The work of Dr. Biesbroek is supported by a Young Talent Fellowship from the Brain Center Rudolf Magnus of the University Medical Center Utrecht. This work and the Meta VCI Map consortium are supported by Vici Grant 918.16.616 from ZonMw, The Netherlands, Organisation for Health Research and Development, to Geert Jan Biessels. The authors would like to thank Dr. Tanja C.W. Nijboer for sharing scans that were used in one of the figures.

The authors disclose no conflicts of interest.

LSM is a powerful tool to study the functional architecture of the human brain. A crucial step in any lesion-symptom mapping study is the preprocessing of imaging data, segmentation of the lesion and registration to a brain template. Here, we report a standardized pipeline for lesion segmentation and registration for the purpose of lesion-symptom mapping. This method can be performed with freely available image processing tools, can be used to process both CT and structural MRI scans, and covers the entire process of pre...

Lesion-symptom mapping (LSM), also called lesion-behavior mapping, is an important tool for studying the functional architecture of the human brain1. In lesion studies, brain function is inferred and localized by studying patients with acquired brain lesions. The first case studies linking neurological symptoms to specific brain locations performed in the nineteenth century&#160;already provided fundamental insights into the anatomical correlates of language and several other cognitive processes2. Yet, the neuroanatomical correlates of many aspects of cognition and&#160;other brain functions remained elusive. In the past decades, improved structural brain imaging methods and technical advances have enabled large-scale in vivo LSM studies with high spatial resolution (i.e., at the level of individual voxels or specific cortical/subcortical regions of interest)1,2. With these methodological advances, LSM has become an increasingly popular method in cognitive neuroscience and continues to offer new insights into the neuroanatomy of cognition and neurological symptoms3. A crucial step in any LSM study is the accurate segmentation of lesions and registration to a brain template. However, a comprehensive tutorial for the preprocessing of brain imaging data for the purpose of LSM is lacking.
Provided here is a complete tutorial for a standardized lesion segmentation and registration method. This method provides researchers with a pipeline for&#160;standardized brain image processing and an overview of potential pitfalls that must be avoided. The presented image processing pipeline was developed through international collaborations4 and is part of the framework of the recently founded Meta VCI map consortium, whose purpose is performing multicenter lesion-symptom mapping studies in vascular cognitive impairment &#60;www.metavcimap.org&#62;5. This method has been designed to process both CT and MRI scans from multiple vendors and heterogeneous scan protocols to allow combined processing of imaging datasets from different sources. The required RegLSM software and all other software needed for this protocol is freely available except for MATLAB, which requires a license. This tutorial focuses on the segmentation and registration of brain infarcts, but this image processing pipeline can also be used for other lesions, such as white matter hyperintensities6.
Prior to initiating an LSM study, a basic understanding of the general concepts and pitfalls is required. Several detailed guidelines and a hitchhiker&#39;s guide are available1,3,6. However, these reviews do not provide a detailed hands-on tutorial for the practical steps involved in gathering and converting brain scans to a proper format, segmenting the brain infarct, and registering the scans to a brain template. The present paper provides such a tutorial. General concepts of LSM are provided in the introduction with references for further reading on the subject.
General aim of lesion-symptom mapping studies
From the perspective of cognitive neuropsychology, brain injury can be used as a model condition to better understand the neuronal underpinnings of certain cognitive processes and to obtain a more complete picture of the cognitive architecture of the brain1. This is a classic approach in neuropsychology that was first applied in post-mortem studies in the nineteenth century by pioneers like Broca and Wernicke2. In the era of functional brain imaging, the lesion approach has remained a crucial tool in neuroscience because it provides proof that lesions in a specific brain region disrupt task performance, while functional imaging studies demonstrate brain regions that are activated during the task performance. As such, these approaches provide complementary information1.
From the perspective of clinical neurology, LSM studies can clarify the relationship between the lesion location and cognitive functioning in patients with acute symptomatic infarcts, white matter hyperintensities, lacunes, or other lesion types (e.g., tumors). Recent studies have shown that such lesions in strategic brain regions are more relevant in explaining cognitive performance than global lesion burden2,5,7,8. This approach has the potential to improve understanding of the pathophysiology of complex disorders (in this example, vascular cognitive impairment) and may provide opportunities for developing new diagnostic and prognostic tools or supporting treatment strategies2.
LSM also has applications beyond the field of cognition. In fact, any variable can be related to lesion location, including clinical symptoms, biomarkers, and functional outcome. For example, a recent study determined infarct locations that were predictive of functional outcome after ischemic stroke10.
Voxel-based versus region of interest-based lesion-symptom mapping
To perform lesion-symptom mapping, lesions need to be segmented and registered to a brain template. During the registration procedure, each patient&#39;s brain is spatially aligned (i.e., normalized or registered to a common template) to correct for differences in brain size, shape, and orientation so that each voxel in the lesion map represents the same anatomical structure for all patients7. In standard space, several types of analyses can be performed, which are briefly summarized here.
