Posts Tagged fMRI

[ARTICLE] BCI Training Effects on Chronic Stroke Correlate with Functional Reorganization in Motor-Related Regions: A Concurrent EEG and fMRI Study – Full Text PDF

Abstract

Brain–computer interface (BCI)-guided robot-assisted training strategy has been increasingly applied to stroke rehabilitation, while few studies have investigated the neuroplasticity change and functional reorganization after intervention from multimodality neuroimaging perspective. The present study aims to investigate the hemodynamic and electrophysical changes induced by BCI training using functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) respectively, as well as the relationship between the neurological changes and motor function improvement. Fourteen chronic stroke subjects received 20 sessions of BCI-guided robot hand training.
Simultaneous EEG and fMRI data were acquired before and immediately after the intervention. Seed-based functional connectivity for resting-state fMRI data and effective connectivity analysis for EEG were processed to reveal the neuroplasticity changes and interaction between different brain regions. Moreover, the relationship among motor function improvement, hemodynamic changes, and electrophysical changes derived from the two neuroimaging modalities was also investigated.
This work suggested that (a) significant motor function improvement could be obtained after BCI training therapy, (b) training effect significantly correlated with functional connectivity change between ipsilesional M1 (iM1) and contralesional Brodmann area 6 (including premotor area (cPMA) and supplementary motor area (SMA)) derived from fMRI, (c) training effect significantly correlated with information flow change from cPMA to iM1 and strongly correlated with information flow change from SMA to iM1 derived from EEG, and (d) consistency of fMRI and EEG results illustrated by the correlation between functional connectivity change and information flow change. Our study showed changes in the brain after the BCI training therapy from chronic stroke survivors and provided a better understanding of neural mechanisms, especially the interaction among motor-related brain regions during stroke recovery. Besides, our finding demonstrated the feasibility and consistency of combining multiple neuroimaging modalities to investigate the neuroplasticity change.[…]

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[Book Review] Introduction to Neuroimaging Analysis

A Book Review on
Introduction to Neuroimaging Analysis

Mark Jenkinson and Michael Chappell, (Oxford: Oxford University Press), 2018, 276 pages, ISBN: 978-0198816300 (also available as E-book).

Magnetic resonance imaging (MRI) has become an essential research tool in human neuroscience, and MRI data analysis is a critical skill for students and researchers in the field. Learning—and teaching—the fundamentals of MRI data analysis is tricky, time-consuming and sometimes frustrating. Quantitative analyses of structural and functional MRI depend on knowledge in many disparate areas, such as MR physics, statistics. and computer programming, but also neurophysiology and neuroanatomy. Most of all, MRI data analysis is a practical skill and requires the knowledge of one of the major analysis software packages.

A new book by Jenkinson and Chappell, both at the University of Oxford and experienced teachers of MRI analysis, aims to instruct students and researchers who are new to the field of neuroimaging research.

Chapter 1 of Introduction to Neuroimaging Analysis provides a brief overview of the main MRI modalities, walks the reader through the several steps of a first, imaginary neuroimaging study and gives a concise introduction to MR physics and scanner hardware. Chapter 2 brings a more in-depth description of MRI modalities, primarily structural, functional, and diffusion MRI. The rest of the book discusses quantitative techniques of MRI analysis. Chapter 3, providing a concise and clear summary of structural, functional, and diffusion imaging pipelines, is the heart of the book. If you are a newbie to MRI analysis, you would want to read this chapter, and read it several times to absorb the many concepts introduced here. Additional chapters on brain extraction, registration as well as motion and distortion correction present a comprehensive discussion of major steps in data preprocessing. Finally, chapter 7 illustrates a more advanced method, surface-based analysis of structural and functional data.

The book is accompanied by additional chapters on the Neuroimaging Primers website. The MRI Physics for Neuroimaging appendix provides a short, non-technical overview of the basics of MR physics. The General Linear Model for Neuroimaging appendix is a very well-written and illustrated introduction to the centerpiece of statistical analysis of MRI.

The unique feature of this book is the use of Example Boxes that provide practical knowledge and skills for MRI analysis. Many of these boxes are linked to separate web pages on the Neuroimaging Primers website. These web pages start with instructions how to install and run FSL, the authors’ favorite neuroimaging software package (of which Mark Jenkinson is principal developer). FSL is a software package that includes programs for the analysis of structural MRI, task-based and resting state functional MRI and diffusion tensor imaging and that runs on Unix systems (such as Mac OS), on Linux machines and inside a virtual Linux machine on Windows PCs.

