Posts Tagged functional magnetic resonance imaging

[ARTICLE] Peak Activation Shifts in the Sensorimotor Cortex of Chronic Stroke Patients Following Robot-assisted Rehabilitation Therapy – Full Text

ABSTRACT

Background:

Ischemic stroke is the most common cause of complex chronic disability and the third leading cause of death worldwide. In recovering stroke patients, peak activation within the ipsilesional primary motor cortex (M1) during the performance of a simple motor task has been shown to exhibit an anterior shift in many studies and a posterior shift in other studies.

Objective:

We investigated this discrepancy in chronic stroke patients who completed a robot-assisted rehabilitation therapy program.

Methods:

Eight chronic stroke patients with an intact M1 and 13 Healthy Control (HC) volunteers underwent 300 functional magnetic resonance imaging (fMRI) scans while performing a grip task at different force levels with a robotic device. The patients were trained with the same robotic device over a 10-week intervention period and their progress was evaluated serially with the Fugl-Meyer and Modified Ashworth scales. Repeated measure analyses were used to assess group differences in locations of peak activity in the sensorimotor cortex (SM) and the relationship of such changes with scores on the Fugl-Meyer Upper Extremity (FM UE) scale.

Results:

Patients moving their stroke-affected hand had proportionally more peak activations in the primary motor area and fewer peak activations in the somatosensory cortex than the healthy controls (P=0.009). They also showed an anterior shift of peak activity on average of 5.3-mm (P<0.001). The shift correlated negatively with FM UE scores (P=0.002).

Conclusion:

A stroke rehabilitation grip task with a robotic device was confirmed to be feasible during fMRI scanning and thus amenable to be used to assess plastic changes in neurological motor activity. Location of peak activity in the SM is a promising clinical neuroimaging index for the evaluation and monitoring of chronic stroke patients.

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Fig. (2). Spatial distributions of peak brain activation during a grip task with both hands. Dots (red, left hemisphere stroke patients; green, healthy control subjects) are overlaid on an International Consortium for Brain Mapping brain surface template (ICBM-152).

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[WEB PAGE] What is Functional Magnetic Resonance Imaging (fMRI)?

By Hannah Devlin

Functional magnetic resonance imaging, or fMRI, is a technique for measuring brain activity. It works by detecting the changes in blood oxygenation and flow that occur in response to neural activity – when a brain area is more active it consumes more oxygen and to meet this increased demand blood flow increases to the active area. fMRI can be used to produce activation maps showing which parts of the brain are involved in a particular mental process.

The development of FMRI in the 1990s, generally credited to Seiji Ogawa and Ken Kwong, is the latest in long line of innovations, including positron emission tomography (PET) and near infrared spectroscopy (NIRS), which use blood flow and oxygen metabolism to infer brain activity. As a brain imaging technique FMRI has several significant advantages:

1. It is non-invasive and doesn’t involve radiation, making it safe for the subject.
2. It has excellent spatial and good temporal resolution.
3. It is easy for the experimenter to use.

The attractions of FMRI have made it a popular tool for imaging normal brain function – especially for psychologists. Over the last decade it has provided new insight to the investigation of how memories are formed, language, pain, learning and emotion to name but a few areas of research. FMRI is also being applied in clinical and commercial settings.

How Does an fMRI Work?

The cylindrical tube of an MRI scanner houses a very powerful electro-magnet. A typical research scanner has a field strength of 3 teslas (T), about 50,000 times greater than the Earth’s field. The magnetic field inside the scanner affects the magnetic nuclei of atoms. Normally atomic nuclei are randomly oriented but under the influence of a magnetic field the nuclei become aligned with the direction of the field. The stronger the field the greater the degree of alignment. When pointing in the same direction, the tiny magnetic signals from individual nuclei add up coherently resulting in a signal that is large enough to measure. In fMRI it is the magnetic signal from hydrogen nuclei in water (H2O) that is detected.

The key to MRI is that the signal from hydrogen nuclei varies in strength depending on the surroundings. This provides a means of discriminating between gray matter, white matter and cerebral spinal fluid in structural images of the brain.

Oxygen is delivered to neurons by hemoglobin in capillary red blood cells. When neuronal activity increases there is an increased demand for oxygen and the local response is an increase in blood flow to regions of increased neural activity.

Hemoglobin is diamagnetic when oxygenated but paramagnetic when deoxygenated. This difference in magnetic properties leads to small differences in the MR signal of blood depending on the degree of oxygenation. Since blood oxygenation varies according to the levels of neural activity these differences can be used to detect brain activity. This form of MRI is known as blood oxygenation level dependent (BOLD) imaging.

fMRI BOLD Effect

One point to note is the direction of oxygenation change with increased activity. You might expect blood oxygenation to decrease with activation, but the reality is a little more complex. There is a momentary decrease in blood oxygenation immediately after neural activity increases, known as the “initial dip” in the hemodynamic response. This is followed by a period where the blood flow increases, not just to a level where oxygen demand is met, but overcompensating for the increased demand. This means the blood oxygenation actually increases following neural activation. The blood flow peaks after around 6 seconds and then falls back to baseline, often accompanied by a “post-stimulus undershoot”.

What Does an fMRI Scan Look Like?

fMRI Scan

The image shown is the result of the simplest kind of fMRI experiment. While lying in the MRI scanner the subject watched a screen which alternated between showing a visual stimulus and being dark every 30 second. Meanwhile the MRI scanner tracked the signal throughout the brain. In brain areas responding to the visual stimulus you would expect the signal to go up and down as the stimulus is turned on and off, albeit blurred slightly by the delay in the blood flow response.

Researchers look at activity on a scan in voxels — or volume pixels, the smallest distinguishable box-shaped part of a three-dimensional image. The activity in a voxel is defined as how closely the time-course of the signal from that voxel matches the expected time-course. Voxels whose signal corresponds tightly are given a high activation score, voxels showing no correlation have a low score and voxels showing the opposite (deactivation) are given a negative score. These can then be translated into activation maps.

