Posts Tagged brain

[WEB PAGE] Individual frequency can be used to control brain activity – News

Reviewed by Emily Henderson, B.Sc.Aug 17 2020

Individual frequency can be used to specifically influence certain areas of the brain and thus the abilities processed in them – solely by electrical stimulation on the scalp, without any surgical intervention. Scientists at the Max Planck Institute for Human Cognitive and Brain Sciences have now demonstrated this for the first time.

Stroke, Parkinson’s disease and depression – these medical illnesses have one thing in common: they are caused by changes in brain functions. For a long time, research has therefore been conducted into ways of influencing individual brain functions without surgery in order to compensate for these conditions.

Scientists at the Max Planck Institute for Human Cognitive and Brain Sciences in Leipzig, Germany, have taken a decisive step. They have succeeded in precisely influencing the functioning of a single area of the brain. For a few minutes, they inhibited exactly the area that processes the sense of touch by specifically intervening in its rhythm. As a result, the area that was less networked with other brain regions, its so-called functional connectivity, decreased, and thus also the exchange of information with other brain networks.

This was possible because the researchers had previously determined each participant’s individual brain rhythm that occurs when perceiving touch. With the personal frequency, they were able to modulate the targeted areas of the brain one at a time in a very precise manner using what is known as transcranial alternating current stimulation. “This is an enormous advance,” explains Christopher Gundlach, first author of the underlying study. “In previous studies, connectivity fluctuated extensively when the current was distributed in different areas of the brain. The electrical current randomly sought its own path in the brain and thus affected different brain areas simultaneously in a rather imprecise manner.

In a preliminary study, the neuroscientists had already observed that this form of stimulation not only reduces the exchange of the targeted brain networks with other networks, it also affects the brain’s ability to process information, in this case the sense of touch. When the researchers inhibited the responsible somatosensory network, the perception threshold increased. The study participants only perceived stimuli when they were correspondingly strong. When, on the other hand, they stimulated the region, the threshold value dropped and the study participants already felt very gentle electrical stimuli.

The deliberate change in brain rhythm lasted only briefly. As soon as the stimulation is switched off, the effect disappears again. Nevertheless, the results are an important step towards a targeted therapy for diseases or disorders caused by disturbed brain functions”.

Bernhard Sehm, Study Leader

Targeted brain stimulation could help to improve, direct and, if necessary, attenuate the flow of information.

Source: Max Planck Institute for Human Cognitive and Brain Sciences

Journal reference: Gundlach, C., et al. (2020) Reduction of somatosensory functional connectivity by transcranial alternating current stimulation at endogenous mu-frequency.  NeuroImage.

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This guide describes a sampling of these at-home biofeedback assistive technology (AT) devices that may help users better understand, interpret, and manage depressive effects that involve your brain, heart, and muscles. Biofeedback AT devices are designed to assist with monitoring and voluntarily controlling certain mental and physical functions such as increasing mental focus, regulating breathing, or relaxing muscles to get brainwaves, heartrate, and muscle tension levels back to normal intensities.

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[WEB PAGE] International researchers propose new classification system of seizures

Epilepsy is a wide-spread neurological disorder that affects around 50 million people worldwide. It is characterized by recurrent epileptic seizures, which are sudden bursts of electrical activity in the brain. There are many different types of seizures, and a person with epilepsy can experience more than one type.

Clinicians today use EEG measurements, with electrodes either placed on a patient’s scalp or inside the brain, to identify when and where a seizure begins. But these measurements alone do not always provide enough information to understand the type of seizure and make optimal decisions regarding treatment.

Now, an international team of researchers led by Aix-Marseille University in France and the University of Michigan has proposed a new classification system of seizures based on a deep understanding and mathematical modelling of brain oscillations. “It represents the first objective and unbiased taxonomy of its kind”, says one of the lead authors, HBP-scientist Prof. Viktor Jirsa from Aix-Marseille University.

The researchers used “bifurcation theory” – a method commonly used in fields such as physics and engineering – to analyze data from over a hundred patients across the globe. Researchers from the University of Melbourne and Monash University, both in Australia, the University of Freiburg in Germany, and Kyoto University in Japan also contributed to the work. Seizures with similar properties were categorized into groups.

