The split hand phenomenon refers to predominant wasting of thenar muscles and is an early and specific feature of amyotrophic lateral sclerosis (ALS). A novel split hand index (SI) was developed to quantify the split hand phenomenon, and its diagnostic utility was assessed in ALS patients. The split hand index was derived by dividing the product of the compound muscle action potential (CMAP) amplitude recorded over the abductor pollicis brevis and first dorsal interosseous muscles by the CMAP amplitude recorded over the abductor digiti minimi muscle. In order to assess the diagnostic utility of the split hand index, ALS patients were prospectively assessed and their results were compared to neuromuscular disorder patients. The split hand index was significantly reduced in ALS when compared to neuromuscular disorder patients (P<0.0001). Limb-onset ALS patients exhibited the greatest reduction in the split hand index, and a value of 5.2 or less reliably differentiated ALS from other neuromuscular disorders. Consequently, the split hand index appears to be a novel diagnostic biomarker for ALS, perhaps facilitating an earlier diagnosis.
23 Related JoVE Articles!
The use of Biofeedback in Clinical Virtual Reality: The INTREPID Project
Institutions: Istituto Auxologico Italiano, Università Cattolica del Sacro Cuore.
Generalized anxiety disorder (GAD) is a psychiatric disorder characterized by a constant and unspecific anxiety that interferes with daily-life activities. Its high prevalence in general population and the severe limitations it causes, point out the necessity to find new efficient strategies to treat it. Together with the cognitive-behavioral treatments, relaxation represents a useful approach for the treatment of GAD, but it has the limitation that it is hard to be learned. The INTREPID project is aimed to implement a new instrument to treat anxiety-related disorders and to test its clinical efficacy in reducing anxiety-related symptoms. The innovation of this approach is the combination of virtual reality and biofeedback, so that the first one is directly modified by the output of the second one. In this way, the patient is made aware of his or her reactions through the modification of some features of the VR environment in real time. Using mental exercises the patient learns to control these physiological parameters and using the feedback provided by the virtual environment is able to gauge his or her success. The supplemental use of portable devices, such as PDA or smart-phones, allows the patient to perform at home, individually and autonomously, the same exercises experienced in therapist's office. The goal is to anchor the learned protocol in a real life context, so enhancing the patients' ability to deal with their symptoms. The expected result is a better and faster learning of relaxation techniques, and thus an increased effectiveness of the treatment if compared with traditional clinical protocols.
Neuroscience, Issue 33, virtual reality, biofeedback, generalized anxiety disorder, Intrepid, cybertherapy, cyberpsychology
Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
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
Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
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
Computerized Dynamic Posturography for Postural Control Assessment in Patients with Intermittent Claudication
Institutions: University of Sydney, University of Hull, Hull and East Yorkshire Hospitals, Addenbrookes Hospital.
Computerized dynamic posturography with the EquiTest is an objective technique for measuring postural strategies under challenging static and dynamic conditions. As part of a diagnostic assessment, the early detection of postural deficits is important so that appropriate and targeted interventions can be prescribed. The Sensory Organization Test (SOT) on the EquiTest determines an individual's use of the sensory systems (somatosensory, visual, and vestibular) that are responsible for postural control. Somatosensory and visual input are altered by the calibrated sway-referenced support surface and visual surround, which move in the anterior-posterior direction in response to the individual's postural sway. This creates a conflicting sensory experience. The Motor Control Test (MCT) challenges postural control by creating unexpected postural disturbances in the form of backwards and forwards translations. The translations are graded in magnitude and the time to recover from the perturbation is computed.
Intermittent claudication, the most common symptom of peripheral arterial disease, is characterized by a cramping pain in the lower limbs and caused by muscle ischemia secondary to reduced blood flow to working muscles during physical exertion. Claudicants often display poor balance, making them susceptible to falls and activity avoidance. The Ankle Brachial Pressure Index (ABPI) is a noninvasive method for indicating the presence of peripheral arterial disease and intermittent claudication, a common symptom in the lower extremities. ABPI is measured as the highest systolic pressure from either the dorsalis pedis or posterior tibial artery divided by the highest brachial artery systolic pressure from either arm. This paper will focus on the use of computerized dynamic posturography in the assessment of balance in claudicants.
