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Pubmed Article
How many separable sources? Model selection in independent components analysis.
PUBLISHED: 03-27-2015
Unlike mixtures consisting solely of non-Gaussian sources, mixtures including two or more Gaussian components cannot be separated using standard independent components analysis methods that are based on higher order statistics and independent observations. The mixed Independent Components Analysis/Principal Components Analysis (mixed ICA/PCA) model described here accommodates one or more Gaussian components in the independent components analysis model and uses principal components analysis to characterize contributions from this inseparable Gaussian subspace. Information theory can then be used to select from among potential model categories with differing numbers of Gaussian components. Based on simulation studies, the assumptions and approximations underlying the Akaike Information Criterion do not hold in this setting, even with a very large number of observations. Cross-validation is a suitable, though computationally intensive alternative for model selection. Application of the algorithm is illustrated using Fisher's iris data set and Howells' craniometric data set. Mixed ICA/PCA is of potential interest in any field of scientific investigation where the authenticity of blindly separated non-Gaussian sources might otherwise be questionable. Failure of the Akaike Information Criterion in model selection also has relevance in traditional independent components analysis where all sources are assumed non-Gaussian.
The ability to adjust behavior to sudden changes in the environment develops gradually in childhood and adolescence. For example, in the Dimensional Change Card Sort task, participants switch from sorting cards one way, such as shape, to sorting them a different way, such as color. Adjusting behavior in this way exacts a small performance cost, or switch cost, such that responses are typically slower and more error-prone on switch trials in which the sorting rule changes as compared to repeat trials in which the sorting rule remains the same. The ability to flexibly adjust behavior is often said to develop gradually, in part because behavioral costs such as switch costs typically decrease with increasing age. Why aspects of higher-order cognition, such as behavioral flexibility, develop so gradually remains an open question. One hypothesis is that these changes occur in association with functional changes in broad-scale cognitive control networks. On this view, complex mental operations, such as switching, involve rapid interactions between several distributed brain regions, including those that update and maintain task rules, re-orient attention, and select behaviors. With development, functional connections between these regions strengthen, leading to faster and more efficient switching operations. The current video describes a method of testing this hypothesis through the collection and multivariate analysis of fMRI data from participants of different ages.
22 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
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Major Components of the Light Microscope
Authors: Victoria Centonze Frohlich.
Institutions: University of Texas Health Science Center at San Antonio (UTHSCSA).
The light microscope is a basic tool for the cell biologist, who should have a thorough understanding of how it works, how it should be aligned for different applications, and how it should be maintained as required to obtain maximum image-forming capacity and resolution. The components of the microscope are described in detail here.
Basic Protocols, Issue 17, Current Protocols Wiley, Microscopy, Objectives, Condenser, Eyepiece
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High-resolution Spatiotemporal Analysis of Receptor Dynamics by Single-molecule Fluorescence Microscopy
Authors: Titiwat Sungkaworn, Finn Rieken, Martin J. Lohse, Davide Calebiro.
Institutions: University of Würzburg, Germany.
Single-molecule microscopy is emerging as a powerful approach to analyze the behavior of signaling molecules, in particular concerning those aspect (e.g., kinetics, coexistence of different states and populations, transient interactions), which are typically hidden in ensemble measurements, such as those obtained with standard biochemical or microscopy methods. Thus, dynamic events, such as receptor-receptor interactions, can be followed in real time in a living cell with high spatiotemporal resolution. This protocol describes a method based on labeling with small and bright organic fluorophores and total internal reflection fluorescence (TIRF) microscopy to directly visualize single receptors on the surface of living cells. This approach allows one to precisely localize receptors, measure the size of receptor complexes, and capture dynamic events such as transient receptor-receptor interactions. The protocol provides a detailed description of how to perform a single-molecule experiment, including sample preparation, image acquisition and image analysis. As an example, the application of this method to analyze two G-protein-coupled receptors, i.e., β2-adrenergic and γ-aminobutyric acid type B (GABAB) receptor, is reported. The protocol can be adapted to other membrane proteins and different cell models, transfection methods and labeling strategies.
Bioengineering, Issue 89, pharmacology, microscopy, receptor, live-cell imaging, single-molecule, total internal reflection fluorescence, tracking, dimerization, protein-protein interactions
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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
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Analysis of Tubular Membrane Networks in Cardiac Myocytes from Atria and Ventricles
Authors: Eva Wagner, Sören Brandenburg, Tobias Kohl, Stephan E. Lehnart.