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This article is courtesy of FMRIB Centre, Department of Clinical Neurology, University of Oxford. It was written by Hannah Devlin, with additional contributions by Irene Tracey, Heidi Johansen-Berg and Stuart Clare. Copyright © 2005-2008 FMRIB Centre

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[ARTICLE] Simultaneous Transcranial Magnetic Stimulation and Functional Magnetic Resonance Imaging: Aspects of Technical Implementation – Full Text

The simultaneous transcranial magnetic stimulation (TMS) and functional magnetic resonance imaging (fMRI) offers a unique opportunity to non-invasively stimulate brain circuits while simultaneously monitoring changes in brain activity. However, to take advantage of this multimodal technique, some technical issues need to be considered/addressed. In this work, we evaluated technical issues associated with the setup and utilization of this multimodal tool, such as the use of a large single-channel radio frequency (rf) coil, and the artifacts induced by TMS when interleaved with the echo-planar imaging (EPI) sequence. We demonstrated that good image quality can be achieved with this rf coil and that the adoption of axial imaging orientation in conjunction with a safe interval of 100 ms, between the TMS pulse and imaging acquisition, is a suitable combination to eliminate potential image artifacts when using the combined TMS-fMRI technique in 3-T MRI scanners.

Introduction

The concurrent transcranial magnetic stimulation (TMS) and functional magnetic resonance imaging (fMRI) provides a non-invasive method for real-time evaluation of neuronal activity induced by TMS. It has the potential to identify brain areas of functional relevance to acute TMS, supporting causal brain connectivity and brain – behavior inferences across the entire brain (see Table 1). Therefore, it poses a step forward toward understanding the underlying mechanism of magnetic stimulation.TABLE 1

Table 1. A summary listing some of the previous TMS-fMRI work.

However, before taking advantage of this multimodal technique, some technical difficulties (Bohning et al., 1998Bestmann et al., 2003aWeiskopf et al., 2009Bungert et al., 2012Navarro de Lara et al., 2017) need to be addressed. A full assessment on passive (presence of a TMS coil) and active (during magnetic stimulation) image artifacts induced by TMS have been previously reported (Bestmann et al., 2003a), in which one of the first MRI compatible TMS coils, developed by Magstim, was used, and images were acquired on a 2-T scanner. Although new MRI-compatible TMS coils have been developed, 3-T scanners have become the primary imaging research tool, and imaging software and hardware have advanced significantly in recent years; only brief assessments have been reported lately on either passive (Bungert et al., 2012Navarro de Lara et al., 2017) or active (Navarro de Lara et al., 2017) TMS-induced image artifacts. Therefore, a comprehensive evaluation on the use of this multimodal tool in its current state is needed.

In this work, we aim to provide an update on the technical aspects of this multimodal tool based on the latest developments of the MRI and TMS techniques. Due to the lack of inner space from most multichannel radio frequency (rf) coils, whole brain imaging acquisition may only be achieved using single-channel birdcage rf coils when combined with TMS; therefore, imaging quality associated with the use of a birdcage rf coil was accessed. Potential image artifacts (passive and active) induced by the latest version of an MRI-compatible TMS coil, on images acquired with echo-planar imaging (EPI) sequences, at a 3-T Prisma Siemens scanner, were also evaluated. Our work demonstrated that this multimodal technique can be easily used when these technical issues are addressed.

Methods

Phantoms and Human Participant

Two phantoms were used in the study to assess quality of images acquired from two rf coils, as well as passive and active image artifacts in the TMS-MRI setup:

1. Bottle phantom: a cylindrical plastic bottle phantom (diameter = 4.3 in, length = 7.9 in) provided by Siemens for standard costumer quality assurance (3.75 g NiSO4 × 6H2O, 5 g NaCl per 1,000 g H2O dist., Siemens Medical Solutions United States, Inc., Malvern, PA);

2. ACR phantom: an American College of Radiology (ACR) MRI phantom (diameter = 8 in, length = 6.82 in., J. M. Specialty Parts Inc. San Diego, CA).

A healthy adult (male, 25 years of age) participated in this study. The participant gave written informed consent approved by the institutional review board of the National Institute on Drug Abuse.

Data Acquisition

MRI Scanning

Images were acquired at a 3-T Prisma Siemens system. A transmit-receive (Tx/Rx) single-channel birdcage head rf coil and a 20-channel head rf coil were used for image quality evaluation (rf coil comparison). Images acquired with the 20-channel coil had either parallel imaging (IPAT) ON (acceleration factor = 2) or OFF, whereas parallel image was not available for the Tx/Rx single-channel coil. Images acquired with the 20-channel coil had prescan normalize ON, but those acquired with the Tx/Rx-coil had it OFF. FMRI data were acquired using a single-shot gradient-echo (GRE) echo-planar imaging (EPI) sequence.

rf Coil Comparison

EPI scans were performed on the bottle phantom and the ACR phantom with the following imaging parameters:

1. Bottle phantom: TE/TR(20-ch)/TR(Tx/Rx) = 27/2,000/2,130 ms, in-plane resolution 3.4 × 3.4 × 4 mm3, 39 slices (Tx/Rx and 20-channel – IPAT ON)/34 slices (20-channel – IPAT OFF), 100 volumes, axial orientation;

2. ACR phantom: TE(20ch)/TE(Tx/Rx)/TR = 27/20/2,000 ms, in-plane resolution 3.4 × 3.4 × 4 mm3, 39 slices (20-channel – IPAT ON), 20 volumes, axial orientation.

TMS-Induced Image Artifacts

Further data acquisition to evaluate the passive and active image artifacts induced by TMS were conducted with the Tx/Rx head coil, since it is the only commercially available volume coil that can fit the TMS coil and its holder inside, along with the scanning object: either the bottle phantom or the participant’s head. The following imaging parameters were used: echo time (TE)/repetition time (TR) of 27/2,500 ms, tr-delay of 500 ms, in-plane resolution of 3.4 × 3.4 × 4.4 mm3, 36 slices per volume, and 20 volumes were acquired to evaluate passive artifacts (with the phantom and the participant) and 50 volumes were acquired to evaluate active image artifacts with the bottle phantom. The anatomical image of the participant head was acquired with a high-resolution (1 × 1 × 1 mm3) T1-weighted magnetization-prepared rapid gradient echo (MPRAGE) sequence covering the whole brain.

Different imaging orientations were used, with and without the presence of the TMS coil, to evaluate the passive image artifacts. Initially, images were acquired with the bottle phantom in the three orthogonal orientations: axial, coronal, and sagittal for both TMS conditions (with and without TMS coil). Following images of the human brain were acquired in axial and oblique (axial 30° rotation on the x-axis direction, as well as tilted on the y- and z-axis direction to follow the head orientation – Supplementary Figure S3) orientations with the TMS coil, but only the oblique images were acquired without the TMS coil. Finally, images in the oblique orientation were acquired on the bottle phantom to evaluate the active artifacts.