A crude lesion-subtraction analysis can be performed to show the difference in lesion distribution in patients with deficits compared to patients without deficits. The resulting subtraction map show regions that are more often damaged in patients with deficits and spared in patients without deficits1. Though a lesion-subtraction analysis can provide some insights into correlates of a specific function, it provides no statistical proof and is now mostly used when the sample size is too low to provide enough statistical power for voxel-based lesion-symptom mapping.
In voxel-based lesion-symptom mapping, an association between the presence of a lesion and cognitive performance is determined at the level of each individual voxel in the brain (Figure 1). The main advantage of this method is the high spatial resolution. Traditionally, these analyses have been performed in a mass-univariate approach, which warrants correction for multiple testing and introduces a spatial bias caused by inter-voxel correlations that are not taken into account1,10,11. Recently developed approaches that do take inter-voxel correlations into account (usually referred to as multivariate lesion-symptom mapping methods, such as Bayesian analysis13, support vector regression4,14, or other machine learning algorithms15) show promising results and appear to improve the sensitivity and specificity of findings from voxel-wise LSM analyses compared to traditional methods. Further improvement and validation of multivariate methods for voxel-wise LSM is an ongoing process. The best method choice for specific lesion-symptom mapping depends on many factors, including the distribution of lesions, outcome variable, and underlying statistical assumptions of the methods.
In the region of interest (ROI)-based lesion-symptom mapping, an association between the lesion burden within a specific brain region and cognitive performance is determined (see Figure 1 in Biesbroek et al.2 for an illustration). The main advantage of this method is that it considers the cumulative lesion burden within an anatomical structure, which in some cases may be more informative than a lesion in a single voxel. On the other hand, ROI-based analyses have limited power for detecting patterns that are only present in a subset of voxels in the region16. Traditionally, ROI-based lesion-symptom mapping is performed using logistic or linear regression. Recently, multivariate methods that deal better with collinearity have been introduced (e.g., Bayesian network analysis17, support vector regression4,18, or other machine learning algorithms19), which may improve the specificity of findings from lesion-symptom mapping studies.
Patient selection
In LSM studies, patients are usually selected based on a specific lesion type (e.g., brain infarcts or white matter hyperintensities) and the time interval between diagnosis and neuropsychological assessment (e.g., acute vs. chronic stroke). The optimal study design depends on the research question. For example, when studying the functional architecture of the human brain, acute stroke patients are ideally included because functional reorganization has not yet occurred in this stage, whereas chronic stroke patients should be included when studying the long-term effects of stroke on cognition. A detailed description of considerations and pitfalls in patient selection is provided elsewhere7.
Brain image preprocessing for the purpose of lesion-symptom mapping
Accurate lesion segmentation and registration to a common brain template are crucial steps in lesion-symptom mapping. Manual segmentation of lesions remains the gold standard for many lesion types, including infarcts7. Provided is a detailed tutorial on criteria for manual infarct segmentation on CT scans, diffusion weighted imaging (DWI), and fluid-attenuated inversion recovery (FLAIR) MRI sequences in both acute and chronic stages. The segmented infarcts (i.e., the 3D binary lesion maps) need to be registered before any across-subject analyses are performed. This protocol uses the registration method RegLSM, which was developed in a multicenter setting4. RegLSM applies linear and non-linear registration algorithms based on elastix20 for both CT and MRI, with an additional CT processing step specifically designed to enhance registration quality of CT scans21. Furthermore, RegLSM allows for using different target brain templates and an (optional) intermediate registration step to an age-specific CT/MRI template22. The possibility of processing both CT and MRI scans and its customizability regarding intermediate and target brain templates makes RegLSM a highly suitable image processing tool for LSM. The entire process of preparing and segmenting CT/MRI scans, registration to a brain template, and manual corrections (if required) are described in the next section.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/59653/59653fig1.jpg" /> 
Figure 1: Schematic illustration of the concept of voxel-based lesion-symptom mapping. The upper part shows the brain image pre-processing steps consisting of segmenting the lesion (an acute infarct in this case) followed by registration to a brain template (the MNI-152 template in this case). Below, a part of the registered binary lesion map of the same patient is shown as a 3D grid, where each cube represents a voxel. Taken together with the lesion maps of 99 other patients, a lesion overlay map is generated. For each voxel, a statistical test is performed to determine the association between lesion status and cognitive performance. The chi-squared test shown here is just an example, any statistical test could be used. Typically, hundreds of thousands of voxels are tested throughout the brain, followed by a correction for multiple comparisons. <a href="https://www-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/59653/59653fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