They found sixteen types of seizure dynamics – or ‘dynamotypes’ – with distinct characteristics. “Similar to the periodic table of elements in chemistry, we demonstrated the existence of a clear classification system of seizures”, says Jirsa.

The system could lead clinicians to a better understanding of seizures and how they should be treated. “Seizure types react differently to treatments. For instance, some seizures can be stopped through electric stimulation, others not, dependent on their dynamotype. The systems scientific basis is theory work developed around the Epileptor, a central epilepsy model we developed in the Human Brain Project that is also at the heart of a large clinical trial running now”, the researcher explains.

Classification, however, is not explanation. There is much work ahead of us to better understand epilepsy mechanisms. This is where EBRAINS will play a key role, as it provides the tools connecting cellular, network and brain imaging signals aiding in mechanism discovery. ”

Prof. Viktor Jirsa, HBP-Scientist from Aix-Marseille University

EBRAINS is a new shared digital brain research infrastructure for the European Union that the Human Brain Project (HBP) is building.

Within the HBP, Jirsa and his team had first begun adapting the open network simulator The Virtual Brain towards applications in epilepsy. The work has laid the foundations for project EPINOV (“Improving EPilepsy surgery management and progNOsis using Virtual brain technology”) a multi-year project involving more than a dozen French hospitals that is funded by the French state. EPINOV tests whether the use of the personalized HBP modeling technology for epilepsy networks can improve surgery preparation in drug-resistant patients.

via International researchers propose new classification system of seizures


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[WEB PAGE] Taking magnetic resonance imaging into a new dimension

EU-funded ATTRACT consortium explores how to enrich MRI scans with mixed reality headsets and other high-tech wizardry.

Copyright: MRBrainS

The complexity of the brain makes operating on this organ one of the most challenging tasks in medicine. But a group of Italian researchers is trying to make intricate neurosurgery easier with holographic brain-mapping software, which highlights and labels crucial areas and blood vessels right before the surgeon’s eyes.

Their project, called MRbrainS, feeds brain activity data from functional magnetic resonance imaging (fMRI) into dedicated software for brain-mapping (called a neuronavigator), then integrates that into a mixed reality headset, which overlays 3D digital images on to the wearer’s view of the real world.  These images can be linked to objects in the real world and remain anchored to them as the wearer looks around.MRBrainS is one of eight research projects focused on magnetic resonance imaging (MRI) with support from ATTRACT, a €20 million consortium funded by the EU and led by CERN that has awarded €100,000 each to 170 technology projects. First developed in the 1970s, MRI is a medical imaging technology that uses magnetic fields and radio waves to create detailed images of internal organs, tissues and bones.Today’s neuronavigators display 2D images on screens, forcing the surgeon to mentally link what’s on the display with the patient lying on the operating table. That’s difficult and “slows down the whole procedure,” says principal investigator Antonio Ferretti. But using a mixed reality headset to tie 3D information directly to what surgeons see in front of them means they can rely on hand-eye coordination, “which is easier if your hands are in front of you, in the same direction you are looking,” explains Ferretti.Another problem MRbrainS aims to solve is that today’s neuronavigators don’t incorporate fMRI data. Unlike regular MRI or computed topography (CT), fMRI shows activity in different parts of the patient’s brain, something that previously could only be determined during invasive surgery with targeted electrical pulses, called direct cortical stimulation. The different steps necessary to push fMRI data to a neuronavigator currently require various different software packages, most of which are designed for researchers, not surgeons. Turning the neuronavigator’s output into a mixed reality image is yet another challenge.For now, MRbrainS uses the Microsoft HoloLens mixed-reality headset. But Ferreti said a longer-term goal could be to design a new headset that integrates the feed from a surgical microscope with all the other data sources in a single device. “This is why we hope that in future development we could involve larger companies,” says Ferretti, an assistant professor of physics at Annunzio University’s Institute for Advanced Biomedical Technologies (ITAB), which runs MRbrainS in partnership with the University of Pavia startup SerVE.

