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Executive Function Is Associated With Off-Line Motor Learning in People With Chronic Stroke

Al-dughmi, Mayis PT; Al-Sharman, Alham PT, PhD; Stevens, Suzanne MD; Siengsukon, Catherine F. PT, PhD

Journal of Neurologic Physical Therapy: April 2017 - Volume 41 - Issue 2 - p 101–106
doi: 10.1097/NPT.0000000000000170
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Background and Purpose: Sleep has been shown to promote off-line motor learning in individuals following stroke. Executive function ability has been shown to be a predictor of participation in rehabilitation and motor recovery following stroke. The purpose of this study was to explore the association between executive function and off-line motor learning in individuals with chronic stroke compared with healthy control participants.

Methods: Seventeen individuals with chronic stroke (>6 months poststroke) and 9 healthy adults were included in the study. Participants underwent 3 consecutive nights of polysomnography, practiced a continuous tracking task the morning of the third day, and underwent a retention test the morning after the third night. Participants underwent testing on 4 executive function tests after the continuous tracking task retention test.

Results: Participants with stroke showed a significant positive correlation between the off-line motor learning score and performance on the Trail-Making Test from Delis-Kaplan Executive Function System (r = 0.652; P = 0.005), while the healthy control participants did not. Regression analysis showed that the Trail-Making Test–Delis-Kaplan Executive Function System is a significant predictor of off-line motor learning (P = 0.008).

Discussion and Conclusions: This is the first study to demonstrate that better performance on an executive function test of attention and set-shifting predicts a higher magnitude of off-line motor learning in individuals with chronic stroke. This emphasizes the need to consider attention and set-shifting abilities of individuals following stroke as these abilities are associated with motor learning. This in turn could affect learning of activities of daily living and impact functional recovery following stroke.

Video Abstract available for more insights from the authors (see Video, Supplemental Digital Content 1, http://links.lww.com/JNPT/A166).

Departments of Physical Therapy and Rehabilitation Science (A.L-D., C.F.S.) and Neurology (S.S.), University of Kansas Medical Center, Kansas City; and Department of Rehabilitation Sciences, Jordan University of Science and Technology, Irbid, Jordan (A.A-S.).

Correspondence: Catherine F. Siengsukon, PT, PhD, Department of Physical Therapy and Rehabilitation Science, University of Kansas Medical Center, 3901 Rainbow Blvd, Mailstop 2002, Kansas City, KS 66160 (csiengsukon@kumc.edu).

This work was supported by the Scientist Development grant (09SDG2060618) awarded to C.F.S. from the American Heart Association.

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).

The authors declare no conflict of interest.

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INTRODUCTION

Growing evidence demonstrates that sleep promotes motor learning in individuals following stroke.1,2 Individuals with chronic stroke who slept between practice and retest demonstrated off-line motor learning (meaning without additional physical practice) of a continuous tracking task (CTT) while those who stayed awake did not.1,2 However, it remains unclear what factors mediate off-line motor skill learning in people with stroke. Understanding which factors enhance or inhibit off-line motor learning may enable clinicians to facilitate or reduce the impact of those factors to hasten motor learning and potentially motor recovery.

In young people who are healthy, the type of instruction provided3,4 and the type of task used5,6 mediate sleep-dependent off-line motor learning. Sleep also facilitates learning of a functional motor task in healthy middle-aged and older adults.7 However, limited information is available to determine whether these same factors mediate sleep-dependent off-line motor learning in people with stroke. Evidence suggests that individuals with chronic stroke experience learning of both an explicit and implicit version of a CTT following a period of sleep but not following a period of being awake.1 A recent study indicates that certain sleep parameters as well as motor function, stroke severity, and time since stroke occurrence are associated with off-line motor learning in people with chronic stroke.2 However, those associations were weak or moderate in strength, leading to further need to consider the contribution of other factors to sleep-dependent off-line motor learning in people with chronic stroke.

