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Predicting future clinical changes of MCI patients using longitudinal and multimodal biomarkers.
Accurate prediction of clinical changes of mild cognitive impairment (MCI) patients, including both qualitative change (i.e., conversion to Alzheimers disease (AD)) and quantitative change (i.e., cognitive scores) at future time points, is important for early diagnosis of AD and for monitoring the disease progression. In this paper, we propose to predict future clinical changes of MCI patients by using both baseline and longitudinal multimodality data. To do this, we first develop a longitudinal feature selection method to jointly select brain regions across multiple time points for each modality. Specifically, for each time point, we train a sparse linear regression model by using the imaging data and the corresponding clinical scores, with an extra group regularization to group the weights corresponding to the same brain region across multiple time points together and to allow for selection of brain regions based on the strength of multiple time points jointly. Then, to further reflect the longitudinal changes on the selected brain regions, we extract a set of longitudinal features from the original baseline and longitudinal data. Finally, we combine all features on the selected brain regions, from different modalities, for prediction by using our previously proposed multi-kernel SVM. We validate our method on 88 ADNI MCI subjects, with both MRI and FDG-PET data and the corresponding clinical scores (i.e., MMSE and ADAS-Cog) at 5 different time points. We first predict the clinical scores (MMSE and ADAS-Cog) at 24-month by using the multimodality data at previous time points, and then predict the conversion of MCI to AD by using the multimodality data at time points which are at least 6-month ahead of the conversion. The results on both sets of experiments show that our proposed method can achieve better performance in predicting future clinical changes of MCI patients than the conventional methods.
Authors: Phoebe Spetsieris, Yilong Ma, Shichun Peng, Ji Hyun Ko, Vijay Dhawan, Chris C. Tang, David Eidelberg.
Published: 06-26-2013
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.
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Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
Authors: Marc N. Coutanche, Sharon L. Thompson-Schill.
Institutions: University of Pennsylvania.
It is now appreciated that condition-relevant information can be present within distributed patterns of functional magnetic resonance imaging (fMRI) brain activity, even for conditions with similar levels of univariate activation. Multi-voxel pattern (MVP) analysis has been used to decode this information with great success. FMRI investigators also often seek to understand how brain regions interact in interconnected networks, and use functional connectivity (FC) to identify regions that have correlated responses over time. Just as univariate analyses can be insensitive to information in MVPs, FC may not fully characterize the brain networks that process conditions with characteristic MVP signatures. The method described here, informational connectivity (IC), can identify regions with correlated changes in MVP-discriminability across time, revealing connectivity that is not accessible to FC. The method can be exploratory, using searchlights to identify seed-connected areas, or planned, between pre-selected regions-of-interest. The results can elucidate networks of regions that process MVP-related conditions, can breakdown MVPA searchlight maps into separate networks, or can be compared across tasks and patient groups.
Neuroscience, Issue 89, fMRI, MVPA, connectivity, informational connectivity, functional connectivity, networks, multi-voxel pattern analysis, decoding, classification, method, multivariate
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Dynamic Visual Tests to Identify and Quantify Visual Damage and Repair Following Demyelination in Optic Neuritis Patients
Authors: Noa Raz, Michal Hallak, Tamir Ben-Hur, Netta Levin.
Institutions: Hadassah Hebrew-University Medical Center.
In order to follow optic neuritis patients and evaluate the effectiveness of their treatment, a handy, accurate and quantifiable tool is required to assess changes in myelination at the central nervous system (CNS). However, standard measurements, including routine visual tests and MRI scans, are not sensitive enough for this purpose. We present two visual tests addressing dynamic monocular and binocular functions which may closely associate with the extent of myelination along visual pathways. These include Object From Motion (OFM) extraction and Time-constrained stereo protocols. In the OFM test, an array of dots compose an object, by moving the dots within the image rightward while moving the dots outside the image leftward or vice versa. The dot pattern generates a camouflaged object that cannot be detected when the dots are stationary or moving as a whole. Importantly, object recognition is critically dependent on motion perception. In the Time-constrained Stereo protocol, spatially disparate images are presented for a limited length of time, challenging binocular 3-dimensional integration in time. Both tests are appropriate for clinical usage and provide a simple, yet powerful, way to identify and quantify processes of demyelination and remyelination along visual pathways. These protocols may be efficient to diagnose and follow optic neuritis and multiple sclerosis patients. In the diagnostic process, these protocols may reveal visual deficits that cannot be identified via current standard visual measurements. Moreover, these protocols sensitively identify the basis of the currently unexplained continued visual complaints of patients following recovery of visual acuity. In the longitudinal follow up course, the protocols can be used as a sensitive marker of demyelinating and remyelinating processes along time. These protocols may therefore be used to evaluate the efficacy of current and evolving therapeutic strategies, targeting myelination of the CNS.
