Retrospective Exploratory Analysis of Task-Specific Effects on Brain Activity after Stroke

Background: Evidence supports cortical reorganization in sensorimotor areas induced by constraint-induced movement therapy (CIMT). However, only a few studies examined the neural plastic changes as a function of task specificity. This provoked us to retrospectively analyze a previously unpublished imaging dataset from chronic stroke survivors before and after participation in the signature CIMT protocol. This exploratory analysis aims to evaluate the functional brain activation changes during a precision and a power grasp task in chronic stroke survivors who received two-weeks of CIMT compared to a control group. Materials and methods: Fourteen chronic stroke survivors, randomized to CIMT (n=8) or non-CIMT (n=6), underwent functional MRI (fMRI) before and after a two-week period. During scan runs, participants performed two different grasp tasks (precision, power). Pre to post changes in laterality index (LI) were compared by group and task for two predetermined motor regions of interest: dorsal premotor cortex (PMd) and primary motor cortex (MI). Results: Two weeks of CIMT resulted in a relative increase in activity in a key region of the motor network, the PMd of the lesioned hemisphere, under precision grasp task conditions compared to a non-treatment control group. However, no changes in LI were observed in MI for either task or group. Conclusion: These findings provide evidence for the task specificity effects of CIMT in the promotion of recovery-supportive cortical reorganization in chronic stroke survivors.

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Development and Clinical Evaluation of a Web-Based Upper Limb Home Rehabilitation System Using a Smartwatch and Machine Learning Model for Chronic Stroke Survivors: Prospective Comparative Study (Preprint)

10.2196/preprints.17216 ◽

2019 ◽

Author(s):

Sang Hoon Chae ◽

Yushin Kim ◽

Kyoung-Soub Lee ◽

Hyung-Soon Park

Keyword(s):

Machine Learning ◽

Home Care ◽

Comparative Study ◽

Internal Rotation ◽

Chronic Stroke ◽

Control Group ◽

Stroke Survivors ◽

Home Based ◽

Rehabilitation System ◽

Prospective Comparative Study

BACKGROUND Recent advancements in wearable sensor technology have shown the feasibility of remote physical therapy at home. In particular, the current COVID-19 pandemic has revealed the need and opportunity of internet-based wearable technology in future health care systems. Previous research has shown the feasibility of human activity recognition technologies for monitoring rehabilitation activities in home environments; however, few comprehensive studies ranging from development to clinical evaluation exist. OBJECTIVE This study aimed to (1) develop a home-based rehabilitation (HBR) system that can recognize and record the type and frequency of rehabilitation exercises conducted by the user using a smartwatch and smartphone app equipped with a machine learning (ML) algorithm and (2) evaluate the efficacy of the home-based rehabilitation system through a prospective comparative study with chronic stroke survivors. METHODS The HBR system involves an off-the-shelf smartwatch, a smartphone, and custom-developed apps. A convolutional neural network was used to train the ML algorithm for detecting home exercises. To determine the most accurate way for detecting the type of home exercise, we compared accuracy results with the data sets of personal or total data and accelerometer, gyroscope, or accelerometer combined with gyroscope data. From March 2018 to February 2019, we conducted a clinical study with two groups of stroke survivors. In total, 17 and 6 participants were enrolled for statistical analysis in the HBR group and control group, respectively. To measure clinical outcomes, we performed the Wolf Motor Function Test (WMFT), Fugl-Meyer Assessment of Upper Extremity, grip power test, Beck Depression Inventory, and range of motion (ROM) assessment of the shoulder joint at 0, 6, and 12 months, and at a follow-up assessment 6 weeks after retrieving the HBR system. RESULTS The ML model created with personal data involving accelerometer combined with gyroscope data (5590/5601, 99.80%) was the most accurate compared with accelerometer (5496/5601, 98.13%) or gyroscope data (5381/5601, 96.07%). In the comparative study, the drop-out rates in the control and HBR groups were 40% (4/10) and 22% (5/22) at 12 weeks and 100% (10/10) and 45% (10/22) at 18 weeks, respectively. The HBR group (n=17) showed a significant improvement in the mean WMFT score (P=.02) and ROM of flexion (P=.004) and internal rotation (P=.001). The control group (n=6) showed a significant change only in shoulder internal rotation (P=.03). CONCLUSIONS This study found that a home care system using a commercial smartwatch and ML model can facilitate participation in home training and improve the functional score of the WMFT and shoulder ROM of flexion and internal rotation in the treatment of patients with chronic stroke. This strategy can possibly be a cost-effective tool for the home care treatment of stroke survivors in the future. CLINICALTRIAL Clinical Research Information Service KCT0004818; https://tinyurl.com/y92w978t