Institutions: Heart Research Center Goettingen, University Medical Center Goettingen, German Center for Cardiovascular Research (DZHK) partner site Goettingen, University of Maryland School of Medicine.
In cardiac myocytes a complex network of membrane tubules - the transverse-axial tubule system (TATS) - controls deep intracellular signaling functions. While the outer surface membrane and associated TATS membrane components appear to be continuous, there are substantial differences in lipid and protein content. In ventricular myocytes (VMs), certain TATS components are highly abundant contributing to rectilinear tubule networks and regular branching 3D architectures. It is thought that peripheral TATS components propagate action potentials from the cell surface to thousands of remote intracellular sarcoendoplasmic reticulum (SER) membrane contact domains, thereby activating intracellular Ca2+ release units (CRUs). In contrast to VMs, the organization and functional role of TATS membranes in atrial myocytes (AMs) is significantly different and much less understood. Taken together, quantitative structural characterization of TATS membrane networks in healthy and diseased myocytes is an essential prerequisite towards better understanding of functional plasticity and pathophysiological reorganization. Here, we present a strategic combination of protocols for direct quantitative analysis of TATS membrane networks in living VMs and AMs. For this, we accompany primary cell isolations of mouse VMs and/or AMs with critical quality control steps and direct membrane staining protocols for fluorescence imaging of TATS membranes. Using an optimized workflow for confocal or superresolution TATS image processing, binarized and skeletonized data are generated for quantitative analysis of the TATS network and its components. Unlike previously published indirect regional aggregate image analysis strategies, our protocols enable direct characterization of specific components and derive complex physiological properties of TATS membrane networks in living myocytes with high throughput and open access software tools. In summary, the combined protocol strategy can be readily applied for quantitative TATS network studies during physiological myocyte adaptation or disease changes, comparison of different cardiac or skeletal muscle cell types, phenotyping of transgenic models, and pharmacological or therapeutic interventions.
Bioengineering, Issue 92, cardiac myocyte, atria, ventricle, heart, primary cell isolation, fluorescence microscopy, membrane tubule, transverse-axial tubule system, image analysis, image processing, T-tubule, collagenase
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Combining Magnetic Sorting of Mother Cells and Fluctuation Tests to Analyze Genome Instability During Mitotic Cell Aging in Saccharomyces cerevisiae
Authors: Melissa N. Patterson, Patrick H. Maxwell.
Institutions: Rensselaer Polytechnic Institute.
Saccharomyces cerevisiae has been an excellent model system for examining mechanisms and consequences of genome instability. Information gained from this yeast model is relevant to many organisms, including humans, since DNA repair and DNA damage response factors are well conserved across diverse species. However, S. cerevisiae has not yet been used to fully address whether the rate of accumulating mutations changes with increasing replicative (mitotic) age due to technical constraints. For instance, measurements of yeast replicative lifespan through micromanipulation involve very small populations of cells, which prohibit detection of rare mutations. Genetic methods to enrich for mother cells in populations by inducing death of daughter cells have been developed, but population sizes are still limited by the frequency with which random mutations that compromise the selection systems occur. The current protocol takes advantage of magnetic sorting of surface-labeled yeast mother cells to obtain large enough populations of aging mother cells to quantify rare mutations through phenotypic selections. Mutation rates, measured through fluctuation tests, and mutation frequencies are first established for young cells and used to predict the frequency of mutations in mother cells of various replicative ages. Mutation frequencies are then determined for sorted mother cells, and the age of the mother cells is determined using flow cytometry by staining with a fluorescent reagent that detects bud scars formed on their cell surfaces during cell division. Comparison of predicted mutation frequencies based on the number of cell divisions to the frequencies experimentally observed for mother cells of a given replicative age can then identify whether there are age-related changes in the rate of accumulating mutations. Variations of this basic protocol provide the means to investigate the influence of alterations in specific gene functions or specific environmental conditions on mutation accumulation to address mechanisms underlying genome instability during replicative aging.
Microbiology, Issue 92, Aging, mutations, genome instability, Saccharomyces cerevisiae, fluctuation test, magnetic sorting, mother cell, replicative aging
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In Situ Neutron Powder Diffraction Using Custom-made Lithium-ion Batteries
Authors: William R. Brant, Siegbert Schmid, Guodong Du, Helen E. A. Brand, Wei Kong Pang, Vanessa K. Peterson, Zaiping Guo, Neeraj Sharma.