Medicine, Issue 86, Optic neuritis, visual impairment, dynamic visual functions, motion perception, stereopsis, demyelination, remyelination
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Examining the Characteristics of Episodic Memory using Event-related Potentials in Patients with Alzheimer's Disease
Authors: Erin Hussey, Brandon Ally.
Institutions: Vanderbilt University.
Our laboratory uses event-related EEG potentials (ERPs) to understand and support behavioral investigations of episodic memory in patients with amnestic mild cognitive impairment (aMCI) and Alzheimer's disease (AD). Whereas behavioral data inform us about the patients' performance, ERPs allow us to record discrete changes in brain activity. Further, ERPs can give us insight into the onset, duration, and interaction of independent cognitive processes associated with memory retrieval. In patient populations, these types of studies are used to examine which aspects of memory are impaired and which remain relatively intact compared to a control population. The methodology for collecting ERP data from a vulnerable patient population while these participants perform a recognition memory task is reviewed. This protocol includes participant preparation, quality assurance, data acquisition, and data analysis. In addition to basic setup and acquisition, we will also demonstrate localization techniques to obtain greater spatial resolution and source localization using high-density (128 channel) electrode arrays.
Medicine, Issue 54, recognition memory, episodic memory, event-related potentials, dual process, Alzheimer's disease, amnestic mild cognitive impairment
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Dried Blood Spot Collection of Health Biomarkers to Maximize Participation in Population Studies
Authors: Michael W. Ostler, James H. Porter, Orfeu M. Buxton.
Institutions: Harvard School of Public Health, Brigham and Women's Hospital, Harvard Medical School, Pennsylvania State University.
Biomarkers are directly-measured biological indicators of disease, health, exposures, or other biological information. In population and social sciences, biomarkers need to be easy to obtain, transport, and analyze. Dried Blood Spots meet this need, and can be collected in the field with high response rates. These elements are particularly important in longitudinal study designs including interventions where attrition is critical to avoid, and high response rates improve the interpretation of results. Dried Blood Spot sample collection is simple, quick, relatively painless, less invasive then venipuncture, and requires minimal field storage requirements (i.e. samples do not need to be immediately frozen and can be stored for a long period of time in a stable freezer environment before assay). The samples can be analyzed for a variety of different analytes, including cholesterol, C-reactive protein, glycosylated hemoglobin, numerous cytokines, and other analytes, as well as provide genetic material. DBS collection is depicted as employed in several recent studies.
Medicine, Issue 83, dried blood spots (DBS), Biomarkers, cardiometabolic risk, Inflammation, standard precautions, blood collection
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Hydrogel Nanoparticle Harvesting of Plasma or Urine for Detecting Low Abundance Proteins
Authors: Ruben Magni, Benjamin H. Espina, Lance A. Liotta, Alessandra Luchini, Virginia Espina.
Institutions: George Mason University, Ceres Nanosciences.
Novel biomarker discovery plays a crucial role in providing more sensitive and specific disease detection. Unfortunately many low-abundance biomarkers that exist in biological fluids cannot be easily detected with mass spectrometry or immunoassays because they are present in very low concentration, are labile, and are often masked by high-abundance proteins such as albumin or immunoglobulin. Bait containing poly(N-isopropylacrylamide) (NIPAm) based nanoparticles are able to overcome these physiological barriers. In one step they are able to capture, concentrate and preserve biomarkers from body fluids. Low-molecular weight analytes enter the core of the nanoparticle and are captured by different organic chemical dyes, which act as high affinity protein baits. The nanoparticles are able to concentrate the proteins of interest by several orders of magnitude. This concentration factor is sufficient to increase the protein level such that the proteins are within the detection limit of current mass spectrometers, western blotting, and immunoassays. Nanoparticles can be incubated with a plethora of biological fluids and they are able to greatly enrich the concentration of low-molecular weight proteins and peptides while excluding albumin and other high-molecular weight proteins. Our data show that a 10,000 fold amplification in the concentration of a particular analyte can be achieved, enabling mass spectrometry and immunoassays to detect previously undetectable biomarkers.
Bioengineering, Issue 90, biomarker, hydrogel, low abundance, mass spectrometry, nanoparticle, plasma, protein, urine
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Chemically-blocked Antibody Microarray for Multiplexed High-throughput Profiling of Specific Protein Glycosylation in Complex Samples
Authors: Chen Lu, Joshua L. Wonsidler, Jianwei Li, Yanming Du, Timothy Block, Brian Haab, Songming Chen.
