*Copyright © 2017 Li, Zhuang, Niu, Bao, Xie and Lan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

*Kevin B. Wilkins 1,2, Meriel Owen1,2, Carson Ingo1 , Carolina Carmona1 , Julius P. A. Dewald1,2,3,4 and Jun Yao1,2\**

*1Department of Physical Therapy and Human Movement Sciences, Northwestern University, Chicago, IL, United States, 2Northwestern University Interdepartmental Neuroscience, Northwestern University, Chicago, IL, United States, 3Department of Biomedical Engineering, Northwestern University, Chicago, IL, United States, 4Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago, IL, United States*

#### *Edited by:*

*Xiaogang Hu, University of North Carolina at Chapel Hill, United States*

#### *Reviewed by:*

*Ping Zhou, University of Texas Health Science Center at Houston, United States Guang H. Yue, Kessler Foundation, United States*

*\*Correspondence: Jun Yao<EMAIL_ADDRESS>
#### *Specialty section:*

*This article was submitted to Stroke, a section of the journal Frontiers in Neurology*

> *Received: 25 April 2017 Accepted: 01 June 2017 Published: 14 June 2017*

#### *Citation:*

*Wilkins KB, Owen M, Ingo C, Carmona C, Dewald JPA and Yao J (2017) Neural Plasticity in Moderate to Severe Chronic Stroke Following a Device-Assisted Task-Specific Arm/ Hand Intervention. Front. Neurol. 8:284. doi: 10.3389/fneur.2017.00284*

Currently, hand rehabilitation following stroke tends to focus on mildly impaired individuals, partially due to the inability for severely impaired subjects to sufficiently use the paretic hand. Device-assisted interventions offer a means to include this more severe population and show promising behavioral results. However, the ability for this population to demonstrate neural plasticity, a crucial factor in functional recovery following effective post-stroke interventions, remains unclear. This study aimed to investigate neural changes related to hand function induced by a device-assisted task-specific intervention in individuals with moderate to severe chronic stroke (upper extremity Fugl-Meyer < 30). We examined functional cortical reorganization related to paretic hand opening and gray matter (GM) structural changes using a multimodal imaging approach. Individuals demonstrated a shift in cortical activity related to hand opening from the contralesional to the ipsilesional hemisphere following the intervention. This was driven by decreased activity in contralesional primary sensorimotor cortex and increased activity in ipsilesional secondary motor cortex. Additionally, subjects displayed increased GM density in ipsilesional primary sensorimotor cortex and decreased GM density in contralesional primary sensorimotor cortex. These findings suggest that despite moderate to severe chronic impairments, post-stroke participants maintain ability to show cortical reorganization and GM structural changes following a device-assisted task-specific arm/hand intervention. These changes are similar as those reported in post-stroke individuals with mild impairment, suggesting that residual neural plasticity in more severely impaired individuals may have the potential to support improved hand function.

Keywords: stroke, hand rehabilitation, EEG, cortical reorganization, voxel-based morphometry, functional electrical stimulation, gray matter, neuroplasticity

## INTRODUCTION

Nearly 800,000 people experience a new or recurrent stroke each year in the US (1). Popular therapies, such as constraint-induced movement therapy (CIMT), utilize intense task-specific practice of the affected limb to improve arm/hand function in acute and chronic stroke with mild impairments (2, 3). Neuroimaging results partially attribute the effectiveness of these arm/hand interventions to cortical reorganization in the ipsilesional hemisphere following training in acute and mild chronic stroke (4). Unfortunately, CIMT requires certain remaining functionality in the paretic hand to execute the tasks, and only about 10% of screened patients are eligible (5), thus disqualifying a large population of individuals with moderate to severe impairments. Recently, studies using device-assisted task-specific interventions specifically targeted toward moderate to severe chronic stroke reported positive clinical results (6–8). However, these studies primarily focus on clinical measures, but it is widely accepted that neural plasticity is a key factor for determining outcome (9–11). Consequently, it remains unclear whether moderate to severe chronic stroke [upper extremity Fugl-Meyer Assessment (UEFMA) < 30] maintains the ability to demonstrate neural changes following an arm/hand intervention.

Neural changes induced by task-specific training have been investigated widely using animal models (12). For instance, monkeys or rodents trained on a skilled reach-to-grasp task express enlarged representation of the digits of the hand or forelimb in primary motor cortex (M1) following training as measured by intracortical microstimulation (13, 14). Additionally, rapid local structural changes in the form of dendritic growth, axonal sprouting, myelination, and synaptogenesis occur (15–18). Importantly, both cortical and structural reorganization corresponds to motor recovery following rehabilitative training in these animals (19, 20).

The functional neural mechanisms underlying effective taskspecific arm/hand interventions in acute and chronic stroke subjects with mild impairments support those seen in the animal literature described above. Several variations of task-specific combined arm/hand interventions, including CIMT, bilateral task-specific training, and hand-specific robot-assisted practice, have shown cortical reorganization such as increased sensorimo tor activity and enlarged motor maps in the ipsilesional hemisphere related to the paretic arm/hand (21–24). These results suggest increased recruitment of residual resources from the ipsilesional hemisphere and/or decreased recruitment of contralesional resources following training. Although the evidence for a pattern of intervention-driven structural changes remains unclear in humans, several groups have shown increases in gray matter (GM) density in sensorimotor cortices (25), along with increases in fractional anisotropy in ipsilesional corticospinal tract (CST) (26) following task-specific training in acute and chronic stroke individuals with mild impairments.

The extensive nature of neural damage in moderate to severe chronic stroke may result in compensatory mechanisms, such as contralesional or secondary motor area recruitment (27). These individuals show increased contralesional activity when moving their paretic arm, which correlates with impairment (28, 29) and may be related to the extent of damage to the ipsilesional CST (30). This suggests that more impaired individuals may increasingly rely on contralesional corticobulbar tracts such as the corticoreticulospinal tract to activate the paretic limb (29). These tracts lack comparable resolution and innervation to the distal parts of the limb, thus sacrificing functionality at the paretic arm/hand (31). Since this population is largely ignored in current arm/hand interventions, it is unknown whether an arm/hand intervention for these more severely impaired post-stroke individuals will increase recruitment of residual ipsilesional corticospinal resources. These ipsilesional CSTs maintain the primary control of hand and finger extensor muscles (32) and are thus crucial for improved hand function. Task-specific training assisted by a device may reengage and strengthen residual ipsilesional corticospinal resources by training distal hand opening together with overall arm use.

The current study seeks to determine whether individuals with moderate to severe chronic stroke maintain the ability to show cortical reorganization and/or structural changes alongside behavioral improvement following a task-specific intervention. We hypothesize that following a device-assisted task-specific intervention, moderate to severe chronic stroke individuals will show similar functional and structural changes as observed in mildly impaired individuals, demonstrated by (i) a shift in cortical activity related to paretic hand opening from the contralesional hemisphere toward the ipsilesional hemisphere and (ii) an increase in GM density in sensorimotor cortices in the ipsilesional hemisphere.

## MATERIALS AND METHODS

#### Subjects

Eight individuals with chronic hemiparetic stroke (age: 63.5 ± 4) and moderate to severe impairment (UEFMA: 11–24) participated in this study. Clinical information for each subject is provided in **Table 1** and lesion locations in **Figure 1**. All individuals were screened for inclusion by a licensed physical therapist. Inclusion criteria include a UEFMA between 10 and 30 out of 66, no cognitive

*AROM, active range of motion; BBT, Box and Blocks Test; BG, basal ganglia; FMA, Fugl-Meyer Assessment; IC, internal capsule; Occ, occipital lobe; Par, parietal lobe; Thal, thalamus; UE, upper extremity.*

or perceptual impairment, no botulinum toxin within the last 6 months, MRI compatibility, no lesion including sensorimotor cortices, the ability to elicit enough EMG activity at wrist/finger extensors, and the ability for the FES to generate a hand opening of at least 4 cm between the thumb and the index finger. This study was approved by the Northwestern University institutional review board, and all subjects gave informed consent.

#### Experimental Protocols Intervention

Subjects participated in a 7-week intervention consisting of three 2-h visits per week. During the visit, subjects completed 20–30 trials of the following sequence of movements: (1) reaching out toward a jar, (2) driving the wrist/finger extensors to open the paretic hand, (3) grabbing the jar, (4) bringing the jar back toward themselves, and (5) releasing the jar. The weight, distance/ height, and orientation of the jar relative to the subject were progressively altered to increase the challenge to each subject, as determined by the physical therapist. All subjects started the motor task with the arm supported by the table. Depending on ability, subjects were encouraged to progressively lift the paretic limb actively. During the task, a novel EMG-FES device, called ReIn-Hand, was used to assist paretic hand opening (see Figure S1 in Supplementary Material). This device recorded EMG activities from eight muscles (deltoid, biceps brachii, triceps, extensor communis digitorum, extensor carpi radialis (ECR), flexor digitorum profundus, flexor carpi radialis (FCR), and abductor pollicis). While the user performed the functional reaching and opening, the ReIn-Hand detected hand opening by extracting EMG features to trigger an Empi transcutaneous electrical neuro-stimulation device (Vista, CA, USA). The stimulation electrodes were applied to the wrist/finger extensors with the following settings: biphasic waveform, frequency = 50 Hz ± 20%, pulse width = 300 μs, amplitude = sufficient for maximal hand opening without discomfort, and duration = 3 s. The novelty of this device is that even with the increased expression of the flexion synergy at the elbow (33), wrist, and fingers (34, 35) during reaching that is prevalent in this population, the device can still detect the hand opening and drive the paretic hand open, thus allowing for a user-driven stimulation to support functional usage of the paretic hand and arm. All participants could successfully use the device to complete the described task (including opening, grasping, and releasing), although some subjects experienced difficulty in sufficiently supinating the hand when releasing the jar to keep it upright on the table. Additionally, the physical therapist stretched the hand and arm at the beginning of the experiment and between trials to effectively elicit hand openings with the EMG-FES device.

## Pre- and Post-Intervention Tests

#### *Clinical Assessments*

For each subject, within 1 week prior to and following the intervention, a licensed physical therapist completed a set of clinical assessments, with the motor-related parts including UEFMA, Box and Blocks Test (BBT), and active range of motion (AROM) averaged over the II and V digit.

#### *Structural Imaging of the Brain*

Within 2 weeks prior to and following the intervention, subjects participated in MRI scans at Northwestern University's Center for Translation Imaging on a 3 TS Prisma scanner with a 64-channel head coil. Structural T1-weighted scans were acquired using an MP-RAGE sequence (TR = 2.3 s, TE = 2.94 ms, FOV 256 mm × 256 mm) producing an isotropic voxel resolution of 1 mm × 1 mm × 1 mm. Visual inspection of acquired images was performed immediately following the data acquisition to guarantee no artifacts and stable head position.

#### *Functional Imaging Related to Hand Opening*

Within 1 week prior to and following the intervention, subjects also participated in an EEG experiment. During the EEG experiment, participants sat in a Biodex chair (Biodex Medical Systems, Shirley, NY, USA), which restrained the trunk with straps crossing the chest and abdomen. The subject's paretic arm was placed in a forearm-hand orthosis attached to the end effector of an admittance controlled robotic device (ACT3D) instrumented with a six degree of freedom load cell (JR3, Inc., Woodland, CA, USA). At the beginning of each trial, subjects moved their hand to a home position, with the shoulder at 85° abduction, 40° flexion, and the elbow at 90° flexion angle. The subject then received an auditory cue. Following the cue, subjects relaxed at the home position for 5–7 s and then self-initiated a maximum attempted paretic hand opening with the arm resting on a haptic table. Subjects were instructed to avoid eye movements by focusing on a point and avoid movements of other body parts during the performance of each trial, which was visually confirmed by the experimenter. Subjects performed 60–70 trials of attempted paretic hand opening, broken into blocks (one block consisted of 20–30 trials). Rest periods varied between 15 and 60 s between trials and 10 min between blocks. The typical duration of the experiment was around 5–6 h, including ~2 h of setup, ~1 h for lunch, and ~2 h of data collection.

Scalp recordings were made with a 160-channel high-density EEG system using active electrodes (Biosemi, Inc., Active II, Amsterdam, The Netherlands) mounted on a stretchable fabric cap based on a 10/20 system. Simultaneously, EMGs were recorded from the ECR, FCR, and intermediate deltoid of the paretic arm. All data were sampled at 2,048 Hz. The impedance was kept below 5 kΩ for the duration of the experiment. Additionally, the positions of EEG electrodes on the subject's scalp were recorded with respect to a coordinate system defined by the nasion and preauricular notches using a Polaris Krios handheld scanner and reflective markers (NDI, ON, Canada). This allowed for coregistration of EEG electrodes with each subject's anatomical MRI data. Due to post-stroke abnormal synergy, finger/wrist extensors and flexors, and often the shoulder abductors, usually co-activate together when performing maximal hand opening (34). Therefore, in order to provide a reliable indicator of movement onset, EMGs were simultaneously recorded from the ECR, FCR, and anterior deltoid (IDL) of the paretic arm.

