Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2022 Jan 1.
Published in final edited form as: Exp Brain Res. 2020 Nov 1;239(1):151–160. doi: 10.1007/s00221-020-05964-y

Neural correlates of within-session practice effects in mild motor impairment after stroke: a preliminary investigation

Elizabeth Regan 1, Julius Fridriksson 2, Sydney Y Schaefer 3, Chris Rorden 4, Leonardo Bonilha 5, Jennapher Lingo VanGilder 3, Jill Campbell Stewart 1
PMCID: PMC7887039  NIHMSID: NIHMS1643024  PMID: 33130906

Abstract

While the structural integrity of the corticospinal tract (CST) has been shown to support motor performance after stroke, the neural correlates of within-session practice effects are not known. The purpose of this preliminary investigation was to examine the structural brain correlates of within-session practice effects on a functional motor task completed with the more impaired arm after stroke. Eleven individuals with mild motor impairment (mean age 57.0 ± 9.4 years, mean months post-stroke 37.0 ± 66.1, able to move ≥26 blocks on the Blocks and Blocks Test) due to left hemisphere stroke completed structural MRI and practiced a functional motor task that involved spooning beans from a start cup to three distal targets. Performance on the motor task improved with practice (p=0.004), although response was variable. Baseline motor performance (Block 1) correlated with integrity of the CST (r=−0.696) while within-session practice effects (change from Block 1 to Block 3) did not. Instead, practice effects correlated with degree of lesion to the superior longitudinal fasciculus (r=0.606), a pathway that connects frontal and parietal brain regions previously shown to support motor learning. This difference between white matter tracts associated with baseline motor performance and within-session practice effects may have implications for understanding response to motor practice and the application of brain-focused intervention approaches aimed at improving hand function after stroke.

Keywords: Motor practice, upper extremity, stroke, diffusion imaging

Introduction

Behavioral practice based on the principles of motor skill learning is an important component of rehabilitation interventions aimed at improving arm motor function after stroke (Winstein and Stewart 2006; Winstein and Wolf 2008). Although individuals post-stroke generally have the capacity to learn a novel motor skill (Boyd et al. 2007; Meehan et al. 2011; Pohl et al. 2006; Winstein et al. 1999), variability in responsiveness to practice is often present. One factor in predicting responsiveness to motor practice may be the residual structural integrity of white matter pathways that support motor learning.

While the neural correlates of motor performance (i.e., how well a task is initially performed) after stroke have been well-studied, less is known about the neural correlates of within-session practice effects in response to motor practice (i.e., how much a task improves over repetitions). Structural integrity of the corticospinal tract (CST), the primary descending motor pathway, has been shown to support motor performance after stroke (Burke et al. 2014; Lewis et al. 2020; Lindenberg et al. 2010; Stewart et al. 2017). However, functional connections between the frontal and parietal brain regions have been shown to support the learning of a novel motor skill in both nondisabled individuals (Lin et al. 2012; Wu et al. 2014) and individuals post-stroke (Zhou et al. 2018). Thus, the structural integrity of the superior longitudinal fasciculus (SLF) that connects frontal and parietal brain regions may be related to within-session practice effects after stroke.

To better study within-session practice effects and motor learning in the context of stroke rehabilitation, we have developed an upper extremity motor task that is more functional and naturalistic than other reaching paradigms that do not involve any grasping or object manipulation. The task involves moving beans with a spoon from a start container to target cups in a specified order (Schaefer and Hengge 2016). This task has shown both within-session practice effects and longer-term learning in young adults (Schaefer and Lang 2012), older adults (Schaefer et al. 2015; Schaefer and Duff 2015) and individuals post-stroke (Schaefer et al. 2013). Moreover, training on this task has been shown to generalize to other functional upper extremity tasks (Schaefer et al. 2013; Walter et al. 2019). Importantly, initial responsiveness to practice predicts 1-month retention performance in older adults (Schaefer and Duff 2015), suggesting that within-session practice effects on this functional motor task may provide important information on long-term retention.

Therefore, the purpose of this preliminary study was to investigate the structural brain correlates of within-session practice effects on a functional motor task completed with the more impaired upper extremity in individuals with mild motor impairment due to chronic stroke. We hypothesized that while the structural integrity of the descending motor pathway (CST) would correlate with baseline motor performance, the integrity of frontal-parietal pathways (SLF) would correlate with within-session practice effects.

