Visuospatial Skills Explain Differences in the Ability to Use Propulsion Biofeedback Post-stroke

Abstract

Background and Purpose:

Visual biofeedback can be used to help people post-stroke reduce biomechanical gait impairments. Using visual biofeedback engages an explicit, cognitively demanding motor learning process. Participants with better overall cognitive function are better able to use visual biofeedback to promote locomotor learning; however, which specific cognitive domains are responsible for this effect are unknown. We aimed to understand which cognitive domains were associated with performance during acquisition and immediate retention when using visual biofeedback to increase paretic propulsion in individuals post-stroke.

Methods:

Participants post-stroke completed cognitive testing, which provided scores for different cognitive domains, including executive function, immediate memory, visuospatial/constructional skills, language, attention, and delayed memory. Next, participants completed a single session of paretic propulsion biofeedback training, where we collected treadmill-walking data for 20 min with biofeedback and 2 min without biofeedback. We fit separate regression models to determine if cognitive domain scores, motor impairment (measured with the lower-extremity Fugl-Meyer), and gait speed could explain propulsion error and variability during biofeedback use and recall error during immediate retention.

Results:

Visuospatial/constructional skills and motor impairment best-explained propulsion error during biofeedback use (adjusted R² = 0.56, P = 0.0008), and attention best-explained performance variability (adjusted R² = 0.17, P = 0.048). Language skills best-explained recall error during immediate retention (adjusted R² = 0.37, P = 0.02).

Discussion and Conclusions:

These results demonstrate that specific cognitive domain impairments explain variability in locomotor learning outcomes in individuals with chronic stroke. This suggests that with further investigation, specific cognitive impairment information may be useful to predict responsiveness to interventions and personalize training parameters to facilitate locomotor learning.

INTRODUCTION

Reducing biomechanical gait impairments is an important goal for individuals post-stroke and a common component of gait rehabilitation.1 This is, in part, because biomechanical gait impairments, such as step length asymmetry and reduced paretic swing knee flexion, are associated with increased metabolic cost2,3 and fall risk.4-6 People post-stroke can alter biomechanical gait impairments (including increasing paretic propulsion7 and reducing step length asymmetry8,9) using visual biofeedback,10 but the ability to do so varies from person to person. Recent evidence suggests that this inter-individual variability may be due to post-stroke cognitive impairment.11

Using visual biofeedback to alter gait impairments is an explicit, cognitively demanding motor learning task.12 Participants are given external information about their task performance and asked to reduce their movement error.13 This requires cognitive processing, as participants must understand the information contained in the biofeedback, its relationship to their movements, and the instructions for the task. Cognitive impairment is common post-stroke,14 with deficits in visuospatial skills, attention, executive function, language, and memory among the most commonly affected domains.15-17 Previous work found that participants with higher fluid cognition scores had better performance when gait biofeedback was present and better 24-hour retention of a biofeedback-driven gait pattern.11 However, fluid cognition is a global measure of cognition reflecting someone’s ability to adapt to new information and solve problems18; therefore, it is still unknown which specific cognitive domains are important for performance during practice and retention of an explicitly learned locomotor skill.

There is no evidence linking specific cognitive domains to performance during and immediate retention of a gait biofeedback task, but there are logical roles for commonly affected domains. For example, visuospatial skills may play a role in understanding the visual biofeedback display and how movements map to the display. Attention may be important to direct cognitive resources to focus on the information contained in the biofeedback display, as opposed to other aspects of the environment (eg, the experimenter, feet on the treadmill, etc.). Executive function, which is the ability to identify goals and adapt plans to achieve those goals,19-21 could play a role in integrating the biofeedback information and using it to accomplish the biofeedback goal. Language skills may be required to understand verbalized instructions. Finally, during a retention test, recalling the biofeedback-driven gait pattern likely requires intact memory. Currently, we do not understand if one or a combination of these domains is associated with biofeedback-driven performance and immediate retention of the biofeedback-driven gait pattern. Identifying specific cognitive domains involved in motor learning tasks may facilitate the development of more personalized motor rehabilitation approaches by individualizing training parameters based on specific cognitive impairments.22

Here, we aimed to understand which cognitive domains were associated with performance, performance variability, and immediate retention during a paretic propulsion biofeedback task in individuals post-stroke. Because there is little research informing our understanding of the relationship between specific cognitive domains and biofeedback-driven changes in gait impairments, we performed an exploratory analysis to understand which cognitive domains contribute to locomotor performance, variability during performance, and immediate retention when using visual biofeedback. We considered the following 6 commonly impacted domains of cognition: immediate memory, visuospatial/constructional skills, language, attention, delayed memory, and executive function.