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A novel experimental setup combining EEG and robotics to investigate brain activity driving controlled reaching movements in chronic stroke survivors

2007 IEEE 10th International Conference on Rehabilitation Robotics ◽

10.1109/icorr.2007.4428527 ◽

2007 ◽

Author(s):

Albert Chen ◽

Jun Yao ◽

Julius P. A. Dewald

Keyword(s):

Brain Activity ◽

Chronic Stroke ◽

Reaching Movements ◽

Experimental Setup ◽

Stroke Survivors ◽

And Robotics

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Development and Clinical Evaluation of a Web-Based Upper Limb Home Rehabilitation System Using a Smartwatch and Machine Learning Model for Chronic Stroke Survivors: Prospective Comparative Study

JMIR mhealth and uhealth ◽

10.2196/17216 ◽

2020 ◽

Vol 8 (7) ◽

pp. e17216

Author(s):

Sang Hoon Chae ◽

Yushin Kim ◽

Kyoung-Soub Lee ◽

Hyung-Soon Park

Keyword(s):

Machine Learning ◽

Home Care ◽

Comparative Study ◽

Internal Rotation ◽

Chronic Stroke ◽

Control Group ◽

Stroke Survivors ◽

Home Based ◽

Rehabilitation System ◽

Prospective Comparative Study

Background Recent advancements in wearable sensor technology have shown the feasibility of remote physical therapy at home. In particular, the current COVID-19 pandemic has revealed the need and opportunity of internet-based wearable technology in future health care systems. Previous research has shown the feasibility of human activity recognition technologies for monitoring rehabilitation activities in home environments; however, few comprehensive studies ranging from development to clinical evaluation exist. Objective This study aimed to (1) develop a home-based rehabilitation (HBR) system that can recognize and record the type and frequency of rehabilitation exercises conducted by the user using a smartwatch and smartphone app equipped with a machine learning (ML) algorithm and (2) evaluate the efficacy of the home-based rehabilitation system through a prospective comparative study with chronic stroke survivors. Methods The HBR system involves an off-the-shelf smartwatch, a smartphone, and custom-developed apps. A convolutional neural network was used to train the ML algorithm for detecting home exercises. To determine the most accurate way for detecting the type of home exercise, we compared accuracy results with the data sets of personal or total data and accelerometer, gyroscope, or accelerometer combined with gyroscope data. From March 2018 to February 2019, we conducted a clinical study with two groups of stroke survivors. In total, 17 and 6 participants were enrolled for statistical analysis in the HBR group and control group, respectively. To measure clinical outcomes, we performed the Wolf Motor Function Test (WMFT), Fugl-Meyer Assessment of Upper Extremity, grip power test, Beck Depression Inventory, and range of motion (ROM) assessment of the shoulder joint at 0, 6, and 12 months, and at a follow-up assessment 6 weeks after retrieving the HBR system. Results The ML model created with personal data involving accelerometer combined with gyroscope data (5590/5601, 99.80%) was the most accurate compared with accelerometer (5496/5601, 98.13%) or gyroscope data (5381/5601, 96.07%). In the comparative study, the drop-out rates in the control and HBR groups were 40% (4/10) and 22% (5/22) at 12 weeks and 100% (10/10) and 45% (10/22) at 18 weeks, respectively. The HBR group (n=17) showed a significant improvement in the mean WMFT score (P=.02) and ROM of flexion (P=.004) and internal rotation (P=.001). The control group (n=6) showed a significant change only in shoulder internal rotation (P=.03). Conclusions This study found that a home care system using a commercial smartwatch and ML model can facilitate participation in home training and improve the functional score of the WMFT and shoulder ROM of flexion and internal rotation in the treatment of patients with chronic stroke. This strategy can possibly be a cost-effective tool for the home care treatment of stroke survivors in the future. Trial Registration Clinical Research Information Service KCT0004818; https://tinyurl.com/y92w978t

