Posts Tagged Paralysis

[BLOG POST] PathMaker Neurosystems – Publication of First Clinical Trial Results for MyoRegulator® Device for Non-Invasive Treatment of Spasticity

PathMaker Neurosystems Inc. (“PathMaker”), a clinical-stage bioelectronic medicine company developing breakthrough non-invasive systems for the treatment of patients with spasticity and paralysis has announced the first publication of clinical trial results for its MyoRegulator® device for the non-invasive treatment of spasticity. Published in Bioelectronic Medicine, the results provide the first clinical evidence using MyoRegulator to treat upper extremity spasticity in subjects with chronic stroke. MyoRegulator is an investigational medical device and is limited by US Federal law to investigational use only.

Spasticity is a chronic condition characterised by painful muscle contractions and is common in patients suffering from stroke, cerebral palsy, multiple sclerosis, spinal cord injury, traumatic brain injury and other neurological disorders. Management of spasticity is a difficult challenge and is currently managed primarily by pharmacological agents and injected botulinum neurotoxins, and there is tremendous unmet medical need.  MyoRegulator is a first-in-class non-invasive device based on PathMaker’s proprietary DoubleStim™ technology (combining anodal trans-spinal direct current stimulation (tsDCS) and peripheral nerve direct current stimulation (pDCS)), which provides simultaneous non-invasive stimulation intended to suppress hyperexcitable spinal neurons involved with spasticity.

“Current pharmacological approaches to managing spasticity have, at best, short-term efficacy, are confounded by adverse effects, and are often unpleasant for the patient,” said co-author Zaghloul Ahmed, Ph.D., Professor and Chairman, Department of Physical Therapy and Professor, Center for Developmental Neuroscience, CUNY and Scientific Founder of PathMaker Neurosystems. “The initial study results demonstrate the potential of a novel, non-invasive treatment to reduce spasticity and improve functional recovery in patients with upper motor neuron syndrome after stroke.”

The publication, Non-Invasive Treatment of Patients with Upper Extremity Spasticity Following Stroke Using Paired Trans-spinal and Peripheral Direct Current Stimulation, was authored by researchers at Feinstein Institute for Medical Research at Northwell Health (Manhasset, NY) led by Bruce Volpe, M.D. The study included patients with upper limb hemiparesis and wrist spasticity at least 6 months after their initial stroke in a single-blind, sham-controlled, crossover design study to test whether MyoRegulator treatment reduces chronic upper-extremity spasticity.

Twenty subjects received five consecutive 20-minute daily treatments with sham stimulation followed by a 1-week washout period, then five consecutive 20-minute daily treatments with active stimulation. Subjects were told that the order of active or sham stimulation would be randomized. Clinical and objective measures of spasticity and motor function were collected before the first session of each condition (baseline), immediately following the last session of each condition, and weekly for 5 weeks after the completion of active treatments. The results demonstrated significant group mean reductions from baseline in both Modified Tardieu Scale scores (summed across the upper limb, P<0.05), and in objectively measured muscle resistance at the wrist flexor (P<0.05) following active treatment as compared to following sham treatment. Motor function also improved significantly (measured by the Fugl-Meyer and Wolf Motor Function Test; P<0.05 for both tests) after active treatment, even without additional prescribed activity or training. The effect of the active MyoRegulator treatment was durable for the 5-week follow-up period.

We are highly encouraged by these clinical results which demonstrate the potential of MyoRegulator to improve outcomes for patients suffering from spasticity, without the need for surgery or drugs. Building on these results and our ongoing clinical trial in Europe, we expect to initiate a US multi-center, pivotal, double-blind clinical trial supported by the National Institute of Neurological Disorders and Stroke (NINDS) in early 2020.

Nader Yaghoubi, M.D., Ph.D., President and Chief Executive Officer of PathMaker

 

About PathMaker Neurosystems Inc.

