Archive for category Radiology

[Abstract] The Structural and Functional Neuroanatomy of Post-Stroke Depression and Executive Dysfunction: A Review of Neuroimaging Findings and Implications for Treatment

Abstract

Post-stroke depression and executive dysfunction co-occur and are highly debilitating. Few treatments alleviate both depression and executive dysfunction after stroke. Understanding the brain network changes underlying post-stroke depression with executive dysfunction can inform the development of targeted and efficacious treatment. In this review, we synthesize neuroimaging findings in post-stroke depression and post-stroke executive dysfunction and highlight the network commonalities that may underlie this comorbidity. Structural and functional alterations in the cognitive control network, salience network, and default mode network are associated with depression and executive dysfunction after stroke. Specifically, post-stroke depression and executive dysfunction are both linked to changes in intrinsic functional connectivity within resting state networks, functional over-connectivity between the default mode and salience/cognitive control networks, and reduced cross-hemispheric frontoparietal functional connectivity. Cognitive training and noninvasive brain stimulation targeted at these brain network abnormalities and specific clinical phenotypes may help advance treatment for post-stroke depression with executive dysfunction.

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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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[NEWS] M-CUBE aims to take MRI technology to the next level

Reviewed by Emily Henderson, B.Sc.Sep 25 2020

It is hard to see inside the human body, but because it is vital for diagnosing certain illnesses, several techniques have been developed and perfected over the last century.

One of these is magnetic resonance imaging, or MRI. The MRI scan uses a strong magnetic field and radio waves to generate images of parts of the body that can’t be seen as well with X-rays, CT scans or ultrasound. It produces detailed cross-sectional images that can be turned into three-dimensional pictures.

It works by using a magnetic field to order the hydrogen atoms in the body’s water molecules and then sends them radio waves from an antenna.

After the interaction, the atoms send the waves back with an intensity that depends on the type of tissue reached. The process then builds up a map of the body tissue.

MRI is painless and very useful for diagnosing many types of conditions with the advantage of avoiding significant radiation (unlike X-rays or CT scans).

For example, MRI can help doctors to see inside joints, ligaments, muscles, and tendons, which makes it helpful for detecting various sports injuries. It is also used to examine internal body structures and diagnose a variety of disorders, such as strokes, tumours, aneurysms, and spinal cord injuries.

However, the technology isn’t perfect. The wave atoms sent back can be very weak, meaning that the resulting images can contain dark areas. A conventional way to try and solve this issue is to deploy more antennas but the process is expensive and complicated as the antenna signals can often interfere with each other.

This is where the M-CUBE project comes in. Coordinated by Aix Marseille University (AMU) and led by the Fresnel Institute and the Centre for Magnetic Resonance in Biology and Medicine (CRMBM) it is supported by an interdisciplinary consortium of 6 universities, 2 research centres and 2 companies. Funding comes from the EU’s Future and Emerging Technologies (FET) programme.

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The aim of M-CUBE is to improve MRI technology by making several prototype antennas from “metamaterials”. These are artificial materials engineered to have electromagnetic properties that don’t exist in nature.

Their custom-designed structures can make them especially sensitive to certain types of electromagnetic waves, allowing practitioners to have more precise control over them.

Thanks to the different metamaterials employed, different antennas will be able to be combined in transmission without their signals interfering with each other. The waves sent back by the atoms can therefore be measured more accurately, making the images sharper, and minimising dark areas.

To date, the prototypes created are working and confirming that the medical imagery has improved with them. For this reason, they will be applied in existing and new MRI systems such as “1.3 TESLA MRI” or “3 TESLA MRI”.

The good outcomes have also allowed the project to obtain ethical authorisations from the National Regulation Authorities to do experiments on humans for the 7 TESLA MRI equipment.

This has led to the launch of M-CUBE’s follow up project – the H2020 funded M-One project, which seeks to transform the technologies developed within M-CUBE into actual medical devices that will become the global gold standard in ultra-high field MRI.

These improvements in medical imaging will mean more accurate diagnosis and a higher capacity to provide “patient-centred” solutions.