Additional web pages cover, for example, analysis of structural MRI (such as tissue-type segmentation), diffusion tensor imaging (such as tractography) and task-based functional MRI. These web pages act as short online tutorials; they contain MRI data sets for download and demonstrate how to analyze the data in FSL and visualize the results using FSL’s image viewer FSLeyes. We ran several of these tutorials and all worked well. The data downloaded from the tutorial web page not only include the original data set, but also FSL’s results folder. This allows you to check and comprehend the results of every single analysis introduced on the web pages without running the analysis yourself or even installing FSL. These tutorials are ideal for homework, they are written with the student in mind and easy to follow.

Running FSL through the graphical user interface requires only rudimentary UNIX skills, which are introduced on the “Getting Started” web page. Those who long for additional UNIX proficiency simply take the link to the FSL course web page, containing Introduction to UNIX videos and a UNIX handout. To sum up, if you are FSL-inclined and teaching an introductory course on MRI data analysis covering the basics of structural MRI, fMRI, and DTI, this book is everything you need. If the main focus of your course is task-based functional MRI, you may need more detail and you may want to add recent review articles or chapters of the established fMRI textbooks (see below) to the reading list.

If you prefer a different software package, let’s say AFNI, Brainvoyager or SPM, the new textbook by Jenkinson and Chappell is still a very helpful resource. The main text of the book and the online appendices are software-independent. The original and the analyzed data sets on the web pages can be inspected by any nifti file viewer. The web page on MRI artifacts, e.g., offers 25 MRI data sets covering various artifacts. Of course, the web pages describe the commands FSL uses to perform a certain analysis step. If you would like your student to learn a non-FSL package through these web pages, you would need to provide the necessary commands and code.

Jenkinson and Chappell’s book is the most recent of several publications on neuroimaging methods. The textbook by Huettel, Song and McCarthy, Functional Magnetic Resonance Imaging, now in its 3rd edition, has gained a wide readership among students and is used in numerous university courses (Huettel et al., 2014). The textbook by Huettel et al. is useful for undergraduate and graduate students who expect a more detailed, narrative account and who are primarily interested in task-based fMRI. The Handbook of Functional MRI Data Analysis by Poldrack, Mumford, and Nichols presents a more concise and slightly more technical discussion of fMRI analysis techniques (Poldrack et al., 2011). If your focus is on resting state fMRI, you may turn to Introduction to Resting State fMRI Functional Connectivity by Bijsterbosch, Smith and Beckmann, a recent addition to the Oxford Neuroimaging Primers series (Bijsterbosch et al., 2018).

Taken together, Jenkinson and Chappell have crafted an easily accessible and highly readable introduction to the analysis of structural MRI, functional MRI and diffusion tensor imaging. With the online tutorials, tightly connected with the main text, the reader will obtain useful hands-on methodological experience. The combination of main text and online tutorial makes this book an ideal companion for students and researchers who plan to undergo their first steps in structural, functional, or diffusion tensor imaging analysis.

Hopefully, this book will find many readers and see a 2nd edition in the near future. In the next edition, a few points should be revised. As the general linear model has become so important for MRI analysis, the authors may consider including their online chapter on the general linear model in the 2nd edition of the printed book. If necessary, Appendix A, Introduction to Brain Anatomy, might be removed and made online-only. Moreover, the online tutorial on Physiological noise modeling, announced but not available yet, would be very much appreciated. Finally, there is one crucial concept in neuroimaging analysis that we missed in this, otherwise excellent, book: a detailed description of methods for multiple comparison correction. This topic has been widely discussed, even in popular media, following the publication of Eklund et al.’s paper in 2016 (Eklund et al., 2016).

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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] How brain imaging provides predictive biomarkers for therapeutic success in the context of virtual reality cognitive training

Highlight

VR environments help improve rehabilitation of impaired complex cognitive functions

Combining neuroimaging and VR boosts ecological validity, generates practical gains

These are the first neurofunctional predictive biomarkers of VR cognitive training

Abstract

As Virtual reality (VR) is increasingly used in neurological disorders such as stroke, traumatic brain injury, or attention deficit disorder, the question of how it impacts the brain’s neuronal activity and function becomes essential. VR can be combined with neuroimaging to offer invaluable insight into how the targeted brain areas respond to stimulation during neurorehabilitation training. That, in turn, could eventually serve as a predictive marker for therapeutic success. Functional magnetic resonance imaging (fMRI) identified neuronal activity related to blood flow to reveal with a high spatial resolution how activation patterns change, and restructuring occurs after VR training. Portable and quiet, electroencephalography (EEG) conveniently allows the clinician to track spontaneous electrical brain activity in high temporal resolution. Then, functional near-infrared spectroscopy (fNIRS) combines the spatial precision level of fMRIs with the portability and high temporal resolution of EEG to constitute an ideal measuring tool in virtual environments (VEs). This narrative review explores the role of VR and concurrent neuroimaging in cognitive rehabilitation.

