Twelve stroke survivors at least three months post cerebral stroke were recruited (Table 1). Study inclusion required an ability to achieve 20° wrist ab/adduction at the more-affected side with minimal resistance in the gravity-eliminated position (score of 2+/5 in manual muscle testing) (14). Exclusion criteria were: 1) non-cerebral stroke, 2) <23 points on Mini Mental State Examination (15), 3) markedly increased muscle tone as indicated by > 1+ on the Modified Ashworth Scale (16), 4) other medical conditions affecting upper limb sensorimotor function, 5) inability to perceive VTF on either forearm, and 6) lack of MRI records confirming stroke diagnosis. Participants were recruited via an outpatient neurology clinic, local stroke support groups, and mailing to addresses retrieved from the clinical data depository of the University of Minnesota Clinical and Translational Science Institute (Figure 1). All participants lived at home in independent self-care. Ten neurologically-intact adults matched by age, gender, and hand dominance were recruited to serve as non-stroke controls for age and gender (Median age: 71 years, range: 44 to 79 years; 6 women, 4 men). The study protocol was approved by the Human Research Protection Program of University of Minnesota. Written informed consent was obtained from all participants prior to data collection. Study data are available from the corresponding author upon reasonable request.
Table 1
Demographics and clinical evaluation for participants with stroke
ID
|
Gender
|
Age (years)
|
Time post stroke (months)
|
Lesion side
|
Lesion location
|
Type
|
FMA-UL
(0 – 66)
|
S03
|
W
|
57
|
27
|
L
|
Cortical and subcortical parietal lobe
|
ischemic
|
66
|
S04
|
M
|
73
|
11
|
L
|
EC, putamen, PVWM
|
ischemic
|
66
|
S05
|
M
|
47
|
4
|
L
|
Posterior subcortical frontal, BG, posterior limb of IC
|
ischemic
|
65
|
S06
|
W
|
74
|
6
|
R
|
Thalamus, putamen
|
hemorrhagic
|
64
|
S07
|
M
|
63
|
7
|
L
|
Corona radiate
|
ischemic
|
65
|
S08
|
W
|
42
|
13
|
R
|
Superior thalamus, cortical and subcortical temporal and occipital lobe
|
ischemic
|
64
|
S09
|
W
|
63
|
5
|
R
|
Frontal (precentral gyrus), parietal (postcentral gyrus), occipital lobe
|
ischemic
|
66
|
S10
|
M
|
65
|
26
|
L & R
|
Cortical and subcortical occipital lobe, L & R thalamus
|
ischemic
|
46*
|
S11
|
M
|
71
|
55
|
R
|
Thalamus
|
hemorrhagic
|
42*
|
S12
|
W
|
68
|
6
|
L
|
Frontal (precentral gyrus)
|
ischemic
|
65
|
S13
|
M
|
60
|
49
|
L
|
Subcortical frontal and parietal
|
ischemic
|
58
|
S14
|
W
|
56
|
14
|
L
|
Frontal (precentral gyrus), parietal (postcentral gyrus)
|
ischemic
|
64
|
Ave.
|
6 Women/6 Men
|
62
|
18
|
4 R/ 7 L / 1 both
|
3 cortical / 7 subcortical/ 2 both
|
10 ischemic
|
61
|
Note. *Impaired wrist position sense indicated by Erasmus MC modified Nottingham Sensory Assessment. FMA-UL, Fugl-Meyer Assessment Upper Limb. EC, external capsule. PVWM, periventricular white matter. BG, basal ganglia. IC, internal capsule.
|
Study Design
The study employed a pre-post design with a single control group. Participants completed the pretest and one intervention session on Day 1, the second intervention and the posttest on Day 2 with retention testing at Day 5 (Figure 2A).
Apparatus
A wrist/hand exoskeleton robotic system (the WristBot) allowing full ROM at three degrees-of-freedom (DOF; wrist flexion/extension, wrist abduction/adduction and forearm pronation/supination; see Figure 2B) was used for training and assessment (for a full technical description of the robot see (17, 18)). It generated appropriate torques to passively displace the hand smoothly to a joint position. Optical encoders measured angular displacement at a high resolution (0.0065° in wrist abduction/adduction (AA); 0.0075° in wrist flexion/extension (FE) (19). Robotic control was implemented through Matlab Simulink code (MathWorks, Natick, Massachusetts, USA).