#### Data Analysis Reorganization of Cortical Activity Related to Hand Opening

EEG data were aligned to the earliest EMG onset of the three muscles and segmented from −2,200 to +200 ms (with EMG onset at 0 ms) using Brain Vision Analyzer 2 software (Brain Products, Gilching, Germany). Data were then visually inspected for the presence of artifacts. Trials exhibiting artifacts (e.g., eye blinks) were eliminated from further analysis. The remaining EEG trials were baseline-corrected (from −2,180 to −2,050 ms), low-pass-filtered at 70 Hz, and ensemble-averaged. The averaged EEG signals were down-sampled to 256 Hz and imported into CURRY 6 (Compumedics Neuroscan Ltd., El Paso, TX, USA). The cortical current density strength (μA/mm2 ) in the time between 150 and 100 ms prior to EMG onset was computed using the standardized low-resolution electromagnetic brain tomography (sLORETA) method (Lp = 1) based on a subject-specific boundary element method model with the regulation parameter automatically adjusted to achieve more than 99% variance counted (36, 37). Possible sources were located on a cortical layer with 3 mm distance between each node. Although the inverse calculation was performed over the whole cortex, only the activity in bilateral sensorimotor cortices was further analyzed. Specific regions of interest (ROI) included bilateral primary sensorimotor cortices [primary motor cortex (M1) + primary sensory cortex (S1)] and secondary motor cortices [supplementary motor area (SMA) + premotor area (PM)].

To investigate the shift of cortical activity related to hand opening, we used the estimated current density strengths to calculate a laterality index [LI = (I − C)/(I + C)], where I and C are the current density strengths from the ipsilesional and contralesional sensorimotor cortices, respectively (i.e., combined primary sensorimotor and secondary motor cortices). LI reflects the relative contributions of each cerebral hemisphere to the source activity, with a value close to +1 for an ipsilesional source distribution and −1 for a contralesional source distribution.

Additionally, we quantified a cortical activity ratio CAR <sup>=</sup> ∑ ∑ *S S n n m m* 1 for each of the four ROIs, where *S* represents the

1 current density strength of one of the nodes, and *n* and *m* represent the number of nodes in the ROI and whole sensorimotor cortices, respectively. The cortical activity ratio reflects the relative strength from one ROI as normalized by the total combined strength of the four ROIs.

#### Structural Changes in GM Density

Anatomical T1 data were analyzed with FSL-voxel-based morphometry (VBM) 1.1 (https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FSLVBM; Oxford University, Oxford, United Kingdom) (38) using FSL tools (39). First, T1 images for participants who have left hemisphere lesions were flipped to ensure that the lesions of all subjects were in the right hemisphere. The T1 images were then brain-extracted using the Brain Extraction Tool and segmented into GM using FAST4. The resulted GM partial volume images were aligned to Montreal Neurological Institute (MNI) 152 standard space using the affine registration tool FLIRT and averaged to create a study-specific GM template. Subsequently, individual GM partial volume images in native space were non-linearly registered to this template using FNIRT, modulated to correct for local expansion or contraction due to the non-linear component of the spatial transformation, and then smoothed with an isotropic Gaussian kernel with a sigma of 3 mm. Finally, a voxel-wise General Linear Model was applied with Threshold-Free Cluster Enhancement (40) to detect changes in GM density following the intervention. Voxel-based threshold of changes in GM density was set at *p* < 0.001 uncorrected.

#### Statistical Analysis

Statistics were performed using SPSS (IBM, V23). Clinical and neural measures were examined for normality using a Shapiro– Wilk test. A Wilcoxon signed rank test was used if assumptions of normality were not met. A paired *t*-test was performed on LI. A 2 (time) × 4 (region) repeated measures ANOVA was performed on the cortical activity ratio. We performed *post hoc* paired *t*-tests when a main ANOVA effect was found. Significance was set at *p* < 0.05. Individual data are depicted for all significant findings.

#### RESULTS

#### Changes in Arm/Hand Function following EMG-FES Task-Specific Training

**Table 1** shows pre and post BBT and AROM scores. Notably, most subjects initially scored a 0 on the pre-assessment BBT and showed 0° of AROM due to the severity of their motor impairments at the arm/hand. The clinical data violated the assumptions of normality based on the Shapiro–Wilk test. Therefore, a Wilcoxon signed rank test was used and reported a significant increase in BBT following the intervention (average increase of 1.9 blocks per minute, *p* = 0.03; **Table 1**) and AROM (average increase of 9.9°, *p* = 0.03; **Table 1**), indicating improvement of paretic arm/hand control, although FMA did not change.

## Cortical Reorganization Related to the Hand

**Figure 2A** shows an example of ensemble-averaged EEG for the 160 channels for Subject 1. There is a clear baseline from roughly −2 to −1.5 s prior to EMG onset and then a slow increase in electrical potential when approaching EMG onset, consistent with the Bereitschaftspotential. The reconstructed cortical activity for Subject 1 while performing hand opening on the table is depicted in **Figure 2B** pre-intervention and in **Figure 2C** post-intervention. This subject showed bilateral activity in sensorimotor cortex prior to the intervention as seen in **Figure 2B** and dominant ipsilesional activity following the intervention as seen in **Figure 2C**. We quantified the pre- and post-intervention LI in each of the participants (see results in **Figure 3**). A paired *t*-test found a significant increase in LI following the intervention [*t*(7) = 3.09, *p* = 0.02], signifying a post-intervention shift toward the ipsilesional hemisphere.

To further investigate regions responsible for the postintervention LI changes, we quantified the pre- and postintervention cortical activity ratios for primary sensorimotor (M1/S1) and secondary motor (SMA/PM) cortices (see results in **Figure 4**). A 2 (time) × 4 (region) repeated measures ANOVA found a significant time × region interaction [*F*(1,7) = 3.47, *p* = 0.03]. *Post hoc* paired *t*-tests found that following the

intervention, there was a decrease in the cortical activation ratio in contralesional M1/S1 (*p* = 0.04) and a trending increase in ipsilesional SMA/PM (*p* = 0.06) related to paretic hand opening.

## GM Density

Following the intervention, subjects displayed significantly greater GM density in M1 and S1 in the lesioned hemisphere (*x* = 52, *y* = −16, *z* = 30, *t*-value = 2.55, *p* < 0.001) and a decrease in GM density in M1 and S1 in the non-lesioned hemisphere (*x* = −46, *y* = −20, *z* = 60, *t*-value = 2.41, *p* < 0.001; *x* = −44, *y* = −18, *z* = 36, *t*-value = 2.79, *p* < 0.001) as depicted in **Figures 5A,B**.

intervention for paretic hand opening. Positive LI indicates predominantly ipsilesional activity. \*indicates *p* < 0.05.

Additionally, subjects displayed greater GM density in the thalamus in the lesioned hemisphere (*x* = 2, *y* = −20, *z* = 10, *t*-value = 3.13, *p* < 0.001) as shown in **Figure 5C**. A complete list of significant regions is provided in Table S1 in Supplementary Material.

## DISCUSSION

The present study investigated neural changes in individuals with moderate to severe stroke following an EMG-FES-assisted task-specific arm/hand intervention. Specifically, we found a shift of sensorimotor cortical activity related to hand opening from contralesional to ipsilesional cortex, along with structural changes in the form of increased ipsilesional M1/S1 and decreased contralesional M1/S1 GM density. Although similar deviceassisted hand/arm training in this population has been investigated before to examine behavioral improvements (7, 41, 42), this study provides evidence for corresponding neural changes even in this more severe chronic population.

## Shift toward Ipsilesional Hemisphere

As expected, before the intervention, subjects showed cortical activity predominantly from the contralesional hemisphere related to open the paretic hand, as reflected by the overall negative LI. This contralesional activity may suggest an increased reliance on low-resolution contralesional corticobulbar pathways such as the corticoreticulospinal tract (31, 43) for general paretic arm function. In fact, more severely impaired subjects actually tend to involuntarily close the hand and activate shoulder muscles when asked to open (35), which may reflect this increased reliance on ipsilateral corticobulbar pathways that innervate primarily flexor hand and proximal muscles compared to extensors (44). These

depicted on sagittal, coronal, and axial sections (left to right) on Montreal Neurological Institute T1 slices. Sections show the maximum effect on (A) ipsilesioned M1/S1, (B) contralesional M1/S1, and (C) ipsilesional thalamus. Les indicates the side of the lesioned hemisphere. Color maps indicate the t values at every voxel. A statistical threshold was set at *p* < 0.001 uncorrected.

pathways lack sufficient innervation to extensor muscles of the hand to produce appropriate hand opening (45) and are often associated with greater motor impairment (29, 31).

Effective hand/arm interventions in mildly impaired poststroke individuals have reported a post-intervention shift toward ipsilesional sensorimotor areas (46, 47). This shift is thought to be a beneficial since it may indicate increased use of ipsilesional CSTs, which maintain the primary innervations to the extensor muscles of the hand (32). Intervention-induced shifts toward the ipsilesional hemisphere have rarely been investigated in more severely impaired post-stroke individuals, especially not for arm/hand training partially due to the lack of inclusion of these subjects in arm/hand interventions. In this study, we found that a ReIn-Hand-assisted arm/hand intervention induced a positive change in LI. Our results suggest that even moderate to severe chronic stroke subjects maintain the ability to show similar cortical reorganization back toward the ipsilesional hemisphere following task-specific training as seen in more mild subjects. This ipsilesional shift may suggest decreased recruitment of contralesional corticobulbar pathways and increased reliance on ipsilesional CSTs during paretic hand opening, which may allow for greater functionality at the hand as seen by the increase in BBT and AROM. Additionally, it could reflect increased ability to actually drive hand opening when instructed rather than involuntary closing and activating proximal muscles (35). It is worth noting that only six out eight participants exhibited this intervention-induced shift despite all showing improvements on either BBT or AROM, possibly reflecting compensatory behavioral strategies following the intervention rather than recovery in these two participants.

## Changes in Cortical Activity Driving LI Shift

We calculated the cortical activity ratio in each sensorimotor region to further elucidate which regions were contributing to the LI shift. Following the intervention, subjects showed decreased activity in contralesional primary sensorimotor cortex (M1/S1) and a trending increase in ipsilesional secondary motor cortex (SMA/PM).

Increased contralesional primary sensorimotor cortex activity is associated with greater impairment following stroke (48, 49) and greater damage to CST (50, 51). Therefore, this decreased activity could reflect either decreased recruitment of contralesional descending motor pathways or changes in interhemispheric balance between primary sensorimotor cortices (52) and thus allow for increased functional usage of the affected hand.

Stroke patients tend to activate secondary motor areas more following greater CST damage (51) and show positive correlations between ipsilesional secondary motor area activation and recovery (53, 54). The increased recruitment of ipsilesional SMA/PM may be due to increased recruitment of direct projections to the spinal cord (55), although these connections are not as efficacious as connections from M1 to the spinal cord (56). Alternatively, plasticity within intrinsic cortico-cortico neuronal connections in M1 (57) may allow increased communication between SMA/PM and M1 following injury. Thus, ipsilesional secondary motor areas may serve as a potential avenue for functionally relevant cortical reorganization *via* either descending or intrinsic connections in addition to removal of contralesional cortical activity.

## Increased GM Density in Ipsilesional Sensorimotor Cortex

Previous work demonstrated significant decreases in GM volume in ipsilesional precentral gyrus following a subcortical stroke, which was associated with greater impairment (58). However, following task-specific training, mild chronic stroke subjects showed increases in GM density in ipsilesional sensorimotor cortex (25), and increases in perilesional GM density were associated with better recovery in acute stroke (59). Similarly, we found increased ipsilesional M1/S1 GM density following the intervention in our moderate to severe stroke population. Additionally, a significant positive correlation was found between changes in LI and changes in GM density in ipsilesional M1/S1 following the intervention (*R*<sup>2</sup> = 0.70, *p* < 0.05; Figure S2 in Supplementary Material), showing that activity shifting to the ipsilesional hemisphere was associated with increased ipsilesional M1/S1 GM density.

Increases in GM density may suggest potential synaptogenesis, dendritic growth, or gliogenesis at the cortex (60). Thus, these changes may be due to new synapse formation and dendritic growth commonly seen in animal training models (61). Additionally, these subjects likely experienced cortical atrophy prior to the intervention due to disuse of the paretic limb, which may have been partially remedied following the intervention due to increased use of the paretic arm/hand. Despite greater damage to ipsilesional descending motor tracts, these severely impaired individuals demonstrate the ability to reorganize ipsilesional primary sensorimotor cortices.

In these more severely impaired post-stroke individuals, we also found intervention-induced decreases in contralesional M1/S1 GM density, which were not reported before in mildly impaired individuals. This decrease may be specific to more severe patients since post-stroke, increased use of the contralesional hemisphere occurs to a greater degree in severely impaired individuals compared with milder individuals (29). The decrease in GM density in contralesional M1/S1 may indicate a decrease in dendritic complexity or synapses in these areas (62). These structural changes may be a result of decreased activation in these areas due to decreased recruitment during movement or overall decreased use (63, 64). Alternatively, they may be due to decreased tonic activity in these contralesional sensorimotor areas, which is thought to be a contributor to hyperexcitability in the brainstem and subsequent increased tone in this population (65, 66).