Institutions: Institute for Hepatitis and Virus Research, Thomas Jefferson University , Drexel University College of Medicine, Van Andel Research Institute, Serome Biosciences Inc..
In this study, we describe an effective protocol for use in a multiplexed high-throughput antibody microarray with glycan binding protein detection that allows for the glycosylation profiling of specific proteins. Glycosylation of proteins is the most prevalent post-translational modification found on proteins, and leads diversified modifications of the physical, chemical, and biological properties of proteins. Because the glycosylation machinery is particularly susceptible to disease progression and malignant transformation, aberrant glycosylation has been recognized as early detection biomarkers for cancer and other diseases. However, current methods to study protein glycosylation typically are too complicated or expensive for use in most normal laboratory or clinical settings and a more practical method to study protein glycosylation is needed. The new protocol described in this study makes use of a chemically blocked antibody microarray with glycan-binding protein (GBP) detection and significantly reduces the time, cost, and lab equipment requirements needed to study protein glycosylation. In this method, multiple immobilized glycoprotein-specific antibodies are printed directly onto the microarray slides and the N-glycans on the antibodies are blocked. The blocked, immobilized glycoprotein-specific antibodies are able to capture and isolate glycoproteins from a complex sample that is applied directly onto the microarray slides. Glycan detection then can be performed by the application of biotinylated lectins and other GBPs to the microarray slide, while binding levels can be determined using Dylight 549-Streptavidin. Through the use of an antibody panel and probing with multiple biotinylated lectins, this method allows for an effective glycosylation profile of the different proteins found in a given human or animal sample to be developed. Introduction Glycosylation of protein, which is the most ubiquitous post-translational modification on proteins, modifies the physical, chemical, and biological properties of a protein, and plays a fundamental role in various biological processes1-6. Because the glycosylation machinery is particularly susceptible to disease progression and malignant transformation, aberrant glycosylation has been recognized as early detection biomarkers for cancer and other diseases 7-12. In fact, most current cancer biomarkers, such as the L3 fraction of α-1 fetoprotein (AFP) for hepatocellular carcinoma 13-15, and CA199 for pancreatic cancer 16, 17 are all aberrant glycan moieties on glycoproteins. However, methods to study protein glycosylation have been complicated, and not suitable for routine laboratory and clinical settings. Chen et al. has recently invented a chemically blocked antibody microarray with a glycan-binding protein (GBP) detection method for high-throughput and multiplexed profile glycosylation of native glycoproteins in a complex sample 18. In this affinity based microarray method, multiple immobilized glycoprotein-specific antibodies capture and isolate glycoproteins from the complex mixture directly on the microarray slide, and the glycans on each individual captured protein are measured by GBPs. Because all normal antibodies contain N-glycans which could be recognized by most GBPs, the critical step of this method is to chemically block the glycans on the antibodies from binding to GBP. In the procedure, the cis-diol groups of the glycans on the antibodies were first oxidized to aldehyde groups by using NaIO4 in sodium acetate buffer avoiding light. The aldehyde groups were then conjugated to the hydrazide group of a cross-linker, 4-(4-N-MaleimidoPhenyl)butyric acid Hydrazide HCl (MPBH), followed by the conjugation of a dipeptide, Cys-Gly, to the maleimide group of the MPBH. Thus, the cis-diol groups on glycans of antibodies were converted into bulky none hydroxyl groups, which hindered the lectins and other GBPs bindings to the capture antibodies. This blocking procedure makes the GBPs and lectins bind only to the glycans of captured proteins. After this chemically blocking, serum samples were incubated with the antibody microarray, followed by the glycans detection by using different biotinylated lectins and GBPs, and visualized with Cy3-streptavidin. The parallel use of an antibody panel and multiple lectin probing provides discrete glycosylation profiles of multiple proteins in a given sample 18-20. This method has been used successfully in multiple different labs 1, 7, 13, 19-31. However, stability of MPBH and Cys-Gly, complicated and extended procedure in this method affect the reproducibility, effectiveness and efficiency of the method. In this new protocol, we replaced both MPBH and Cys-Gly with one much more stable reagent glutamic acid hydrazide (Glu-hydrazide), which significantly improved the reproducibility of the method, simplified and shorten the whole procedure so that the it can be completed within one working day. In this new protocol, we describe the detailed procedure of the protocol which can be readily adopted by normal labs for routine protein glycosylation study and techniques which are necessary to obtain reproducible and repeatable results.