METHODS

Participants

Twenty-nine participants with chronic stroke (>6 months) completed a single session of paretic propulsion biofeedback training. Participants were recruited from the community and through the Registry for Aging and Rehabilitation Evaluation database at the University of Southern California. The specific inclusion criteria for enrollment are provided in supplemental digital content 1, available at: http://links.lww.com/JNPT/A479. The University of Southern California Institutional Review Board approved the experimental procedures, and all participants provided informed consent before beginning the experiment. This study is registered on clinicaltrials.gov (NCT04411303).

Cognitive Testing

Participants completed the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS)23 and the Trail Making B test.24 The RBANS is a comprehensive cognitive testing battery that provides normalized scores for immediate memory, visuospatial/constructional skills, language, attention, and delayed memory. The RBANS also provides a total score, which accounts for all tested cognitive domains. Data for all participants were age-normalized using the RBANS scoring manual. We did not perform race-based normalization on these data to align with the recommendation from the American Academy of Clinical Neuropsychology.25 For all domains, higher scores indicate better cognitive function. All RBANS were independently double-scored to identify and resolve any discrepancies in scoring. When discrepancies were found, the 2 scorers discussed the results and resolved the discrepancies. The Trail Making B test provided a score for executive function and was scored on time to correct completion in seconds.26 We included the Trail Making B Test because the RBANS does not have a dedicated executive function score, and impairment in executive function is common post-stroke.14 Higher scores indicate worse executive function. The test was terminated after 300 s.

Gait Analysis Procedures

We collected kinematic and kinetic data while participants walked on an instrumented dual-belt treadmill. Details about the data acquisition and data processing procedures can be found in supplemental digital content 1, available at: http://links.lww.com/JNPT/A479. Before beginning data collection, we used a custom staircase algorithm to determine the participant’s comfortable treadmill walking speed.27 The participants walked at their comfortable speed during all trials. First, participants walked for 2 min without biofeedback (Figure 1A). Participants then completed four 5-min biofeedback trials. During the biofeedback trials, participants walked with paretic propulsion biofeedback. Our paretic propulsion biofeedback code provided the paretic limb’s real-time anterior ground reaction force during stance (Figure 1B, purple curve and Figure 1C, purple bar). It also provided peak propulsion end-point feedback, allowing participants to visualize their previous peak propulsion value (Figure 1B, Figure 1C, blue dot). The goal was set at +50% of the difference between paretic and non-paretic limbs at baseline,7 with a ±5 N tolerance goal zone (Figure 1B & Figure 1C, orange rectangle). Participants were given the following instructions before the first biofeedback trial with a static visual of the biofeedback display (Figure 1C): “You’ll see on the screen how much you push off the treadmill with your weaker leg while you are walking. The purple bar represents how hard you are pushing off the treadmill in real time. The blue dot shows the hardest you pushed off on the step before. The goal is to make the blue dot land in the orange line with each step.” The participants were not provided with a practice session using the biofeedback before completing the first biofeedback trial. Finally, participants walked for 2 min without biofeedback to assess immediate retention and were given the following instructions: “You will not be provided any feedback during this trial. You should try to walk in the same way you did in the trials when you used the feedback. So, whatever you were doing to meet the feedback goal, I want you to recreate that.”29

Figure 1.