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Improvement of Hand Motor Function after Radial Shock Wave Therapy in Chronic Stroke Patients

Indonesian Journal of Physical Medicine & Rehabilitation ◽

10.36803/2019.ikfr.07(1).01 ◽

2019 ◽

pp. 2-12

Author(s):

M. Kamaluddin K ◽

Erna Setiawati ◽

Tanti Ajoe Kesoema

Keyword(s):

Shock Wave ◽

Motor Function ◽

Hand Function ◽

Chronic Stroke ◽

Control Group ◽

Shock Wave Therapy ◽

Stroke Patients ◽

Before And After ◽

And Control ◽

Experimental Group

IIntroduction: The Radial Shock Wave Therapy (RSWT) expected could improve spasticity and hand function in chronic stroke patients. This study aimed to find out the improvement of hand function after RSWT as an additional therapy in chronic stroke patients. Methods: Design study was a randomized controlled trial, in December 2018. The patients were assigned randomly to the experimental group (Infrared, Stretching, and RSWT) and control group (Infrared and Stretching) for six weeks. Hand motor function was measured using Fugl-Meyer Motor Assesment (FMA) before and after intervention. Results: The median values of wrist FMA scores in the experimental and control group before and after intervention were 2 vs 5 (p=0.001) and 3 vs 4 (p<0.001) respectively. The median values of hand FMA scores in the experimental and control group before and intervention were 4 vs 6 (p=0,.001) and 4 vs 5 (p<0.001). However, the delta between before and after intervention was higher in experimental group. Conclusion: The improvement of wrist and hand FMA scores after added treatment by RSWT was tend to higher.Keywords: Spasticity, Hand Function, Stroke, Radial Shock Therapy, Fugl-Meyer Motor Assesment

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Feasibility and effect of interactive telerehabilitation on balance in individuals with chronic stroke: a pilot study

Journal of NeuroEngineering and Rehabilitation ◽

10.1186/s12984-021-00866-8 ◽

2021 ◽

Vol 18 (1) ◽

Author(s):

Shih-Ching Chen ◽

Chueh-Ho Lin ◽

Sheng-Wen Su ◽

Yu-Tai Chang ◽

Chien-Hung Lai

Keyword(s):

Pilot Study ◽

Effect Size ◽

Exercise Intervention ◽

Chronic Stroke ◽

Control Group ◽

Stroke Survivors ◽

Post Intervention ◽

Timed Up And Go ◽

Kinect Camera ◽

Experimental Group

Abstract Background Stroke survivors need continuing exercise intervention to maintain functional status. This study assessed the feasibility and efficacy of an interactive telerehabilitation exergaming system to improve balance in individuals with chronic stroke, compared to conventional one-on-one rehabilitation. Methods In this prospective case–control pilot study, 30 Taiwanese individuals with chronic stroke were enrolled and randomly allocated to an experimental group and a control group. All participants received intervention 3 times per week for 4 weeks in the study hospital. The experiment group underwent telerehabilitation using a Kinect camera-based interactive telerehabilitation system in an independent room to simulate home environment. In contrast, the control group received conventional one-on-one physiotherapy in a dedicated rehabilitation area. The effectiveness of interactive telerehabilitation in improving balance in stroke survivors was evaluated by comparing outcomes between the two groups. The primary outcome was Berg Balance Scale (BBS) scores. Secondary outcomes were performance of the Timed Up and Go (TUG) test, Modified Falls Efficacy Scale, Motricity Index, and Functional Ambulation Category. Results Comparison of outcomes between experimental and control groups revealed no significant differences between groups at baseline and post-intervention for all outcome measures. However, BBS scores improved significantly in both groups (control group: p = 0.01, effect size = 0.49; experimental group: p = 0.01, effect size = 0.70). Completion times of TUG tests also improved significantly in the experimental group (p = 0.005, effect size = 0.70). Conclusion The Kinect camera-based interactive telerehabilitation system demonstrates superior or equal efficacy compared to conventional one-on-one physiotherapy for improving balance in individuals with chronic stroke. Trial registration ClinicalTrials.gov. NCT03698357. Registered October 4, 2018, retrospectively registered.