Operating inside the womb with mixed reality

Similarly, the ATTRACT-funded MIFI project is developing a mixed reality system that integrates MRI, ultrasound, and endoscopic video for surgery on unborn children. In-utero surgery is especially difficult, because doctors “need to operate on a patient inside another patient,” notes Mario Ceresa, MIFI’s principal investigator. That patient is very small and delicate, and depends on an amniotic sac that can quickly collapse, so “the interventions are very difficult, because there is a lot of time pressure,” adds Ceresa, a postdoctoral researcher at Pompeu Fabra University (UPF) in Barcelona. Another challenge is that since the operation is keyhole surgery, the surgeon has only a very narrow field of view inside the womb through an endoscope, a tiny camera on the end of a long, thin fibre optic cable.MIFI aims to improve the surgeon’s field of view by displaying a virtual 3D image of the mother’s womb in mixed reality, on top of what the doctor sees in front of them. The project applies machine learning to pre-operative ultrasound and MRI scans to identify relevant blood vessels—some of which are extremely small—and to help the surgeon find them in the womb, even if the baby has moved in the meantime.Though the system isn’t meant to be specific to one condition, for development purposes MIFI is focusing on surgery to correct a condition called Twin-to-Twin Transfusion Syndrome (TTTS). This is where twins that share a placenta — monochorionic twins — are threatened by a blood flow imbalance; the condition occurs in one third of monochorionic twins, and kills both in more than 90 per cent of cases. TTTS can be treated by separating the twins’ blood circulation, using a laser to coagulate the tiny blood vessels. MIFI is a partnership between UPF, research firm Vicomtech, and Sant Joan de Déu Hospital in Barcelona.

Finding scars fast        

MERIT-VA is another ATTRACT project trying to improve the way major surgery is carried out. The researchers, based at the Teknon Medical Centre in Barcelona, UPF, and software firm Galgo Medical, are using machine learning to analyse data from MRI scans and electrocardiograms (ECGs) to improve planning of a particular type heart surgery.Scar tissue formed after a heart attack can disrupt the heart’s natural electrical pulses by directing the current where it shouldn’t go, causing an irregular heartbeat (arrythmia). The condition is treated by inserting tiny catheters into the heart that destroy the problem tissues with radio waves. These catheters contain sensors that provide their position in 3D and detect electrical signals to identify the tissues that need removing. This information can then be displayed on an electro-anatomical map (EAM) to guide the surgeon.But building this map using the catheters can take hours, increasing the risk that something will go wrong during surgery. The condition also frequently recurs after treatment. The more the surgeon knows about which scars to target and where to find them, the quicker the procedure and the greater the chance of curing the condition without destroying excess tissue unnecessarily.Doctors can predict where in the heart the problem is likely to be found by looking at ECG charts, and MRI has been shown to improve this pre-op planning. MERIT-VA, therefore, aims to improve such predictions further by using machine learning to analyse and integrate ECG and MRI information. The goal is to make the surgery quicker, less risky, and more successful.In another project, called QP-MRI, researchers at the University of Turin and the University of Aberdeen are using a variable-field strength MRI scanner to monitor the structural integrity of a new type of medical implant. The implants, used to repair bodily tissues, such as bone, cartilage or corneas, are made from a biodegradable polymer lattice, bonded to an amino acid called polyhistidine, which shows up brightly in MRI scans. When the lattice begins to break down, the MRI signature of the polyhistidine fades.The lattices are supposed to break down once their job is done, but the point is to ensure they don’t deteriorate too early. Such polymer lattices are already in medical use; QP-MRI’s novelty is the use of polyhistidine as a contrast agent, along with an MRI scanner capable of operating at variable magnetic field strengths, designed by the team at Aberdeen.“Our system uses a completely new mechanism in order to produce contrast in an MRI machine,” says principal investigator Simonetta Geninatti Crich, a professor of molecular biology at Turin. Geninatti explains the existing MRI contrast agents carry health risks, which is why polyhistidine is a desirable alternative. But in order for it to work, new, lower-field strength scanners are needed. “You can detect this signal only if you are able to work at a low magnetic field strengths of about 30 milliteslas, more or less,” whereas conventional MRI scanners work at around 1 tesla, she adds.