Source: https://www.sciencedirect.com/science/article/abs/pii/S0149763420304218?dgcid=rss_sd_all&utm_campaign=RESR_MRKT_Researcher_inbound&utm_medium=referral&utm_source=researcher_app

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[BLOG POST] Brain Imaging: What Are the Different Types? – BrainLine

Positron Emission Topography
Positron Emission Topography (PET) measures brain metabolism. Different applications of PET allow one to “see” pathology associated with Alzheimer’s disease, for instance, that cannot be visualized any other way. Used in a different way, PET also allows doctors to see how different areas of the brain use oxygen or glucose – both very important to understanding not just what the damage might look like but also how the brain provides energy to itself.
T1-Weighted MRI
The T1-Weighted MRI is the standard imaging test and part of every general MRI exam. It provides doctors with a very clear view of brain anatomy and structure. It can also show damage in brain injury but generally only when the damage is very significant.
T2-Weighted MRI
The T2-Weighted MRI is also a standard part of every MRI exam. But unlike T1-weighted imaging, the T2 allows visualization of severe diffuse axonal injury such as what is expected following severe TBI.
Diffusion Weighted Magnetic Resonance Imaging
Diffusion Weighted MRI (DWI) shows alterations in tissue integrity. In ischemic injury — such as many types of stroke or when blood is not able to get to all parts of the brain — there is a chemical reaction in the cells. As the cells die because of lack of blood flow (with oxygen), there is an increase in sodium and this changes (increases) the amount of water in the tissue. DWI is very sensitive to this change. In fact, using DWI, doctors can identify a stroke or ischemic injury within seconds of occurrance.
Fluid-Attenuated Inversion Recovery MRI
Fluid-Attenuated Inversion Recovery (FLAIR) MRI is also sensitive to water content in brain tissue. This is very useful in patients who have reductions in brain tissue following an injury. Most commonly, however, FLAIR is used to visualize alterations in tissue in diseases such as multiple sclerosis.
Diffusion Tensor Imaging
Diffusion Tensor Imaging (DTI) shows white matter tracts in brain tissue. These tracts allow different parts of the brain to talk to each other. Think of the brain as if it were a computer. With DTI doctors can see and measure the “cables” connecting parts of the brain. DTI can provide information about damage to parts of the nervous system as well as about connections among brain regions.
Gradient Record MRI
Gradient Record MRI (GRE) shows blood or hemorrhaging in the brain tissue. This is very important in acute head injury. CT scans are also very useful in this stage but sometimes miss very small bleeds ― or so called microbleeds ― in the brain. MRI and types of MRI more sensitive to blood can identify these and allow doctors to monitor the patient.
Functional MRI
Functional MRI (fMRI) is a newer type of MRI that takes advantage of the iron in blood and the fact that when neurons fire there is ― eventually ― an increase in local iron in the areas where the neurons fired. For this imaging test, doctors ask patients to do something while in the MRI machine like opening and closing their right hand for 30 seconds and then opening and closing their left hand for 30 seconds. Then, the doctors model the change in signal associated with an increase in blood related to that task. So, areas involved in opening the right hand will show increased signal. This allows images to be created that reveal how the brain does tasks. This is potentially useful in TBI when the brain structures all appear normal but the brain is functioning in a different way. It is important to know that fMRI is not approved for clinical use for diagnosis of TBI.

via Brain Imaging: What Are the Different Types? | BrainLine

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[WEB PAGE] fMRI vs. SPECT Scan for the Brain: Know Your Options

By: Dr. Mark Allen PhD on February 3rd, 2020

If you’re struggling to recover after a brain injury, dealing with healthcare providers is often a frustrating process. Unless you have a clear, severe injury, they might be dismissive of your symptoms or just may not have enough treatment options to help you. Oftentimes, they’ll order an MRI or a CT scan.

But MRI and CT scans will only show structural damage. So, they’re helpful if you have a severe traumatic brain injury, but if you’ve had a mild traumatic brain injury (mTBI, aka concussion), they likely won’t show anything. If your structural MRI scan comes back as normal, many doctors won’t do much in the way of follow-up.

However, that doesn’t mean you’re out of options. If you’re here, you’ve probably heard of SPECT scans and functional MRI. These imaging tests can show dysfunction resulting from an mTBI.

But which one is right for you? In this post, we’ll explain:

  • What SPECT imaging and fMRIs actually are
  • What it’s like to get a SPECT scan or fMRI
  • What they can show, and what that means for your diagnosis.

Note: If you’re experiencing symptoms that won’t go away after a concussion, we can help. On average, our patients improve by 75% after treatment. To learn about diagnosis and treatment options, sign up for a free consultation.

All About Brain SPECT Scans

A cartoon graphic of a woman lying down in an fMRI machine.

What is a SPECT Scan?

