Contralesional Crossed Facilitation Impairs Bimanual Force Coordination in Chronic Stroke Survivors with Moderate-to-Mild Motor Impairment

Abstract

Background:

After stroke, impaired bimanual coordination reduces quality of life, where precise coordination of force between arms is essential for daily activities. Effective coordination relies on balanced interhemispheric communication, which induces crossed facilitation between primary motor cortices (M1). Intracortical inhibition influences both crossed facilitation and bimanual coordination in neurologically intact individuals. This study examines whether GABA_B-mediated inhibition in ipsilesional M1 influences crossed facilitation from contralesional M1 and its relationship with bimanual coordination post-stroke.

Methods:

Thirteen chronic stroke participants performed dynamic and isometric bimanual force grip task. In the dynamic task, the paretic hand maintained 30% of maximal voluntary contraction while the non-paretic hand varied force levels (low-mid-high). Cross-covariance coefficient between hands measured interference from non-paretic hand to paretic hand. In the isometric task, transcranial magnetic stimulation assessed crossed facilitation via motor evoked potential (MEP) and intracortical inhibition via cortical silent period (CSP) in ipsilesional M1 under varying bimanual force conditions (paretic: rest, 5%, 30%; non-paretic: rest, 10%, 30%, 70%).

Results:

Results showed variable bimanual interference post-stroke, with greater interference in less impaired individuals and under high non-paretic force. Crossed facilitation increased with higher force asymmetry and lower paretic effort, particularly in less impaired participants, but became more variable as paretic effort increased (during PH 30%: NPH 70%). Under the high asymmetry condition, GABA_B-mediated disinhibition was most pronounced and greater crossed facilitation was associated with increased bimanual interference.

Conclusion:

These findings suggest that reduced inhibitory tone may contribute to the regulation of crossed facilitation, and bimanual coordination deficits may be driven by excessive crossed facilitation. Future work will examine other ipsilesional factors regulating crossed facilitation, as targeted asymmetric training and neuromodulation may help improve bimanual coordination in individuals with moderate-to-mild motor impairment.

Keywords

bimanual interference bimanual coordination crossed facilitation intracortical inhibition transcranial magnetic stimulation stroke

Introduction

Bimanual activities such as dressing, bathing, and eating are essential for daily living and functional independence. Asymmetric bimanual movements are particularly important, as each hand plays a distinct yet coordinated role (Guiard, 1987). For example, the non-dominant hand typically stabilizes an object, such as holding a jar, while the dominant hand performs dynamic actions, like unscrewing the lid (Woytowicz et al., 2018; Yuk et al., 2024). However, stroke survivors with upper-limb hemiparesis have difficulty incorporating the affected arm and hand into daily activities and often experience poor bimanual coordination (Molad & Levin, 2022), characterized by impairments in activation timing (Kantak et al., 2016; Lodha et al., 2012a), spatial positioning, and force control (Kang & Cauraugh, 2014; Nguyen et al., 2023; Patel & Lodha, 2019, 2020).

Bimanual coordination relies on dynamic interhemispheric interactions (Carson, 2005; Swinnen, 2002) including crossed facilitation, a phenomenon that voluntary contraction of one arm facilitates the contralateral corticospinal pathway (Bunday & Perez, 2012; Chiou et al., 2018). Symmetrical movements are considered neuronally efficient due to balanced crossed facilitation, which enhances interhemispheric coupling (Grefkes et al., 2008; Meister et al., 2010; Stinear & Byblow, 2002). In contrast, bimanual asymmetric movements require regulating crossed facilitation to allow partial uncoupling of limbs, enabling two limbs to perform different roles for a common goal (Bortoletto et al., 2021; Tazoe et al., 2013). Previous study has shown that extent of crossed facilitation creates behavioral challenges, particularly during tasks that require asymmetric force production, highlighting its role in bimanual behavior (Cunningham et al., 2017).

In neurologically intact individuals, when maintaining unequal forces with both hands, the hand producing the lower force exhibits greater error, and this interference at the behavioral level (i.e., bimanual interference) intensifies as the force asymmetry becomes more pronounced (Eliassen, 2000; Fling & Seidler, 2012b; Jeeves et al., 1988). Bimanual interference is regulated by interhemispheric and intracortical inhibition. Interhemispheric inhibition occurs when inputs from one hemisphere activate inhibitory interneurons in the opposite hemisphere via the corpus callosum (Avanzino et al., 2007; Lee et al., 2007; Liao et al., 2018; Perez & Cohen, 2008; Tazoe et al., 2013; Trompetto et al., 2004). Intracortical inhibition, via GABA_A and GABA_B circuits, suppresses cortical activity to preserve excitability balance, where GABA_B is thought to modulate GABA_A (Connelly et al., 2013; Werhahn et al., 1999). Here, we focus on GABA_B-mediated tonic inhibition, which stabilizes motor excitability, prevents premature movements, and supports independent limb control (Cirillo et al., 2018; Cowie et al., 2016; Di Lazzaro et al., 2006; McDonnell et al., 2006; Paulus et al., 2008) GABA_B activity can be examined using the TMS-evoked cortical silent period (CSP), where the early phase of the silent period reflects spinal inhibition and the later phase of the silent period reflects GABA_B-mediated intracortical inhibition (Chen et al., 1999; Paulus et al., 2008).