Medicine, Issue 82, Posture, Computerized dynamic posturography, Ankle brachial pressure index, Peripheral arterial disease, Intermittent claudication, Balance, Posture, EquiTest, Sensory Organization Test, Motor Control Test
Clinical Examination Protocol to Detect Atypical and Classical Scrapie in Sheep
Institutions: Animal Health and Veterinary Laboratories Agency Weybridge.
The diagnosis of scrapie, a transmissible spongiform encephalopathy (TSEs) of sheep and goats, is currently based on the detection of disease-associated prion protein by post mortem
tests. Unless a random sample of the sheep or goat population is actively monitored for scrapie, identification of scrapie cases relies on the reporting of clinical suspects, which is dependent on the individual's familiarization with the disease and ability to recognize clinical signs associated with scrapie. Scrapie may not be considered in the differential diagnosis of neurological diseases in small ruminants, particularly in countries with low scrapie prevalence, or not recognized if it presents as nonpruritic form like atypical scrapie. To aid in the identification of clinical suspects, a short examination protocol is presented to assess the display of specific clinical signs associated with pruritic and nonpruritic forms of TSEs in sheep, which could also be applied to goats. This includes assessment of behavior, vision (by testing of the menace response), pruritus (by testing the response to scratching), and movement (with and without blindfolding). This may lead to a more detailed neurologic examination of reporting animals as scrapie suspects. It could also be used in experimental TSE studies of sheep or goats to evaluate disease progression or to identify clinical end-point.
Infectious Diseases, Issue 83, transmissible spongiform encephalopathy, sheep, atypical scrapie, classical scrapie, neurologic examination, scratch test, menace response, blindfolding
The Multiple Sclerosis Performance Test (MSPT): An iPad-Based Disability Assessment Tool
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
From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data
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
A Multi-Modal Approach to Assessing Recovery in Youth Athletes Following Concussion
Institutions: Holland Bloorview Kids Rehabilitation Hospital, University of Toronto, University of Toronto.
Concussion is one of the most commonly reported injuries amongst children and youth involved in sport participation. Following a concussion, youth can experience a range of short and long term neurobehavioral symptoms (somatic, cognitive and emotional/behavioral) that can have a significant impact on one’s participation in daily activities and pursuits of interest (e.g.,
school, sports, work, family/social life, etc.
). Despite this, there remains a paucity in clinically driven research aimed specifically at exploring concussion within the youth sport population, and more specifically, multi-modal approaches to measuring recovery. This article provides an overview of a novel and multi-modal approach to measuring recovery amongst youth athletes following concussion. The presented approach involves the use of both pre-injury/baseline testing and post-injury/follow-up testing to assess performance across a wide variety of domains (post-concussion symptoms, cognition, balance, strength, agility/motor skills and resting state heart rate variability). The goal of this research is to gain a more objective and accurate understanding of recovery following concussion in youth athletes (ages 10-18 years). Findings from this research can help to inform the development and use of improved approaches to concussion management and rehabilitation specific to the youth sport community.
Medicine, Issue 91, concussion, children, youth, athletes, assessment, management, rehabilitation
Design and Implementation of an fMRI Study Examining Thought Suppression in Young Women with, and At-risk, for Depression
Institutions: McMaster University, McMaster University, University of Calgary, McMaster University.
Ruminative brooding is associated with increased vulnerability to major depression. Individuals who regularly ruminate will often try to reduce the frequency of their negative thoughts by actively suppressing them. We aim to identify the neural correlates underlying thought suppression in at-risk and depressed individuals. Three groups of women were studied; a major depressive disorder group, an at-risk group (having a first degree relative with depression) and controls. Participants performed a mixed block-event fMRI paradigm involving thought suppression, free thought and motor control periods. Participants identified the re-emergence of “to-be-suppressed” thoughts (“popping” back into conscious awareness) with a button press. During thought suppression the control group showed the greatest activation of the dorsolateral prefrontal cortex, followed by the at-risk, then depressed group. During the re-emergence of intrusive thoughts compared to successful re-suppression of those thoughts, the control group showed the greatest activation of the anterior cingulate cortices, followed by the at-risk, then depressed group. At-risk participants displayed anomalies in the neural regulation of thought suppression resembling the dysregulation found in depressed individuals. The predictive value of these changes in the onset of depression remains to be determined.
Behavior, Issue 99, Major Depressive Disorder, Risk, Thought Suppression, fMRI, Women, Rumination, Thought Intrusion
Flying Insect Detection and Classification with Inexpensive Sensors
Institutions: University of California, Riverside, University of California, Riverside, University of São Paulo - USP, ISCA Technologies.
An inexpensive, noninvasive system that could accurately classify flying insects would have important implications for entomological research, and allow for the development of many useful applications in vector and pest control for both medical and agricultural entomology. Given this, the last sixty years have seen many research efforts devoted to this task. To date, however, none of this research has had a lasting impact. In this work, we show that pseudo-acoustic optical sensors can produce superior data; that additional features, both intrinsic and extrinsic to the insect’s flight behavior, can be exploited to improve insect classification; that a Bayesian classification approach allows to efficiently learn classification models that are very robust to over-fitting, and a general classification framework allows to easily incorporate arbitrary number of features. We demonstrate the findings with large-scale experiments that dwarf all previous works combined, as measured by the number of insects and the number of species considered.
Bioengineering, Issue 92, flying insect detection, automatic insect classification, pseudo-acoustic optical sensors, Bayesian classification framework, flight sound, circadian rhythm
Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
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
A Zebrafish Model of Diabetes Mellitus and Metabolic Memory
Institutions: Rosalind Franklin University of Medicine and Science, Rosalind Franklin University of Medicine and Science.
Diabetes mellitus currently affects 346 million individuals and this is projected to increase to 400 million by 2030. Evidence from both the laboratory and large scale clinical trials has revealed that diabetic complications progress unimpeded via the phenomenon of metabolic memory even when glycemic control is pharmaceutically achieved. Gene expression can be stably altered through epigenetic changes which not only allow cells and organisms to quickly respond to changing environmental stimuli but also confer the ability of the cell to "memorize" these encounters once the stimulus is removed. As such, the roles that these mechanisms play in the metabolic memory phenomenon are currently being examined.
We have recently reported the development of a zebrafish model of type I diabetes mellitus and characterized this model to show that diabetic zebrafish not only display the known secondary complications including the changes associated with diabetic retinopathy, diabetic nephropathy and impaired wound healing but also exhibit impaired caudal fin regeneration. This model is unique in that the zebrafish is capable to regenerate its damaged pancreas and restore a euglycemic state similar to what would be expected in post-transplant human patients. Moreover, multiple rounds of caudal fin amputation allow for the separation and study of pure epigenetic effects in an in vivo
system without potential complicating factors from the previous diabetic state. Although euglycemia is achieved following pancreatic regeneration, the diabetic secondary complication of fin regeneration and skin wound healing persists indefinitely. In the case of impaired fin regeneration, this pathology is retained even after multiple rounds of fin regeneration in the daughter fin tissues. These observations point to an underlying epigenetic process existing in the metabolic memory state. Here we present the methods needed to successfully generate the diabetic and metabolic memory groups of fish and discuss the advantages of this model.
Medicine, Issue 72, Genetics, Genomics, Physiology, Anatomy, Biomedical Engineering, Metabolomics, Zebrafish, diabetes, metabolic memory, tissue regeneration, streptozocin, epigenetics, Danio rerio, animal model, diabetes mellitus, diabetes, drug discovery, hyperglycemia
Basics of Multivariate Analysis in Neuroimaging Data
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
Generation of Comprehensive Thoracic Oncology Database - Tool for Translational Research
Institutions: University of Chicago, University of Chicago, Northshore University Health Systems, University of Chicago, University of Chicago, University of Chicago.
The Thoracic Oncology Program Database Project was created to serve as a comprehensive, verified, and accessible repository for well-annotated cancer specimens and clinical data to be available to researchers within the Thoracic Oncology Research Program. This database also captures a large volume of genomic and proteomic data obtained from various tumor tissue studies. A team of clinical and basic science researchers, a biostatistician, and a bioinformatics expert was convened to design the database. Variables of interest were clearly defined and their descriptions were written within a standard operating manual to ensure consistency of data annotation. Using a protocol for prospective tissue banking and another protocol for retrospective banking, tumor and normal tissue samples from patients consented to these protocols were collected. Clinical information such as demographics, cancer characterization, and treatment plans for these patients were abstracted and entered into an Access database. Proteomic and genomic data have been included in the database and have been linked to clinical information for patients described within the database. The data from each table were linked using the relationships function in Microsoft Access to allow the database manager to connect clinical and laboratory information during a query. The queried data can then be exported for statistical analysis and hypothesis generation.
Medicine, Issue 47, Database, Thoracic oncology, Bioinformatics, Biorepository, Microsoft Access, Proteomics, Genomics
Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging
Institutions: The Johns Hopkins University.
In 2010 approximately 68,720 melanomas will be diagnosed in the US alone, with around 8,650 resulting in death 1
. To date, the only effective treatment for melanoma remains surgical excision, therefore, the key to extended survival is early detection 2,3
. Considering the large numbers of patients diagnosed every year and the limitations in accessing specialized care quickly, the development of objective in vivo
diagnostic instruments to aid the diagnosis is essential. New techniques to detect skin cancer, especially non-invasive diagnostic tools, are being explored in numerous laboratories. Along with the surgical methods, techniques such as digital photography, dermoscopy, multispectral imaging systems (MelaFind), laser-based systems (confocal scanning laser microscopy, laser doppler perfusion imaging, optical coherence tomography), ultrasound, magnetic resonance imaging, are being tested. Each technique offers unique advantages and disadvantages, many of which pose a compromise between effectiveness and accuracy versus ease of use and cost considerations. Details about these techniques and comparisons are available in the literature 4
Infrared (IR) imaging was shown to be a useful method to diagnose the signs of certain diseases by measuring the local skin temperature. There is a large body of evidence showing that disease or deviation from normal functioning are accompanied by changes of the temperature of the body, which again affect the temperature of the skin 5,6
. Accurate data about the temperature of the human body and skin can provide a wealth of information on the processes responsible for heat generation and thermoregulation, in particular the deviation from normal conditions, often caused by disease. However, IR imaging has not been widely recognized in medicine due to the premature use of the technology 7,8
several decades ago, when temperature measurement accuracy and the spatial resolution were inadequate and sophisticated image processing tools were unavailable. This situation changed dramatically in the late 1990s-2000s. Advances in IR instrumentation, implementation of digital image processing algorithms and dynamic IR imaging, which enables scientists to analyze not only the spatial, but also the temporal thermal behavior of the skin 9
, allowed breakthroughs in the field.
In our research, we explore the feasibility of IR imaging, combined with theoretical and experimental studies, as a cost effective, non-invasive, in vivo optical measurement technique for tumor detection, with emphasis on the screening and early detection of melanoma 10-13
. In this study, we show data obtained in a patient study in which patients that possess a pigmented lesion with a clinical indication for biopsy are selected for imaging. We compared the difference in thermal responses between healthy and malignant tissue and compared our data with biopsy results. We concluded that the increased metabolic activity of the melanoma lesion can be detected by dynamic infrared imaging.
Medicine, Issue 51, Infrared imaging, quantitative thermal analysis, image processing, skin cancer, melanoma, transient thermal response, skin thermal models, skin phantom experiment, patient study
Determination of Molecular Structures of HIV Envelope Glycoproteins using Cryo-Electron Tomography and Automated Sub-tomogram Averaging
Institutions: National Institutes of Health, University of Cambridge , National Institutes of Health, Massachusetts Institute of Technology, William Fremd High School, University of Virginia , Duke University , Yale University, University of Notre Dame , Washington University in St. Louis , National Institutes of Health, Thomas Jefferson High School for Science and Technology.
Since its discovery nearly 30 years ago, more than 60 million people have been infected with the human immunodeficiency virus (HIV) (www.usaid.gov). The virus infects and destroys CD4+ T-cells thereby crippling the immune system, and causing an acquired immunodeficiency syndrome (AIDS) 2
. Infection begins when the HIV Envelope glycoprotein "spike" makes contact with the CD4 receptor on the surface of the CD4+ T-cell. This interaction induces a conformational change in the spike, which promotes interaction with a second cell surface co-receptor 5,9
. The significance of these protein interactions in the HIV infection pathway makes them of profound importance in fundamental HIV research, and in the pursuit of an HIV vaccine.
The need to better understand the molecular-scale interactions of HIV cell contact and neutralization motivated the development of a technique to determine the structures of the HIV spike interacting with cell surface receptor proteins and molecules that block infection. Using cryo-electron tomography and 3D image processing, we recently demonstrated the ability to determine such structures on the surface of native virus, at ˜20 Å resolution 9,14
. This approach is not limited to resolving HIV Envelope structures, and can be extended to other viral membrane proteins and proteins reconstituted on a liposome. In this protocol, we describe how to obtain structures of HIV envelope glycoproteins starting from purified HIV virions and proceeding stepwise through preparing vitrified samples, collecting, cryo-electron microscopy data, reconstituting and processing 3D data volumes, averaging and classifying 3D protein subvolumes, and interpreting results to produce a protein model. The computational aspects of our approach were adapted into modules that can be accessed and executed remotely using the Biowulf GNU/Linux parallel processing cluster at the NIH (https://biowulf.nih.gov). This remote access, combined with low-cost computer hardware and high-speed network access, has made possible the involvement of researchers and students working from school or home.