PathMaker Neurosystems is a clinical stage bioelectronic medicine company developing breakthrough non-invasive systems for the treatment of patients with chronic neuromotor conditions. With offices in Boston (US) and Paris (France), we are collaborating with world-class institutions to rapidly bring to market disruptive products for treating spasticity, paralysis and muscle weakness. In January 2019, we announced a collaboration and distribution agreement with WeHealth Digital Medicine to commercialise the MyoRegulator® device worldwide, excluding US and Japan territories retained by PathMaker.  More than 48 million patients in the US, Europe and China suffer disabilities due to stroke, cerebral palsy, multiple sclerosis, spinal cord injury, traumatic brain injury, Parkinson’s disease and other neurological disorders. At PathMaker, we are opening up a new era of non-invasive neurotherapy for patients suffering from chronic neuromotor conditions. For more information, please visit the company website at www.pmneuro.com.

Source: PathMaker Neurosystems Inc.

via PathMaker Neurosystems – Publication of First Clinical Trial Results for MyoRegulator® Device for Non-Invasive Treatment of Spasticity | ACNR | Online Neurology Journal

, , , , ,

Leave a comment

[WEB PAGE] Reconnecting the Disconnected: Restoring Movement in Paralyzed Limbs – Video

"Moving an arm can involve more than 50 different muscles," UA professor Andrew Fuglevand said. "Replicating how the brain naturally coordinates the activities of these muscles is extremely challenging."

“Moving an arm can involve more than 50 different muscles,” UA professor Andrew Fuglevand said. “Replicating how the brain naturally coordinates the activities of these muscles is extremely challenging.”

UA professor Andrew Fuglevand is using artificial intelligence to stimulate multiple muscles to elicit natural movement in ways previous methods have been unable to do.
Dec. 20, 2018
Andrew Fuglevand

Andrew Fuglevand

Scientists now know that the brain controls movement in people by signaling groups of neurons to tell the muscles when and where to move. Researchers also have learned it takes a complex orchestration of many signals to produce even seemingly simple body movements.

If any of these signals are blocked or broken, such as from a spinal cord injury or stroke, the messages from the brain to the muscles are unable to connect, causing paralysis. The person’s muscles are functional, but they no longer are being sent instructions.

Andrew Fuglevand, professor of physiology at the University of Arizona College of Medicine – Tucson and professor of neuroscience at the UA College of Science, has received a $1.2 million grant from the National Institutes of Health to study electrical stimulation of the muscles as a way to restore limb movements in paralyzed individuals. Fuglevand’s goal is to restore voluntary movement to a person’s own limbs rather than relying on external mechanical or robotic devices.

Producing a wide range of movements in paralyzed limbs has been unsuccessful so far because of the substantial challenges associated with identifying the patterns of muscle stimulation needed to elicit specified movements, Fuglevand explained.

“Moving a finger involves as many as 20 different muscles at a time. Moving an arm can involve more than 50 different muscles. They all work together in an intricate ‘dance’ to produce beautifully smooth movements,” he said. “Replicating how the brain naturally coordinates the activities of these muscles is extremely challenging.”

Recent advances in “machine learning,” or artificial intelligence, are making the impossible possible.

Fuglevand, who also is an affiliate professor of biomedical engineering and teaches neuroscience courses at the UA, is employing machine learning to mimic and replicate the patterns of brain activity that control groups of muscles. Tiny electrodes implanted in the muscles replay the artificially generated signals to produce complex movements.

“If successful, this approach would greatly expand the repertoire of motor behaviors available to paralyzed individuals,” he said.

“More than 5 million Americans are living with some form of paralysis, and the leading causes are stroke and spinal injury,” said Nicholas Delamere, head of the UA Department of Physiology. “New innovations in artificial intelligence, developed by scientists like Fuglevand and his team, are allowing them to decode subtle brain signals and make brain-machine interfaces that ultimately will help people move their limbs again.”

“The headway researchers have made in our understanding of artificial intelligence, machine learning and the brain is incredible,” said UA President Robert C. Robbins. “The opportunity to incorporate AI to brain-limb communication has life-changing potential, and while there are many challenges to optimize these interventions, we are really committed to making this step forward. I am incredibly excited to track Dr. Fuglevand’s progress with this new grant.”

