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Pubmed Article
Gaussian Mixture Models and Model Selection for [18F] Fluorodeoxyglucose Positron Emission Tomography Classification in Alzheimer's Disease.
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PLoS ONE
PUBLISHED: 04-29-2015
We present a method to discover discriminative brain metabolism patterns in [18F] fluorodeoxyglucose positron emission tomography (PET) scans, facilitating the clinical diagnosis of Alzheimer's disease. In the work, the term "pattern" stands for a certain brain region that characterizes a target group of patients and can be used for a classification as well as interpretation purposes. Thus, it can be understood as a so-called "region of interest (ROI)". In the literature, an ROI is often found by a given brain atlas that defines a number of brain regions, which corresponds to an anatomical approach. The present work introduces a semi-data-driven approach that is based on learning the characteristics of the given data, given some prior anatomical knowledge. A Gaussian Mixture Model (GMM) and model selection are combined to return a clustering of voxels that may serve for the definition of ROIs. Experiments on both an in-house dataset and data of the Alzheimer's Disease Neuroimaging Initiative (ADNI) suggest that the proposed approach arrives at a better diagnosis than a merely anatomical approach or conventional statistical hypothesis testing.
Authors: Rashna D. Balsara, Sarah E. Chapman, Ian M. Sander, Deborah L. Donahue, Lucas Liepert, Francis J. Castellino, W. Matthew Leevy.
Published: 12-28-2014
ABSTRACT
Stroke is the third leading cause of death among Americans 65 years of age or older1. The quality of life for patients who suffer from a stroke fails to return to normal in a large majority of patients2, which is mainly due to current lack of clinical treatment for acute stroke. This necessitates understanding the physiological effects of cerebral ischemia on brain tissue over time and is a major area of active research. Towards this end, experimental progress has been made using rats as a preclinical model for stroke, particularly, using non-invasive methods such as 18F-fluorodeoxyglucose (FDG) coupled with Positron Emission Tomography (PET) imaging3,10,17. Here we present a strategy for inducing cerebral ischemia in rats by middle cerebral artery occlusion (MCAO) that mimics focal cerebral ischemia in humans, and imaging its effects over 24 hr using FDG-PET coupled with X-ray computed tomography (CT) with an Albira PET-CT instrument. A VOI template atlas was subsequently fused to the cerebral rat data to enable a unbiased analysis of the brain and its sub-regions4. In addition, a method for 3D visualization of the FDG-PET-CT time course is presented. In summary, we present a detailed protocol for initiating, quantifying, and visualizing an induced ischemic stroke event in a living Sprague-Dawley rat in three dimensions using FDG-PET.
19 Related JoVE Articles!
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Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
Authors: Phoebe Spetsieris, Yilong Ma, Shichun Peng, Ji Hyun Ko, Vijay Dhawan, Chris C. Tang, David Eidelberg.
Institutions: The Feinstein Institute for Medical Research.
The scaled subprofile model (SSM)1-4 is a multivariate PCA-based algorithm that identifies major sources of variation in patient and control group brain image data while rejecting lesser components (Figure 1). Applied directly to voxel-by-voxel covariance data of steady-state multimodality images, an entire group image set can be reduced to a few significant linearly independent covariance patterns and corresponding subject scores. Each pattern, termed a group invariant subprofile (GIS), is an orthogonal principal component that represents a spatially distributed network of functionally interrelated brain regions. Large global mean scalar effects that can obscure smaller network-specific contributions are removed by the inherent logarithmic conversion and mean centering of the data2,5,6. Subjects express each of these patterns to a variable degree represented by a simple scalar score that can correlate with independent clinical or psychometric descriptors7,8. Using logistic regression analysis of subject scores (i.e. pattern expression values), linear coefficients can be derived to combine multiple principal components into single disease-related spatial covariance patterns, i.e. composite networks with improved discrimination of patients from healthy control subjects5,6. Cross-validation within the derivation set can be performed using bootstrap resampling techniques9. Forward validation is easily confirmed by direct score evaluation of the derived patterns in prospective datasets10. Once validated, disease-related patterns can be used to score individual patients with respect to a fixed reference sample, often the set of healthy subjects that was used (with the disease group) in the original pattern derivation11. These standardized values can in turn be used to assist in differential diagnosis12,13 and to assess disease progression and treatment effects at the network level7,14-16. We present an example of the application of this methodology to FDG PET data of Parkinson's Disease patients and normal controls using our in-house software to derive a characteristic covariance pattern biomarker of disease.
Medicine, Issue 76, Neurobiology, Neuroscience, Anatomy, Physiology, Molecular Biology, Basal Ganglia Diseases, Parkinsonian Disorders, Parkinson Disease, Movement Disorders, Neurodegenerative Diseases, PCA, SSM, PET, imaging biomarkers, functional brain imaging, multivariate spatial covariance analysis, global normalization, differential diagnosis, PD, brain, imaging, clinical techniques
50319
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Basics of Multivariate Analysis in Neuroimaging Data
Authors: Christian Georg Habeck.