Methods

Participants

Eleven individuals with chronic stroke participated in this study. Participants were recruited from a larger multi-lab study on brain-behavior relationships in chronic stroke (Fridriksson et al. 2016; Peters et al. 2018; Stark et al. 2019). For this larger study, participants were included if they (1) had a single left hemispheric stroke occurring at least 6 months prior, (2) were able to follow simple instructions, and (3) could walk 8 meters independently with or without an assistive device. Exclusion criteria consisted of (1) any contraindication for MRI, (2) clinical history of dementia, alcohol abuse, psychiatric disorder, co-morbid neurologic conditions, or (3) severe vision or visual-spatial impairments. For the current study, individuals had to be able to hold a spoon with or without modifications (e.g. grip build up) with the contralesional hand. Motor status was defined by several clinical measures including the Box & Blocks Test (BBT), a measure of hand function (Mathiowetz et al. 1985a), the Nine-hole peg test (9-HPT), a measure of hand dexterity (Mathiowetz et al. 1985b), hand grip strength (average of three trials with a handheld dynamometer (Bohannon 1986; Riddle et al. 1989)), and the Stroke Impact Scale (SIS) Hand Domain, a 5-item self-report measure on perceived difficulty to use the more impaired hand to compete daily activities (Duncan et al. 1999). Spatial working memory was assessed by the Spatial Span test (Wechsler 1997). Both forward and backward spatial span tests were completed leading to maximum score of 32. All participants provided written informed consent prior to participation through a protocol approved by the Institutional Review Board at the University of South Carolina.

Functional upper extremity task

Participants completed 15 trials of a functional motor task with the contralesional upper extremity. This task has been adapted from a functional hand assessment (Jebsen et al. 1969), and has been shown to induce within-session practice effects (Schaefer et al. 2015; Schaefer et al. 2013; Schaefer et al. 2018). One trial consisted of the participant spooning nine raw kidney beans, one at a time, from a start cup to three distal target cups (https://osf.io/phs57/wiki/Functional_reaching_task_Regan_et_al/); beans were dropped in sequential order into the left cup, middle cup, and right cup. Each trial began with the participant holding a plastic spoon next to the start cup which held 50 beans. On a verbal “go” signal, the participant began spooning one bean at a time; the trial ended when the 9th bean dropped into the final cup. Participants could hold the spoon using whatever grip they chose.

Participants completed 15 trials of the motor task during a single session. No feedback, coaching or encouragement was provided aside from instructions to continue until the trial was complete. This was by design to minimize any effect on participant expectation or a ‘prime’ by the experimenter, which have been shown to influence motor learning (Chua et al. 2020; McKay et al. 2012; Wulf et al. 2012). Performance time (time from the go signal to when the 9th bean was dropped in the cup) and errors (beans dropped outside of a cup or more than one bean spooned) for each trial were recorded. If an error was made, the participant was instructed to continue the trial until 9 movements from the start cup to a target cup had been made (3 movements to each cup in sequential order) that were successful in dropping a bean into the target cup. Performance times were averaged into five trial blocks for analyses (three blocks that included 5 trials each). Baseline motor performance was defined as Block 1 mean performance time (i.e. mean of the first five trials). Within-session practice effects were defined as the change in performance time from Block 1 to Block 3.

Brain Imaging

Participants underwent a single MRI session on a 3T Siemens scanner. A scanner upgrade was completed in the middle of data collection. Therefore, seven participants completed the MRI session on a 3T Prisma with a 20-channel head coil and four participants completed the MRI session on a 3T Trio with a 12-channel head coil. Diffusion-weighted images were acquired using echo planar imaging; two sequences were acquired in opposite phase encoding directions (for 7 participants: TR=5250 ms, TE=80 ms, 80 axial slices, 1.5 mm3 voxel size; for 4 participants: TR=4987 ms, TE=79.2 ms, 50 axial slices, 2.3 mm3 voxel size). Each sequence included 36 volumes with noncollinear diffusion directions at a b-value of 1000 s/mm2 and 5 or 7 volumes with b-value of 0. High resolution T1-weighted (TR=2250 ms, TE=4.52 ms/4.15 ms, 1 mm3 voxel size) and T2 weighted images (TR=2800 ms, TE=402 ms, 1 mm3 voxel size) were also acquired.

Stroke lesions were manually outlined on the T2 image by a neurologist who was blinded to motor scores, which were then coregistered to the T1 image. T1-weighted images were normalized into standard MNI space utilizing enantiomorphic unified segmentation-normalization routines in Statistical Parametric Mapping (SPM) 12 (Wellcome Department of Cognitive Neurology, London, UK) (Rorden et al. 2012), which also applied a lesion-mask cost function (Brett et al. 2001). Diffusion images were undistorted using the FMRIB Software Library’s (FSL; FMRIB Center, Oxford, UK) TOPUP and Eddy tools (Andersson et al. 2003; Andersson and Sotiropoulos 2016) and the skull was removed using the FSL BET tool. FSL’s dtifit tool was used to create voxelwise maps of fractional anisotropy (FA). FA is a measure of white matter structural integrity with values ranging between 0 and 1; higher values indicate greater alignment of white matter in a primary direction (Mori and Zhang 2006). In order to improve registration between T1 and FA spaces, the skull-stripped T1 image was nonlinearly normalized to match the undistorted FA images.

