Altered Cortical Activity During a Finger Tap in People with Stroke

doi:10.21203/rs.3.rs-3331958/v1

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Altered Cortical Activity During a Finger Tap in People with Stroke

https://doi.org/10.21203/rs.3.rs-3331958/v1

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published 09 May, 2024

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This study describes temporal patterns of cortical activity during a simple finger movement in people with stroke to understand how temporal patterns of cortical activation and network connectivity align with prolonged muscle contraction at the end of a task. We investigated changes in the EEG temporal patterns in the beta band (13-26Hz) of people with chronic stroke (N = 10, 7F/3M) and controls (N = 10, 7F/3M), during and after a cued movement of the index finger. We quantified the change in beta band EEG power relative to baseline as activation at each electrode and the change in beta band task-based coherence (tbCoh) relative to baseline coherence as connectivity between EEG electrodes. Contrary to controls, finger tap cortical activity in the stroke group was spatially distributed bilaterally, and measurements from the post task period lacked a positive change in beta power relative to baseline, which has been described as event-related synchronization in controls. In addition, the stroke group exhibited no discernible reduction in tbCoh between the ipsilesional sensorimotor and frontal regions of the cortex during the post task period, which was a notable feature of tbCoh in controls. Our results suggest that divergent cortical activation patterns coupled with changes in connectivity between the sensorimotor and frontal cortices in the stroke group might explain clinical observations of prolonged muscle activation in people with stroke. This prolonged activation might be attributed to the combination of cortical reorganization and changes to sensory feedback post-stroke.

Event-related desynchronization

Event-related synchronization

EEG

Cortical activation

Connectivity

Beta band

The purpose of this study was to characterize cortical activation and network connectivity during a simple finger motor task in stroke survivors using electroencephalography (EEG). Neuroplasticity after strokes that affect motor function is reflected by a structural (Carmichael and Chesselet 2002; Kalinosky et al. 2013; Sotelo et al. 2020) and functional (Cramer and Crafton 2006) cortical reorganization to the nonprimary motor areas in the ipsilesional or the sensorimotor contralesional cortex (Chollet et al. 1991; Rossini et al. 1998; Ward et al. 2003a, b; Ward and Cohen 2004; Cramer and Crafton 2006; Kalinosky et al. 2013, 2019; Grefkes and Ward 2014). Based on EEG and MEG studies, residual motor impairments after stroke are proportional to the cortical activation changes in spatial distribution and magnitude (Platz 2000; Leocani and Comi 2006; Steogonpień et al. 2011; Rossiter et al. 2014). Like cortical activation, cortical connectivity (the coupling between various brain regions) is altered post-stroke (Gerloff et al. 2006; De Vico Fallani et al. 2009; Grefkes and Ward 2014; Wu et al. 2015; Snyder et al. 2021). For example, Bönstrup and colleagues used high-density EEG to show a stronger coupling between parietal and frontal regions of the brain after stroke (Bönstrup et al. 2018). Similarly, Gerloff and colleagues explored changes in connectivity using EEG coherence analysis, finding that the networks used for simple tasks in people with stroke are similar to those used to perform higher-order tasks by healthy adults (Gerloff et al. 2006). Strens and colleagues showed that this dependency on alternate networks reduces with recovery (Strens et al. 2004). While several EEG studies have documented motor impairment associated with spatial changes in activity (Platz 2000; Leocani et al. 2001; Steogonpień et al. 2011; Rossiter et al. 2014) or connectivity (Strens et al. 2004; Gerloff et al. 2006; De Vico Fallani et al. 2009; Wu et al. 2015; Bönstrup et al. 2018), the EEG temporal patterns associated with motor impairments have not been extensively described and could provide insight into cortical contributions to motor impairments.