<table><tbody><tr><td>dcm2niix</td><td>N/A</td><td>N/A</td><td>free download https://github.com/rordenlab/dcm2niix</td></tr><tr><td>ITK-SNAP</td><td>N/A</td><td>N/A</td><td>free download www.itksnap.org</td></tr><tr><td>MATLAB</td><td>MathWorks</td><td>N/A</td><td>Version 2015a or higher</td></tr><tr><td>MRIcron</td><td>N/A</td><td>N/A</td><td>free download https://www.nitrc.org/projects/mricron</td></tr><tr><td>RegLSM</td><td>N/A</td><td>N/A</td><td>free download www.metavcimap.org/support/software-tools</td></tr><tr><td>SPM12b</td><td>N/A</td><td>N/A</td><td>free download https://www.fil.ion.ucl.ac.uk/spm/</td></tr></tbody></table>

brain infarct segmentation and registration on mri or ct for lesion-symptom mapping

In lesion-symptom mapping (LSM), brain function is inferred by relating the location of acquired brain lesions to behavioral or cognitive symptoms in a group of patients. With recent advances in brain imaging and image processing, LSM has become a popular tool in cognitive neuroscience. LSM can provide fundamental insights into the functional architecture of the human brain for a variety of cognitive and non-cognitive functions. A crucial step in performing LSM studies is the segmentation of lesions on brains scans of a large group of patients and registration of each scan to a common stereotaxic space (also called standard space or a standardized brain template). Described here is an open-access, standardized method for infarct segmentation and registration for the purpose of LSM, as well as a detailed and hands-on walkthrough based on exemplary cases. A comprehensive tutorial for the manual segmentation of brain infarcts on CT scans and DWI or FLAIR MRI sequences is provided, including criteria for infarct identification and pitfalls for different scan types. The registration software provides multiple registration schemes that can be used for processing of CT and MRI data with heterogeneous acquisition parameters. A tutorial on using this registration software and performing visual quality checks and manual corrections (which are needed in some cases) is provided. This approach provides researchers with a framework for the entire process of brain image processing required to perform an LSM study, from gathering of the data to final quality checks of the results.

Exemplar cases of brain infarct segmentations on CT (Figure 3), DWI (Figure 5), and FLAIR (Figure 6) images, and subsequent registration to the MNI-152 template are provided here. The registration results shown in Figure 3B and Figure 5C were not entirely successful, as there was misalignment near the frontal horn of the ventricle. The regis...

Watch this Scientific Journal Video about Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping at JoVE.com

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Provided here is a practical tutorial for an open-access, standardized image processing pipeline for the purpose of lesion-symptom mapping. A step-by-step walkthrough is provided for each processing step, from manual infarct segmentation on CT/MRI to subsequent registration to standard space, along with practical recommendations and illustrations with exemplary cases.

In lesion-symptom mapping (LSM), brain function is inferred by relating the location of acquired brain lesions to behavioral or cognitive symptoms in a ...

brain-infarct-segmentation-registration-on-mri-or-ct-for-lesion

Universidad San Sebastian

Research

JoVE Journal

Behavior

47.7K Views.  Utrecht University. Provided here is a practical tutorial for an open-access, standardized image processing pipeline for the purpose of lesion-symptom mapping. A step-by-step walkthrough is provided for each processing step, from manual infarct segmentation on CT/MRI to subsequent registration to standard space, along with practical recommendations and illustrations with exemplary cases.