Machine learning dissects the detail

The MAGRes project aims to make MRI more effective at monitoring glioblastoma—an extremely aggressive form of brain cancer—by identifying subtle variations in MRI scans. The MAGRes researchers use magnetic resonance spectroscopy imaging (MRSI) to identify metabolic changes in the tumour. They then link these results to barely-perceptible changes in ordinary MRI scans, in order to develop new machine learning models for analysing MRI. The idea is not for glioblastoma patients to undergo MRSI—which takes much longer than MRI—but for MRSI research to make MRI analysis more effective.“This metabolic information can appear before anatomical information seen by MRI,” explains Ana Paula Candiota, MAGRes principal investigator and postdoctoral researcher at the Network Centre for Biomedical Research and the Autonomous University of Catalonia. The hypothesis is that “we can use the metabolic information to try to guide ourselves to find things on the [MRI] image that maybe we did not know,” she adds.The purpose of MAGRes is to detect as early as possible whether a patient’s treatment is having any effect or whether it needs to be changed, since glioblastoma patients are tragically short on time. Average life expectancy with treatment is a little over one year, and only a small percentage of patients survive five years.Candiota says she also took part in experiments that eliminated glioblastoma in half of affected mice, without recurrence, by reducing the frequency of chemotherapy treatment to give the immune system more time to attack the tumour. But trying this method in humans is difficult because doctors and patients are suspicious of the hope of getting better results from less chemotherapy, she warns. The only human trials so far involved patients who “were in the last days” and had failed to respond to any treatment, so in all likelihood, nothing could be done for them. “That’s not fair,” notes Candiota.In a similar vein to MAGRes, the IMAGO project aims to develop new models of MRI analysis using a technique called single particle tracking (SPT) to monitor the behaviour of light in sample tissues. Unlike MRI, SPT can identify tiny, sub-microscopic features, but MRI can “see” inside the body whereas SPT can’t. The IMAGO experiments aim to link the characteristics of different samples to subtle variations in MRI data, so that more information can be gleaned from MRI scans. The project is a partnership between Italy’s National Research Council and the Sapienza University of Rome.Meanwhile, the DentMRI project is using low-strength MRI scanning to improve dental care, by providing the first ever images of teeth and gums together that are good enough for medical diagnosis. The researchers, based at the Polytechnic University of Valencia and MRI equipment manufacturer Tesoro Imaging, have developed a prototype scanner that can accommodate objects of up to a cubic centimetre, and the goal is to build one large enough for a person to put their head inside for a dental scan.

Enabling electronics at extreme temperatures

The Low Temperature Communication Link (LTCL) project could help to make MRI equipment more efficient by redesigning the way the powerful magnets inside an MRI scanner are connected to the rest of the system.MRI magnets are kept cool with liquid helium, which has a boiling point of -269° Celsius, or about four Kelvins. Normal electronics can’t function at such low temperatures, so they are built outside the cryogenic vessel that contains the magnets and connected with wires. But LTCL aims to develop electronics that could work inside the cryogenic container, with a wireless communications link and wireless power supply to the normal temperature environment outside.The LTCL researchers at CERN, startup Oxford Instruments, and the French Alternative Energies and Atomic Energy Commission (CEA) say this would bring a number of advantages—not just for MRI, but for any technology that relies on cryogenic equipment. For example, engineers would have more freedom in how they design the cryogenic containers because there would be no need for physical connections to the outside. Furthermore, bringing the electronics closer to the data source—the magnet—would cut interference and give more accurate readings.

Discover more ATTRACT projects developing MRI technologies and innovative solutions for society here. Also, save the date for the ATTRACT online conference – Igniting the Deep Tech Revolution.

via Taking magnetic resonance imaging into a new dimension | Science|Business

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[News] New noninvasive ultrasound neuromodulation technique for epilepsy treatment

Reviewed by Emily Henderson, B.Sc.May 15 2020

Epilepsy is a central nervous system disorder characterized by recurrent seizures resulting from excessive excitation or inadequate inhibition of neurons.