One of the manifestations of motor impairments in people with stroke is difficulty terminating volitional muscle activity (Chae et al. 2002, 2006; Seo et al. 2009). Chae and colleagues found significant delays in the initiation and termination of paretic muscles compared to their non-paretic counterparts (Chae et al. 2002, 2006). The authors attributed the delay in termination of both the upper and lower extremity muscles to decreased inhibition along descending pathways to the muscles and restructuring of cortical pathways. Along with changes in inhibitory cortical networks, Seo and colleagues concluded that muscle activity termination in the paretic limb is also negatively influenced by changes in sensory processing during a grip task (Seo et al. 2009). Further, EEG signals associated with the elbow and shoulder in preparation for a motor task have greater overlap in both the ipsilesional and contralesional hemispheres in people with stroke, compared to controls (Yao et al. 2009; Yao and Dewald 2018). These studies suggest that the primary mechanisms supporting the inability to volitionally relax muscles are a combination of 1. cortical reorganization that alters the cortical drive to the motoneuron pools, 2. reliance on alternate descending pathways as a compensatory mechanism during a motor task and 3. alterations in sensory processing or feedback (Dewald et al. 1995; Kamper and Rymer 2001; Chae et al. 2002, 2006; Lang and Schieber 2003; Seo et al. 2009). While these mechanisms likely contribute to altered temporal patterns of cortical activity, a direct measure of cortical activation and connectivity patterns during a motor task is needed to confirm that prolonged muscle activation arises from the cortex.

The current study examined cortical activation and connectivity changes in people with chronic stroke during and after finger tap. We collected EEG data while participants moved their index finger. During and at the end of the task, we quantified cortical activation using the change in EEG power relative to baseline in the beta band (13-26Hz) (Stancák and Pfurtscheller 1996; Pfurtscheller and Lopes da Silva 1999) and quantified connectivity between cortical areas using task-based coherence between EEG electrodes (Rappelsberger et al. 1994; Gerloff et al. 2006; Snyder et al. 2019). We hypothesized that: 1. Cortical activation persists when relaxation should occur at the end of the task, and 2. changes in connectivity between cortical regions correspond to the termination of the motor command at the end of the task.

Study Participants

Ten chronic stroke participants (66.3 ± 11.29 years old, 7 females) and ten controls (66 ± 11.35 years old, 7 females) participated in this study. The stroke participants completed the study using their paretic hand, and the controls used their dominant hand. The study included controls with no neurological deficits and individuals with chronic stroke (> one-year post-stroke) with no other neurological conditions. People with stroke were excluded from the study if they could not voluntarily fully flex or extend their paretic index finger at the metacarpophalangeal (MCP) joint, unassisted. We computed the impairment level for all stroke participants using the Fugl-Meyer upper extremity assessment (Fugl-Meyer et al. 1975). The average Fugl Meyer scores were 57.33 ± 8.33 (max: 66); we did not measure the Fugl Meyer in one participant due to participant-related constraints. Table 1 contains the demographics and clinical characteristics of the participants. This study was performed in line with the principles of the Declaration of Helsinki, and all participants consented in writing to join in the study per the Institutional Review Board at Marquette University (HR – 3256).

Table 1

Demographic and clinical characteristics of stroke participants (S) and controls (C). The hand used for performing the task was the paretic side for stroke participants and the dominant side for controls. The reported Fugl-Meyer value is the upper extremity motor score (maximum of 66). A score of 66 was assumed for controls (Anatomical locations: ACA – Anterior Cerebral Artery, MCA – Middle Cerebral Artery, PCA – Posterior Cerebral Artery) (Gender: F – Female, M – Male)
ID	Age (yrs.)/ Gender	Time Since Stroke (yrs.)	Type of Stroke	Anatomical Location of Stroke	Cortical Subcortical Both	Study Arm	Fugl- Meyer
S501	81/M	1	Hemorrhagic	Right Temporal and Right Occipital	Cortical	Left	-
S502	67/F	1	Ischemic	Posterior Circulation (Brainstem, Cerebellum, Embolic)	Subcortical	Right	53
S504	73/F	2	Ischemic	Right PCA	Cortical	Left	50
S505	66/F	2	Ischemic	-	-	Left	38
S506	81/F	3	Ischemic	Left MCA Upper Division, Left Temporal	Cortical	Right	65
S509	48/F	3	Ischemic	Left MCA	Both	Right	61
S511	68/F	4	Ischemic	Right ACA, Right MCA	Both	Left	62
S512	70/F	1	Ischemic	Left MCA	Both	Right	63
S513	59/M	9	Hemorrhagic	Left Frontal and Left Parietal	Cortical	Right	64
S514	50/M	4	Hemorrhagic	Right Frontal and Right Parietal	Cortical	Left	60
C601	70/M	0	-	-	-	Right	66
C602	68/M	0	-	-	-	Right	66
C603	69/F	0	-	-	-	Right	66
C604	72/F	0	-	-	-	Right	66
C607	48/F	0	-	-	-	Right	66
C609	72/M	0	-	-	-	Right	66
C610	65/F	0	-	-	-	Right	66
C612	83/F	0	-	-	-	Right	66
C613	68/F	0	-	-	-	Right	66
C614	45/F	0	-	-	-	Left	66

Study Apparatus

Participants rested their arm on a manipulandum with a shoulder abduction angle of 30 degrees and elbow flexion of 90 degrees to perform the task. The participants sat in an adjustable chair, and their shoulder was neutral in the horizontal plane with no flexion or extension. The wrist position was at 30 degrees supination of the forearm.