Video: Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

MRI and PET in Mouse Models of Myocardial Infarction

Myocardial infarction is one of the leading causes of death in the Western world. The similarity of the mouse heart to the human heart has made it an ideal model for testing novel therapeutic strategies.
In vivo magnetic resonance imaging (MRI) gives excellent views of the heart noninvasively with clear anatomical detail, which can be used for accurate functional assessment. Contrast agents can provide basic measures of tissue viability but these are nonspecific. Positron emission tomography (PET) is a complementary technique that is highly specific for molecular imaging, but lacks the anatomical detail of MRI. Used together, these techniques offer a sensitive, specific and quantitative tool for the assessment of the heart in disease and recovery following treatment.
In this paper we explain how these methods are carried out in mouse models of acute myocardial infarction. The procedures described here were designed for the assessment of putative protective drug treatments. We used MRI to measure systolic function and infarct size with late gadolinium enhancement, and PET with fluorodeoxyglucose (FDG) to assess metabolic function in the infarcted region. The paper focuses on practical aspects such as slice planning, accurate gating, drug delivery, segmentation of images, and multimodal coregistration. The methods presented here achieve good repeatability and accuracy maintaining a high throughput.

We describe how to perform MRI and PET imaging of the mouse heart. The protocol is tailored to assess treatment efficacy in models of myocardial infarction and heart failure.

Myocardial infarction is one of the leading causes of death in the Western world. The similarity of the mouse heart to the human heart has made it an ...

Medicine

Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly

Obtaining in vivo human brain tissue volumetrics from MRI is often complicated by various technical and biological issues. These challenges are exacerbated when significant brain atrophy and age-related white matter changes (e.g. Leukoaraiosis) are present. Lesion Explorer (LE) is an accurate and reliable neuroimaging pipeline specifically developed to address such issues commonly observed on MRI of Alzheimer's disease and normal elderly. The pipeline is a complex set of semi-automatic procedures which has been previously validated in a series of internal and external reliability tests1,2. However, LE's accuracy and reliability is highly dependent on properly trained manual operators to execute commands, identify distinct anatomical landmarks, and manually edit/verify various computer-generated segmentation outputs.
LE can be divided into 3 main components, each requiring a set of commands and manual operations: 1) Brain-Sizer, 2) SABRE, and 3) Lesion-Seg. Brain-Sizer's manual operations involve editing of the automatic skull-stripped total intracranial vault (TIV) extraction mask, designation of ventricular cerebrospinal fluid (vCSF), and removal of subtentorial structures. The SABRE component requires checking of image alignment along the anterior and posterior commissure (ACPC) plane, and identification of several anatomical landmarks required for regional parcellation. Finally, the Lesion-Seg component involves manual checking of the automatic lesion segmentation of subcortical hyperintensities (SH) for false positive errors.
While on-site training of the LE pipeline is preferable, readily available visual teaching tools with interactive training images are a viable alternative. Developed to ensure a high degree of accuracy and reliability, the following is a step-by-step, video-guided, standardized protocol for LE's manual procedures.

Lesion Explorer (LE) is a semi-automatic, image-processing pipeline developed to obtain regional brain tissue and subcortical hyperintensity lesion volumetrics from structural MRI of Alzheimer's disease and normal elderly. To ensure a high level of accuracy and reliability, the following is a video-guided, standardized protocol for LE's manual procedures.

Obtaining in vivo human brain tissue volumetrics from MRI is often complicated by various technical and biological issues. These challenges are ...

Assessing Cortical Cerebral Microinfarcts on High Resolution MR Images

Cerebral microinfarcts are frequent findings in the post-mortem human brain, and are related to cognitive decline and dementia. Due to their small sizes it is challenging to study them on clinical MRI scans. It was recently demonstrated that cortical microinfarcts can be depicted with MRI scanners using high magnetic field strengths (7T). Based on this experience, a proportion of these lesions is also visible on lower resolution 3T MRI. These findings were corroborated with ex vivo imaging of post-mortem human brain tissue, accompanied by histopathological verification of possible cortical microinfarcts.
Here an ex vivo imaging protocol is presented, for the purpose of validating MR observed cerebral microvascular pathology with histological evaluation. Furthermore, guidelines are provided for the assessment of cortical microinfarcts on both in vivo 7T and 3T MR images. These guidelines provide researchers with a tool to rate cortical microinfarcts on in vivo images of larger patient samples, to further unravel their clinical relevance in cognitive decline and dementia, and establish these lesions as a novel biomarker of cerebral small vessel disease.