TMS

The MRI-compatible TMS coil (Air Cooled Coil MRI-B91, MagVenture Inc., Alpharetta, GA) was appended to the MRI-compatible TMS holder (MagVenture Inc., Alpharetta, GA), which was attached to the MRI bed. This holder allows to position the TMS coil inside the Tx/Rx coil, which has a cylindrical shape, through the back of the rf coil. The TMS coil was connected to the stimulator (MagPro X100, MagVenture Inc., Alpharetta, GA) seated outside the MRI scanner room, through a long cable passing through the waive-guide on the filter wall of the scanner room.

Imaging With the Phantom (Passive and Active TMS-Induced Image Artifacts)

In the MRI suite, the MRI-compatible TMS coil was positioned over the left side of bottle phantom oblique to the xy-plane as displayed on Figure 1B, to mimic the coil position intended to be used during the brain imaging.

Figure 1. (A) A picture of the Tx/Rx and 20-channel coils at the top-left corner. MRI signal intensity (mean), temporal standard deviation (tSD), and temporal signal-to-noise ratio (tSNR) images are shown for echo-planar imaging (EPI) images of the bottle phantom acquired with both rf coils (Tx/Rx and 20-channel); tSNR differences between 20-channel acquisitions (with and without parallel imaging) and Tx/Rx acquisitions (Diff-tSNR) are also shown; NP, no parallel imaging. (B) Schematic design of the transcranial magnetic stimulation (TMS) coil positioned over the left side of the phantom, oblique to the xy-plane. Axial view of the mean EPI images is displayed for the axial, sagittal, and coronal data acquisition of the bottle phantom with and without the TMS coil. The difference images (without – with TMS coil) are also displayed. (C) Brain EPI images for the axial and oblique data acquisition acquired with the TMS coil, positioned over the left dorsolateral prefrontal cortex (DLPFC, MNI = -50,30,36, highlighted in red), in addition to the oblique acquisition without the TMS coil are shown, on coronal and axial views.

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[Abstract + References] Complex network changes during a virtual reality rehabilitation protocol following stroke: a case study

Abstract

Stroke is one of the main causes of disabilities caused by injuries to the human central nervous system, yielding a wide range of mild to severe impairments that can compromise sensorimotor and cognitive functions. Although rehabilitation protocols may improve function of stroke survivors, patients often reach plateaus while undergoing therapy. Recently, virtual reality (VR) technologies have been paired with traditional rehabilitation aiming to improve function recovery after stroke. Aiming to better understand structural brain changes due to VR rehabilitation protocols, we modeled the brain as a graph and extracted three measures representing the network’s topology: degree, clustering coefficient and betweenness centrality (BC). In this single case study, our results indicate that all metrics increased on the ipsilesional hemisphere, while remaining about the same at the contrale-sional site. Particularly, the number of functional connections increased in the lesion area overtime. In addition, the BC displayed the highest variations, and in brain regions related to the patient’s cognitive and motor impairments; hence, we argue that this measure could be regarded as an indicative for brain plasticity mechanisms.
1. J-H. Shin , H. Ryu & S. H. Jang . A task-specific interactive game-based virtual reality rehabilitation system for patients with stroke: a usability test and two clinical experiments. Journal of NeuroEngineering and Rehabilitation. 2014: 11-32

2. M. S. Cameirão , S. B. i Badia , E. D. Oller & P. F. M. J. Verschure . Neurorehabilitation using the virtual reality based Rehabilitation Gaming System: methodology, design, psychometrics, usability and validation. Journal of NeuroEngineering and Rehabilitation. 2010: 7-48

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5. Page S. J. , Fulk G. D. , Boyne P. Clinically Important Differences for the Upper-Extremity Fugl-Meyer Scale in People With Minimal to Moderate Impairment Due to Chronic Stroke. Physical Therapy 92(6): 791798, 2012. doi: 10.2522/ptj.20110009

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7. NK. Logothetis , J. Pauls , M. Augath , T. Trinath , A. Oeltermann . Neurophysiological investigation of the basis of the fMRI signal. Nature. 2001. 412(6843):150-7

8. M.D. Fox , M. E. Raichle . Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nat Rev Neurosci. 2007. 8(9):700-11.

9. de Campos, B. M. , Coan, A. C. , Lin Yasuda, C. , Casseb, R. F. and Cendes, F. (2016), Large-scale brain networks are distinctly affected in right and left mesial temporal lobe epilepsy. Hum. Brain Mapp. doi: 10.1002/hbm.23231

10. J. D. Power , A. L. Cohen , S. M. Nelson , G. S. Wig , K. A. Barnes , J. A. Church , A. C. Vogel , T. O. Laumann , F. M. Miezin , B. L. Schlagger , S. E. Petersen . Functional network organization of the human brain. Neuron. 2011: 72(4): 665 – 678.

11. Rubinov M. and Sporns O. Complex network measures of brain connectivity: Uses and interpretations. NeuroImage 2010, 52(3): 1059-1069. doi: 10.1016/j.neuroimage.2009.10.003

12. M. E. J. Newman . A measure of betweenness centrality based on random walks. Soc. Netw. 2005. 27: 39 – 57.

 

via Complex network changes during a virtual reality rehabilitation protocol following stroke: a case study – IEEE Conference Publication

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[Editorial] Functional brain mapping of epilepsy networks: methods and applications – Neuroscience