Stroke survivors exhibit reduced coordination when performing static and dynamic bimanual forces (Lodha et al., 2012a), along with greater force variability and greater task-dependent temporal deviation between the two hands (Kang & Cauraugh, 2014, 2015; Lai et al., 2019; Lodha et al., 2012b; Patel & Lodha, 2019). These deficits suggest difficulties in interlimb coordination, potentially due to underlying neuronal disruptions that lead to unregulated crossed facilitation and abnormal intracortical inhibition. In the chronic stage of stroke, beyond six months post-ictus, intracortical mechanisms remain altered, where they exhibit persistent intracortical disinhibition (Tang et al., 2019; Washabaugh et al., 2024). Such disruption of intracortical inhibition may impair the regulation of crossed facilitation.

In this study, we investigated bimanual interference during dynamic and isometric force production tasks in individuals with chronic stroke, building on prior studies that have characterized bimanual interference in able-bodied participants using similar paradigms (Cunningham et al., 2017; Fling & Seidler, 2012b; Perez & Cohen, 2008). To assess bimanual interference, participants maintained isometric force with the paretic hand while producing dynamic force with the non-paretic hand, resulting in varying degrees of bimanual asymmetry. We hypothesized that when force increases from the non-paretic hand, greater errors will be observed in the paretic hand. We also assessed crossed facilitation and GABA_B-mediated intracortical inhibition using TMS during unimanual and bimanual isometric tasks. We hypothesize that post-stroke disruption of GABA_B-mediated inhibitory control leads to excessive crossed facilitation, increased bimanual interference, resulting in reduced limb independence during bimanual asymmetric activities.

Methods

Participants and Clinical Evaluation of Impairment

Participants aged 18 to 90 years who had experienced their first-ever ischemic or hemorrhagic stroke with chronic (≥ 6 months) upper limb hemiparesis were included in the study. Eligible participants exhibited movements in the fingers, thumb, and wrist. Exclusion criteria were based on contraindications for TMS (Rossi et al., 2021) and included the presence of a cardiac pacemaker, metallic implants in the head, a history of recurrent seizures, or use of bupropion, which lowers the seizure threshold. The Institutional Review Board of the MetroHealth System approved of this study, and all participants provided written informed consent.

Upper-Extremity Fugl-Meyer (UEFM): UEFM was used to measure motor impairment of the paretic upper-limb (Fugl-Meyer et al., 1975). UEFM is a reliable and valid measure of post-stroke upper limb motor impairment (Duncan et al., 1983). The UEFM items consider synergy patterns, isolated strength, coordination, and hypertonia. Volitional movement of the upper limb (shoulder, elbow, forearm, wrist, and hand) is examined in and out of synergies. Each item is graded on a 3-point ordinal scale (0, cannot perform; 1, perform partially; and 2, perform fully) and summed to provide a maximum score of 66.

Experiment set-up and Experimental Tasks

Participants were seated in an armchair with relaxed shoulders, elbows positioned at a 90-degree angle, and hands resting naturally in a mid-prone position (palms facing inward). They held a strain gauge-based isometric hand dynamometer (Model HD-BTA, Vernier) in each hand using a power grip (Figure 1A). Force signals were sampled at 4000 Hz (PowerLab 8/35, ADInstruments Inc., Colorado Springs, CO, USA) and streamed in real-time from LabChart 8 Software (ADInstruments Inc., Colorado Springs, CO, USA) to a custom MATLAB program (MathWorks^®, Natick, MA, USA) to provide visual feedback during the bimanual force production tasks (described below). Surface electromyography (EMG) signals were recorded from the paretic and non-paretic flexor carpi radialis (FCR) using the PowerLab 8/35 system. EMG signals were amplified (×500), sampled at 4000 Hz and band-pass filtered online between 10 and 2000 Hz. LabChart 8 software was used for data collection, and all data was stored for offline analysis.

Figure 1.

A. Experiment Setup: A Schematic of a Participant Performing the Bimanual Force Production Task. For Neurophysiology Assessment, TMS Was Delivered at Ipsilesional M1. B. Dynamic Force Production Task: Participants Were Instructed to Match Their Grip Force to the Target Level, as Indicated by the Position of the Ball Relative to the Horizontal Bar. Participants Maintained the Grip Force in the Paretic Hand at 30% Level While Changing the Non-Paretic Hand Grip Force to Various Levels. C. Static Force Production Task: To Perform Neurophysiology Assessment in the Ipsilesional M1 Under Various Asymmetric Grip Force Conditions Between Two Hands, Force Targets Were Set to 12 Combinations of Bimanual Force. The Paretic Hand Was at Either Rest, 5%, or 30% Force Level While the Non-Paretic Hand Was at Either Rest, 10%, 30%, and 70% Force Level.

