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DOI: 10.1007/s12541-012-0275-0
A Computer-based Finger-tapping System for Evaluating Movement of the Affected Hand Following Stroke: A Pilot Study Jongsang Son1, A Ra Ko2, Young Hee Lee3, and Youngho Kim1,# 1 Department of Biomedical Engineering and Institute of Medical Engineering, Yonsei University, Wonju, South Korea, 220-710 2 Medical Devices Management Division, Korea Food & Drug Administration, Cheongwon, South Korea, 363-700 3 Department of Rehabilitation, Yonsei University Wonju College of Medicine, Wonju, South Korea, 220-701 # Corresponding Author / E-mail:
[email protected], TEL: +82-33-760-2859, FAX: +82-33-760-2806 KEYWORDS: Rehabilitation, Neuroplasticity, Noninvasive brain stimulation, Motor sequence task
The objective of this study was to develop a computer-based finger-tapping system and to investigate whether the system is effective for evaluating motor function by performing pilot tests of the effects of three interventions, transcranial direct current stimulation (tDCS), peripheral sensory stimulation (PSS), and PSS combined with tDCS (PSS + tDCS), on the movement of the affected hand in stroke patients. The developed system consists of two parts, the finger-tapping input system and finger-tapping analysis software. The finger-tapping input system can detect the states of four buttons and transmit these states to the finger-tapping analysis software, which measures the reaction time. Three stroke patients participated in an experimental session to test the effects of each intervention on motor performance. Each session included three blocks for the determination of (1) baseline of motor performance (Base), (2) the motor performance after each form of stimulation (Pre), and (3) the motor performance following motor training and rest (Post). The results showed that the mean response times for all blocks (Base, Pre, and Post) did not differ significantly with tDCS and PSS, but significant decreases were found between Base and Post and between Pre and Post. In addition, there was no significant difference among the interventions at Pre; however, PSS + tDCS facilitated considerably greater training effects than tDCS or PSS at Post. The developed system was able to evaluate motor function through motor training after a combination of PSS and tDCS. In the near future, we plan to apply the developed system to more patients with relatively good motor function to extend our findings to a general outcome, and expect that these results could be used to design an appropriate rehabilitative approach. Manuscript received: June 11, 2012 / Accepted: July 2, 2012
1. Introduction Stroke is one of the most common diseases in neurology, and at least 80% of stroke survivors are left with motor function deficits.1 Therefore, proper rehabilitation approaches are needed to improve their reduced motor function. Many researchers have discovered that noninvasive brain stimulation (NBS) enhances the beneficial effects of motor training on cortical plasticity,2 and this phenomenon is described as neuroplasticity.3 Transcranial direct current stimulation (tDCS) was shown to be effective in improving motor function after stroke by modulating the excitability of targeted brain regions by altering neuronal membrane potentials based on the polarity of the current transmitted through the scalp via sponge electrodes. Additionally, transcranial magnetic stimulation can help to promote speech and motor recovery by reducing transcallosal inhibition.
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Somatosensory stimulation also improves motor cortical excitability and could influence training effects in stroke patients.4 Recently, NBS has been combined with simultaneous peripheral stimulation to further enhance the facilitating effects of NBS, especially with tDCS due to its ease of use, noninvasiveness, safety, and the possibility of combining it with other methods.3 However, a number of previous studies have focused on using changes in motor evoked potentials (MEPs) in order to evaluate the effects of various types of stimulation. In terms of rehabilitation, quantitatively evaluating the motor functions themselves might be a more useful approach than measuring MEPs to facilitate the design of an appropriate rehabilitation method. A finger-tapping test (FTT) has been used frequently to examine hand movement. According to Strauss et al. (2006), FTT can be used to detect both motor and cognitive impairments in neuropsychological
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examinations. Groot-Driessen et al. (2006) investigated whether the speed of finger-tapping can predict the degree of recovery in stroke patients, comparing other clinical tools such as the Barthel Index, the Frenchay Activities Index, and the Sickness Impact Profile 68. The results showed that the speed of finger tapping is not superior to other standardized predictors from a clinical point of view. However, several alternative opinions have also been expressed. For example, Jiménez-Jiménez et al. (2010) showed that patients with essential trauma have a marked impairment in the performance of repetitive finger movements assessed with FTT; thus, the speed of finger tapping might be a functionality predictor for motor speed. Silva et al. (2012) also supported the use of a finger tapping test to evaluate motor speed and procedural learning in healthy controls and patients with schizophrenia or schizoaffective disorder. There are various opinions on the speed of finger-tapping and the functional outcome, but it has not yet been verified that the speed of finger-tapping is related to motor functionality. The main goal of the present study was to develop a computerbased finger-tapping system that displays a number corresponding to specific motor sequences given prior to the test, e.g., index-middlelittle-ring-index, and records the reaction time after displaying the number. In order to determine whether this system is effective for evaluating motor functionality, we performed pilot tests of the effects of three interventions, tDCS, peripheral sensory stimulation (PSS), and PSS combined with tDCS (PSS + tDCS), on movements of the affected hand in stroke patients.