The increases in GM density seen in the thalamus in our results may be due to the repeated use of electrical stimulation throughout the intervention. Although we focused on the motor changes in this study, it is likely that these subjects show sensory neural changes as well due to the augmented afferent feedback generated by the EMG-FES device. Therefore, it is not surprising to see changes in the thalamus due to its central role as a sensory relay station for both the cutaneous and proprioceptive sensory modalities (67).

## Limitations

The main limitation of the current study is the small sample size. Despite the relatively small n, we observed consistent patterns of functional and structural changes. These changes signify the importance of examining the potential neural mechanisms found here in a larger population of moderate to severe chronic stroke subjects. Additionally, there was no control group in the present study. However, this study was aimed at investigating whether this population maintained the ability to show neural changes following an intervention, rather than answering the question of what is the optimal intervention for this population. Another potential confounding factor from the task-specific intervention is the amount of stretching. However, stretching on its own is unlikely to drive the functional and structural changes found in this study (68), even though it may temporarily reduce the stretch reflex activation of wrist and finger flexors (69). Additionally, reduced flexion synergy and subsequent decreased involuntary shoulder abduction/adduction force generation during hand opening (34) could contribute to interventioninduced changes in LI.

One of the primary long-term goals of the current study is to substantially increase the population included in task-specific therapy. Although the current ReIn-Hand device allowed our cohort of moderate to severe chronic stroke individuals to participate in task-specific training, it does require both detectable extensor EMGs to drive the device and responsiveness to FES to create sufficient hand opening. In our experience, limiting our inclusion criteria to an FMA ≥10 satisfied these requirements in most of initially screened participants (18 out of 20). However, due to the current sample size, it is difficult to accurately specify the portion of individuals who could utilize the ReIn-Hand device. However, considering that only ~5% of nearly 800 poststroke individuals in the Clinical Neuroscience Research Registry hosted by the Rehabilitation Institute of Chicago and Northwestern University exhibit FMA scores less than 10, it clearly substantially increases coverage compared with conventional task-specific training.

#### CONCLUSION

The present study shows the ability of even moderate to severe chronic stroke subjects to show cortical reorganization at both the functional and structural levels following a device-assisted task-specific intervention in a manner resembling that seen in mild chronic stroke subjects. Despite the tendency to focus on acute or mild chronic stroke patients in hand function rehabilitation, the current study encourages the continued push to use devices to involve moderate to severe chronic stroke subjects in task-specific arm/hand rehabilitation.

## ETHICS STATEMENT

This study was carried out in accordance with the recommendations of Northwestern University Institutional Review Board

## REFERENCES

with written informed consent from all subjects. All subjects gave written informed consent in accordance with the Declaration of Helsinki. The protocol was approved by Northwestern University Institutional Review Board.

## AUTHOR CONTRIBUTIONS

KW helped with design of the intervention and neuroimaging pre-/posttests, acquired the EEG data, ran intervention sessions, conducted EEG and VBM data analysis, contributed to interpretation, and was primary author of the manuscript. MO acquired MRI data, aided in the VBM data analysis, and contributed to interpretation and manuscript writing. CI aided in MRI data acquisition, VBM data analysis, and contributed to interpretation and manuscript writing. CC helped with the design of the intervention, ran intervention sessions, acquired the EEG data, conducted pre/post clinical assessments, conducted clinical measure data analysis, and contributed to interpretation. JD helped with the design of the intervention and neuroimaging pre-/posttests and provided interpretation to the data as well as contributions to manuscript writing. JY was the primary designer of the intervention and neuroimaging pre-/posttests, ran the intervention sessions, aided in the acquisition of the EEG data, aided in EEG analysis, provided interpretation to the data, and contributed to manuscript writing.

#### ACKNOWLEDGMENTS

The authors want to acknowledge Dr. Justin Drogos for assistance with the intervention and Dr. Daniel Corcos for manuscript feedback.

## FUNDING

This work was supported by an HHS grant 90IF0090-01-00 (formerly DOE NIDRR H133G120287) and NICHD 2RO1H-D039343 grants.

## SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at http://journal.frontiersin.org/article/10.3389/fneur.2017.00284/ full/#supplementary-material.

stroke: a multicentre, parallel-group randomised trial. *Lancet Neurol* (2014) 13(2):159–66. doi:10.1016/S1474-4422(13)70305-3

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Copyright © 2017 Wilkins, Owen, Ingo, Carmona, Dewald and Yao. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

# Multiparameter electromyography analysis of the Masticatory Muscle activities in Patients with Brainstem stroke at Different head Positions

*Chuyao Jian1†, Miaoluan Wei 1†, Jie Luo1 \*, Jiayin Lin1 , Wen Zeng1 , Weitian Huang2 and Rong Song1*

*1 Key Laboratory of Sensing Technology and Biomedical Instrument of Guang Dong Province, Guangdong Provincial Engineering and Technology Center of Advanced and Portable Medical Devices, Sun Yat-sen University, Guangzhou, China, 2 Department of Stroke Rehabilitation, Guangdong Work Injury Rehabilitation Center, Guangzhou, China*

#### *Edited by:*

*Xiaoyan Li, University of Texas, United States*

#### *Reviewed by:*

*Xu Zhang, University of Science and Technology of China, China Jinsook Roh, Rehabilitation Institute of Chicago, United States*

*\*Correspondence:*

*Jie Luo<EMAIL_ADDRESS>
*† These authors have contributed equally to this work.*

#### *Specialty section:*

*This article was submitted to Stroke, a section of the journal Frontiers in Neurology*

> *Received: 03 March 2017 Accepted: 08 May 2017 Published: 29 May 2017*

#### *Citation:*

*Jian C, Wei M, Luo J, Lin J, Zeng W, Huang W and Song R (2017) Multiparameter Electromyography Analysis of the Masticatory Muscle Activities in Patients with Brainstem Stroke at Different Head Positions. Front. Neurol. 8:221. doi: 10.3389/fneur.2017.00221*

The performance of the masticatory muscle is frequently affected and presents high heterogeneity poststroke. Surface electromyography (EMG) is widely used to quantify muscle movement patterns. However, only a few studies applied EMG analysis on the research of masticatory muscle activities poststroke, and most of which used single parameter—root mean squares (RMS). The aim of this study was to fully investigate the performance of masticatory muscle at different head positions in healthy subjects and brainstem stroke patients with multiparameter EMG analysis. In this study, 15 healthy subjects and six brainstem stroke patients were recruited to conduct maximum voluntary clenching at five different head positions: upright position, left rotation, right rotation, dorsal flexion, and ventral flexion. The EMG signals of bilateral temporalis anterior and masseter muscles were recorded, and parameters including RMS, median frequency, and fuzzy approximate entropy of the EMG signals were calculated. Two-way analysis of variance (ANOVA) with repeated measures and Bonferroni *post hoc* test were used to evaluate the effects of muscle and head position on EMG parameters in the healthy group, and the non-parametric Wilcoxon signed rank test was conducted in the patient group. The Welch–Satterthwaite *t*-test was used to compare the between-subject difference. We found a significant effect of subject and muscles but no significant effect of head positions, and the masticatory muscles of patients after brainstem stroke performed significantly different from healthy subjects. Multiparameter EMG analysis might be an informative tool to investigate the neural activity related movement patterns of the deficient masticatory muscles poststroke.

Keywords: stroke, entropy, vestibular stimulation, masticatory muscles, median frequency

## INTRODUCTION

Stroke, a cerebrovascular disease with a high incidence and a high mortality rate, is divided into hemorrhagic and ischemic types (1). The cerebral damage in survived patients was often left with sequela of motor dysfunction and abnormal muscle activation. Patients frequently have disorders of masticatory system, with masticatory muscle activity, bite force, flexibility of tongue, lip force and chewing performance affected, especially after brainstem stroke (2). It is important for stroke patients to go through a rehabilitation training and recover the masticatory function. However, the impairments of the masticatory system are usually of a high heterogeneity, and the effect of the masticatory rehabilitation training should be enhanced. To improve the effect of training, the overall characteristics of the impaired masticatory system and their responses to neural stimuli in patients after stroke should be studied.

The motor nucleus of the trigeminal in the brainstem is responsible for providing motor innervation to the masticatory muscles (3). The vestibular stimuli caused by changing head positions can affect the performance of the masticatory muscles. Evidences shown in the studies of Funakoshi et al. (4) and Deriu et al. (5) proved that the activation of masticatory muscles was different at different head positions. On the other hand, Kushiro et al. (6) showed a reverse effect that chewing gums increased postural stability when people stood uprightly. Besides, animal experiments suggested that there could be an anatomical connection between the vestibular nuclei and trigeminal motoneurons in the brainstem (7–9). We hypothesized that the impairment of the brainstem might interrupt the responses of the masticatory system to the changes in head position. However, former studies were performed in healthy people. Therefore, the effect of changes in head position on the performance of the masticatory muscles needs further studies.

Surface electromyography (EMG) is a useful tool for quantifying the activation patterns of muscles. However, there were only a few studies evaluating the impairment of masticatory system based on an EMG signal analysis. Cruccu et al. (10) and Wang et al. (11) used a linear EMG parameter, root mean squares (RMS), to evaluate the excitability of muscles (12) and found that the activation of the masticatory muscles was lower in the affected side of patients poststroke during clenching. Another linear parameter, median frequency (MDF), is also widely used to describe spectral characteristics of EMG signals with a good specificity and sensitivity in reflecting the muscle electrophysiology (13, 14). Due to the non-linearity and complexity of EMG signals, it has been reported that non-linear EMG parameters should be included (15, 16). Entropy, as a non-linear parameter, is introduced to assess the complexity of the EMG signal. Approximate entropy (ApEn) and sample entropy were two of the most used entropy estimations. Giannasi et al. (17) evaluated the reliability of several EMG parameters in the masticatory muscles of cerebral palsy patients and demonstrated that RMS, MDF, and ApEn were most reliable. Fuzzy approximate entropy (fApEn) uses an exponential fuzzy function to enhance the consistency and monotonicity, and it is considered as an improved version of ApEn (18). Previous studies reported that fApEn was related to motor unit recruiting and firing (19). A combination of above linear and non-linear EMG analysis would help to obtain more information, from different perspectives, about the performance of the masticatory muscles.

This study aimed to investigate whether there existed a certain activation pattern of the masticatory muscles in patients after brainstem stroke. Fifteen healthy subjects and six brainstem stroke patients were recruited and requested to occlude as hard as possible at five different head positions: (1) upright position; (2) turning left by 30° (left rotation); (3) turning right by 30° (right rotation); (4) turning up by 30° (dorsal flexion); (5) turning down by 30° (ventral flexion). In the meanwhile, surface EMG signals of bilateral temporal anterior and masseter muscles were recorded and analyzed with RMS, MDF, and fApEn.

## MATERIALS AND METHODS

#### Subject Recruitment

Six brainstem stroke patients (five males, one female, mean age was 59.33 ± 13.79 years) were recruited from Stroke Rehabilitation Department at Guangdong Work Injury Rehabilitation Center. This study received permission from local ethics committee and volunteered to take part in this study. They all signed the written informed consent about the purpose and procedures of the study prior to the experiment. The neurologist accompanied beside the patients during the experiment to avoid accidents. The clinical diagnosis of the patients was evaluated by neurologist based on MRI or CT scanning images. No patient had a history of neurological disorders or symptoms prior to stroke. The Function Oral Intake Scale (FOIS) was used to evaluate the masticatory function of patients. Low scores indicated weak masticatory function. The exclusion criteria were: associated diseases such as dental problems, missing teeth (between the premolar and molar in the left and right side), temporomandibular disorder, feeling painful during clenching and changing head positions, and under an orthodontic treatment. The demographic and clinical characteristics of all the patients were listed in **Table 1**. Specially, one of the patients had no FOIS score, but he did not have any facial asymmetry and did a good job in the experiment.

Fifteen healthy subjects (8 males, 7 females, age: 22 ± 2 years) were recruited from Sun Yat-sen University. All subjects were in general good health and had a normal occlusion without any pathologic changes in the orofacial myofunction, masticatory system, or cervical spine. The inclusion criteria were completeness of natural permanent teeth, i.e., at least 28 teeth, including complete bilateral molar and premolar. Thirteen healthy subjects habitually chewed with their right sides. One of the remaining two subjects usually used the left side to chew, and the other had no habitually chewing side.