To examine the relationship between brain structure and baseline motor performance/within-session practice effects, mean FA and lesion load from two a priori regions of interest (ROI) were extracted utilizing the John Hopkins University (JHU) atlas: corticospinal tract (CST) and superior longitudinal fasciculus (SLF). CST was chosen as this pathway has been shown to support movement in individuals post-stroke (Burke et al. 2014; Lewis et al. 2020; Lindenberg et al. 2010; Stewart et al. 2017). The SLF is a pathway that connects frontal and parietal brain regions, areas shown to play a role in motor learning (Daselaar et al. 2003; Hardwick et al. 2013; Lin et al. 2012; Wu et al. 2014). The structural integrity of white matter pathways was quantified using two approaches in each ROI, lesion load and mean FA. Lesion load was quantified as the percentage of voxels within the ROI that overlapped with the stroke lesion. Since the relationship between FA and motor behavior can be affected by the inclusion or exclusion of lesioned voxels in the mean FA calculation (Archer et al. 2017), mean FA from each ROI in the lesioned hemisphere was extracted with and without lesioned voxels removed; only lesion-corrected FA values which represent the integrity of the residual, non-lesioned tissue were used in analyses. An FA ratio (FA lesioned hemisphere/FA nonlesioned hemisphere) was calculated to determine relative integrity of the lesioned side compared to the nonlesioned side for each individual.

Statistical Analysis

All data were normally distributed according to the Shapiro-Wilk test (p>0.05). A repeated measures analysis of variance (ANOVA) was used to examine within-session practice effects across the three blocks of practice of the motor task. Brain structure-motor behavior relationships were examined by calculating bivariate Pearson correlations between FA ratio or lesion load (% damaged) in each ROI and baseline motor performance and between FA ratio/lesion load in each ROI and within-session practice effects. The strength of correlations was interpreted based on the value of the correlation coefficient: r < 0.25 = little or no relationship; r of 0.25 to 0.50 = fair; r of 0.50 to 0.75 = moderate; r > 0.75 = strong (Portney and Watkins 2009). For this preliminary investigation, significance level was set at p<.05 for all analyses.

Results

Participants

Participants had a mean age of 57.0 ± 9.4 years, were on average 37.0 ± 66.1 months post-stroke (Table 1), and presented with a mix of cortical and subcortical lesions (Figure 1). The right hand was the dominant hand for all but one participant (Subject 9). Overall, participants presented with mild motor impairment; all individuals were able to move a minimum of 26 blocks in one minute on the BBT and completed the 9-HPT in 60 sec or less. However, 9 out of 11 participants reported continued difficultly in using the more affected hand to complete functional activities (SIS Hand Score < 100).

Table 1.

Participant Demographics

ID Age Sex Months Post-Stroke Lesion Volume (mm3) Spatial Span Total Grip Strength (kg) BBT 9-HPT (sec) SIS-Hand Score Baseline Motor Performance (Block 1) Within-Session Effects (Block 3-Block1)
1 64 M 13.3 191668 5 26.0 38 50.5 70 36.4 1.15
2 55 M 12.2 55412 4 44.7 66 20.7 90 19.67 −0.16
3 53 M 25.0 65991 19 44.0 83 21.8 100 18.25 −1.12
4 49 M 11.3 2094 13 58.3 48 27.2 95 23.01 −3.97
5 54 M 10.6 2292 8 18.0 26 60.0 45 38.62 −4.69
6 59 M 8.9 26058 14 37.0 41 40.5 80 39.10 −1.80
7 74 M 20.5 52941 13 29.0 43 36.2 50 31.35 −7.57
8 64 M 10.4 37515 12 61.3 50 30.4 100 24.29 −4.75
9 44 M 22.0 90340 21 48.7 59 26.5 95 33.01 −3.41
10 45 F 38.9 55588 13 21.0 44 27.9 85 29.23 −0.15
11 66 M 234.7 106006 12 43.3 36 34.1 60 32.97 −6.19
Mean 57 37.0 62355 12.2 39.2 48.6 34.2 79.1 29.63 −2.94
Open in a new tab

Grip Strength, Box & Blocks Test (BBT), and Nine Hole Peg Test (9-HPT) represent the contralesional hand; SIS=Stroke Impact Scale; M=Male; F=Female; Spatial Span Total max score=32; SIS Hand Score max score=100; Values for Motor Performance and Within-Session Effects represent time in sec

Fig. 1.

Fig. 1

Open in a new tab

Summary mask of stroke lesions. Color represents number of participants with a lesion in that voxel

Within-Session Practice Effects on Functional Upper Extremity Task

Overall, participants improved performance on the functional upper extremity task with practice. Errors (e.g. dropped bean) were low across blocks (mean error <1 per block); total errors across all 15 trials by individual participants ranged from 0 to 6 with a median of 2. Motor performance time significantly improved over the three blocks of practice (F2,20 = 7.24, p=0.004) from an average of 29.63 ± 7.37 seconds in Block 1 to 26.69 ± 7.48 seconds in Block 3 (Figure 2), although the degree of improvement varied between participants (Table 1). This corresponded to a mean rate of 3.29 ± 0.82 sec/bean in Block 1 and 2.97 ± 0.83 sec/bean in Block 3. A previous study using a version of this motor task in individuals post-stroke with a greater overall level of motor impairment reported a mean rate of 7.20 ± 7.39 sec/bean for the more impaired arm and 2.39 ± 0.45 sec/bean for the less impaired arm in the first block of practice (Schaefer et al. 2013). Within-session changes on the motor task did not correlate with measures of motor function (BBT, 9-HPT) or spatial working memory (Spatial Span; r<0.328, p>0.326).