A high resolution ex vivo 7T MR imaging protocol is presented, to perform MR-guided histopathological validation of microvascular pathology in post-mortem human brain tissue. Furthermore, guidelines are provided for the assessment of cortical microinfarcts on in vivo 7T as well as 3T MR images.

Cerebral microinfarcts are frequent findings in the post-mortem human brain, and are related to cognitive decline and dementia. Due to their small sizes ...

A Magnetic Resonance Imaging Protocol for Stroke Onset Time Estimation in Permanent Cerebral Ischemia

MRI provides a sensitive and specific imaging tool to detect acute ischemic stroke by means of a reduced diffusion coefficient of brain water. In a rat model of ischemic stroke, differences in quantitative T1 and T2 MRI relaxation times (qT1 and qT2) between the ischemic lesion (delineated by low diffusion) and the contralateral non-ischemic hemisphere increase with time from stroke onset. The time dependency of MRI relaxation time differences is heuristically described by a linear function and thus provides a simple estimate of stroke onset time. Additionally, the volumes of abnormal qT1 and qT2 within the ischemic lesion increase linearly with time providing a complementary method for stroke timing. A (semi)automated computer routine based on the quantified diffusion coefficient is presented to delineate acute ischemic stroke tissue in rat ischemia. This routine also determines hemispheric differences in qT1 and qT2 relaxation times and the location and volume of abnormal qT1 and qT2 voxels within the lesion. Uncertainties associated with onset time estimates of qT1 and qT2 MRI data vary from &plusmn; 25 min to &plusmn; 47 min for the first 5 hours of stroke. The most accurate onset time estimates can be obtained by quantifying the volume of overlapping abnormal qT1 and qT2 lesion volumes, termed 'Voverlap' (&plusmn; 25 min) or by quantifying hemispheric differences in qT2 relaxation times only (&plusmn; 28 min). Overall, qT2 derived parameters outperform those from qT1. The current MRI protocol is tested in the hyperacute phase of a permanent focal ischemia model, which may not be applicable to transient focal brain ischemia.

A protocol for stroke onset time estimation in a rat model of stroke exploiting quantitative magnetic resonance imaging (qMRI) parameters is described. The procedure exploits diffusion MRI for delineation of the acute stroke lesion and quantitative T1 and T2 (qT1 and qT2) relaxation times for timing of stroke.

MRI provides a sensitive and specific imaging tool to detect acute ischemic stroke by means of a reduced diffusion coefficient of brain water. In a rat ...

Neuroscience

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

Within neuroimaging research, a number of recent studies have discussed the impact of between-study differences in volumetric findings that are thought to result from the use of different segmentation tools to generate brain volumes. Here, processing pipelines for seven automated tools that can be used to segment grey matter within the brain are presented. The protocol provides an initial step for researchers aiming to find the most accurate method for generating grey matter volumes from T1-weighted MRI scans. Steps to undertake detailed visual quality control are also included in the manuscript. This protocol covers a range of potential segmentation tools and encourages users to compare the performance of these tools within a subset of their data before selecting one to apply to a full cohort. Furthermore, the protocol may be further generalized to the segmentation of other brain regions.

This protocol describes the process of applying seven different automated segmentation tools to structural T1-weighted MRI scans to delineate grey matter regions that can be used for the quantification of grey matter volume.

Within neuroimaging research, a number of recent studies have discussed the impact of between-study differences in volumetric findings that are thought to ...

PET Imaging of Neuroinflammation Using [11C]DPA-713 in a Mouse Model of Ischemic Stroke

Neuroinflammation is central to the pathological cascade following ischemic stroke. Non-invasive molecular imaging methods have the potential to provide critical insights into the temporal dynamics and role of certain neuroimmune interactions in stroke. Specifically, Positron Emission Tomography (PET) imaging of translocator protein 18 kDa (TSPO), a marker of activated microglia and peripheral myeloid-lineage cells, provides a means to detect and track neuroinflammation in vivo. Here, we present a method to accurately quantify neuroinflammation using [11C]N,N-Diethyl-2-[2-(4-methoxyphenyl)-5,7-dimethylpyrazolo[1,5-a]pyrimidin-3-yl]acetamide ([11C]DPA-713), a promising second generation TSPO-PET radiotracer, in distal middle cerebral artery occlusion (dMCAO) compared to sham-operated mice. MRI was performed 2 days post-dMCAO surgery to confirm stroke and define the infarct location and volume. PET/Computed Tomography (CT) imaging was carried out 6 days post-dMCAO to capture the peak increase in TSPO levels following stroke. Quantitation of PET images was conducted to assess the uptake of [11C]DPA-713 in the brain and spleen of dMCAO and sham mice to assess central and peripheral levels of inflammation. In vivo [11C]DPA-713 brain uptake was confirmed using ex vivo autoradiography.