Research reported in this release was supported by the National Institutes of Health, National Institute of Neurological Disorders and Stroke, under grant No. 1R01NS102259-01A1. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
A version of this article originally appeared on the UA Health Sciences website:https://opa.uahs.arizona.edu/newsroom/news/2018/reconnecting-disconnected-ua-physiology-professor-receives-12m-nih-grant-use-ai

 

via Reconnecting the Disconnected: Restoring Movement in Paralyzed Limbs | UANews

, , , , , , , ,

Leave a comment

[WEB SITE] Building a better brain-computer interface

Building a better brain-computer interface

Photo of a dummy BrainGate interface. Credit: Paul Wick/Wikimedia Commons

October 2, 2018 by Matt Miles, Medical Xpress

Brain-computer interfaces, or BCIs, represent relatively recent advances in neurotechnology that allow computer systems to interact directly with human or animal brains. This technology is particularly promising for use in cases of spinal cord injury or paralysis. In these situations, patients may be able to use neural decoders that access part of their brain to operate a prosthetic limb or even to re-animate a paralyzed limb through functional electrical stimulation (FES).

Michael A. Schwemmer and colleagues, in a recent Nature Medicine article, detail their research on BCIs using  decoders with a participant with tetraplegia due to spinal cord injury. Their research focuses on addressing several key needs identified by end-users of BCI systems, namely: high , minimal daily setup, rapid response time, and multifunctionality—all of which are characteristics heavily influenced by a BCI’s particular neural decoding algorithm.

Schwemmer’s group describes several different approaches to training and testing three variations on neural network decoders (NN-BCI) in comparison with each other and a benchmark support vector machine (SVM) decoder. The four BCI decoder paradigms were developed and tested over the course of several years in association with a 27-year-old male participant with tetraplegia. The participant had a 96-channel microelectrode array implanted in the area of his left primary motor cortex corresponding to the hand and arm. Using intracortical data collected from 80 sessions over 865 days, the investigators trained and evaluated these BCI decoders. These sessions consisted of two 104-second blocks of a four-movement task: index extension, index flexion, wrist extension, and wrist flexion.

The initial neural network (NN)  was developed and calibrated using data from the first 40 sessions (80 blocks); it was not updated over the second half of the training/testing period, and is referred to here as the fixed neural network (fNN) model. From the fNN, two other neural network models were created: a supervised updating (sNN) model and an unsupervised updating (uNN) model. Both models used data from the first block of the second 40-session (updating/testing) period. The sNN model’s algorithm relies on explicit training labels, that is, known timing and type of movement, whereas the uNN model relies on undifferentiated or unknown direct input in relation to intended action of the limb. The second block of the second 40-session period was used for accuracy testing of all models—fNN, sNN, uNN, and SVM.

The purpose of using four separate models here was to test and demonstrate various aspects of the three neural network models in relation to each other and the benchmark SVM model. For instance, the supervised neural network (sNN) model was updated daily (during the first block of the second 40-session period) and compared directly with the daily-retrained SVM model. The fixed neural network (fNN) model was provided to demonstrate that a BCI could sustain accuracy for over a year with no updates.

The unsupervised neural network (uNN) was perhaps the most interesting comparator, as we shall see, because it attempted to combine the improved accuracy gained from daily updates but without the consequent daily setup time required by the sNN model. Accuracy was the key performance measure in all tests, defined here as a percentage of correctly predicted time-bins in the second block of the second 40 sessions; the criterion of greater than 90% accuracy was one of the four end-user requirements originally articulated at the outset of the study.

The sNN consistently outperformed the daily-retrained SVM: in 37 out of 40 sessions, its accuracy was > 90%, whereas the SVM only achieved > 90% accuracy in 12 sessions. The fNN also outperformed the SVM in 36 of 40 sessions; it achieved > 90% accuracy in 32 sessions. The fNN accuracy was, not surprisingly, lower than the accuracy of the sNN, and both fixed decoders, fNN and SVM, declined in accuracy over the course of the study period, in contrast to the daily-updated decoders.

Perhaps the most interesting finding of this research however, is the performance of the unsupervised neural network (uNN), which outperformed both fixed models in terms of accuracy, while also meeting the end-user requirement of minimal daily set-up. Where the sNN model required explicit daily training, the uNN incorporated data from general use in its update schema, which required no such daily set-up. In comparison with the fNN, a performance gap emerged over time, and the benefits of the uNN distinguished themselves. The uNN also outperformed the SVM in terms of response time, another key end-user requirement.