Fig. 2.

Fig. 2

Open in a new tab

Within-session changes in motor task performance. Values represent mean trial time with standard error bars for each trial

Relationship between Brain Structure and Within-Session Practice Effects

Stroke lesions involved the SLF ROI in nine participants but did not overlap with the CST ROI in any participant (Table 2). Therefore, for the SLF, a lesioned-corrected FA value that represented the structural integrity of residual tissue was extracted and used in analyses. Mean FA was lower in the lesioned hemisphere compared to the nonlesioned hemisphere in both the SLF and CST (p<0.01) (Table 2). For examination of brain-behavior relationships, FA ratio (lesioned/nonlesioned) for both pathways (CST, SLF) and lesion load for the SLF were utilized.

Table 2.

Mean Lesion Load, FA, and FA Ratio

Number of Participants with Lesion to ROI Lesion Load (%) Mean FA Lesion Corrected Mean FA FA Ratio
SLF Lesioned 9 27.8 (29.1) 0.33 (0.09) 0.38* (0.05)
0.86 (0.11)
SLF Non-Lesioned 0.43 (0.03)
CST Lesioned 0 0.37* (0.09)
0.88 (0.14)
CST Non-Lesioned 0.42 (0.06)
Open in a new tab

Values represent mean (standard deviation). SLF=superior longitudinal fasciculus; CST=corticospinal tract; FA=fractional anisotropy; Mean FA Corrected=mean FA with lesioned voxels removed; FA Ratio=lesioned FA/nonlesioned FA.

*

p<0.01 for differences between lesioned (left) and nonlesioned (right) side

CST FA ratio showed a significant and moderate negative relationship with baseline motor performance (r=−0.696, p=0.017) but did not correlate with within-session practice effects (r=−0.112, p=0.743) (Figure 3). Individuals with a higher FA ratio tended to have shorter (i.e., better) performance times in Block 1. In contrast, SLF lesion load showed a significant positive, moderate relationship with within-session practice effects (r=0.606, p=0.048) but not with baseline motor performance (r=0.038, p=0.912). In other words, individuals with relatively less lesion to the SLF tended to show more improvement on the motor task over the course of practice. Total lesion volume and SLF FA ratio did not significantly correlate with either baseline motor performance or within-session practice effects (r<0.370, p>0.263).

Fig. 3.

Fig. 3

Open in a new tab

Relationship between corticospinal tract (CST) FA ratio and motor performance (A) and within-session practice effects (B), between lesion load of the superior longitudinal fasciculus (SLF) and motor performance (C) and within-session practice effects (D), and between SLF FA ratio and motor performance (E) and within-session practice effects (F). Each data point represents an individual participant. FA Ratio=lesioned FA/nonlesioned FA. *p<0.05 for correlation

Discussion

The purpose of this study was to investigate the structural brain correlates of within-session practice effects on a functional motor task completed with the more impaired hand in individuals with mild motor impairment due to stroke. On average, motor performance improved with practice, however, the amount of within-session improvement varied between individuals. While baseline motor performance correlated with the structural integrity of the CST, within-session practice effects correlated with the structural integrity of the SLF. This difference between the neural correlates of motor performance and the neural correlates of within-session practice effects may have implications for rehabilitation aimed at improving arm and hand function after stroke.

The degree of lesion to the SLF correlated with within-session practice effects on the motor task; individuals with less lesion to this ROI showed more improvement over practice (decrease in time to complete). Because the SLF provides structural connection between frontal and parietal brain regions (Makris et al. 2005), this study provides additional insights into the underlying mechanisms of variability of motor training responsiveness between individuals after stroke. Functional activation in motor, premotor, and parietal cortices has been shown to support motor learning in nondisabled individuals (Daselaar et al. 2003; Hardwick et al. 2013; Lin et al. 2012; Wu et al. 2014). Recent work in individuals post-stroke showed that resting-state frontoparietal functional connectivity predicted performance gains after practice on a set of computer-based, visuomotor tracking tasks with the more impaired arm (Zhou et al. 2018). Additionally, previous studies in older adults found that learning of the motor task used in the current study was related to visuospatial function (Lingo VanGilder et al. 2018; Schaefer and Duff 2017), a function that relies on the SLF (Chechlacz et al. 2015). Here, we show that damage to the SLF due to stroke may impede within-session practice gains on a functional, motor task. Future studies on practice-related changes on a motor task after stroke should consider the role of frontal-parietal pathways and visuospatial function.