Participants performed two visually guided bimanual force production tasks: a dynamic-isometric bimanual coordination to evaluate bimanual interference and bimanual static force-production to evaluate neurophysiology. These tasks were programmed in MATLAB and presented on a television monitor. In this task, there is a red ball in the vertical box and a green target bar that horizontally crosses the vertical box (Figure 1A). The grip force measured from the hand dynamometer controlled the height of the red ball. Participants were instructed to match the force level for each hand indicated by the green target bar shown in the respective task box. To calibrate the target force individually, the maximal grip strength (i.e., maximal voluntary contraction (MVC)) was determined for each participant for both hands. Participants performed three MVC trials, each separated by a minimum of 30 s. After calculating the average MVC from these three MVC trials, the average MVC value was used to calculate the % target force in bimanual force production tasks.

Due to the potential for TMS to disrupt steady force output during the static task, neurophysiological and behavioral measurements were collected in separate but similar task contexts. Cortical excitability was assessed during static contractions that reflect key force conditions occurring during the dynamic task. While this approach does not capture moment-to-moment fluctuations in brain activity during movement, it provides more stable estimates of brain state under behaviorally relevant motor conditions.

Dynamic-Isometric Bimanual Coordination: We adapted a visuomotor task from previous motor skill learning studies (Lafe et al., 2023; Reis et al., 2009). In this task, participants viewed two horizontal bars on the screen representing ± 5% of their target MVC for each hand (Figure 1B). Participants were instructed to maintain a steady isometric grip force with their paretic hand at 30% of their MVC throughout the task. Simultaneously, they were guided to produce force levels with their non-paretic hand in a fixed sequence: 30%, 70%, 10%, 30%, 10%, and 30% of MVC. This sequence allowed for transitions through various force levels (low to high, high to low, or intermediate levels), where the target force changes every three seconds (total task duration: 21 s). The task was repeated 20 times, with at least 1-min rest periods, and more if needed, between trials to minimize fatigue. Participants were familiarized with the task prior to testing.

Bimanual Static Force Production Task: The paretic hand produced three force conditions (rest, 5%, and 30% of MVC), while the non-paretic hand produced four force conditions (rest, 10%, 30%, and 70% of MVC), resulting in a total of 12 combinations of bimanual force conditions (Figure 1C). While two hands were producing the targeted forces, TMS was applied to the ipsilesional primary motor cortex (M1) (see below for details). Similar to the dynamic force production task, participants viewed two horizontal bars on a screen, which indicated ±5% of their target MVC for each hand. They were instructed to keep a red ball within these bounds throughout the trial.

Transcranial Magnetic Stimulation

Single-pulse transcranial magnetic stimulation (Magstim 200², Magstim Company Ltd, Whitland, UK) was delivered using a Magstim D70² figure-of-eight coil to the ipsilesional primary motor cortex. All participants heads were registered to the Montreal Neurological Institute Template brain to stereotactically guide TMS (Brainsight Software, Rogue Research, Montreal, Canada). TMS-induced motor evoked potentials (MEPs) were recorded from the paretic flexor carpi radialis muscle. The TMS coil (figure of eight, D70², Magstim Company Ltd, Whitland, UK) was positioned tangential to the scalp, with the handle angled 45° backward and laterally to the midsagittal axis (posterior-anterior direction). To identify the motor “hotspot,” we located the scalp site that elicited a reliable MEP (≥ 50 µV in at least 5 out of 10 trials) with the lowest percent machine stimulator output (%MSO), defined as the resting motor threshold (rMT). Starting over the “hand-knob” on the MNI template, the coil position was adjusted with a 10 mm spatial resolution in all directions, with the %MSO initially reduced in larger steps before transitioning to 1% increments until the MEP was no longer reliable. Each time a new site required a lower %MSO with reliable MEPs present, it became the new center, and the process repeated until no lower-threshold site was found in all directions. If multiple sites produced reliable MEPs at the lowest %MSO, we selected the one with the greatest MEP consistency, stable MEP amplitudes, and/or visible muscle contraction of the FCR.

We measured MEPs from the ipsilesional primary motor cortex during the bimanual static force production task. We determined a suprathreshold %MSO for each participant that produced an MEP amplitude ∼50% of the maximal MEP of the FCR for each paretic hand condition (rest, 5%, and 30% MVC). The %MSOs were used for each respective condition throughout the experiment. For each force combination, ten TMS pulses were delivered at a frequency of ∼0.2 Hz. Several breaks were given to minimize fatigue.

Data Analysis

To investigate bimanual motor control and neurophysiological mechanisms, we analyzed three key aspects of performance: bimanual interference, crossed facilitation, and intracortical inhibition. Bimanual interference was assessed through the dynamic-isometric bimanual coordination task, examining both dynamic and static interactions between the paretic and non-paretic hand. Crossed facilitation and intracortical inhibition were examined through the bimanual static force production task. Crossed facilitation was evaluated by comparing MEP amplitudes across bimanual conditions relative to unimanual performance, providing changes in corticospinal excitability with changes in relative force of the non-paretic hand. Intracortical inhibition was examined using CSP duration, recorded during bimanual paretic hand isometric conditions at 30% MVC relative to the unimanual condition. Offline signal organization, processing, and analysis were performed using TMS Analysis Toolbox (Cunningham et al., 2021). The TMS Analysis Toolbox has a graphical user interface and allows users to organize large datasets and perform basic and advanced analyses of common TMS-related outcomes on individual or average signal TMS trials.