Molecular Biology, Issue 63, Glycoproteins, glycan-binding protein, specific protein glycosylation, multiplexed high-throughput glycan blocked antibody microarray
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Adaptation of Semiautomated Circulating Tumor Cell (CTC) Assays for Clinical and Preclinical Research Applications
Authors: Lori E. Lowes, Benjamin D. Hedley, Michael Keeney, Alison L. Allan.
Institutions: London Health Sciences Centre, Western University, London Health Sciences Centre, Lawson Health Research Institute, Western University.
The majority of cancer-related deaths occur subsequent to the development of metastatic disease. This highly lethal disease stage is associated with the presence of circulating tumor cells (CTCs). These rare cells have been demonstrated to be of clinical significance in metastatic breast, prostate, and colorectal cancers. The current gold standard in clinical CTC detection and enumeration is the FDA-cleared CellSearch system (CSS). This manuscript outlines the standard protocol utilized by this platform as well as two additional adapted protocols that describe the detailed process of user-defined marker optimization for protein characterization of patient CTCs and a comparable protocol for CTC capture in very low volumes of blood, using standard CSS reagents, for studying in vivo preclinical mouse models of metastasis. In addition, differences in CTC quality between healthy donor blood spiked with cells from tissue culture versus patient blood samples are highlighted. Finally, several commonly discrepant items that can lead to CTC misclassification errors are outlined. Taken together, these protocols will provide a useful resource for users of this platform interested in preclinical and clinical research pertaining to metastasis and CTCs.
Medicine, Issue 84, Metastasis, circulating tumor cells (CTCs), CellSearch system, user defined marker characterization, in vivo, preclinical mouse model, clinical research
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Controlling Parkinson's Disease With Adaptive Deep Brain Stimulation
Authors: Simon Little, Alek Pogosyan, Spencer Neal, Ludvic Zrinzo, Marwan Hariz, Thomas Foltynie, Patricia Limousin, Peter Brown.
Institutions: University of Oxford, UCL Institute of Neurology.
Adaptive deep brain stimulation (aDBS) has the potential to improve the treatment of Parkinson's disease by optimizing stimulation in real time according to fluctuating disease and medication state. In the present realization of adaptive DBS we record and stimulate from the DBS electrodes implanted in the subthalamic nucleus of patients with Parkinson's disease in the early post-operative period. Local field potentials are analogue filtered between 3 and 47 Hz before being passed to a data acquisition unit where they are digitally filtered again around the patient specific beta peak, rectified and smoothed to give an online reading of the beta amplitude. A threshold for beta amplitude is set heuristically, which, if crossed, passes a trigger signal to the stimulator. The stimulator then ramps up stimulation to a pre-determined clinically effective voltage over 250 msec and continues to stimulate until the beta amplitude again falls down below threshold. Stimulation continues in this manner with brief episodes of ramped DBS during periods of heightened beta power. Clinical efficacy is assessed after a minimum period of stabilization (5 min) through the unblinded and blinded video assessment of motor function using a selection of scores from the Unified Parkinson's Rating Scale (UPDRS). Recent work has demonstrated a reduction in power consumption with aDBS as well as an improvement in clinical scores compared to conventional DBS. Chronic aDBS could now be trialed in Parkinsonism.
Medicine, Issue 89, Parkinson's, deep brain stimulation, adaptive, closed loop
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Ultrasound Assessment of Endothelial-Dependent Flow-Mediated Vasodilation of the Brachial Artery in Clinical Research
Authors: Hugh Alley, Christopher D. Owens, Warren J. Gasper, S. Marlene Grenon.
Institutions: University of California, San Francisco, Veterans Affairs Medical Center, San Francisco, Veterans Affairs Medical Center, San Francisco.
The vascular endothelium is a monolayer of cells that cover the interior of blood vessels and provide both structural and functional roles. The endothelium acts as a barrier, preventing leukocyte adhesion and aggregation, as well as controlling permeability to plasma components. Functionally, the endothelium affects vessel tone. Endothelial dysfunction is an imbalance between the chemical species which regulate vessel tone, thombroresistance, cellular proliferation and mitosis. It is the first step in atherosclerosis and is associated with coronary artery disease, peripheral artery disease, heart failure, hypertension, and hyperlipidemia. The first demonstration of endothelial dysfunction involved direct infusion of acetylcholine and quantitative coronary angiography. Acetylcholine binds to muscarinic receptors on the endothelial cell surface, leading to an increase of intracellular calcium and increased nitric oxide (NO) production. In subjects with an intact endothelium, vasodilation was observed while subjects with endothelial damage experienced paradoxical vasoconstriction. There exists a non-invasive, in vivo method for measuring endothelial function in peripheral arteries using high-resolution B-mode ultrasound. The endothelial function of peripheral arteries is closely related to coronary artery function. This technique measures the percent diameter change in the brachial artery during a period of reactive hyperemia following limb ischemia. This technique, known as endothelium-dependent, flow-mediated vasodilation (FMD) has value in clinical research settings. However, a number of physiological and technical issues can affect the accuracy of the results and appropriate guidelines for the technique have been published. Despite the guidelines, FMD remains heavily operator dependent and presents a steep learning curve. This article presents a standardized method for measuring FMD in the brachial artery on the upper arm and offers suggestions to reduce intra-operator variability.