Another important aspect of this study with regard to NNs focused on transfer learning, whereby new movements can be added to the existing repertoire with minimal additional training and data. In this case, “hand open” and “hand close” were added to the previous four movements, and all decoders were rebuilt. Here too, unsupervised updating was used to build an unsupervised transfer  (utNN), which, after only one session of training oupterformed the SVM model.

Finally, the previous research—all of which was conducted in an “offline” setting—was applied, via the participant’s FES-controlled hand and forearm, to show that a transfer learning uNN trained on the original four-movement task could be used to quickly create a new decoder to control, in real time, an open hand and three grips (can, fork, and peg). In a test of the system, the participant was able to perform all three hand movement grip tasks, with no failures, in 45 attempts. Previously, he was only able to perform one grip task successfully.

In summarizing how the results of their study relate to the main end-user expectations previously described, the investigators cite the following achievements: “(i) using deep NNs to create robust neural decoders that sustain high fidelity BCI control for more than a year without retraining; (ii) introducing a new updating procedure that can improve performance using data obtained through regular system use; (iii) extension of functionality through transfer learning using minimal additional data; and (iv) introducing a decoding framework that simultaneously addresses these four competing aspects of BCI performance (accuracy, speed, longevity, and multifunctionality). In addition, we provide a clinical demonstration that a decoder calibrated using historical data of imagined hand movements with no feedback can be successfully used in real-time to control FES-evoked grasp function for object manipulation.”

Schwemmer and colleagues go on to offer a more in-depth discussion of their results amidst the broader landscape of BCI research, and offer commentary on some of the specific challenges and limitations of their experiment. While noting that the median response time for uNN decoders (0.9 s) is still faster than that of SVM decoders (1.1 s), they acknowledge that a target of 750 ms or less is probably closer to realistic end-user expectations.

Ultimately they conclude: “We have demonstrated that decoders based on NNs may be superior to other implementations because new functions can be easily added after the initial decoder calibration using transfer learning. Crucially, we show that this secondary update to add more movements requires a minimal amount of additional data.” And “insights gained from offline data and analyses can carry over to a realistic online BCI scenario with minimal additional data collection.”

 Explore further: Using multi-task learning for low-latency speech translation

More information: Michael A. Schwemmer et al. Meeting brain–computer interface user performance expectations using a deep neural network decoding framework, Nature Medicine(2018). DOI: 10.1038/s41591-018-0171-y

via Building a better brain-computer interface

, , , , , ,

Leave a comment

[Thesis] Robotic rehabilitation of upper-limb after stroke – Implementation of rehabilitation control strategy on robotic manipulator – Full Text PDF

Abstract
Globally, stroke is one of the main causes of permanent neurological damage \cite{WHO}. Partial or total paralysis of the extremities is the most common complication, with paralysis in upper-limbs being the most prevalent. Efficient and available rehabilitation therapy is essential for the patient’s recovery process. Traditional physical therapy is a resource intensive and commonly used approach. Research on robotic rehabilitation aims to provide a viable rehabilitation tool.The main objective of this master thesis is to design and implement a safe real-time control system on an industrial six-axis manipulator with an external force/torque sensor for robotic upper-limb rehabilitation of stroke patients.The chosen approach is to implement control strategies directly in the tool frame of the manipulator. This is achieved by utilizing the UR5 from Universal Robot and the Mini45 F/T sensor from ATI Industrial Automation. Two verification test are chosen based on activities of daily life (ADL). The best low-level control strategy is achieved by indirect force and torque control through a joint velocity interface.The UR5 firmware operates with an unknown internal controller. An external controller is designed incrementally to investigate the unknown system dynamics and find the best possible low-level performance. Numerous safety mechanisms are added to the external controller. Four high-level control strategies are developed and implemented.Three main safety-related challenges with robotic rehabilitation are identified. Two of them are related to and solved by the external force/torque sensor. The third challenge is related to the self-collisions inside the workspace of the UR5 manipulator. This challenge is also applicable to all six-axis robot manipulators. The three challenges are analyzed and solved with a safety-oriented approach.The safety and functionality of the robotic rehabilitation system are experimentally verified. The behaviour of the rehabilitation modes is analyzed and discussed based on raw data and video recordings.The conclusion is that robotic upper-limb rehabilitation of stroke patients utilizing the UR5 manipulator and the Mini45 F/T sensor is safe and feasible.

via Robotic rehabilitation of upper-limb after stroke – Implementation of rehabilitation control strategy on robotic manipulator

, , , , , , ,

Leave a comment

[WEB PAGE] What are the various treatment options for paralysis?