SPECT stands for “single photon emission computed tomography.” That’s a mouthful, so here’s what it translates to:

Patients are injected with a radioactive isotope that emits gamma rays (electromagnetic radiation emitted by a decaying atom). As the isotope (also known as a radioactive tracer) travels through the patient’s bloodstream, it continues to emit radiation. (We know how that sounds, but it really is a safe level of radiation).

A special “gamma camera” is positioned above the patient and able to detect the gamma rays emitted by the isotope. A computer then triangulates in 3-dimensional space where the gamma rays are coming from.

In other words, it looks at multiple data points to figure out where the isotope was in your body when the radiation was emitted.

Over time, collecting multiple “images” can show physicians if blood flow in your body has been impacted by your condition.

Note: You may be more familiar with a PET scan (positron emission tomography). The imaging technique is very similar to SPECT scanning, since both use radioactive tracers to investigate blood flow. PET has greater resolution and fewer image artifacts, but it has other drawbacks.

What Can a SPECT Scan Show?

A SPECT scan for the brain shows an average over 10 - 15 min of scanning, but an fMRI can show each second of brain activity.

SPECT scans can be used to look at the heart, brain, and a few other things. In the brain, it can be used in evaluating conditions such as neurodegenerative diseases, stroke, seizures, tumors, and other brain trauma. In the heart, it’s most commonly used to view how well the heart is moving the blood that comes through it.

Because of the delay between when the isotope emits gamma rays and when the camera records them, combined with the inaccuracy of having to triangulate their position, SPECT images are an average over time rather than an instantaneous picture. In many cases, it can take 10-15 minutes to get that average image.

Because of that, it can identify certain conditions better than others. If you’ve had a concussion, brain SPECT imaging can confirm whether you have or have not sustained brain dysfunction after the injury. Unfortunately, it often can’t provide more detailed information about specific regions and how they were affected.

How Long Does a SPECT Scan Take?

The time you spend on a SPECT scan depends somewhat on where you’re getting the scan and why you’re getting the scan. They generally range from 1-2 hours (including the time needed for the injection).

The actual scan itself can take as few as 30 minutes.

How Much Does a SPECT Scan Cost?

SPECT scans are one of the more affordable ways to image the brain, but prices can fluctuate a lot based on location, the purpose of the scan, and what additional interpretation is involved.

According to MDsave, brain SPECT scans range from $1,300 to over $3,500.

Does a SPECT Scan Have Any Side Effects?

The main possible side effect of a SPECT scan is having an allergic reaction to the injected isotope. Some people can have bruising and soreness around the site of the injection as well.

If you’re nervous about the radiation, that’s understandable. As far as Western medicine is concerned, however, it’s perfectly harmless. Most people get more radiation from being on a plane at 30,000 feet in the air than they would from a SPECT brain scan.

All About Brain fMRI Scans

fMRI scan

What is an fMRI?

fMRI scans (functional nuclear magnetic resonance imaging) work through a combination of radio waves and magnets. Engineers have figured out how to magnetize soft tissues — such as the brain — very precisely. When you send radio waves through those magnetized tissues, the magnetic field changes the radio wave.

Sensors detect even minimal changes in the radio waves to form a 3D image of the scanned tissue. The only limits to how fine the imaging can become are human ingenuity and engineering skill.

In a structural MRI, that information is used to examine the physical integrity of the brain (or any other organ being imaged). It should show any physical brain damage you’ve sustained. A functional MRI is used to observe blood flow. Since increased cerebral blood flow is tied to increased brain activity, fMRI can show how the brain calls for resources during a given task.

If you’re trying to understand the difference between a structural MRI and a functional MRI, in terms of what it means for patients, this article will help.

What Can an fMRI Show?

A detailed fMRI scan

fMRIs can show detailed images of blood flow in internal organs. In brain imaging, this means doctors and researchers can see how the brain is managing its oxygen supply and whether the right regions respond in the right way when given a certain task.

For example, we found that patients who have post-concussion syndrome (the condition in which symptoms don’t go away after a concussion) will show tell-tale signs of hyperactivity and hypoactivity in the affected brain regions. Thanks to fMRI, we’re able to pinpoint for post-concussion patients which areas of the brain are dysfunctional and in what way.

To learn more about how we do that, you can read about functional neurocognitive imaging (fNCI), the specific type of fMRI we use.

How Long Does an fMRI Take?

Because fMRI is used more in the research setting than the clinical setting as yet, scan times can vary dramatically. The more you need to know, the longer the scan will take.

At Cognitive FX, an fNCI takes about 45 minutes. During that time, patients take six different cognitive tests while we image their brain to learn how it responds to that stimulation.

How Much Does fMRI Cost?

That depends on who you’re asking — researchers might pay hundreds of dollars per hour for access to one, but if you’re part of a clinical trial, it might be free. That said, if you’re looking to get a brain fMRI for diagnostic purposes, you’ll be charged for both the scan and whatever diagnostic analysis is performed.