*M, male; F, female; H, hemorrhage; I, ischemia; FOIS, Function Oral Intake Scale; –, without the diagnosis of clinical doctor; L, left; R, right; W, without facial asymmetry.*

#### Recording System

Surface EMG signals were recorded with disposable Ag–AgCl bipolar electrodes. Before the placement of the electrodes, the skin beneath the electrodes was cleaned with 70% alcohol. Surface electrodes were positioned on the muscular bellies of bilateral temporal anterior and masseter muscles of subjects, paralleled to muscular fibers with an inter-electrode distance of 20 mm. For the temporal anterior muscle, the electrodes were placed vertically along the anterior margin of the muscle, while for the masseter muscle, at the lower third of the line between the lateral angle of eye and the gonial angle. Subjects were requested to clench to adjust the location of electrodes. The reference electrodes were positioned at the corresponding ipsilateral elbow joint.

The activation of muscles was recorded by a four-channel EMG amplifier, sampled at 1,000 Hz by a 16-bit data acquisition card (DAQ USB-6341, National Instrument Corporation, Austin, TX, USA). The bandwidth of the on-board analog band-pass filter was 10–500 Hz. A program was designed in LabVIEW™ (LabVIEW 2012, National Instruments Corporation, Austin, TX, USA) to store the data.

#### Experimental Protocol

**Figure 1** described the schematic diagram of the experimental setup. During the experiment, subjects seated straightly on a fixed chair with their hands put on the knees. The knee joints were kept at 90°.  Point O on the smooth wall represented the upright position. When the subjects turned their heads to straightly face the other four points, they actually turn their head 30° in the four directions: left, right, up, and down. The height of point O was determined by the height of the participant's eyes. *X*1 was measured before the experiment. The positions of the other four points on the wall representing head positions of left rotation, right rotation, dorsal flexion, and ventral flexion were determined by *D X* = × ° <sup>1</sup> tan30 .

A short training was conducted before the experiment. Then the subjects were requested to turn their head to straightly face the five different markers on the wall in turn to induce static vestibular stimuli. After each change in head position, the subjects followed the verbal instruction of beginning the clench at 7 s (**Figure 2**, command), such as "clench," and they occluded as hard as possible [to generate maximum voluntary clenching: maximal voluntary clenching (MVC)]. The occlusion phase of a trial lasted for 10 s. At each head position, the trial was repeated four times, so the task consisted of five 4-trial blocks. To avoid fatigue, there was a 30-s rest after each trial, and a 2-min rest between each two blocks.

#### Data Analysis

A representative example of raw EMG signals of the left masseter muscle of three subjects during MVC in the upright position was displayed in **Figure 2**. Patient 1 could not eat with mouth and was tube dependent, and his FOIS score was 1. Patient 2 had a FOIS score of 5. Although the two patients had different FOIS scores, their raw EMG signals were similar. The delay between the clenching command and the actual EMG clenching onset was due to the response time of the research assistant and the subject. The duration of the MVC phase was 10 s in each trial, and a 5-s analysis window was added from 3 to 8 s of the clench phase (**Figure 2**). The raw data were preprocessed with a fourth-order 10–300 Hz band-pass digital Butterworth filter. RMS, MDF and fApEn were calculated using the filtered EMG signals*.*

Root mean squares represented the amplitude of the signals, and it was calculated using the following formula:

$$\text{RMS} = \sqrt{\frac{1}{N} \sum\_{i} \mu\_i^2}$$

where *ui* was the filtered EMG signals (*i* = 0, 1, …, *N* − 1), and *N* was the length of the EMG signals. MDF was defined as the

frequency point that divided the spectrum into two equal parts. To calculate MDF, the following equation should be satisfied.

$$\sum\_{j=1}^{\text{MDF}} P\_j = \sum\_{j=\text{MDF}}^{M} P\_j = \frac{1}{2} \sum\_{j=1}^{M} P\_j$$

where *Pj* represents the power spectrum of the filtered EMG, *j* represents the *j*-th discrete frequency, and *M* represents the bandwidth of the power spectrum analysis.

The calculation steps of fApEn were as follows:

$$X\_i^m = \left\{\mu(i), \mu(i+1), \dots, \mu(i+m-1)\right\} - \mu(0), i = 1, 2, \dots, n$$

$$N - m + 1, n = N - m + 1$$

where *Xi <sup>m</sup>* represented an *m* dimensional vector reconstructed with the filtered EMG signals *u*(*i*). *u*0 (*i*) was the average value of the *m* discrete filtered EMG data, and was defined as *u i <sup>m</sup> u i <sup>j</sup> j m* 0 1 0 1 ( ) = + ( ) = − ∑ . The distance between two different *<sup>m</sup>*

dimensional vectors was calculated as

$$d\_{\vec{\eta}}^{\mathfrak{m}} = \max\_{i \star j} \left| X\_i^{\mathfrak{m}} - X\_j^{\mathfrak{m}} \right|$$

where *Xi m* and *Xj m* were the two reconstructed *m* dimensional vectors, and *dij <sup>m</sup>* was the distance between *Xi m* and *Xj m* . According to the concept of fuzzy entropy (18), the smaller the distance, the higher the similarity. The similarity degree of *Xi m* and *Xj m* was determined by a fuzzy function of *dij m* , *n* and *r*:

$$D\_{\vec{y}}^{\pi} \left( n, r \right) = \exp \left( - \left( \frac{d\_{\vec{y}}^{\pi}}{n} \right)' \right)$$

where *Dij m* denoted the similarity degree, *r* was the similarity tolerance should be predefined, the parameter *n* reweighted the contribution of distance *dij <sup>m</sup>*, e.g., a larger *n* indicating more contribution of smaller distances of *dij m* .

$$\mathcal{Q}^{m}\left(n,r\right) = \frac{1}{n-m} \sum\_{i=1}^{N-m} \left(\frac{1}{N-m} \sum\_{j=1, j\neq i}^{N-m} D\_{ij}^{m}\right)$$

where ∅*<sup>m</sup>*( ) *n r*, was averaged similarity. According to above equations, *Xi m*+ { }1 and ∅*<sup>m</sup>*+<sup>1</sup> could be calculated in the same manner, and

$$\text{fApEn}(m, n, r, N) = \ln \mathcal{Q}'''(n, r) - \ln \mathcal{Q}^{m+1}(n, r)$$

where fApEn was the fuzzy approximate entropy of an *N* sample time series.

It was important to set proper values of *m*, *r*, and *n* before calculating the fApEn. Previous studies recommended *m* to use 2 or 3 because the length of the physiological signal was often not long enough to satisfy the need of the lager value of *m* (18), and *m* = 2 was the choice in this study. The similarity boundary was determined by the value of *r* and *n*. A too narrow boundary increased the sensitivity to noise, while a too wide one resulted in information loss. Besides, Sun et al. used the EMG data of a stroke patient to test how the fApEn changed with the values of *N* and *r*, and they found that when *N* > 300 and *r* ranged from 0.02 to 1, there was no crossover effect in the performance of the fApEn (19). Consequently, *N*, *r*, and *n* were respectively set to 1,000, 0.15, and 2 in this study (18–20).

All above calculations were processed in Matlab (Matlab R2014a, MathWorks Inc., Natick, MA, USA).

#### Statistical Analysis

All the parameters were described as mean ± SD in the paper. The level of significance was set at 0.05 (*P* < 0.05). Levene test was used to test for normal distribution of the data. If the Levene test result showed no significant deviations from variance homogeneity, the data were analyzed by three-way analysis of variance (ANOVA) with repeated measures. The three factors were: subject (healthy and patient), muscle (left temporal, right temporal, left masseter, right masseter) and head position (upright position, left rotation, right rotation, ventral flexion, dorsal flexion). If a large variance with non-normal distribution in the data of a subject group, a non-parametric test should be selected to analyze the data (21). Song et al. (22) suggested that the Wilcoxon signed rank test should be applied to analyze the statistical significance in a small sample size of subjects with high heterogeneity. In the current study, the sample size of the patient group was small. It is complex to perform a non-parametric test of multi-factors and their interactions (23, 24). If the patients' data did not pass the Levene test and did not display normality, we would use the Wilcoxon signed rank test to analyze the withinsubject effects of muscle, head position and their interaction in the patient group based on manually paired data. In addition, if one of the effect was non-significant, we merged the data in the corresponding groups and performed the Wilcoxon signed rank test again to perform multiple comparison. If the healthy group passed the Levene test, a two-way analysis of variance (ANOVA) with repeated measures was used to evaluate the influences of the two within-subject factors. Bonferroni *post hoc* test was used to detect the subgroup differences after the ANOVA comparison. To compare the between-subject differences with unequalvariance data, the Welch–Satterthwaite *t*-test was applied and the significant within-subject effect was controlled. All the statistical procedures were computed using the Statistical for Social Science (SPSS) version 22.0.

#### RESULTS

**Figure 3** shows performance of the four masticatory muscles, in terms of RMS, MDF and fApEn, at different head positions. Some trends in the activities of masticatory muscles could be observed in the healthy subjects. For example, when they turned their head to the left, the muscles in the left was activated more than the contralateral ones', while the muscles in the right had higher RMS means when turning to the right. However, no such trend was observed in the patients. Besides, fApEn means in the patient group had a much larger SD, which surpassed the effect of head position.

The healthy subject's data passed the Levene test (RMS: *P* = 0.367; MDF: *P* = 0.563; fApEn: *P* = 0.667), but the patients' data did not pass the Levene test (RMS: *P*= 0.001; MDF: *P*= 0.013; fApEn: *P* < 0.001).

The two-way repeated measure ANOVA results showed that muscle had significant effect (RMS: *P* = 0.001; MDF: *P* = 0.416; fApEn: *P* = 0.179), but head position and muscle × head position interaction had no significant effect (head position: *P* = 0.905; interaction: *P* = 0.818). Bonferroni *post hoc* test showed that the RMS mean of the left masseter muscle was significantly lower than all the other muscles.

The Wilcoxon signed rank test conducted in the patient group showed a significant effect of Muscle but no significant effect of Head position (data not shown) and muscle × head position interaction (**Table 2**). Besides, the Wilcoxon signed rank test (**Figure 4**) indicated that the *RMS* means of any paired muscles were significantly different (*P* < 0.007) except that of the right masseter and temporal muscles, the scores of bilateral masseter muscles were significantly higher than those of bilateral temporal muscles in MDF (*P*< 0.005), and the masseter muscles had significantly higher fApEn means than the ipsilateral temporal muscles (*P* < 0.005).

We observed significant between-subject *RMS* differences in all muscles except the left masseter muscle (*P* < 0.001). The patient group showed significantly higher *MDF* means in the bilateral masseter muscles (*P* < 0.001). Besides, significant lower *fApEn* means were detected in all the muscles except the left masseter muscle (*P* < 0.005) (**Figure 4**).

For each parameter, we plotted a case-by-case plot to compare each patient's mean value with the subject mean in the healthy group (**Figure 5**). We found that the *RMS* means of Patients 1–4 and Patients 5 and 6 clustered into two patterns: Type I and Type II. Type I was characterized by significantly less activation in the right masticatory muscles, while Type II was characterized by equal weakness of all the four muscles. For the MDF, we found that all the patients' data varied in the same way, and the mean values of the bilateral masseter muscles laid above the healthy subjects' corresponding means. The fApEn case-by-case plot showed a large heterogeneity of patients.

Figure 3 | The root mean squares (RMS) (A), median frequency (MDF) (B), fuzzy approximate entropy (C) means and SDs of the four masticatory muscles at different head positions in healthy subjects and patients. Temp., temporal anterior muscle; Mass., masseter muscle; Pos., position; Rot., rotation; Flex., flexion.

#### Table 2 | The result of the Wilcoxon signed rank test for statistical significance of muscle **×** head position interaction on the three electromyography parameters.

*Head positionsi : one of the head positions in the task; Head positionsj : a different head position which was used to pair with Head positionsi ; Temp., temporal anterior muscle; Mass., masseter muscle; Pos., position; Rot., rotation; Flex., flexion.*

## DISCUSSION

This study was designed to investigate the activation pattern of masticatory muscle at different head positions in the healthy subjects and brainstem stroke patients when performing the maximal voluntary clenching (MVC). RMS, MDF and fApEn were used to analyze the EMG data. Significant differences between the healthy subjects and the patients were found in certain muscles.

## Comparison between the Healthy Subjects and Stroke Patients

The lower RMS means of the stroke patients' right masticatory muscles compared with those of the healthy subjects' implied that the muscle excitation of their right muscles was quite low. Wang et al. (11) and Cruccu et al. (10) found that the affected side activated lower during clenching. The reduction of the muscle activation in patients was consistent with previous studies.

Figure 4 | The root mean squares (RMS) (A), median frequency (MDF) (B), fuzzy approximate entropy (fApEn) (C) means and SDs of the four masticatory muscles in healthy subjects and patients. \**P* < 0.05; Temp., temporal anterior muscle; Mass., masseter muscle.

Compared to healthy subjects, the MDF means of the bilateral masseter muscles in patients was higher. Since MDF was associated with the conduction velocity in the muscle fiber and the action duration of motor unit (25), a higher MDF mean might suggest a higher conduction velocity and a smaller action duration. The shift of the spectrum toward higher frequencies might implied that, after brainstem stroke, the proportion of fast-twitch motor units that were enrolled in masticatory movement was lower (26). Another possible interpretation of the spectrum shift might be the deficiency of the monoaminergic neurons in the brainstem poststroke. The monoaminergic input amplifies the small motor neurons' current non-linearly and causes the slowtwitch motor units to sustain the contraction even after the action potential is over (27). After brainstem stroke, the monoaminergic regulation of the input–output properties might be affected (28, 29), and the contribution of the slow-twitch motor units in the masseter muscles was then lowered.