When there is a loss of muscular functioning in an area or sensory loss on area resulting usually from any damage to central nervous system, there is paralysis. Some of the probable causes of this dangerous condition are polio, stroke, excessive trauma or multiple sclerosis, etc. There may be complete paralysis or partial paralysis. It is mainly of two kinds, namely, paraplegia and quadriplegia. Paralysis is the consequence when the brain fails to send signals to various regions of the body. This may result from a variety of reasons. Stroke accounts for 30% of paralysis cases and is the major cause. However, one can choose paralysis treatment depending on the severity of the condition and the region which is paralyzed.

How is paralysis diagnosed?

On the event of any failure of muscular functioning or sensory loss on certain area, it is important to visit a medical practitioner immediately. To diagnose the condition, he prescribes a series of tests including CT Scan, MRI, X-Ray, Electromyography. If at all it is necessary, the patient may be suggested a neurologist. After paralysis is confirmed, the treatment begins. Certain types of paralysis may be cured and this mainly includes partial paralysis. You can ask the doctor whether the recovery is possible or not. No matter what the cause of the condition, the treatment procedure will be almost the same. Whatever treatment you choose for recovery, the treatment provider will try and restore brain and body connection. This is the only way to bring about recovery.

Some of the basic treatment options for paralysis

Wearable device running on electricity is the most basic treatment for paralysis. This wearable electronic device is also used for stroke treatment. It improves arm functioning and restores motion in the arms. When you wear this device, it delivers electrical current to activate the muscles of arms and legs. This technique of motion restoration is also termed as FES or Functional Electrical Stimulation. It can recover the feet or lower legs from paralysis. The use of FES along with specific exercises can bring about a relief.

Some of the best treatment options for paralysis 

If anyone of your loved one is suffering from paralysis, read the following section to learn how to reduce the symptoms:

  • Surgery can address physical barriers. It may be that there is an object fixed in the brain or spinal cord of the person. It needs to be got rid of. Through the surgery, certain portions of the spinal cord can also be fused together.
  • Some paralysis medication may be used to reduce swelling, inflammation and infection on the area. If there is chronic pain, it may be addressed with medicines.
  • Continuous monitoring of the person is mandatory to ensure that this condition does not get worse
  • Psychotherapy can help a lot. Support groups may teach you how to cope with this critical situation.
  • To restore muscular and nerve functioning, you may be asked to do certain exercises. Occupational therapy can also help a lot. Work on the injuries and practice them as much as possible. Physical therapy may reverse paralysis by rewiring the brain.
  • Some people got great results from alternative treatments like chiropractic care, massage therapy and acupuncture treatment.

If there are breathing difficulties, problem in the bowel movement, take immediate treatment for them. Again, surgery is an effective sleep apnea treatment. Whether it is sleep apnea or paralysis, immediate medical attention is required.

via What are the various treatment options for paralysis?- The New Indian Express

, , , ,

Leave a comment

[Abstract + References] Classifying Imaginary Hand Movement through Electroencephalograph Signal for Neuro-rehabilitation

Abstract

Brain-Computer-Interface (BCI) has been widely used in the field of neuro-rehabilitation such as automatic controls based on brain commands to upper and lower extremity prosthesis devices in patients with paralysis. In a post-stroke period, approximately 50% of stroke sufferers have unilateral motor deficits leading to a chronic decline in chronic upper extremity function. Stroke affects patients in their productive and elderly age which is potentially creating new problems in national health development. BCI can be used to aid post-stroke patient recovery, thus motion detection and classification is essential for optimizing BCI device control. Therefore, this study aims to distinguish several hand functions such as grasping, pinching, and hand lifting from releasing movement in accordance with the usual movements performed during post-stroke rehabilitation based on brain signals obtained from electroencephalogram (EEG). In this study, the information that obtained from the processing of EEG signals were be used as inputs for artificial neural networks then classified to distinguish two types of imaginary hand movements (grasping v. releasing, pinching v. releasing, hand lifting v. releasing). The results of these classifications using Extreme Learning Machine (ELM) based on spectral analysis and CSP (Common Spatial Pattern) calculation show that ELM and CSP was a good feature in distinguishing two types of motion with software/system accuracy average above 95%. This could be useful for optimizing BCI devices in neuro-rehabilitation, such as combining with Functional Electrical Stimulator (FES) device as a self-therapy for post-stroke patient.