At Cognitive FX, charges for an fNCI can run from $3,500 to $5,250, depending on several factors (such as whether you pay in full at the visit or via a payment plan, get the scan as part of a treatment package, etc.).

Does an fMRI Have Any Side Effects?

fMRI does not have any side effects per se, but there are situations in which you might not be able to use it. Some types of foreign metal objects in your body (such as surgical implants, braces, or even permanent eye-liner) may prohibit you from entering the MRI scanner. However, the imaging facility will provide you with full details before you commit to undergoing the scan.

If you have extreme anxiety or fear of enclosed spaces, that would also pose a challenge. fMRI is completed in an enclosed space and is very loud (you are given earplugs, headphones, and cushioning to make the noise more tolerable).

SPECT vs. fMRI: Which is Better?

fMRI vs SPECT scans

fMRI is a higher quality test than SPECT, for a few reasons. However, which functional neuroimaging test you need depends on your situation.

The spatial and temporal resolution of fMRI is significantly better: fMRI can see things down to a few millimeters, whereas SPECT resolution is on the centimeter scale. 

In other words, fMRI has at least 10x better spatial resolution.

When it comes to temporal (time) resolution, there’s no comparison. SPECT gives an image from 10-15 minutes of activity at a time. fMRI, on the other hand, can give a second-by-second picture of how your brain reacts to given stimuli.

While both methods can show if your brain has been affected by a concussion, fMRI can tell you which parts of your brain were affected (Thalamus? Basal ganglia? Prefrontal cortex?) and how (hyperactive or hypoactive). The latter information is far more useful: If we know which areas of the brain are affected, we can tailor treatment to target those regions. This insight into how your brain function has been impacted by injury is invaluable during treatment.

SPECT makes more sense than fMRI in the case of easier-to-see conditions such as stroke and seizure. Since a SPECT scan is typically cheaper than fMRI, there’s no reason not to use it when it will do the job. But for concussion diagnosis, fMRI provides much more robust, clinically useful data.

fMRI for Concussion Diagnosis

All that being said, it’s important to mention that neither fMRI nor SPECT can be used to diagnose a concussion unless the doctor reading the scans has the right information and tools available.

At Cognitive FX, we do a type of fMRI called fNCI, or functional neurocognitive imaging. It’s what allows us to pinpoint which brain regions were affected (as mentioned above).

fNCI uses the same technology of an fMRI, but the imaging process involves having patients perform standardized tasks while in the MRI machine. Over years of research, we built a database of healthy and unhealthy brains performing these tasks. We know which areas of the brain are supposed to be active during these tasks, and how much or little these areas should respond to each separate task.

When we give a patient an fNCI, we analyze the images of their brain to see which areas of their brain are not working optimally. This process allows for accurate diagnosis of injuries that hinder brain function (such as concussion, but sometimes other conditions like brain dysfunction from carbon monoxide poisoning.)

After the test, we meet with patients to discuss their results and how that will affect their treatment. Patients get an overall score and several pages breaking down how each brain region we scanned performed. Here’s an example that one of our patients agreed to share:

Exam 1: Matrix Reasoning Test Findings for Olivia

From Olivia’s story: “The fNCI showed that my thalamus was hypoactive and my basal ganglia was completely out to lunch. 3 standard deviations from normal basically means that there was no activation seen in that area on the fMRI. All the work was being routed around it — causing fatigue and stress on the rest of my brain. The inferior frontal gyrus was trending toward hyperactive (using too many resources for given tasks).”

Because fNCI is a kind of fMRI, the test is noninvasive and harmless. There is no radiation, however, the same restrictions around metal apply.

What You Can Do About Concussion Recovery

For many patients, concussion symptoms resolve after about two weeks. But for others, those symptoms just won’t seem to go away. If that describes your situation, you may have post-concussion syndrome. We’ve listed some of the symptoms in the chart below:

Post-Concussion Symptoms list

Your best bet is to seek treatment from a doctor or clinic that specializes in post-concussion treatment. If you’d like to know more about how we can help you, sign up for a free consultation.

Or, if you’d like to learn about choosing a clinic, here’s our post on the best concussion clinics in the U.S.

Active Recovery

In the meantime, there are things you can do to improve your chances at a good recovery. Do your best to fit these into every day:

  1. Plenty of rest (if you’re having difficulty, this post on post-concussion sleep may help).
  2. Light physical activity for 30 minutes per day (at whatever level you can tolerate without causing symptoms). Here’s our guide to exercising safely after a concussion.
  3. Cognitive activities like reading or logic puzzles, as tolerated.
  4. Work or school activities, as tolerated.

Conclusion

Knowing whether you need an fMRI or SPECT scan comes down to a matter of how much you need to know in order to receive effective treatment. If you’re suffering from post-concussion syndrome, a SPECT scan is better than nothing, but an fMRI is significantly more useful than a SPECT scan.