Institutions: Columbia University.
Multivariate analysis techniques for neuroimaging data have recently received increasing attention as they have many attractive features that cannot be easily realized by the more commonly used univariate, voxel-wise, techniques1,5,6,7,8,9. Multivariate approaches evaluate correlation/covariance of activation across brain regions, rather than proceeding on a voxel-by-voxel basis. Thus, their results can be more easily interpreted as a signature of neural networks. Univariate approaches, on the other hand, cannot directly address interregional correlation in the brain. Multivariate approaches can also result in greater statistical power when compared with univariate techniques, which are forced to employ very stringent corrections for voxel-wise multiple comparisons. Further, multivariate techniques also lend themselves much better to prospective application of results from the analysis of one dataset to entirely new datasets. Multivariate techniques are thus well placed to provide information about mean differences and correlations with behavior, similarly to univariate approaches, with potentially greater statistical power and better reproducibility checks. In contrast to these advantages is the high barrier of entry to the use of multivariate approaches, preventing more widespread application in the community. To the neuroscientist becoming familiar with multivariate analysis techniques, an initial survey of the field might present a bewildering variety of approaches that, although algorithmically similar, are presented with different emphases, typically by people with mathematics backgrounds. We believe that multivariate analysis techniques have sufficient potential to warrant better dissemination. Researchers should be able to employ them in an informed and accessible manner. The current article is an attempt at a didactic introduction of multivariate techniques for the novice. A conceptual introduction is followed with a very simple application to a diagnostic data set from the Alzheimer s Disease Neuroimaging Initiative (ADNI), clearly demonstrating the superior performance of the multivariate approach.
JoVE Neuroscience, Issue 41, fMRI, PET, multivariate analysis, cognitive neuroscience, clinical neuroscience
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Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy
Authors: Zulfi Haneef, Agatha Lenartowicz, Hsiang J. Yeh, Jerome Engel Jr., John M. Stern.
Institutions: Baylor College of Medicine, Michael E. DeBakey VA Medical Center, University of California, Los Angeles, University of California, Los Angeles.
Functional connectivity MRI (fcMRI) is an fMRI method that examines the connectivity of different brain areas based on the correlation of BOLD signal fluctuations over time. Temporal Lobe Epilepsy (TLE) is the most common type of adult epilepsy and involves multiple brain networks. The default mode network (DMN) is involved in conscious, resting state cognition and is thought to be affected in TLE where seizures cause impairment of consciousness. The DMN in epilepsy was examined using seed based fcMRI. The anterior and posterior hubs of the DMN were used as seeds in this analysis. The results show a disconnection between the anterior and posterior hubs of the DMN in TLE during the basal state. In addition, increased DMN connectivity to other brain regions in left TLE along with decreased connectivity in right TLE is revealed. The analysis demonstrates how seed-based fcMRI can be used to probe cerebral networks in brain disorders such as TLE.
Medicine, Issue 90, Default Mode Network (DMN), Temporal Lobe Epilepsy (TLE), fMRI, MRI, functional connectivity MRI (fcMRI), blood oxygenation level dependent (BOLD)
51442
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Cortical Source Analysis of High-Density EEG Recordings in Children
Authors: Joe Bathelt, Helen O'Reilly, Michelle de Haan.
Institutions: UCL Institute of Child Health, University College London.
EEG is traditionally described as a neuroimaging technique with high temporal and low spatial resolution. Recent advances in biophysical modelling and signal processing make it possible to exploit information from other imaging modalities like structural MRI that provide high spatial resolution to overcome this constraint1. This is especially useful for investigations that require high resolution in the temporal as well as spatial domain. In addition, due to the easy application and low cost of EEG recordings, EEG is often the method of choice when working with populations, such as young children, that do not tolerate functional MRI scans well. However, in order to investigate which neural substrates are involved, anatomical information from structural MRI is still needed. Most EEG analysis packages work with standard head models that are based on adult anatomy. The accuracy of these models when used for children is limited2, because the composition and spatial configuration of head tissues changes dramatically over development3.  In the present paper, we provide an overview of our recent work in utilizing head models based on individual structural MRI scans or age specific head models to reconstruct the cortical generators of high density EEG. This article describes how EEG recordings are acquired, processed, and analyzed with pediatric populations at the London Baby Lab, including laboratory setup, task design, EEG preprocessing, MRI processing, and EEG channel level and source analysis. 
Behavior, Issue 88, EEG, electroencephalogram, development, source analysis, pediatric, minimum-norm estimation, cognitive neuroscience, event-related potentials 
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Cerenkov Luminescence Imaging of Interscapular Brown Adipose Tissue
Authors: Xueli Zhang, Chaincy Kuo, Anna Moore, Chongzhao Ran.
Institutions: Massachusetts General Hospital/Harvard Medical School, China Pharmaceutical University, Perkin Elmer.