Study Measurements

A 64-channel active electrode actiCAP (Brain Products GmbH, Munich, Germany), set in the conventional 10–20 international system, was used to collect EEG data. The reference electrode was at FCz, and the ground electrode was at AFz. First, we placed the Cz electrode in line with the preauricular points and the nasion and inion points to align the EEG cap to each participant's head. Next, we applied SuperVisc gel (Brain Products, GmbH, Munich, Germany) to the surface electrodes to lower their impedances below 10kOhms. EEG signals sampled at 1kHz were bandpass filtered (0.3 and 200Hz), notch filtered (60Hz), and amplified using the Scan 4.5 software and a Synamps 2 EEG system (Compumedics® NeuroscanTM, Charlotte, USA).

Study Protocol

Participants produced cued index finger movements, with movement targeted at the MCP joint using a visual cue to initiate the movement. The study consisted of 80 trials. Participants performed each trial using the index finger of their paretic arm (stroke) or dominant arm (controls). Each trial consisted of baseline (2s before visual cue onset), Finger Tap (0s at visual cue onset to 1s post visual cue onset), Post Task (1s to 2s post visual cue onset), and rest periods. Before each trial, participants started from a resting position where they rested the index finger upon the thumb. The participants quickly extended their index finger at visual cue onset and returned to the resting position. We provided visual cues using a computer monitor placed at eye level. A custom-built LabVIEW (National Instruments Corporation, Austin, Texas) program controlled the visual cues and generated pulses to synchronize the cue onset to the EEG data. During rest, the visual cue was a dark green circle on the screen with the word "Wait" written inside, and at cue onset, this circle turned bright green, and the word changed to "Go." Each visual cue lasted 500ms, and the cues appeared at random intervals that varied between 5s and 7s. After recording the first 40 trials, the participants took a short break before recording the second 40 trials to maximize focus and break the monotony. Both the stroke participants and controls performed the task by quickly extending their finger after the cue and returning to the resting position without delay. We closely monitored the participants during each trial for consistent performance and to minimize other muscle contractions. We rejected trials where the participant did not quickly extend and return to the resting position or where we observed evidence of activity in nontargeted muscles.

Data Analysis

EEG Preprocessing and Referencing: We used the EEGLAB toolbox (version v13.4.4b) (Delorme and Makeig 2004), FieldTrip (version 2016-01-03) (Oostenveld et al. 2011), and custom MATLAB scripts (version 2016a & 2018a, MathWorks, Natick, Massachusetts) to analyze the EEG data. All EEG data were filtered using a fourth-order, zero-phase Butterworth filter (1–50Hz). Each epoch started at baseline (2s before visual cue onset (t = 0)) and ended 4s post cue onset resulting in 80 epochs. The manual rejection function of FieldTrip (ft_rejectvisual) was then used to remove outlier epochs and noisy EEG channels from baseline-corrected epochs (average # of epochs/channels removed: 20.6/2.8). The Adaptive Mixture Independent Component Analysis (AMICA) from EEGLAB was then applied to remove artifacts and other noise from the EEG data. Independent component analysis is a blind source separation technique used to separate the signals of interest and noise in the EEG data (Delorme et al. 2012). Of the various source separation algorithms, the AMICA function (Palmer et al. 2011) has shown good performance in separating artifactual signal components such as eye blinks and muscle activity from signal components (Delorme et al. 2012). These artifactual signal characteristics can be identified as noisy components (Delorme et al. 2012; Puce and Hämäläinen 2017). We removed noisy components from our EEG signals using AMICA (average # of components removed: 15.5) and then reconstructed the EEG signals without the noise.

We used the common average reference, preferred for EEG analysis (Valdes-Sosa et al. 2017), for activation analysis but averaged mastoid electrodes (Electrodes Tp9 and Tp10) for connectivity analysis (Rappelsberger et al. 1994; Snyder et al. 2019). Note that EEG records cortical signals at the scalp from electrodes placed close to each other. As cortical signals pass through various biological media, the signal is attenuated, and there is spatial smearing, leading to a volume conduction effect (Cohen 2017; Snyder et al. 2019, 2021). Therefore, we computed the surface Laplacian values to minimize this volume conduction effect. Using a spherical spline algorithm, we used the CSD Toolbox (Version 1.1) to calculate the surface Laplacian (Perrin et al. 1989; Kayser and Tenke 2006). Note that the spherical spline algorithm is a more robust estimate of the EEG montage's edge electrodes than the computation of surface Laplacian using the local Hjorth estimate (Kayser and Tenke 2015).

For analysis, we switched the hemispheres for right hemisphere stroke datasets so the left hemisphere would represent the lesioned/contralateral side for all participants. The baseline period was defined as 2s before the visual cue onset till cue onset (-2s to 0s), the Finger Tap period was defined from visual cue onset at 0s to 1s post cue (0s to 1s), and the Post Task period was 1s post visual cue to 2s post cue (1s to 2s). Data from 2s to 4s was inspected, but not used for further analysis because there was no evidence of consistent cortical activation in this time window.

Activation Analysis: We used the change in beta band power of the surface Laplacian-corrected EEG epochs to estimate cortical activation. Note that beta-band power reductions relative to baseline represent cortical activation during a motor task (Pfurtscheller and Lopes da Silva 1999). The amplitude was squared and averaged across epochs after bandpass filtering at 13-26Hz using an IIR fourth-order Butterworth filter for each electrode. We calculated percent change from baseline using Eq. (1) to normalize the beta power data for further analyses,

$$\varvec{\%}\varvec{C}\varvec{h}\varvec{a}\varvec{n}\varvec{g}\varvec{e} \left(\varvec{t}\right)= \frac{(\varvec{A} \left(\varvec{t}\right)-\varvec{R})}{\varvec{R}} \mathbf{*} 100$$

where %Change(t) represents the percent change in beta power relative to baseline. A(t) represents the power at each time point, and R is the averaged beta power across the baseline period. ERD is manifested as a reduction in power relative to baseline and typically occurs during a motor task. At the same time, event-related synchronization (ERS) is an increase in power relative to baseline, often observed at the end of a motor task (Pfurtscheller et al. 1996, 1999; Pfurtscheller and Lopes da Silva 1999). Thus, ERD will be reported as the percent decrease in beta power (13-26Hz) relative to baseline (Eq. 1). Similarly, beta ERS will be reported as a percent increase in beta band power relative to baseline.

Source localization of EEG data was performed using the Brainstorm toolbox (Version 3.4 19 May 2016) to visualize the spatial distribution of activation across the cortical surface. For source localization, we used the preprocessed EEG data (noisy channels, noisy trials, bad components removed), re-referenced the preprocessed EEG to a common average reference, then switched the hemispheres in people with a right hemisphere stroke, so the left hemisphere was the ipsilesional side. The default MNI/Colin 27 anatomical brain template (Holmes et al. 1998) was used to normalize the location of each participant's distributed current dipole map. The inverse model for source localization was calculated in Brainstorm using a depth-weighted minimum L2 norm estimator of cortical current density (Hämäläinen and Ilmoniemi 1994). The forward model (OpenMEEG) (Gramfort et al. 2010) estimate was found using a boundary element model before the computation of the inverse model. Source localized data calculated for each trial were then bandpass filtered between 13-26Hz using a zero-phase fourth-order Butterworth filter. Finally, Hilbert transformed, filtered data were squared and averaged across trials to obtain the power to calculate the event-related desynchronization (ERD) for displaying the source localized EEG data.