PET Imaging of Neuroinflammation Using [11C]DPA-713 in a Mouse Model of Ischemic Stroke

Positron Emission Tomography (PET) imaging of translocator protein 18 kDa (TSPO) provides a non-invasive means to visualize the dynamic role of neuroinflammation in the development and progression of brain diseases. This protocol describes TSPO-PET and ex vivo autoradiography to detect neuroinflammation in a mouse model of ischemic stroke.

Neuroinflammation is central to the pathological cascade following ischemic stroke. Non-invasive molecular imaging methods have the potential to provide ...

Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI&#8212;Application in Premanifest Huntington's Disease

Recent advances in MRI offer a variety of useful markers to identify neurodegenerative diseases. In Huntington&#39;s disease (HD), regional brain atrophy begins many years prior to the motor onset (during the &#34;premanifest&#34; period), but the spatiotemporal pattern of regional atrophy across the brain has not been fully characterized. Here we demonstrate an online cloud-computing platform, &#34;MRICloud&#34;, which provides atlas-based whole-brain segmentation of T1-weighted images at multiple granularity levels, and thereby, enables us to access the regional features of brain anatomy. We then describe a regression model that detects statistically significant inflection points, at which regional brain atrophy starts to be noticeable, i.e. the &#34;change-point&#34;, with respect to a disease progression index. We used the CAG-age product (CAP) score to index the disease progression in HD patients. Change-point analysis of the volumetric measurements from the segmentation pipeline, therefore, provides important information of the order and pattern of structural atrophy across the brain. The paper illustrates the use of these techniques on T1-weighted MRI data of premanifest HD subjects from a large multicenter PREDICT-HD study. This design potentially has wide applications in a range of neurodegenerative diseases to investigate the dynamic changes of brain anatomy.

This paper describes a statistical model for volumetric MRI data analysis, which identifies the &#34;change-point&#34; when brain atrophy begins in premanifest Huntington&#39;s disease. Whole-brain mapping of the change-points is achieved based on brain volumes obtained using an atlas-based segmentation pipeline of T1-weighted images.

Recent advances in MRI offer a variety of useful markers to identify neurodegenerative diseases. In Huntington&#39;s disease (HD), regional brain atrophy ...

Comprehensive Autopsy Program for Individuals with Multiple Sclerosis

We describe a rapid tissue donation program for individuals with multiple sclerosis (MS) that requires scientists and technicians to be on-call 24/7, 365 days a year. Participants consent to donate their brain and spinal cord. Most patients were followed by neurologists at the Cleveland Clinic Mellen Center for MS Treatment and Research. Their clinical courses and neurological disabilities are well-characterized. Soon after death, the body is transported to the MS Imaging Center, where the brain is scanned in situ by 3 T magnetic resonance imaging (MRI). The body is then transferred to the autopsy room, where the brain and spinal cord are removed. The brain is divided into two hemispheres. One hemisphere is immediately placed in a slicing box and alternate 1 cm-thick slices are either fixed in 4% paraformaldehyde for two days or rapidly frozen in dry ice and 2-methylbutane. The short-fixed brain slices are stored in a cryopreservation solution and used for histological analyses and immunocytochemical detection of sensitive antigens. Frozen slices are stored at -80 &#176;C and used for molecular, immunocytochemical, and in situ hybridization/RNA scope studies. The other hemisphere is placed in 4% paraformaldehyde for several months, placed in the slicing box, re-scanned in the 3 T magnetic resonance (MR) scanner and sliced into centimeter-thick slices. Postmortem in situ MR images (MRIs) are co-registered with 1 cm-thick brain slices to facilitate MRI-pathology correlations. All brain slices are photographed and brain white-matter lesions are identified. The spinal cord is cut into 2 cm segments. Alternate segments are fixed in 4% paraformaldehyde or rapidly frozen. The rapid procurement of postmortem MS tissues allows pathological and molecular analyses of MS brains and spinal cords and pathological correlations of brain MRI abnormalities. The quality of these rapidly-processed postmortem tissues (usually within 6 h of death) is of great value to MS research and has resulted in many high-impact discoveries.