Brown adipose tissue (BAT), widely known as a “good fat” plays pivotal roles for thermogenesis in mammals. This special tissue is closely related to metabolism and energy expenditure, and its dysfunction is one important contributor for obesity and diabetes. Contrary to previous belief, recent PET/CT imaging studies indicated the BAT depots are still present in human adults. PET imaging clearly shows that BAT has considerably high uptake of 18F-FDG under certain conditions. In this video report, we demonstrate that Cerenkov luminescence imaging (CLI) with 18F-FDG can be used to optically image BAT in small animals. BAT activation is observed after intraperitoneal injection of norepinephrine (NE) and cold treatment, and depression of BAT is induced by long anesthesia. Using multiple-filter Cerenkov luminescence imaging, spectral unmixing and 3D imaging reconstruction are demonstrated. Our results suggest that CLI with 18F-FDG is a practical technique for imaging BAT in small animals, and this technique can be used as a cheap, fast, and alternative imaging tool for BAT research.
Medicine, Issue 92, Cerenkov luminescence imaging, brown adipose tissue, 18F-FDG, optical imaging, in vivo imaging, spectral unmixing
51790
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Topographical Estimation of Visual Population Receptive Fields by fMRI
Authors: Sangkyun Lee, Amalia Papanikolaou, Georgios A. Keliris, Stelios M. Smirnakis.
Institutions: Baylor College of Medicine, Max Planck Institute for Biological Cybernetics, Bernstein Center for Computational Neuroscience.
Visual cortex is retinotopically organized so that neighboring populations of cells map to neighboring parts of the visual field. Functional magnetic resonance imaging allows us to estimate voxel-based population receptive fields (pRF), i.e., the part of the visual field that activates the cells within each voxel. Prior, direct, pRF estimation methods1 suffer from certain limitations: 1) the pRF model is chosen a-priori and may not fully capture the actual pRF shape, and 2) pRF centers are prone to mislocalization near the border of the stimulus space. Here a new topographical pRF estimation method2 is proposed that largely circumvents these limitations. A linear model is used to predict the Blood Oxygen Level-Dependent (BOLD) signal by convolving the linear response of the pRF to the visual stimulus with the canonical hemodynamic response function. PRF topography is represented as a weight vector whose components represent the strength of the aggregate response of voxel neurons to stimuli presented at different visual field locations. The resulting linear equations can be solved for the pRF weight vector using ridge regression3, yielding the pRF topography. A pRF model that is matched to the estimated topography can then be chosen post-hoc, thereby improving the estimates of pRF parameters such as pRF-center location, pRF orientation, size, etc. Having the pRF topography available also allows the visual verification of pRF parameter estimates allowing the extraction of various pRF properties without having to make a-priori assumptions about the pRF structure. This approach promises to be particularly useful for investigating the pRF organization of patients with disorders of the visual system.
Behavior, Issue 96, population receptive field, vision, functional magnetic resonance imaging, retinotopy
51811
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Harnessing the Bioorthogonal Inverse Electron Demand Diels-Alder Cycloaddition for Pretargeted PET Imaging
Authors: Thomas Reiner, Jason S. Lewis, Brian M. Zeglis.
Institutions: Memorial Sloan Kettering Cancer Center.
Due to their exquisite affinity and specificity, antibodies have become extremely promising vectors for the delivery of radioisotopes to cancer cells for PET imaging. However, the necessity of labeling antibodies with radionuclides with long physical half-lives often results in high background radiation dose rates to non-target tissues. In order to circumvent this issue, we have employed a pretargeted PET imaging strategy based on the inverse electron demand Diels-Alder cycloaddition reaction. The methodology decouples the antibody from the radioactivity and thus exploits the positive characteristics of antibodies, while eschewing their pharmacokinetic drawbacks. The system is composed of four steps: (1) the injection of a mAb-trans-cyclooctene (TCO) conjugate; (2) a localization time period during which the antibody accumulates in the tumor and clears from the blood; (3) the injection of the radiolabeled tetrazine; and (4) the in vivo click ligation of the components followed by the clearance of excess radioligand. In the example presented in the work at hand, a 64Cu-NOTA-labeled tetrazine radioligand and a trans-cyclooctene-conjugated humanized antibody (huA33) were successfully used to delineate SW1222 colorectal cancer tumors with high tumor-to-background contrast. Further, the pretargeting methodology produces high quality images at only a fraction of the radiation dose to non-target tissue created by radioimmunoconjugates directly labeled with 64Cu or 89Zr. Ultimately, the modularity of this protocol is one of its greatest assets, as the trans-cyclooctene moiety can be appended to any non-internalizing antibody, and the tetrazine can be attached to a wide variety of radioisotopes.
Bioengineering, Issue 96, Positron Emission Tomography, Click Chemistry, Pretargeting, Tetrazine, Trans-cyclooctene, Inverse Electron Demand Diels-Alder Cycloaddition
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Human Brown Adipose Tissue Depots Automatically Segmented by Positron Emission Tomography/Computed Tomography and Registered Magnetic Resonance Images
Authors: Aliya Gifford, Theodore F. Towse, Ronald C. Walker, Malcolm J. Avison, E. Brian Welch.