Connectivity Analysis: Volume conduction and reference electrodes can contribute to erroneous estimation of EEG coherence during analysis (Nunez et al. 1997). We mitigated these effects by using an averaged mastoid (Rappelsberger et al. 1994), surface Laplacian (Kayser and Tenke 2006), and task-based coherence (Gerloff et al. 2006) in our coherence analysis. We computed EEG coherence between all electrode pairs across both hemispheres during the Finger Tap and Post Task periods, using magnitude squared coherence (MSC) as shown in Eq. (2),

$$\mathbf{M}\mathbf{S}\mathbf{C} \left(\mathbf{f}\right)= \frac{{\left|\varvec{C}\varvec{x}\varvec{y}\left(\varvec{f}\right)\right|}^{2}}{\varvec{C}\varvec{x}\varvec{x}\left(\varvec{f}\right)\varvec{*}\varvec{C}\varvec{y}\varvec{y}\left(\varvec{f}\right)}$$

where Cxy(f) is the cross-spectrum between electrode pairs (x and y) at each frequency (f) of interest, Cxx(f) and Cyy(f) are the auto spectra of the electrodes x and y, respectively. Each trial was divided into six non-overlapping windows, each with 1s of data. For each participant, the connectivity matrix was 65x65x6 for each frequency. The connectivity matrix was calculated within the beta band frequency range of 13–26 Hz and then averaged across the frequency range. We calculated task-based coherence (tbCoh) by subtracting average coherence across baseline (-2s to 0s) from the Finger Tap (0s to 1s) and Post Task (1s to 2s) coherence data (Rappelsberger et al. 1994). Electrodes of interest for this analysis were C3 in the ipsilesional motor region, Fz in the frontal region, and C4 in the contralesional motor region. The sensorimotor and frontal regions of the brain demonstrate movement-related changes in activation and connectivity in healthy controls and may show alterations related to the stroke (Leocani et al. 1997). Therefore, we averaged tbCoh values across the time window during the Finger Tap and Post Task for the connectivity between electrodes of interest and other electrodes.

Statistical Analysis

The beta ERD and beta ERS values were chosen at electrodes C3 in the contralateral/ipsilesional hemisphere and C4 in the ipsilateral/contralesional hemisphere as they are at the center of the hand sensorimotor region in each hemisphere. For each electrode, we calculated the area under the curve above and below 0% change from baseline from 0s to 2s (since the timing of beta ERD and beta ERS varied for each participant, we looked at these values over the 0s to 2s time window). Negative areas under the curve were designated as ERD (ERD C3 and ERD C4), and positive areas under the curve were ERS (ERS C3 and ERS C4). Statistical comparison of beta ERD and beta ERS in the 0s to 2s time window between the stroke group and controls was performed using one-way MANOVA followed by separate independent T-tests (α = 0.05). For the one-way Multivariate Analysis of Variance (MANOVA), we tested the equality of covariances using Box’s M test. We also tested the dependent variables for normality using the Shapiro-Wilk test and homogeneity of variance using Levene's test. For the connectivity statistical analysis, we performed a one-way MANOVA test for coherence between electrode pairs (C3: Fz, C3: C4, and Fz: C4) for Finger Tap (0s to 1s) and Post Task (1s – 2s).

ERD/ERS Timeseries and Statistics

Significant differences in beta ERD were observed between the stroke group and controls in both hemispheres, and ERS was significantly different between the stroke group and controls in the lesioned hemisphere. The dependent variables ERD C3, ERD C4, ERS C3, and ERS C4, were the areas below (ERD) or above (ERS) the baseline in both groups between 0s to 2s. For activation statistical analysis of the dependent variables, we performed one-way MANOVA to test if these variables are different between the stroke group and controls. The MANOVA assumption of homogeneity of variance-covariance using Box's M test (Box's M = 6.76) was assumed (F(6,2347) = 0.92, p = 0.48). The multivariate analysis for the dependent variables (ERD C3, ERD C4, ERS C3, And ERS C4), was statistically significant (F(4,15) = 8.67, p < 0.001, Wilk’s l = 0.302, partial h² = 0.698).

Follow-up independent t-tests were performed for the dependent variables. ERD C3 (ipsilesional hemisphere) in the stroke group (-51.48 ± 27.0% change from baseline) was significantly larger than in controls (-31.23 ± 12.98% change from baseline) and the means were significantly different at t (18) = 2.14, p = 0.047. ERD C4 (contralesional hemisphere) in the stroke group (-51.57 ± 26.88% change from baseline) was significantly greater than ERD C4 in controls (-29.04 ± 18.27% change from baseline), t(18) = 2.19, p = 0.042 (Fig. 1b). In addition, the stroke group also had significantly lower ERS at C3 (7.41 ± 7.75% change from baseline) compared to controls (44.17 ± 18.22% change from baseline), t(18) = 5.871, p < 0.001. There was no significant difference between groups in ERS at C4 (p > 0.05).