Intervention
Participants sat on a height-adjustable chair. The medial-lateral wrist joint axis was aligned with the axis of rotation of the WristBot. During intervention, only one degree of freedom - AA was trained. Participants grasped the robot handle and made continuous, small amplitude wrist AA movements to position a virtual ball into a target area on a tilt-able table viewed on a display (Figure 2B) the rotation scaling factor was set to translate each degree of wrist motion to one degree of tilt angle of the virtual table. Motion of the virtual ball towards the target was generated by tilting the table using FE and AA corresponding to the two coordinated axes of rotations: the dynamics of the virtual ball was simulated by considering its mass and gravity force generated by the inclination of the table and, consequently imposing a kinematics on its trajectory. Participants completed a single 24-minute session on Day1 and Day2 for a total of 48 minutes of training. Both sessions began with a 5-minute familiarization phase, then continued with three 8-minute training blocks. During the familiarization phase, participants learned to associate VTF with the ball-target distance and ball speed in the presence of visual feedback of the virtual table/ball system. After familiarization with vision, participants continued practicing with eyes closed for the remainder of training relying solely on vibro-tactile feedback to complete the task of moving the virtual ball to the target zone. VTF was provided by three light-weight vibratory motors (9 mm in diameter, 25 mm in length, 4.6 g; Model 307-100, Precision Microdrives Ltd., London, United Kingdom). Two vibratory motors were placed along the longitudinal axis of the training forearm at a distance that users verbally reported that they could differentiate (Figure 2B). They encoded ball position and distance of the ball relative to the target. Vibration frequency increased in three levels (80, 90, trains of 100 Hz pulses) with the distance from the ball to the target. Preliminary work from our group established that a 10Hz difference in signal is discernible on the forearm and all participants reported that they were able to differentiate the differences in vibration frequency. The distal vibrator turned on when the ball was on the right side of the target, while the proximal vibration motor was switched on when the ball was the left side of target. A third vibrator placed on the non-performing forearm encoded ball speed by vibrating between 75-98 Hz (Higher frequency indicated higher ball speed). A trial was completed when the ball stayed within the target area for 5 seconds. Target locations on the table changed between trials (left, center, right). The difficulty level increased after every 6 trials by altering the virtual dynamics of the system (i.e., increasing ball mass, decreasing table friction).
Primary Outcome Measure
Just-noticeable difference (JND) threshold
Participants sat in a height-adjustable chair. The tested forearm was secured with a Velcro strap to the splint of the robot to minimize movement during testing. Vision was occluded. Pink noise provided via headphones masked external sounds that could provide position information. The participant’s wrist was displaced from an initial position of 10° wrist adduction (ulnar deviation) at a constant angular velocity of 6°/s. Two stimuli were presented in each trial: A reference position of 5° wrist abduction and a comparison position. The comparison position was always more abducted than the reference position. The order of the two positions was randomized. In each trial, participants verbally identified the stimulus with the larger amplitude in response to “Which position was the farthest from the starting position?” The stimuli difference in the subsequent trial was determined based on the participant’s response by an adaptive psi-marginal algorithm (20). A correct response was followed by a smaller stimulus difference than the previous trial and vice versa. For the first trial, stimulus difference was set at 1.9°, which was 25% higher than the threshold of a healthy young adult cohort (19). Breaks were scheduled every 10 to 15 trials. The JND threshold represented the smallest stimulus intensity that the participant can discriminate based on the fitted performance function after 30 trials. The method’s test-retest reliability was r = 0.99. The average within-subject variability was 0.09° (19).
Secondary Outcome Measures
Accuracy of wrist tracing
To examine the transfer effect of the proposed wrist proprioceptive training on the untrained motor task, participants held the handle of the device and actively traced templates presented on a computer monitor by using the WristBot to control a cursor on the monitor. Wrist flexion/extension was mapped to linear horizontal, wrist abduction/adduction on to linear vertical cursor movement. The task consisted of 5 shapes: horizontal line, vertical line, triangle, figure of eight, and ellipse (Figure 2C). Shapes were scaled to 60% of the participant’s active ROM in the respective DOF to avoid confounding by end-range muscle tightness. The reference trace template was always visible. A target circle was presented to indicate the desired tracing direction. Participants started tracing with a wrist flexion movement. The obtained angular position time-series data were filtered offline using a low-pass 4th-order Butterworth filter (cut-off frequency: 2.5 Hz). The minimal distance of each cursor sampling point with respect to the template indicated the instantaneous tracing error. For each shape, the mean and standard deviation of the instantaneous tracing errors were calculated for each participant and used as variables for subsequent analysis.