The results of M-CUBE and higher resolution imaging will allow patient conditions to be diagnosed much earlier than before and they could even have interesting implications for the diagnosing of epilepsy, Parkinson or detection of osteoporosis – diseases that the consortium did not expect to detect at the beginning of the project.

Source: https://www.news-medical.net/news/20200925/M-CUBE-aims-to-to-take-MRI-technology-to-the-next-level.aspx

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[WEB SITE] The Whole Brain Atlas

  • Keith A. Johnson, M.D. (keith@bwh.harvard.edu)
  • J.Alex Becker (jabecker@mit.edu)

Neuroimaging of specific neurological conditions.

The Whole Brain Atlas 

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[Editorial] Identifying Neuroimaging-Based Markers for Distinguishing Brain Disorders – Full Text

Editorial on the Research Topic
Identifying Neuroimaging-Based Markers for Distinguishing Brain Disorders

The current diagnosis of brain disorders heavily relies on clinical presentation. Neuroimaging is gaining more importance with the potential to provide useful markers in revealing biological substrates and benefit brain disorder diagnosis. Magnetic resonance imaging (MRI), electroencephalography (EEG), positron emission tomography (PET), diffusion tensor image (DTI), and magnetoencephalography (MEG) have been widely applied to measure the brain structure, decode the brain function, and explore the disease mechanism from different aspects. This Research Topic takes action by publishing 24 papers that proposed new methods for identifying biomarkers from these modalities and utilized neuroimaging measures to differentiate between patients with brain disorders or differentiate patients from healthy controls. Papers in the topic involved different disorders such as schizophrenia (SZ), autism spectrum disorder (ASD), Alzheimer’s disease (AD), attention-deficit/hyperactivity disorder (ADHD), and epilepsy.

Resting-state functional MRI (fMRI) has been successful in estimating brain functional networks and connectivity via data-driven methods (Calhoun et al., 2001Beckmann et al., 2005Du and Fan, 2013), providing features for the classification between various brain disorders and the prediction of disorder progression (Du et al., 20152018bArbabshirani et al., 2017). There has been evidence that brain functional connectivity is time-varying, and clustering (e.g., K-means) and decomposition methods can be used to extract connectivity states from dynamic connectivity patterns (Hutchison et al., 2013Allen et al., 2014Calhoun et al., 2014Preti et al., 2017). Most previous dynamic connectivity studies focused on the dynamics of the connectivity between different brain regions or networks (Damaraju et al., 2014Yu et al., 2015Du et al., 201620172018a). A study by Bhinge et al. proposed a novel approach to measure both the voxelwise spatial variability in functional networks and the dynamic functional network connectivity (dFNC). Time-varying spatial networks were estimated by a constrained independent vector analysis. Their method successfully captured distinct information between healthy controls and SZ patients, resulting in relatively high classification accuracy by using dynamic spatial information. Another shortcoming of previous dynamic analyses is that clustering was often performed to all time-varying connectivity matrices without considering their temporal relationship. In the topic, Espinoza et al. incorporated the temporal variation of functional network connectivity into clustering, thus providing more information than regular dFNC method in investigating differences between SZ patients and healthy controls. In another study, Zhao et al. also focused on improving the clustering performance in identifying reliable connectivity states from dynamic connectivity. They used the node centrality of brain regions rather than the original functional connectivity strengths as features, showing that repeatable dynamic features can be found between repeated scans. All these new methods would benefit the biomarker identification from dynamic functional connectivity. On the other hand, potential neuroimaging biomarkers are most meaningful when they can be replicable and used to predict new subjects in clinical practice (Jiang et al., 2018). A previous study (Sun et al., in press) proposed a connectome-based predictive model that can be used to predict depressive rating changes and remission status of major depressive disorder (MDD) patients. In the topic, Zhu et al. identified abnormal brain connections in the lateral habenula and thalamus, and found that they may serve as connectome-based biomarkers to predict the precursor to MDD. Luo et al. investigated the MDD in terms of the functional connectivity between the brainstem regions and other brain regions, providing a new insight for the neurobiology of MDD. Cui et al. proposed a method to integrate local and global properties of brain functional networks for improving the classification performance between early mild cognitive impairment (EMCI) and healthy control groups, based on the minimum spanning tree and graph kernel techniques. A work from Yang et al. obtained high classification accuracy by fusing amplitude of low frequency fluctuation (ALFF) and fractional ALFF features for distinguishing individuals with subjective cognitive decline, patients with amnestic mild cognitive impairment, patients with AD, and healthy controls. Xu, Yang et al. investigated the altered resting-state whole brain functional connectivity in premature ejaculation patients compared to healthy controls via a classification method. Using fMRI, network topological property is one of the most important techniques to elucidate the brain function (Wang et al., 2010Bullmore and Sporns, 2012). One study by Liu et al. revealed the alterations in diabetes mellitus patients using long-range and short-range functional connectivity degree. Xu, Guo et al. compared the topological properties between diabetes mellitus patients and healthy controls using fMRI connectivity. Wang, Tao et al. studied the topology of frontal parietal attention network in children with ADHD using a graph theory analysis method including the minimum spanning tree technique.