This multidisciplinary research topic is a collection of contemporary advances in neuroimaging applied to mapping functional brain networks in epilepsy. With technology such as simultaneous electroencephalography and functional magnetic resonance imaging (EEG-fMRI) now more readily available, it is possible to non-invasively map epileptiform activity throughout the entire brain at millimetre resolution. This research topic includes original research studies, technical notes and reviews of the field. Due to the multidisciplinary nature of the domain, the topic spans two journals: Frontiers in Neurology (Section: Epilepsy) and Frontiers in Neuroscience (Section: Brain Imaging Methods).
In this editorial we consider the outcomes of the multidisciplinary work presented in the topic. With the benefit of time elapsed since the original papers were published, we can see that the works are making a substantial impact in the field. At the time of writing, this topic had well over 27,000 full-paper downloads (including over 18,000 for the 15 papers in the Epilepsy section, and over 9,000 for the 8 papers in the Brain Imaging Methods section). Several papers in the topic have climbed the tier in Frontiers and received an associated invited commentary, demonstrating there is substantial interest in this research area.
Reviews
The topic’s review papers set the scene for the original research papers and synthesise contemporary thinking in epilepsy research and neuroimaging methods. We see that Epilepsy, whether of a “generalised” or “focal” origin, is increasingly recognised as a disorder of large-scale brain networks. At one level it is self-evident that otherwise healthy functional networks are recruited during epileptic activity, as this is what generates patient perceptions of their epileptic aura. For example, the epileptic aura of mesial temporal lobe epilepsy can include an intense sensation of familiarity (déjà vu) associated with involvement of the hippocampus, and unpleasant olfactory auras which may reflect involvement of adjacent olfactory cortex. As seizures spread more widely throughout the brain, presumably along pre-existing neural pathways, patients lose control of certain functions; for example, their motor system in the case of generalised convulsions, or aspects of awareness in seizures that remain localised to non-motor brain regions. Yet these functions return when the seizure abates, implying involved brain regions are also responsible for normal brain function. What has been less clear, and difficult to investigate until the advent of functional neuroimaging, is precisely which brain networks are involved (especially in ‘generalised’ epilepsy syndromes), and the extent to which functional networks are perturbed during seizures, inter-ictal activity, and at other times.
Functional imaging evidence of brain abnormalities in temporal lobe epilepsy is explored in (Caciagli et al., 2014), including evidence of dysfunction in limbic and other specific brain networks, as well as global changes in network topography derived from resting-state fMRI. Archer et al systematically review the functional neuroimaging of a particularly severe epilepsy phenotype, Lennox-Gastaut Syndrome (LGS), illustrating well how different forms of brain pathology can manifest in a similar clinical phenotype, simply by the nature of the healthy networks that the underlying pathology perturbs (Archer et al., 2014). Similarly, the mechanisms of absence seizure generation are reviewed by (Carney and Jackson, 2014), revealing that it too has a signature pattern of large-scale functional brain network perturbation. The ability to make such observations has considerable clinical significance, as highlighted in the review by (Pittau et al., 2014).
The tantalising proposition that there may be a common treatment target for all focal epilepsy phenotypes is also explored in a review of the piriform cortex by (Vaughan and Jackson, 2014). The piriform cortex was first implicated as a common brain region associated with spread of interictal discharges in focal epilepsy in an experiment that analysed the spatially normalised functional imaging data of a heterogeneous group of focal epilepsy patients (Laufs et al., 2011). This finding, since replicated (Flanagan et al., 2014), led Vaughan & Jackson to explore in detail what is known of the piriform cortex. Their findings reveal the piriform has several features that likely predispose it to involvement in focal epilepsy, and features that also explain many of the peculiar symptoms experienced by patients, from olfactory auras to the characteristic nose-wiping that many patients perform postictally. This work points to the need for future studies to determine whether the piriform might be an effective target for deep brain stimulation or other targeted therapy to prevent the spread of epileptiform activity.
Original research
Temporal lobe epilepsy is investigated in several papers in this topic. One of these studies also introduces a new exploratory method, Shared and specific independent component analysis (SSICA), that builds upon independent component analysis to perform between-group network comparison (Maneshi et al., 2014). In application to mesial temporal lobe epilepsy (MTLE) and healthy controls, three distinct reliable networks were revealed: two that exhibited increased activity in patients (a network including hippocampus and amygdala bilaterally, and a network including postcentral gyri and temporal poles), and a network identified as specific to healthy controls (i.e. effectively decreased in patients, consisting of bilateral precuneus, anterior cingulate, thalamus, and parahippocampal gyrus). These finding give mechanistic clues to the cognitive impairments often reported in patients with MTLE. Further clues are revealed in a study of the dynamics of fMRI and its functional connectivity (Laufs et al., 2014). Compared to healthy controls, temporal variance of fMRI was seen to be most increased in the hippocampi of TLE patients, and variance of functional connectivity to this region was increased mainly in the precuneus, the supplementary and sensorimotor, and the frontal cortices. More severe disruption of connectivity in these networks during seizures may explain patients’ cognitive dysfunction (Laufs et al., 2014). Yang and colleagues also show that it may be possible to use fMRI functional connectivity to lateralise TLE (Yang et al., 2015), which could be a useful clinical tool.
Mechanistic explanations of symptomatology beyond the seizure onset zone can also be revealed with conventional nuclear medicine techniques such as 18F-FDG-PET. This is demonstrated in a study of Occipital Lobe Epilepsy by Wong and colleagues, who observed that patients with automatisms have metabolic changes extending from the epileptogenic occipital lobe into the ipsilateral temporal lobe, whereas in patients without automatisms the 18F-FDG-PET was abnormal only in the occipital lobe (Wong et al., 2014).
The clinical significance of the ability to non-invasively study functional brain networks extends to understanding the impact of surgery on brain networks. This Frontiers research topic includes an investigation by Doucet and colleagues revealing that temporal lobe epilepsy and surgery selectively alter the dorsal, rather than the ventral, default-mode network (Doucet et al., 2014).
Another approach to better understand the mechanisms of seizure onset and broader symptomatology is computational modelling. It can track aspects of neurophysiology than cannot be readily measured: for example effective connectivity and mean membrane potential dynamics are shown by (Freestone et al., 2014) to be estimable using model inversion. In a proof-of-principle experiment with simulated data, they demonstrate that by tailoring the model to subject-specific data, it may be possible for the framework to identify a seizure onset site and the mechanism for seizure initiation and termination. Also in this topic, Petkov and colleagues utilise a computational model of the transition into seizure dynamics to explore how conditions favourable for seizures relate to changes in functional networks. They find that networks with higher mean node degree are more prone to generating seizure dynamics in the model, thus providing a mathematical mechanistic explanation for increasing node degree causing increased ictogenicity (Petkov et al., 2014).