Somatosensory evoked potentials
To obtain a neural correlate of proprioceptive function, we recorded somatosensory evoked potentials. SEPs were induced by median nerve stimulation applied to the trained wrist via electrical stimulation (S88 stimulator with SIU 5 stimulus isolation unit; Grass Technologies, West Warwick, RI, USA). Square-wave pulses of 0.2 ms duration were delivered at 2 Hz and at the voltage sufficient to induce a noticeable thumb adductor twitch. 1200 stimuli were delivered in two blocks, with a break at the 600th stimulus.
EEG data were recorded continuously from nine Ag/AgCl electrodes mounted on an elastic cap using the ANT Neuro eego system (Medical Imaging Solutions GmbH, Berlin, Germany). The montage covered the primary sensorimotor cortical area (Fz, F3/4, FC1/2, FC3/4, Cz, C3/4, CP3/4, CP5/6, P3/4) on the contralateral hemisphere of the stimulated wrist and bilateral mastoid processes based on the standard international 10–20 system. Signals recorded from bilateral mastoid processes were used to re-reference the scalp recording offline. All signals were sampled at 2 kHz or 4 kHz with a 24-bit A/D-converter. EEG data were processed using EEGLAB (21) and ERPLAB toolboxes (22). First, continuous EEG signals were visually inspected to remove visible electromyographic or movement artifacts. Second, data were resampled to 1000 Hz and baseline correction was performed using the average value. Third, data were filtered using a 2nd-order Butterworth high-pass (cut-off: 0.1 Hz) and a low-pass (cut-off: 200 Hz) filter in series. Signals were then re-referenced to the average signals recorded from bilateral mastoid processes. Lastly, the continuous signals were segmented into 300-ms epochs with 100 ms before and 200 ms after the onset of the electrical stimulus. Artifact rejection was performed through a moving average method that flagged epochs containing peak-to-peak amplitudes higher than 100 µV in 200-ms moving window in a 100-ms step. Artifact-free epochs were then averaged to generate the grand average for each participant session (89% of total epochs were accepted after artifact rejection).
Three measures of early somatosensory cortical processing were extracted based on the individual SEP waveforms for each participant: (1) peak latency of N30, defined as the first negative peak from the frontal electrodes (F3/4, FC1/2, FC3/4) after 28 ms (23), (2) peak-to-peak amplitude of P27-N30, and (3) P45, where P27 refers to the positive peak prior to N30, occurring between 22-28 ms after stimulus (24). P45 is the positive peak following N30.
Statistical Analysis
Distributions of JND across the three measurements were not significantly different from normal distribution based on Shapiro-Wilk tests (p values >0.05) and a 2 x 3 (group: stroke and control x measurement time [pretest, posttest and retention]) mixed ANOVA was performed to examine the change of JND over time with the comparison between the stroke and control groups. Pearson correlation coefficients (r) were computed for bivariate analysis of JND thresholds. For other variables, half of the tracing errors were not normally distributed as indicated by Shapiro-Wilk test. Even with one outlier (> 2 interquartile range [IQR]) removed across the tasks, distributions of six variables were still not normally distributed. The SEP variables N30 peak-to-peak amplitude, N30 latency, and P45 latency were not normally distributed as indicated by Shapiro-Wilk test. Therefore, to account for non-normal distribution, nonparametric Friedman tests were employed to examine changes in tracing errors and all SEP variables over the three measurements for the stroke and control groups respectively. Kendall’s w was calculated to indicate the effect size of Friedman’s test by normalizing the chi square statistics obtained in the Friedman’s test by the number of participants (N) and degrees of freedom (i.e. the number of repeated measures – 1)(25). Kendall’s w indicates the percentage of variance among the ranks explained by the repeated measures, similar to eta squared used in ANOVA designs. Spearman correlation coefficients (rs) were computed for bivariate analysis for tracing errors and SEP measures. Significance level was set at α = 0.05.