If you’ve been suffering from lingering symptoms after a concussion and haven’t found relief, you’re not imagining things: 10-20% of people who have had a concussion endure lingering symptoms that do not go away without treatment. We’ve written at length about some of the more common issues our patients face, such as headachesmemory problemsrelentless fatigue, and more.

To learn more about treatment options and what you can do next, sign up for a free consultation.  

via fMRI vs. SPECT Scan for the Brain: Know Your Options

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[Abstract] Neural Correlates of Passive Position Finger Sense After Stroke

Background. Proprioception of fingers is essential for motor control. Reduced proprioception is common after stroke and is associated with longer hospitalization and reduced quality of life. Neural correlates of proprioception deficits after stroke remain incompletely understood, partly because of weaknesses of clinical proprioception assessments.

Objective. To examine the neural basis of finger proprioception deficits after stroke. We hypothesized that a model incorporating both neural injury and neural function of the somatosensory system is necessary for delineating proprioception deficits poststroke.

Methods. Finger proprioception was measured using a robot in 27 individuals with chronic unilateral stroke; measures of neural injury (damage to gray and white matter, including corticospinal and thalamocortical sensory tracts), neural function (activation of and connectivity of cortical sensorimotor areas), and clinical status (demographics and behavioral measures) were also assessed.

Results. Impairment in finger proprioception was present contralesionally in 67% and bilaterally in 56%. Robotic measures of proprioception deficits were more sensitive than standard scales and were specific to proprioception. Multivariable modeling found that contralesional proprioception deficits were best explained (r2 = 0.63; P = .0006) by a combination of neural function (connectivity between ipsilesional secondary somatosensory cortex and ipsilesional primary motor cortex) and neural injury (total sensory system injury).

Conclusions. Impairment of finger proprioception occurs frequently after stroke and is best measured using a quantitative device such as a robot. A model containing a measure of neural function plus a measure of neural injury best explained proprioception performance. These measurements might be useful in the development of novel neurorehabilitation therapies.

via Neural Correlates of Passive Position Finger Sense After Stroke – Morgan L. Ingemanson, Justin R. Rowe, Vicky Chan, Jeff Riley, Eric T. Wolbrecht, David J. Reinkensmeyer, Steven C. Cramer, 2019

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[ARTICLE] Efficacy and Brain Imaging Correlates of an Immersive Motor Imagery BCI-Driven VR System for Upper Limb Motor Rehabilitation: A Clinical Case Report – Full Text

To maximize brain plasticity after stroke, a plethora of rehabilitation strategies have been explored. These include the use of intensive motor training, motor-imagery (MI), and action-observation (AO). Growing evidence of the positive impact of virtual reality (VR) techniques on recovery following stroke has been shown. However, most VR tools are designed to exploit active movement, and hence patients with low level of motor control cannot fully benefit from them. Consequently, the idea of directly training the central nervous system has been promoted by utilizing MI with electroencephalography (EEG)-based brain-computer interfaces (BCIs). To date, detailed information on which VR strategies lead to successful functional recovery is still largely missing and very little is known on how to optimally integrate EEG-based BCIs and VR paradigms for stroke rehabilitation. The purpose of this study was to examine the efficacy of an EEG-based BCI-VR system using a MI paradigm for post-stroke upper limb rehabilitation on functional assessments, and related changes in MI ability and brain imaging. To achieve this, a 60 years old male chronic stroke patient was recruited. The patient underwent a 3-week intervention in a clinical environment, resulting in 10 BCI-VR training sessions. The patient was assessed before and after intervention, as well as on a one-month follow-up, in terms of clinical scales and brain imaging using functional MRI (fMRI). Consistent with prior research, we found important improvements in upper extremity scores (Fugl-Meyer) and identified increases in brain activation measured by fMRI that suggest neuroplastic changes in brain motor networks. This study expands on the current body of evidence, as more data are needed on the effect of this type of interventions not only on functional improvement but also on the effect of the intervention on plasticity through brain imaging.

Introduction

Worldwide, stroke is a leading cause of adult long-term disability (Mozaffarian et al., 2015). From those who survive, an increased number is suffering with severe cognitive and motor impairments, resulting in loss of independence in their daily life such as self-care tasks and participation in social activities (Miller et al., 2010). Rehabilitation following stroke is a multidisciplinary approach to disability which focuses on recovery of independence. There is increasing evidence that chronic stoke patients maintain brain plasticity, meaning that there is still potential for additional recovery (Page et al., 2004). Traditional motor rehabilitation is applied through physical therapy and/or occupational therapy. Current approaches of motor rehabilitation include functional training, strengthening exercises, and range of movement exercises. In addition, techniques based on postural control, stages of motor learning, and movement patterns have been proposed such as in the Bobath concept and Bunnstrom approach (amongst others) (Bobath, 1990). After patients complete subacute rehabilitation programs, many still show significant upper limb motor impairment. This has important functional implications that ultimately reduce their quality of life. Therefore, alternative methods to maximize brain plasticity after stroke need to be developed.