Structural integrity of the CST correlated with baseline motor performance but not with within-session practice effects. CST integrity as measured by diffusion-weighted imaging has been suggested as a biomarker of the motor system after stroke (Boyd et al. 2017) and the integrity of this descending motor pathway has been shown to correlate with level of motor impairment (Burke et al. 2014; Cassidy et al. 2018; Lindenberg et al. 2010; Stinear et al. 2007). Most studies investigating motor behaviour-brain structure relationships after stroke have utilized clinical measures of motor impairment or function to characterize the motor system. Clinical measures of the motor system, however, typically provide a cross-sectional picture of general movement ability and not the ability to acquire a new skill with practice. The relationship between CST integrity and baseline motor performance found in the current study (greater integrity correlated with faster baseline performance) is consistent with this previous work using clinical measures.

The repetitive practice of functional tasks is an important component of many intervention approaches in arm rehabilitation after stroke (Winstein and Wolf 2008). The functional, naturalistic motor task used in the current study aligns with the types of tasks frequently practiced during rehabilitation. The within-session practice effects found in this study are consistent with previous studies using a similar version of this task (Schaefer et al. 2015; Schaefer et al. 2018) including in individuals post-stroke (Schaefer et al. 2013). Given that initial responsiveness to practice predicts 1-month retention performance in older adults (Schaefer and Duff 2015), these within-session practice effects may provide predictive information on long-term learning potential after stroke and should be investigated in future studies.

We focused on structural measures of the brain in the current study, specifically anatomical structural scans and diffusion-weighted images. One advantage of these structural scans over functional MRI is that they can be collected on individuals with a wide array of motor capacity levels while at rest. Both lesion load and FA were used to define white matter integrity in each ROI. Each of these measures has been used to define white matter integrity after stroke (Burke et al. 2014; Cassidy et al. 2018; Lindenberg et al. 2010; Riley et al. 2011; Stinear et al. 2007). The SLF ROI had some degree of lesion in most participants while the CST ROI was not lesioned in any participant (see Table 2). The CST ROI in the JHU atlas is located in the brainstem, limiting the likelihood of lesion to this ROI in the current study. While the CST may have been lesioned in more rostral portions of the tract, the ROI used in the current study provided an FA value that was not affected by lesion (Archer et al. 2017) and was located in a portion of the tract that often shows the strongest relationships between FA and motor behavior (Archer et al. 2017; Schaechter et al. 2009). This difference in lesion load between the CST and SLF ROIs may explain why lesion load, a measure that reflects degree of stroke-damage, correlated with behavior in the SLF but FA, a measure that reflects the structural integrity of residual tissue, correlated with behavior in the CST. The optimal measure of structural integrity of white matter pathways (e.g. lesion load or FA) after stroke remains unknown (Cassidy et al. 2018; Findlater et al. 2019) and may differ based on the pathway of interest or approach taken to define integrity (i.e. location of the ROI along the tract).

This was a preliminary investigation with a small sample size; thus, the results of the current analyses should be interpreted within the context of this limitation. However, the findings of this study support future studies in larger populations that may include other motor pathways, such as interhemispheric motor connections, shown to support movement after stroke (Lindenberg et al. 2012; Stewart et al. 2017; Wang et al. 2012). This study investigated within-session practice effects but did not have a true measure of learning (i.e., a delayed retention test) and did not allow the investigation of the strategy used to improve performance (e.g. straighter hand path versus increased reach speed). While previous work suggests that within-session practice effects predict long-term retention in older adults (Schaefer and Duff 2015), future studies should include a retention period and additional instrumentation to validate this finding in individuals post-stroke and elucidate changes in strategy with practice. Previous work has shown that visuospatial perception was related to skill retention one week after cognitively-intact older adults trained on this motor task (Lingo VanGilder et al. 2018). In the present study, we measured spatial working memory, which did not correlate with within-session practice effects. Other measures of visuospatial function (e.g. perception, construction, etc.) may show a relationship with within-session practice effects and should be considered in future studies. Further, this study did not investigate or collect information on participants’ self-efficacy, attention, or motivation. We do, however, acknowledge that individual differences in these factors may also explain variance in within-session practice effects (Lewthwaite and Wulf 2017), and should be measured in future studies. Finally, this study included a sample of convenience. All participants had a left hemisphere stroke and mild motor impairment and most participants were male. Indeed, some degree of hand function was required for completion of the motor task used in this study, although modifications that enlarge the spoon grip can make the task more accessible for individuals with greater impairment (Schaefer et al. 2013). Thus, it is unknown whether the results of this study apply to individuals with greater motor impairment or individuals with a right hemisphere stroke.

Conclusions

In conclusion, individuals with mild motor impairment due to stroke showed practice related improvements on a naturalistic, functional motor task, although the magnitude of improvement varied between individuals. Baseline motor performance correlated with structural integrity of the corticospinal tract while within-session practice effects correlated with structural integrity of the superior longitudinal fasciculus. This difference between the neural correlates of baseline motor performance and within-session practice effects may have implications for understanding response to motor practice and the application of brain-focused intervention approaches aimed at improving hand function after stroke. Future studies on the neural correlates of practice-induced changes in motor behavior after stroke should consider the role of the frontal-parietal connections contained in the superior longitudinal fasciculus.

Acknowledgements:

This work was supported by grants from the National Institutes of Health (R01 DC014664, T32 GM081740, K01 AG047926, F31 AG062057) and the American Heart Association (15SDG24970011).

