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Increased Sensorimotor Cortex Activation With Decreased Motor Performance During Functional Upper Extremity Tasks Poststroke

Lim, Shannon B. MPT, MSc, BKin; Eng, Janice J. BSR (PT/OT), MSc, PhD

Journal of Neurologic Physical Therapy: July 2019 - Volume 43 - Issue 3 - p 141–150
doi: 10.1097/NPT.0000000000000277
Research Articles
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Background and Purpose: Current literature has focused on identifying neuroplastic changes associated with stroke through tasks and in positions that are not representative of functional rehabilitation. Emerging technologies such as functional near-infrared spectroscopy (fNIRS) provide new methods of expanding the area of neuroplasticity within rehabilitation. This study determined the differences in sensorimotor cortex activation during unrestrained reaching and gripping after stroke.

Methods: Eleven individuals with chronic stroke and 11 neurologically healthy individuals completed reaching and gripping tasks under 3 conditions using their (1) stronger, (2) weaker, and (3) both arms together. Performance and sensorimotor cortex activation using fNIRS were collected. Group and arm differences were calculated using mixed analysis of covariance (covariate: age). Pairwise comparisons were used for post hoc analyses. Partial Pearson correlations between performance and activation were assessed for each task, group, and hemisphere.

Results: Larger sensorimotor activations in the ipsilesional hemisphere were found for the stroke compared with healthy group for reaching and gripping conditions despite poorer performance. Significant correlations were observed between gripping performance (with the weaker arm and both arms simultaneously) and sensorimotor activation for the stroke group only.

Discussion and Conclusions: Stroke leads to significantly larger sensorimotor activation during functional reaching and gripping despite poorer performance. This may indicate an increased sense of effort, decreased efficiency, or increased difficulty after stroke. fNIRS can be used for assessing differences in brain activation during movements in functional positions after stroke. This can be a promising tool for investigating possible neuroplastic changes associated with functional rehabilitation interventions in the stroke population.

Video Abstract available for more insights from the authors (see Video Abstract, Supplemental Digital Content 1, available at: http://links.lww.com/JNPT/A269).

Rehabilitation Research Program, GF Strong Rehabilitation Centre, Vancouver, Canada (S.B.L., J.J.E.); Rehabilitation Sciences, Faculty of Graduate Studies, University of British Columbia, Vancouver, Canada (S.B.L.); and Department of Physical Therapy, University of British Columbia, Vancouver, Canada (J.J.E.).

Correspondence: Janice J. Eng, BSR (PT/OT), MSc, PhD, University of British Columbia, 212-2177 Wesbrook Mall, Vancouver, BC V6T 1Z3, Canada (janice.eng@ubc.ca).

We acknowledge support from the Canadian Institutes of Health Research, Canada Research Chairs Program (FND-143340), and Canada Foundation for Innovation/Infrastructure Operating Fund (Sponsor Identifier: 36035) in Collaboration with British Columbia Knowledge Development Fund to J.J.E. and University of British Columbia, Four-Year Doctoral Fellowship Program to S.B.L.

Results from this study have previously been presented at the 2017 artScientific Symposium and the 2017 Canadian Stroke Congress. The abstract is published in the International Journal of Stroke, 12(4S).

The authors declare no conflict of interest.

Supplemental digital content is available for this article. Direct URL citation appears in the printed text and is provided in the HTML and PDF versions of this article on the journal's Web site (www.jnpt.org).

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INTRODUCTION

Stroke is the leading cause of long-term disability in Canada, with approximately 405 000 Canadians currently living with its long-lasting effects.1 While the site of injury and the specific presentation of symptoms are heterogeneous, up to 70% of these individuals experience upper extremity hemiparesis,2 and even after rehabilitation, greater than 65% of this population have difficulty utilizing their affected limb in activities of daily living.3 Decreased use of the paretic arm can lead to chronic pain and weakness, decreased bone density,4 cerebral cortex changes,5 and an overall decrease in quality of life.6 In addition, stroke rehabilitation and continual care are costly for the health care system.7 Therefore, it is important to maximize patient recovery in an effective and efficient manner.