So far, there is growing evidence that action observation (AO) (Celnik et al., 2008) and motor imagery (MI) improve motor function (Mizuguchi and Kanosue, 2017) but techniques based on this paradigm are not widespread in clinical settings. As motor recovery is a learning process, the potential of MI as a training paradigm relies on the availability of an efficient feedback system. To date, a number of studies have demonstrated the positive impact of virtual-reality (VR) based on neuroscientific grounds on recovery, with proven effectiveness in the stroke population (Bermúdez i Badia et al., 2016). However, patients with no active movement cannot benefit from current VR tools due to low range of motion, pain, fatigue, etc. (Trompetto et al., 2014). Consequently, the idea of directly training the central nervous system was promoted by establishing an alternative pathway between the user’s brain and a computer system.

This is possible by using electroencephalography (EEG)-based Brain-Computer Interfaces (BCIs), since they can provide an alternative non-muscular channel for communication and control to the external world (Wolpaw et al., 2002), while they could also provide a cost-effective solution for training (Vourvopoulos and Bermúdez, 2016b). In rehabilitation, BCIs could offer a unique tool for rehabilitation since they can stimulate neural networks through the activation of mirror neurons (Rizzolatti and Craighero, 2004) by means of action-observation (Kim et al., 2016), motor-intent and motor-imagery (Neuper et al., 2009), that could potentially lead to post-stroke motor recovery. Thus, BCIs could provide a backdoor to the activation of motor neural circuits that are not stimulated through traditional rehabilitation techniques.

In EEG-based BCI systems for motor rehabilitation, Alpha (8–12 Hz) and Beta (12–30 Hz) EEG rhythms are utilized since they are related to motor planning and execution (McFarland et al., 2000). During a motor attempt or motor imagery, the temporal pattern of the Alpha rhythms desynchronizes. This rhythm is also named Rolandic Mu-rhythm or the sensorimotor rhythm (SMR) because of its localization over the sensorimotor cortices. Mu-rhythms are considered indirect indications of functioning of the mirror neuron system and general sensorimotor activity (Kropotov, 2016). These are often detected together with Beta rhythm changes in the form of an event-related desynchronization (ERD) when a motor action is executed (Pfurtscheller and Lopes da Silva, 1999). These EEG patterns are primarily detected during task-based EEG (e.g., when the participant is actively moving or imagining movement) and they are of high importance in MI-BCIs for motor rehabilitation.

A meta-analysis of nine studies (combined N = 235, sample size variation 14 to 47) evaluated the clinical effectiveness of BCI-based rehabilitation of patients with post-stroke hemiparesis/hemiplegia and concluded that BCI technology could be effective compared to conventional treatment (Cervera et al., 2018). This included ischemic and hemorrhagic stroke in both subacute and chronic stages of stoke, between 2 to 8 weeks. Moreover, there is evidence that BCI-based rehabilitation promotes long-lasting improvements in motor function of chronic stroke patients with severe paresis (Ramos-Murguialday et al., 2019), while overall BCI’s are starting to prove their efficacy as rehabilitative technologies in patients with severe motor impairments (Chaudhary et al., 2016).

The feedback modalities used for BCI motor rehabilitation include: non-embodied simple two-dimensional tariffs on a screen (Prasad et al., 2010Mihara et al., 2013), embodied avatar representation of the patient on a screen or with augmented reality (Holper et al., 2010Pichiorri et al., 2015), neuromuscular electrical stimulation (NMES) (Kim et al., 2016Biasiucci et al., 2018). and robotic exoskeletal orthotic movement facilitation (Ramos-Murguialday et al., 2013Várkuti et al., 2013Ang et al., 2015). In addition, it has been shown that multimodal feedback lead to a significantly better performance in motor-imagery (Sollfrank et al., 2016) but also multimodal feedback combined with motor-priming, (Vourvopoulos and Bermúdez, 2016a). However, there is no evidence which modalities are more efficient in stroke rehabilitation are.

Taking into account all previous findings in the effects of multimodal feedback in MI training, the purpose of this case study is to examine the effect of the MI paradigm as a treatment for post-stroke upper limb motor dysfunction using the NeuRow BCI-VR system. This is achieved through the acquisition of clinical scales, dynamics of EEG during the BCI treatment, and brain activation as measured by functional MRI (fMRI). NeuRow is an immersive VR environment for MI-BCI training that uses an embodied avatar representation of the patient arms and haptic feedback. The combination of MI-BCIs with VR can reinforce activation of motor brain areas, by promoting the illusion of physical movement and the sense of embodiment in VR (Slater, 2017), and hence further engaging specific neural networks and mobilizing the desired neuroplastic changes. Virtual representation of body parts paves the way to include action observation during treatment. Moreover, haptic feedback is added since a combination of feedback modalities could prove to be more effective in terms of motor-learning (Sigrist et al., 2013). Therefore, the target of this system is to be used by patients with low or no levels of motor control. With this integrated BCI-VR approach, severe cases of stroke survivors may be admitted to a VR rehabilitation program, complementing traditional treatment.