A factor that has not yet been considered but warrants investigation is the contribution of cognitive function to sleep-dependent off-line motor learning. In particular, executive function processes such as initiation, sequencing, attention, set-shifting, and planning are often needed for the proper execution of motor skills, particularly those skills requiring manual dexterity.8–10 In addition to the contribution of executive function to motor learning, executive function was found to be an independent predictor of active participation in rehabilitation and recovery following stroke.11 Executive function deficits can impact the ability of the individual to effectively participate in the rehabilitation intervention due to the inability to maintain consistency of performance, initiate movements, inhibit impulsive behaviors, and follow rehabilitation instructions.11,12 Therefore, the main purpose of this study was to examine the association between executive function and off-line motor learning. This may identify a need to consider both cognition and sleep during stroke rehabilitation and may help guide clinicians on structuring treatment plans to optimize functional outcomes.

A secondary purpose of this study was to examine which sleep parameters are associated with executive function in individuals with chronic stroke. Sleep is classified into 2 stages: nonrapid eye movement sleep and rapid eye movement (REM) sleep. Nonrapid eye movement sleep has 3 substages of increasing depth: N1, N2, and N3.13 Polysomnographic (PSG) studies in individuals with stroke show changes in sleep parameters including decreased sleep efficiency, increased total sleep time, increased time spent in N1 sleep, and less time spent in N3 sleep.14–17 Increased time spent in N3 and REM sleep were associated with better cognitive function in both the subacute and chronic stages following stroke.18 However, it is unclear which sleep parameters are associated with executive function, in particular, in individuals with chronic stroke. Understanding sleep changes following stroke is critical as studies have shown that cognition including executive function is impacted by sleep quality in individuals with stroke19,20 as well as in healthy individuals.21–24

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METHODS

The participants with stroke were recruited from the Stroke Registry at the University of Kansas Medical Center, area stroke support groups, and personal referral from consented participants, area physicians, or study personnel. To participate, individuals had to be 40 to 75 years of age, have no untreated sleep disorders, maintain a regular sleep schedule (defined for the purpose of this study as averaging 6-9 hours of sleep per night), and score at least 26 on the Mini-Mental State Examination25 to ensure no evidence of dementia. Individuals with stroke were included if they presented with a unilateral stroke in the middle cerebral artery distribution, which was confirmed by magnetic resonance imaging and experienced the stroke at least 6 months prior. Individuals with acute medical problems, uncontrolled depression, uncorrected vision loss, who smoke, have a history of psychiatric admission, multiple strokes, transient ischemic attacks, or extensive white matter disease were excluded from participating. This study was performed in accordance with the University of Kansas Medical Center's Institutional Review Board.

Twenty-six individuals with stroke and 10 control participants were enrolled in the study. Nine individuals with stroke and 1 control participant were excluded from data analysis. Six individuals with stroke were excluded because of having an Apnea-Hypopnea Index of 15 or more on the acclimation night of polysomnography. One participant with stroke was excluded because of not completing the cognitive tests and another one was excluded when the magnetic resonance image showed that the stroke occurred in the brain stem. One participant with stroke and 1 control participant were excluded after being identified as extreme outliers on box-plot analysis for normality for the executive function tests. Therefore, 17 individuals with chronic stroke (mean age = 58.5 ± 11.52 years; 12 females and 5 males) and 9 control participants (mean age = 62.4 ± 11.85 years; 5 females and 4 males) were included in data analysis.

The methods of this study have been described previously2 as these data are part of a larger study. In brief, participants underwent 3 consecutive nights of PSG. The first night served as an acclimation night and allowed for the identification of participants with undiagnosed sleep disorders. The second night served as a baseline night, and the third night served as the experimental night. The PSG outcome measures of interest from the experimental night were sleep efficiency; time spent in N1, N2, N3, and REM sleep; and percent sleep period time spent in N1, N2, N3, and REM sleep. Participants practiced an implicit CTT the morning of the third day and underwent a retention test the morning after the third night. Participants with stroke performed the task with their less-affected hand, and control participants were matched for hand use. The outcome measure of interest from the CTT is the off-line learning score, which is the improvement in performance on the repeated segment from the last practice block to the retention block. Participants underwent testing on 4 executive function tests following completion of the CTT retention test.