Multiple sclerosis is an inflammatory demyelinating disease with no cure. Analysis of brain tissue provides important clues to understanding the pathogenesis of the disease. Here we discuss the methodology and downstream analysis of MS brain tissue collected through a unique rapid autopsy program in operation at the Cleveland Clinic.

We describe a rapid tissue donation program for individuals with multiple sclerosis (MS) that requires scientists and technicians to be on-call 24/7, 365 ...

Quantification of Cerebral Perfusion using Laser Speckle Imaging and Infarct Volume using MRI in a Pre-clinical Model of Posterior Circulation Stroke

Background: Basilar artery occlusion (BAO) is a subset of posterior circulation stroke that carries a mortality as high as 90%.&#160; The current clinical standard to diagnose ischemic stroke include computerized tomography (CT), CT angiography and perfusion and magnetic resonance imaging (MRI). Large animal pre-clinical models to accurately reflect the clinical disease as well as methods to assess stroke burden and evaluate treatments are lacking.
Methods: We describe a canine model of large vessel occlusion (LVO) stroke in the posterior circulation, and developed a laser speckle imaging (LSI) protocol to monitor perfusion changes in real time.&#160; We then utilized high b-value DWI (b=1800s/mm2) MRI to increase detection sensitivity. We also evaluated the ability of magnetic resonance angiography (MRA) to assess arterial occlusion and correlate with DSA. Finally, we verified infarct size from apparent diffusion coefficient (ADC) mapping with histology.&#160;
Results:&#160; Administration of thromboembolism occluded the basilar artery as tracked by DSA (n=7).&#160; &#160;LSI correlated with DSA, demonstrating a reduction in perfusion after stroke onset that persisted throughout the experiment, allowing us to monitor perfusion in real time.&#160; DWI with an optimized b-value for dogs illustrated the stroke volume and allowed us to derive ADC and magnetic resonance angiography (MRA) images. The MRA performed at the end of the experiment correlated with DSA performed after occlusion. Finally, stroke burden on MRI correlated with histology.
Conclusions: Our studies demonstrate real time perfusion imaging using LSI of a canine thromboembolic LVO model of posterior circulation stroke, which utilizes multimodal imaging important in the diagnosis and treatment of ischemic stroke.

A canine model of LVO stroke was utilized to develop laser speckle imaging to monitor cerebral perfusion in real-time.&#160; Diffusion-weighted MRI was optimized to image infarct volume utilizing a high b-value, enabling ADC and MRA, correlated with DSA at the time of stroke.&#160; Finally, ADC reconstructions correlated with histological findings.

Background: Basilar artery occlusion (BAO) is a subset of posterior circulation stroke that carries a mortality as high as 90%.&#160; The current clinical ...

A Model of Transient Cerebral Ischemia by Occlusion of Middle Cerebral Artery

Transient Middle Cerebral Artery Occlusion Model of Stroke

Stroke stands as a major cause of death or chronic disability globally. Nevertheless, existing optimal treatments are limited to reperfusion therapies during the acute phase of ischemic stroke. To gain insights into stroke physiopathology and develop innovative therapeutic approaches, in vivo rodent models of stroke play a fundamental role. The availability of genetically modified animals has particularly propelled the use of mice as experimental stroke models.
In stroke patients, occlusion of the middle cerebral artery (MCA) is a common occurrence. Consequently, the most prevalent experimental model involves intraluminal occlusion of the MCA, a minimally invasive technique that doesn't require craniectomy. This procedure involves inserting a monofilament through the external carotid artery (ECA) and advancing it through the internal carotid artery (ICA) until it reaches the branching point of the MCA. After a 45 min arterial occlusion, the monofilament is removed to allow reperfusion. Throughout the process, cerebral blood flow is monitored to confirm the reduction during occlusion and subsequent recovery upon reperfusion. Neurological and tissue outcomes are evaluated using behavioral tests and magnetic resonance imaging (MRI) studies.

Author Spotlight: Assessing Ischemic Stroke Damage Through Middle Cerebral Artery Occlusion Model 

This protocol describes the model of transient focal cerebral ischemia in mice through intraluminal occlusion of the middle cerebral artery. Additionally, examples of outcome assessment are shown using magnetic resonance imaging and behavioral tests.

Stroke stands as a major cause of death or chronic disability globally. Nevertheless, existing optimal treatments are limited to reperfusion therapies ...

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

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