Institutions: University of Sydney, University of Wollongong, Australian Synchrotron, Australian Nuclear Science and Technology Organisation, University of Wollongong, University of New South Wales.
Li-ion batteries are widely used in portable electronic devices and are considered as promising candidates for higher-energy applications such as electric vehicles.1,2 However, many challenges, such as energy density and battery lifetimes, need to be overcome before this particular battery technology can be widely implemented in such applications.3 This research is challenging, and we outline a method to address these challenges using in situ NPD to probe the crystal structure of electrodes undergoing electrochemical cycling (charge/discharge) in a battery. NPD data help determine the underlying structural mechanism responsible for a range of electrode properties, and this information can direct the development of better electrodes and batteries. We briefly review six types of battery designs custom-made for NPD experiments and detail the method to construct the ‘roll-over’ cell that we have successfully used on the high-intensity NPD instrument, WOMBAT, at the Australian Nuclear Science and Technology Organisation (ANSTO). The design considerations and materials used for cell construction are discussed in conjunction with aspects of the actual in situ NPD experiment and initial directions are presented on how to analyze such complex in situ data.
Physics, Issue 93, In operando, structure-property relationships, electrochemical cycling, electrochemical cells, crystallography, battery performance
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Automated Quantification of Hematopoietic Cell – Stromal Cell Interactions in Histological Images of Undecalcified Bone
Authors: Sandra Zehentmeier, Zoltan Cseresnyes, Juan Escribano Navarro, Raluca A. Niesner, Anja E. Hauser.
Institutions: German Rheumatism Research Center, a Leibniz Institute, German Rheumatism Research Center, a Leibniz Institute, Max-Delbrück Center for Molecular Medicine, Wimasis GmbH, Charité - University of Medicine.
Confocal microscopy is the method of choice for the analysis of localization of multiple cell types within complex tissues such as the bone marrow. However, the analysis and quantification of cellular localization is difficult, as in many cases it relies on manual counting, thus bearing the risk of introducing a rater-dependent bias and reducing interrater reliability. Moreover, it is often difficult to judge whether the co-localization between two cells results from random positioning, especially when cell types differ strongly in the frequency of their occurrence. Here, a method for unbiased quantification of cellular co-localization in the bone marrow is introduced. The protocol describes the sample preparation used to obtain histological sections of whole murine long bones including the bone marrow, as well as the staining protocol and the acquisition of high-resolution images. An analysis workflow spanning from the recognition of hematopoietic and non-hematopoietic cell types in 2-dimensional (2D) bone marrow images to the quantification of the direct contacts between those cells is presented. This also includes a neighborhood analysis, to obtain information about the cellular microenvironment surrounding a certain cell type. In order to evaluate whether co-localization of two cell types is the mere result of random cell positioning or reflects preferential associations between the cells, a simulation tool which is suitable for testing this hypothesis in the case of hematopoietic as well as stromal cells, is used. This approach is not limited to the bone marrow, and can be extended to other tissues to permit reproducible, quantitative analysis of histological data.
Developmental Biology, Issue 98, Image analysis, neighborhood analysis, bone marrow, stromal cells, bone marrow niches, simulation, bone cryosectioning, bone histology
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An Assay for Measuring the Effects of Ethanol on the Locomotion Speed of Caenorhabditis elegans
Authors: Andrew G. Davies, GinaMari G. Blackwell, Richard C. Raabe, Jill C. Bettinger.
Institutions: Virginia Commonwealth University, Virginia Commonwealth University.
Alcohol use disorders are a significant public health concern, for which there are few effective treatment strategies. One difficulty that has delayed the development of more effective treatments is the relative lack of understanding of the molecular underpinnings of the effects of ethanol on behavior. The nematode, Caenorhabditis elegans (C. elegans), provides a useful model in which to generate and test hypotheses about the molecular effects of ethanol. Here, we describe an assay that has been developed and used to examine the roles of particular genes and environmental factors in behavioral responses to ethanol, in which locomotion is the behavioral output. Ethanol dose-dependently causes an acute depression of crawling on an agar surface. The effects are dynamic; animals exposed to a high concentration demonstrate an initial strong depression of crawling, referred to here as initial sensitivity, and then partially recover locomotion speed despite the continued presence of the drug. This ethanol-induced behavioral plasticity is referred to here as the development of acute functional tolerance. This assay has been used to demonstrate that these two phenotypes are distinct and genetically separable. The straightforward locomotion assay described here is suitable for examining the effects of both genetic and environmental manipulations on these acute behavioral responses to ethanol in C. elegans.