Bimanual Interference: Interference between the paretic and non-paretic hands during the dynamic–isometric bimanual coordination task was quantified using the zero-lag (without delay) cross-covariance coefficient (r), with values closer to 1 indicating stronger interference and values closer to 0 indicating weaker interference. Therefore, in this task, successful performance of the paretic hand, which was required to maintain at 30% MVC, was defined as minimal covariation, indicating stable force output in the paretic hand while allowing dynamic force modulation in the non-paretic hand. The cross-covariance analysis emphasizes temporal alignment of signal patterns between two signals (i.e., between limb force signals) and accounts for magnitude differences by assessing pattern similarity rather than absolute amplitude. We chose this analysis due to its strong relevance to our comparison between the non-paretic and paretic hand, which typically differ in force amplitude. This analysis captures the temporal similarity between two time-varying force signals, emphasizing the alignment of force-changes patterns overtime rather than the absolute magnitude of force. This allows for better comparison across the range of motor severity.

We first calculated the cross-covariance coefficient across the entire task to quantify overall bimanual interference. We then focused on segments where the non-paretic hand transitioned from a lower to a higher force target (10% → 30%, 10% → 70%, and 30% → 70%) to test whether interference varied with the magnitude of the non-paretic force grip change. Each segment was 3 s in duration, the time allotted for the non-paretic hand to adjust force, corresponding to the shaded regions in Figure 2A.

Figure 2.

A. Representative Trial Force Trajectory of Participant 4 (UEFM = 27) who Showed low Bimanual Correlation; B. Representative Trial Trajectory of Participant 10 (UEFM = 61) who Showed High Bimanual Correlation; C. A Linear Regression Between Upper Extremity Fugl Meyer Score and Whole Phase Correlation Between Force Profiles of NPH and PH; D. a box Plot Comparing Correlation Coefficient Between NPH Force Change Conditions: 10 to 30 is Smaller Change; 30 to 70 is mediocre Change; and 10 to 70 is Larger Change (Correlation of the Forces in the Shaded Areas in A). Higher the Correlation Coefficient, More Similar the Force Profile Between two Hands. Asterisk Indicates a Statistically Significant Difference (p < 0.05).

Crossed facilitation: To assess crossed facilitation, we measured MEPs from the paretic FCR during the bimanual static force production task. Each participant completed three conditions with the non-paretic hand at rest and the paretic hand at rest, 5%, or 30% MVC (i.e., rest|rest, 5%|rest, 30%|rest). For each of these paretic hand force levels, we tested three conditions where the non-paretic hand generated 10%, 30%, or 70% MVC, resulting in nine additional conditions. Ten MEPs were collected per condition and their peak-to-peak amplitudes averaged. For each paretic hand force level (rest, 5%, and 30% MVC), crossed facilitation was quantified as a ratio:

M E P R a t i o = \frac{M e a n M E P (P H (r e s t, 5 %, 30 %) | a c t i v e N P H (10 %, 30 %, 70 %)}{M e a n M E P (P H (r e s t, 5 %, 30 %) | N P H a t r e s t)}

This yielded nine MEP ratio values per participant. A ratio greater than 1 indicated crossed facilitation in the paretic hand due to activation of the non-paretic hand.

Intracortical Inhibition: Intracortical inhibition was assessed using the CSP, defined as the transient suppression of voluntary EMG activity in a contracting muscle following contralateral TMS. CSP analysis was performed for conditions in which the paretic hand maintained 30% MVC (30%|rest, 30%|10%, 30%|30%, 30%|70%), as 30% MVC produced a robust EMG signal to allow for sufficient EMG suppression. EMG signals were time-aligned to the TMS pulse and averaged across trials, then full-wave rectified. CSP onset was determined using the mean consecutive difference (MCD) threshold method (Garvey et al., 2001). The upper and lower variation limits were calculated from the pre-stimulus EMG (–110 to −10 ms) as the mean ± 2.66 × |MCD|, encompassing 95% of baseline activity. The lower limit was used to detect CSP onset within a 20–100 ms post-TMS window when the EMG signal first cross below the threshold, and multiple potential CSP offsets were similarly detected when EMG exceeded the threshold. Because EMG of patient populations often results in noisier signals, we used an adaptive moving MCD method to avoid detecting CSP offsets caused by brief EMG bursts. For each potential offset, the MCD-based threshold calculated from the following 100 ms had to remain above the original 2.66 × |MCD| threshold derived from the pre-stimulus period. This method, compared to the 50% variation limit over the subsequent 5 ms (Garvey et al., 2001), was more accurate for our dataset. All onsets and offsets were visually inspected for accuracy.

To account for the known relationship between CSP duration and MEP amplitude (Hupfeld et al., 2020; Orth & Rothwell, 2004), each participant's CSP duration was first normalized by dividing it by the corresponding MEP peak-to-peak amplitude:

N o r m a l i z e d C S P = \frac{C S P d u r a t i o n}{M E P a m p l i t u d e}

To assess modulation during bimanual conditions, normalized CSP values were then expressed as a ratiorelative to the unimanual condition (30% PH | NPH at rest):

C S P R a t i o = \frac{N o r m a l i z e d C S P (P H : 30 % | N P H : 10 %, 30 %, 70 %)}{N o r m a l i z e d C S P (P H : 30 % | N P H : r e s t)}

A ratio of 1 indicates no change from the unimanual condition, while values above or below 1 reflect increased or decreased intracortical inhibition due to bimanual force production, respectively.