Immunology, Issue 58, HIV, Envelope glycoprotein, membrane protein, vaccine design, cryo-electron tomography, transmission electron microscopy, structural biology, high school science, scientific outreach, scientific visualization, National Institutes of Health, National Cancer Institute, National Library of Medicine
Assessment and Evaluation of the High Risk Neonate: The NICU Network Neurobehavioral Scale
Institutions: Brown University, Women & Infants Hospital of Rhode Island, University of Massachusetts, Boston.
There has been a long-standing interest in the assessment of the neurobehavioral integrity of the newborn infant. The NICU Network Neurobehavioral Scale (NNNS) was developed as an assessment for the at-risk infant. These are infants who are at increased risk for poor developmental outcome because of insults during prenatal development, such as substance exposure or prematurity or factors such as poverty, poor nutrition or lack of prenatal care that can have adverse effects on the intrauterine environment and affect the developing fetus. The NNNS assesses the full range of infant neurobehavioral performance including neurological integrity, behavioral functioning, and signs of stress/abstinence. The NNNS is a noninvasive neonatal assessment tool with demonstrated validity as a predictor, not only of medical outcomes such as cerebral palsy diagnosis, neurological abnormalities, and diseases with risks to the brain, but also of developmental outcomes such as mental and motor functioning, behavior problems, school readiness, and IQ. The NNNS can identify infants at high risk for abnormal developmental outcome and is an important clinical tool that enables medical researchers and health practitioners to identify these infants and develop intervention programs to optimize the development of these infants as early as possible. The video shows the NNNS procedures, shows examples of normal and abnormal performance and the various clinical populations in which the exam can be used.
Behavior, Issue 90, NICU Network Neurobehavioral Scale, NNNS, High risk infant, Assessment, Evaluation, Prediction, Long term outcome
Utilizing Transcranial Magnetic Stimulation to Study the Human Neuromuscular System
Institutions: Ohio University.
Transcranial magnetic stimulation (TMS) has been in use for more than 20 years 1
, and has grown exponentially in popularity over the past decade. While the use of TMS has expanded to the study of many systems and processes during this time, the original application and perhaps one of the most common uses of TMS involves studying the physiology, plasticity and function of the human neuromuscular system. Single pulse TMS applied to the motor cortex excites pyramidal neurons transsynaptically 2
(Figure 1) and results in a measurable electromyographic response that can be used to study and evaluate the integrity and excitability of the corticospinal tract in humans 3
. Additionally, recent advances in magnetic stimulation now allows for partitioning of cortical versus spinal excitability 4,5
. For example, paired-pulse TMS can be used to assess intracortical facilitatory and inhibitory properties by combining a conditioning stimulus and a test stimulus at different interstimulus intervals 3,4,6-8
. In this video article we will demonstrate the methodological and technical aspects of these techniques. Specifically, we will demonstrate single-pulse and paired-pulse TMS techniques as applied to the flexor carpi radialis (FCR) muscle as well as the erector spinae (ES) musculature. Our laboratory studies the FCR muscle as it is of interest to our research on the effects of wrist-hand cast immobilization on reduced muscle performance6,9
, and we study the ES muscles due to these muscles clinical relevance as it relates to low back pain8
. With this stated, we should note that TMS has been used to study many muscles of the hand, arm and legs, and should iterate that our demonstrations in the FCR and ES muscle groups are only selected examples of TMS being used to study the human neuromuscular system.
Medicine, Issue 59, neuroscience, muscle, electromyography, physiology, TMS, strength, motor control. sarcopenia, dynapenia, lumbar
Nerve Excitability Assessment in Chemotherapy-induced Neurotoxicity
Institutions: University of New South Wales , University of New South Wales , University of New South Wales .