Besides fMRI, other modalities including structural MRI (sMRI), EEG, PET, and MEG can be utilized to provide useful indicators. Xu, Chen et al. reported the first study to show structural and functional brain abnormalities in patients with hemifacial spasm using both fMRI and sMRI. Long et al. found that various brain parcellation schemes may result in different classification performance by using voxel-based morphometry measures summarized in brain regions as features in classifying MCI patients and healthy controls. Yan et al. proposed a new matrix regression method that showed a promise in predicting cognitive data of AD using voxel-based morphometry. Zeng et al. focused on the prediction of medication response in herpes zoster patients by applying a searchlight algorithm and support vector machine on the voxel-based brain morphometry measures. A paper by Ma et al. showed the tissue-specific changes in gray matter and white matter of the mouse model of tauopathy based on the in vivo and ex vivo conditions, emphasizing the importance of longitudinal analysis. Based on DTI data, Qin et al. applied the graph theory and network-based statistic methods to compare the impairments between obsessive-compulsive disorder and SZ. Wang, Li et al. revealed the abnormality in the hemispheric topological asymmetries in bipolar disorder using DTI-based network analysis. Using both DTI and fMRI network measures, Park et al. reported the changes of individuals with eating disorder and found the brain regions associated with the behaviors. In another study, Hu et al. used the partial least square technique to aid the minimum variance beamforming approach for source imaging with MEG arrays, and verified its effectiveness in simulated data and epilepsy data. Using EEG activity, Simões et al. identified group differences between patients with ASD and healthy controls under the visual stimulation and mental imagery tasks, revealing a possible biomarker of face emotional imagery network of ASD. Shah et al. explored the possible mechanism of depression in human immunodeficiency virus (HIV) by analyzing the longitudinal PET images of an animal model of HIV.

Since different types of neuroimaging techniques reflect the brain’s function and structure from different angles, it has been largely acknowledged that through the fusion of complementary information from different modalities, biomarkers of mental illness may be identified more precisely (Sui et al., 2018). Efficient methods that can draw valid conclusions from high dimensional multimodal imaging, cognitive or genetic data are urgently needed (Calhoun and Sui, 2016Qi et al., 2019). In the topic, Acar et al. applied an advanced coupled matrix and tensor factorizations (CMTF) method to the data of EEG, fMRI, and sMRI collected from patients with schizophrenia and healthy controls to reveal linked biomarkers across different modalities. Compared to joint ICA, they revealed more meaningful and reproducible biomarkers. Besides the neuroimaging studies on brain disorders, increasing work has recognized the role of genetics in the etiology of many complex disorders (e.g., schizophrenia; Lin et al., 2018Chen et al., 2019). Imaging genetics, a rising field to bridge genetics and neuroimaging, aims to investigate the genetic risk of various imaging endophenotypes in relation to diseases, and identify biomarkers (genetic and imaging) to facilitate the disease diagnosis (Lin et al., 2014). In the topic, Jiang et al. reviewed the current imaging genetics studies on schizophrenia, particularly in revealing the heterogeneity within schizophrenia, and also discussed the potential of imaging genetics in refining disease diagnosis.