Statistics

All analyses were conducted in RStudio (R version 4.4.3) using linear regression [lm] and linear mixed-effects models [lmer] where participants were included as random intercept. Residual normality was assessed via the Shapiro–Wilk test [shapiro.test], with log-transformation applied when assumptions were violated. Tukey-adjusted estimated marginal means were used for post-hoc comparisons. For the dynamic-isometric bimanual task, descriptive statistics were computed for the peak cross-covariation coefficient between hands. A linear regression tested the association between cross-covariation and motor impairment (UEFM). To identify transitions contributing most to bimanual interference, a mixed-effects model assessed the effect of transition condition (10–30%, 30–70%, 10–70% MVC), adjusting for UEFM. Crossed facilitation was analyzed using a mixed-effects model with log-transformed MEP ratio as the outcome. Fixed effects included paretic and non-paretic hand effort levels and their interaction, adjusting for UEFM. GABA_B-mediated inhibition was evaluated via CSP ratios across non-paretic hand force levels (10%, 30%, 70% MVC) during a constant 30% MVC effort by the paretic hand. Condition effects were tested using a mixed-effects model adjusting for UEFM. One sample t-test [test] determined whether CSP ratio at each level differed from a normalized value of 1. To examine the relationship between inhibition and crossed facilitation, a mixed-effects model was used with normalized MEP ratio as the outcome and fixed effects for CSP ratio, condition, their interaction, and UEFM; subject-level variability was modeled with random intercepts and slopes. Last, as an exploratory analysis, hierarchical linear regression tested whether cross-facilitation (MEP ratio) or intracortical inhibition (CSP ratio) predicted bimanual interference (cross-covariation coefficient) as a function of motor impairment. For each predictor, a baseline model was compared to a model including its interaction with UEFM [anova], and parameter estimates were obtained [summary]. R code (using Google Colab) and accompany data (.csv files) are available at https://github.com/CunninghamLab/Publication-Data.

Results

Demographics and Motor Impairment

Thirteen individuals with chronic upper-extremity hemiparesis following unilateral stroke participated in the study (Table 1). Briefly, the average age was 65 ± 10 years (range: 51–83), and three participants were female. Six had left-sided and seven had right-sided paresis. Eleven were pre-stroke right-hand dominant. UEFM scores were available for twelve participants (mean: 37 ± 13); one score was missing due to a therapist scheduling conflict. MEPs were present in eleven participants. Time since stroke averaged 63 ± 45 months (range: 15–160).

Table 1.

Demographic and Clinical Measures.

ID	Age/Sex	Race	Affected Side	Hand Dominance	Stroke type	Months since stroke	MEP	UEFM
1	51/M	White	L	R	Hemorrhagic	18	+	20
2	66/F	White	R	R	Hemorrhagic	45	+	20
3	63/M	White	R	R	Hemorrhagic	15	-	25
4	80/M	White	L	R	Ischemic	65	+	27
5	83/M	Black/African American	R	R	Ischemic	160	+	31
6	60/M	Black/African American	L	R	Hemorrhagic	90	+	40
7	75/M	White	L	L	Ischemic	96	+	40
8	75/M	White	R	R	Hemorrhagic	92	+	42
9	63/F	White	R	R	Ischemic	20	+	44
10	60/M	White	L	R	Ischemic	137	-	46
11	57/F	Black/African American	R	R	Hemorrhagic	54	+	51
12	62/M	White	L	L	Ischemic	71	+	61
13	56/M	White	R	R	Ischemic	38	+	-
	65.5 ± 9.8 /10(M) 3(F)	10 (white) 3 (Black/African American)	6(L)/ 7(R)	11(R)/2(L)	6(Hem)/7(Isch)	63.3±45.0	11(+)	37.3±12.8

All thirteen participants completed the dynamic-isometric bimanual coordination task, and cross-covariation coefficients between the paretic and non-paretic hands were calculated for all participants. One participant (13) was excluded from the motor impairment analysis due to a missing UEFM score. Two participants (3 and 10) were excluded from the crossed facilitation analysis due to absent MEP responses. Four participants (1, 3, 10, and 13) were excluded from the intracortical inhibition analysis due to unquantifiable CSP durations, caused by excessive background EMG (1 and 13) or absent MEPs (3 and 10). The remaining eight participants were included in the hierarchical regression models evaluating whether crossed facilitation, inhibition, and UEFM predict bimanual interference.

Bimanual Interference

During the dynamic–isometric bimanual task, paretic hand performance varied across participants, averaging 20 ± 8.2% MVC (range: 3.3–36.1% MVC; Suppl. Figure 2). The non-paretic hand achieved target levels of 14 ± 6%, 33 ± 5%, and 66 ± 5% MVC. Achieved paretic hand %MVC was evaluated as a covariate in all regression models but did not explain additional variance (p = 0.43, 0.71, and 0.74) and was thus excluded from final models.

