β-Oscillations Reflect Recovery of the Paretic Upper Limb in Subacute Stroke

What fucking stupidity.  Tell me EXACTLY how this is going to help survivors recover. EXACTLY!  

And this from Sept. 2015 was not enough to answer your questions on β-oscillations? Hopefully your answer is not subacute vs. chronic.

Reinforcement learning of self-regulated β-oscillations for motor restoration in chronic stroke Sept. 2015

β-Oscillations Reflect Recovery of the Paretic Upper Limb in Subacute Stroke

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Chih-Wei Tang, MD, Fu-Jung Hsiao, PhD, Po-Lei Lee, PhD, ...

First Published April 23, 2020 Research Article

https://doi.org/10.1177/1545968320913502

Article information

Abstract

Background.
Recovery of upper limb function post-stroke can be partly predicted by initial motor function, but the mechanisms underpinning these improvements have yet to be determined. Here, we sought to identify neural correlates of post-stroke recovery using longitudinal magnetoencephalography (MEG) assessments in subacute stroke survivors.  
Methods.
First-ever, subcortical ischemic stroke survivors with unilateral mild to moderate hand paresis were evaluated at 3, 5, and 12 weeks after stroke using a finger-lifting task in the MEG. Cortical activity patterns in the β-band (16-30 Hz) were compared with matched healthy controls.  
Results.
All stroke survivors (n=22; 17 males) had improvements in action research arm test (ARAT) and Fugl-Meyer upper extremity (FM-UE) scores between 3 and 12 weeks. At 3 weeks post-stroke the peak amplitudes of the movement-related ipsilesional β-band event-related desynchronization (β-ERD) and synchronization (β-ERS) in primary motor cortex (M1) were significantly lower than the healthy controls (p<0.001) and were correlated with both the FM-UE and ARAT scores (r=0.51-0.69, p<0.017). The decreased β-ERS peak amplitudes were observed both in paretic and non-paretic hand movement particularly at 3 weeks post-stroke, suggesting a generalized disinhibition status. The peak amplitudes of ipsilesional β-ERS at week 3 post-stroke correlated with the FM-UE score at 12 weeks (r=0.54, p=0.03) but no longer significant when controlling for the FM-UE score at 3 weeks post-stroke. 
Conclusions.
Although early β-band activity does not independently predict outcome at 3 months after stroke, it mirrors functional changes, giving a potential insight into the mechanisms underpinning recovery of motor function in subacute stroke.

Keywords stroke, motor recovery, magnetoencephalography, β-oscillations, event-related synchronization, event-related desynchronization

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Brain Activity on Observation of Another Person’s Action: A Magnetoencephalographic Study

So we still don't know if action observation works in stroke rehab and the protocols for it. You'll just have to completely guess on your own.

Brain Activity on Observation of Another Person’s Action: A Magnetoencephalographic Study 

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Jumpei Mizuno ,^*1 Masashi Kawamura ,^*1 and Minoru Hoshiyama ^*1Affiliations¹Nagoya University

*Mizuno, Kawamura, and Hoshiyama are with the Dept. of Occupational Therapy, Graduate School of Medicine, School of Health Sciences, Nagoya University, Nagoya, Japan. Hoshiyama is also with the Brain & Mind Research Center, Nagoya University, Nagoya, Japan.

Address author correspondence to Minoru Hoshiyama at hosiyama@met.nagoya-u.ac.jp.

Volume: 22 Issue: 4 Pages: 377-390

https://doi.org/10.1123/mc.2017-0053

• ABSTRACT
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Brain activity was recorded using a whole-head magnetoencephalography system followed by coherence analysis to assess neural connectivity in 10 healthy right-handed adults to clarify differences in neural connectivity in brain regions during action observation from several perspectives. The subjects were instructed to observe and memorize or imitate the hand action from a first-person or second-person visual perspective. The brain activity in coherence was modified among frontal and central, sensorimotor, and mirror neuron system-related regions based on the visual perspectives of finger movements. The regional activity in coherence changed similarly under the imitation and observation tasks compared with the condition of observing static hand figures. The information from different visual perspectives of body movements was processed in the frontal–central regions related to sensorimotor processes and partially in mirror neuron system.

Brain Scans Could Help Detect Concussions

But better than eye tracking software? Available on the sideline of football games? Faster than 60 seconds?

SyncThink gets FDA approval for eye-tracking device to test for concussions in 60 seconds April 2016

New Technology Advances Eye Tracking As Biomarker for Brain Function and Recovery from Brain Injury Dec. 2014 

Oculogica’s eye-tracking machine could tell you if you’ve had a concussion  July 2014 

http://www.rdmag.com/article/2016/12/brain-scans-could-help-detect-concussions?

There may be a new method in helping to detect concussions that conventional scans might miss.
Researchers from Simon Fraser University in Burnaby, Canada have found that high-resolution brain scans along with computational analysis could help play a role in detecting concussions.
The recent study shows how magnetoencephalography (MEG), which maps interactions between regions of the brain, could detect greater levels of neural changes than typical clinical imaging tools such as MRI or CAT scans.
Concussions are typically diagnosed using the existing diagnostic tools along with other self-reporting measures, such as headaches and fatigues.
"Changes in communication between brain areas, as detected by MEG, allowed us to detect concussion from individual scans, in situations where MRI or CT failed," Vasily Vakorin, Ph.D., a scientist with the Behavioral and Cognitive Neuroscience Institute based at SFU and SFU's ImageTech Lab, a new facility at Surrey Memorial Hospital and co-lead author of the study, said in a statement.
This breakthrough can be especially vital in the world of sports, where concussion detection and treatment has come under fire in the last decade.
Using the new techniques could lead to better diagnosis of mild traumatic brain injuries, which are common among football players and cannot be detected on conventional scans.
 The researchers conducted the study by taking MEG scans of 41 men between 20 and 44 years of age where half had been diagnosed with concussions within the past three months.
The study shows that the concussions were associated with alterations in the interactions between different brain areas, meaning there were observable changes in how areas of the brain communicate with one another.
The new diagnostic methods were proven to provide important measurements of changes in the brain during concussion recovery. MEG was described by Vakorin and fellow study co-author Sam Doesburg as an “unprecedented combination of ‘excellent temporal and spatial relationship’ for reading brain activity to better diagnose concussion where other methods fail.”
The researchers now hope to refine their understanding of specific neural changes associated with concussions to further improve detection, treatment and recovery processes.
The study, which appeared in PLOS Computational Biology, can be viewed here.
About 300,000 sports-related concussions occur each year nationwide among all ages. In high school athletics, they occur at a rate of almost three per 10,000 games or practices.
Concussion detection has become especially important in recent years.
According to University of Arkansas concussion researcher R.J. Elbin, the lead author of a different study, evidence suggests up to 50 percent of concussions in teen sports aren't reported. Athletes are sometimes not aware they've experienced a concussion, or they suspect a head injury but continue playing because "they don't want to let their teammates down."
Many youth football and other sports leagues have become particularly cognizant of concussions by requiring coaches to take preseason classes and mandating an athletic trainer present at every game and practice.
In New Jersey high school athletics there is a five-to-six-day protocol that must be completed by concussed athletes where they slowly progress through different tests before they can return to the playing field.
Another study estimated that in 2013 more than 2 million concussions from sports or play activities occur in the U.S. with many not receiving treatment.
The concussion problem also occurs on the professional level.
The NFL has received criticism and has been subject to lawsuits in recent years filed by ex-players for hiding the risks of football on the brain.
Dr. Bennet Omalu, who was recently portrayed by Will Smith in the 2015 film “Concussion,” has discovered that several deceased football players were diagnosed with degenerative brain diseases chronic traumatic encephalopathy (CTE).
For more information on Omalu and some other concussion-related data, visit  R&D Magazine’s previous article.

 

 

Magnetoencephalography in Stroke Recovery and Rehabilitation

This would be so cool if it could truly detect neuroplastic changes. We might finally get to objective diagnosis and objective recovery statistics. But only if we overthrow our fucking failures of stroke associations.
http://journal.frontiersin.org/article/10.3389/fneur.2016.00035/full?

Andrea Paggiaro¹, Niels Birbaumer^1,2, Marianna Cavinato¹*, Cristina Turco¹, Emanuela Formaggio¹, Alessandra Del Felice³, Stefano Masiero³ and Francesco Piccione¹

• ¹Laboratory of Neurophysiology and Magnetoencephalography, Department of Neurophysiology, Institute of Care and Research, S.Camillo Hospital Foundation, Venice, Italy
• ²Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany
• ³Section of Rehabilitation, Department of Neuroscience, University of Padova, Padova, Italy

Magnetoencephalography (MEG) is a non-invasive neurophysiological technique used to study the cerebral cortex. Currently, MEG is mainly used clinically to localize epileptic foci and eloquent brain areas in order to avoid damage during neurosurgery. MEG might, however, also be of help in monitoring stroke recovery and rehabilitation. This review focuses on experimental use of MEG in neurorehabilitation. MEG has been employed to detect early modifications in neuroplasticity and connectivity, but there is insufficient evidence as to whether these methods are sensitive enough to be used as a clinical diagnostic test. MEG has also been exploited to derive the relationship between brain activity and movement kinematics for a motor-based brain–computer interface. In the current body of experimental research, MEG appears to be a powerful tool in neurorehabilitation, but it is necessary to produce new data to confirm its clinical utility.

Introduction

The introduction in the early 1980s of magnetoencephalography (MEG) recording devices boosted its clinical application: multichannel MEG provided a superior spatial resolution compared to electroencephalography (EEG) and the possibility of detecting dipoles tangential to the cortical surface were its main advantages. MEG was initially deployed in the presurgical evaluation of epileptic foci, given both the reliability in localizing superficial cortical epileptic foci (1) and the precise indications for placement of intracranial electrodes (2). It became subsequently obvious that processing of natural language is more accessible with MEG than with EEG or functional magnetic resonance imaging (fMRI) because the magnetic field changes can be more precisely free from noise and artifacts (3). The high variability in the localization of frontal and parietal language processing sources creates considerable difficulties for the neurosurgeon to discriminate between eloquent areas involved in speech and language and “silent” brain tissue, so that the removal of tumors and other malformations of the brain and its vasculatum becomes a challenging operation. The combination of MEG and structural MRI provides the optimal solution to this problem because of the small fiducials positioning and localization errors (i.e., approximately 2 mm) assuring a reliable coregistration of functional and structural data (4).

With the installation of the new generation MEG having more than 250 sensors able to provide even further improved spatial resolution and accessibility of source localization algorithms (see below) to deeper brain structures and cerebellum, MEG technology has been successfully introduced to resolve the more complex problems of ­recovery and brain reorganization after stroke and other types of brain injury. Particularly, recovery prediction and assessment has become the focus of interest in clinical use of MEG in rehabilitation.

Magnetoencephalography has maintained part of its advantages even after the introduction of high-density EEG, consisting of a spatial sampling up to more than 250 electrodes. Although signals detected by the two recording techniques appear to be generated by different limbs of the same circuit, recent studies (5–8) have suggested that they have at least partially distinct generators. Indeed, MEG is particularly sensitive to activity originating in the cortex directly underlying sensors and is insensitive to radial dipoles, whereas EEG seems to reflect volume conducted activity and is sensitive to radial and tangential dipoles (9). Thus, the two techniques should be considered mutually complementary rather than mutually exclusive.