Cortical Activation Maps

The topographical and source localized maps showing the spatial distribution of cortical activation demonstrated qualitative differences between the stroke group and controls. Cortical activity observed using the beta ERD indicated a bilateral reduction in power relative to baseline over brain sensorimotor cortical regions during Finger Tap in both groups (Fig. 2a). In addition, Fig. 2a shows the presence of beta ERS (shown using warm colors) in the controls, which was not present in the stroke group. The spatiotemporal distribution of beta ERD in response to Finger Tap showed a bilateral spread away from the sensorimotor areas in the source localization map for the stroke group, which was not seen in controls (Fig. 2b).

Connectivity Across C3, Fz, and C4

Connectivity was assessed using task-based coherence calculated as tbCoh values for C3, Fz, and C4 during the Finger Tap and Post Task. Topographic images of tbCoh between electrodes C3, Fz, and C4 and the rest of the electrodes are shown in Fig. 3. tbCoh values between each electrode with the rest of the cortical regions formed similar spatial patterns in both groups during Finger Tap. Although similar patterns were seen between groups, the magnitude of tbCoh was lower in the stroke group compared to controls. In controls Post Task, connectivity measures between C3 and the frontal regions and Fz and the contralateral sensorimotor region showed negative tbCoh values. There were no discernible connectivity patterns between C3, Fz, or C4 electrodes and the rest of the brain in the Post Task period for participants with stroke. During Post Task, the functional connectivity patterns differed for participants, resulting in averaged patterns with no pronounced connectivity in the stroke group. Overall, the tbCoh values suggest a modest divergence of connectivity patterns between the stroke group and controls during Finger Tap which was more prominent during Post Task.

For coherence statistical analysis, we performed separate one-way MANOVAs for Finger Tap and Post Task to test if coherence between electrode pairs of C3:C4, C3:Fz, and C4:Fz are different between the stroke group and controls (Fig. 4). The MANOVA assumption of homogeneity of variance-covariance using Box's M test (Box's M = 6.76) was valid (F(6,2347) = 0.92, p = 0.48). Using Wilk's criterion, the multivariate connectivity analysis for Finger Tap was not significant (p = 0.49).

For Post Task, Box’s M test (Box’s M = 14.18) assumed homogeneity of variance-covariance (F(6,2347) = 14.18, p = 0.072). The multivariate connectivity analysis for Post Task was statistically significant (F(3,16) = 4.64, p = 0.016, Wilk’s l = 0.54, partial h² = 0.47). A follow-up one way ANOVA for each electrode pair for Post Task showed a significant difference between the stroke group and controls for electrode pair C3:Fz (F(1,18) = 6.21, p = 0.023) but not for electrode pair C3:C4 (p = 0.14), and C4:Fz (p = 0.73) (see Figure 4).

Our objective was to characterize cortical activation and connectivity changes during and after a cued simple finger tap motor task. Our primary novel findings supported our hypotheses and were that the stroke group continued cortical activation even after the end of the task, and the connectivity between cortical regions in the stroke group changed compared to the controls. Cortical activity, quantified as the change in the beta band (13-26Hz) power relative to baseline, showed a positive inflection (ERS) in controls that was not present in stroke (Fig. I). Further, cortical activity during Finger Tap was spatially dispersed in the stroke group compared to controls (Fig. II). Post Task decreases in task-based coherence (tbCoh) seen as negative coherence values between the ipsilesional sensorimotor regions, and the frontal regions of the cortex were observed in controls but not in people with stroke (Fig. III), suggesting a reduction in cortical connectivity in stroke. These results suggest that alterations in cortical activity and associated connectivity between the sensorimotor and frontal cortices might underlie clinical observations of prolonged muscle activation in people with stroke.