The executive function tests consisted of the Stroop Test,26 which assesses cognitive flexibility and response inhibition, Trail-Making Test (TMT) from Delis-Kaplan Executive Function System (D-KEFS),27 which assesses attention and set-shifting, the Verbal Fluency Task (VFT),28 which assesses semantic memory and response monitoring to avoid repetition, and the d2 task,29 which assesses visual scanning speed. For the Stroop Test, the participant completed 2 trials. The first trial consisted of a list of Xs printed in different colors on a piece of paper. The participants were instructed to name the color of the Xs. In the second trial, a list of word colors was printed in colored ink not congruent with the name of the word (eg, the word “red” printed in blue ink). Participants were given 45 seconds to name the color of as many words as possible. An interference score30 that reflects the change in performance as the task becomes more challenging was calculated by subtracting the second trial from the first trial and dividing by the first trial. For the TMT D-KEFS,27 participants used a paper and pencil to “connect the dots” in 4 conditions: number sequences (1-2-3-4 ... 16), letter sequences (A-B-C-D ... P), number-letter sequences (1-A-2-B-3-C ... 16-P.), and motor speed (trace dashed lines from each target). The TMT D-KEFS was used instead of the standard TMT because it uses longer trials and provides more specific interpretation of executive function abilities.27 In all conditions, participants were instructed to perform as quickly and accurately as possible. A normalizing score was calculated to reflect the progressive increasing difficulty of the TMT task using the following equation: conditions 1 − 3 [(1 + 2 − 3)/1 + 2)]. Condition 4 was used as a control for upper limb motor abilities in the data analysis. For the VFT, participants were instructed to name as many words as they could that started with a certain letter of the alphabet within 60 seconds. Three letters (F, A, S) were used for the VFT and an average score of the correct responses of all the 3 letters was calculated. For the d2 task, the participants were given a piece of standard-sized paper consisting of 14 lines of the letter “d” printed in rows with 0 to 2 small dots above and/or below the “d.” The participant was instructed to cross out using a pencil only the occurrences of the letter “d” with 2 dots at any location (ie, with the 2 dots on top the letter, below the letter, or one above and one below the letter). The participants were given 20 seconds to complete each line. At the end of 20 seconds, the participants were required to begin the next line. The total number of target ds correctly marked during each 20-second trial is recorded, and then the numbers of target ds marked from each line are summed and used for analysis.

SPSS version 20.0 was used to perform all statistical analysis and α was set at 0.05. Descriptive statistics including age, sex, global cognitive function (measured using the Mini-Mental State Examination), and sleep quality (measured using the Pittsburg Sleep Quality Index)31 were calculated. The data were analyzed for normality using the Shapiro-Wilks Test, Skewness and Kurtosis Test, as well as Box plots and normal Q-Q plots. Independent t tests were used to compare the performance on the executive function tests between individuals with stroke and the control participants when the assumption of normality was satisfied. Pearson correlations were utilized to explore the associations between sleep parameters and executive function as well as between executive function and off-line motor learning in the participants with stroke and the control group. When the normality assumption for any outcome measure was not satisfied, Mann-Whitney-Wilcoxon and Spearman rank correlation coefficient tests were used. Stepwise linear regression was utilized to explore whether performance on the executive function tests predicted off-line motor learning in individuals with stroke.

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RESULTS

There was no significant difference in the demographic variables between the participants with stroke and the control participants (Table 1). As previously reported in the larger sample,2 individuals with stroke in the current sample demonstrated a significant improvement in performance on the tracking task following sleep (P = 0.002) while the control participants did not (P = 0.931). Independent t test showed no significant difference in performance on the executive function tests between the participants with stroke and the control participants (Table 2).