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Asymmetric effective connectivity within frontoparietal motor network underlying motor imagery and motor execution

10.1101/2020.10.22.351106 ◽

2020 ◽

Author(s):

Takeshi Ogawa ◽

Hideki Shimobayashi ◽

Jun-ichiro Hirayama ◽

Motoaki Kawanabe

Keyword(s):

Motor Imagery ◽

Primary Motor Cortex ◽

Brain Activity ◽

Effective Connectivity ◽

Premotor Cortex ◽

Brain Regions ◽

Neural Representation ◽

Motor Execution ◽

Positive Effects ◽

Motor Network

AbstractBoth imagery and execution of motor controls consist of interactions within a neuronal network, including frontal motor-related regions and posterior parietal regions. To reveal neural representation in the frontoparietal motor network, several approaches have been proposed: one is decoding of actions/modes related to motor control from the spatial pattern of brain activity; another is to estimate effective connectivity, which means a directed association between two brain regions within motor regions. However, a motor network consisting of multiple brain regions has not been investigated to illustrate network representation depending on motor imagery (MI) or motor execution (ME). Here, we attempted to differentiate the frontoparietal motor-related networks based on the effective connectivity in the MI and ME conditions. We developed a delayed sequential movement and imagery (dSMI) task to evoke brain activity associated with data under ME and MI in functional magnetic resonance imaging (fMRI) scanning. We applied a linear non-Gaussian acyclic causal model to identify effective connectivity among the frontoparietal motor-related brain regions for each condition. We demonstrated higher effective connectivity from the contralateral dorsal premotor cortex (dPMC) to the primary motor cortex (M1) in ME than in MI. We mainly identified significant direct effects of dPMC and ventral premotor cortex (vPMC) to the parietal regions. In particular, connectivity from the dPMC to the superior parietal lobule (SPL) in the same hemisphere showed significant positive effects across all conditions. Instead, interlateral connectivities from vPMC to SPL showed significantly negative effects across all conditions. Finally, we found positive effects from A1 to M1 in the same hemisphere, such as the audio motor pathway. These results indicated that sources of motor command originated from d/vPMC and influenced M1 as achievements of ME and MI, and the parietal regions as integration of somatosensory and visual representation during finger tapping. In addition, sequential sounds may functionally facilitate temporal motor processes.

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Elevations in EEG Power Spectra During a Go/No-go Task Following Acute Psychosocial Stress in Preschool Children

10.31234/osf.io/xwre8 ◽

2020 ◽

Author(s):

Ryan Jeffrey Giuliano ◽

Chantal P Delaquis ◽

Leslie E Roos

Keyword(s):

Preschool Children ◽

Psychosocial Stress ◽

Acute Stress ◽

Brain Activity ◽

Power Spectra ◽

Control Group ◽

Before And After ◽

Eeg Power ◽

Acute Psychosocial Stress

Despite substantial research emphasizing the role of chronic stress on children’s brain function, remarkably little is known about the more immediate effects of acute stress on brain activity. Here, we analyzed changes in power spectra of the electroencephalogram (EEG) during a Go/No-go task collected both before and after a laboratory psychosocial stressor validated for preschool children. Significant increases in EEG power were observed broadly across bandwidths and electrode sites from pre- to post- manipulation measurements. Follow-up comparisons illustrate that pre- to post- increases in EEG power were more pronounced for the Stressor group than the Control group, particularly for the delta (1-3 Hz), high alpha (9-12 Hz) and beta (12-20 Hz) bandwidths. While exploratory, these results are amongst the first pieces of evidence documenting the effects of acute psychosocial stress on brain activity in young children.

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Robotic-based ACTive somatoSENSory (Act.Sens) retraining on upper limb functions with chronic stroke survivors: study protocol for a pilot randomised controlled trial

Pilot and Feasibility Studies ◽

10.1186/s40814-021-00948-3 ◽

2021 ◽

Vol 7 (1) ◽

Author(s):

Ananda Sidarta ◽

Yu Chin Lim ◽

Christopher Wee Keong Kuah ◽

Yong Joo Loh ◽

Wei Tech Ang

Keyword(s):

Randomised Controlled Trial ◽

Study Protocol ◽

Upper Limb ◽

Controlled Trial ◽

Chronic Stroke ◽

Community Dwelling ◽

Control Group ◽

Stroke Survivors ◽

Pilot Randomised Controlled Trial ◽

Randomised Controlled

Abstract Background Prior studies have established that senses of the limb position in space (proprioception and kinaesthesia) are important for motor control and learning. Although nearly one-half of stroke patients have impairment in the ability to sense their movements, somatosensory retraining focusing on proprioception and kinaesthesia is often overlooked. Interventions that simultaneously target motor and somatosensory components are thought to be useful for relearning somatosensory functions while increasing mobility of the affected limb. For over a decade, robotic technology has been incorporated in stroke rehabilitation for more controlled therapy intensity, duration, and frequency. This pilot randomised controlled trial introduces a compact robotic-based upper-limb reaching task that retrains proprioception and kinaesthesia concurrently. Methods Thirty first-ever chronic stroke survivors (> 6-month post-stroke) will be randomly assigned to either a treatment or a control group. Over a 5-week period, the treatment group will receive 15 training sessions for about an hour per session. Robot-generated haptic guidance will be provided along the movement path as somatosensory cues while moving. Audio-visual feedback will appear following every successful movement as a reward. For the same duration, the control group will complete similar robotic training but without the vision occluded and robot-generated cues. Baseline, post-day 1, and post-day 30 assessments will be performed, where the last two sessions will be conducted after the last training session. Robotic-based performance indices and clinical assessments of upper limb functions after stroke will be used to acquire primary and secondary outcome measures respectively. This work will provide insights into the feasibility of such robot-assisted training clinically. Discussion The current work presents a study protocol to retrain upper-limb somatosensory and motor functions using robot-based rehabilitation for community-dwelling stroke survivors. The training promotes active use of the affected arm while at the same time enhances somatosensory input through augmented feedback. The outcomes of this study will provide preliminary data and help inform the clinicians on the feasibility and practicality of the proposed exercise. Trial registration ClinicalTrials.gov NCT04490655. Registered 29 July 2020.

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Cortical activation associated with motor preparation can be used to predict the freely chosen effector of an upcoming movement and reflects response time: An fMRI decoding study

10.1101/295345 ◽

2018 ◽

Author(s):

Satoshi Hirose ◽

Isao Nambu ◽

Eiichi Naito

Keyword(s):

Response Time ◽

Primary Motor Cortex ◽

Response Times ◽

Brain Activity ◽

Premotor Cortex ◽

Brain Regions ◽

Cortical Activation ◽

List Type ◽

Action Execution ◽

Motor Representations

AbstractMotor action is prepared in the human brain for rapid initiation at the appropriate time. Recent non-invasive decoding techniques have shown that brain activity for action preparation represents various parameters of an upcoming action. In the present study, we demonstrated that a freely chosen effector can be predicted from brain activity measured using functional magnetic resonance imaging (fMRI) before initiation of the action. Furthermore, the activity was related to response time (RT). We measured brain activity with fMRI while 12 participants performed a finger-tapping task using either the left or right hand, which was freely chosen by them. Using fMRI decoding, we identified brain regions in which activity during the preparatory period could predict the hand used for the upcoming action. We subsequently evaluated the relationship between brain activity and the RT of the upcoming action to determine whether correct decoding was associated with short RT. We observed that activity in the supplementary motor area, dorsal premotor cortex, and primary motor cortex measured before action execution predicted the hand used to perform the action with significantly above-chance accuracy (approximately 70%). Furthermore, in most participants, the RT was shorter in trials for which the used hand was correctly predicted. The present study showed that preparatory activity in cortical motor areas represents information about the effector used for an upcoming action, and that well-formed motor representations in these regions are associated with reduced response times.HighlightsBrain activity measured by fMRI was used to predict freely chosen effectors.M1/PMd and SMA activity predicted the effector hand prior to action initiation.Response time was shorter in trials in which effector hand was correctly predicted.Freely chosen action is represented in the M1/PMd and SMA.Well-formed preparatory motor representations lead to reduced response time.

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