Ultrasound stimulation has recently emerged as a noninvasive method for modulating brain activity; however, its range and effectiveness for different neurological disorders, such as Parkinson’s Disease, Epilepsy and Depression, have not been fully elucidated.

Researchers from the Shenzhen Institutes of Advanced Technology (SIAT) of the Chinese Academy of Sciences developed a noninvasive ultrasound neuromodulation technique, which could potentially modulate neuronal excitability without any harm in the brain.

Low-intensity pulsed ultrasound and ultrasound neuromodulation system were prepared for non-human primate model of epilepsy and human epileptic tissues experiments, respectively.

The results showed that ultrasound stimulation could exert an inhibitory influence on epileptiform discharges and improve behavioral seizures in a non-human primate epileptic model.

Ultrasound stimulation inhibited epileptiform activities with an efficiency exceeding 65% in biopsy specimens from epileptic patients in vitro.

The mechanism underlying the inhibition of neuronal excitability could be due to adjusting the balance of excitatory-inhibitory (E/I) synaptic inputs by the increased activity of local inhibitory neurons. In addition, the variation of temperature among these brain slices was less than 0.64°C during the experimental procedure.

The study demonstrated for the first time that low-intensity pulsed ultrasound improved electrophysiological activities and behavioral outcomes in a non-human primate model of epilepsy and suppressed epileptiform activities of neurons from human epileptic slices.

It provided evidence for the potential clinical use of non-invasive low-intensity pulsed ultrasound stimulation for epilepsy treatment.

Source: Chinese Academy of Sciences Headquarters

Journal reference: Lin, Z., et al. (2020) Non-invasive ultrasonic neuromodulation of neuronal excitability for treatment of epilepsy.

BiopsyBrainCentral Nervous SystemDepressionEpilepsyin vitroNervous SystemNeuromodulationNeuronsTheranosticsUltrasound

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[ARTICLE] Advances in brain imaging in multiple sclerosis – Full Text

Brain imaging is increasingly used to support clinicians in diagnosing multiple sclerosis (MS) and monitoring its progression. However, the role of magnetic resonance imaging (MRI) in MS goes far beyond its clinical application. Indeed, advanced imaging techniques have helped to detect different components of MS pathogenesis in vivo, which is now considered a heterogeneous process characterized by widespread damage of the central nervous system, rather than multifocal demyelination of white matter. Recently, MRI biomarkers more sensitive to disease activity than clinical disability outcome measures, have been used to monitor response to anti-inflammatory agents in patients with relapsing–remitting MS. Similarly, MRI markers of neurodegeneration exhibit the potential as primary and secondary outcomes in clinical trials for progressive phenotypes. This review will summarize recent advances in brain neuroimaging in MS from the research setting to clinical applications.


In the last decade, magnetic resonance imaging (MRI) has emerged as a fundamental imaging biomarker for multiple sclerosis (MS). Currently, MRI plays a key role in several aspects of the disease including diagnosis,1 prognosis2 and treatment response assessment.3

Over the last few years, developments in brain imaging acquisition and post-processing have advanced the field and have made tremendous contributions to our understanding of disease-specific pathogenetic mechanisms.4 This has improved the accuracy of MS diagnosis and differentiation from other inflammatory diseases of the central nervous system (CNS).5 Furthermore, promising imaging biomarkers are now used to reflect pathological processes occurring in progressive MS.6 This has culminated in the recent use of advanced imaging technique measures as outcomes in phase II and III MS clinical trials of disease-modifying and neuroprotective therapies.7

There is expanding scientific literature on brain imaging in MS. Therefore, we constrained our review to the clinical advances in human brain MRI achieved over the last 5 years in the MS field. Although positron emission tomography (PET)8 and optical coherence tomography (OCT)9 are currently emerging as key tools in the understanding of MS pathophysiology and in monitoring the disease, these neuroimaging techniques were not included in our search criteria.