One area that has been highly debated for rehabilitation efficacy is the side of arm training. Numerous reviews have stated conflicting and inconclusive results pertaining to benefits of the paretic (affected) arm or bilateral arm training8–10 and a few studies have recently investigated the effects of the nonparetic (less-affected) arm training.11,12 Investigating how stroke itself affects neural activation during unilateral and bilateral upper extremity activities may help explain the mechanisms underlying such training.

In individuals living with the chronic effects of stroke, nonnormal brain activation is commonly seen with irregular activation in both the ipsi- and contralesional hemispheres during movement. A meta-analysis of 20 studies13 calculated increases in contralesional primary motor cortex, and bilateral premotor and supplementary motor areas with use of the paretic hand compared with healthy individuals. Systematically reviewing 22 functional magnetic resonance imaging (fMRI) and positron emission tomography studies, Buma et al14 reported general initial increases in contra-, ipsi-, and perilesional activation during paretic upper extremity movement in individuals with cortical and subcortical strokes when compared with healthy adults. In addition, as paretic arm performance increased with training, these authors also showed that in many, but not all participants, activation decreased in areas such as the contralesional motor cortex (ie, ipsilateral to the movement arm), which is not typically activated in healthy individuals. Previous reviews have also reported increases in cortical activation of motor supporting areas (bilateral premotor and supplementary motor areas) later in recovery that are associated with greater function,15 although the opposite has also been reported.16

The majority of previously mentioned evidence utilized neuroimaging techniques that require an individual to remain fairly still, especially at the head, and recorded in the supine position. While there are many advantages to these techniques, such as high spatial resolution and penetration depth using fMRI, the functional imaging data acquired from these studies may not be truly indicative of the neural correlates involved during rehabilitation tasks. Thus, assessment of brain activation during upright, unrestrained, functional tasks is needed. Functional near-infrared spectroscopy (fNIRS) is an emerging neuroimaging device that has the capabilities of determining cortical activation while the participant is mobile. Similar to fMRI, fNIRS is an indirect measure of cortical activation that utilizes the neurovascular coupling theory to estimate changes in brain activity.17 Near-infrared light emitted by this device is absorbed by areas high in oxyhemoglobin or deoxyhemoglobin content and is measured through detectors placed on the individual's head. When an increase in brain activity occurs, a typical overall increase in oxyhemoglobin concentration and a slight decrease in deoxyhemoglobin are observed.17 Due to its portability, fNIRS has been used to investigate cortical activation during various mobile tasks after stroke.18,19 To our knowledge, no work has been done to compare sensorimotor cortex activation of paretic, nonparetic, and bilateral arm movements poststroke using fNIRS.

Therefore, the primary purpose of this study was to investigate differences in cortical brain activation during performance of upper extremity activities in an upright position after stroke and in neurologically healthy individuals. Based on the current evidence, we hypothesized that greater sensorimotor cortex activation would be observed in the stroke group compared with the neurologically healthy group, particularly when using the weaker arm. For our secondary measures, we hypothesize that (1) individuals in the stroke group will perform worse than the control group when using their weaker arm and (2) cortical activation in the contralateral hemisphere (eg, ipsilesional hemisphere during paretic arm movements) will positively correlate with task performance.

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METHODS

Participants

Community-dwelling individuals with stroke and neurologically healthy individuals were recruited for the study. All participants provided written and informed consent in accordance to the University of British Columbia Clinical Research Ethics Board. Participants who have not had a stroke (healthy) had no known current or previous neurological or upper extremity deficits. Inclusion criteria for individuals with stroke (stroke) include stroke onset greater than 6 months previous, confirmed infarct/hemorrhage through MRI, CT, or clinical evaluation, presence of active scapular elevation against gravity, palpable wrist extension, and ability to sit independently without supports. Exclusions include individuals with unstable cardiovascular status, significant upper extremity musculoskeletal or neurological conditions other than stroke, or receptive aphasia. Only right-handed participants were recruited (confirmed by the Edinburgh Handedness Inventory20) into both groups. Participants were recruited through advertisements placed around the community, word of mouth, and previous participants from the Rehabilitation Research Program who agreed to be contacted for future studies.