Table 1

Table 1

Table 2

Table 2

When examining the relationship between off-line motor learning and performance on the executive function tests for the participants with stroke, there was a significant positive correlation between the off-line motor learning score and performance on the TMT D-KEFS (r = 0.652, P = 0.005) (Figure 1) which indicates that better performance on the TMT is associated with a higher magnitude of off-line motor learning (a lower score on the TMT indicates that less time was required to complete the test and a more negative off-line learning score indicates a larger magnitude of improvement on the tracking task from practice to the retention testing). There were nonsignificant negligible or weak correlations between the off-line motor learning score and performance on the Stroop Test, VFT, and d2 test (Table 3). For the control group, there were no significant correlations between off-line motor learning and performance on the executive function tests (Table 3). The PSG variables of interest showed no significant associations with performance on any of the 4 executive function tests (Table 4).

Figure 1

Figure 1

Table 3

Table 3

Table 4

Table 4

In the regression analysis, the 4 executive function tests were used as the predictor variables for off-line motor learning score for the stroke group. The overall model was significant (P = 0.041), explaining 54% of the variance in the off-line learning score (R2 = 0.539) (Table 5). The only executive function test that had a significant contribution in the model was the TMT D-KEFS (P = 0.013). Stepwise linear regression showed that the TMT D-KEFS was the only variable that predicts off-line motor learning (P = 0.005) and explained 43% (R2 = 0.425) of the variance in the off-line motor learning score. The model remained significant after controlling for condition 4 (motor speed) of the TMT D-KEFS (P = 0.008). We further considered the 2 individuals who demonstrated the largest magnitude of change on the tracking task (−7.44° and −5.77°). Notably, the person with the largest magnitude was the youngest individual in the sample (41 years of age) and the other individual was 52 years of age (which is below the mean of the sample). The stepwise linear regression was repeated with age added as a control. The TMT D-KEFS remained a significant predictor of off-line motor learning (P = 0.034) and explained 40.3% (R2 = 0.403) of the variance in off-line motor learning.

Table 5

Table 5

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DISCUSSION

This study is the first to demonstrate that better performance on the TMT D-KEFS is associated with increased magnitude of off-line motor skill learning in individuals with chronic stroke. Furthermore, performance on the TMT D-KEFS was shown to be a significant independent predictor of off-line motor learning in individuals with chronic stroke even after controlling for age. This study advances the body of knowledge of what factors mediate off-line motor learning in people with chronic stroke.

Attention and set-shifting are 2 important factors that contribute to the performance of motor skills.32,33 The CTT used in this study requires both attention and set-shifting to be performed well. The participants need to maintain attention to focus on the task to accurately match the cursor with the movement of the target. Furthermore, the participants must be able to “set-shift” between a random segment and a repeated segment every trial to accurately adapt to and learn the constantly shifting target. Therefore, the TMT D-KEFS was likely the only executive function measure found to be a significant independent predictor of off-line motor learning in this study due to the high degree of attentional and set-shifting demands of the CTT. It is also possible that the association is due to similarity in motor movements that are required to perform the TMT D-KEFS and the tracking task. We do not believe that motor ability is driving the association as performance on the TMT D-KEFS was found to be a significant predictor of off-line motor learning even after controlling for motor speed, and individuals used their less affected upper extremity to perform the tracking task.