References

Badan Penelitian dan Pengembangan Kesehatan. Riset Kesehatan Dasar 2013, Available at : http://www.depkes.go.id/resources/download/general/Hasil%20Riskesdas%202013.pdf, accesed February 2017.

J. A. Franck. Concise Arm and Hand Rehabilitation Approach in Stroke. vol. 3. no. 4. 2015.

N. Birbaumer. A. R. Murguialday. and L. Cohen. Brain-computer interface in paralysis. Curr. Opin. Neurol. vol. 21. no. 6. pp. 634–8. 2008.

J. J. Daly. R. Cheng. J. Rogers. K. Litinas. K. Hrovat. and M. Dohring. Feasibility of a New Application of Noninvasive Brain Computer Interface (BCI): A Case Study of Training for Recovery of Volitional Motor Control After Stroke. J. Neurol. Phys. Ther. vol. 33. no. 4. pp. 203–211. 2009.

K. K. Ang. C. Guan. K. S. Phua. C. Wang. L. Zhou. K. Y. Tang. G. J. Ephraim Joseph. C. W. K. Kuah. and K. S. G. Chua. Brain-computer interface-based robotic end effector system for wrist and hand rehabilitation: results of a three-armed randomized controlled trial for chronic stroke.. Front. Neuroeng. vol. 7. no. July. p. 30. 2014.

E. Buch. C. Weber. L. G. Cohen. C. Braun. M. A. Dimyan. T. Ard. J. Mellinger. A. Caria. S. Soekadar. A. Fourkas. and N. Birbaumer. Think to move: A neuromagnetic brain-computer interface (BCI) system for chronic stroke. Stroke. vol. 39. no. 3. pp. 910–917. 2008.

G.-B. Huang. Q. Zhu. C. Siew. G. H. Ã. Q. Zhu. C. Siew. G.-B. Huang. Q. Zhu. and C. Siew. Extreme learning machine: Theory and applications. Neurocomputing. vol. 70. no. 1–3. pp. 489–501. 2006.

Emotiv Insight User Manual. 2015, Availabe at : https://www.emotiv.com, accessed June 2017

P. Szachewicz. Classification of Motor Imagery for Brain-Computer Interfaces. p. 50. 2013.

B. Shoelson. edfRead, Available at : https://www.mathworks.com/matlabcentral/fileexchange/ 31900-edfread, accesed February 2017.

J. Ethridge and W. Weaver. Common Spatial Patterns Alogarithm. MatlabCentral. 2009. .

Q. Yuan. W. Zhou. S. Li. and D. Cai. Epileptic EEG classification based on extreme learning machine and nonlinear features. Epilepsy Res. vol. 96. no. 1–2. pp. 29–38. 2011.

G. Huang. Introduction to Extreme Learning Machines. Hands-on Work. Mach. Learn. Biomed. Informatics 2006. 2006.

M. H.. A. Samaha. and K. AlKamha. Automated Classification of L/R Hand Movement EEG Signals using Advanced Feature Extraction and Machine Learning. Int. J. Adv. Comput. Sci. Appl. vol. 4. no. 6. p. 6. 2013.

G. Lange. C. Y. Low. K. Johar. F. A. Hanapiah. and F. Kamaruzaman. Classification of Electroencephalogram Data from Hand Grasp and Release Movements for BCI Controlled Prosthesis. Procedia Technol. vol. 26. pp. 374–381. 2016.