Behavior, Issue 98, ethanol, alcohol, behavior, sensitivity, acute functional tolerance, Caenorhabditis elegans, worm, locomotion, computer tracking, assay
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How to Ignite an Atmospheric Pressure Microwave Plasma Torch without Any Additional Igniters
Authors: Martina Leins, Sandra Gaiser, Andreas Schulz, Matthias Walker, Uwe Schumacher, Thomas Hirth.
Institutions: University of Stuttgart.
This movie shows how an atmospheric pressure plasma torch can be ignited by microwave power with no additional igniters. After ignition of the plasma, a stable and continuous operation of the plasma is possible and the plasma torch can be used for many different applications. On one hand, the hot (3,600 K gas temperature) plasma can be used for chemical processes and on the other hand the cold afterglow (temperatures down to almost RT) can be applied for surface processes. For example chemical syntheses are interesting volume processes. Here the microwave plasma torch can be used for the decomposition of waste gases which are harmful and contribute to the global warming but are needed as etching gases in growing industry sectors like the semiconductor branch. Another application is the dissociation of CO2. Surplus electrical energy from renewable energy sources can be used to dissociate CO2 to CO and O2. The CO can be further processed to gaseous or liquid higher hydrocarbons thereby providing chemical storage of the energy, synthetic fuels or platform chemicals for the chemical industry. Applications of the afterglow of the plasma torch are the treatment of surfaces to increase the adhesion of lacquer, glue or paint, and the sterilization or decontamination of different kind of surfaces. The movie will explain how to ignite the plasma solely by microwave power without any additional igniters, e.g., electric sparks. The microwave plasma torch is based on a combination of two resonators — a coaxial one which provides the ignition of the plasma and a cylindrical one which guarantees a continuous and stable operation of the plasma after ignition. The plasma can be operated in a long microwave transparent tube for volume processes or shaped by orifices for surface treatment purposes.
Engineering, Issue 98, atmospheric pressure plasma, microwave plasma, plasma ignition, resonator structure, coaxial resonator, cylindrical resonator, plasma torch, stable plasma operation, continuous plasma operation, high speed camera
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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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From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data
Authors: Wen-Ting Tsai, Ahmed Hassan, Purbasha Sarkar, Joaquin Correa, Zoltan Metlagel, Danielle M. Jorgens, Manfred Auer.
Institutions: Lawrence Berkeley National Laboratory, Lawrence Berkeley National Laboratory, Lawrence Berkeley National Laboratory.
Modern 3D electron microscopy approaches have recently allowed unprecedented insight into the 3D ultrastructural organization of cells and tissues, enabling the visualization of large macromolecular machines, such as adhesion complexes, as well as higher-order structures, such as the cytoskeleton and cellular organelles in their respective cell and tissue context. Given the inherent complexity of cellular volumes, it is essential to first extract the features of interest in order to allow visualization, quantification, and therefore comprehension of their 3D organization. Each data set is defined by distinct characteristics, e.g., signal-to-noise ratio, crispness (sharpness) of the data, heterogeneity of its features, crowdedness of features, presence or absence of characteristic shapes that allow for easy identification, and the percentage of the entire volume that a specific region of interest occupies. All these characteristics need to be considered when deciding on which approach to take for segmentation. The six different 3D ultrastructural data sets presented were obtained by three different imaging approaches: resin embedded stained electron tomography, focused ion beam- and serial block face- scanning electron microscopy (FIB-SEM, SBF-SEM) of mildly stained and heavily stained samples, respectively. For these data sets, four different segmentation approaches have been applied: (1) fully manual model building followed solely by visualization of the model, (2) manual tracing segmentation of the data followed by surface rendering, (3) semi-automated approaches followed by surface rendering, or (4) automated custom-designed segmentation algorithms followed by surface rendering and quantitative analysis. Depending on the combination of data set characteristics, it was found that typically one of these four categorical approaches outperforms the others, but depending on the exact sequence of criteria, more than one approach may be successful. Based on these data, we propose a triage scheme that categorizes both objective data set characteristics and subjective personal criteria for the analysis of the different data sets.
Bioengineering, Issue 90, 3D electron microscopy, feature extraction, segmentation, image analysis, reconstruction, manual tracing, thresholding
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Quantum State Engineering of Light with Continuous-wave Optical Parametric Oscillators
Authors: Olivier Morin, Jianli Liu, Kun Huang, Felippe Barbosa, Claude Fabre, Julien Laurat.
Institutions: Université Pierre et Marie Curie, Ecole Normale Supérieure, CNRS, East China Normal University, Universidade de São Paulo.
Engineering non-classical states of the electromagnetic field is a central quest for quantum optics1,2. Beyond their fundamental significance, such states are indeed the resources for implementing various protocols, ranging from enhanced metrology to quantum communication and computing. A variety of devices can be used to generate non-classical states, such as single emitters, light-matter interfaces or non-linear systems3. We focus here on the use of a continuous-wave optical parametric oscillator3,4. This system is based on a non-linear χ2 crystal inserted inside an optical cavity and it is now well-known as a very efficient source of non-classical light, such as single-mode or two-mode squeezed vacuum depending on the crystal phase matching. Squeezed vacuum is a Gaussian state as its quadrature distributions follow a Gaussian statistics. However, it has been shown that number of protocols require non-Gaussian states5. Generating directly such states is a difficult task and would require strong χ3 non-linearities. Another procedure, probabilistic but heralded, consists in using a measurement-induced non-linearity via a conditional preparation technique operated on Gaussian states. Here, we detail this generation protocol for two non-Gaussian states, the single-photon state and a superposition of coherent states, using two differently phase-matched parametric oscillators as primary resources. This technique enables achievement of a high fidelity with the targeted state and generation of the state in a well-controlled spatiotemporal mode.
Physics, Issue 87, Optics, Quantum optics, Quantum state engineering, Optical parametric oscillator, Squeezed vacuum, Single photon, Coherent state superposition, Homodyne detection
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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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Creating Objects and Object Categories for Studying Perception and Perceptual Learning
Authors: Karin Hauffen, Eugene Bart, Mark Brady, Daniel Kersten, Jay Hegdé.
Institutions: Georgia Health Sciences University, Georgia Health Sciences University, Georgia Health Sciences University, Palo Alto Research Center, Palo Alto Research Center, University of Minnesota .
In order to quantitatively study object perception, be it perception by biological systems or by machines, one needs to create objects and object categories with precisely definable, preferably naturalistic, properties1. Furthermore, for studies on perceptual learning, it is useful to create novel objects and object categories (or object classes) with such properties2. Many innovative and useful methods currently exist for creating novel objects and object categories3-6 (also see refs. 7,8). However, generally speaking, the existing methods have three broad types of shortcomings. First, shape variations are generally imposed by the experimenter5,9,10, and may therefore be different from the variability in natural categories, and optimized for a particular recognition algorithm. It would be desirable to have the variations arise independently of the externally imposed constraints. Second, the existing methods have difficulty capturing the shape complexity of natural objects11-13. If the goal is to study natural object perception, it is desirable for objects and object categories to be naturalistic, so as to avoid possible confounds and special cases. Third, it is generally hard to quantitatively measure the available information in the stimuli created by conventional methods. It would be desirable to create objects and object categories where the available information can be precisely measured and, where necessary, systematically manipulated (or 'tuned'). This allows one to formulate the underlying object recognition tasks in quantitative terms. Here we describe a set of algorithms, or methods, that meet all three of the above criteria. Virtual morphogenesis (VM) creates novel, naturalistic virtual 3-D objects called 'digital embryos' by simulating the biological process of embryogenesis14. Virtual phylogenesis (VP) creates novel, naturalistic object categories by simulating the evolutionary process of natural selection9,12,13. Objects and object categories created by these simulations can be further manipulated by various morphing methods to generate systematic variations of shape characteristics15,16. The VP and morphing methods can also be applied, in principle, to novel virtual objects other than digital embryos, or to virtual versions of real-world objects9,13. Virtual objects created in this fashion can be rendered as visual images using a conventional graphical toolkit, with desired manipulations of surface texture, illumination, size, viewpoint and background. The virtual objects can also be 'printed' as haptic objects using a conventional 3-D prototyper. We also describe some implementations of these computational algorithms to help illustrate the potential utility of the algorithms. It is important to distinguish the algorithms from their implementations. The implementations are demonstrations offered solely as a 'proof of principle' of the underlying algorithms. It is important to note that, in general, an implementation of a computational algorithm often has limitations that the algorithm itself does not have. Together, these methods represent a set of powerful and flexible tools for studying object recognition and perceptual learning by biological and computational systems alike. With appropriate extensions, these methods may also prove useful in the study of morphogenesis and phylogenesis.
Neuroscience, Issue 69, machine learning, brain, classification, category learning, cross-modal perception, 3-D prototyping, inference
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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
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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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The Generation of Higher-order Laguerre-Gauss Optical Beams for High-precision Interferometry
Authors: Ludovico Carbone, Paul Fulda, Charlotte Bond, Frank Brueckner, Daniel Brown, Mengyao Wang, Deepali Lodhia, Rebecca Palmer, Andreas Freise.
Institutions: University of Birmingham.
Thermal noise in high-reflectivity mirrors is a major impediment for several types of high-precision interferometric experiments that aim to reach the standard quantum limit or to cool mechanical systems to their quantum ground state. This is for example the case of future gravitational wave observatories, whose sensitivity to gravitational wave signals is expected to be limited in the most sensitive frequency band, by atomic vibration of their mirror masses. One promising approach being pursued to overcome this limitation is to employ higher-order Laguerre-Gauss (LG) optical beams in place of the conventionally used fundamental mode. Owing to their more homogeneous light intensity distribution these beams average more effectively over the thermally driven fluctuations of the mirror surface, which in turn reduces the uncertainty in the mirror position sensed by the laser light. We demonstrate a promising method to generate higher-order LG beams by shaping a fundamental Gaussian beam with the help of diffractive optical elements. We show that with conventional sensing and control techniques that are known for stabilizing fundamental laser beams, higher-order LG modes can be purified and stabilized just as well at a comparably high level. A set of diagnostic tools allows us to control and tailor the properties of generated LG beams. This enabled us to produce an LG beam with the highest purity reported to date. The demonstrated compatibility of higher-order LG modes with standard interferometry techniques and with the use of standard spherical optics makes them an ideal candidate for application in a future generation of high-precision interferometry.
Physics, Issue 78, Optics, Astronomy, Astrophysics, Gravitational waves, Laser interferometry, Metrology, Thermal noise, Laguerre-Gauss modes, interferometry
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Simultaneous Multicolor Imaging of Biological Structures with Fluorescence Photoactivation Localization Microscopy
Authors: Nikki M. Curthoys, Michael J. Mlodzianoski, Dahan Kim, Samuel T. Hess.
Institutions: University of Maine.
Localization-based super resolution microscopy can be applied to obtain a spatial map (image) of the distribution of individual fluorescently labeled single molecules within a sample with a spatial resolution of tens of nanometers. Using either photoactivatable (PAFP) or photoswitchable (PSFP) fluorescent proteins fused to proteins of interest, or organic dyes conjugated to antibodies or other molecules of interest, fluorescence photoactivation localization microscopy (FPALM) can simultaneously image multiple species of molecules within single cells. By using the following approach, populations of large numbers (thousands to hundreds of thousands) of individual molecules are imaged in single cells and localized with a precision of ~10-30 nm. Data obtained can be applied to understanding the nanoscale spatial distributions of multiple protein types within a cell. One primary advantage of this technique is the dramatic increase in spatial resolution: while diffraction limits resolution to ~200-250 nm in conventional light microscopy, FPALM can image length scales more than an order of magnitude smaller. As many biological hypotheses concern the spatial relationships among different biomolecules, the improved resolution of FPALM can provide insight into questions of cellular organization which have previously been inaccessible to conventional fluorescence microscopy. In addition to detailing the methods for sample preparation and data acquisition, we here describe the optical setup for FPALM. One additional consideration for researchers wishing to do super-resolution microscopy is cost: in-house setups are significantly cheaper than most commercially available imaging machines. Limitations of this technique include the need for optimizing the labeling of molecules of interest within cell samples, and the need for post-processing software to visualize results. We here describe the use of PAFP and PSFP expression to image two protein species in fixed cells. Extension of the technique to living cells is also described.
Basic Protocol, Issue 82, Microscopy, Super-resolution imaging, Multicolor, single molecule, FPALM, Localization microscopy, fluorescent proteins
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A Protocol for Phage Display and Affinity Selection Using Recombinant Protein Baits
Authors: Rekha Kushwaha, Kim R. Schäfermeyer, A. Bruce Downie.
Institutions: University of Kentucky .