UEFM score was not a significant predictor of bimanual interference (F(1, 10) = 1.57, p = 0.24, R² = 0.14). After removing one outlier (triangle in Figure 2C), however, the association strengthened: higher UEFM scores (indicating less motor impairment) were associated with greater bimanual interference (β = 0.01, F(1, 9) = 7.75, p = 0.021, R² = 0.46).

There was a significant main effect on bimanual interference during low to high transitions (F(2,22) = 10.27, p < 0.001). Post-hoc pairwise comparisons showed that interference was significantly lower during the 10–30% transition (−0.19 ± 0.23) compared to both 30–70% (0.14 ± 0.41, p = .01) and 10–70% (0.27 ± 0.33, p < 0.001). No difference was observed between the 30–70% and 10–70% transitions (p = 0.51) (Figure 2D).

Bimanual Static Force Task, Crossed Facilitation and Intracortical Inhibition

Bimanual static force production task: The paretic hand was maintained at 0.1 ± 0.6%, 11.7 ± 6.1%, and 31.2 ± 7.5% MVC across the three targeted force levels. At the 0% target, force was stable across non-paretic hand conditions (F(3, 36) = 1.17, p = 0.33). At the 5% target, force increased slightly (3.6 ± 2.4%) during 70% non-paretic effort. At the 30% target, the overall model was significant (F(3, 36) = 3.02, p = 0.042); however, force remained largely stable, with no significant pairwise differences and a marginal difference between the 10% and 70% conditions (p = 0.07; Suppl. Figure 3). The non-paretic hand was maintained at 0.0 ± 0.1%, 13.0 ± 5.7%, 31.2 ± 6.6%, and 64.9 ± 8.8% MVC (Suppl. Figure 3). To account for general variability in paretic hand performance, %MVC was initially included as a covariate in all regression models but did not explain additional variance (p = 0.68 [crossed facilitation], p = 0.62 [intracortical inhibition], p = .38 [interaction]) and was excluded from final models.

Crossed facilitation: There is a significant main effect of non-paretic hand effort (F(2, 77) = 24.96, p < 0.001) and paretic hand effort (F(2, 77) = 21.38, p < 0.001), as well as a significant interaction between non-paretic and paretic hand effort (F(4, 77) = 3.56, p = 0.01; Figure 3). UEFM was also a significant predictor (F(1, 77) = 16.15, p < 0.001), with greater motor function associated with increased crossed facilitation. Post-hoc comparisons showed that at 70% non-paretic effort, crossed facilitation was greater when the paretic hand was at rest compared to 5% MVC (p < 0.05) and 30% MVC (p < 0.001), and greater at 5% compared to 30% MVC (p < 0.001). At 30% non-paretic effort, crossed facilitation was significantly greater at rest compared to 30% MVC (p < 0.01). No significant differences were observed at lower non-paretic effort levels. Notably, MEP responses at 30% paretic effort during 70% nonparetic effort were highly variable (range: 0.6–1.6), indicating inter-individual differences in crossed facilitation modulation when the paretic hand was exerting 30% effort.

Figure 3.

A. Representative MEP in the PH at various Force Conditions. Faded MEP is When NPH was at Rest and Bold MEP is When NPH was at 70% Force Level. the Difference in MEP Amplitude Between two Overlapped Profiles Indicate Gradually Decreasing Crossed Facilitation as PH Produces Force. B. Group Level Comparison of the Crossed Facilitation Between 9 Combinations of Bimanual Force Conditions. MEP Ratio (MEP in Bimanual Condition/ MEP in Unimanual Condition) Greater Than 1 Indicates the Extent of Crossed Facilitation in the Ipsilesional M1. Asterisk Indicates a Statistically Significant Difference (p < 0.05).

Intracortical Inhibition: There is a significant main effect of condition on normalized CSP ratio (F(2, 23) = 3.76, p < 0.05; Figure 4A), while UEFM was not a significant predictor. Post-hoc comparisons showed significantly reduced inhibition at 70% MVC compared to 30% MVC (p < 0.05), with no other differences. One-sample tests against a reference value of 1 indicated significantly reduced inhibition at 70% MVC (p < 0.05), while ratios at 10% and 30% MVC did not differ from 1.

Figure 4.

A. Silent Period Ratio in the PH Comparing Three NPH Force Conditions (10% vs. 30% vs. 70%). B. A Linear Regression Between Silent Period Ratio and MEP Ratio in Each NPH Force Condition. Silent Period Ratio Greater Than 1 Indicates longer Silent Period (More Inhibition) and Less Than 1 Indicate Shorter Silent Period (Less Inhibition) in the Bimanual Condition Compared to the Unimanual Condition. MEP Ratio Greater Than 1 Indicates Greater MEP Amplitude (Greater Crossed Facilitation) and Less Than 1 Indicate Smaller MEP Amplitude (Smaller Crossed Facilitation) in the Bimanual Condition Compared to the Unimanual Condition. Asterisk Indicates a Statistically Significant Difference (p < 0.05). Cross Sign Indicates a Statistically Significant Difference from Ratio of 1 (a Dotted Line).