Institutions: Vanderbilt University, Vanderbilt University School of Medicine, Vanderbilt University Medical Center, Vanderbilt University.
Reliably differentiating brown adipose tissue (BAT) from other tissues using a non-invasive imaging method is an important step toward studying BAT in humans. Detecting BAT is typically confirmed by the uptake of the injected radioactive tracer 18F-Fluorodeoxyglucose (18F-FDG) into adipose tissue depots, as measured by positron emission tomography/computed tomography (PET-CT) scans after exposing the subject to cold stimulus. Fat-water separated magnetic resonance imaging (MRI) has the ability to distinguish BAT without the use of a radioactive tracer. To date, MRI of BAT in adult humans has not been co-registered with cold-activated PET-CT. Therefore, this protocol uses 18F-FDG PET-CT scans to automatically generate a BAT mask, which is then applied to co-registered MRI scans of the same subject. This approach enables measurement of quantitative MRI properties of BAT without manual segmentation. BAT masks are created from two PET-CT scans: after exposure for 2 hr to either thermoneutral (TN) (24 °C) or cold-activated (CA) (17 °C) conditions. The TN and CA PET-CT scans are registered, and the PET standardized uptake and CT Hounsfield values are used to create a mask containing only BAT. CA and TN MRI scans are also acquired on the same subject and registered to the PET-CT scans in order to establish quantitative MRI properties within the automatically defined BAT mask. An advantage of this approach is that the segmentation is completely automated and is based on widely accepted methods for identification of activated BAT (PET-CT). The quantitative MRI properties of BAT established using this protocol can serve as the basis for an MRI-only BAT examination that avoids the radiation associated with PET-CT.
Medicine, Issue 96, magnetic resonance imaging, brown adipose tissue, cold-activation, adult human, fat water imaging, fluorodeoxyglucose, positron emission tomography, computed tomography
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A Comprehensive Protocol for Manual Segmentation of the Medial Temporal Lobe Structures
Authors: Matthew Moore, Yifan Hu, Sarah Woo, Dylan O'Hearn, Alexandru D. Iordan, Sanda Dolcos, Florin Dolcos.
Institutions: University of Illinois Urbana-Champaign, University of Illinois Urbana-Champaign, University of Illinois Urbana-Champaign.
The present paper describes a comprehensive protocol for manual tracing of the set of brain regions comprising the medial temporal lobe (MTL): amygdala, hippocampus, and the associated parahippocampal regions (perirhinal, entorhinal, and parahippocampal proper). Unlike most other tracing protocols available, typically focusing on certain MTL areas (e.g., amygdala and/or hippocampus), the integrative perspective adopted by the present tracing guidelines allows for clear localization of all MTL subregions. By integrating information from a variety of sources, including extant tracing protocols separately targeting various MTL structures, histological reports, and brain atlases, and with the complement of illustrative visual materials, the present protocol provides an accurate, intuitive, and convenient guide for understanding the MTL anatomy. The need for such tracing guidelines is also emphasized by illustrating possible differences between automatic and manual segmentation protocols. This knowledge can be applied toward research involving not only structural MRI investigations but also structural-functional colocalization and fMRI signal extraction from anatomically defined ROIs, in healthy and clinical groups alike.
Neuroscience, Issue 89, Anatomy, Segmentation, Medial Temporal Lobe, MRI, Manual Tracing, Amygdala, Hippocampus, Perirhinal Cortex, Entorhinal Cortex, Parahippocampal Cortex
50991
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A Dual Tracer PET-MRI Protocol for the Quantitative Measure of Regional Brain Energy Substrates Uptake in the Rat
Authors: Maggie Roy, Scott Nugent, Sébastien Tremblay, Maxime Descoteaux, Jean-François Beaudoin, Luc Tremblay, Roger Lecomte, Stephen C Cunnane.
Institutions: Université de Sherbrooke, Université de Sherbrooke, Université de Sherbrooke, Université de Sherbrooke.
We present a method for comparing the uptake of the brain's two key energy substrates: glucose and ketones (acetoacetate [AcAc] in this case) in the rat. The developed method is a small-animal positron emission tomography (PET) protocol, in which 11C-AcAc and 18F-fluorodeoxyglucose (18F-FDG) are injected sequentially in each animal. This dual tracer PET acquisition is possible because of the short half-life of 11C (20.4 min). The rats also undergo a magnetic resonance imaging (MRI) acquisition seven days before the PET protocol. Prior to image analysis, PET and MRI images are coregistered to allow the measurement of regional cerebral uptake (cortex, hippocampus, striatum, and cerebellum). A quantitative measure of 11C-AcAc and 18F-FDG brain uptake (cerebral metabolic rate; μmol/100 g/min) is determined by kinetic modeling using the image-derived input function (IDIF) method. Our new dual tracer PET protocol is robust and flexible; the two tracers used can be replaced by different radiotracers to evaluate other processes in the brain. Moreover, our protocol is applicable to the study of brain fuel supply in multiple conditions such as normal aging and neurodegenerative pathologies such as Alzheimer's and Parkinson's diseases.