Seizure prediction is an area of considerable research, and in this topic Cook and colleagues reveal intriguing characteristics in the long-term temporal pattern of seizure onset. They confirmed that human inter-seizure intervals follow a power law, and they found evidence of long-range dependence. Specifically, the dynamics that led to the generation of a seizure in most patients appeared to be affected by events that took place much earlier (as little as 30 minutes prior and up to 40 days prior in some patients) (Cook et al., 2014). The authors rightly note that this information could be valuable for individually-tuned seizure prediction algorithms.
Several methodological papers in this Frontiers Topic prove there remains considerable potential to improve neuroimaging methods as applied to the study of epilepsy. For example, (Mullinger et al., 2014) reveal the critical importance of the accuracy of physical models if one is to optimise lead positioning in functional MRI with simultaneous EEG. Confirming with computer modelling and phantom measurements that lead positioning can have a substantial effect on the amplitude of the MRI gradient artefact present on the EEG, they optimised the positions in a novel cap design. However, whilst this substantially reduced gradient artefact amplitude on the phantom, it made things worse when used on human subjects. Thus, improvement is required in model accuracy if one is to make accurate predictions for the human context.
Reduction of artefact, particularly cardioballistic and non-periodic motion artefact, remains a challenge for off-the-shelf MRI-compatible EEG systems. However, for over a decade, the Jackson group in Melbourne has dealt well with this issue using insulated carbon-fibre artefact detectors, physically but not electrically attached to the scalp (Masterton et al., 2007). In the present topic, they provide detailed instructions for building such detectors and interfacing them with a commercially available MRI-compatible EEG system (Abbott et al., 2015). This team also previously developed event-related ICA (eICA), to map fMRI activity associated with inter-ictal events observed on EEG (Masterton et al., 2013b). The method is capable of distinguishing separate sub-networks characterised by differences in spatio-temporal response (Masterton et al., 2013a). The eICA approach frees one from assumptions regarding the shape of the time-course of the neuronal and haemodynamic response associated with inter-ictal activity (which can vary according to spike type, can vary from conventional models and may include pre-spike activity (Masterton et al., 2010); issues explored further in the present topic by (Faizo et al., 2014) and (Jacobs et al., 2014)). However, the effectiveness of eICA can be affected by fMRI noise or artefact. In the present topic we see that application of a fully automated de-noising algorithm (SOCK) is now recommended, as it can substantially improve the quality of eICA results (Bhaganagarapu et al., 2014).
The ability to detect activity associated with inter-ictal events can also be improved with faster image acquisition. Magnetic Resonance Encephalography (MREG) is a particularly fast fMRI acquisition method (TR=100ms) that achieves its speed using an under-sampled k-space trajectory (Assländer et al., 2013; Zahneisen et al., 2012). This has now been applied in conjunction with simultaneous EEG, to reveal that the negative fMRI response in the default-mode network is larger in temporal compared to extra-temporal epileptic spikes (Jacobs et al., 2014).
The default mode network and its relationship to epileptiform activity is also examined in several other papers in this topic. In a pilot fMRI connectivity study of Genetic Generalised Epilepsy and Temporal Lobe Epilepsy patients, (Lopes et al., 2014) observed that intrinsic connectivity in portions of the default mode network appears to increase several seconds prior to the onset of inter-ictal discharges. The authors suggest that the default mode network connectivity may facilitate IED generation. This is plausible, although causality is difficult to establish and it is possible that something else drives both the connectivity and EEG changes (Abbott, 2015).
Complicating matters further is the question of what connectivity means. There are many ways in which connectivity can be assessed. Jones and colleagues have discovered that some of these do not necessarily correlate well with each other. They examined connectivity between measurements made with intracranial electrodes, connectivity assessed using simultaneous BOLD fMRI and intracranial electrode stimulation, connectivity between low-frequency voxel measures of fMRI activity, and a diffusion MRI measure of connectivity – an integrated diffusivity measure along a connecting pathway (Jones et al., 2014). They found only mild correlation between these four measures, implying they assess quite different features of brain networks. More research in this domain would therefore be valuable.
Whatever the measure of connectivity utilised, most evidence of alterations in connectivity in epilepsy has been obtained from comparison of a group of patients with a group of healthy controls. However, a new method called Detection of Abnormal Networks in Individuals (DANI) is now proposed by (Dansereau et al., 2014). This method is designed to detect the organisation of brain activity in stable networks, which the authors call modularity. The conventional definition of modularity refers to the degree to which networks can be segregated into distinct communities, usually estimated by maximising within-group nodal links, and minimising between group links (Girvan and Newman, 2002; Rubinov and Sporns, 2010). Dansereau take a novel approach to this concept, instead evaluating the stability of each resting state network across replications of a bootstrapped clustering method (Bellec et al., 2010). In the DANI approach, the degree to which an individual’s functional connectivity modular pattern deviates from a population of controls is quantified. Whilst application of the method to epilepsy patients is preliminary, significant changes were reported likely related to the epileptogenic focus in 5 of the 6 selected focal epilepsy patients studied. In several patients, modularity changes in regions distant from the focus were also observed, adding further evidence that the pervasive network effects of focal epilepsy can extend well beyond the seizure onset zone.
When it comes to application of EEG-fMRI to detect the seizure onset zone, there is typically a trade-off between specificity and sensitivity, with the added complication that activity or network changes may also occur in brain regions other than the ictal onset zone. The distant activity may be due to activity propagation from the onset zone, pervasive changes in functional networks creating a ‘permissive state’, or in some cases might be the brain’s attempt to prevent seizures. Specificity and sensitivity of EEG-fMRI to detect the ictal onset zone is explored by (Tousseyn et al., 2014). They determined how rates of true and false positives and negatives varied with voxel height and cluster size thresholds, both for the full statistical parametric map, and for the single cluster that contained the voxel of maximum statistical significance. The latter conferred the advantage of reducing positives remote from the seizure onset zone. As a result, it appeared to be more robust to variations in statistical threshold than analysis of the entire map. One needs to be cautious however, given the small numbers of patients studied, and the fact that the “optimal” settings were determined using receiver operator characteristic curves of the same study data. It remains to be seen how well this might generalise to a different study.
Perhaps the greatest potential for future advancement in EEG-fMRI is in methods to make the most of the all the information captured by each modality. This is highlighted by the work of Deligianni et al, demonstrating with a novel analysis framework the potential to obtain more information on the human functional connectome by utilising EEG and fMRI together (Abbott, 2016; Deligianni et al., 2014).
We hope that you enjoy this collection of papers providing a broad snapshot of advances in brain mapping methods and application to better understand epilepsy.