The aim of this review was to describe advances in brain MRI imaging used to support the diagnosis of MS and to characterize the pathological mechanisms underlying clinical activity and progression. Finally, we intended also to present the recent impact of these advances on clinical trials in MS. For these purposes, the review was conducted using literature from Embase and PubMed using the following keywords: multiple sclerosis; magnetic resonance imaging; brain; pathogenesis; diagnosis; progression. As regards clinical trials, we focused on completed phase II and III trials in relapsing–remitting MS (RR-MS) or progressive MS using clinical trials databases, such as and

Recent advances in neuroimaging considering different brain locations are listed in Figure 1.


Figure 1. Advances in brain imaging in multiple sclerosis in different brain locations.
CVS, central vein sign; DGM, deep grey matter; DMD, disease-modifying drug; ihMT, inhomogeneous magnetization transfer; MRI, magnetic resonance imaging; MRS, magnetic resonance spectroscopy; MWF, myelin water fraction; NODDI, neurite orientation dispersion and density imaging; PET, positron emission tomography; qMT, quantitative magnetization transfer; SEL, slowly expanding lesion; TSC, total sodium concentration.


Continue —-> Advances in brain imaging in multiple sclerosis – Rosa Cortese, Sara Collorone, Olga Ciccarelli, Ahmed T. Toosy, 2019

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[TED-Ed] Is marijuana bad for your brain? Anees Bahji. – Video Animation

In 1970, marijuana was classified as a schedule 1 drug in the United States: the strictest designation possible, meaning it was completely illegal and had no recognized medical uses. Today, marijuana’s therapeutic benefits are widely acknowledged, but a growing recognition for its medical value doesn’t answer the question: is recreational marijuana use bad for your brain? Anees Bahji investigates.

via Is marijuana bad for your brain? – Anees Bahji | TED-Ed

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[ARTICLE] Self-Support Biofeedback Training for Recovery From Motor Impairment After Stroke – Full Text


Unilateral arm paralysis is a common symptom of stroke. In stroke patients, we observed that self-guided biomechanical support by the nonparetic arm unexpectedly triggered electromyographic activity with normal muscle synergies in the paretic arm. The muscle activities on the paretic arm became similar to the muscle activities on the nonparetic arm with self-supported exercises that were quantified by the similarity index (SI). Electromyogram (EMG) signals and functional near-infrared spectroscopy (fNIRS) of the patients (n=54) showed that self-supported exercise can have an immediate effect of improving the muscle activities by 40–80% according to SI quantification, and the muscle activities became much more similar to the muscle activities of the age-matched healthy subjects. Using this self-supported exercise, we investigated whether the recruitment of a patient’s contralesional nervous system could reactivate their ipsilesional neural circuits and stimulate functional recovery. We proposed biofeedback training with self-supported exercise where the muscle activities were visualized to encourage the appropriate neural pathways for activating the muscles of the paretic arm. We developed the biofeedback system and tested the recovery speed with the patients (n=27) for 2 months. The clinical tests showed that self-support-based biofeedback training improved SI approximately by 40%, Stroke Impairment Assessment Set (SIAS) by 35%, and Functional Independence Measure (FIM) by 20%.


Stroke is the leading cause of long-term disability worldwide. Of more than 750,000 stroke victims in the United States each year [1], approximately two-thirds survive and require immediate rehabilitation to recover lost brain functions [2]. These stroke rehabilitation programs, of which direct and indirect costs were estimated to be 73.7 billion dollars in 2010 [3], aim to help survivors gain physical independence and better quality of life.

Stroke damage typically interrupts blood flow within one brain hemisphere, resulting in unilateral motor deficits, sensory deficits, or both. The preservation of long-term neural and synaptic plasticity is essential for the functional reorganization and recovery of neural pathways disrupted by stroke [4]–[5][6]. Stroke survivors typically require long-term, intensive rehabilitation training due to the length of time required for these recovery processes [7], [8]. The typical time course for partial recovery of arm movement after mild to moderate unilateral stroke damage is 2 to 6 months, depending on the severity of tissue damage and the latency of treatment initiation [9], [10]; however, patients with severe damage require additional months to years of rehabilitation. Given the economic burden on patients’ families and the medical system, novel rehabilitation methods that promote rapid and complete functional recovery are needed, along with a better understanding of the functional mechanisms and neural circuits that can participate in potential therapeutic processes. The identification of rehabilitation methods that can more effectively recover brain functions in the damaged hemisphere by re-engaging dormant motor functions should be a major global objective, from both economic and societal perspectives. Such an objective would require the interface of biology, medical research, and clinical practice [4].