Footnotes

Conflict of Interest

The authors declare that they have no conflict of interest.

Compliance with Ethical Standards

Ethical Approval

The protocol for this study was approved by the University of South Carolina Institutional Review Board and was in accordance with the ethical standards of the 1964 Declaration of Helsinki and its amendments. All participants provided written informed consent prior to study participation.

Publisher's Disclaimer: This Author Accepted Manuscript is a PDF file of an unedited peer-reviewed manuscript that has been accepted for publication but has not been copyedited or corrected. The official version of record that is published in the journal is kept up to date and so may therefore differ from this version.

References

  1. Andersson JL, Skare S, Ashburner J (2003) How to correct susceptibility distortions in spin-echo echo-planar images: application to diffusion tensor imaging. Neuroimage 20:870–888. doi: 10.1016/s1053-8119(03)00336-7 [DOI] [PubMed] [Google Scholar]
  2. Andersson JLR, Sotiropoulos SN (2016) An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging. Neuroimage 125:1063–1078. doi: 10.1016/j.neuroimage.2015.10.019 [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Archer DB, Patten C, Coombes SA (2017) Free-water and free-water corrected fractional anisotropy in primary and premotor corticospinal tracts in chronic stroke. Hum Brain Mapp 38:4546–4562. doi: 10.1002/hbm.23681 [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Bohannon RW (1986) Test-retest reliability of hand-held dynamometry during a single session of strength assessment. Phys Ther 66:206–209. doi: 10.1093/ptj/66.2.206 [DOI] [PubMed] [Google Scholar]
  5. Boyd LA et al. (2017) Biomarkers of Stroke Recovery: Consensus-Based Core Recommendations from the Stroke Recovery and Rehabilitation Roundtable. Neurorehabil Neural Repair 31:864–876. doi: 10.1177/1545968317732680 [DOI] [PubMed] [Google Scholar]
  6. Boyd LA, Quaney BM, Pohl PS, Winstein CJ (2007) Learning implicitly: effects of task and severity after stroke. Neurorehabil Neural Repair 21:444–454 [DOI] [PubMed] [Google Scholar]
  7. Brett M, Leff AP, Rorden C, Ashburner J (2001) Spatial normalization of brain images with focal lesions using cost function masking. Neuroimage 14:486–500. doi: 10.1006/nimg.2001.0845 [DOI] [PubMed] [Google Scholar]
  8. Burke E, Dodakian L, See J, McKenzie A, Riley JD, Le V, Cramer SC (2014) A multimodal approach to understanding motor impairment and disability after stroke. J Neurol 261:1178–1186. doi: 10.1007/s00415-014-7341-8 [DOI] [PubMed] [Google Scholar]
  9. Cassidy JM, Tran G, Quinlan EB, Cramer SC (2018) Neuroimaging Identifies Patients Most Likely to Respond to a Restorative Stroke Therapy. Stroke 49:433–438. doi: 10.1161/strokeaha.117.018844 [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Chechlacz M, Gillebert CR, Vangkilde SA, Petersen A, Humphreys GW (2015) Structural Variability within Frontoparietal Networks and Individual Differences in Attentional Functions: An Approach Using the Theory of Visual Attention. J Neurosci 35:10647–10658. doi: 10.1523/jneurosci.0210-15.2015 [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Chua LK, Wulf G, Lewthwaite R (2020) Choose your words wisely: Optimizing impacts on standardized performance testing. Gait Posture 79:210–216. doi: 10.1016/j.gaitpost.2020.05.001 [DOI] [PubMed] [Google Scholar]
  12. Daselaar SM, Rombouts SA, Veltman DJ, Raaijmakers JG, Jonker C (2003) Similar network activated by young and old adults during the acquisition of a motor sequence. Neurobiol Aging 24:1013–1019 [DOI] [PubMed] [Google Scholar]
  13. Duncan PW, Wallace D, Lai SM, Johnson D, Embretson S, Laster LJ (1999) The stroke impact scale version 2.0. Evaluation of reliability, validity, and sensitivity to change. Stroke 30:2131–2140 [DOI] [PubMed] [Google Scholar]
  14. Findlater SE et al. (2019) Comparing CST Lesion Metrics as Biomarkers for Recovery of Motor and Proprioceptive Impairments After Stroke. Neurorehabil Neural Repair:1545968319868714. doi: 10.1177/1545968319868714 [DOI] [PubMed] [Google Scholar]
  15. Fridriksson J, Yourganov G, Bonilha L, Basilakos A, Den Ouden DB, Rorden C (2016) Revealing the dual streams of speech processing. Proc Natl Acad Sci U S A 113:15108–15113. doi: 10.1073/pnas.1614038114 [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Hardwick RM, Rottschy C, Miall RC, Eickhoff SB (2013) A quantitative meta-analysis and review of motor learning in the human brain. Neuroimage 67:283–297. doi: 10.1016/j.neuroimage.2012.11.020 [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Jebsen RH, Taylor N, Trieschmann RB, Trotter MJ, Howard LA (1969) An objective and standardized test of hand function. Archives of physical medicine and rehabilitation 50:311–319 [PubMed] [Google Scholar]
  18. Lewis AF, Myers M, Heiser J, Kolar M, Baird JF, Stewart JC (2020) Test-retest reliability and minimal detectable change of corticospinal tract integrity in chronic stroke. Hum Brain Mapp. doi: 10.1002/hbm.24961 [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Lewthwaite R, Wulf G (2017) Optimizing motivation and attention for motor performance and learning. Current opinion in psychology 16:38–42. doi: 10.1016/j.copsyc.2017.04.005 [DOI] [PubMed] [Google Scholar]
  20. Lin CH, Chiang MC, Wu AD, Iacoboni M, Udompholkul P, Yazdanshenas O, Knowlton BJ (2012) Enhanced motor learning in older adults is accompanied by increased bilateral frontal and fronto-parietal connectivity. Brain Connect 2:56–68. doi: 10.1089/brain.2011.0059 [doi] [DOI] [PubMed] [Google Scholar]
  21. Lindenberg R, Renga V, Zhu LL, Betzler F, Alsop D, Schlaug G (2010) Structural integrity of corticospinal motor fibers predicts motor impairment in chronic stroke. Neurology 74:280–287. doi: 10.1212/WNL.0b013e3181ccc6d9 [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Lindenberg R, Zhu LL, Ruber T, Schlaug G (2012) Predicting functional motor potential in chronic stroke patients using diffusion tensor imaging. Hum Brain Mapp 33:1040–1051. doi: 10.1002/hbm.21266 [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Lingo VanGilder J, Hengge CR, Duff K, Schaefer SY (2018) Visuospatial function predicts one-week motor skill retention in cognitively intact older adults. Neurosci Lett 664:139–143. doi: 10.1016/j.neulet.2017.11.032 [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Makris N, Kennedy DN, McInerney S, Sorensen AG, Wang R, Caviness VS Jr., Pandya DN (2005) Segmentation of subcomponents within the superior longitudinal fascicle in humans: a quantitative, in vivo, DT-MRI study. Cereb Cortex 15:854–869. doi: 10.1093/cercor/bhh186 [DOI] [PubMed] [Google Scholar]
  25. Mathiowetz V, Volland G, Kashman N, Weber K (1985a) Adult norms for the Box and Block Test of manual dexterity. Am J Occup Ther 39:386–391 [DOI] [PubMed] [Google Scholar]
  26. Mathiowetz V, Weber K, Kashman N, Volland G (1985b) Adult norms for the nine hole peg test of finger dexterity. Occupational Therapy Journal of Research 5:24–38 [DOI] [PubMed] [Google Scholar]
  27. McKay B, Lewthwaite R, Wulf G (2012) Enhanced expectancies improve performance under pressure. Front Psychol 3:8. doi: 10.3389/fpsyg.2012.00008 [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Meehan SK, Randhawa B, Wessel B, Boyd LA (2011) Implicit sequence-specific motor learning after subcortical stroke is associated with increased prefrontal brain activations: an fMRI study. Hum Brain Mapp 32:290–303. doi: 10.1002/hbm.21019 [doi] [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Mori S, Zhang J (2006) Principles of diffusion tensor imaging and its applications to basic neuroscience research. Neuron 51:527–539. doi: 10.1016/j.neuron.2006.08.012 [DOI] [PubMed] [Google Scholar]
  30. Peters DM et al. (2018) Cortical disconnection of the ipsilesional primary motor cortex is associated with gait speed and upper extremity motor impairment in chronic left hemispheric stroke. Hum Brain Mapp 39:120–132. doi: 10.1002/hbm.23829 [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Pohl PS, McDowd JM, Filion D, Richards LG, Stiers W (2006) Implicit learning of a motor skill after mild and moderate stroke. Clin Rehabil 20:246–253 [DOI] [PubMed] [Google Scholar]
  32. Portney L, Watkins M (2009) Foundations of Clinical Research: Applications to Practice. 3rd edn. Prentice-Hall, Upper Saddle River, NJ [Google Scholar]
  33. Riddle DL, Finucane SD, Rothstein JM, Walker ML (1989) Intrasession and intersession reliability of hand-held dynamometer measurements taken on brain-damaged patients. Phys Ther 69:182–194. doi: 10.1093/ptj/69.3.182 [DOI] [PubMed] [Google Scholar]
  34. Riley JD, Le V, Der-Yeghiaian L, See J, Newton JM, Ward NS, Cramer SC (2011) Anatomy of stroke injury predicts gains from therapy. Stroke 42:421–426. doi: 10.1161/strokeaha.110.599340 [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Rorden C, Bonilha L, Fridriksson J, Bender B, Karnath HO (2012) Age-specific CT and MRI templates for spatial normalization. Neuroimage 61:957–965. doi: 10.1016/j.neuroimage.2012.03.020 [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Schaechter JD, Fricker ZP, Perdue KL, Helmer KG, Vangel MG, Greve DN, Makris N (2009) Microstructural status of ipsilesional and contralesional corticospinal tract correlates with motor skill in chronic stroke patients. Hum Brain Mapp 30:3461–3474 [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Schaefer SY, Dibble LE, Duff K (2015) Efficacy and Feasibility of Functional Upper Extremity Task-Specific Training for Older Adults With and Without Cognitive Impairment. Neurorehabil Neural Repair 29:636–644. doi: 10.1177/1545968314558604 [DOI] [PubMed] [Google Scholar]