Unlike controls, the stroke group exhibited a continued presence of ERD until 1.5s post-movement offset and a lack of beta band ERS. Although not directly measured in this study, there is evidence of people with stroke having difficulty volitionally relaxing their muscles, such as delays in termination of muscle activity in the upper extremity (Chae et al. 2002; Seo et al. 2009). The beta band ERS differences between the stroke group and controls might contribute to the difficulty in movement termination seen in people with stroke. In controls, return to synchronization or beta band ERS is associated with a return to the "idling state" (Pfurtscheller 1992), an active inhibition of the motor network at the end of the task (Fry et al. 2016; Heinrichs-Graham et al. 2017), or sensory feedback marking the end of the task (Cassim et al. 2001; Houdayer et al. 2006). Problems terminating muscle activity and the lack of ERS observed in the current study might be related to damage to somatosensory pathways in stroke survivors. While motor deficits are readily observed after stroke, functional outcomes are worse when patients have both motor and sensory impairments (Carey 1995; Patel et al. 2000; Platz 2000). These studies highlight the impact of sensory problems on motor function in stroke survivors. The relay of signals between the motor cortex, the periphery, and back to the motor cortex forms a closed neural loop of beta oscillations that has even been suggested to underlie muscle synergies (Aumann and Prut 2015). In other words, beta oscillations are associated with the connections of the motor cortex to afferent pathways involved in motor commands (Baker 2007). Although the stroke participants in this study had low levels of impairment and did not show visible signs of an inability to deactivate the finger after the task, the ERS was not just delayed in time. Instead the ERS did not appear to be present. In people with spinal cord injury, ERS is attenuated following a brisk toe plantar flexion, possibly due to a deficit in the sensory feedback (Gourab and Schmit 2010). In addition, ERS following ankle movement is reduced in healthy adults when sensory feedback is attenuated using prolonged vibration (Lee and Schmit 2018). Thus, the loss of somatosensory feedback after stroke could underlie the absence of ERS in the current study; however, it is unlikely to be the only cause. We may also be able to attribute the satisfactory performance of the task, despite the lack of ERS, to the simplicity and shorter time duration of the task itself.

Losses in active inhibition of motor commands and resetting of motor regions to an idling state after stroke likely contribute to the loss of ERS. According to Heinrichs-Graham and colleagues, afferent sensory feedback and active inhibition work together to give rise to ERS (Heinrichs-Graham et al. 2017). Similarly, Alegre and colleagues found increased ERS following a cue of "no-go" and during a cue to inhibit movements, suggesting the involvement of networks associated with active inhibition at the end of the task (Alegre et al. 2004). Parameters related to the complexity of the task or force generated relative to the task (Fry et al. 2016) regulate the level of ERS in healthy adults, and both active and passive movements also result in ERS (Cassim et al. 2001). ERS is also seen during imagined movements of feet (Pfurtscheller et al. 2005) and hands (Pfurtscheller and Lopes da Silva 1999), which might minimize the sensory feedback necessary for movement termination. Pfurtscheller and colleagues imply that returning to an idling state or resetting cortical networks might result in ERS with inputs from primary and non-primary motor networks (Pfurtscheller et al. 2005). As ERS is seen during active, passive, or imagined movements and found to be somatotopically arranged (Salmelin et al. 1995), the delay in termination of cortical activation seen as a lack of ERS in stroke could be the result of a lack of sensory feedback. However, changes to networks contributing to active inhibition, or an inability to reset to the "idling state" are likely to also play a role.

Similarly, differences in connectivity between the sensorimotor cortices and the frontal cortex in stroke participants (compared to controls) provide a potential mechanism for motor impairments in people with stroke. The increase in connectivity between the motor cortices and the frontal lobe during a motor task, followed by a decrease in connectivity following the task, occurs with self-paced movements in the healthy controls (Leocani et al. 1997). We saw a similar pattern in controls in the current study; however, people with stroke did not show a negative coherence after the task (Fig. III). Post Task, a negative coherence indicates a decoupling of networks at the end of the task (Gerloff et al. 2006). This lack of modulation of connectivity of the primary motor areas with frontal lobe regions near the supplementary motor area could contribute to challenges in the initiation and termination of muscle activity in stroke survivors.