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Demographic Data

Age and sex were documented for both groups. For the stroke group, time since stroke, affected side, type of stroke, and upper extremity functional performance were also collected prior to the task. Specific details of the stroke (ie, onset, type, and location) were obtained from medical records when available. Functional performance was determined through the Action Research Arm Test (ARAT). The ARAT consists of 4 components including grasp, grip, pinch, and gross movements with the upper extremity. The maximum total score is 57, with a higher score equating to greater function.21

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Protocol

Tasks

Participants were seated unrestrained at a table, with both feet on the ground and their back resting on the backrest of a chair. They were asked to complete 2 different tasks: (1) reaching and (2) gripping. For the reaching task, 2 Box and Block22 sets were placed in front of the participant in an orientation that allowed forward reaching across the barrier instead of the typical lateral reach (Figure 1A). As quickly as possible, while keeping their back rested on the backrest, participants were asked to grab a 2.5-cm3 block from the box and reach over the barrier to place the block in the other box. For each condition, participants continuously performed this task for 20 seconds, with 15 seconds of rest between the 10 trials. This task was chosen because it is a common outcome tool in stroke clinical trials and involves reaching, grasping, and manipulating small objects—all of which are key functions of the upper extremity. For the gripping task, participants sat with their elbows flexed at 90° and their forearms resting on the armrest by their side. Participants were asked to grip a dynamometer as hard as possible for 5 seconds, 5 times with 30 seconds of rest between trials. A grip task was chosen because it provides a standardized measure of maximal motor output and has been shown to relate to upper limb performance in activities of daily living.23

Figure 1

Figure 1

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Conditions

The reaching and gripping tasks were performed under 3 conditions with the participants' (1) stronger arm (dominant for healthy, nonparetic for stroke), (2) weaker arm (nondominant for healthy, paretic for stroke), or (3) both arms together. The order of each task and condition was randomized for every participant.

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Outcome Measures

Behavioral Performance (Secondary Outcome)

For the reaching task, the number of successful reach sequences (grasp, reach, and release into the box) was averaged for each trial and condition. For the both-arms condition, the total number of blocks moved per trial was divided by 2 and then averaged to account for blocks moved by each hand. For the gripping task, the maximum grip strength per trial was determined through the calibrated hand-held dynamometer (JAMAR, Lafayette Instruments) and the average was calculated for each condition.

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Cortical Activity (Primary Outcome)

fNIRS (NIRSport, NIRx Medical Technologies) was used to investigate differences in cortical activation through calculating the change in hemoglobin with each task and condition. A typical hemodynamic response to a task consists of an increase in oxyhemoglobin and a slight decrease in deoxyhemoglobin over regions of neural activation,24 which is consistent with the physiological basis of indirectly recording neural activation through fMRI.25 As fNIRS is able to capture both hemoglobin species, the difference in hemoglobin species (HbDiff = oxyhemoglobin − deoxyhemoglobin) was used as an indicator of cortical activation. Comparison of the combined response through HbDiff, opposed to assessing oxyhemoglobin and deoxyhemoglobin separately, has been done in several previous studies.26–28 This allows for assessment of an overall change in neural activation rather than providing a fractionated picture of brain activity that may be confounded by other factors such as superficial blood flow changes. The fNIRS cap was placed on the participant's head using the anatomical landmarks and calculations indicated by the international 10/10 system.29 Source and detector probes were placed 3 cm apart (see Figure 1B for exact placement). This configuration resulted in 20 channels over bilateral sensorimotor cortices. Data were recorded at a sampling rate of 7.81 Hz.

Preprocessing of the raw voltage data was computed using nirsLAB (NIRx Medical Technologies). First, channels in which an adequate reading was not obtained during calibration were excluded from further analysis. Further noisy channels were then removed based on coefficient of variation calculations of greater than 15%.26 To remove any “jumps” and spikes in the data due to motion, discontinuities were removed from raw data by determining points that were 5 standard deviations from the mean and subtracting subsequent points by that value. Data were then filtered between 0.01 and 0.2 Hz17 to remove nonevoked components such as heart rate. Hemodynamic states (oxy- and deoxyhemoglobin) were then calculated using the modified Beer-Lambert law with differential pathlength factors determined by each participants' age to account for increases in skull thickness with aging.30

Data were imported into MATLAB (MathWorks) where each hemoglobin species was baseline corrected to 5 seconds prior to each trial onset using a customized script. HbDiff was then calculated on a trial-by-trial basis. Average amplitudes and area under the curves were calculated for each channel for each condition and task. Two different variables were included to capture possible differences in hemoglobin responses between groups and conditions. For example, if the shape of the responses was similar between the 2 groups, then calculating the average amplitude would be a valid variable for between-group comparisons. However, if the hemodynamic responses were different, for example, a greater sustained increase in HbDiff for one group and a sharp peak was observed in the other, then a better comparator variable would be the area under the curve. To account for the slow hemodynamic return to baseline postactivity, amplitudes and areas were calculated from the onset of the task to 10 seconds posttask: 0 to 30 seconds and 0 to 15 seconds for the reaching and gripping tasks, respectively. The variables were then averaged across each hemisphere (ie, one data point for each hemisphere) for each condition (3 conditions) and task (2 tasks).

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Controlling for Lesion Side

As the paretic side was not consistent across all participants in the stroke group, cortical recordings were adjusted according to paresis side (ie, for an individual with right-sided paresis, left and right hemisphere recordings were flipped prior to statistical analysis and pooled with cortical recordings from individuals with left-sided paresis). Cortical recordings from fNIRS data will be referred to as “contralesional” and “ipsilesional” hemisphere for the stroke group. For the healthy group, we refer to the left (dominant) and right (nondominant) hemispheres, which were compared with the stroke group contralesional and ipsilesional hemispheres, respectively.

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Statistical Analyses

Behavioral Performance

A 2 (group: healthy, stroke) by 3 (condition: stronger, weaker, both) mixed-measures analysis of variance (ANOVA) was conducted to assess the main effects of arm for the reaching tasks. The effects of arm on the gripping task were assessed using a 2 (group) by 4 (condition: stronger, weaker, stronger arm in the both-arms condition, weaker arm in the Both-arms condition) mixed ANOVA. The group-condition interaction was conducted to look at differences between groups for each arm used, and a subsequent analysis of covariance (ANCOVA) was conducted to account for the covariate of age between the groups.31 Pairwise post hoc comparisons were conducted with Bonferroni corrections to account for multiple comparisons.

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Cortical Activity

Four separate 2 (group) by 3 (condition) mixed-measures ANOVAs and ANCOVAs were conducted as earlier for each hemisphere (contralesional/left, ipsilesional/right) and task (reaching, gripping). Pairwise post hoc comparisons with Bonferroni corrections were conducted to account for multiple comparisons.

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Relationship Between Behavioral Performance and Cortical Activity

Partial Pearson correlations controlling for age were calculated between the performance and fNIRS data collected over each hemisphere for the stroke and healthy groups. Each task, condition, and group were assessed separately.

Greenhouse-Geisser corrections were conducted and reported if violations of sphericity occurred. All tests were conducted using SPSS22. Alpha was set at 0.05.