via Frontiers | Editorial: Functional brain mapping of epilepsy networks: methods and applications | Neuroscience

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[Abstract] Impact of transcranial direct current stimulation on structural plasticity of the somatosensory system

Significance Recently, it has been shown that repeated anodal transcranial direct current stimulation (tDCS) applied to the somatosensory system induces functionalchanges at its site of application in the primary somatosensory cortex and therefore at an early stage of information processing (Hilgenstock, Weiss, Huonker, & Witte, ). The present study complements this finding by showing that tDCS also affects the somatosensory system on a structural level, however, at a late stage of decision making, probably at the stage of decision readout. Thus, tDCS seems capable of inducing changes at all levels of the somatosensory processing hierarchy.

Abstract

While there is a growing body of evidence regarding the behavioral and neurofunctional changes in response to the longitudinal delivery of transcranial direct current stimulation (tDCS), there is limited evidence regarding its structural effects. Therefore, the present study was intended to investigate the effect of repeatedly applied anodal tDCS over the primary somatosensory cortex on the gray matter (GM) and white matter (WM) compartment of the brain. Structural tDCS effects were, moreover, related to effects evidenced by functional imaging and behavioral assessment. tDCS was applied over the course of 5 days in 25 subjects with concomitant assessment of tactile acuity of the right and left index finger as well as imaging at baseline, after the last delivery of tDCS and at follow‐up 4 weeks thereafter. Irrespective of the stimulation condition (anodal vs. sham), voxel‐based morphometry revealed a behaviorally relevant decrease of GM in the precuneus co‐localized with a functional change of its activity. Moreover, there was a decrease in GM of the bilateral lingual gyrus and the right cerebellum. Diffusion tensor imaging analysis showed an increase of fractional anisotropy exclusively in the tDCSanodal condition in the left frontal cortex affecting the final stretch of a somatosensory decision making network comprising the middle and superior frontal gyrus as well as regions adjacent to the genu of the corpus callosum. Thus, this is the first study in humans to identify structural plasticity in the GM compartment and tDCS‐specific changes in the WM compartment in response to somatosensory learning.

 

via Impact of transcranial direct current stimulation on structural plasticity of the somatosensory system – Hirtz – 2018 – Journal of Neuroscience Research – Wiley Online Library

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[ARTICLE] Impact of transcranial direct current stimulation on structural plasticity of the somatosensory system – Full Text

Abstract

While there is a growing body of evidence regarding the behavioral and neurofunctional changes in response to the longitudinal delivery of transcranial direct current stimulation (tDCS), there is limited evidence regarding its structural effects. Therefore, the present study was intended to investigate the effect of repeatedly applied anodal tDCS over the primary somatosensory cortex on the gray matter (GM) and white matter (WM) compartment of the brain. Structural tDCS effects were, moreover, related to effects evidenced by functional imaging and behavioral assessment. tDCS was applied over the course of 5 days in 25 subjects with concomitant assessment of tactile acuity of the right and left index finger as well as imaging at baseline, after the last delivery of tDCS and at follow‐up 4 weeks thereafter. Irrespective of the stimulation condition (anodal vs. sham), voxel‐based morphometry revealed a behaviorally relevant decrease of GM in the precuneus co‐localized with a functional change of its activity. Moreover, there was a decrease in GM of the bilateral lingual gyrus and the right cerebellum. Diffusion tensor imaging analysis showed an increase of fractional anisotropy exclusively in the tDCSanodal condition in the left frontal cortex affecting the final stretch of a somatosensory decision making network comprising the middle and superior frontal gyrus as well as regions adjacent to the genu of the corpus callosum. Thus, this is the first study in humans to identify structural plasticity in the GM compartment and tDCS‐specific changes in the WM compartment in response to somatosensory learning.

1 INTRODUCTION

Recently, we reported effects of repeatedly applied anodal transcranial direct current stimulation (tDCS) on tactile acuity learning of the dominant (right) index finger (IF) and concomitant neural changes as investigated by functional magnetic resonance imaging (fMRI) (Hilgenstock, Weiss, Huonker, & Witte, 2016). Relying on the same study, this article focuses on the structural underpinnings of these findings as investigated by voxel‐based morphometry (VBM) and diffusion tensor imaging (DTI).

tDCS is the application of a weak current exerting local effects on membrane potential (Bindman, 1965; Purpura & McMurtry, 1965) and neurotransmitter release (e.g., Clark, Coffman, Trumbo, & Gasparovic, 2011; Hone‐Blanchet, Edden, & Fecteau, 2016) as well as global effects on network functioning (e.g., Bachtiar, Near, Johansen‐Berg, & Stagg, 2015; Kim, Stephenson, Morris, & Jackson, 2014; Polania, Nitsche, & Paulus, 2011; Polania, Paulus, & Nitsche, 2012). Therefore, tDCS has widely been applied to study cognition, motor, and somatosensory functioning (e.g., Das, Holland, Frens, & Donchin, 2016; Stagg & Nitsche, 2011) but also to explore its beneficial potential in pathological states (e.g., Allman et al., 2016; Lindenberg, Renga, Zhu, Nair, & Schlaug, 2010; Lindenberg, Zhu, & Schlaug, 2012; Mori et al., 2013). With regard to the somatosensory system, anodal tDCS induces short‐term (e.g., Fujimoto, Yamaguchi, Otaka, Kondo, & Tanaka, 2014; Fujimoto et al., 2016; Ragert, Vandermeeren, Camus, & Cohen, 2008) as well as long‐term (Hilgenstock et al., 2016) effects on tactile acuity. After 5 days of anodal tDCS delivery, there was a profound and bilateral improvement of tactile acuity that persisted for at least 4 weeks. These improvements in tactile acuity were accompanied by changes in brain metabolism interpreted to indicate a more effective recruitment of neural machinery to process somatosensory information (Hilgenstock et al., 2016). Yet, despite the widespread application of tDCS and insights into changes in brain metabolism and connectivity, structural changes in the gray matter (GM) and white matter (WM) compartment of the brain in response to its repeated application have hardly been investigated.