Experimental paradigm. (A) Treadmill walking paradigm. Participants walked on the treadmill at their comfortable speed for baseline, biofeedback, and retention trials and rested as long as needed between trials. (B) Schematic anterior-posterior GRF trace and corresponding components of the visual biofeedback display. The anterior-posterior GRF signals represented by the dashed lines are not shown in the biofeedback display. The solid purple section of the curve corresponds to the paretic GRF signal that was displayed in the real-time biofeedback. The blue dot indicates the peak paretic propulsion in value that was provided as end-point biofeedback. The goal (orange dashed line) was set at +50% of the difference between peak paretic propulsion and peak non-paretic propulsion at baseline. The bounds of the goal zone were ±5 N (orange rectangle). (C) Snapshot of real-time paretic propulsion biofeedback display. The purple bar represents the real-time anterior-posterior GRF signal at a given time. The blue dot represents peak propulsion and appears after every step to provide end-point feedback to the participant. The orange rectangle represents the goal zone. Abbreviations: GRF; ground reaction force.

Data Analyses

We calculated 3 dependent variables: biofeedback performance, performance variability, and immediate retention. Biofeedback performance – the ability of the participants to use the biofeedback to increase paretic propulsion – was represented by normalized propulsion error averaged over the final 30 strides of biofeedback training.28 We completed 5 steps to calculate normalized propulsion error: (1) Calculate peak propulsion as the most positive value of the anterior/posterior ground reaction force during the stance phase for both limbs. (2) Calculate propulsion error for each stride (Figure 2B). Propulsion error was defined as the absolute distance of peak propulsion to the closest border of the goal zone (Figure 2A). Of note, this method was used to calculate propulsion error at baseline, even though the goal was not visible to participants during the baseline walking trial. We chose to calculate the absolute error (as opposed to a directional error) because we were interested in the participants’ ability to manipulate propulsion to achieve the provided goal. Therefore, either over- or undershooting the target was considered an error. (3) Average the propulsion error over the final 30 strides of the baseline trial. (4) Divide the propulsion error value for each stride by the average baseline propulsion error and multiply by 100 to obtain a normalized propulsion error (Figure 2C), where a value of 100% represents the average propulsion error during baseline walking without biofeedback. Any value under 100% reflects reduced normalized propulsion error relative to baseline performance, and any value over 100% reflects increased normalized propulsion error relative to baseline performance. We chose to normalize each participant’s propulsion error to their baseline error to allow for comparison between individuals, as the goal was personalized for each participant. (5) Average normalized propulsion error over the final 30 strides of biofeedback training. We chose this bin to capture the plateau in performance after ample practice with the biofeedback. This value was used for the biofeedback performance analysis. Figure 2 displays the transformation between peak propulsion, propulsion error, and normalized propulsion error of a representative participant’s data.

Figure 2.

Propulsion data used to calculate normalized propulsion error for a representative participant. (A) Peak paretic propulsion across strides. The horizontal dashed lines represent the goal propulsion. (B) Propulsion error across strides. Propulsion error was calculated for each stride as the absolute distance between peak propulsion and the closest goal zone border. Propulsion error was then averaged over the final 30 baseline strides (between vertical dashed lines). (C) Normalized propulsion error across strides. The propulsion error value for each stride was divided by the average baseline propulsion error and multiplied by 100 to obtain normalized propulsion error. A normalized propulsion error of 100% is equivalent to baseline error (horizontal dashed line). The average of the final 30 biofeedback strides was our performance outcome measure (between vertical dashed lines). The difference between the average final 30 biofeedback and the average first immediate retention strides was our immediate retention outcome measure.

We quantified the participants’ variability during performance by calculating the coefficient of variation of paretic propulsion across the final 30 strides of biofeedback training. The coefficient of variation was calculated by dividing the standard deviation of paretic propulsion by the mean paretic propulsion and multiplying by 100%.

To capture immediate retention we calculated recall error, defined as the difference between the average normalized propulsion error over the final 30 biofeedback strides and the average of the first 30 strides of the immediate retention trial (similar to previous work).29,30 This allows us to infer retention of the newly learned walking pattern based on how much of the gait pattern they can recall without the biofeedback. A recall error of 0% indicates that participants normalized propulsion error was the same during immediate retention as their performance at the end of biofeedback training. Positive recall error values mean they were further away from the goal compared to the end of biofeedback training, and negative values mean participants were closer to the goal at immediate retention compared to the end of biofeedback training.

Statistical Analyses

All analyses were conducted in R (4.2.2).31 We performed linear regression with best-subsets selection to determine the cognitive domains associated with performance, performance variability, and immediate retention. Best-subsets selection evaluates each possible combination of predictors and identifies the combination of predictors that best explains the outcome variable, resulting in a single model.32 We included terms for Lower-Extremity Fugl-Meyer (LE-FM), overground gait speed, immediate memory, visuospatial/constructional skills, language, attention, delayed memory, and executive function. We included a LE-FM term as a potential predictor to account for the possibility that motor impairment plays a role in the participants ability to increase paretic propulsion, and overground gait speed as a measure of walking function. We performed this process 3 times, once with each of our dependent variables (normalized propulsion error, coefficient of variation, and recall error). We chose the models with the lowest Bayesian information criterion score. We verified that the final models meet linear regression assumptions using the performance package.33

For all analyses, we excluded participants whose average baseline propulsion magnitude ±1 standard deviation captured the propulsion biofeedback goal. We did this to ensure that participants had to increase their propulsion over their baseline propulsion magnitude to meet the goal. For the immediate retention analysis, we excluded additional participants who did not improve performance during the biofeedback task (normalized propulsion error >95% at the end of biofeedback training).

RESULTS

We included 18 participants in the performance and performance variability analyses. Eleven participants were excluded because their baseline propulsion magnitude ±1 standard deviation captured the propulsion goal. Five participants did not improve performance during the biofeedback task and were excluded from the immediate retention analysis. Clinical demographics for participants included in these analyses are included in Table 1. Our sample had RBANS total scores ranging from 54 to 106, suggesting we captured a large range of cognitive impairment. Participants had an average normalized propulsion error of 59.9 (44.0), an average coefficient of variation of 23.7 (13.4), and an average recall error of 7.6 (25.6).

Table 1.

Clinical Demographics of Participants Included in the Analyses

Age	Race/Ethnicity	Affected Side	10 m Walk Speed (m/s)	Treadmill Speed (m/s)	LEFM	IM	V/C	Language	Attention	DM	EF
63	Asian	Right	1.19	0.61	28	94	92	92	82	102	136
a53	Asian	Right	1.07	0.55	29	57	69	75	97	68	182
35	White/Hispanic	Right	1.39	0.78	24	81	69	112	85	60	122
31	White/Hispanic	Left	1.38	0.85	29	65	87	64	75	60	73
61	White/Hispanic	Left	0.73	0.39	22	73	75	90	82	92	93
59	Asian	Left	1.19	0.74	24	90	89	87	103	108	109
61	White	Right	0.51	0.5	24	85	112	92	97	84	66
53	Asian	Right	1.17	0.6	25	57	87	79	82	91	189
78	Asian	Left	0.86	0.49	24	94	105	96	88	113	95
46	White/Hispanic	Left	0.95	0.54	24	94	60	118	56	98	68
55	White/Hispanic	Left	1.04	0.54	28	69	378	64	75	75	74
63	White/Hispanic	Left	0.92	0.56	18	100	109	98	109	110	71
a56	Black	Left	0.81	0.52	11	94	60	87	68	99	300
a64	White	Right	0.92	0.48	23	94	87	90	75	112	140
a60	Black	Right	0.42	0.23	12	53	58	54	43	64	300
69	White/Hispanic	Left	0.23	0.3	22	81	64	85	53	86	198
a44	White/Hispanic	Right	1.07	0.58	16	53	66	85	53	60	267
45	Black	Left	0.59	0.37	17	103	69	83	85	98	300

The maximum Lower-Extremity Fugl-Meyer score is 34. Scores for visuospatial/constructional, language, attention, immediate memory, and delayed memory are from the Repeatable Battery for the Assessment of Neuropsychological Status. Executive function scores are from the Trail Making B Test and are scored in seconds to completion. Participants excluded from the immediate retention analysis are denoted with a superscript letter (^a).

Abbreviations: DM, delayed memory; EF, executive function; IM, immediate memory; LEFM, Lower-Extremity Fugl-Meyer; V/C, visuospatial/constructional.

Visuospatial/constructional and LE-FM scores best-explained normalized propulsion error during biofeedback training (adjusted R2 = 0.59, P = 0.0005; Figure 3A), suggesting that participants with better visuospatial/constructional skills and less motor impairment were better able to use the information contained in the biofeedback display to improve performance. To understand the relative contribution of each predictor to normalized propulsion error, we visualized the relationship between our outcome and one predictor while controlling for the other predictor using added variable plots.34 When controlling for the influence of LE-FM, we see a negative relationship between visuospatial/constructional scores and normalized propulsion error (Figure 3B, top). When controlling for visuospatial/constructional scores, we similarly see a negative relationship between LE-FM and normalized propulsion error, albeit weaker (Figure 3B, bottom). This suggests that the visuospatial/constructional score has a greater contribution to normalized propulsion error than the LE-FM score. Individual coefficient estimates for all models are provided in Table 2. It is important to note that the best-subsets selection process employed here selects the combination of predictors that best-explains the outcome variable, resulting in a non-significant term being included in our final model (LE-FM).

Figure 3.

Biofeedback performance results. (A) Relationship between visuospatial/constructional skills, LE-FM, and normalized propulsion error averaged over the final 30 strides of biofeedback training (n = 18). A normalized propulsion value of 100% is equivalent to baseline propulsion error (horizontal dashed line). (B) Added variable plots for relationship between visuospatial/constructional skills (top) and LE-FM (bottom) and normalized propulsion error. Abbreviations: RBANS, Repeatable Battery for the Assessment of Neuropsychological Status; LE-FM, Lower-Extremity Fugl-Meyer.

Table 2.

Model Results

	β	SE	95% CI	P-value
Performance model
Intercept	243.5	37.0	[164.7, 322.4]	<0.0001
LE-FM	−2.4	1.4	[−5.3, 0.6]	0.1
Visuospatial/Constructional	−1.6	0.4	[−2.6, −0.7]	0.002
Performance variability model
Intercept	50.7	13.0	[23.2, 78.2]	0.001
Attention	−0.34	0.2	[−0.7, −0.002]	0.048
Immediate retention model
Intercept	91.3	36.7	[10.5, 172.1]	0.03
Language	−0.9	0.4	[−1.8, −0.04]	0.04

Abbreviations: LE-FM, Lower-Extremity Fugl-Meyer.

Attention scores best explained the coefficient of variation across the final 30 s of biofeedback training (adjusted R2 = 0.17, P = 0.048; Figure 4A). Participants with higher attention scores had a more consistent propulsion output throughout performance, suggesting that attention plays a role in the consistency of the biofeedback-driven walking pattern execution.

Figure 4.

Performance variability and immediate retention results. (A) Relationship between attention and coefficient of variation in performance (n = 18). (B) Relationship between language skills and recall error (n = 13). A recall error of 0% represents exact recall (horizontal dashed line). Negative recall error means that participants were closer to the goal at immediate retention than at the end of biofeedback training. Positive recall error means that participants were further from the goal at immediate retention than at the end of biofeedback training. The linear model fit is plotted in red, with 95% confidence intervals in gray. Abbreviation: RBANS, Repeatable Battery for the Assessment of Neuropsychological Status.

Language scores best-explained recall error (adjusted R2 = 0.27, P = 0.04; Figure 4B). Participants with higher language scores were able to maintain or move closer to the propulsion goal immediately after the biofeedback was removed.

For all analyses, we have included supplemental digital content 2, Tables 1-3 (available at: http://links.lww.com/JNPT/A480) to show which variables were selected at each stage of the best-subsets selection process.

DISCUSSION

We aimed to understand which cognitive domains were associated with performance, performance variability, and immediate retention of a paretic propulsion biofeedback task in individuals post-stroke. We found that visuospatial/constructional skills and motor impairment were associated with performance, attention was associated with performance variability, and language skills were associated with immediate retention. These results are the first to identify an association between specific cognitive domains and the biofeedback-driven acquisition, variability, and immediate retention of a newly learned locomotor behavior.

Our data indicated that participants with better visuospatial/constructional skills exhibited less error while performing a paretic propulsion biofeedback task compared to those with lower scores. The RBANS visuospatial/constructional subscale consists of line orientation and figure copy tasks, and lower scores indicate impairment in processing and using visual information.35 To use the information contained in the biofeedback display, participants must understand how their propulsion magnitude maps onto the visual display. They must also internalize the visual error on the screen to adjust their propulsion output. Based on their poorer performance with the biofeedback task, this was more challenging for participants with lower visuospatial/constructional skills. This suggests that commonly used rehabilitation approaches that require processing visual information – such as taping step length targets on the floor or practicing specific movements in front of a mirror – may be less effective for participants with visuospatial/constructional impairments. These individuals may instead benefit from an alternative biofeedback method, such as auditory biofeedback, which can also be used to increase paretic propulsion in individuals post-stroke.36 However, future research is needed to understand the cognitive domains that influence motor learning for other modes of biofeedback.

Previous work by French et al.11 found that fluid cognition, not visuospatial working memory, was related to the acquisition and 24-hour retention of a biofeedback-driven walking pattern in individuals post-stroke. Importantly, the visuospatial measures used in the aforementioned study measured a different aspect of visuospatial cognition than our study. Visuospatial working memory reflects someone’s ability to store and manipulate visual information,37 and visuospatial/constructional skills reflect someone’s ability to process and use visual information.35 Our work expands upon the findings of French et al.11 by identifying the specific cognitive domains associated with both the acquisition and immediate retention of a biofeedback-driven walking pattern.

In addition to the visuospatial/constructional cognitive domain, our selected performance model also included the LE-FM score, accounting for motor impairment. This is unsurprising, as motor impairment likely contributes to an individual’s ability to increase paretic propulsion. It is possible that the participant is unable to physically increase propulsion and, therefore, cannot reach the biofeedback goal regardless of cognitive impairment. Including the LE-FM score in the model partially accounts for this possibility. However, it is likely that it does not entirely capture motor impairment, particularly since the LE-FM is not measured during gait. Adding other gait-specific measures of motor impairment may better address this concern in future studies.

We found that participants with better attention scores had lower performance variability during biofeedback training, indicating that attention plays a role in the execution consistency of the biofeedback-driven walking pattern. This is consistent with work relating attention and gait variability in individuals with Parkinson Disease.38 Additionally, functional connectivity between the dorsal attentional and default networks, which reflect the ability to allocate and sustain attention during a task, are related to gait variability in older adults and individuals with Parkinsons Disease.39 To use the propulsion biofeedback, participants had to direct cognitive resources to sustain attention on the display. This may explain why participants with poorer attentional abilities exhibited greater variability during the acquisition of the biofeedback-driven walking pattern.

Our work is the first to find that language skills play a role in the immediate retention of a biofeedback-driven walking pattern. The language subscale on the RBANS consists of picture naming and semantic fluency tasks, and lower scores indicate expressive (producing language)19 or receptive (perceiving and understanding language)19 impairments.35 Based on these results, we posit that the participants in our study with lower language scores had more difficulty comprehending the verbal instructions for the immediate retention test, which previous work has shown to impact retention of a biofeedback-driven walking behavior.40 It is also important to consider that we performed an immediate retention test (approximately 2 min after completion of biofeedback training), which did not provide a consolidation period for long-term learning.41 Therefore, while we found an association between language skills and immediate retention, future work is needed to understand if language skills are also associated with the long-term retention of a biofeedback-driven walking pattern.

Although we observed an association between language skills and immediate retention of our biofeedback-driven walking pattern, retention of upper extremity tasks has most often been related to visuospatial function.22,30,42-45 For example, the RBANS visuospatial/constructional score has been associated with 1-week retention of an upper extremity task in neurotypical older adults.42 In addition, previous studies have found that visuospatial working memory (not captured in our visuospatial/constructional score) is associated with 1-month retention of an upper extremity task in neurotypical adults30,43-45 and individuals post-stroke.22 Interestingly, French et al.11 found that visuospatial working memory did not explain any more variance than the fluid cognition score in 24-hour retention of a biofeedback-driven locomotor behavior. This suggests that visuospatial working memory may not play the same role in the retention of a visually guided walking pattern as it does in a reaching task. This difference could be explained by the distinct types of motor skills required for each task, as recent work demonstrated that participants with better visuospatial working memory have better retention of fine, but not gross, upper extremity motor skills.45 Altering gait impairments requires improvement and retention of gross motor skills.

