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Pubmed Article
Automatic ROI selection in structural brain MRI using SOM 3D projection.
PLoS ONE
PUBLISHED: 01-01-2014
This paper presents a method for selecting Regions of Interest (ROI) in brain Magnetic Resonance Imaging (MRI) for diagnostic purposes, using statistical learning and vector quantization techniques. The proposed method models the distribution of GM and WM tissues grouping the voxels belonging to each tissue in ROIs associated to a specific neurological disorder. Tissue distribution of normal and abnormal images is modelled by a Self-Organizing map (SOM), generating a set of representative prototypes, and the receptive field (RF) of each SOM prototype defines a ROI. Moreover, the proposed method computes the relative importance of each ROI by means of its discriminative power. The devised method has been assessed using 818 images from the Alzheimer's disease Neuroimaging Initiative (ADNI) which were previously segmented through Statistical Parametric Mapping (SPM). The proposed algorithm was used over these images to parcel ROIs associated to the Alzheimer's Disease (AD). Additionally, this method can be used to extract a reduced set of discriminative features for classification, since it compresses discriminative information contained in the brain. Voxels marked by ROIs which were computed using the proposed method, yield classification results up to 90% of accuracy for controls (CN) and Alzheimer's disease (AD) patients, and 84% of accuracy for Mild Cognitive Impairment (MCI) and AD patients.
Authors: Hans-Peter Müller, Jan Kassubek.
Published: 07-28-2013
ABSTRACT
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.
19 Related JoVE Articles!
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A Comprehensive Protocol for Manual Segmentation of the Medial Temporal Lobe Structures
Authors: Matthew Moore, Yifan Hu, Sarah Woo, Dylan O'Hearn, Alexandru D. Iordan, Sanda Dolcos, Florin Dolcos.
Institutions: University of Illinois Urbana-Champaign, University of Illinois Urbana-Champaign, University of Illinois Urbana-Champaign.
The present paper describes a comprehensive protocol for manual tracing of the set of brain regions comprising the medial temporal lobe (MTL): amygdala, hippocampus, and the associated parahippocampal regions (perirhinal, entorhinal, and parahippocampal proper). Unlike most other tracing protocols available, typically focusing on certain MTL areas (e.g., amygdala and/or hippocampus), the integrative perspective adopted by the present tracing guidelines allows for clear localization of all MTL subregions. By integrating information from a variety of sources, including extant tracing protocols separately targeting various MTL structures, histological reports, and brain atlases, and with the complement of illustrative visual materials, the present protocol provides an accurate, intuitive, and convenient guide for understanding the MTL anatomy. The need for such tracing guidelines is also emphasized by illustrating possible differences between automatic and manual segmentation protocols. This knowledge can be applied toward research involving not only structural MRI investigations but also structural-functional colocalization and fMRI signal extraction from anatomically defined ROIs, in healthy and clinical groups alike.
Neuroscience, Issue 89, Anatomy, Segmentation, Medial Temporal Lobe, MRI, Manual Tracing, Amygdala, Hippocampus, Perirhinal Cortex, Entorhinal Cortex, Parahippocampal Cortex
50991
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Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly
Authors: Joel Ramirez, Christopher J.M. Scott, Alicia A. McNeely, Courtney Berezuk, Fuqiang Gao, Gregory M. Szilagyi, Sandra E. Black.
Institutions: Sunnybrook Health Sciences Centre, University of Toronto.
Obtaining in vivo human brain tissue volumetrics from MRI is often complicated by various technical and biological issues. These challenges are exacerbated when significant brain atrophy and age-related white matter changes (e.g. Leukoaraiosis) are present. Lesion Explorer (LE) is an accurate and reliable neuroimaging pipeline specifically developed to address such issues commonly observed on MRI of Alzheimer's disease and normal elderly. The pipeline is a complex set of semi-automatic procedures which has been previously validated in a series of internal and external reliability tests1,2. However, LE's accuracy and reliability is highly dependent on properly trained manual operators to execute commands, identify distinct anatomical landmarks, and manually edit/verify various computer-generated segmentation outputs. LE can be divided into 3 main components, each requiring a set of commands and manual operations: 1) Brain-Sizer, 2) SABRE, and 3) Lesion-Seg. Brain-Sizer's manual operations involve editing of the automatic skull-stripped total intracranial vault (TIV) extraction mask, designation of ventricular cerebrospinal fluid (vCSF), and removal of subtentorial structures. The SABRE component requires checking of image alignment along the anterior and posterior commissure (ACPC) plane, and identification of several anatomical landmarks required for regional parcellation. Finally, the Lesion-Seg component involves manual checking of the automatic lesion segmentation of subcortical hyperintensities (SH) for false positive errors. While on-site training of the LE pipeline is preferable, readily available visual teaching tools with interactive training images are a viable alternative. Developed to ensure a high degree of accuracy and reliability, the following is a step-by-step, video-guided, standardized protocol for LE's manual procedures.