Recently, Allman et al. (2016) were the first to show the capability of tDCS to induce structural changes in the GM compartment of the brain in response to repeatedly applied tDCS in stroke patients undergoing a course of 9 days of tDCS delivery with concomitant daily motor training. To the best of the authors’ knowledge, no study has investigated changes in GM in the somatosensory system in response to somatosensory training or the delivery of tDCS. Studies in the blind, however, indicate changes in GM focusing on the visual system. For example, Modi, Bhattacharya, Singh, Tripathi, and Khushu (2012) observed GM decreases in the lingual gyrus (primary visual cortex), the precuneus, the cerebellum, especially lobule VIIIa and intraparietal areas as well as an GM increase in the middle frontal gyrus (BA 6). Likewise, Voss, Pike, and Zatorre (2014) observed a significantly lower GM density in parts of the visual system and adjacent regions (pre‐/cuneus) in late blinded opposed to sighted individuals.

Changes in the WM compartment of the brain have primarily been investigated by studying fractional anisotropy (FA) (Zheng & Schlaug, 2015) that indicates organizational and directional changes in the diffusivity of water molecules in the WM compartment (Basser, 1995). Lindenberg, Nachtigall, Meinzer, Sieg, and Flöel (2013) could show that effects of tDCS on motor performance in stroke patients depended on the integrity of transcallosal and corticospinal fibers as characterized by FA (Lindenberg et al., 20122013). Moreover, Zheng and Schlaug (2015) were the first to provide evidence of a behaviorally relevant increase in FA of the so‐called alternate motor fibers (cortico‐rubro‐spinal and cortico‐reticulo‐spinal fibers) in response to repeated anodal tDCS in stroke patients. While there is no study investigating FA changes in the somatosensory domain in response to tDCS, Debowska et al. (2016) revealed changes after training of Braille reading in sighted individuals affecting the primary and secondary somatosensory system, the visual system, and the middle and superior frontal gyrus. In the blind, there is a decrease in WM in the visual system (BA 17, 18) and an increase in the superior frontal gyrus (Modi et al., 2012).

Thus, this article was intended to provide insight into where changes in tactile (acuity) perception emerge both in the GM and WM compartment of the brain (sham stimulation) and how these changes are modified by the repeated delivery of anodal tDCS. Moreover, we were interested in how somatosensory learning (sham stimulation) and its modification by anodal tDCS are implemented by combining findings from the analysis of VBM and DTI data with our previously reported findings from the analysis of fMRI and behavioral data (Hilgenstock et al., 2016). To this end, the analysis of fMRI data will be extended. Given the current state of research, we hypothesized that somatosensory learning and the repeated delivery of anodal tDCS will affect the visual system and its adjacent brain regions as well as prefrontal areas, especially the middle and superior frontal gyrus. There are only a few reports of sex‐specific tDCS‐induced effects (e.g., Chaieb, Antal, & Paulus, 2008; Fumagalli et al., 2010; Kuo, Paulus, & Nitsche, 2006). Yet, to also investigate the possibility of sex‐specific effects of tDCS in the somatosensory domain, we conducted additional exploratory analyses.[…]

 

Continue —> Impact of transcranial direct current stimulation on structural plasticity of the somatosensory system – Hirtz – 2018 – Journal of Neuroscience Research – Wiley Online Library

Figure 2 
(a) Significant decrease of gray matter (GM) density (red) and increase of the BOLD response (green) over the course of study projected on the MNI152 template provided by FSL, family‐wise error‐corrected on a cluster‐level at p < .05. (b) Changes in the BOLD response (green) over the course of the study and changes in relative GM volume (red) relative to baseline at the fifth day of the study (T2) and at follow‐up 4 weeks thereafter (T3) separately plotted for the sham (dark green/dark red) and anodal (light green/light red) condition (Mean ± SEM). (c) Pearson correlation between changes in relative GM volume at T3 relative to T1 and the GOT threshold (millimeter) of the right and left IF. Regression line and 95% confidence interval [Color figure can be viewed at wileyonlinelibrary.com]

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[Abstract] Neuroimaging in epilepsy

Purpose of review Epilepsy neuroimaging is important for detecting the seizure onset zone, predicting and preventing deficits from surgery and illuminating mechanisms of epileptogenesis. An aspiration is to integrate imaging and genetic biomarkers to enable personalized epilepsy treatments.

Recent findings The ability to detect lesions, particularly focal cortical dysplasia and hippocampal sclerosis, is increased using ultra high-field imaging and postprocessing techniques such as automated volumetry, T2 relaxometry, voxel-based morphometry and surface-based techniques. Statistical analysis of PET and single photon emission computer tomography (STATISCOM) are superior to qualitative analysis alone in identifying focal abnormalities in MRI-negative patients. These methods have also been used to study mechanisms of epileptogenesis and pharmacoresistance.

Recent language fMRI studies aim to localize, and also lateralize language functions. Memory fMRI has been recommended to lateralize mnemonic function and predict outcome after surgery in temporal lobe epilepsy.

Summary Combinations of structural, functional and post-processing methods have been used in multimodal and machine learning models to improve the identification of the seizure onset zone and increase understanding of mechanisms underlying structural and functional aberrations in epilepsy.

via Neuroimaging in epilepsy : Current Opinion in Neurology

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[Abstract+References] Action, observation or imitation of virtual hand movement affect differently regions of the mirror neuron system and the default mode network

Abstract

Virtual reality (VR)-based paradigms use visual stimuli that can modulate visuo-motor networks leading to the stimulation of brain circuits. The aims of this study were to compare the changes in blood-oxygenation level dependent (BOLD) signal when watching and imitating moving real (RH) and virtual hands (VH) in 11 healthy participants (HP). No differences were found between the observation of RH or VH making this VR-based experiment a promising tool for rehabilitation protocols. VH-imitation involved more the ventral premotor cortex (vPMC) as part of the mirror neuron system (MNS) compared to execution and VH-observation conditions. The dorsal-anterior Precuneus (da-Pcu) as part of the Precuneus/posterior Cingulate Cortex (Pcu/pCC) complex, a key node of the Default Mode Network (DMN), was also less deactivated and therefore more involved. These results may reflect the dual visuo-motor roles for the vPMC and the implication of the da-Pcu in the reallocation of attentional and neural resources for bimodal task management. The ventral Pcu/pCC was deactivated regardless of the condition confirming its role in self-reference processes. Imitation of VH stimuli can then modulate the activation of specific areas including those belonging to the MNS and the DMN.