Interestingly, 2 participants performed considerably better on the task without biofeedback than with biofeedback (reflected as a negative recall error in Figure 4B). These participants had high language and low visuospatial scores (see rows 2 and 10 in Table 1). It is possible that the participants’ impaired visuospatial skills led to worse performance with the biofeedback, and when the biofeedback was removed, they were able to use the verbal instructions to better produce the intended gait pattern. These data highlight the potential importance of considering the distribution of cognitive impairment to individually tailor rehabilitation interventions.

Collectively, these results suggest that neuropsychological testing may be valuable for interpreting motor learning and rehabilitation outcomes.11,46 Collecting detailed information about the distribution of cognitive impairment across domains of cognition – beyond that provided by standard screening tools – may help to better explain individual variation in response to experimental paradigms. Standard screening tools such as the Montreal Cognitive Assessment and Mini-Mental State Exam may categorize a participant as having normal cognitive function despite impairments in specific cognitive domains.47 While full neuropsychological testing may not be feasible in many rehabilitation research settings, the RBANS can be administered in 30 min or less – providing sub-scale scores for 5 different cognitive domains with minimal time burden. Collecting detailed data on specific cognitive domain impairments will allow for a deeper understanding of how these impairments affect post-stroke rehabilitation outcomes. With further investigation, we may be able to identify training parameters, such as the mode, frequency, or practice schedule, that can be individualized based on specific cognitive impairments. Further defining the relationship between cognitive impairments and rehabilitation responsiveness would also inform the design of future studies that explore using cognitive training to enhance motor rehabilitation. Recent evidence suggests that visuospatial skills can be trained in young neurotypical adults,48 but more research is needed to understand if the same is observed in people post-stroke or if visuospatial training influences motor skill retention.

Limitations

This study has a few limitations. First, we included a relatively small number of participants in our exploratory analyses, which introduces the possibility that the associations found in this work may not generalize to a larger sample. However, this work provides the foundation to inform future, hypothesis-driven research with larger sample sizes. Second, we did not include all factors that may contribute to individual variability in locomotor learning outcomes in our model. Future work should investigate potential physical, psychological, or social factors that may contribute to locomotor learning. Third, we considered both over- and undershooting the propulsion goal to be an error. It is possible that overshooting the goal may have resulted in more symmetric propulsion between limbs (see supplemental digital content 2 and Figure 1, available at: http://links.lww.com/JNPT/A480) This may have caused participants to overshoot the goal despite the instructions of the task, as they may have been implicitly biased towards walking with more symmetric propulsion. It is also possible that our results would differ if we had considered directional error rather than absolute error. However, there is limited data available to inform a hypothesis about the influence of directional errors on the acquisition and retention of a new walking pattern. Fourth, our immediate retention instructions may have been too ambiguous, and participants may have interpreted the objective differently (ie, keep trying to hit the previously displayed target or continue walking like you were when the feedback was on). This may be why 2 participants walked with propulsion values closer to the target during retention, but further studies are needed to understand the influence of instruction on the acquisition and retention of locomotor skills. Fifth, we had to exclude 11 participants because their baseline propulsion magnitude ±1 standard deviation captured the propulsion goal, suggesting the goal was not sufficiently challenging for some participants. Finally, since we used the same goal-setting method for each participant, the goal difficulty may have been different between individuals. Personalizing the propulsion goal (similar to Genthe et al.7) would provide a more consistent level of challenge between participants.

CONCLUSION

We found that visuospatial/constructional skills and motor impairment were associated with acquiring a biofeedback-driven locomotor behavior, attention was associated with performance variability, and language skills were associated with the immediate retention of that behavior. These results demonstrate that information on specific cognitive domain impairments explains variability in locomotor learning outcomes. This suggests that with further investigation, we may be able to use information on specific cognitive impairments to predict responsiveness to interventions and personalize training parameters to facilitate locomotor learning after a stroke.