2. Methods 2.1 Development of a computer-based finger-tapping system Our computer-based finger-tapping system consists of two parts (Fig. 1): a finger-tapping input system and finger-tapping analysis software. The finger-tapping input system includes four identical membrane buttons, which are each 40 × 15 × 10 mm (length × width × height), and a microcontroller (ATmega128, Atmel, USA), which allows detection of the state (i.e., pressed or released) of each button and the transmission via RS232 communication protocol of the state to
a personal computer, on which the finger-tapping analysis software is installed. The finger-tapping analysis software was written in the C# programming language (Visual C# 2010 Express, Microsoft, USA). The screen design and options were intuitively organized for easy operation. The software allows the user to define the affected hand, and to enter the specific motor sequences and the time duration for a test. The reaction time is measured by a System.Windows.Forms.Timer class in C#, and is defined as the time taken for the software to receive the state of the button from the finger-tapping input system after displaying the number corresponding to the entered sequences. The response time data are stored in an array during a test, and then exported to an ASCII file after the test.
2.2 Evaluation procedure as a pilot study In order to determine whether our system is effective for evaluating motor functionality, we performed pilot tests of the effects of tDCS combined with PSS on movements of the affected hand in three stroke patients (Table 1). Baseline evaluations included a medical history, the Korean version of the Mini-Mental State Examination (MMSE-K), the Fugl-Meyer Scale (FMS), and the Modified Ashworth Scale (MAS). The mean MMSE-K score was 29.7 (SD = 0.6, range = 29-30), and the mean FMS of the upper extremity was 69.2% (SD = 26.6, range = 43.9-97.0). Inclusion criteria were a unilateral lesion on brain imaging and the ability to perform the finger sequence task independently. The exclusion criteria were an intracranial metallic implant, cerebellar or brain stem lesion, and serious cognitive deficits (MMSE-K < 22). The subjects gave informed consent prior to commencing the experiments which were approved by the Institutional Review Board of the Yonsei University Wonju College of Medicine. The subjects were required to practice a finger sequence task for half an hour per session, with three sessions in a week. The task involved pressing four buttons using the fingers of the impaired hand, repeating four motor patterns as quickly and accurately as possible for three minutes (Fig. 2a). The motor patterns were set as index (2) to little (5) to middle (3) to ring (4) to index (2), 4 to 3 to 5 to 2 to 4, 3
Table 1 Subject information S1 S2 S3 Age (years) 49 62 43 Sex Mb M M Time after stroke 5 83 13 (months) Diagnosis S-ICHc on Rd (Affected Thalamus ICH (L) S-ICH (R) side) (Le) f MMSE-K 29 30 30 FMSg (%) 43.9 66.7 97.0 MASh 2 1 0 PSSj PSS + tDCS Stimulation tDCSi a
SDa 9.7
33.7
42.9
29.7 69.2
0.6 26.6
standard deviation; bmale; csubcortical intracerebral hemorrhage; right; eleft; fKorean version of mini-mental state examination; gFuglMeyer scale of upper extremity; hmodified Ashworth scale; itranscranial direct current stimulation; jperipheral sensory stimulation d
Fig. 1 Configuration of our computer-based finger tapping system
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to 2 to 4 to 5 to 3, and 5 to 2 to 4 to 3 to 5.9 Subsequently, they participated in an experimental session (Fig. 3). Each session consisted of three blocks in series for the determination of (1) baseline motor performance (Base), (2) the motor performance after each form of stimulation (20 minutes of tDCS, one hour of PSS, or one hour of PSS combined with 20 minutes of tDCS) (Pre), and (3) the motor performance after an 18-minute motor training and a 30minute rest (Post). The motor performance was determined by the response time. The tDCS was applied using the anode positioned over the ipsilesional M1 and the cathode over the contralateral supraorbital region for 20 minutes (2 mA).10 Anodal tDCS (Phoresor II Auto PM750, IOMED, Inc., USA) was delivered through a 6 × 4 cm sponge electrode (Daeyang Medical Co., Ltd., Korea) placed on the patient’s scalp while the patient was sitting. PSS was applied using a transcutaneous electrical nerve stimulation program in Bio-Trac Plus (EMS Physio Ltd., UK) for one hour. The burst train for producing a more comfortable muscle contraction (pulse width: 100 ìs, frequency: 2 Hz)11 was delivered through two electrodes (5 × 50 mm) attached to the region of the median and ulnar nerves on the affected side. The magnitude of the PSS was determined as the maximum current level, but less than 100 mA, with no visible movements of the abductor digiti minimi and flexor pollicis brevis muscles.
2.3 Data analysis Only the response time data from 30 seconds to 2 minutes in the three-minute data sequence were used in order to avoid warm-up and fatigue effects (Fig. 3).9 To compare the changes in the selected data among three blocks for each intervention, paired t-tests were performed with a significant level of p < 0.017 corrected by the Bonferroni method. In addition, the selected data at Pre and Post relative to Base were analyzed using ANOVA in order to investigate the effects of the interventions. Based on a significant level of p < 0.05, post hoc analyses were conducted and corrected for multiple comparisons with LSD tests. All statistical results were calculated using SPSS Statistics 17.0 (SPSS Inc., USA).
Fig. 2 (a) Finger sequence task and (b) Experimental session
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3. Results and Discussion The mean response times for all blocks (Base, Pre, and Post) did not differ significantly with tDCS and PSS. With PSS + tDCS, there was no difference between the Base and Pre trials (p = 0.573), but significant decreases were found between Base and Post (p = 0.000), and between Pre and Post (p = 0.001) (Fig. 3a). The greatest changes in the response time at Pre relative to Base occurred in tDCS (approximately -6%), and the least was in PSS (approximately -1%); there was no statistical effects of the interventions. At Post, however, PSS + tDCS facilitated considerably greater training effects than tDCS (p = 0.039) or PSS (p = 0.013), showing an approximately 20% decrease (Fig. 3b). The purpose of this study was to develop a computer-based fingertapping system that displays specific motor sequences and records the reaction times. In addition, pilot tests of the effects of three interventions were performed in order to investigate whether the developed system is effective for evaluating motor performance. Since only one patient participated in each intervention, our results might be insufficient to extend our findings to a general outcome. However, it might be meaningful to verify the intervention effects on an individual level since a rehabilitative outcome would be highly dependent on a patient’s own motor function. In addition, since our purpose was to determine whether the developed system is useful for assessing motor performance in stroke patients, we believe that the results support its feasibility for evaluating rehabilitative effects. According to our results, the three interventions did not affect to the mean response time at Pre in comparison to Base, and only the PSS + tDCS facilitated training exhibited significant effects at Post. Thus, we were unable to identify any evidence indicating whether tDCS or PSS was more effective for enhancing motor function immediately after each intervention, but at least the combined
Fig. 3 Comparison of (a) the response time among three blocks in each intervention and (b) the effects of interventions at Pre and Post
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stimulation (PSS + tDCS) might be effective for enhancing motor performance through motor training. Our results are similar to those reported by Celnik et al. (2009). They hypothesized that the combination of both tDCS and peripheral nerve stimulation (PNS) would enhance the motor training effects, and compared the mean number of correct key presses in 30-second intervals to verify the effects of interventions, i.e., PNS as a sham combined with tDCS as a sham (PNSsham + tDCSsham); PNS + tDCSsham, which is equivalent to PSS in our study; PNSsham + tDCS, which is equivalent to tDCS in our study; and PNS + tDCS, which is equivalent to PSS + tDCS in our study. Their results for PNSsham + tDCS and PNS + tDCSsham showed no motor training effects, but PNS + tDCS facilitated training effects more than PNS + tDCSsham and tDCS + PNSsham. Although the mean response time was chosen as the primary outcome measure in our study, rather than the mean number of correct key presses in 30-second intervals, both results showed that the combination of PSS or PNS with tDCS yielded better results than any single intervention. Since FMS was comparatively different among the subjects who participated in this study, the mean response time appears to be divided into two groups of participant (above 1.5 s and near 0.5 s) in Fig. 3. The best effects of the interventions were exhibited by subject S3, who had the highest FMS (97.0%), while no improvements were evident for subjects S1 (FMS: 43.9%) or S2 (FMS: 66.7%). Based on these preliminary results, we cannot conclude that the outcome was consistent even if S1 or S2 participated in a session with PSS + tDCS. In previous studies,2,4,10 the mean FMS was 92.0% (SD = 5.9%, range = 79.0-100.0%). This might mean that these interventions would be more beneficial in patients with relatively good motor function. Thus, further studies are required to verify effects of various interventions on motor training with patients who have scored above 90%. In conclusion, the developed system was able to evaluate motor functionalities through motor training after a combination of PSS and tDCS. Performance improvements after PSS + tDCS were more noticeable than those measured after PSS or tDCS alone. In the near future we plan to apply the developed system to more patients with relatively good motor function to extend our findings to general outcomes, and expect that these results could be used to design or select a proper rehabilitative approach for stroke patients.
ACKNOWLEDGEMENT This research was financially supported by the Ministry of Knowledge Economy (MKE) and Korea Institute for Advancement of Technology (KIAT) through the Research and Development for Regional Industry (70011192), and was also supported by the Technology Innovation Program (Industrial Strategic Technology Development Program, 10032055) funded by the Ministry of Knowledge Economy (MKE, Korea).
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