of recommendations for SEMG sensors and sensor placement procedures. Journal of Electromyography and Kinesiology, 10, 361–374. https://doi.org/10.1016/S1050-6411(00)00027-4

28.

Hinder

M. R.

Schmidt

M. W.

Garry

M. I.

Carroll

T. J.

Summers

J. J.

(2011). Absence of cross-limb transfer of performance gains following ballistic motor practice in older adults. Journal of Applied Physiology, 110, 166–175. https://doi.org/10.1152/japplphysiol.00958.2010

29.

Hortobagyi

Richardson

S. P.

Lomarev

Shamim

Meunier

Russman

Dang

Hallett

(2011). Interhemispheric plasticity in humans. Medicine & Science in Sports & Exercise, 43, 1188–1199. https://doi.org/10.1249/MSS.0b013e31820a94b8

30.

Howell

D. C.

(2010). Statistical methods for psychology, 8 Edition. Cengage Wadsworth.

31.

Hsu

J. C.

(1996). Multiple comparisons : theory and methods, 1st Ed.. Chapman & Hall.

32.

Kamper

D. G.

Fischer

H. C.

Conrad

M. O.

Towles

J. D.

Rymer

W. Z.

Triandafilou

K. M.

(2014). Finger-thumb coupling contributes to exaggerated thumb flexion in stroke survivors. Journal of Neurophysiology, 111, 2665–2674. https://doi.org/10.1152/jn.00413.2013

33.

Kattla

Lowery

M. M.

(2010). Fatigue related changes in electromyographic coherence between synergistic hand muscles. Experimental Brain Research, 202, 89–99. https://doi.org/10.1007/s00221-009-2110-0

34.

Keenan

K. G.

Massey

W. V.

Walters

T. J.

Collins

J. D.

(2012). Sensitivity of EMG-EMG coherence to detect the common oscillatory drive to hand muscles in young and older adults. Journal of Neurophysiology, 107, 2866–2875. https://doi.org/10.1152/jn.01011.2011

35.

Kidgell

D. J.

Sale

M. V.

Semmler

J. G.

(2006). Motor unit synchronization measured by cross-correlation is not influenced by short-term strength training of a hand muscle. Experimental Brain Research, 175, 745–753. https://doi.org/10.1007/s00221-006-0724-z

36.

Kilner

J. M.

Alonso-Alonso

Fisher

Lemon

R. N.

(2002). Modulation of synchrony between single motor units during precision grip tasks in humans. The Journal of Physiology, 541, 937–948. https://doi.org/10.1113/jphysiol.2001.013305

37.

Kilner

J. M.

Baker

S. N.

Salenius

Hari

Lemon

R. N.

(2000). Human cortical muscle coherence is directly related to specific motor parameters. The Journal of Neuroscience, 20, 8838–8845. https://doi.org/10.1523/JNEUROSCI.20-23-08838.2000

38.

Kilner

J. M.

Baker

S. N.

Salenius

Jousmaki

Hari

Lemon

R. N.

(1999). Task-dependent modulation of 15–30 hz coherence between rectified EMGs from human hand and forearm muscles. The Journal of Physiology, 516(Pt 2), 559–570. https://doi.org/10.1111/j.1469-7793.1999.0559v.x

39.

Kleim

J. A.

Jones

T. A.

(2008). Principles of experience-dependent neural plasticity: Implications for rehabilitation after brain damage. Journal of Speech, Language, and Hearing Research : JSLHR, 51, S225–S239. https://doi.org/10.1044/1092-4388(2008/018)

40.

Krauth

Schwertner

Vogt

Lindquist

Sailer

Sickert

Lamprecht

Perdikis

Corbet

Millan

Hinrichs

Heinze

H.-J.

Sweeney-Reed

C. M.

(2019). Cortico-Muscular Coherence Is Reduced Acutely Post-stroke and Increases Bilaterally During Motor Recovery: A Pilot Study. Frontiers in neurology, 10. https://doi.org/10.3389/fneur.2019.00126

41.

Larsen

L. H.

Jensen

Christensen

M. S.

Lundbye-Jensen

Langberg

Nielsen

J. B.

(2016). Changes in corticospinal drive to spinal motoneurones following tablet-based practice of manual dexterity. Physiological Reports, 4. https://doi.org/10.14814/phy2.12684

42.

Larsen

L. H.

Zibrandtsen

I. C.

Wienecke

Kjaer

T. W.

Christensen

M. S.

Nielsen

J. B.

Langberg

(2017). Corticomuscular coherence in the acute and subacute phase after stroke. Clinical Neurophysiology, 128, 2217–2226. https://doi.org/10.1016/j.clinph.2017.08.033

43.