The fApEn means of all the muscles in the stroke patients were smaller than those in healthy subjects. Previous studies reported that fApEn might be related to the amount of recruited motor units and the firing rate (30). Therefore, the decrease of fApEn in the three muscles might be due to the changes in the neurological control after stroke, such as reduction of the number of recruited motor unit or lowering of the firing rate. The decreased firing rate in stroke patients was recently proven by a high-density EMG study (31). The large variability of fApEn in stroke patients suggested that large arithmetic cancels should happen when calculating means, hence some real situation might be hidden behind the fApEn means. In the case-by-case plot (**Figure 5C**), Patient 3 and 6 showed marked lower fApEn values of the left masseter muscle, while the other patients' data lied around the healthy subject's fApEn mean.

#### Influences of Muscles

For healthy subjects, the right masseter muscle yielded a higher RMS compared to the left one, which could be interpreted by different activations of the working-side and the balancing-side of the masseter muscles (32). It might reflect the truth that most (13/15) of the healthy subjects in this study habitually chewed with their right side. However, we observed no significantly different MDF and fApEn among different muscles. It might suggest that the neurological control of the jaw muscles in healthy subjects may be homologous.

For patients, mean values of the RMS and MDF between bilateral masseter muscles were statistically different, and the bilateral temporal muscles exhibited significantly different RMS but non-significantly different MDF. The lower RMS means of the right masticatory muscles might be due to the weakness of the right masticatory muscles. According to the case-by-case RMS plot, four patients exhibited right-side weakness, but only two of them had right face sagging (**Table 1**). EMG analysis might reveal the unseen weakness of the patients. MDF is a parameter often used to characterize fatigue (33), and both masseter and temporal muscles yield lower MDFs in a sustain clench fatigue experiment (34). Previous studies have reported that successive recruitment of new motor units might be the reason of spectrum shift during muscular fatigue (35). The task in our study was fatigue-free, and the higher MDF in the right masseter muscle might due to the smaller action duration of the motor units (36).

The large variability of the fApEn implied that it might be more sensitive to the pathological status of the patients. From the case-by-case fApEn plot, we observed that most of the data points of patients were lower than the corresponding healthy subjects' means, which was in agreement with previous study showing smaller fApEn in the patient group (37). Combined fApEn with RMS, we might obtain more information about the masticatory function of the patient. For example, although Patient 5 and 6 had Type II RMS muscle pattern, the right masseter muscle's data of Patient 5 lied within the mean ± SD interval of healthy subjects, while Patient 6's data lied markedly beneath that interval. It suggested a larger deficiency of the neuromuscular control of the right side in Patient 6, and this patient had a form of right face sagging (**Table 1**).

#### Influences of Head Positions

Changing head position is a kind of static vestibular stimulation (5), which interferences balance control. In the other hand, occlusion is widely interrelated to gaze stabilization (38) and balance control (39) through activating complex nervous reflexes (40). A previous study (4) observed two types of EMG responses of the masticatory muscles to changes in head position. In the balanced type, the corresponding bilateral muscles (e.g., left and right temporal muscles) were activated equally when subjects performing ventral and dorsal flexion, while ipsilateral activation was observed in the rotation or tilting of the head. The unbalanced type exhibited asymmetrical and irregular activations. Although our results showed no statistical significance of head position, there exhibited similar trends to Ref. (4), i.e., the right muscles of the healthy subjects were much more activated when rotating to the right. The neuromuscular mechanism of changes in head position influencing muscle activation might be due to the tonic neck reflex, which was demonstrated with rats (41). The reasons why head position was a non-significant effect in our study might be due to the interindividual variation, also pointed out in the previous study (4), and the different experimental protocol we used, i.e., we instructed the subjects to clench as hard as possible instead of recording their rest EMG signals. Biting hard activated the whole muscle synchronously (42), and the weak tonic-neck-reflex origin EMG signal would be buried in the large firing signal. However, the sternocleidomastoid muscles would be co-activated (43) during maximum voluntary clenching. Therefore, the tonic neck reflex might be actually enhanced (44) although we could not see it. From the case-by-case EMG parameter values to head position curve of patients (data not show), we found again the high heterogeneity of patients' performance, but the clinical relevance was not clear.

## Limitation of the Study

In the current study, we have investigated the muscle activities in response to variations in head position using three EMG parameters in both the healthy subjects and brainstem stroke patients. However, several limitations still should be addressed. First, we recruited the healthy youths rather than age-matched healthy adults. It should be noted that age might be one of the factors that influenced the EMG parameters, but age effect was not included in this study. In this preliminary study, we mainly focused on whether the three EMG parameters were discriminating and suited for assessing the masticatory function poststroke. The age effect should be further studied in the future. Second, more patients should be recruited in the future study to improve the statistical power of the study.

## CONCLUSION

In this study, three EMG parameters, RMS, MDF and fApEn, were used to evaluate the activities of bilateral masseter and temporal muscles at different head positions in healthy subjects and brainstem stoke patients. We found that subject and muscle effected significantly on these parameters, but head position was a non-significant effect. The stroke patient group performed differently during MVC compared with healthy subjects and exhibited a large heterogeneity. Several patterns and trends were detected using multiparameter EMG analysis. Multiparameter EMG analysis might provide rich information and should be a potential useful tool of quantifying the neural activity related movement patterns of the deficient masticatory muscles poststroke.

## ETHICS STATEMENT

This study received permission from local ethics committee. All the participants were volunteered to take part in this study and signed the written informed consent about the purpose and procedures of the study prior to the experiment.

## AUTHOR CONTRIBUTIONS

Both CJ and MW conducted most of the experiments, collected and analyzed the data, interpreted the results, and finished the draft manuscript. JL designed the study, helped to analyze the data, and interpreted the results and revised the manuscript. JL and WZ participated in the data collection and analysis. WH conducted part of the experiments, recruited the subjects, collected data, and interpreted the results. RS designed the study and helped to interpret part of the results.

## ACKNOWLEDGMENTS

The authors would like to thank Yunchu Hou, Jiaru Cheng, and Yingjian Wu for doing the experiments and collecting the data. The authors also would like to thank Jiangtao Sun for revising the manuscript.

## FUNDING

This research was supported by the grant from National Nature Science Foundation of China (No. 61403430).

## REFERENCES

electromyographic activity in healthy subjects. *Cranio* (2009) 27(3): 159–66. doi:10.1179/crn.2009.024

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Copyright © 2017 Jian, Wei, Luo, Lin, Zeng, Huang and Song. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

*Chenyun Dai1 , Nina L. Suresh2,3, Aneesha K. Suresh4 , William Zev Rymer2,3 and Xiaogang Hu1 \**

*<sup>1</sup> Joint Department of Biomedical Engineering, University of North Carolina at Chapel Hill and North Carolina State University, Raleigh, NC, USA, 2Sensory Motor Performance Program, Rehabilitation Institute of Chicago, Chicago, IL, USA, 3Department of Physical Medicine and Rehabilitation, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA, 4Committee on Computational Neuroscience, University of Chicago, Chicago, IL, USA*

After a cerebral stroke, a series of changes at the supraspinal and spinal nervous system can alter the control of muscle activation, leading to persistent motor impairment. However, the relative contribution of these different levels of the nervous system to impaired muscle activation is not well understood. The coherence of motor unit (MU) spike trains is considered to partly reflect activities of higher level control, with different frequency band representing different levels of control. Accordingly, the objective of this study was to quantify the different sources of contribution to altered muscle activation. We examined the coherence of MU spike trains decomposed from surface electromyogram (sEMG) of the first dorsal interosseous muscle on both paretic and contralateral sides of 14 hemispheric stroke survivors. sEMG was obtained over a range of force contraction levels at 40, 50, and 60% of maximum voluntary contraction. Our results showed that MU coherence increased significantly in delta (1–4 Hz), alpha (8–12 Hz), and beta (15–30 Hz) bands on the affected side compared with the contralateral side, but was maintained at the same level in the gamma (30–60 Hz) band. In addition, no significant alteration was observed across medium–high force levels (40–60%). These results indicated that the common synaptic input to motor neurons increased on the paretic side, and the increased common input can originate from changes at multiple levels, including spinal and supraspinal levels following a stroke. All these changes can contribute to impaired activation of affected muscles in stroke survivors. Our findings also provide evidence regarding the different origins of impaired muscle activation poststroke.

Keywords: motor unit, coherence, stroke, synchronization, surface electromyogram

## INTRODUCTION

After a cerebral stroke, a series of changes at the spinal and supraspinal levels of the nervous system can influence the control of muscle activation, leading to different motor impairment. One convenient way of identifying the different levels of contributions to altered muscle activation is to characterize the discharge patterns of motor unit (MU), given that MU discharge activities at the populational level can now be readily obtained from the skin surface (1, 2). Since different alpha motor neurons receive common synaptic excitations from spinal and supraspinal pathways during sustained contraction (3–6), the discharging times of MU should exhibit some degree of correlation.

#### *Edited by:*

*Ayrton R. Massaro, Hospital Sirio-Libanes, Brazil*

#### *Reviewed by:*

*Rakesh Pilkar, Kessler Foundation, USA Dong Feng Huang, Sun Yat-sen University, China*

> *\*Correspondence: Xiaogang Hu<EMAIL_ADDRESS>
#### *Specialty section:*

*This article was submitted to Stroke, a section of the journal Frontiers in Neurology*

*Received: 18 November 2016 Accepted: 25 April 2017 Published: 15 May 2017*

#### *Citation:*

*Dai C, Suresh NL, Suresh AK, Rymer WZ and Hu X (2017) Altered Motor Unit Discharge Coherence in Paretic Muscles of Stroke Survivors. Front. Neurol. 8:202. doi: 10.3389/fneur.2017.00202*

**169**

The cross-correlation analysis of MU spike trains in the time domain has been widely used to study the connectivity between the motor neuron pool and the spinal or cortical inputs (7–11). However, the relative strength of correlation across different levels cannot be systematically quantified using this approach.

In contrast, the coherence analysis, which reflects the crosscorrelation of MU spike trains in the frequency domain, provides complementary and important information from another perspective that can reveal these activities of higher level control (12–14). Previous studies have established the findings that the coherence of MU spike trains under 60 Hz is critically important and can be separated into four different frequency bands, including delta band (1–4 Hz), alpha band (8–12 Hz), beta band (15–30 Hz), and gamma band (30–60 Hz) (5, 15–17). Moreover, physiological origins for the synchronization of each band have been widely accepted. Specifically, the delta band is thought to reflect the common modulation of firing rates (18, 19); the alpha band highly depends on the feedback from muscle spindles and possibly results from rhythmical activities of the spinal reflex loop (20, 21); the beta band may reflect cortical and subcortical activities (15, 19); and the gamma band also represents cortical activities (19).

Previous studies have found that the MU synchronization of certain frequency bandwidths change under atypical conditions. A recent study reported an increase of MU coherence in delta, alpha, and beta bands after muscle fatigue (17). Another recent study (22) examined the variation of coherence with muscle pain. Their results indicated that muscle pain led to an increase in the coherence of MU spike trains in the delta band, but a decrease in the alpha band. Given the direct and indirect cortical and subcortical projections to the motor neuron pool, it can be conjectured that the alteration of synaptic inputs to motor neurons may influence the MU coherence at different frequency bandwidths. To date, few studies have examined the possible changes of MU synchronization in stroke survivors (23, 24). These studies were mostly based on electroencephalogram—electromyogram (EMG) synchronization rather than the coherence of MU spike train itself—and did not specifically quantify the variation of coherence in each frequency corresponding to specific physiological implications.

Accordingly, the objective of our current study was to systematically investigate the potentially altered control at different levels in contribution to impaired finger muscle activation in stroke survivors. In our study, MU spike trains were acquired from the decomposition of surface electromyogram (sEMG) of the first dorsal interosseous (FDI) muscle at a wide range of index finger abduction forces on both paretic and contralateral sides of hemispheric stroke survivors. The discharge coherence of concurrently active MUs was calculated across four frequency bandwidths at different force levels and was compared between the affected and contralateral muscles. The finger muscle was examined because stroke survivors tend to show persistent impairment in finger muscle activation. Particularly, the FDI muscle was tested because FDI is the only muscle involved in index finger abduction force, which can avoid force contribution of other muscles, and because the FDI is superficial and is accessible from the skin surface during EMG recordings. Our findings provide evidence that there are substantial changes in the common input, arising from the spinal and supraspinal circuitry, to the motor neuron pool innervating affected muscles, which can modify the control of muscle activation of stroke survivors.