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RESULTS

Thirteen healthy and 12 individuals with chronic stroke participated in the study. Data from 2 healthy participants were not included in the analysis due to difficulties with obtaining fNIRS data (n = 1: hair too thick, n = 1: inappropriate cap head size for probe congruency). Data from 1 stroke individual were excluded due to technical errors in recording task onset. Therefore, data from 11 healthy (mean age 27 ± 3.4; 5 female) and 11 stroke (mean age 68 ± 5.9; 1 female) individuals were included in this study. See Table 1 for descriptive statistics on demographic data from the stroke group. Overall, complete data from all 20 channels were obtained from 9 participants. For the remaining participants, a range of 1 to 4 channels were removed across the whole head with no obvious pattern or differences in channel exclusion between hemispheres or group.

Table 1

Table 1

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Behavioral Performance

For the reaching task (Figure 2A), a significant group-condition interaction (F(2,32) = 12.08, P < 0.001, ηp2 = 0.43) was found, in addition to significant main effects of condition (F(2,32) = 66.62, P < 0.001, ηp2 = 0.81) and nonsignificant main effects of group (F(1,15) = 3.83, P = 0.069, ηp2 = 0.20). Post hoc comparisons indicated significantly poorer performance (ie, less blocks transferred) in the healthy group for the both-arms condition (Figure 2A, bar 5) compared with either the stronger or weaker arm (Figure 2A, bar 1 and 3, respectively). In the stroke group, the poorest performance was during the both-arms condition (Figure 2A, bar 6) followed by the weaker (paretic) arm (Figure 2A, bar 4) and then the stronger arm (Figure 2A, bar 2) with the best performance. For each condition, the healthy group performed significantly better than the stroke group.

Figure 2

Figure 2

For the gripping task (Figure 2B), significant main effects of condition (F(3,57) = 8.726, P = 0.003, ηp2 = 0.315, Greenhouse-Geisser corrected for violation in sphericity assumption) were observed where higher grip strengths were found when the stronger hand was used unilaterally (Figure 2B, bar 1) or bilaterally (Figure 2B, bar 3) compared with when the weaker hand was used unilaterally (Figure 2B, bar 2) but not bilaterally. There was no difference in grip performance when gripping was done unilaterally compared with bilaterally. Although the healthy group had higher gripping values for each condition, no significant main effects of group (F(1,18) = 1.000, P = 0.331, ηp2 = 0.053) and no group-condition interaction (F(3,57) = 2.394, P = 0.125, ηp2 = 0.112, Greenhouse-Geisser corrected) effects were observed.

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Cortical Activity

At the onset of both tasks, fNIRS recordings showed a typical hemodynamic response function with a slow increase in oxyhemoglobin and a slight decrease in deoxyhemoglobin. For the 20-second reaching task, a sustained increase in HbDiff was observed during the entire task, with a distinct peak at 5.55 ± 0.263 seconds for the healthy group and a gradual increase in HbDiff peaking at 20.10 ± 0.105 seconds after task onset for the stroke group (Figure 3A). For the 5-second gripping task, distinct peaks were observed at 5.59 ± 0.06 seconds for the healthy group and 5.08 ± 0.06 seconds for the stroke group (Figure 3B).

Figure 3

Figure 3

For the majority of tasks and conditions, similar results were observed when assessing the average amplitude and the area under the curve of the HbDiff response. The only difference between the 2 parameters was a significant main effect of group observed for the area under the curve parameter with greater HbDiff change in the stroke compared with healthy group for the ipsilesional/right hemisphere during the reaching task. No significant group differences were observed for the average amplitude parameter in this hemisphere for this task. Due to the difference in the shape of the response for the reaching task, the results from the area under the curve variable are presented for the remaining sections. While performing the reaching task, significant main effects of condition (F(2,40) = 9.814, P < 0.001, ηp2 = 0.329) and group (F(1,19) = 6.525, P = 0.019, ηp2 = 0.256) were observed in the ipsilesional (stroke)/right hemisphere (healthy), where significantly greater HbDiff changes were calculated during reaching with the weaker arm compared with either the stronger arm or both arms together. Ipsilesional cortex activation was significantly greater in the stroke group compared with the right hemisphere in the healthy group. No interaction effects for the ipsilesional (stroke)/right (healthy) (F(2,40) = 2.117, P = 0.134, ηp2 = 0.096) or contralesional (stroke)/left (healthy) (F(2,40) = 0.022, P = 0.979, ηp2 = 0.001) hemispheres and no significant main effects of condition (F(2,40) = 2.704, P = 0.079, ηp2 = 0.119) or group (F(1,19) = 3.217, P = 0.089, ηp2 = 0.145) were observed in the contralesional (stroke)/left (healthy) hemisphere (Table 2).