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Source: https://www.frontiersin.org/articles/10.3389/fnins.2020.00327/full

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

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

Copyright: MRBrainS

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

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

Operating inside the womb with mixed reality

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

Finding scars fast        

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

Machine learning dissects the detail

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

Enabling electronics at extreme temperatures

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

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

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

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[Abstract + References] Improving wrist imaging through a multicentre educational intervention: The challenge of orthogonal projections

In relation to wrist imaging, the accepted requirement is two orthogonal projections obtained at 90°, each with the wrist in neutral position. However, the literature and anecdotal experience suggests that this principle is not universally applied.

This multiphase study was undertaken across eight different hospitals sites. Compliance with standard UK technique was confirmed if there was a change in ulna orientation between the dorsi-palmar (DP) and lateral wrist projections. A baseline evaluation for three days was randomly identified from the preceding three months. An educational intervention was implemented using a poster to demonstrate standard positioning. To measure the impact of the intervention, further evaluation took place at two weeks (early) and three months (late).

Across the study phases, only a minority of radiographs demonstrated compliance with the standard technique, with an identical anatomical appearance of the distal ulna across the projections. Initial compliance was 16.8% (n = 40/238), and this improved to 47.8% (n = 77/161) post-intervention, but declined to 32.8% (n = 41/125) within three months. The presence of pathology appeared to influence practice, with a greater proportion of those with an abnormal radiographic examination demonstrating a change in ulna appearances in the baseline cohort (p < 0.001) and the late post-intervention group (p = 0.002) but not in the examinations performed two weeks after staff education (p = 0.239).

Assessment of image quality is critical for diagnosis and treatment monitoring. Yet poor compliance with standard anatomical principles was evident. A simple educational intervention resulted in a transient improvement in wrist positioning, but the impact was not sustained over time.

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via Improving wrist imaging through a multicentre educational intervention: The challenge of orthogonal projections – Beverly Snaith, Scott Raine, Lynsey Fowler, Christopher Osborne, Sophie House, Ryan Holmes, Emma Tattersall, Emma Pierce, Melanie Dobson, James W Harcus,

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[WEB PAGE] What Does Chocolate and Peanut Butter Have to Do with Stroke? Find Out Here

Posted by Debbie Overman | Jan 16, 2020   

What Does Chocolate and Peanut Butter Have to Do with Stroke? Find Out Here

Calling it his “chocolate and peanut butter moment,” a University at Buffalo researcher has developed a brain model designed to offer insights into damage caused by stroke and other injuries that represents a combination of two existing approaches that seems obvious.

The model is designed to create a digital simulation environment that can serve as a testing ground for hypotheses about specific damage caused by neurological issues. The researcher’s background in computer modeling made his advancement of combining existing approaches seem obvious–hence, the “chocolate and peanut butter moment,” a media release from University at Buffalo explains.

“This model is tied accurately to the functional connectivity of the brain and is able to demonstrate realistic patterns of cognitive impairment,” says Christopher McNorgan, an assistant professor of psychology in UB’s College of Arts and Sciences, in the release. “Since the model reflects how the brain is connected, we can manipulate it in ways that provide insights, for example, into the areas of a patient’s brain that might be damaged.

“This recent work doesn’t prove that we have a digital facsimile of the human brain, but the findings indicate that the model is performing in a way that is consistent with how the brain performs, and that at least suggests that the model is taking on properties that are moving in the direction of possibly one day creating a facsimile.”

The findings, published in NeuroImage, provide a powerful means of identifying and understanding brain networks and how they function, which could lead to what once were unrealized possibilities for discovery and understanding.

Explaining McNorgan’s model starts with a look at the two fundamental components of its design: functional connectivity and multivariate pattern analyses (MVPA).