Our study showed bilateral cortical activation as beta band ERD in both the stroke and control groups. Our results suggest that both hemispheres contribute to the action stage of a motor task. Several studies have quantified beta ERD in the contralateral hemisphere in healthy controls (Leocani et al. 1997; Pfurtscheller et al. 1999; Formaggio et al. 2013; Snyder et al. 2019), but there is also evidence of ipsilateral hemispheric involvement during a motor task, as shown in our study. Chen et al. used repetitive Transcranial Magnetic Stimulation (rTMS) to briefly disrupt the ipsilateral motor cortex during a sequence of finger movements and found timing errors even with simple sequences (Chen et al. 1997), suggesting that both hemispheres contribute to a simple motor task. The primary communication pathway between the brain and the muscles involves the corticospinal tract, and this communication occurs via both hemispheres (Kandel et al. 2000; Lang and Schieber 2004; McMorland et al. 2015). In stroke, functional magnetic resonance imaging studies (Cramer et al. 1997; Carey et al. 2002; Cramer and Crafton 2006; Calautti et al. 2007; Rehme et al. 2012), and MEG (Rossini et al. 1998), and EEG (Platz 2000; Leocani et al. 2001; Steogonpień et al. 2011; Rossiter et al. 2014) have both described a shift in cortical activation to non-primary sensorimotor areas in the ipsilesional hemisphere or the primary sensorimotor areas of the contralesional hemisphere as well as a reduction in activation. Thus, we expected a reduction in the magnitude of activation and a shift in activation to non-primary sensorimotor regions of the brain. While the spatial distribution of beta ERD in the stroke group was diffuse compared to controls, including non-primary sensorimotor regions of both hemispheres, we did not observe a reduction in activation. These results agree with the visual task based activation maps generated during a functional imaging study in people with stroke by Kalinosky and colleagues, who saw an increase in activation in the contralesional hemisphere (Kalinosky et al. 2019). The similarity between the stroke group and controls may be due to our stroke group's low level of impairment, based on the Fugl Meyer upper extremity scores.

Similarly, with task-based connectivity between C3 (ipsilesional region), C4 (contralesional region), and Fz (frontal region) in people with stroke, we found a similar pattern, but lower magnitude compared to controls during Finger Tap (as shown in Fig. III). These findings are consistent with magnetic resonance imaging (Feydy et al. 2002; Grefkes et al. 2008; Mintzopoulos et al. 2009; Crofts et al. 2011; Rehme et al. 2011) and EEG (Strens et al. 2004; Gerloff et al. 2006; De Vico Fallani et al. 2009; Wu et al. 2015; Bönstrup et al. 2018) studies that have found a reduction in intra-hemispheric and inter-hemispheric connectivity post-stroke. Ipsilateral networks typically play a supportive role during motor tasks in the uninjured brain, but if the primary motor cortex is lesioned, the ipsilateral networks can act as primary networks (Feydy et al. 2002). These studies highlight the variable nature of connectivity changes seen in stroke. The reduced task-based connectivity for the stroke group seen in our study is consistent with these previously reported changes to cortical connectivity in people with stroke.

There were several limitations to this study. While we accounted and corrected for EEG data collection and analysis-related issues such as movement artifacts and volume conduction, we could not eliminate all contaminations due to extraneous muscle activity associated with arm stabilization. Specifically, it was difficult to separate EEG signal from EMG associated with arm and trunk stabilization during analysis. We secured the torso of the participants with straps to minimize trunk-related muscle activity, although we could not tightly secure the arm to the manipulandum due to EMG electrodes on the forearm of the participants. We monitored and minimized non-task-related movements in participants. We also observed the participants to ensure that they performed the task uniformly and according to the study protocol. However, we could not document the movement level using EMG as it was impossible to isolate the muscle involved in the task. The participants with stroke had low levels of impairment (average Fugl Meyer = 57.33 ± 8.33) and could perform the task successfully (full range of motion). However, as the presence of ERD has been noted during active, passive, and imagined movements (Pfurtscheller et al. 1999; Neuper et al. 2006; Wörtz and Pfurtscheller 2006), we cannot eliminate the possibility of imagined movement if the stroke participants were mentally coaching themselves during the study. We did not instrument the participants to document movement kinematics and timing, which might have aided in the interpretation of the results. We also had a heterogeneous group of stroke participants with varying, albeit smaller degrees of impairment (including but not limited to motor impairments), so we could not generalize the level of compensation across the stroke group.

In conclusion, our study has highlighted that at the end of a cued simple motor task, people with stroke demonstrated prolonged beta ERD and a lack of beta ERS. Contrary to controls, task-based connectivity did not show negative coherence between the ipsilesional hemisphere and the frontal region. Our results concur with other studies that have explored the spatial characteristics of cortical activation and connectivity but incorporated the temporal component that contributes to motor impairments, which is pertinent to volitional termination of muscle activation during a motor task in people with stroke.

The authors did not receive support from any extramural organization for the submitted work. The authors have no relevant financial or non-financial interests to disclose.

Data Availability Statement

The datasets generated during and analyzed during the current study are available from the corresponding author on reasonable request.

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Altered Cortical Activity During a Finger Tap in People with Stroke

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