Crossed facilitation and Intracortical Inhibition Interaction: GABA_B-mediated intracortical inhibition significantly predicted crossed facilitation (β = –0.37, p < 0.05), with greater inhibition associated with reduced facilitation. There was no significant interaction with condition, and UEFM was not a significant covariate. The model showed a strong overall fit, with marginal r² = 0.69 and conditional r² = 0.89. Follow-up regressions by condition (controlling for UEFM) yielded r² values of 0.51 (p = 0.068) at 10% MVC, 0.85 at 30% MVC (p < 0.001), and 0.69 at 70% MVC (p < 0.01) (Figure 4B).

Intracortical Inhibition, Crossed Facilitation and Bimanual Interference

Given the high variability in crossed facilitation when the paretic limb exerted 30% effort during 70% non-paretic effort (range: 0.6–1.6), we conducted an exploratory analysis to identify predictors of bimanual interference under this condition. Crossed facilitation tended to show a positive association with bimanual interference (F(1,9) = 5.16, p = 0.0506, β = 0.39, R² = 0.36; Figure 5A), a noteworthy association despite not meeting the conventional threshold for statistical significance. Adding impairment did not explain additional variance (p = 0.19). Intracortical inhibition was not a significant predictor (p = 0.52, Figure 5B), and including impairment likewise did not explain additional variance (p = 0.22). To account for variability in paretic hand performance during the bimanual static force task, %MVC was added as a covariate, but it did not explain additional variance (p = 0.43).

Figure 5.

Linear Regression Between Bimanual Interference (BI) and A: Crossed Facilitation (CF) B: Intracortical Inhibition. Normalized MEP Ratio > 1 Indicates Greater MEP Amplitude (Greater Crossed Facilitation) in the Bimanual Condition vs Unimanual Condition. Normalized Silent Period Ratio > 1 Indicates longer Silent Period (More Inhibition) in the Bimanual Condition vs Unimanual Condition.

Discussion

This study assessed bimanual interference in individuals with chronic post-stroke hemiparesis during a task where the non-paretic hand generated dynamic grip forces and the paretic hand maintained an isometric contraction. We also assessed crossed facilitation and GABA_B-mediated intracortical inhibition, using TMS-evoked CSP, during asymmetric force production. Our main findings are: 1) bimanual interference post-stroke is variable and is greater in those with less motor impairment, especially under high non-paretic hand force demands; 2) Crossed facilitation increases with higher force asymmetry and lower paretic hand effort, more so in the less impaired individuals, but becomes more variable as paretic effort increases and 3) higher GABA_B-mediated inhibition suppresses crossed facilitation, but disinhibition occurs with higher force asymmetry; and 4) crossed facilitation may negatively impact bimanual interference post-stroke. Overall, increased inhibitory tone may facilitate better regulation of crossed facilitation, supporting interlimb uncoupling. However, reduced ipsilesional inhibitory tone and excessive crossed facilitation originating from the contralesional hemisphere may contribute to bimanual coordination deficits in individuals with mild to moderate post-stroke impairment.

Crossed facilitation and bimanual interference are thought to arise from neural crosstalk between motor commands, largely mediated by interhemispheric interactions through the corpus callosum(Arya & Pandian, 2014; Swinnen, 2002). Evidence from split-brain patients provides support, with reduced interference observed when drawing different shapes after callosotomy(Eliassen et al., 1999). Following stroke, however, corpus callosum damage is common, with greater disruption associated with more severe motor impairment (Hayward et al., 2017). Individuals with more severe deficits often exhibit increased neural crosstalk during unimanual movements, most evident as mirror movements of the non-paretic limb during voluntary paretic limb activity, whereas the reverse pattern is rarely observed (Kim et al., 2003). In contrast, our results show that during bimanual movements, variable force generation by the non-paretic limb results in persistent bimanual interference and crossed facilitation within the ipsilesional hemisphere, effects that are more pronounced in individuals with milder impairment.

Crossed facilitation increased with greater non-paretic effort, particularly when the paretic hand was at rest or minimally active but was reduced and more variable when the paretic hand exerted more force. This partially aligns with previous findings from able-bodied studies (Cunningham et al., 2017; Perez & Cohen, 2008). Perez and Cohen (2008) showed that facilitation in the primary motor cortex rises with ipsilateral force when the opposite hand is at rest, whereas our prior work demonstrated that moderate contralateral activity (30% MVC) suppresses facilitation below unimanual levels (Cunningham et al., 2017). In stroke, during bimanual force production, facilitation with greater non-paretic effort was reduced but not suppressed, remaining comparable to unimanual levels and highly variable across participants (range: 0.6–1.6). This variability partly explained differences in bimanual interference, where greater facilitation tended to associate with more interference (p = .0506). In contrast to neurologically intact individuals, where moderate crossed facilitation supports coordination (Cunningham et al., 2017; Fling & Seidler, 2012a), our findings suggest that after stroke, excessive facilitation may disrupt bimanual coordination. Effective bimanual coordination may therefore depend on a balance between excessive and insufficient regulation of crossed facilitation.

The altered level of crossed facilitation post stroke during bimanual asymmetries, may reflect altered intracortical inhibition dynamics. Transcallosal signals are not inherently excitatory or inhibitory. Instead, their effect depends on the inhibitory tone of the receiving hemisphere and whether callosal inputs synapse onto excitatory (glutamatergic) or inhibitory (GABAergic) interneurons (Avanzino et al., 2007). In our earlier study (Cunningham et al., 2017), suppressed crossed facilitation was largely mediated by increased intracortical inhibition, particularly GABA_A-mediated, however, there was no change in the degree of intracortical inhibition during the high asymmetry condition (test hand 30%|condition hand 70%). The present study tested the role of GABA_B-mediated inhibition, which provides tonic inhibitory control to stabilize excitability, prevent premature movements, and support independent limb control (Cirillo et al., 2018; Cowie et al., 2016; McDonnell et al., 2006). While GABA_B-mediated inhibition suppressed crossed facilitation overall (Figure 4B), we observed greater disinhibition during high asymmetry (PH|30%, NPH|70%). By contrast, smaller asymmetries (PH|30% with NPH|10% or 30%) showed no clear differences, suggesting inhibition does not scale linearly with force but instead requires a threshold imbalance to be exceeded.

Overall, high non-paretic hand force with moderate paretic hand engagement results in both bimanual interference (Figure 2D) and disinhibition (Figure 4A). Reduced suppression of crossed facilitation in stroke highlights the need to systematically investigate intracortical inhibition, where GABA_B thought to regulate GABA_A – mediated inhibition (Connelly et al., 2013; Werhahn et al., 1999), as a driver of post-stroke bimanual interference and control. This is particularly relevant given evidence that persistent intracortical disinhibition is common after stroke (Tang et al., 2019; Washabaugh et al., 2024) and thus may contribute to underlying bimanual motor deficits.

Limitations

Visuomotor tasks were performed with independent feedback for each limb, while necessary for our aims, may introduce attentional confounds due to the use of two visual targets. Participants may have prioritized the limb producing greater force, and reduced attention to the paretic-hand performance. Although the CSP reflects both spinal and cortical inhibition, making it difficult to isolate cortical contributions, it was chosen over long-interval intracortical inhibition to reduce the number of trials and minimize fatigue in this exploratory study. Lastly, the sample size was modest, but the repeated-measures design helps improve statistical sensitivity and supports the value of this study in informing future research on post-stroke inhibitory control and bimanual interference.

Conclusion

This study shows that bimanual interference post-stroke may, in part, be shaped by contralesional crossed facilitation and GABA_B-mediated intracortical inhibition. Further, our findings suggest that effective bimanual coordination may depend on a balance between excessive and insufficient regulation of crossed facilitation. Clinically, this has important implications, as stroke survivors often operate in a persistently asymmetric state due to the consistent weakness of the paretic hand. In such conditions, contralesional activity may dominate and disrupt paretic hand performance, exacerbating bimanual coordination deficits during everyday tasks, even in the moderate-to-mildly impaired. This highlights the potential value of targeted asymmetric training and neuromodulation to improve bimanual coordination in individuals with less severe impairment.

Supplemental Material

sj-docx-1-rnn-10.1177_09226028251395702 - Supplemental material for Contralesional Crossed Facilitation Impairs Bimanual Force Coordination in Chronic Stroke Survivors with Moderate-to-Mild Motor Impairment

Supplemental material, sj-docx-1-rnn-10.1177_09226028251395702 for Contralesional Crossed Facilitation Impairs Bimanual Force Coordination in Chronic Stroke Survivors with Moderate-to-Mild Motor Impairment by Rifeng Jin, Jisung Yuk, Shreya Ramani and David A Cunningham in Restorative Neurology and Neuroscience

Footnotes

Aknowledgements

The authors thank Amy Friedl for her contributions to participant recruitment and administration of the Upper Extremity Fugl-Meyer assessment. We also acknowledge the ReproRehab Program (NIH NICHD/NCMRR R25HD105583) for training on the use of Google Colab to support reproducible data analysis. Additionally, we acknowledge the use of ChatGPT-3.5 to assist in generating Figure 1A and to improve readability during the writing process, with suggestions reviewed, and edited or rejected by authors as needed. The tool was utilized in full compliance with APA ethical policies, and the authors take full responsibility for the content.

Author Contributions

DAC designed research; RJ, JY, and DAC performed research. RJ, JY, SR, and DAC analyzed data. RJ, JY and DAC wrote the paper.

Funding

The work was supported by the National Institutes of Health (NIH), including the National Institute of Child Health and Human Development (NICHD) and the National Center for Medical Rehabilitation Research (NCMRR), with grant numbers R01HD109299 and K12HD093427 to DAC.

National Center for Medical Rehabilitation Research, (grant number K12HD093427, R01HD109299).

Declaration of Conflicting Interests

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

Data Availability

The processed data (.csv) and accompanying R-script are available at . An interactive version of the R script is also accessible via Google Colab (downloadable from GitHub) for reproducibility.

Disclosures

The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the materials discussed in the manuscript.

Supplemental Material

Supplemental material for this article is available online.

ORCID iDs

Rifeng Jin

Jisung Yuk

David A Cunningham

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