## MATERIALS AND METHODS

#### Experimental Apparatus and Procedures Participants

Experimental data from 14 chronic hemiparetic stroke participants (detailed demographic information is shown in **Table 1**) were acquired at the Rehabilitation Institute of Chicago. All participants provided written informed consent. The experimental protocols were approved by the Institutional Review Board (#STU00084379) at Northwestern University.

The major inclusion criteria for stroke participants included (1) ability to provide written informed consent; (2) ability to communicate with and understand the instructions of the experimenter; (3) the duration since stroke >6 months; (4) impairment level of hand function measured with the Chedoke-McMaster score ranged from 2 to 6; (5) no medication; (6) no upper extremity inflammation, recent injury, pain, or other concurrent severe medical illness; (7) no history of vascular impairment.

#### Experimental Setup

The detailed information about the experimental setup has been reported in a previous study (25, 26). Briefly, participants sat in the experimental apparatus with their upper arm comfortably placed on a support, their forearm oriented at a full pronation position, secured with a cast and located in a ring mount interface attached to a forearm rest, and their wrist held neutral with respect to flexion/extension (**Figure 1A**). Their middle, ring, and little fingers were abducted away from the index finger resting on a supporting frame. The index finger casted and fixed to another ring mount interface was placed at approximately 60° apart from the thumb and directly attached to a 6 degree-of-freedom load cell (ATI, Inc., Apex, NC, USA). The recorded isometric

#### Table 1 | Participant demographic information.

*Age, year of age; Years, years since stoke; Side, paretic side; Chedoke, Chedoke-McMaster stroke assessment ranging from 1 to 7, with 1 being the most severe impairment; Fugl-Meyer, Fugl-Meyer assessment ranging from 1 to 66, with 1 being the most severe impairment; M, male; F, female; R, right; L, left.*

index finger abduction forces were low-pass filtered at 200 Hz cutoff frequency and sampled at 2 kHz, and a high sampling rate is required to avoid aliasing effect from channel cross-talk. The participant's skin above the FDI muscle was scrubbed with alcohol pads to reduce background noise. A five-pin sensor array (Delsys, Inc., Natick, MA, USA) was placed on the skin surface. Five 0.5-mm diameter cylindrical probes are located at the center of sensor array within a 5 mm × 5 mm square (**Figure 1B**). The sensor array and a reference electrode were connected to a Delsys Bagnoli sEMG system to record sEMG signals generated by the pairwise differentiation of these five electrodes. The sEMG recordings were amplified by a 1,000 gain and filtered with a bandwidth of 20–2 kHz with a sampling frequency at 20 kHz (**Figure 1C**).

#### Procedures

The data collection included two main sessions (one for each side) with same experimental protocols. Before the two main sessions, participants were required to perform maximum voluntary contractions (MVCs) for 3 s. Three repeated trials with 60 s rest between trials were tested to obtain the largest force value, which was designated as the MVC. To ensure a fair comparison between two sides, the MVC of paretic side was used during the force tasks on the contralateral side, such that the two sides produced the same absolute forces. Then, participants performed five repeated trials for five isometric contraction levels at 20, 30, 40, 50, and 60% MVC. The force trajectory contained a 5-s quiescent period for baseline noise calculation, an up-ramp increasing at 10% MVC/s, a constant force at prescribed MVC for 12 s, a down-ramp decreasing at 10% MVC/s, and a final 3-second quiescent period. The order of the force levels was randomly selected for each participant. One minute of rest period was provided between trials to avoid cumulative fatigue.

## Data Analysis

#### Preprocessing

The sEMG and force recordings selected for further data analysis must satisfy the following requirements: (1) no sudden change in the up-ramp force; (2) the variability of force during steady state within ±2 SDs of background force level; (3) the sEMG signal with the baseline noise level within ±10 μV, and signal-to-noise ratio greater than 5 (signal-to-noise ratio was defined as peak– peak amplitude of the EMG signal at steady state contractions divided by peak–peak amplitude of the baseline noise). Based on the criteria above, three trials for each MVC level per participant were finally selected for the coherence analysis. During the analysis, the accepted number of MU must be over eight to be included in the analysis because the large number of MUs resulted in an accurate coherence estimate.

#### MU Acceptance

All raw sEMG recordings were automatically decomposed using Nawab's algorithm (1). The autodecomposition algorithm extracted the firing times, and four different MU action potential waveforms (from the four-channel EMG recordings) of each identified MU spike train. Then, a robust postexamination method, spike trigger average (STA) algorithm (27), was used to determine which MUs were retained for further analysis. The STA method reestimated MU action potential templates based on the firing times, MU action potential templates, and raw EMG signals obtained from Delsys. A high agreement of two different algorithms for each MU provided confidence regarding the reliability of decomposition results. The STA method calculated the coefficient of variation (CV) for peak–peak amplitude of MU action potential templates and the maximum correlation coefficient between STA MU action potential estimation and Delsys MU action potential templates. Only MUs with a mean correlation >0.7 and CV <0.3 across four channels were selected for data analysis based on our previous studies (17, 27, 28).

#### Coherence Calculation

The magnitude of coherence increased substantially with the number of MU spike trains selected. Therefore, using more MUs for coherence calculation was recommended (29, 30), providing a better estimation than a small number of MUs. Moreover, the coherence values can only be compared across different trials when the same amount of MU spike trains was analyzed. Since some trials only had a small number of accepted MU spike trains, eight MUs were selected for coherence calculation. For those trials with more than eight accepted MUs, the same amount of MU spike trains was randomly chosen from accepted MUs pool. A total of eight spike trains were randomly separated into two groups and then summed up into two composite spike trains (CSTs). The Welch's averaged, modified periodogram method (31), adopted by multiple previous MU coherence studies (16, 17, 22), was performed to calculate the magnitude of squared coherence *Cxy*(*f*) between the two CSTs:

$$C\_{xy}(f) = \frac{\left|P\_{xy}(f)\right|^2}{P\_{xx}(f)P\_{yy}(f)},\tag{1}$$

where *Pxy*(*f*) is the cross-spectrum mean of two CSTs, and *Pxx*(*f*) and *Pyy*(*f*) are, respectively, their autospectrum densities. The calculation of the coherence-square used the MATLAB function "mscohere" with a length of 1,024 sample segments tapered by a Hann window and overlapped by 75% to estimate the entire frequency spectrum. The data processing steps are summarized in **Figure 2**. To obtain a better coherence estimate, the parameter selection for the coherence calculation was based on a previous study (32). In addition, to reduce the effect caused by random selection, 100 repeated tests were operated and averaged to acquire final coherence estimation for each trial. [An exemplar coherence estimation of the affected and contralateral sides of a stroke survivor is shown in **Figure 3** (top)].

The confidence limit for coherence estimate was

$$\gamma\_{1-\alpha}^2 = 1 - \alpha^{\left[1/\left(\text{HDOF}-1\right)\right]},\tag{2}$$

where α is (1 − α)% confidence level, and EDOF is the equivalent degree of freedom for Hann window (33).

Four different bands—delta band (1–4 Hz), alpha band (8–12 Hz), beta band (15–30 Hz), and gamma band (30–60 Hz) were analyzed separately due to their different physiological meanings. We began by exploring the potential influence of three factors (two sides, four bands, and three MVC levels) on coherence. The magnitude of coherence for each band was quantized by its mean band coherence:

$$\text{MBC} = \frac{\int\_{f\_i}^{f\_i} \mathcal{C}\_{xy}(f) df}{B},\tag{3}$$

where *Cxy*(*f*) is the magnitude of squared coherence, *B* is the width of one frequency band, and *f*1 and *f*2 are the lower and upper bounds of the corresponding band, respectively.

#### Correlation Analysis

A linear regression was performed to investigate the relationship between the possible factors (age, years poststroke, and the severity of impairment measured with Fugl-Meyer assessment) and the increased percentage of coherence on the paretic side compared with the contralateral side. We chose Fugl-Meyer assessment

Figure 3 | Top: non-standardized coherence; bottom: z-coherence. An exemplar frequency-coherence plots from a representative stroke participant at 40% maximum voluntary contraction. The solid thick red line represents the coherence of the affected side, and the blue line represents the contralateral side. Thin lines present SE.

instead of Chedoke because the values of Fugl-Meyer assessment spread across a wider range from 1 to 66, which can give a better resolution for linear regression, and the correlation coefficient between Fugl-Meyer and Chedoke was 0.82. Therefore, only Fugl-Meyer was included in the regression.

#### Statistics

The coherence estimate was examined mainly on three aspects: comparisons of coherence amplitude across different MVC levels, comparisons between the affected and contralateral side, and comparisons of coherence amplitude across different frequency bands. The differences were tested statistically using a three-way repeated measures ANOVA, with *post hoc* pairwise comparisons conducted using Bonferroni correction method. A significance level of *p* = 0.05 was used. To satisfy the ANOVA test assumption, all coherence values (*C*) were transformed to Fisher's values (FZ), which has been used in previous MU coherence studies (16, 34, 35). The fisher's z-transformation equation is

$$\text{FZ} = \arctan h \sqrt{\text{C}}.\tag{4}$$

#### RESULTS

After decomposition and cross validation between STA and Delsys, the overall number of accepted MU spike trains is shown in **Table 2**. Low contraction levels that yielded a small number of MUs caused two main issues: (1) the number of accepted MUs cannot satisfy the requirement of coherence calculation (≥8). The "<8" column in **Table 2** presents the number of trials with number of MUs < 8; (2) the random grouping algorithm for coherence calculation may be biased due to a small MU pool. Therefore, 40, 50, and 60% MVC were used for the final data analysis, while 20 and 30% MVC were excluded. (See Section "Discussion" for further details regarding the choice of MU numbers.) For these participants, under 40, 50, and 50% contraction levels, the average number of accepted MU spike trains was 17.73 ± 5.57 per single trial for the affected side and 20.27 ± 6.38 for the contralateral side; while the corresponding mean firing rates were 13.55 ± 4.04 pulse per second (pps) and 12.90 ± 3.31 pps, respectively.

A three-way repeated measures ANOVA was tested across three isometric contraction levels (40, 50, and 60%) on two sides and four different frequency bands. The ANOVA results showed that there is an interaction [*F*(3,39) = 9.708, *p*< 10<sup>−</sup><sup>4</sup> ] between the side and frequency band. However, the third factor MVC showed no

Table 2 | The number of accepted motor units across different maximum voluntary contraction levels on two sides.

significant difference [*F*(2,26) = 1.666, *p*= 0.209], and also showed no interaction with other factors {[*F*(2,26) = 0.030, *p* = 0.971] between MVC and side; [*F*(6,78) = 0.982, *p* = 0.443] between MVC and frequency band; and [*F*(6,78) = 0.086, *p* = 0.997] for all three}.

The interaction (side × frequency band) led us to conduct further *post hoc* pairwise comparisons separately. First, we compared the difference between paretic side and contralateral side for each individual band. The pairwise comparisons showed that the coherences were significantly different for delta band (*p* = 0.013), alpha band (*p* = 0.005), and beta band (*p* = 0.022), between the affected and contralateral sides, but not different for gamma band (*p* = 0.717). The average z-transformed mean coherence across all participants for delta band, alpha band, and beta band on affected side were 0.2720 ± 0.0254, 0.3468 ± 0.0282, and 0.2881 ± 0.0150, which were higher than those on the contralateral side (0.2116 ± 0.0112, 0.2620 ± 0.0082, and 0.2625 ± 0.0078, respectively, as shown in **Figure 4**). The baseline coherence was 0.2123 ± 0.004 and is comparable to the gamma band coherence.

Second, *post hoc* pairwise comparisons were tested across four bands on two sides separately, and both showed a significant difference. For the paretic side, **Figure 5** (top) shows *post hoc* evaluation, which revealed that (1) the alpha band had the highest mean coherence (*p* < 0.05); (2) the gamma band had the lowest mean value (*p* < 0.05); (3) the magnitude of delta band and beta hand had no statistical difference (*p* = 0.284). For the contralateral side, the *post hoc* comparison (**Figure 5**, bottom) showed that (1) the alpha and beta bands had significantly high coherence compared with the delta band and gamma band (*p* < 0.001); (2) the magnitude of the alpha and beta bands, or the delta and gamma bands had no statistical difference (*p* > 0.9).

Third, a linear regression was performed between three possible factors (age, years of poststroke, and the severity of stroke

Table 3 | The fitting coefficients with corresponding *p* values, the *r*<sup>2</sup> value of the linear regression on four bands.

*Individual p-values are shown in parentheses.*

*\*Significance at 95% level.*

measured using Fugl-Meyer) and the increased percentage of coherence on paretic side compared with contralateral side. Separate regression was performed on the four different bands. **Table 3** shows the fitting coefficients with the *r*<sup>2</sup> value of each frequency band. The *r*<sup>2</sup> values of the linear regression for the delta, alpha, beta, and gamma bands were 0.372, 0.364, 0.264, and 0.505, respectively. The results showed that the three factors tended to have a linear relation with the increased coherence value.