Table 2

Table 2

During the gripping task, significant main effects of group (F(1,19) = 10.807, P = 0.004, ηp2 = 0.363) were calculated showing larger HbDiff changes in the ipsilesional hemisphere for the stroke compared with the right hemisphere of the healthy group. No significant main effects of condition (F(2,40) = 0.680, P = 0.512, ηp2 = 0.033) or interaction effects (F(2,40) = 2.415, P = 0.102, ηp2 = 0.108) were observed for the ipsilesional (stroke)/right (healthy) and no condition (F(2,40) = 3.104, P = 0.056, ηp2 = 0.134), interaction (F(2,40) = 0.282, P = 0.756, ηp2 = 0.014) or group (F(1,19) = 4.093, P = 0.057, ηp2 = 0.177) effects were observed for the contralesional (stroke)/left (healthy) hemisphere (Table 2).

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Relationship Between Behavioral Performance and Cortical Activity

Significant correlations, controlling for age, were observed between gripping performance (for the weaker and both-arms conditions) and cortical activation in both hemispheres for the stroke group only with a higher maximum grip force related to higher changes in HbDiff (Figure 4). No significant correlations were observed for the healthy group or during gripping with the nonparetic hand for the stroke group (Table 3).

Figure 4

Figure 4

Table 3

Table 3

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DISCUSSION

This is the first study to examine and compare sensorimotor cortex activation during upright unilateral and bilateral upper extremity tasks in the stroke population. As we hypothesized, significantly greater cortical activation was observed in the stroke compared with healthy group, and this occurred despite the lower performance by the stroke group. There were no consistent differences between the cortical activation when reaching with the stronger, weaker, or both arms, and the significant differences between groups were exclusively found in the ipsilesional hemisphere. In addition, partly supporting our hypothesis, significantly greater activation was related to greater performance in the stroke group only and only for the gripping tasks involving the paretic arm. This relationship was observed in both the ipsi- and contralesional hemispheres.

Current results showing significant increases in ipsilesional sensorimotor cortex after stroke are in accordance with previous work in humans, which used small motions such as finger tapping, upper extremity flexion/extension,14 or isolated gripping tasks.32 Previous studies using fNIRS have also shown similar results to those obtained through other, more constrained imaging methods.33 In using fNIRS, we were able to record brain activity during large and forceful antigravity tasks that are similar to those assessed during rehabilitation. The ability to capture neural activation through fNIRS provides the opportunity to assess more clinically relevant outcomes and tasks that are otherwise restricted in other devices.

Interestingly, the shape of the hemodynamic response differed slightly between the stroke and healthy groups during the reaching task. Healthy individuals showed an increase in the hemodynamic response that peaked at approximately 5 seconds, which is the typical response, and remained above baseline throughout the task. The hemodynamic response for the stroke group, on the other hand, displayed a gradual increase in HbDiff that peaked toward the end of the task. Some groups have suggested differences in the hemodynamic response after a stroke.34,35 Sakatani and colleagues35 reviewed results from fMRI and fNIRS data in individuals poststroke and found evoked increases in deoxyhemoglobin in comparison to the typical decrease of deoxyhemoglobin in healthy individuals. This difference, however, was most prominent in the severe stroke groups, and similar oxygenation responses were observed in the moderate stroke groups. The majority of individuals within the current study demonstrated excellent functional capabilities, as indicated by the ARAT, and did not show evoked increases in deoxyhemoglobin. Instead, our hemodynamic findings may likely be a result of the task performance. The stroke group moved less blocks over the barrier compared with the healthy group, indicating that they moved slower throughout the task. Although we did not collect kinematic data during the study, it is likely that individuals in the stroke group took longer to acquire their rhythm and peak speed during the task and thus resulted in a later hemodynamic time to peak. On the contrary, a similar time to peak was observed during the single motion, short burst gripping task. This further suggests that the hemodynamic response is not altered in this stroke population and differences observed in the reaching task are likely performance induced.