Lee

Hinder

M. R.

Gandevia

S. C.

Carroll

T. J.

(2010). The ipsilateral motor cortex contributes to cross-limb transfer of performance gains after ballistic motor practice. Journal of Physiology, 588, 201–212. https://doi.org/10.1113/jphysiol.2009.183855

44.

Lemon

R. N.

(2008). Descending pathways in motor control. Annual Review of Neuroscience, 31, 195–218. https://doi.org/10.1146/annurev.neuro.31.060407.125547

45.

Lowery

M. M.

Myers

L. J.

Erim

(2007). Coherence between motor unit discharges in response to shared neural inputs. Journal of Neuroscience Methods, 163, 384–391. https://doi.org/10.1016/j.jneumeth.2007.03.011

46.

Mathiowetz

Kashman

Volland

Weber

Dowe

Rogers

(1985b). Grip and pinch strength: Normative data for adults. Archives of Physical Medicine and Rehabilitation, 66(2), 69–74. https://www.ncbi.nlm.nih.gov/pubmed/3970660

47.

Mathiowetz

Volland

Kashman

Weber

(1985a). Adult norms for the box and block test of manual dexterity. The American Journal of Occupational Therapy, 39, 386–391. https://doi.org/10.5014/ajot.39.6.386

48.

McManus

X. G.

Rymer

W. Z.

Suresh

N. L.

Lowery

M. M.

(2016). Muscle fatigue increases beta-band coherence between the firing times of simultaneously active motor units in the first dorsal interosseous muscle. Journal of Neurophysiology, 115, 2830–2839. https://doi.org/10.1152/jn.00097.2016

49.

Midway

Robertson

Flinn

Kaller

(2020). Comparing multiple comparisons: Practical guidance for choosing the best multiple comparisons test. PeerJ, 8, e10387. https://doi.org/10.7717/peerj.10387

50.

Mima

Toma

Koshy

Hallett

(2001). Coherence between cortical and muscular activities after subcortical stroke. Stroke, 32, 2597–2601. https://doi.org/10.1161/hs1101.098764

51.

Muellbacher

Ziemann

Wissel

Dang

Kofler

Facchini

Boroojerdi

Poewe

Hallett

(2002). Early consolidation in human primary motor cortex. Nature, 415, 640–644. https://doi.org/10.1038/nature712

52.

Nowak

D. A.

Grefkes

Dafotakis

Eickhoff

Kust

Karbe

Fink

G. R.

(2008). Effects of low-frequency repetitive transcranial magnetic stimulation of the contralesional primary motor cortex on movement kinematics and neural activity in subcortical stroke. Archives of Neurology, 65, 741–747. https://doi.org/10.1001/archneur.65.6.741

53.

Omlor

Patino

Hepp-Reymond

M. C.

Kristeva

(2007). Gamma-range corticomuscular coherence during dynamic force output. NeuroImage, 34, 1191–1198. https://doi.org/10.1016/j.neuroimage.2006.10.018

54.

Patten

Neudorf

D. S.

Nguyen RM

K. L. N.

(2025). Stroke. In Nielsen

J. B.

(Eds.), Neuroplasticity-based neurorehabilitation (pp. 259–274). Springer.

55.

Patten

White

Lodha

(2013). Unilateral activation of the less affected limb to task failure facilitates the ipsilesional hemisphere post-stroke. Soc for Neurosci.

56.

Perez

M. A.

Cohen

L. G.

(2008). Mechanisms underlying functional changes in the primary motor cortex ipsilateral to an active hand. The Journal of Neuroscience, 28, 5631–5640. https://doi.org/10.1523/JNEUROSCI.0093-08.2008

57.

Perez

M. A.

Lundbye-Jensen

Nielsen

J. B.

(2006). Changes in corticospinal drive to spinal motoneurones following visuo-motor skill learning in humans. The Journal of Physiology, 573, 843–855. https://doi.org/10.1113/jphysiol.2006.105361

58.

Poh

Riek

Carroll

T. J.

(2013). Ipsilateral corticospinal responses to ballistic training are similar for various intensities and timings of TMS. Acta Physiologica, 207, 385–396. https://doi.org/10.1111/apha.12032

59.

Power

H. A.

Norton

J. A.

Porter

C. L.

Doyle

Hui

Chan

K. M.

(2006). Transcranial direct current stimulation of the primary motor cortex affects cortical drive to human musculature as assessed by intermuscular coherence. The Journal of Physiology, 577, 795–803. https://doi.org/10.1113/jphysiol.2006.116939

60.