## DISCUSSION

The coherence of MU spike trains is considered to partly reveal the relations between the motor neuron pool and spinal/ superaspinal network, and the coherence of each frequency bandwidth can reflect the relative connectivity between the motor neuron pool and the upstream circuitry. Our current study quantified the changes of the coherence of each frequency band on the affected side, compared with the contralateral side of hemiparetic stroke survivors. Our results provided strong evidence that the coherences of three frequency bands (delta, alpha, and beta) showed a significant increase compared with the contralateral side. The coherence of the delta band and the alpha band increased substantially on the affected side (increased by 28.5 and 32.4%, respectively), compared with the contralateral side. The coherence in the beta band on the affected side also revealed moderate increase (by 9.6%), compared with the contralateral side. However, the magnitude of the gamma band coherence remained unchanged. Our general findings indicate that there are substantial changes in the common input to the motor neuron pool, arising from the spinal and supraspinal circuitry, which can modify the control of muscle activations after a stroke.

## Influence of Muscle Activation Level on Coherence

We found that the coherence estimates are consistent across a range of medium–high force levels (40–60%), indicating that the relative contribution of common synaptic input to the motor neuron pool is not sensitive to the levels of muscle contraction. The findings suggest that we can use a single prespecified muscle activation level (40–60% MVC) to estimate the coherence at different frequency ranges. It should be emphasized that the MVC levels affect the number of recruited MUs. During the coherence analysis, only a fixed number of MUs are randomly selected to the pool. Therefore, if the number of MUs used for the coherence analysis was identical, the MVC level within 40–60% MVC did not influence the estimate of coherence. However, the coherence estimate at force levels outside of the range was not examined, because lower force levels tend to yield a smaller number of MUs (see **Table 2**, some 20 and 30% trials cannot meet the minimum requirement), which may bias the coherence calculation. In contrast, higher forces tend to induce early muscle fatigue, which has been shown as a factor that can affect MU coherence (17). These factors can potentially bias our current coherent estimation, due to the sampling requirement of the coherence analysis and a relative steady MU discharge rate based on the assumption of the discharge spectrum analysis.

## Physiological Implications for the Variation of Each Band

Although the partition of frequency band range from different studies was not identical, the four specific ranges were divided similarly, and the corresponding physiological concepts have been widely accepted (17, 19, 22). In our present study, the gamma band always maintained at a stable and low power level on both paretic and contralateral sides. A substantial increase of the delta and alpha bands coherence was observed in the affected muscle in our stroke cohort. It is believed that the delta coherence is associated with the modulation of mean firing rate of the MUs. The increased delta coherence may reflect a compensatory change in response to the reduced firing rate and the reduced modulation of firing rate of MUs observed in stroke survivors (36). The large SE (see **Figure 4**) on the affected side also illustrated that the increased rate of these common inputs was different across participants, possibly due to the different severity of stroke. The alpha band coherence is associated with afferent feedback/spinal reflex contributions. In addition, previous studies indicated that the coherence between MUs increased with increasing muscle spindle activities (20, 22, 37). A flexed finger posture, potentially due to hyperreflexia, is a common feature in spastic stroke survivors. In addition, hyperreflexia is one common pathology in stroke survivors (38, 39), and it is conceivable that there is an increased spinal reflex contribution to the MU activation. However, we do not have direct evidence that the FDI muscle is hyperreflexia in our stroke cohort, since spasticity is not routinely assessed in the finger muscles.

The increase of common synaptic inputs affected beta band that revealed cortical and subcortical activities and short-term MU synchronization. The increased coherence in beta band results provided a consistent evidence with previous studies, which reported that the coherence of MU firing times had significant linear relationship with shared motor neuron inputs (6, 40). The stronger correlated higher level input to the FDI motor neuron pool may reflect more centralized control with a few cortical neurons directly projecting to the whole motor neuron pool, rather than having a complex network directly and indirectly modulating the activation of the motor neuron pool. In addition, a reduction of inhibitory high level input, due to stroke, could also contribute to more synchronized input. Clearly, additional studies are necessary to verify these potential changes.

## Correlations with Subject Demographic Information

To identify potential associations between the change of coherence in the affected FDI and the participant clinical information, separate multiple linear regression was performed for each coherence bandwidth. A moderate overall correlation was found as indicated by the *r*<sup>2</sup> values. However, the individual demographic factors were not significant predictors on the change of coherence. A lack of strong correlations may arise from multiple aspects. First, the heterogeneous features of our stroke cohort of 14 participants may limit our correlation estimates, and a larger sample size may yield a better fit. Second, the change of coherence at different bandwidth may have stronger associations with other clinical features such as the lesion location or the integrity of the cortical spinal track, and the Fugl-Meyer or time poststroke may not be specific enough to show a strong correlation.

## CONCLUSION

In conclusion, we observed a significant increase of MU spike train coherence on the affected side of stroke survivors in the delta band, the alpha band, and the beta band, compared with the contralateral side. Our findings indicate that changes at multiple levels, including spinal and supraspinal levels, can all contribute to altered activation of affected muscles in stroke survivors. Further studies may be necessary to investigate possible relations between common drive and other frequency bands for stroke survivors

## REFERENCES

and to explore if the increase in common drive on paretic side can lead to MU synchronization across different muscles.

## AUTHOR CONTRIBUTIONS

CD analyzed the data and drafted the manuscript; NS, AS, and XH acquired the data; CD, NS, AS, WR, and XH discussed the study and revised the manuscript.

## FUNDING

This work was supported by National Institutes of Health Award Number: R03 NS085492-01A1.

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Copyright © 2017 Dai, Suresh, Suresh, Rymer and Hu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

# Advanced Myoelectric Control for Robotic Hand-Assisted Training: Outcome from a Stroke Patient

*Zhiyuan Lu1 , Kai-yu Tong2 , Henry Shin1 , Sheng Li1 and Ping Zhou1,3\**

*1Department of Physical Medicine and Rehabilitation, University of Texas Health Science Center at Houston, TIRR Memorial Hermann Research Center, Houston, TX, USA, 2Division of Biomedical Engineering, Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong, Hong Kong, 3Guangdong Work Injury Rehabilitation Center, Guangzhou, China*

A hand exoskeleton driven by myoelectric pattern recognition was designed for stroke rehabilitation. It detects and recognizes the user's motion intent based on electromyography (EMG) signals, and then helps the user to accomplish hand motions in real time. The hand exoskeleton can perform six kinds of motions, including the whole hand closing/opening, tripod pinch/opening, and the "gun" sign/opening. A 52-year-old woman, 8 months after stroke, made 20× 2-h visits over 10 weeks to participate in robot-assisted hand training. Though she was unable to move her fingers on her right hand before the training, EMG activities could be detected on her right forearm. In each visit, she took 4× 10-min robot-assisted training sessions, in which she repeated the aforementioned six motion patterns assisted by our intent-driven hand exoskeleton. After the training, her grip force increased from 1.5 to 2.7 kg, her pinch force increased from 1.5 to 2.5 kg, her score of Box and Block test increased from 3 to 7, her score of Fugl–Meyer (Part C) increased from 0 to 7, and her hand function increased from Stage 1 to Stage 2 in Chedoke–McMaster assessment. The results demonstrate the feasibility of robot-assisted training driven by myoelectric pattern recognition after stroke.

#### *Edited by:*

*Ayrton R. Massaro, Hospital Sirio-Libanes, Brazil*

#### *Reviewed by:*

*Jun Yao, Northwestern University, USA Michele Linda Callisaya, University of Tasmania, Australia*

> *\*Correspondence: Ping Zhou<EMAIL_ADDRESS>
#### *Specialty section:*

*This article was submitted to Stroke, a section of the journal Frontiers in Neurology*

> *Received: 07 October 2016 Accepted: 03 March 2017 Published: 20 March 2017*

#### *Citation:*

*Lu Z, Tong KY, Shin H, Li S and Zhou P (2017) Advanced Myoelectric Control for Robotic Hand-Assisted Training: Outcome from a Stroke Patient. Front. Neurol. 8:107. doi: 10.3389/fneur.2017.00107*

Keywords: electromyography, myoelectric pattern recognition, hand exoskeleton, rehabilitation, case report

## INTRODUCTION

Robot-assisted upper limb training is considered to be more efficient (1) and economic (2) than conventional therapy in neurorehabilitation. Controlling the robot with the user's own electromyography (EMG) signals connects the user's intended motion and his actual movements. It can therefore enhance therapeutic effects and promote motor learning (3–5). Various EMG-driven robots and exoskeletons have been developed for neurorehabilitation (6–8), primarily based on one-to-one mapping, which typically maps one channel of EMG signal to a corresponding single degree-offreedom (DOF) or variable such as speed and torque using a conventional "on-off " or proportional strategy. Robots based on such control strategy work well on training joints with only a few DOFs such as elbow and wrist. However, a human hand has up to 27 DOFs (9) and is controlled by complex temporal and spatial coordination of multiple muscles. It is therefore not feasible to regain hand dexterity through conventional control strategies. Myoelectric pattern-recognition techniques have been developed to extract motion intentions from EMG signals (10, 11). The extracted intentions can then be used to control a multiple-DOF robot such as a prosthesis (12). Previous studies have also shown that motion intentions can still be extracted after neurological impairment (13–15). We therefore developed an intent-driven hand training system. The system employs an exoskeleton hand, which is controlled by myoelectric pattern recognition. As soon as the user's intention is detected (usually within 250 ms), the system is able to assist to accomplish the intended motions (16).

## CASE REPORT

#### Subject

A 52-year-old woman participated in this robotic hand-assisted training 8 months after stroke. She was right-handed before stroke and had hemiplegia on her right side after her stroke. She was able to walk independently with an ankle foot orthosis but had difficulties in moving her right arm. Her fingers were flexed naturally. She was unable to move any of the fingers on her right hand, but EMG signals were able to be recorded from her forearm. Her Fugl–Meyer score (Part A–D, max 66) was 16, with a 0 in Part C (Hand, max 14). She had no pain when her whole hand was passively opened or closed. She did not receive any other hand or upper limb therapies while participating in this study. During her visits, she was able to understand and follow all the instructions.

#### Exoskeleton Hand

The exoskeleton hand, Hand of Hope (Rehab-Robotics, Hong Kong), was used in this study to help the subject move her hand (**Figure 1**). The exoskeleton hand has five individual fingers. Each finger is actuated by a linear actuator that can pull and push linearly. The mechanical design of the fingers converts these linear movements into the rotations of a virtual metacarpophalangeal (MCP) joint and a virtual proximal interphalangeal (PIP) joint. Both joints rotate together to help the hand perform closing and opening movements (7). The motion range is 55° and 65° for MCP and PIP joints, respectively. The subject's palm and five fingers are fixed to the exoskeleton hand with Velcro belts. Each finger can be bent or straightened individually by the exoskeleton hand. The exoskeleton hand stands on a brace, which also supports the

subject's forearm, so that the subject can be totally relaxed when attached to the exoskeleton. The exoskeleton hand used in this study can perform six different motion patterns, including hand closing (HC); hand opening (HO); thumb, index, and middle fingers closing (TIMC or tripod pinch); thumb, index, and middle fingers opening; middle, ring, and little fingers closing (MRLC or the "gun" sign); and middle, ring, and little fingers opening. The exoskeleton hand can perform HC, TIMC, or MRLC when it is open. However, after performing any one from these three patterns, it can only return to the original open status (e.g., there is no direct way from the "tripod pinch" to the "gun" sign).

Conventional EMG control of the device was applied in previous studies for training hand opening/closing function for stroke survivors (17, 18). In order to make all these six motion patterns available for the subject, a myoelectric pattern-recognition system was developed for this study to control the exoskeleton hand. This system is able to detect and recognize the subject's muscle activity patterns, indicate his/her intended hand motions, and then assist the patient in accomplishing these motions in real time. When the subject tries to perform a hand motion, EMG signals can be detected from those activated muscles. The myoelectric pattern-recognition system then extracts the motion intent from these EMG signals and maps the intent into control commands. The exoskeleton hand therefore performs the same motion as the subject's intent, so that the subject can accomplish the motion with both robotic assistance and his/her own participation.

#### Protocol

The subject made 20 visits (experiments) for the robot-assisted training, 2 visits per week. During the experiment, the subject was seated comfortably in a chair, next to a small height-adjustable side table. The exoskeleton hand was placed beside her on the table on her right side. Her right hand was fixed in the exoskeleton, and her forearm was placed on the brace (**Figure 1**). The exoskeleton was placed and locked on the brace. Therefore, the subject's right arm and hand could be totally relaxed instead of resisting gravity. The height of the table was adjusted to make the angle between her upper arm and her trunk about 45°, and the angle between her upper arm and her forearm about 90°. The subject was free to move her left hand. She was also allowed to move her right arm by moving the brace when she took breaks between two training sessions. Considering that the virtual palm was locked, the subject's right hand was always in a neutral position, and her forearm was never rotated even when she moved the brace. The brace would be moved to its initial position before another training session began.