For many years, traditional brain-based models have relied on a general linear approach. This method looks at every spot in the brain and how those areas respond to stimuli. This approach is used in traditional studies of functional connectivity, which rely on functional magnetic resonance imaging (fMRI) to explore how the brain is wired. A linear model assumes a direct relationship between two things, such as the visual region of the brain becoming more or less active when a light flickers on or off.

While linear models excel at identifying which areas are active under certain conditions, they often fail to detect complicated relationships potentially existing among multiple areas. That’s the domain of more recent advances, like MVPA, a “teachable” machine-learning technique that operates on a more holistic level to evaluate how activity is patterned across brain regions.

MVPA is non-linear. Assume for instance that there’s a set of neurons dedicated to recognizing the meaning of a stop sign. These neurons are not active when we see something red or something octagonal because there’s not a one-to-one linear mapping between being red and being a stop sign (an apple isn’t a stop sign), nor between being octagonal and being a stop sign (a board room table isn’t a stop sign).

“A non-linear response ensures that they do light up when we see an object that is both red and octagonal,” McNorgan explains, the release continues.

“For this reason, non-linear methods like MVPA have been at the core of so-called ‘Deep Learning’ approaches behind technologies, such as the computer vision software required for self-driving cars.”

But MVPA uses brute force machine-learning techniques. The process is opportunistic, sometimes confusing coincidence with correlation. Even ideal models require researchers to provide evidence that activity in the theoretical model would also be present under the same conditions in the brain.

On their own, both traditional functional connectivity and MVPA approaches have limitations, and integrating results generated by each of these approaches requires considerable effort and expertise for brain researchers to puzzle out the evidence.

When combined, however, the limitations are mutually constrained —  and McNorgan is the first researcher to successfully integrate functional connectivity and MVPA to develop a machine-learning model that’s explicitly grounded in real-world functional connections among brain regions. In other words, the mutually constrained results are a self-assembling puzzle.

“It was my chocolate and peanut butter moment,” says McNorgan, an expert in neuroimaging and computational modeling.

“I’ve had a particular career trajectory that has allowed me to work extensively with different theoretical models. That background provided a particular set of experiences that made the combination seem obvious in hindsight,” he comments.

To build his models, McNorgan begins by gathering the brain data that will teach them the patterns of brain activity that are associated with each of three categories – in this case, tools, musical instruments and fruits. These data came from 11 participants who imagined the appearance and sound of familiar category examples, like hammers, guitars and apples, while undergoing an MRI scan. These scans indicate which areas are more or less active based on blood oxygen levels.

“There are certain patterns of activity across the brain that are consistent with thinking about one category versus another,” says McNorgan. “We might think of this as a neural fingerprint.”

These MRI patterns were then digitized and used to train a series of computer models to recognize which activity patterns were associated with each category.

“After training, models are given previously unseen activity patterns,” he explains. “Significantly above-chance classification accuracy indicates that the models have learned a generalizable relationship between specific brain activity patterns and thinking about a specific category.”

To test whether the digital brain models produced by this new method were more realistic, McNorgan gave them “virtual lesions” by disrupting activations in regions known to be important for each of the categories.

He found that the mutually constrained models showed classification errors consistent with the lesion location. For example, lesions to areas thought to be important for representing tools disrupted accuracy for tool patterns, but not the other two categories. By comparison, other versions of models not trained using the new method did not show this behavior.

“The model now suggests how brain areas that might not appear to be important for encoding information when considered individually may be important when it’s functioning as part of a larger configuration or network,” he says. “Knowing these areas may help us understand why someone who suffered a stroke or other injury is having trouble making these distinctions.”