Neuroscience, Issue 82, positron emission tomography (PET), 18F-fluorodeoxyglucose, 11C-acetoacetate, magnetic resonance imaging (MRI), kinetic modeling, cerebral metabolic rate, rat
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Functional Interrogation of Adult Hypothalamic Neurogenesis with Focal Radiological Inhibition
Authors: Daniel A. Lee, Juan Salvatierra, Esteban Velarde, John Wong, Eric C. Ford, Seth Blackshaw.
Institutions: California Institute of Technology, Johns Hopkins University School of Medicine, Johns Hopkins University School of Medicine, University Of Washington Medical Center, Johns Hopkins University School of Medicine.
The functional characterization of adult-born neurons remains a significant challenge. Approaches to inhibit adult neurogenesis via invasive viral delivery or transgenic animals have potential confounds that make interpretation of results from these studies difficult. New radiological tools are emerging, however, that allow one to noninvasively investigate the function of select groups of adult-born neurons through accurate and precise anatomical targeting in small animals. Focal ionizing radiation inhibits the birth and differentiation of new neurons, and allows targeting of specific neural progenitor regions. In order to illuminate the potential functional role that adult hypothalamic neurogenesis plays in the regulation of physiological processes, we developed a noninvasive focal irradiation technique to selectively inhibit the birth of adult-born neurons in the hypothalamic median eminence. We describe a method for Computer tomography-guided focal irradiation (CFIR) delivery to enable precise and accurate anatomical targeting in small animals. CFIR uses three-dimensional volumetric image guidance for localization and targeting of the radiation dose, minimizes radiation exposure to nontargeted brain regions, and allows for conformal dose distribution with sharp beam boundaries. This protocol allows one to ask questions regarding the function of adult-born neurons, but also opens areas to questions in areas of radiobiology, tumor biology, and immunology. These radiological tools will facilitate the translation of discoveries at the bench to the bedside.
Neuroscience, Issue 81, Neural Stem Cells (NSCs), Body Weight, Radiotherapy, Image-Guided, Metabolism, Energy Metabolism, Neurogenesis, Cell Proliferation, Neurosciences, Irradiation, Radiological treatment, Computer-tomography (CT) imaging, Hypothalamus, Hypothalamic Proliferative Zone (HPZ), Median Eminence (ME), Small Animal Radiation Research Platform (SARRP)
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3D-Neuronavigation In Vivo Through a Patient's Brain During a Spontaneous Migraine Headache
Authors: Alexandre F. DaSilva, Thiago D. Nascimento, Tiffany Love, Marcos F. DosSantos, Ilkka K. Martikainen, Chelsea M. Cummiford, Misty DeBoer, Sarah R. Lucas, MaryCatherine A. Bender, Robert A. Koeppe, Theodore Hall, Sean Petty, Eric Maslowski, Yolanda R. Smith, Jon-Kar Zubieta.
Institutions: University of Michigan School of Dentistry, University of Michigan School of Dentistry, University of Michigan, University of Michigan, University of Michigan, University of Michigan.
A growing body of research, generated primarily from MRI-based studies, shows that migraine appears to occur, and possibly endure, due to the alteration of specific neural processes in the central nervous system. However, information is lacking on the molecular impact of these changes, especially on the endogenous opioid system during migraine headaches, and neuronavigation through these changes has never been done. This study aimed to investigate, using a novel 3D immersive and interactive neuronavigation (3D-IIN) approach, the endogenous µ-opioid transmission in the brain during a migraine headache attack in vivo. This is arguably one of the most central neuromechanisms associated with pain regulation, affecting multiple elements of the pain experience and analgesia. A 36 year-old female, who has been suffering with migraine for 10 years, was scanned in the typical headache (ictal) and nonheadache (interictal) migraine phases using Positron Emission Tomography (PET) with the selective radiotracer [11C]carfentanil, which allowed us to measure µ-opioid receptor availability in the brain (non-displaceable binding potential - µOR BPND). The short-life radiotracer was produced by a cyclotron and chemical synthesis apparatus on campus located in close proximity to the imaging facility. Both PET scans, interictal and ictal, were scheduled during separate mid-late follicular phases of the patient's menstrual cycle. During the ictal PET session her spontaneous headache attack reached severe intensity levels; progressing to nausea and vomiting at the end of the scan session. There were reductions in µOR BPND in the pain-modulatory regions of the endogenous µ-opioid system during the ictal phase, including the cingulate cortex, nucleus accumbens (NAcc), thalamus (Thal), and periaqueductal gray matter (PAG); indicating that µORs were already occupied by endogenous opioids released in response to the ongoing pain. To our knowledge, this is the first time that changes in µOR BPND during a migraine headache attack have been neuronavigated using a novel 3D approach. This method allows for interactive research and educational exploration of a migraine attack in an actual patient's neuroimaging dataset.