Recently, candidate brain areas that become activated during stroke recovery have been identified in patients and animal models [7]. Brain imaging studies during stroke recovery suggest that the extent of functional motor recovery is associated with an increase in neuronal activity in the sensorimotor cortex of the ipsilesional hemisphere [10]–[11][12]. Other work has suggested that repetitive sensorimotor tasks may promote cortical reorganization and functional recovery in the ipsilesional area by increasing bilateral cortical activity to enhance neuroplasticity [13]. Activation in the contralesional hemisphere is also observed in the early stages of post-stroke patients. This activation has been explained by the emergence of communication in corticospinal projections that are silent in the healthy state [11], and it may also contribute to movement-related neural activity on the ipsilesional limb [14], [15]. Functional brain imaging studies show that activity of the contralesional hemisphere is increased early after stroke and gradually declines as recovery progresses [16]. The functional relevance of contralesional recruitment remains unclear [17], [18]. Some reported studies have linked high abnormal activity to a high inhibitory signaling drive onto the ipsilesional cortex [19], which may be a major contributor to motor impairment [6], [20]. Recent studies have also investigated the benefits of activating the contralesional and/or ipsilesional hemispheres in functional motor recovery using brain-computer interface (BCI) and transcranial magnetic stimulation (TMS) therapies [21], [22].

Current stroke rehabilitation approaches have largely focused on paretic limb rehabilitation interventions such as muscle strengthening and endurance training [23], forced-use therapy [24], constraint-induced exercise [25], robot therapy with biofeedback [26], nonparetic limb interventions (e.g., mirror-therapy [27], [28]), or bilateral/bimanual training [29], [30]. However, to date, none have clearly investigated how the use of a patient’s unaffected neural circuits in the healthy cortical hemisphere, or in the local peripheral circuit, affect the impaired limb in terms of functional rehabilitation of the bilateral cortical sensorimotor network [31].

In this study, we investigated a motor recovery approach for post-stroke unilateral arm impairment that combined sensory feedback, motor control, and motor intention. While observing a patient cohort with unilateral stroke damage and arm movement impairment, we found that a specific self-guided motion, which we termed self-supported exercise, surprisingly reactivated a healthy muscles pattern in the paretic arm. The key of the self-supported exercise is use of the nonparetic arm as a support to help move the paretic arm. First, we will show the observation of appropriate muscle recruitment and reduction of abnormal muscle synergies for post-stroke patients during the self-supported exercise, which are a common problem in stroke recovery [32]. Then, we conduct the experiments of functional imaging and electromyography recordings and characterized the neurobiology and physiology of this self-supported exercise. Based on this mechanism, we designed a rehabilitation program involving biofeedback-aided self-supported exercises that employ a patients’ self-initiated motor intention. The results of the comparative experiments between the feedback training cohorts and the control cohorts show that this method results in efficient recovery from post-stroke motion paralysis. Finally, we discuss the significance of our findings for the design of biologically-based stroke rehabilitation.[…]

via Self-Support Biofeedback Training for Recovery From Motor Impairment After Stroke – IEEE Journals & Magazine

FIGURE 2. - The four types of exercises.

FIGURE 2.The four types of exercises.




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[TED-Ed] The brain-changing benefits of exercise – Wendy Suzuki

What’s the most transformative thing that you can do for your brain today? Exercise! says neuroscientist Wendy Suzuki. Get inspired to go to the gym as Suzuki discusses the science of how working out boosts your mood and memory — and protects your brain against neurodegenerative diseases like Alzheimer’s.

via The brain-changing benefits of exercise – Wendy Suzuki | TED-Ed

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[Infographic] Music & The Brain


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