Medicine, Issue 92, endothelial function, endothelial dysfunction, brachial artery, peripheral artery disease, ultrasound, vascular, endothelium, cardiovascular disease.
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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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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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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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A Restriction Enzyme Based Cloning Method to Assess the In vitro Replication Capacity of HIV-1 Subtype C Gag-MJ4 Chimeric Viruses
Authors: Daniel T. Claiborne, Jessica L. Prince, Eric Hunter.
Institutions: Emory University, Emory University.
The protective effect of many HLA class I alleles on HIV-1 pathogenesis and disease progression is, in part, attributed to their ability to target conserved portions of the HIV-1 genome that escape with difficulty. Sequence changes attributed to cellular immune pressure arise across the genome during infection, and if found within conserved regions of the genome such as Gag, can affect the ability of the virus to replicate in vitro. Transmission of HLA-linked polymorphisms in Gag to HLA-mismatched recipients has been associated with reduced set point viral loads. We hypothesized this may be due to a reduced replication capacity of the virus. Here we present a novel method for assessing the in vitro replication of HIV-1 as influenced by the gag gene isolated from acute time points from subtype C infected Zambians. This method uses restriction enzyme based cloning to insert the gag gene into a common subtype C HIV-1 proviral backbone, MJ4. This makes it more appropriate to the study of subtype C sequences than previous recombination based methods that have assessed the in vitro replication of chronically derived gag-pro sequences. Nevertheless, the protocol could be readily modified for studies of viruses from other subtypes. Moreover, this protocol details a robust and reproducible method for assessing the replication capacity of the Gag-MJ4 chimeric viruses on a CEM-based T cell line. This method was utilized for the study of Gag-MJ4 chimeric viruses derived from 149 subtype C acutely infected Zambians, and has allowed for the identification of residues in Gag that affect replication. More importantly, the implementation of this technique has facilitated a deeper understanding of how viral replication defines parameters of early HIV-1 pathogenesis such as set point viral load and longitudinal CD4+ T cell decline.
Infectious Diseases, Issue 90, HIV-1, Gag, viral replication, replication capacity, viral fitness, MJ4, CEM, GXR25
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Developing Neuroimaging Phenotypes of the Default Mode Network in PTSD: Integrating the Resting State, Working Memory, and Structural Connectivity
Authors: Noah S. Philip, S. Louisa Carpenter, Lawrence H. Sweet.
Institutions: Alpert Medical School, Brown University, University of Georgia.
Complementary structural and functional neuroimaging techniques used to examine the Default Mode Network (DMN) could potentially improve assessments of psychiatric illness severity and provide added validity to the clinical diagnostic process. Recent neuroimaging research suggests that DMN processes may be disrupted in a number of stress-related psychiatric illnesses, such as posttraumatic stress disorder (PTSD). Although specific DMN functions remain under investigation, it is generally thought to be involved in introspection and self-processing. In healthy individuals it exhibits greatest activity during periods of rest, with less activity, observed as deactivation, during cognitive tasks, e.g., working memory. This network consists of the medial prefrontal cortex, posterior cingulate cortex/precuneus, lateral parietal cortices and medial temporal regions. Multiple functional and structural imaging approaches have been developed to study the DMN. These have unprecedented potential to further the understanding of the function and dysfunction of this network. Functional approaches, such as the evaluation of resting state connectivity and task-induced deactivation, have excellent potential to identify targeted neurocognitive and neuroaffective (functional) diagnostic markers and may indicate illness severity and prognosis with increased accuracy or specificity. Structural approaches, such as evaluation of morphometry and connectivity, may provide unique markers of etiology and long-term outcomes. Combined, functional and structural methods provide strong multimodal, complementary and synergistic approaches to develop valid DMN-based imaging phenotypes in stress-related psychiatric conditions. This protocol aims to integrate these methods to investigate DMN structure and function in PTSD, relating findings to illness severity and relevant clinical factors.