 

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[ARTICLE] Leap Motion-based virtual reality training for improving motor functional recovery of upper limbs and neural reorganization in subacute stroke patients – Full Text

 

Abstract

Virtual reality is nowadays used to facilitate motor recovery in stroke patients. Most virtual reality studies have involved chronic stroke patients; however, brain plasticity remains good in acute and subacute patients. Most virtual reality systems are only applicable to the proximal upper limbs (arms) because of the limitations of their capture systems. Nevertheless, the functional recovery of an affected hand is most difficult in the case of hemiparesis rehabilitation after a stroke. The recently developed Leap Motion controller can track the fine movements of both hands and fingers. Therefore, the present study explored the effects of a Leap Motion-based virtual reality system on subacute stroke. Twenty-six subacute stroke patients were assigned to an experimental group that received virtual reality training along with conventional occupational rehabilitation, and a control group that only received conventional rehabilitation. The Wolf motor function test (WMFT) was used to assess the motor function of the affected upper limb; functional magnetic resonance imaging was used to measure the cortical activation. After four weeks of treatment, the motor functions of the affected upper limbs were significantly improved in all the patients, with the improvement in the experimental group being significantly better than in the control group. The action performance time in the WMFT significantly decreased in the experimental group. Furthermore, the activation intensity and the laterality index of the contralateral primary sensorimotor cortex increased in both the experimental and control groups. These results confirmed that Leap Motion-based virtual reality training was a promising and feasible supplementary rehabilitation intervention, could facilitate the recovery of motor functions in subacute stroke patients. The study has been registered in the Chinese Clinical Trial Registry (registration number: ChiCTR-OCH-12002238).

Introduction

Chronic conditions such as stroke are becoming more prevalent as the world’s population ages (Christensen et al., 2009). Although the number of fatalities caused by stroke has fallen in most countries, stroke is still a leading cause of acquired adult hemiparesis (Langhorne et al., 2009; Liu and Duan, 2017). Up to 85% of patients who survive a stroke experience hemiparesis, resulting in impaired movement of an arm and hand (Nakayama et al., 1994). Among them, a large proportion (46% to 95%) remain symptomatic six months after experiencing an ischemic stroke (Kong et al., 2011). The loss of upper limb function adversely affects the quality of life and impedes the normal use of other body parts. The motor function recovery of the upper limbs is more difficult than that of the lower extremities (Kwakkel et al., 1996; Nichols-Larsen et al., 2005; Día and Gutiérrez, 2013). Functional motor recovery in the affected upper extremities in patients with hemiparesis is the primary goal of physical therapists (Page et al., 2001). Evidence suggests that repetitive, task-oriented training of the paretic upper extremity is beneficial (Barreca et al., 2003; Wolf et al., 2006). Rehabilitation intervention is a critical part of the recovery and studies have reported that intensive repeated practice is likely necessary to modify the neural organization and favor the recovery of the functional upper limb motor skills of stroke survivors (Brunnstrom, 1966; Kopp et al., 1999; Taub et al., 1999; Wolf et al., 2006; Nudo, 2011). Meta-analyses of clinical trials have indicated that longer sessions of practice promote better outcomes in the case of impairments, thus improving the daily activities of people after a stroke (Nudo, 2011; Veerbeek et al., 2014; Sehatzadeh, 2015; French et al., 2016). However, the execution of these conventional rehabilitation techniques is tedious, resource-intensive, and often requires the transportation of patients to specialized facilities (Jutai and Teasell, 2003; Teasell et al., 2009).

Virtual reality training is becoming a promising technology that can promote motor recovery by providing high-intensity, repetitive, and task-orientated training with computer programs simulating three-dimensional situations in which patients play by moving their body parts (Saposnik et al., 2010, 2011; Kim et al., 2011; Laver et al., 2015; Tsoupikova et al., 2015). The gaming industry has developed a variety of virtual reality systems for both home and clinical applications (Saposnik et al., 2010; Bao et al., 2013; Orihuela-Espina et al., 2013; Gatica-Rojas and Méndez-Rebolledo, 2014). The most difficult task related to hemiparesis rehabilitation after a stroke is the functional recovery of the affected hand (Carey et al., 2002). To facilitate the functional recovery of a paretic hand along with that of the proximal upper extremity, an ideal virtual reality system should be able to track hand position and motion, which is not a feature of most existing virtual reality systems (Jang et al., 2005; Merians et al., 2009). The leap motion controller developed by Leap Motion (https://www.leapmotion.com) provides a means of capturing and tracking the fine movements of the hand and fingers, while controlling a virtual environment requiring hand-arm coordination as part of the practicing of virtual tasks (Iosa et al., 2015; Smeragliuolo et al., 2016).

Most virtual reality studies have often only involved patients who have experienced chronic stroke (Piron et al., 2003; Yavuzer et al., 2008; Saposnik et al., 2010; da Silva Cameirao et al., 2011). For patients in the chronic stage, who had missed the window of opportunity present at the acute and subacute stages (in which the brain plasticity peaks), rehabilitation-therapy-induced neuroplasticity can only be effective within a relatively narrow range (Chen et al., 2002). No motor function recovery of the hands, six months after the onset of a stroke, indicates a poor prognosis for hand function (Duncan et al., 1992).

We hypothesized that Leap Motion-based virtual reality training would facilitate motor functional recovery of the affected upper limb, as well as neural reorganization in subacute stroke patients. Functional magnetic resonance imaging (fMRI), also called blood oxygenation level-dependent fMRI (BOLD-fMRI), is widely used as a non-invasive, convenient, and economical method to examine cerebral function (Ogawa et al., 1990; Iosa et al., 2015; Yu et al., 2016). In the present study, we evaluated the brain function reorganization by fMRI, as well as the motor function recovery of the affected upper limb in patients with subacute stroke using Leap Motion-based virtual reality training.[…]

Continue —>  Leap Motion-based virtual reality training for improving motor functional recovery of upper limbs and neural reorganization in subacute stroke patients Wang Zr, Wang P, Xing L, Mei Lp, Zhao J, Zhang T – Neural Regen Res

Figure 1: Leap Motion-based virtual reality system and training games.
(A, B) Leap Motion-based virtual reality system; (C) petal-picking game; (D) piano-playing game; (E) robot-assembling game; (F) object-catching with balance board game; (G) firefly game; (H) bee-batting game.

 

 

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