X. Yong and C. Menon. EEG classification of different imaginary movements within the same limb. PLoS One. vol. 10. no. 4. pp. 1–24. 2015.

via Classifying Imaginary Hand Movement through Electroencephalograph Signal for Neuro-rehabilitation | Rahma | Walailak Journal of Science and Technology (WJST)

, , , , , , , , , ,

Leave a comment

[WEB SITE] Scientists develop combined therapy for stroke victim recovery

Scientists in Switzerland have demonstrated that combining a brain-computer interface (BCI) with functional electrical stimulation (FES) can help stroke victims recover greater use of their paralysed limbs – even years after the stroke.

 

stroke-brain-computer-interface

 

Paralysis of an arm and/or leg is one of the most common results of a stroke. However, a team of scientists at the Defitech Foundation Chair in Brain-Machine Interface, in association with other members of EPFL’s Center for Neuroprosthetics, the Clinique Romande de Réadaptation in Sion, and the Geneva University Hospitals, have developed a technique aimed at enabling stroke victims to recover greater use of their paralysed limbs. The scientists’ pioneering approach utilises two existing therapies – a brain-computer interface (BCI) and functional electrical stimulation (FES).

Explaining the key to their approach, José del R. Millán, who holds the Defitech Chair at EPFL, said: “The key is to stimulate the nerves of the paralysed arm precisely when the stroke-affected part of the brain activates to move the limb, even if the patient can’t actually carry out the movement. That helps re-establish the link between the two nerve pathways where the signal comes in and goes out.”.

Combined therapy tested on stroke patients

Twenty-seven patients aged between 36 and 76 took part in the clinical trial. All had a similar lesion that resulted in moderate to severe arm paralysis following a stroke occurring at least ten months earlier. Half of the patients were treated with the scientists’ dual-therapy approach and reported clinically significant improvements. The other half were treated only with FES and served as a control group.

For the first group, the scientists used a BCI system to link the patients’ brains to computers by means of electrodes. This enabled them to pinpoint exactly where the electrical activity occurred in the brain tissue when the patients tried to reach out their hands. Each time the electrical activity was identified the system immediately stimulated the arm muscle controlling the corresponding wrist and finger movements. The patients in the second group also had their arm muscles stimulated, but at random times. This control group enabled the scientists to determine how much of the additional motor-function improvement could be attributed to the BCI system.

 

The scientists noted a significant improvement in arm mobility among patients in the first group after just ten one-hour sessions. When the full round of treatment was completed, some of the first-group patients’ scores on the Fugl-Meyer Assessment – a test used to evaluate motor recovery among patients with post-stroke hemiplegia – were over twice as high as those of the second group.

“Patients who received the BCI treatment showed more activity in the neural tissue surrounding the affected area. Due to their plasticity, they could help make up for the functioning of the damaged tissue,” says Millán.

 

Electroencephalographies (EEGs) of the patients clearly showed an increase in the number of connections among the motor cortex regions of their damaged brain hemisphere, which corresponded with the increased ease in carrying out the associated movements. In addition, the enhanced motor function didn’t seem to diminish with time. Evaluated again 6-12 months later, the patients were found to have lost none of their recovered mobility.

The study results were published in Nature Communications.

via Scientists develop combined therapy for stroke victim recovery

, , , , , , , , ,

1 Comment

[BLOG POST] A Dual-Therapy Approach to Boost Motor Recovery After a Stroke

Stroke victims have a reason to finally smile as a new therapy approach promises to help them recover greater use of their paralyzed leg and/or arm. In a recent study, researchers managed to demonstrate that indeed, broken sensory nerve connections can be reconstructed without surgery, but using two therapies at the same time.

The technique combines functional electrical stimulation (FES) and brain-computer interface BCI to help “resurrect” the use of paralyzed limb (arm/leg). In most cases, paralysis happens to be the most general but hard to bear effects of a stroke. Fortunately, research now seems to have a solution for treating this effect.

Communication Between Nerve Pathways

While the approach may not be something completely out of the horizon, this is the first time experts considered deploying two therapies at the same time on stroke effects (FES + BCI). The therapy works to help reestablish communication between nerve pathways, which ideally corrects how signals come in and go out of the nerve segment endings.

“The goal is to stimulate those nerves thought to have been silenced by the paralysis. This should be the work of the brain. But as the part of the brain tasked to do this may no longer be active enough, the therapy steps in to help reestablish the links between (the brain) and the nerve pathways,” explains Jose del R. Millan, one of the scientists involve in the research, which was pioneered by the Defitech Foundation Brain and Machine Interface.