Using recombinant phage as a scaffold to present various protein portions encoded by a directionally cloned cDNA library to immobilized bait molecules is an efficient means to discover interactions. The technique has largely been used to discover protein-protein interactions but the bait molecule to be challenged need not be restricted to proteins. The protocol presented here has been optimized to allow a modest number of baits to be screened in replicates to maximize the identification of independent clones presenting the same protein. This permits greater confidence that interacting proteins identified are legitimate interactors of the bait molecule. Monitoring the phage titer after each affinity selection round provides information on how the affinity selection is progressing as well as on the efficacy of negative controls. One means of titering the phage, and how and what to prepare in advance to allow this process to progress as efficiently as possible, is presented. Attributes of amplicons retrieved following isolation of independent plaque are highlighted that can be used to ascertain how well the affinity selection has progressed. Trouble shooting techniques to minimize false positives or to bypass persistently recovered phage are explained. Means of reducing viral contamination flare up are discussed.
Biochemistry, Issue 84, Affinity selection, Phage display, protein-protein interaction
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Automated, Quantitative Cognitive/Behavioral Screening of Mice: For Genetics, Pharmacology, Animal Cognition and Undergraduate Instruction
Authors: C. R. Gallistel, Fuat Balci, David Freestone, Aaron Kheifets, Adam King.
Institutions: Rutgers University, Koç University, New York University, Fairfield University.
We describe a high-throughput, high-volume, fully automated, live-in 24/7 behavioral testing system for assessing the effects of genetic and pharmacological manipulations on basic mechanisms of cognition and learning in mice. A standard polypropylene mouse housing tub is connected through an acrylic tube to a standard commercial mouse test box. The test box has 3 hoppers, 2 of which are connected to pellet feeders. All are internally illuminable with an LED and monitored for head entries by infrared (IR) beams. Mice live in the environment, which eliminates handling during screening. They obtain their food during two or more daily feeding periods by performing in operant (instrumental) and Pavlovian (classical) protocols, for which we have written protocol-control software and quasi-real-time data analysis and graphing software. The data analysis and graphing routines are written in a MATLAB-based language created to simplify greatly the analysis of large time-stamped behavioral and physiological event records and to preserve a full data trail from raw data through all intermediate analyses to the published graphs and statistics within a single data structure. The data-analysis code harvests the data several times a day and subjects it to statistical and graphical analyses, which are automatically stored in the "cloud" and on in-lab computers. Thus, the progress of individual mice is visualized and quantified daily. The data-analysis code talks to the protocol-control code, permitting the automated advance from protocol to protocol of individual subjects. The behavioral protocols implemented are matching, autoshaping, timed hopper-switching, risk assessment in timed hopper-switching, impulsivity measurement, and the circadian anticipation of food availability. Open-source protocol-control and data-analysis code makes the addition of new protocols simple. Eight test environments fit in a 48 in x 24 in x 78 in cabinet; two such cabinets (16 environments) may be controlled by one computer.
Behavior, Issue 84, genetics, cognitive mechanisms, behavioral screening, learning, memory, timing
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Measurement of Neurophysiological Signals of Ignoring and Attending Processes in Attention Control
Authors: Agatha Lenartowicz, Gregory V. Simpson, Samantha R. O'Connell, Mark S. Cohen.
Institutions: University of California Los Angeles, Attention Research Institute, University of California Los Angeles.
Attention control is the ability to selectively attend to some sensory signals while ignoring others. This ability is thought to involve two processes: enhancement of sensory signals that are to be attended and the attenuation of sensory signals that are to be ignored. The overall strength of attentional modulation is often measured by comparing the amplitude of a sensory neural response to an external input when attended versus when ignored. This method is robust for detecting attentional modulation, but precludes the ability to assess the separate dynamics of attending and ignoring processes. Here, we describe methodology to measure independently the neurophysiological signals of attending and ignoring using the intermodal attention task (IMAT). This task, when combined with electroencephalography, isolates neurophysiological sensory responses in auditory and visual modalities, when either attending or ignoring, with respect to a passive control. As a result, independent dynamics of attending and of a ignoring can be assessed in either modality. Our results using this task indicate that the timing and cortical sources of attending and ignoring effects differ, as do their contributions to the attention modulation effect, pointing to unique neural trajectories and demonstrating sample utility of measuring them separately.
Behavior, Issue 101, attention, control, executive function, neurophysiology, electroencephalography, event-related potential, attending, ignoring, sustained attention, intermodal, inter-sensory, auditory, visual
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