  38. Schaefer SY, Duff K (2015) Rapid Responsiveness to Practice Predicts Longer-Term Retention of Upper Extremity Motor Skill in Non-Demented Older Adults. Front Aging Neurosci 7:214. doi: 10.3389/fnagi.2015.00214 [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Schaefer SY, Duff K (2017) Within-session and one-week practice effects on a motor task in amnestic mild cognitive impairment. J Clin Exp Neuropsychol 39:473–484. doi: 10.1080/13803395.2016.1236905 [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Schaefer SY, Hengge CR (2016) Testing the concurrent validity of a naturalistic upper extremity reaching task. Exp Brain Res 234:229–240. doi: 10.1007/s00221-015-4454-y [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Schaefer SY, Lang CE (2012) Using dual tasks to test immediate transfer of training between naturalistic movements: a proof-of-principle study. Journal of Motor Behavior 44:313–327. doi: 10.1080/00222895.2012.708367 [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Schaefer SY, Patterson CB, Lang CE (2013) Transfer of training between distinct motor tasks after stroke: implications for task-specific approaches to upper-extremity neurorehabilitation. Neurorehabil Neural Repair 27:602–612. doi: 10.1177/1545968313481279 [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Schaefer SY, Saba A, Baird JF, Kolar MB, Duff K, Stewart JC (2018) Within-Session Practice Effects in the Jebsen Hand Function Test (JHFT). Am J Occup Ther 72:7206345010p7206345011–7206345010p7206345015. doi: 10.5014/ajot.2018.024745 [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Stark BC, Basilakos A, Hickok G, Rorden C, Bonilha L, Fridriksson J (2019) Neural organization of speech production: A lesion-based study of error patterns in connected speech. Cortex 117:228–246. doi: 10.1016/j.cortex.2019.02.029 [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Stewart JC et al. (2017) Role of corpus callosum integrity in arm function differs based on motor severity after stroke. NeuroImage Clinical 14:641–647. doi: 10.1016/j.nicl.2017.02.023 [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Stinear CM, Barber PA, Smale PR, Coxon JP, Fleming MK, Byblow WD (2007) Functional potential in chronic stroke patients depends on corticospinal tract integrity. Brain 130:170–180 [DOI] [PubMed] [Google Scholar]
  47. Walter CS, Hengge CR, Lindauer BE, Schaefer SY (2019) Declines in motor transfer following upper extremity task-specific training in older adults. Exp Gerontol 116:14–19. doi: 10.1016/j.exger.2018.12.012 [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Wang LE, Tittgemeyer M, Imperati D, Diekhoff S, Ameli M, Fink GR, Grefkes C (2012) Degeneration of corpus callosum and recovery of motor function after stroke: a multimodal magnetic resonance imaging study. Hum Brain Mapp 33:2941–2956. doi: 10.1002/hbm.21417 [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Wechsler D (1997) Wechsler adult intelligence scale–Third Edition (WAIS-III). NCS Pearson, San Antonio, TX [Google Scholar]
  50. Winstein CJ, Merians AS, Sullivan KJ (1999) Motor learning after unilateral brain damage. Neuropsychologia 37:975–987 [DOI] [PubMed] [Google Scholar]
  51. Winstein CJ, Stewart JC (2006) Conditions of task practice for individuals with neurologic impairments In: Selzer ME, Clarke S, Cohen LG, Duncan PW, Gage FH (eds) Textbook of Neural Repair and Rehabilitation. Cambridge University Press, New York, pp 89–102 [Google Scholar]
  52. Winstein CJ, Wolf SL (2008) Task-oriented training to promote upper extremity recovery. In: Stein J, RL H, Macko R, Winstein C, Zorowitz R (eds) Stroke recovery & rehabilitation. Demos Medical, New York, pp 267–290 [Google Scholar]
  53. Wu J, Srinivasan R, Kaur A, Cramer SC (2014) Resting-state cortical connectivity predicts motor skill acquisition. Neuroimage 91:84–90. doi: 10.1016/j.neuroimage.2014.01.026 [DOI] [PMC free article] [PubMed] [Google Scholar]
  54. Wulf G, Chiviacowsky S, Lewthwaite R (2012) Altering mindset can enhance motor learning in older adults. Psychol Aging 27:14–21 [DOI] [PubMed] [Google Scholar]
  55. Zhou RJ et al. (2018) Predicting Gains With Visuospatial Training After Stroke Using an EEG Measure of Frontoparietal Circuit Function. Frontiers in neurology 9:597. doi: 10.3389/fneur.2018.00597 [DOI] [PMC free article] [PubMed] [Google Scholar]

RESOURCES