Degrees of Paralysis

The work, which also appears in the latest issue of Nature Communications focused on mid-age and aged adults of between 36 to 76, and involved 27 volunteers with varying stroke effects. A section of the patients had moderate paralysis, while for the rest the cases were considered as severe arm paralysis occurring less than a year prior to the dual-therapy.

Representing half of the volunteering team, 14 of the patients took the dual-therapy and the results found a significant lasting improvement in the ability to initiate control of their affected arms. The other half of the volunteers took the functional electrical stimulation (FES) treatment only and acted as a control team to help monitor progress.

Hunting for the Brain Signals

Now, the scientists introduced the BCI system to access the patient’s brain response, linking the same to computers via electrodes. The exact task was to pinpoint the specific areas the electrical signals showed up in the brain as the patient tried to pick something using the affected arm.

When the electrical activity was spotted the system immediately stimulated the concerned muscle in the wrist and finger to have it respond to the signal. Patients in the “control” group had their muscles stimulated but not as often as the first team. That was done on purpose to help establish the motor-function improvement that could directly be attributed to the BCI system and the reliability of the same.

Reactivated Tissue and How this Changes Stroke Effect Therapy

Source: braceworks

What makes the research outstanding is that some patients in the first group registered a significant improvement in arm mobility within the first ten one-hour therapy sessions. Using a special test that evaluates motor recovery on post-stroke hemiplegia, called the Fugl-Meyer Assessment, a good number of the patients in the first group improved in their mobility twice in score compared to their counterparts.

The scientists also found an overall increase in connection among the motor cortex areas of their damaged brain, which corresponded with the overall ease in undertaking the associated tasks.

This might, without doubt, be the complete game changer of the way effects of stroke should be treated, because, even after 6 and 12 months – looking at the progress of the patients, their recovered mobility from the dual-therapy was maintained.

 

via A Dual-Therapy Approach to Boost Motor Recovery After a Stroke – Sanvada

, , , , , ,

Leave a comment

[Online Game] Mobility Mission Online Game – Stroke.org

Mobility Mission Online Game

Mobility Mission is an entertaining online game that addresses post-stroke mobility challenges. Stroke is a serious condition, and learning to deal with the effects of surviving a stroke can be challenging. This game will help you gain a better understanding of post-stroke mobility challenges such as spasticity, paralysis, foot drop, as well as management and treatment options you can discuss with your healthcare provider. As you travel through the four levels of the game you will learn how to improve your safety at home and acquire tips to lower your risk of falling. Your journey is waiting!

PLAY NOW

 

via Mobility Mission Online Game | Stroke.org

, , , , , ,

Leave a comment

[WEB SITE] Brain-Machine Interface Shows Potential for Hand Paralysis – Rehab Managment

Published on 

http://www.dreamstime.com/royalty-free-stock-images-hands-grandmother-elderly-image66789689

The use of a brain-machine interface shows potential for helping to restore function in stroke patients with hand paralysis, according to a study of healthy adults published in the Journal of Neuroscience.

According to the study, researchers note that the brain-machine interface, which is designed to combine brain stimulation with a robotic device that controls hand movement, increases the output of pathways connecting the brain and spinal cord.

Researchers Alireza Gharabaghi and colleagues asked participants to imagine opening their hand without actually making any movement while their hand was placed in a device that passively opened and closed their fingers as it received the necessary input from their brain activity. Stimulating the hand area of the motor cortex at the same time, but not after, the robotic device initiated hand movement increased the strength of the neural signal, most likely by harnessing the processing power of additional neurons in the corticospinal tract, explains a media release from the Society for Neuroscience.

However, the signal decreased when participants were not required to imagine moving their hand. Delivering brain stimulation and robotic motor feedback simultaneously during rehabilitation may therefore be beneficial for patients who have lost voluntary muscle control, the release continues.

[Source(s): Society for Neuroscience]

via Brain-Machine Interface Shows Potential for Hand Paralysis – Rehab Managment

, , , , , , , , ,

Leave a comment

%d bloggers like this: