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).

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