Medicine, Issue 89, default mode network, neuroimaging, functional magnetic resonance imaging, diffusion tensor imaging, structural connectivity, functional connectivity, posttraumatic stress disorder
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The Use of Magnetic Resonance Spectroscopy as a Tool for the Measurement of Bi-hemispheric Transcranial Electric Stimulation Effects on Primary Motor Cortex Metabolism
Authors: Sara Tremblay, Vincent Beaulé, Sébastien Proulx, Louis-Philippe Lafleur, Julien Doyon, Małgorzata Marjańska, Hugo Théoret.
Institutions: University of Montréal, McGill University, University of Minnesota.
Transcranial direct current stimulation (tDCS) is a neuromodulation technique that has been increasingly used over the past decade in the treatment of neurological and psychiatric disorders such as stroke and depression. Yet, the mechanisms underlying its ability to modulate brain excitability to improve clinical symptoms remains poorly understood 33. To help improve this understanding, proton magnetic resonance spectroscopy (1H-MRS) can be used as it allows the in vivo quantification of brain metabolites such as γ-aminobutyric acid (GABA) and glutamate in a region-specific manner 41. In fact, a recent study demonstrated that 1H-MRS is indeed a powerful means to better understand the effects of tDCS on neurotransmitter concentration 34. This article aims to describe the complete protocol for combining tDCS (NeuroConn MR compatible stimulator) with 1H-MRS at 3 T using a MEGA-PRESS sequence. We will describe the impact of a protocol that has shown great promise for the treatment of motor dysfunctions after stroke, which consists of bilateral stimulation of primary motor cortices 27,30,31. Methodological factors to consider and possible modifications to the protocol are also discussed.
Neuroscience, Issue 93, proton magnetic resonance spectroscopy, transcranial direct current stimulation, primary motor cortex, GABA, glutamate, stroke
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The Multiple Sclerosis Performance Test (MSPT): An iPad-Based Disability Assessment Tool
Authors: Richard A. Rudick, Deborah Miller, Francois Bethoux, Stephen M. Rao, Jar-Chi Lee, Darlene Stough, Christine Reece, David Schindler, Bernadett Mamone, Jay Alberts.
Institutions: Cleveland Clinic Foundation, Cleveland Clinic Foundation, Cleveland Clinic Foundation, Cleveland Clinic Foundation.
Precise measurement of neurological and neuropsychological impairment and disability in multiple sclerosis is challenging. We report a new test, the Multiple Sclerosis Performance Test (MSPT), which represents a new approach to quantifying MS related disability. The MSPT takes advantage of advances in computer technology, information technology, biomechanics, and clinical measurement science. The resulting MSPT represents a computer-based platform for precise, valid measurement of MS severity. Based on, but extending the Multiple Sclerosis Functional Composite (MSFC), the MSPT provides precise, quantitative data on walking speed, balance, manual dexterity, visual function, and cognitive processing speed. The MSPT was tested by 51 MS patients and 49 healthy controls (HC). MSPT scores were highly reproducible, correlated strongly with technician-administered test scores, discriminated MS from HC and severe from mild MS, and correlated with patient reported outcomes. Measures of reliability, sensitivity, and clinical meaning for MSPT scores were favorable compared with technician-based testing. The MSPT is a potentially transformative approach for collecting MS disability outcome data for patient care and research. Because the testing is computer-based, test performance can be analyzed in traditional or novel ways and data can be directly entered into research or clinical databases. The MSPT could be widely disseminated to clinicians in practice settings who are not connected to clinical trial performance sites or who are practicing in rural settings, drastically improving access to clinical trials for clinicians and patients. The MSPT could be adapted to out of clinic settings, like the patient’s home, thereby providing more meaningful real world data. The MSPT represents a new paradigm for neuroperformance testing. This method could have the same transformative effect on clinical care and research in MS as standardized computer-adapted testing has had in the education field, with clear potential to accelerate progress in clinical care and research.
Medicine, Issue 88, Multiple Sclerosis, Multiple Sclerosis Functional Composite, computer-based testing, 25-foot walk test, 9-hole peg test, Symbol Digit Modalities Test, Low Contrast Visual Acuity, Clinical Outcome Measure
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Getting to Compliance in Forced Exercise in Rodents: A Critical Standard to Evaluate Exercise Impact in Aging-related Disorders and Disease
Authors: Jennifer C. Arnold, Michael F. Salvatore.
Institutions: Louisiana State University Health Sciences Center.
There is a major increase in the awareness of the positive impact of exercise on improving several disease states with neurobiological basis; these include improving cognitive function and physical performance. As a result, there is an increase in the number of animal studies employing exercise. It is argued that one intrinsic value of forced exercise is that the investigator has control over the factors that can influence the impact of exercise on behavioral outcomes, notably exercise frequency, duration, and intensity of the exercise regimen. However, compliance in forced exercise regimens may be an issue, particularly if potential confounds of employing foot-shock are to be avoided. It is also important to consider that since most cognitive and locomotor impairments strike in the aged individual, determining impact of exercise on these impairments should consider using aged rodents with a highest possible level of compliance to ensure minimal need for test subjects. Here, the pertinent steps and considerations necessary to achieve nearly 100% compliance to treadmill exercise in an aged rodent model will be presented and discussed. Notwithstanding the particular exercise regimen being employed by the investigator, our protocol should be of use to investigators that are particularly interested in the potential impact of forced exercise on aging-related impairments, including aging-related Parkinsonism and Parkinson’s disease.
Behavior, Issue 90, Exercise, locomotor, Parkinson’s disease, aging, treadmill, bradykinesia, Parkinsonism
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Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues
Authors: Marcus Cheetham, Lutz Jancke.
Institutions: University of Zurich.
Mori's Uncanny Valley Hypothesis1,2 proposes that the perception of humanlike characters such as robots and, by extension, avatars (computer-generated characters) can evoke negative or positive affect (valence) depending on the object's degree of visual and behavioral realism along a dimension of human likeness (DHL) (Figure 1). But studies of affective valence of subjective responses to variously realistic non-human characters have produced inconsistent findings 3, 4, 5, 6. One of a number of reasons for this is that human likeness is not perceived as the hypothesis assumes. While the DHL can be defined following Mori's description as a smooth linear change in the degree of physical humanlike similarity, subjective perception of objects along the DHL can be understood in terms of the psychological effects of categorical perception (CP) 7. Further behavioral and neuroimaging investigations of category processing and CP along the DHL and of the potential influence of the dimension's underlying category structure on affective experience are needed. This protocol therefore focuses on the DHL and allows examination of CP. Based on the protocol presented in the video as an example, issues surrounding the methodology in the protocol and the use in "uncanny" research of stimuli drawn from morph continua to represent the DHL are discussed in the article that accompanies the video. The use of neuroimaging and morph stimuli to represent the DHL in order to disentangle brain regions neurally responsive to physical human-like similarity from those responsive to category change and category processing is briefly illustrated.
Behavior, Issue 76, Neuroscience, Neurobiology, Molecular Biology, Psychology, Neuropsychology, uncanny valley, functional magnetic resonance imaging, fMRI, categorical perception, virtual reality, avatar, human likeness, Mori, uncanny valley hypothesis, perception, magnetic resonance imaging, MRI, imaging, clinical techniques
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Brain Imaging Investigation of the Neural Correlates of Emotion Regulation
Authors: Sanda Dolcos, Keen Sung, Ekaterina Denkova, Roger A. Dixon, Florin Dolcos.
Institutions: University of Illinois, Urbana-Champaign, University of Alberta, Edmonton, University of Alberta, Edmonton, University of Alberta, Edmonton, University of Alberta, Edmonton, University of Illinois, Urbana-Champaign, University of Illinois, Urbana-Champaign.
The ability to control/regulate emotions is an important coping mechanism in the face of emotionally stressful situations. Although significant progress has been made in understanding conscious/deliberate emotion regulation (ER), less is known about non-conscious/automatic ER and the associated neural correlates. This is in part due to the problems inherent in the unitary concepts of automatic and conscious processing1. Here, we present a protocol that allows investigation of the neural correlates of both deliberate and automatic ER using functional magnetic resonance imaging (fMRI). This protocol allows new avenues of inquiry into various aspects of ER. For instance, the experimental design allows manipulation of the goal to regulate emotion (conscious vs. non-conscious), as well as the intensity of the emotional challenge (high vs. low). Moreover, it allows investigation of both immediate (emotion perception) and long-term effects (emotional memory) of ER strategies on emotion processing. Therefore, this protocol may contribute to better understanding of the neural mechanisms of emotion regulation in healthy behaviour, and to gaining insight into possible causes of deficits in depression and anxiety disorders in which emotion dysregulation is often among the core debilitating features.
Neuroscience, Issue 54, Emotion Suppression, Automatic Emotion Control, Deliberate Emotion Control, Goal Induction, Neuroimaging
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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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Cross-Modal Multivariate Pattern Analysis
Authors: Kaspar Meyer, Jonas T. Kaplan.
Institutions: University of Southern California.