The significant increases we observed in cortical activation also occurred despite the stroke group's overall poorer performance. This may indicate that either greater cortical activation is required to complete the task due to decreased cortical or peripheral efficiency or that a greater sense of effort is experienced by the stroke group. After a stroke, more motor-associated regions are recruited, arguably to help support the deficits in the primary motor areas.36 Peripherally, McCrea et al37 showed greater electromyographic amplitudes and additional muscles recruited after a stroke. This peripheral inefficiency is further supported through kinematic studies where highly variable movement amplitudes and degrees of freedom are also found after strokes.38 Moreover, studies in healthy individuals show that increased sense of effort is correlated to increases in cortical activation independent of muscle activation and movement.39 Thus, while we cannot determine the exact cause of the hyperactivations in our stroke group, we can speculate that a combination of compensations for central and peripheral inefficiencies and/or an increased sense of effort may lead to increases in cortical activation.

Buma et al14 and Rehme et al13 suggest that cortical hyperactivation poststroke is often seen in bilateral hemispheres, especially subacutely after a stroke. As recovery occurs, the contralesional primary motor cortex hyperactivation decreases and brain activation returns to normal ipsilesional (contralateral to the moving arm) activity in some, but not all, individuals.14,40,41 Narrowing the findings to the specific motor regions, it appears that the chronic changes after a stroke often leave an individual with increases in secondary motor areas such as the premotor and supplementary motor areas13 while the primary motor cortex returns to normal.42 Secondary motor cortex increases have also been reported in nonhuman primate studies where direct recordings from the supplementary motor area were similarly associated with recovery after a lesion.36 Interestingly, the current results show hyperactivation in the ipsilesional hemisphere regardless of the arm(s) used in the stroke group (ie, greater activation in the ipsilesional hemisphere was observed even when performing the tasks with the stronger [less affected] arm). This finding may suggest a global change in cortical activation after a stroke rather than changes only associated with movements of the more affected side.

In addition, a significant relationship between gripping performance and cortical activation was found only for the stroke group. Similarly, a systematic review by Kokotilo et al32 found that ipsilesional cortical activation positively correlated with paretic arm force production poststroke. While current results showed a similar ipsilesional relationship, significant correlations were also found for the contralesional hemisphere. Previous work in healthy adults has also shown bilateral hemispheric scaling with a unimanual gripping task.33 These authors suggest that this bilateral scaling could be a result of increased interhemispheric inhibition to the ipsilateral hemisphere (in this case, the contralesional hemisphere), which would be observed as an increased hemodynamic response on both hemispheres. However, they also suggest interhemispheric inhibition to be more applicable for forces less than 20% maximum,33 which was not the case for our task. In addition, these authors suggest the activation of ipsilateral corticospinal tract as an explanation for observed ipsilateral activity. Since the ipsilateral corticospinal tract only constitutes a small proportion of the corticospinal tracts, activation here would be less than that observed in the contralateral tract, which is what we observed in the current study. Another explanation for the bilateral hemisphere activation may be the difficulty of our task. Our participants were asked to complete 5 maximum grip strength trials for 5 seconds each. In comparison to gripping with the nonparetic arm, completing a maximum grip strength with the paretic arm is likely more difficult and effortful. Previous work in both healthy controls and individuals poststroke has consistently shown increased bilateral activation when performing more difficult tasks.43 In contrast, using the stronger arm for the stroke group and maximum grip strength for the healthy group may not be difficult enough to elicit a scaled cortical response or bilateral activation. Therefore, it is possible that the activation observed in the contralesional hemisphere of the stroke group is not maladaptive, rather it is supportive for a more difficult task poststroke.