[Source(s): University at Buffalo, Newswise]

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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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[BOOK] Neuroimaging In Epilepsy Surgery

Russell A. Reeves; Richard Gorniak.Author Information

Introduction

A seizure is a transient occurrence of abnormal excessive or synchronous neuronal activity in the brain. Seizures manifest in different ways based on the anatomic regions of hyperactive neuronal activity. For example, patients may develop focal symptoms due to abnormal activity in the temporal lobe, whereas global signs represent widespread aberrant neuronal activity. Seizures may initially manifest as focal symptoms with subsequent generalization to the remaining cortex. Furthermore, patients may or may not lose consciousness during a seizure, depending on whether or not the limbic structures and brainstem are involved.[1]

Seizure activity in the brain can be caused by numerous anatomic abnormalities such as tumors, infection, inflammatory/autoimmune processes, vascular malformations, stroke, trauma, cortical malformations/dysplasias, gray matter heterotopias, mesial temporal sclerosis, encephaloceles or other acquired or developmental abnormalities.[2] Patients may have seizures due to medical factors such as metabolic derangement, withdrawal, hyperthermia, or toxins as well. However, patients may also suffer from recurrent seizures without known underlying etiology. Patients with at least two unprovoked seizures separated by at least 24 hours may be diagnosed with epilepsy.

Seizure management relies on the treatment of the underlying etiology and/or anti-seizure drug therapy, and, for most patients, part of the evaluation for the underlying cause requires diagnostic workup with imaging. Various diagnostic imaging modalities may be used for patients with recurrent seizures, many adding complementary information for the care of these patients. Furthermore, diagnostic imaging can provide information that localizes epileptogenic lesions in patients with refractory epilepsy that require surgical intervention, potentially obviating the need for invasive electroencephalography (EEG). As such, understanding the uses and limitations of each modality is of critical importance for the treatment of these patients.Go to:

Anatomy

Seizures can manifest as a result of a wide variety of anatomic abnormalities within the brain as well as toxic or metabolic derangements. Anatomic abnormalities that result in seizures can be located nearly anywhere within the brain, though usually involve the neocortex or mesial temporal region, particularly the hippocampi. Because of this, imaging is typically employed to adequately scrutinize all structures of the brain with careful attention directed towards the hippocampi.

The hippocampi are situated within the medial aspect of the temporal lobes bilaterally and occupy the medial floor of the lateral ventricles. They are a core limbic structure, responsible for learning and memory formation. The hippocampus is composed of two distinct gray matter structures known as the cornu ammonis and the dentate gyrus. Anterior to posterior it is divided into the head, body, and tail segments. On coronal imaging, the hippocampal head is characterized by digitations which give its superior surface an undulating contour. The hippocampal body can be recognized by its “jelly roll” or “swiss roll” appearance of the interlocking dentate gyrus, Ammon horn, and intervening strata. White matter tracts from the hippocampus traverse its superior surface, forming the alveus, which condenses into bundles called fimbria, which continue posteriorly as the fornix. The fornix terminates just off of midline within the mamillary body; this white matter tract plays a vital role in the Papez circuit. Adjacent to the head of the hippocampus lies the amygdala and entorhinal cortex.[3]

Hippocampal sulcus remnant cysts and incomplete hippocampal inversion are developmental variants in the hippocampus, which should not be confused with pathology.

The bilateral and symmetric nature of the hippocampi allows for direct comparison during imaging. Unilateral abnormalities may shed light on the underlying etiology of a patient’s refractory epilepsy. However, gross abnormalities of the hippocampi may be bilateral in up to 10% of cases.[3] Additionally, hippocampal lesions can be associated with extra hippocampal epileptogenic lesions. Proper identification of hippocampal abnormalities is critical for patients with medically refractory epilepsy, as surgical resection of the epileptogenic focus is the standard treatment for these patients. Go to:

Plain Films

Plain radiography represents the earliest form of diagnostic imaging. X-rays are used to generate image contrast based on differences in tissue attenuation. Because the soft tissues of the brain exhibit similar attenuation characteristics, the use of plain radiographs to evaluate for structural lesions within the brain is extremely limited. As such, plain radiographs play no role in the diagnostic workup for patients suffering from seizures. Go to:

Computed Tomography

Computed tomography (CT) utilizes helically acquired x-rays and postprocessing techniques to generate cross-sectional images. Modern CT scanners receive x-ray attenuation data in a nearly isotropic manner, which allows the generation of voxels that can be reconstructed in coronal, sagittal, and three-dimensional formats. As with the limitations of plain radiographs, the brain soft tissues are poorly evaluated on routine CT examinations because the attenuation characteristics of the brain soft tissues and many pathologies are similar. Iodinated intravenous contrast can highlight the vascular structures of the brain or areas of enhancement. Seizure activity may result in increased cortical enhancement due to increased cortical perfusion but is typically an unanticipated observation in patients that are not suspected of seizures rather than a sought out diagnostic finding. As such, CT plays a limited role in the imaging workup of patients considering epilepsy surgery.Go to:

Magnetic Resonance

Magnetic resonance imaging (MRI) is the preferred diagnostic modality for patients with seizures. MRI offers excellent signal-to-noise and contrast within the brain. Seizures that are attributed to known metabolic arrangements may not necessarily require further diagnostic studies; however, nearly every patient that suffers from an unexplained seizure should undergo an MRI to evaluate for underlying structural brain abnormalities. Virtually any MRI can be used to assess for mass lesions within the brain, but high field strength scanners, more than 1.5 Tesla, should be used for evaluating patients with epilepsy when possible.[4] Specialized imaging protocols have been developed which optimize subtle signal intensity alterations and anatomic abnormalities within the hippocampi. This is particularly critical for patients undergoing evaluation for surgical management of intractable epilepsy, as small abnormalities within the hippocampi may be undetectable without specialized techniques. 

Patients with medically intractable partial complex epilepsy are most commonly affected by mesial temporal sclerosis (MTS). MRI is essential in identifying MTS, as it has characteristic findings of volume loss and increased T2/FLAIR signal intensity due to hippocampal neuronal cell death and gliosis. There may also be associated atrophy within the ipsilateral amygdala, entorhinal cortex, fornix, or mammillary body. Identifying these subtle differences requires the acquisition of a 1 mm isotropic series with T1 weighting and FLAIR. Reconstructions must be performed perpendicular to the plane of the hippocampi to allow adequate side-to-side comparison. A coronal T2-weighted series should also be obtained with 2 mm slices and sub-mm in-plane resolution to allow both side-to-side comparisons of the hippocampi as well as to delineate the typical internal architecture.[5][6] These sequences are also useful in detecting focal cortical dysplasias, gray matter heterotopias, and small encephaloceles. 

Intravenous contrast may improve the utility of MRI depending on the clinical circumstances. Gadolinium-based contrast agents act to increase the T1 signal, highlighting vascular structures and blood-brain barrier abnormalities. A common approach to patients with seizures is to perform non-enhanced MRI sequences initially and only to administer contrast if the nonenhanced study requires further investigation.[7] That said, patients with intractable epilepsy undergoing evaluation for possible surgical treatment do not routinely require intravenous contrast. Finally, it should be noted that MRI does not require the use of ionizing radiation, where this is a necessary consequence of CT imaging.

Although MRI can be useful for the detection of underlying structural lesions, MRI can also be used to evaluate brain physiology. In patients being evaluated for surgical resection, functional MRI (fMRI) is useful in identifying the language laterality[8] and can, in many instances, replace the invasive Wada test.Go to:

Ultrasonography

Ultrasound utilizes high-frequency sound waves to generate diagnostic images. The advantage of ultrasound is that no ionizing radiation is required for its use. Unfortunately, calcified structures such as the bones of the calvarium preclude adequate sound transmission for an ultrasound to be useful in diagnostic imaging of the brain. As such, ultrasound plays no role in the evaluation of patients with seizures.Go to:

Nuclear Medicine

Nuclear imaging plays an adjunctive role in seizure imaging. Due to its technical limitations, nuclear studies are not considered to be first-line imaging modalities for patients with seizures. However, there are circumstances where nuclear imaging studies add complementary information to that of traditional cross-sectional imaging such as MRI.[4]