Chemotherapy-induced neurotoxicity is a serious consequence of cancer treatment, which occurs with some of the most commonly used chemotherapies1,2
. Chemotherapy-induced peripheral neuropathy produces symptoms of numbness and paraesthesia in the limbs and may progress to difficulties with fine motor skills and walking, leading to functional impairment. In addition to producing troubling symptoms, chemotherapy-induced neuropathy may limit treatment success leading to dose reduction or early cessation of treatment. Neuropathic symptoms may persist long-term, leaving permanent nerve damage in patients with an otherwise good prognosis3
. As chemotherapy is utilised more often as a preventative measure, and survival rates increase, the importance of long-lasting and significant neurotoxicity will increase.
There are no established neuroprotective or treatment options and a lack of sensitive assessment methods. Appropriate assessment of neurotoxicity will be critical as a prognostic factor and as suitable endpoints for future trials of neuroprotective agents. Current methods to assess the severity of chemotherapy-induced neuropathy utilise clinician-based grading scales which have been demonstrated to lack sensitivity to change and inter-observer objectivity4
. Conventional nerve conduction studies provide information about compound action potential amplitude and conduction velocity, which are relatively non-specific measures and do not provide insight into ion channel function or resting membrane potential. Accordingly, prior studies have demonstrated that conventional nerve conduction studies are not sensitive to early change in chemotherapy-induced neurotoxicity4-6
. In comparison, nerve excitability studies utilize threshold tracking techniques which have been developed to enable assessment of ion channels, pumps and exchangers in vivo
in large myelinated human axons7-9
Nerve excitability techniques have been established as a tool to examine the development and severity of chemotherapy-induced neurotoxicity10-13
. Comprising a number of excitability parameters, nerve excitability studies can be used to assess acute neurotoxicity arising immediately following infusion and the development of chronic, cumulative neurotoxicity. Nerve excitability techniques are feasible in the clinical setting, with each test requiring only 5 -10 minutes to complete. Nerve excitability equipment is readily commercially available, and a portable system has been devised so that patients can be tested in situ
in the infusion centre setting. In addition, these techniques can be adapted for use in multiple chemotherapies.
In patients treated with the chemotherapy oxaliplatin, primarily utilised for colorectal cancer, nerve excitability techniques provide a method to identify patients at-risk for neurotoxicity prior to the onset of chronic neuropathy. Nerve excitability studies have revealed the development of an acute Na+
channelopathy in motor and sensory axons10-13
. Importantly, patients who demonstrated changes in excitability in early treatment were subsequently more likely to develop moderate to severe neurotoxicity11
. However, across treatment, striking longitudinal changes were identified only in sensory axons which were able to predict clinical neurological outcome in 80% of patients10
. These changes demonstrated a different pattern to those seen acutely following oxaliplatin infusion, and most likely reflect the development of significant axonal damage and membrane potential change in sensory nerves which develops longitudinally during oxaliplatin treatment10
. Significant abnormalities developed during early treatment, prior to any reduction in conventional measures of nerve function, suggesting that excitability parameters may provide a sensitive biomarker.
Neuroscience, Issue 62, Chemotherapy, Neurotoxicity, Neuropathy, Nerve excitability, Ion channel function, Oxaliplatin, oncology, medicine
Rapid Determination of the Thermal Nociceptive Threshold in Diabetic Rats
Institutions: Wright State University, Universidade São Judas Tadeu.
Painful diabetic neuropathy (PDN) is characterized by hyperalgesia i.e.
, increased sensitivity to noxious stimulus, and allodynia i.e.,
hypersensitivity to normally innocuous stimuli1
. Hyperalgesia and allodynia have been studied in many different rodent models of diabetes mellitus2
. However, as stated by Bölcskei et al
, determination of "pain
" in animal models is challenging due to its subjective nature3
. Moreover, the traditional methods used to determine behavioral responses to noxious thermal stimuli usually lack reproducibility and pharmacological sensitivity3
. For instance, by using the hot-plate method of Ankier4
, flinch, withdrawal and/or licking of either hind- and/or fore-paws is quantified as reflex latencies at constant high thermal stimuli (52-55 °C). However, animals that are hyperalgesic to thermal stimulus do not reproducibly show differences in reflex latencies using those supra-threshold temperatures3,5
. As the recently described method of Bölcskei et al.6
, the procedures described here allows for the rapid, sensitive and reproducible determination of thermal nociceptive thresholds (TNTs) in mice and rats. The method uses slowly increasing thermal stimulus applied mostly to the skin of mouse/rat plantar surface. The method is particularly sensitive to study anti-nociception during hyperalgesic states such as PDN. The procedures described bellow are based on the ones published in detail by Almási et al 5
and Bölcskei et al 3
. The procedures described here have been approved the Laboratory Animal Care and Use Committee (LACUC), Wright State University.
Neuroscience, Issue 63, Diabetes, painful diabetic neuropathy, nociception, thermal nociceptive threshold, nocifensive behavior
Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues
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
) (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
Trajectory Data Analyses for Pedestrian Space-time Activity Study
Institutions: Kean University, University of Wisconsin-Madison.
It is well recognized that human movement in the spatial and temporal dimensions has direct influence on disease transmission1-3
. An infectious disease typically spreads via contact between infected and susceptible individuals in their overlapped activity spaces. Therefore, daily mobility-activity information can be used as an indicator to measure exposures to risk factors of infection. However, a major difficulty and thus the reason for paucity of studies of infectious disease transmission at the micro scale arise from the lack of detailed individual mobility data. Previously in transportation and tourism research detailed space-time activity data often relied on the time-space diary technique, which requires subjects to actively record their activities in time and space. This is highly demanding for the participants and collaboration from the participants greatly affects the quality of data4
Modern technologies such as GPS and mobile communications have made possible the automatic collection of trajectory data. The data collected, however, is not ideal for modeling human space-time activities, limited by the accuracies of existing devices. There is also no readily available tool for efficient processing of the data for human behavior study. We present here a suite of methods and an integrated ArcGIS desktop-based visual interface for the pre-processing and spatiotemporal analyses of trajectory data. We provide examples of how such processing may be used to model human space-time activities, especially with error-rich pedestrian trajectory data, that could be useful in public health studies such as infectious disease transmission modeling.
The procedure presented includes pre-processing, trajectory segmentation, activity space characterization, density estimation and visualization, and a few other exploratory analysis methods. Pre-processing is the cleaning of noisy raw trajectory data. We introduce an interactive visual pre-processing interface as well as an automatic module. Trajectory segmentation5
involves the identification of indoor and outdoor parts from pre-processed space-time tracks. Again, both interactive visual segmentation and automatic segmentation are supported. Segmented space-time tracks are then analyzed to derive characteristics of one's activity space such as activity radius etc.
Density estimation and visualization are used to examine large amount of trajectory data to model hot spots and interactions. We demonstrate both density surface mapping6
and density volume rendering7
. We also include a couple of other exploratory data analyses (EDA) and visualizations tools, such as Google Earth animation support and connection analysis. The suite of analytical as well as visual methods presented in this paper may be applied to any trajectory data for space-time activity studies.
Environmental Sciences, Issue 72, Computer Science, Behavior, Infectious Diseases, Geography, Cartography, Data Display, Disease Outbreaks, cartography, human behavior, Trajectory data, space-time activity, GPS, GIS, ArcGIS, spatiotemporal analysis, visualization, segmentation, density surface, density volume, exploratory data analysis, modelling
Pairwise Growth Competition Assay for Determining the Replication Fitness of Human Immunodeficiency Viruses
Institutions: University of Washington, University of Washington, Walter Reed Army Institute of Research, Henry M. Jackson Foundation.
fitness assays are essential tools for determining viral replication fitness for viruses such as HIV-1. Various measurements have been used to extrapolate viral replication fitness, ranging from the number of viral particles per infectious unit, growth rate in cell culture, and relative fitness derived from multiple-cycle growth competition assays. Growth competition assays provide a particularly sensitive measurement of fitness since the viruses are competing for cellular targets under identical growth conditions. There are several experimental factors to consider when conducting growth competition assays, including the multiplicity of infection (MOI), sampling times, and viral detection and fitness calculation methods. Each factor can affect the end result and hence must be considered carefully during the experimental design. The protocol presented here includes steps from constructing a new recombinant HIV-1 clone to performing growth competition assays and analyzing the experimental results. This protocol utilizes experimental parameter values previously shown to yield consistent and robust results. Alternatives are discussed, as some parameters need to be adjusted according to the cell type and viruses being studied. The protocol contains two alternative viral detection methods to provide flexibility as the availability of instruments, reagents and expertise varies between laboratories.
Immunology, Issue 99, HIV-1, Recombinant, Mutagenesis, Viral replication fitness, Growth competition, Fitness calculation