An interesting finding in this study is that performance on the TMT D-KEFS was not associated with any of the sleep parameters or self-reported sleep quality. This lack of association is in contrast to the results from Siccoli et al,18 who found increased time spent in N3, and REM sleep was associated with better cognitive function (including executive function) in both the subacute and chronic stages poststroke. The discrepancy in findings may be due to a difference in calculation of the reported outcomes. Another reason why no association was found between performance on the TMT D-KEFS and sleep parameters or self-reported sleep quality in our study may be that participants with stroke in our study sample have on average good sleep quality (the mean Pittsburg Sleep Quality Index score for the stroke group is <5; a score >5 indicates poor sleep quality31), and none of the participants with stroke included in the study had untreated sleep disorders. Therefore, in the absence of poor sleep quality or sleep disorders, detecting an association between executive function and the sleep variables may be difficult. Another possibility is that the performance on the TMT D-KEFS may be associated with sleep parameters at certain parts of the night (ie, at the first half of the night in which N3 is predominant or the second half of the night in which N2 is predominant34), which was not analyzed for our study.

Because this study did not include a group that had a testing schedule to allow for assessing the association between executive function performance and motor learning independent of sleep, it is possible that the performance on the TMT D-KEFS might be associated with improved execution of the motor task whether the period between testing and retesting included sleep or being awake. However, we believe this unlikely as prior studies found that sleep enhanced off-line motor learning in individuals with chronic stroke, but a similar period of being awake did not.1,2 Therefore, sleep appears necessary to enhance learning off-line (with no further practice) in people with stroke.

A limitation of this study is the small sample size, which might affect the interpretation of the results. In addition, the findings of this study can be generalized to only those individuals with stroke with minimal to no cognitive impairments as the mean score on the Mini-Mental State Examination to assess global cognitive status was high (mean, 29.4 ± 0.71), and there were no differences in performance on the executive function tests between the individuals with stroke and the control participants (Table 2). It is important to note that changes in executive function typically occur with normal aging,35,36 and variability in executive function ability is typical in people who would be considered cognitively normal.35,37,38 Furthermore, the individuals with stroke in this study on average had good sleep quality (Pittsburg Sleep Quality Index global score is <5), which is contrary to what would be expected in the general stroke population.20,39,40 The fact that the individuals with stroke in this study have good sleep quality limits the understanding of how sleep disturbances would impact the ability to perform and learn a motor task through its association with attention and set-shifting. Future studies should investigate the association between poor sleep quality, executive function, and motor learning in individuals with chronic stroke.

The findings of this study have important clinical implications. In healthy adults, the performance on the TMT D-KEFS is associated with the performance of activities of daily living and overall physical function.41–43 Attention and set-shifting are important factors that allow an individual to effectively and rapidly adapt to different environmental situations, hence, the association with functional performance. Furthermore, functional neuroimaging studies have demonstrated that performance on the TMT highly correlates with frontal cortical regions that are involved in motor control.44,45 Poor performance on the TMT was associated with lower independent functional outcomes,46 predicts poorer driving abilities,47 and predicts mortality in individuals with stroke.48 In the current study, the finding that executive function predicts off-line motor learning may influence the ability to perform or learn motor skills necessary to perform activities of daily living and could potentially impact motor recovery following stroke.

Because of the impact of executive function on motor learning, physical therapists and rehabilitation clinicians should consider screening attentional and set-shifting abilities in individuals poststroke. For example, the TMT D-KEFS could be used to screen for attentional and set-shifting abilities in individuals poststroke. Performance on the TMT D-KEFS may indicate capacity for motor skill learning, which could influence goal-setting and prognosis. Future studies are needed to determine whether executive function is associated with off-line learning of more functionally relevant tasks or whether addressing executive function deficits impacts motor learning and motor recovery following stroke.

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CONCLUSIONS

The findings of this study demonstrate that certain executive function processes (attention and set-shifting) predict off-line motor learning in individuals with chronic stroke. Clinicians should consider screening executive function abilities specifically attention and set-shifting in individuals following stroke. Future research should explore whether rehabilitation treatment plans that facilitate off-line motor learning and executive functioning favorably enhance functional recovery poststroke.

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ACKNOWLEDGMENTS

The authors thank the sleep technicians for their role in the sleep data collection and also thank the individuals who participated in this study.

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Keywords:

attention; human movement system; practice; set-shifting; sleep

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