Positron emission tomography with fluorodeoxyglucose (FDG-PET) allows for metabolic imaging within the brain. The fluorodeoxyglucose is actively taken up by neuronal cells, in an activation-dependent distribution. Thus, FDG uptake is increased in parts of the brain during a seizure, and conversely, uptake is decreased within the seizure focus interictally. These temporal factors contribute significant limitations to FDG-PET imaging, making it technically challenging to obtain the images either during a seizure or immediately after.[9] Furthermore, PET imaging has low resolution compared to CT and MRI, with a resolution limit of approximately 1 cm. Because of this limitation, FDG-PET imaging is often co-registered with either CT or MRI data to provide useful colocalization between the foci of metabolic abnormality and anatomic structures.[5] However, in patients with suspected temporal lobe epilepsy, interictal FDG-PET is frequently useful in seizure localization, especially in patients with normal MRI scans.[10]

Single-photon emission CT (SPECT) produces images through the use of radioisotope production of gamma rays. These radioisotopes are linked to parent molecules, known as radiopharmaceuticals. Radiopharmaceuticals such as Tc99m-HMPAO do not cross the blood-brain barrier and act as perfusion agents within the brain. Through rapid intravenous administration of a radiopharmaceutical within 90 seconds of seizure onset, regions of increased perfusion within the brain can be identified, which correspond to the seizure focus. Similarly, postictal administration results in decreased cerebral blood flow in the epileptogenic center.[4] Subtracting ictal and interictal SPECT studies with coregistration to an MRI (SISCOM) improves the utility of SPECT imaging.[11] However, radiopharmaceutical administration within 90 seconds of seizure onset is technically challenging, limiting the utility of SPECT imaging.[12] This modality is most commonly used when conventional MRI imaging, electroencephalograms, and other adjunct tests are equivocal in seizure focus localization.Go to:

Angiography

As described previously, both CT and magnetic resonance angiography are uncommonly performed in patients with seizure disorders. A notable exception includes patients who are suspected of having underlying ischemic or vascular disease within the head or neck. Outside of these narrow indications, cross-sectional angiography is seldomly performed during the routine seizure workup. Additionally, more invasive tests such as catheter-based angiography may be used for further delineation of vascular pathology but are rarely required. Before surgical interventions, vascular imaging may be warranted, but this is dependent on the individual clinical scenario.Go to:

Patient Positioning

Before the advent of modern CT and MRI scanners, patient positioning was of critical importance to obtain accurate and useful diagnostic images. However, modern scanners can acquire data in an isotropic fashion, which permits post imaging processing and reconstruction.[13] Prior to this technical development, adjusting for differences in patient positioning was not easily performed. Nearly all studies are performed supine with the patient lying in a comfortable position. This is particularly relevant for MRI studies since image quality is significantly degraded with even small patient movements; as such, ensuring that a patient can maintain a particular position long enough for image acquisition is of considerable technical importance. Similar principles also apply to FDG-PET and SPECT imaging studies.Go to:

Clinical Significance

Gaining a full understanding of the various imaging modalities for patients who suffer from seizures is of critical importance. Nearly one-third of patients with epilepsy will not achieve remission with antiepileptic medications alone, and many patients will have underlying anatomic abnormalities that may offer a surgical cure.[2] Specialized MRI protocols are necessary to thoroughly scrutinize the hippocampi, as many of these patients will develop or demonstrate mesial temporal sclerosis. More subtle abnormalities, such as focal cortical dysplasias, require advanced postprocessing techniques and expert neuroradiologists for a full evaluation.[14] Patients with structural lesions concordant with seizure localization on EEG should receive a preoperative fMRI to map regions of eloquent neocortex. In cases where no anatomic lesion is identified on MRI, FDG-PET may offer complementary information, revealing areas of hypometabolism or help guide the placement of intracranial EEG electrodes. Finally, if PET fails to aid localization, ictal, and interictal SPECT imaging can be performed to evaluate dynamic changes in cerebral perfusion, helping direct the placement of intracranial EEG electrodes for definitive localization of an epileptogenic focus. Further advanced imaging equipment and techniques are under development and available at a limited number of institutions, including the use of 7T MRI. This increased field strength offers an opportunity to detect even more subtle lesions, potentially increasing overall imaging sensitivity and providing curative surgery to a greater proportion of patients.[8] Advanced neuroimaging will continue to play an evolving role in the surgical management of patients with medication-refractory epilepsy. Go to:

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