Finally, the rapid development of non-invasive Brain–Machine Interface Research [BMI or also termed brain–computer interfaces (BCI)] during the last 10–15 years (10–12) has launched a completely new and challenging field of application to MEG technologies: on-line recordings from selected MEG–sensor combination has been used to drive exoskeletons and computer switches for therapeutic purposes (see below). With BMI research, MEG has been transformed from a passive recording and documentation/diagnostic device into an active treatment and rehabilitation instrument (13).

The success of BMIs has reactivated the tradition of neurofeedback research, popular in the EEG community from the 60s–80s of the last century (14). MEG allows simultaneous observation and self-control of extremely specific localized dynamic sources of neuromagnetic activity together with widespread, more general, brain activity changes. In addition, the availability of fast computing algorithms for providing feedback of dynamic connectivity changes has introduced a new area of interest for directly manipulating changes and the related functional connectivities of oscillatory brain activity. When such algorithms allow modeling of oscillatory sources’ directionality, the effective connectivity can be estimated by describing how anatomically connected areas interact with each other (15).

More at link.

New Mini-sensor Measures Magnetic Field of the Brain

Our researchers now will be able to listen in on our brain waves and maybe compare them to normal ones.
http://www.alphagalileo.org/ViewItem.aspx?ItemId=120619&CultureCode=en
Successful test at PTB of optical magnetometer with potential applications in brain imaging for neurological diagnostics and in basic research.
In future a new magnetic sensor the size of a sugar cube might simplify the measurement of brain activity. In the magnetically shielded room of Physikalisch-Technische Bundesanstalt (PTB) the sensor has passed an important technical test: Spontaneous as well as stimulated magnetic fields of the brain were detected. This demonstrates the potential of the sensor for medical applications, such as, the investigation of brain currents during cognitive processes with the aim of improving neurological diagnostics. The main advantage of the new sensor developed by NIST in the USA over the conventionally used cryoelectronics is its room temperature operation capability making complicated cooling obsolete. The results have recently been published in the journal "Biomedical Optics Express".
The magnetic field sensor is called Chip-scale Atomic Magnetometer (CSAM) as it uses miniaturized optics for measuring absorption changes in a Rubidium gas cell caused by magnetic fields. The CSAM sensor was developed by NIST (National Institute of Standards and Technology), which is the national metrology institute of the USA. In this cooperation between PTB and NIST each partner contributes his own particular capabilities. PTB’s staff has long standing experience in biomagnetic measurements in a unique magnetically shielded room. NIST contributes the sensors, which are the result of a decade of dedicated research and development.
Up to now the measurement of very weak magnetic fields was the domain of cryoelectronic sensors, the so called superconducting quantum interference device (SQUID). They can be considered as the „gold standard“ for this application, but they have the disadvantage to operate only at very low temperatures close to absolute zero. This makes them expensive and less versatile compared to CSAMs. Even though at present CSAMs are still less sensitive compared to SQUIDs, measurements with a quality comparable to SQUIDs, but at lower costs, might eventually become reality. Due to the cooling requirements, SQUIDs have to be kept apart from the human body by a few centimeters. In contrast to that, CSAMs can be attached closely to the human body. This increases the signal amplitude as the magnetic field from currents inside the human bodydecays rapidly with increasing distance.
An important application is the measurement of the magnetic field distribution around the head, which is called magnetoencephalography (MEG). It enables the characterization of neuronal currents. Such investigations have gained importance during the last few years for  neurologists and neuroscientists. Objective indicators of psychiatric disorders as well as age dependent brain diseases, are urgently needed for the support of today’s clinical diagnostics.
Already in  2010 scientists from NIST and PTB had successfully tested the performance of an earlier version of the present CSAM  by measurements of the magnetic field of the human heart. For the present study the sensor was positioned about 4 mm away from the head of healthy subjects. At the back of the head, the magnetic fields of alpha waves were detected, a basic brain rhythm which occurs spontaneously during relaxation. In another measurement the brain fields due to the processing of tactile stimuli were identified. These fields are extremely weak and the CSAM result was validated by a simultaneous MEG measurement relying on the established SQUID technology.