Rossetti

H. C.

Lacritz

L. H.

Cullum

C. M.

Weiner

M. F.

(2011). Normative data for the Montreal cognitive assessment (MoCA) in a population-based sample. Neurology, 77, 1272–1275. https://doi.org/10.1212/WNL.0b013e318230208a

61.

Rossiter

H. E.

Eaves

Davis

Boudrias

M. H.

Park

C. H.

Farmer

Barnes

Litvak

Ward

N. S.

(2013). Changes in the location of cortico-muscular coherence following stroke. NeuroImage: Clinical, 2, 50–55. https://doi.org/10.1016/j.nicl.2012.11.002

62.

Ruddy

K. L.

Carson

R. G.

(2013). Neural pathways mediating cross education of motor function. Frontiers in Human Neuroscience, 7, 397. https://doi.org/10.3389/fnhum.2013.00397

63.

Ruxton

G. D.

Beauchamp

(2008). Time for some a priori thinking about post hoc testing. Behavioral Ecology, 19, 690–693. https://doi.org/10.1093/beheco/arn020

64.

Ryan

T. A.

(1959). Comments on orthogonal components. Psychological Bulletin, 56, 394–396. https://doi.org/10.1037/h0041280

65.

Santisteban

Teremetz

Bleton

J. P.

Baron

J. C.

Maier

M. A.

Lindberg

P. G.

(2016). Upper limb outcome measures used in stroke rehabilitation studies: A systematic literature review. Plos One, 11, e0154792. https://doi.org/10.1371/journal.pone.0154792

66.

Santos

A. D.-D.

Poston

Jesunathadas

Bobich

L. R.

Hamm

T. M.

Santello

(2010). Influence of fatigue on hand muscle coordination and EMG-EMG coherence during three-digit grasping. Journal of Neurophysiology, 104, 3576–3587. https://doi.org/10.1152/jn.00583.2010

67.

Semmler

J. G.

Sale

M. V.

Meyer

F. G.

Nordstrom

M. A.

(2004). Motor-unit coherence and its relation with synchrony are influenced by training. Journal of Neurophysiology, 92, 3320–3331. https://doi.org/10.1152/jn.00316.2004

68.

Sun

Ledwell

N. M. H.

Boyd

L. A.

Zehr

E. P.

(2018). Unilateral wrist extension training after stroke improves strength and neural plasticity in both arms. Experimental Brain Research, 236(7), 2009–2021. https://doi.org/10.1007/s00221-018-5275-6

69.

Takahashi

Maruyama

Maeda

Etoh

Hirakoba

Kawahira

Rothwell

J. C.

(2009). Unilateral grip fatigue reduces short interval intracortical inhibition in ipsilateral primary motor cortex. Clinical Neurophysiology, 120, 198–203. https://doi.org/10.1016/j.clinph.2008.10.003

70.

Thomas

C. K.

Butler

J. E.

Zijdewind

(2002). Patterns of pathological firing in human motor units. Advances in Experimental Medicine and Biology, 508, 237–244. https://doi.org/10.1007/978-1-4615-0713-0_29

71.

von Carlowitz-Ghori

Bayraktaroglu

Hohlefeld

F. U.

Losch

Curio

Nikulin

V. V.

(2014). Corticomuscular coherence in acute and chronic stroke. Clinical Neurophysiology, 125, 1182–1191. https://doi.org/10.1016/j.clinph.2013.11.006

72.

Welch

P. D.

(1967). Use of fast Fourier transform for estimation of power Spectra - a method based on time averaging over short modified periodograms. IEEE Transactions on Acoustics, Speech, and Signal Processing, 15, 70–73.

73.

Wilcox

R. R.

(1987). New designs in analysis of variance. Annual Review of Psychology, 38, 29–60. https://doi.org/10.1146/annurev.ps.38.020187.000333

74.

Haith

A. M.

Krakauer

J. W.

(2015). Motor control of the hand before and after stroke. In Kansaku

(Ed.), Clinical systems neuroscience (pp. 277–281). Springer.

75.

Yamanaka

Goto

Kawakami

Tateishi

Kondo

Nojima

(2023). Intermuscular coherence during quiet standing in sub-acute patients after stroke: An exploratory study. Brain Sciences, 13. https://doi.org/10.3390/brainsci13121640

76.

Zheng

Peng

G. H.

Wang

(2018). Using Corticomuscular Coherence to Reflect Function Recovery of Paretic Upper Limb after Stroke: A Case Study. Frontiers in Neurology, 8. https://doi.org/10.3389/fneur.2017.00728