Medicine, Issue 88, μ-opioid, opiate, migraine, headache, pain, Positron Emission Tomography, molecular neuroimaging, 3D, neuronavigation
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Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
Authors: Hans-Peter Müller, Jan Kassubek.
Institutions: University of Ulm.
Diffusion tensor imaging (DTI) techniques provide information on the microstructural processes of the cerebral white matter (WM) in vivo. The present applications are designed to investigate differences of WM involvement patterns in different brain diseases, especially neurodegenerative disorders, by use of different DTI analyses in comparison with matched controls. DTI data analysis is performed in a variate fashion, i.e. voxelwise comparison of regional diffusion direction-based metrics such as fractional anisotropy (FA), together with fiber tracking (FT) accompanied by tractwise fractional anisotropy statistics (TFAS) at the group level in order to identify differences in FA along WM structures, aiming at the definition of regional patterns of WM alterations at the group level. Transformation into a stereotaxic standard space is a prerequisite for group studies and requires thorough data processing to preserve directional inter-dependencies. The present applications show optimized technical approaches for this preservation of quantitative and directional information during spatial normalization in data analyses at the group level. On this basis, FT techniques can be applied to group averaged data in order to quantify metrics information as defined by FT. Additionally, application of DTI methods, i.e. differences in FA-maps after stereotaxic alignment, in a longitudinal analysis at an individual subject basis reveal information about the progression of neurological disorders. Further quality improvement of DTI based results can be obtained during preprocessing by application of a controlled elimination of gradient directions with high noise levels. In summary, DTI is used to define a distinct WM pathoanatomy of different brain diseases by the combination of whole brain-based and tract-based DTI analysis.
Medicine, Issue 77, Neuroscience, Neurobiology, Molecular Biology, Biomedical Engineering, Anatomy, Physiology, Neurodegenerative Diseases, nuclear magnetic resonance, NMR, MR, MRI, diffusion tensor imaging, fiber tracking, group level comparison, neurodegenerative diseases, brain, imaging, clinical techniques
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Creating Dynamic Images of Short-lived Dopamine Fluctuations with lp-ntPET: Dopamine Movies of Cigarette Smoking
Authors: Evan D. Morris, Su Jin Kim, Jenna M. Sullivan, Shuo Wang, Marc D. Normandin, Cristian C. Constantinescu, Kelly P. Cosgrove.
Institutions: Yale University, Yale University, Yale University, Yale University, Massachusetts General Hospital, University of California, Irvine.
We describe experimental and statistical steps for creating dopamine movies of the brain from dynamic PET data. The movies represent minute-to-minute fluctuations of dopamine induced by smoking a cigarette. The smoker is imaged during a natural smoking experience while other possible confounding effects (such as head motion, expectation, novelty, or aversion to smoking repeatedly) are minimized. We present the details of our unique analysis. Conventional methods for PET analysis estimate time-invariant kinetic model parameters which cannot capture short-term fluctuations in neurotransmitter release. Our analysis - yielding a dopamine movie - is based on our work with kinetic models and other decomposition techniques that allow for time-varying parameters 1-7. This aspect of the analysis - temporal-variation - is key to our work. Because our model is also linear in parameters, it is practical, computationally, to apply at the voxel level. The analysis technique is comprised of five main steps: pre-processing, modeling, statistical comparison, masking and visualization. Preprocessing is applied to the PET data with a unique 'HYPR' spatial filter 8 that reduces spatial noise but preserves critical temporal information. Modeling identifies the time-varying function that best describes the dopamine effect on 11C-raclopride uptake. The statistical step compares the fit of our (lp-ntPET) model 7 to a conventional model 9. Masking restricts treatment to those voxels best described by the new model. Visualization maps the dopamine function at each voxel to a color scale and produces a dopamine movie. Interim results and sample dopamine movies of cigarette smoking are presented.
Behavior, Issue 78, Neuroscience, Neurobiology, Molecular Biology, Biomedical Engineering, Medicine, Anatomy, Physiology, Image Processing, Computer-Assisted, Receptors, Dopamine, Dopamine, Functional Neuroimaging, Binding, Competitive, mathematical modeling (systems analysis), Neurotransmission, transient, dopamine release, PET, modeling, linear, time-invariant, smoking, F-test, ventral-striatum, clinical techniques
50358
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Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
Authors: Rangaraj M. Rangayyan, Shantanu Banik, J.E. Leo Desautels.
Institutions: University of Calgary , University of Calgary .