Methodology

Patient Profile

In this pilot study we recruited a 60 years old male patient with left hemiparesis following cerebral infarct in the right temporoparietal region 10 months before. The participant had corrected vision through eyewear, he had 4 years of schooling and his experience with computers was reported as low. Moreover, the patient was on a low dose of diazepam (5 mg at night to help sleep), dual antiplatelet therapy, anti-hypertensive drug and metformin. Hemiparesis was associated with reduced dexterity and fine motor function; however, sensitivity was not affected. Other sequelae of the stroke included hemiparetic gait and dysarthria. Moreover, a mild cognitive impairment was identified which did not interfere with his ability to perform the BCI-VR training. The patient had no other relevant comorbidities. Finally, the patient was undergoing physiotherapy and occupational therapy at the time of recruitment and had been treated with botulinum toxin infiltration 2 months before due to focal spasticity of the biceps brachii.

Intervention Protocol

The patient underwent a 3-weeks intervention with NeuRow, resulting in 10 BCI sessions of a 15 min of exposure in VR training per session. Clinical scales, motor imagery capability assessment, and functional -together with structural- MRI data had been gathered in three time-periods: (1) before (serving as baseline), (2) shortly after the intervention and (3) one-month after the intervention (to assess the presence of long-term changes). Finally, electroencephalographic (EEG) data had been gathered during all sessions, resulting in more than 20 datasets of brain electrical activity.

The experimental protocol was designed in collaboration with the local healthcare system of Madeira, Portugal (SESARAM) and approved by the scientific and ethic committees of the Central Hospital of Funchal. Finally, written informed consent was obtained from the participant upon recruitment for participating to the study but also for the publication of the case report in accordance with the 1964 Declaration of Helsinki.

Assessment Tools

A set of clinical scales were acquired including the following:

1. Montreal Cognitive Assessment (MoCA). MoCA is a cognitive screening tool, with a score range between 0 and 30 (a score greater than 26 is considered to be normal) validated also for the Portuguese population, (Nasreddine et al., 2005).

2. Modified Ashworth scale (MAS). MAS is a 6-point rating scale for measuring spasticity. The score range is 0, 1, 1+, 2, 3, and 4 (Ansari et al., 2008).

3. Fugl-Meyer Assessment (FMA). FMA is a stroke specific scale that assesses motor function, sensation, balance, joint range of motion and joint pain. The motor domain for the upper limb has a maximum score of 66 (Fugl-Meyer et al., 1975).

4. Stroke Impact Scale (SIS). SIS is a subjective scale of the perceived stroke impact and recovery as reported by the patient, validated for the Portuguese population. The score of each domain of the questionnaire ranges from 0 to 100 (Duncan et al., 1999).

5. Vividness of Movement Imagery Questionnaire (VMIQ2). VMIQ2 is an instrument that assess the capability of the participant to perform imagined movements from external perspective (EVI), internal perspective imagined movements (IVI) and finally, kinesthetic imagery (KI) (Roberts et al., 2008).

NeuRow BCI-VR System

EEG Acquisition

For EEG data acquisition, the Enobio 8 (Neuroelectrics, Barcelona, Spain) system was used. Enobio is a wearable wireless EEG sensor with 8 EEG channels for the recording and visualization of 24-bit EEG data at 500 Hz and a triaxial accelerometer. The spatial distribution of the electrodes followed the 10–20 system configuration (Klem et al., 1999) with the following electrodes over the somatosensory and motor areas: Frontal-Central (FC5, FC6), Central (C1, C2, C3, C4), and Central-Parietal (CP5, CP6) (Figure 1A). The EEG system was connected via Bluetooth to a dedicated desktop computer, responsible for the EEG signal processing and classification, streaming the data via UDP through the Reh@Panel (RehabNet Control Panel) for controlling the virtual environment. The Reh@Panel is a free tool that acts as a middleware between multiple interfaces and virtual environments (Vourvopoulos et al., 2013).

FIGURE 1

Figure 1. Experimental setup, including: (A) the wireless EEG system; (B) the Oculus HMD, together with headphones reproducing the ambient sound from the virtual environment; (C) the vibrotactile modules supported by a custom-made table-tray, similar to the wheelchair trays used for support; (D) the visual feedback with NeuRow game. A written informed consent was obtained for the publication of this image.

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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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