Seven bipolar surface electrodes (Delsys 2.1) were attached on the subject's forearm using double-sided tapes, covering the first dorsal interossei, flexor digitorum superficialis, flexor digitorum profundus, extensor digitorum, abductor pollicis longus, extensor digiti minimi, and extensor pollicis longus muscles. The reference electrode was placed on the olecranon. The skin was cleaned using sterile alcohol wipes before electrodes were placed. EMG signals were acquired using a Bagnoli-8 EMG System (Delsys Inc., Boston, MA, USA), which amplified raw EMG signals 10,000 times and filtered the signals using a 20–450 Hz band pass filter. The acquired EMG signals were then input into a desktop running Windows 7 through a data acquisition device, USB-6221 (National Instruments Inc., Austin, TX, USA), which digitized the signals at 1,000 Hz with a 16-bit resolution.

The digitized EMG signals were then analyzed by a myoelectric pattern-recognition program developed for this study, which aimed to extract motion intentions from multi-channel EMG signals. The analysis was based on the EMG signals recorded in the most recent 200 ms (named a processing window) and was performed every 100 ms, so that the recognition result could be updated at 10 Hz (19), which is acceptable for real-time control. A motion detection algorithm was first performed to tell whether this window contained EMG signals that corresponded to the user's voluntary motions. It calculated the mean absolute value (MAV) (20) of the processing window. If the MAV was smaller than a given threshold (it was 80% of the average MAV of EMG signals recorded from this subject at her median force level), no further processing would be performed, and the recognition result was "no motion." Otherwise, a motion was considered to be detected because one or more muscles were active. Then, a pattern-recognition algorithm was performed, in which a support vector machine classifier (21) was applied to recognize motions based on a set of features including root mean square amplitude (20), fourth order auto regressive coefficients (22), and waveform length (20). The output of the classifier was then mapped onto control commands and sent to the hand exoskeleton.

Each visit included five sessions. In the first session, the subject repeated each motion pattern 15 times. Although she was unable to move her fingers, she was encouraged to try controlling the fingers. For each motion pattern, the subject was asked to imagine moving her fingers in the desired motion. The first 2 s of abovethreshold EMG signals were recorded before the exoskeleton subsequently provided assistance corresponding to the motion pattern. These EMG signals recorded in these 2-s periods were used to train the classifier. The next four sessions were all training sessions. In each session, the subject controlled the exoskeleton using her own motion intent. She was asked to try performing all of the six motion patterns and then to follow through with the exoskeleton hand while it moved through its full motion range. When the exoskeleton reached the final range, it stopped, and began waiting for another motion intent from the subject. The subject was therefore encouraged to perform the next motion right after the exoskeleton stopped. The subject was always free to choose any of the executable patterns, but sometimes the experimenter gave suggestions in order to balance the training amount of each motion pattern. Considering the subject's EMG signals were very weak, it was necessary for her to perform motions using about 70% of her maximal force in all these sessions. Consequently, she got fatigued quickly after 8–10 min training. In order to avoid fatigue, each training session was set to 10 min and could be terminated at any time after 8 min. Moreover, she was given as much time as needed to rest between sessions. Therefore, each visit took about 2 h, including approximately 40 min training.

#### RESULTS

Assessments, including grip force (using Jamar Plus + Digital Hand Dynamometer, Patterson Medical, Warrenville, IL, USA), pinch force (using PG-60 Pinch Gauges, B&L Engineering, Santa Ana, CA, USA), Box and Block test (23), Fugl–Meyer (Part C only, ranging from 0 to 14), and Chedoke–McMaster (the Hand Stage only, ranging from 1 to 7) (24), were performed before and after the 20-visit training. Results are shown in **Table 1**. The results of both the grip force and the pinch force were the maximal readings from three measurements. The result of Box and Block test was the highest score of three trials. As to the Fugl–Meyer and Chedoke–McMaster assessment, only the handrelated score were reported because only hand function was trained. The average control accuracy of the 1st and the 20th visit was also calculated. The subject was requested to report every time when the exoskeleton hand performed a motion that was different from her intent. The control accuracy was calculated based on the number of wrong motions and the total number of motions.

After the training, the results of all the assessments were improved dramatically. The grip force and pinch force were almost doubled. She also regained some voluntary finger movements. These improvements were quantified by three functional assessments from different aspects. Before the training, she was not able to perform observable finger movements. Her Fugl–Meyer score (Part C) was 0 and Chedoke–McMaster stage was 1 because she failed to do all the tasks in these assessments. After the training, she could flex all her fingers in a small range, so that she was able to partially perform many of the tasks. For example, she was able to hold a pencil, though loosely. As a result, she obtained 1 point in each grip task in the part C of the Fugl–Meyer assessment except the task "flexion in interphalangeal joints and extension in MCP." Also because of these active motions, she met the criteria of Hand Stage 2 in the Chedoke–McMaster assessment. However, she did not get to Stage 3 because her range of motion was not greater than 50% plus that she did not have opposition to bring the thumb to the index finger. The same functional improvement increased her score in the Box and Block test. Because she was not able to open/close her hand before the training, she developed an alternative way to accomplish this task, which was to push a block into the space between her thumb and other fingers using arm movements. When she managed to push two corners of a block into her hand, she could pick up the block. Holding the block in hand was difficult given that her grip was weak and she was not holding the whole block. As a result, preventing dropping the block half way was more challenging for her, compared with releasing the block. After the training, she used the same way to move the blocks. Although the range of motion for HO was still not large enough to pick up or hold one block, it was easier for her to push the block in. And she could hold the block for a longer

time because of her increased grip force, so that she had more time to move the block over the barrier. The scores of her three trials after training were 4, 7, and 4, respectively, while her highest score before training was 3.

#### DISCUSSION

In the previous studies applying conventional control (17, 18), the exoskeleton was triggered when the EMG amplitude of the monitored muscle(s) exceeded a given threshold, while other muscles' activities were ignored. However, finger motions are generated by coordinating a series of muscles. Pattern-recognition algorithms were therefore introduced in this study in order to analyze the motion patterns of up to seven muscles. These algorithms also made it possible to control the hand exoskeleton in multiple DOFs and provided an approach to training fine motions and improving hand dexterity. Both the myoelectric pattern-recognition techniques and the robot-assisted training are safe. No adverse event was observed, and no discomfort was reported. Although fatigue was reported sometimes, it usually went away after a few minutes break.

This intent-driven control required the subject to be active during the training. This subject was able to activate her muscles though she could not perform finger motions. The real-time assistance from the exoskeleton gave her the feedback of muscle activities and helped her strengthen her motion patterns. Although the subject had severely impaired hand functions before the training (Hand Stage 1 according to the Chedoke– McMaster assessment), she still achieved 75% control accuracy. Our algorithms recognized most of the subject's motion intents correctly, which assisted her in accomplishing these motions. Her hand function improved after the training, and all the assessments showed consistent improvements.

Although the training program for this subject demonstrates promising outcomes, a comprehensive evaluation of the effectiveness of the robotic hand-assisted training driven by myoelectric pattern recognition requires testing with a larger number of stroke subjects. We are aware that for a wide range of stroke patients with mild to severe impairment, some patients may not

## REFERENCES

be suitable for such training due to lack of muscle activity or impaired muscle activity patterns (25, 26), while those stroke subjects who are able to generate muscle activity patterns and achieve reasonable accuracies can participate in the training program. In this regard, a pre-examination or assessment might be necessary to determine the stroke subjects who are able to control the exoskeleton hand with myoelectric pattern recognition, and who can benefit most from the robotic hand aided training.

## ETHICS STATEMENT

This study was approved by the Committee for the Protection of Human Subjects (CPHS) of The University of Texas Health Science Center at Houston and TIRR Memorial Hermann (Houston, TX, USA). All procedures of the study were performed in accordance with the Declaration of Helsinki. The subject gave written and informed consent before the experimental procedures.

## AUTHOR CONTRIBUTIONS

ZL performed experiment, analyzed data, and wrote the manuscript. KT developed the robotic exoskeleton hand and helped with data analysis. HS helped with experiment and data analysis. SL helped with data analysis and interpretation. PZ oversaw the study and helped with experiment, data analysis, and interpretation. All the authors read, revised, and approved the manuscript.

#### ACKNOWLEDGMENTS

The authors would like to thank Kathryn Nedley, OTR, OTD, for performing functional assessments and useful discussions.

## FUNDING

This study was supported by American Heart Association (grant number 16GRNT29100012).

rehabilitation for chronic stroke. *Neurorehabil Neural Repair* (2009) 23:837–46. doi:10.1177/1545968309338191

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Copyright © 2017 Lu, Tong, Shin, Li and Zhou. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

# Alterations of Muscle Activation Pattern in Stroke Survivors during Obstacle Crossing

*Chenming Ma1,2†, Na Chen1†, Yurong Mao1 , Dongfeng Huang1 , Rong Song2 \* and Le Li1 \**

*1Department of Rehabilitation Medicine, Guangdong Engineering Technology Research Center for Rehabilitation Medicine and Clinical Translation, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China, 2Key Laboratory of Sensing Technology and Biomedical Instrument of Guang Dong Province, School of Engineering, Sun Yat-sen University, Guangzhou, China*

Objective: This study investigates changes in the neuromuscular activation pattern of the lower limb muscles in stroke survivors when crossing obstacles of three different heights.

#### *Edited by:*

*Xiaogang Hu, University of North Carolina at Chapel Hill, USA*

#### *Reviewed by:*

*Cliff Klein, Guangdong Provincial Work Injury Rehabilitation Center, China Xu Zhang, University of Science and Technology of China, China*

#### *\*Correspondence:*

*Rong Song<EMAIL_ADDRESS>Le Li<EMAIL_ADDRESS>These authors have contributed*

#### *Specialty section:*

*equally to this work.*

*This article was submitted to Stroke, a section of the journal Frontiers in Neurology*

*†*

*Received: 21 December 2016 Accepted: 16 February 2017 Published: 03 March 2017*

#### *Citation:*

*Ma C, Chen N, Mao Y, Huang D, Song R and Li L (2017) Alterations of Muscle Activation Pattern in Stroke Survivors during Obstacle Crossing. Front. Neurol. 8:70. doi: 10.3389/fneur.2017.00070*

Methods: Eight stroke survivors and eight age-, height-, and gender-matched healthy controls were recruited and instructed to cross over obstacles with heights of 10, 20, and 30% leg length. Surface electromyography (EMG) signals were recorded from the rectus femoris (RF), biceps femoris (BF), tibialis anterior (TA), and medial gastrocnemius (MG) of both limbs. Muscle activation signals were normalized to maximum voluntary contraction. Differences between groups and heights were compared using the root mean square of EMG, the cocontraction index of agonist and antagonist muscles, and power spectral analysis based on the mean power frequency (MPF). The correlations between the calculated variables and clinical scales such as Berg Balance Scale and Fugl-Meyer assessment (FMA) were also examined.

Results: During the leading limb swing phase, the activation levels of all four muscles were greater in the stroke group than the healthy controls (*p* < 0.05), and the TA showed increased activation level with increasing obstacle height in both groups (*p* < 0.05). Cocontraction between the TA and MG was higher in the stroke group during the swing phase of the leading limb and between the RF and BF during the stance phase (*p* < 0.05). Similarly, for the trailing limb, increased cocontractions between the two pairs of agonist and antagonist muscles were found during the stance phase in the stroke group (*p* < 0.05). During the crossing stride, the frequency analysis showed significantly smaller MPF values in all four lower limb muscles in the leading limb of stroke survivors compared with healthy controls (*p* < 0.05). Moreover, significant correlations were found between the FMA scores and the BF and TA activations in the leading limb during the swing phase (*p* < 0.05).

Conclusion: Greater activation levels of the lower limb muscles resulted in higher muscular demands for stroke survivors, which might lead to greater difficulty in maintaining balance. The increased cocontraction during obstacle crossing might be compensation

**183**

for the affected stability and enable safe crossing for stroke survivors. The reduced MPF in the affected limb of the stroke group might be due to impairments in motor units or other complex neuromuscular alterations.

Keywords: stroke, obstacle crossing, electromyography, gait analysis, activation pattern

## INTRODUCTION

Stroke is a leading cause of disability associated with a loss of ability to generate force, which results in activity limitations and has a negative impact on motor function (1). Following a stroke, motor control impairments such as weakness, slow movements, spasticity, fatigue, and incoordination often occur in the lower limbs, which lead to gait abnormalities (2).

Daily walking commonly involves avoiding obstacles, such as doorsteps, stones, and stairs, and stepping across obstacles has been demonstrated to require greater muscle force, increased balance control, and enhanced muscle coordination than level walking (3, 4). One study showed that almost half of the tested stroke survivors failed to step across an obstacle, and their ability to maintain balance was compromised (5). The loss of balance in stroke survivors during obstacle crossing may lead to a high risk of falls and cause soft tissue injuries or fractures. Therefore, it is important to analyze the characteristics of the motion during obstacle crossing in stroke survivors. In spite of compromised balance, it is possible that stroke survivors may use compensatory strategies to avoid falls. It would therefore be helpful to understand the mechanism of preventing falls and ensuring safe crossing.