We demonstrate methods for the detection of architectural distortion in prior mammograms of interval-cancer cases based on analysis of the orientation of breast tissue patterns in mammograms. We hypothesize that architectural distortion modifies the normal orientation of breast tissue patterns in mammographic images before the formation of masses or tumors. In the initial steps of our methods, the oriented structures in a given mammogram are analyzed using Gabor filters and phase portraits to detect node-like sites of radiating or intersecting tissue patterns. Each detected site is then characterized using the node value, fractal dimension, and a measure of angular dispersion specifically designed to represent spiculating patterns associated with architectural distortion. Our methods were tested with a database of 106 prior mammograms of 56 interval-cancer cases and 52 mammograms of 13 normal cases using the features developed for the characterization of architectural distortion, pattern classification via quadratic discriminant analysis, and validation with the leave-one-patient out procedure. According to the results of free-response receiver operating characteristic analysis, our methods have demonstrated the capability to detect architectural distortion in prior mammograms, taken 15 months (on the average) before clinical diagnosis of breast cancer, with a sensitivity of 80% at about five false positives per patient.
Medicine, Issue 78, Anatomy, Physiology, Cancer Biology, angular spread, architectural distortion, breast cancer, Computer-Assisted Diagnosis, computer-aided diagnosis (CAD), entropy, fractional Brownian motion, fractal dimension, Gabor filters, Image Processing, Medical Informatics, node map, oriented texture, Pattern Recognition, phase portraits, prior mammograms, spectral analysis
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Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
Authors: Adrian K.C. Lee, Eric Larson, Ross K. Maddox.
Institutions: University of Washington.
Magneto- and electroencephalography (MEG/EEG) are neuroimaging techniques that provide a high temporal resolution particularly suitable to investigate the cortical networks involved in dynamical perceptual and cognitive tasks, such as attending to different sounds in a cocktail party. Many past studies have employed data recorded at the sensor level only, i.e., the magnetic fields or the electric potentials recorded outside and on the scalp, and have usually focused on activity that is time-locked to the stimulus presentation. This type of event-related field / potential analysis is particularly useful when there are only a small number of distinct dipolar patterns that can be isolated and identified in space and time. Alternatively, by utilizing anatomical information, these distinct field patterns can be localized as current sources on the cortex. However, for a more sustained response that may not be time-locked to a specific stimulus (e.g., in preparation for listening to one of the two simultaneously presented spoken digits based on the cued auditory feature) or may be distributed across multiple spatial locations unknown a priori, the recruitment of a distributed cortical network may not be adequately captured by using a limited number of focal sources. Here, we describe a procedure that employs individual anatomical MRI data to establish a relationship between the sensor information and the dipole activation on the cortex through the use of minimum-norm estimates (MNE). This inverse imaging approach provides us a tool for distributed source analysis. For illustrative purposes, we will describe all procedures using FreeSurfer and MNE software, both freely available. We will summarize the MRI sequences and analysis steps required to produce a forward model that enables us to relate the expected field pattern caused by the dipoles distributed on the cortex onto the M/EEG sensors. Next, we will step through the necessary processes that facilitate us in denoising the sensor data from environmental and physiological contaminants. We will then outline the procedure for combining and mapping MEG/EEG sensor data onto the cortical space, thereby producing a family of time-series of cortical dipole activation on the brain surface (or "brain movies") related to each experimental condition. Finally, we will highlight a few statistical techniques that enable us to make scientific inference across a subject population (i.e., perform group-level analysis) based on a common cortical coordinate space.
Neuroscience, Issue 68, Magnetoencephalography, MEG, Electroencephalography, EEG, audition, attention, inverse imaging
4262
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Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
Authors: Wenan Chen, Ashwin Belle, Charles Cockrell, Kevin R. Ward, Kayvan Najarian.
Institutions: Virginia Commonwealth University, Virginia Commonwealth University Reanimation Engineering Science (VCURES) Center, Virginia Commonwealth University, Virginia Commonwealth University, Virginia Commonwealth University.
In this paper we present an automated system based mainly on the computed tomography (CT) images consisting of two main components: the midline shift estimation and intracranial pressure (ICP) pre-screening system. To estimate the midline shift, first an estimation of the ideal midline is performed based on the symmetry of the skull and anatomical features in the brain CT scan. Then, segmentation of the ventricles from the CT scan is performed and used as a guide for the identification of the actual midline through shape matching. These processes mimic the measuring process by physicians and have shown promising results in the evaluation. In the second component, more features are extracted related to ICP, such as the texture information, blood amount from CT scans and other recorded features, such as age, injury severity score to estimate the ICP are also incorporated. Machine learning techniques including feature selection and classification, such as Support Vector Machines (SVMs), are employed to build the prediction model using RapidMiner. The evaluation of the prediction shows potential usefulness of the model. The estimated ideal midline shift and predicted ICP levels may be used as a fast pre-screening step for physicians to make decisions, so as to recommend for or against invasive ICP monitoring.
Medicine, Issue 74, Biomedical Engineering, Molecular Biology, Neurobiology, Biophysics, Physiology, Anatomy, Brain CT Image Processing, CT, Midline Shift, Intracranial Pressure Pre-screening, Gaussian Mixture Model, Shape Matching, Machine Learning, traumatic brain injury, TBI, imaging, clinical techniques
3871
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Quantification of Atherosclerotic Plaque Activity and Vascular Inflammation using [18-F] Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography (FDG-PET/CT)
Authors: Nehal N. Mehta, Drew A. Torigian, Joel M. Gelfand, Babak Saboury, Abass Alavi.
Institutions: University of Pennsylvania, Perelman School of Medicine, University of Pennsylvania, Perelman School of Medicine, University of Pennsylvania, Perelman School of Medicine.
Conventional non-invasive imaging modalities of atherosclerosis such as coronary artery calcium (CAC)1 and carotid intimal medial thickness (C-IMT)2 provide information about the burden of disease. However, despite multiple validation studies of CAC3-5, and C-IMT2,6, these modalities do not accurately assess plaque characteristics7,8, and the composition and inflammatory state of the plaque determine its stability and, therefore, the risk of clinical events9-13. [18F]-2-fluoro-2-deoxy-D-glucose (FDG) imaging using positron-emission tomography (PET)/computed tomography (CT) has been extensively studied in oncologic metabolism14,15. Studies using animal models and immunohistochemistry in humans show that FDG-PET/CT is exquisitely sensitive for detecting macrophage activity16, an important source of cellular inflammation in vessel walls. More recently, we17,18 and others have shown that FDG-PET/CT enables highly precise, novel measurements of inflammatory activity of activity of atherosclerotic plaques in large and medium-sized arteries9,16,19,20. FDG-PET/CT studies have many advantages over other imaging modalities: 1) high contrast resolution; 2) quantification of plaque volume and metabolic activity allowing for multi-modal atherosclerotic plaque quantification; 3) dynamic, real-time, in vivo imaging; 4) minimal operator dependence. Finally, vascular inflammation detected by FDG-PET/CT has been shown to predict cardiovascular (CV) events independent of traditional risk factors21,22 and is also highly associated with overall burden of atherosclerosis23. Plaque activity by FDG-PET/CT is modulated by known beneficial CV interventions such as short term (12 week) statin therapy24 as well as longer term therapeutic lifestyle changes (16 months)25. The current methodology for quantification of FDG uptake in atherosclerotic plaque involves measurement of the standardized uptake value (SUV) of an artery of interest and of the venous blood pool in order to calculate a target to background ratio (TBR), which is calculated by dividing the arterial SUV by the venous blood pool SUV. This method has shown to represent a stable, reproducible phenotype over time, has a high sensitivity for detection of vascular inflammation, and also has high inter-and intra-reader reliability26. Here we present our methodology for patient preparation, image acquisition, and quantification of atherosclerotic plaque activity and vascular inflammation using SUV, TBR, and a global parameter called the metabolic volumetric product (MVP). These approaches may be applied to assess vascular inflammation in various study samples of interest in a consistent fashion as we have shown in several prior publications.9,20,27,28
Medicine, Issue 63, FDG-PET/CT, atherosclerosis, vascular inflammation, quantitative radiology, imaging
3777
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In Vivo Dynamics of Retinal Microglial Activation During Neurodegeneration: Confocal Ophthalmoscopic Imaging and Cell Morphometry in Mouse Glaucoma
Authors: Alejandra Bosco, Cesar O. Romero, Balamurali K. Ambati, Monica L. Vetter.
Institutions: University of Utah, University of Utah.
Microglia, which are CNS-resident neuroimmune cells, transform their morphology and size in response to CNS damage, switching to an activated state with distinct functions and gene expression profiles. The roles of microglial activation in health, injury and disease remain incompletely understood due to their dynamic and complex regulation in response to changes in their microenvironment. Thus, it is critical to non-invasively monitor and analyze changes in microglial activation over time in the intact organism. In vivo studies of microglial activation have been delayed by technical limitations to tracking microglial behavior without altering the CNS environment. This has been particularly challenging during chronic neurodegeneration, where long-term changes must be tracked. The retina, a CNS organ amenable to non-invasive live imaging, offers a powerful system to visualize and characterize the dynamics of microglia activation during chronic disorders. This protocol outlines methods for long-term, in vivo imaging of retinal microglia, using confocal ophthalmoscopy (cSLO) and CX3CR1GFP/+ reporter mice, to visualize microglia with cellular resolution. Also, we describe methods to quantify monthly changes in cell activation and density in large cell subsets (200-300 cells per retina). We confirm the use of somal area as a useful metric for live tracking of microglial activation in the retina by applying automated threshold-based morphometric analysis of in vivo images. We use these live image acquisition and analyses strategies to monitor the dynamic changes in microglial activation and microgliosis during early stages of retinal neurodegeneration in a mouse model of chronic glaucoma. This approach should be useful to investigate the contributions of microglia to neuronal and axonal decline in chronic CNS disorders that affect the retina and optic nerve.
Medicine, Issue 99, Neuroscience, microglia, neurodegeneration, glaucoma, retina, optic nerve head, confocal scanning laser ophthalmoscopy, live image analysis, segmentation by thresholding, cell morphometry CX3CR1, DBA/2J
52731
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