Multivariate pattern analysis (MVPA) is an increasingly popular method of analyzing functional magnetic resonance imaging (fMRI) data1-4. Typically, the method is used to identify a subject's perceptual experience from neural activity in certain regions of the brain. For instance, it has been employed to predict the orientation of visual gratings a subject perceives from activity in early visual cortices5 or, analogously, the content of speech from activity in early auditory cortices6. Here, we present an extension of the classical MVPA paradigm, according to which perceptual stimuli are not predicted within, but across sensory systems. Specifically, the method we describe addresses the question of whether stimuli that evoke memory associations in modalities other than the one through which they are presented induce content-specific activity patterns in the sensory cortices of those other modalities. For instance, seeing a muted video clip of a glass vase shattering on the ground automatically triggers in most observers an auditory image of the associated sound; is the experience of this image in the "mind's ear" correlated with a specific neural activity pattern in early auditory cortices? Furthermore, is this activity pattern distinct from the pattern that could be observed if the subject were, instead, watching a video clip of a howling dog? In two previous studies7,8, we were able to predict sound- and touch-implying video clips based on neural activity in early auditory and somatosensory cortices, respectively. Our results are in line with a neuroarchitectural framework proposed by Damasio9,10, according to which the experience of mental images that are based on memories - such as hearing the shattering sound of a vase in the "mind's ear" upon seeing the corresponding video clip - is supported by the re-construction of content-specific neural activity patterns in early sensory cortices.
Neuroscience, Issue 57, perception, sensory, cross-modal, top-down, mental imagery, fMRI, MRI, neuroimaging, multivariate pattern analysis, MVPA
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Applying Microfluidics to Electrophysiology
Authors: David T. Eddington.
Institutions: University of Illinois, Chicago.
Microfluidics can be integrated with standard electrophysiology techniques to allow new experimental modalities. Specifically, the motivation for the microfluidic brain slice device is discussed including how the device docks to standard perfusion chambers and the technique of passive pumping which is used to deliver boluses of neuromodulators to the brain slice. By simplifying the device design, we are able to achieve a practical solution to the current unmet electrophysiology need of applying multiple neuromodulators across multiple regions of the brain slice. This is achieved by substituting the standard coverglass substrate of the perfusion chamber with a thin microfluidic device bonded to the coverglass substrate. This was then attached to the perfusion chamber and small holes connect the open-well of the perfusion chamber to the microfluidic channels buried within the microfluidic substrate. These microfluidic channels are interfaced with ports drilled into the edge of the perfusion chamber to access and deliver stimulants. This project represents how the field of microfluidics is transitioning away from proof-of concept device demonstrations and into practical solutions for unmet experimental and clinical needs.
Neuroscience, Issue 8, Biomedical Engineering, Microfluidics, Slice Recording, Electrophysiology, Neurotransmitter, Bioengineering
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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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Monitoring Tumor Metastases and Osteolytic Lesions with Bioluminescence and Micro CT Imaging
Authors: Ed Lim, Kshitij Modi, Anna Christensen, Jeff Meganck, Stephen Oldfield, Ning Zhang.
Institutions: Caliper Life Sciences.
Following intracardiac delivery of MDA-MB-231-luc-D3H2LN cells to Nu/Nu mice, systemic metastases developed in the injected animals. Bioluminescence imaging using IVIS Spectrum was employed to monitor the distribution and development of the tumor cells following the delivery procedure including DLIT reconstruction to measure the tumor signal and its location. Development of metastatic lesions to the bone tissues triggers osteolytic activity and lesions to tibia and femur were evaluated longitudinally using micro CT. Imaging was performed using a Quantum FX micro CT system with fast imaging and low X-ray dose. The low radiation dose allows multiple imaging sessions to be performed with a cumulative X-ray dosage far below LD50. A mouse imaging shuttle device was used to sequentially image the mice with both IVIS Spectrum and Quantum FX achieving accurate animal positioning in both the bioluminescence and CT images. The optical and CT data sets were co-registered in 3-dimentions using the Living Image 4.1 software. This multi-mode approach allows close monitoring of tumor growth and development simultaneously with osteolytic activity.
Medicine, Issue 50, osteolytic lesions, micro CT, tumor, bioluminescence, in vivo, imaging, IVIS, luciferase, low dose, co-registration, 3D reconstruction
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What is Visualize?

JoVE Visualize is a tool created to match the last 5 years of PubMed publications to methods in JoVE's video library.

How does it work?

We use abstracts found on PubMed and match them to JoVE videos to create a list of 10 to 30 related methods videos.

Video X seems to be unrelated to Abstract Y...

In developing our video relationships, we compare around 5 million PubMed articles to our library of over 4,500 methods videos. In some cases the language used in the PubMed abstracts makes matching that content to a JoVE video difficult. In other cases, there happens not to be any content in our video library that is relevant to the topic of a given abstract. In these cases, our algorithms are trying their best to display videos with relevant content, which can sometimes result in matched videos with only a slight relation.