Interestingly, we did not find any significant relationships between the reaching performance and cortical activation in the stroke group. Due to the significantly greater motor output required for the gripping task, the change in cortical activation for the gripping task was double the amplitude of that observed in the reaching task. It is likely that the cortical activation during the reaching task was not large enough to detect a significant scaling effect over the variability (ie, low signal-to-noise ratio).

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Limitations

Although we statistically controlled for age in our analysis and used age-appropriate differential pathlength factors within the modified Beer-Lambert equation, we acknowledge that the functional differences between cortical activation in relation to aging cannot be fully accounted for. There has been significant debate on the effects of aging on neutral activity and hemodynamic responses. Current evidence has suggested increases, decreases, and similar activation in various brain regions with aging, which may be attributed to factors such as changes in central and cerebral vascular compliance, cerebral structural volume, and cortical efficiency.44–46 However, we believe that our results provide an indication of cortical activation differences after a stroke above those that may be observed with aging. If our results were purely a consequence of aging, we would suspect a global difference in brain activation (ie, observed differences in both hemispheres) between the 2 groups. This was not the case; significant differences were primarily observed in the ipsilesional hemisphere and no differences were observed for the contralesional hemisphere. Therefore, our data suggest that the hyperactivations in the ipsilesional hemisphere are at least in part a result of the brain injury. Second, within this study we had not accounted for between-group differences in sex and had more males enrolled in the stroke group. Some evidence suggests differences in cerebral blood flow between sexes, with women showing greater blow flow than men.47 However, this difference appears to diminish with age.47 Behaviorally, it is well documented that males have greater maximum grip strength values compared with females,48 which in turn would affect the cortical output. Nevertheless, the healthy group still showed greater (though statistically not different) grip strengths compared with the stroke group. It is also important to note the heterogeneity of our stroke participants. The participants varied in stroke chronicity, type, and location. While a more homogenous stroke group would decrease the variance within our results, the fact that we still observed significant effects underscores the robust nature of this phenomenon.

In addition, our current experiment is limited by the lack of precise localization of brain recording beneath each optode. Although we meticulously set our channels over the international 10/10 coordinates and can estimate cortical regions from these anatomical landmarks based on previously published algorithms,49 we acknowledge that changes in brain volume and atrophy related to lesion location could potentially skew the estimated locations. For this reason, we used a large array of optodes to cover a broad area over the sensorimotor cortex. In addition, we believe we were able to capture brain activation, especially from our older stroke group who may have significant brain atrophy, because of the observed hyperactivations. If we were not recording over active brain regions and instead over lesion sites or only through cerebrospinal fluid, we would likely observe diminished or no changes in HbDiff. Despite this, future studies would benefit from utilizing a spatial registration system for identifying the specific coordinates of recording on individualized structural MRI scans, especially when attempting to compare specific cortical regions.

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CONCLUSIONS

Results from the current study demonstrate the capabilities of using fNIRS in recording cortical activation during tasks with higher forces and larger motions that are more representative of functional activities performed in rehabilitation and everyday life. These results may further inform rehabilitation intervention targeted at bilateral or nonparetic arm training, as our results show that stroke leads to larger ipsilesional sensorimotor activation during functional reaching and gripping despite poorer performance and irrespective of the arm used. In addition, the ipsilateral brain activations observed in our study may indicate decreased efficiencies or greater movement effort after stroke. Longitudinal studies from stroke onset may provide further insight into how these changes evolve and whether interventions could alter these brain activations and performance relationships.

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Keywords:

brain activation; fNIRS; neuroplasticity stroke; upper limb

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