Medicine, Issue 86, Brain, Vascular Diseases, Magnetic Resonance Imaging (MRI), Neuroimaging, Alzheimer Disease, Aging, Neuroanatomy, brain extraction, ventricles, white matter hyperintensities, cerebrovascular disease, Alzheimer disease
50887
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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
51107
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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
51673
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Training Synesthetic Letter-color Associations by Reading in Color
Authors: Olympia Colizoli, Jaap M. J. Murre, Romke Rouw.
Institutions: University of Amsterdam.
Synesthesia is a rare condition in which a stimulus from one modality automatically and consistently triggers unusual sensations in the same and/or other modalities. A relatively common and well-studied type is grapheme-color synesthesia, defined as the consistent experience of color when viewing, hearing and thinking about letters, words and numbers. We describe our method for investigating to what extent synesthetic associations between letters and colors can be learned by reading in color in nonsynesthetes. Reading in color is a special method for training associations in the sense that the associations are learned implicitly while the reader reads text as he or she normally would and it does not require explicit computer-directed training methods. In this protocol, participants are given specially prepared books to read in which four high-frequency letters are paired with four high-frequency colors. Participants receive unique sets of letter-color pairs based on their pre-existing preferences for colored letters. A modified Stroop task is administered before and after reading in order to test for learned letter-color associations and changes in brain activation. In addition to objective testing, a reading experience questionnaire is administered that is designed to probe for differences in subjective experience. A subset of questions may predict how well an individual learned the associations from reading in color. Importantly, we are not claiming that this method will cause each individual to develop grapheme-color synesthesia, only that it is possible for certain individuals to form letter-color associations by reading in color and these associations are similar in some aspects to those seen in developmental grapheme-color synesthetes. The method is quite flexible and can be used to investigate different aspects and outcomes of training synesthetic associations, including learning-induced changes in brain function and structure.
Behavior, Issue 84, synesthesia, training, learning, reading, vision, memory, cognition
50893
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In vivo Imaging of Optic Nerve Fiber Integrity by Contrast-Enhanced MRI in Mice
Authors: Stefanie Fischer, Christian Engelmann, Karl-Heinz Herrmann, Jürgen R. Reichenbach, Otto W. Witte, Falk Weih, Alexandra Kretz, Ronny Haenold.
Institutions: Jena University Hospital, Fritz Lipmann Institute, Jena, Jena University Hospital.
The rodent visual system encompasses retinal ganglion cells and their axons that form the optic nerve to enter thalamic and midbrain centers, and postsynaptic projections to the visual cortex. Based on its distinct anatomical structure and convenient accessibility, it has become the favored structure for studies on neuronal survival, axonal regeneration, and synaptic plasticity. Recent advancements in MR imaging have enabled the in vivo visualization of the retino-tectal part of this projection using manganese mediated contrast enhancement (MEMRI). Here, we present a MEMRI protocol for illustration of the visual projection in mice, by which resolutions of (200 µm)3 can be achieved using common 3 Tesla scanners. We demonstrate how intravitreal injection of a single dosage of 15 nmol MnCl2 leads to a saturated enhancement of the intact projection within 24 hr. With exception of the retina, changes in signal intensity are independent of coincided visual stimulation or physiological aging. We further apply this technique to longitudinally monitor axonal degeneration in response to acute optic nerve injury, a paradigm by which Mn2+ transport completely arrests at the lesion site. Conversely, active Mn2+ transport is quantitatively proportionate to the viability, number, and electrical activity of axon fibers. For such an analysis, we exemplify Mn2+ transport kinetics along the visual path in a transgenic mouse model (NF-κB p50KO) displaying spontaneous atrophy of sensory, including visual, projections. In these mice, MEMRI indicates reduced but not delayed Mn2+ transport as compared to wild type mice, thus revealing signs of structural and/or functional impairments by NF-κB mutations. In summary, MEMRI conveniently bridges in vivo assays and post mortem histology for the characterization of nerve fiber integrity and activity. It is highly useful for longitudinal studies on axonal degeneration and regeneration, and investigations of mutant mice for genuine or inducible phenotypes.
Neuroscience, Issue 89, manganese-enhanced MRI, mouse retino-tectal projection, visual system, neurodegeneration, optic nerve injury, NF-κB
51274
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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
2715
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A Mouse Model for Pathogen-induced Chronic Inflammation at Local and Systemic Sites
Authors: George Papadopoulos, Carolyn D. Kramer, Connie S. Slocum, Ellen O. Weinberg, Ning Hua, Cynthia V. Gudino, James A. Hamilton, Caroline A. Genco.
Institutions: Boston University School of Medicine, Boston University School of Medicine.
Chronic inflammation is a major driver of pathological tissue damage and a unifying characteristic of many chronic diseases in humans including neoplastic, autoimmune, and chronic inflammatory diseases. Emerging evidence implicates pathogen-induced chronic inflammation in the development and progression of chronic diseases with a wide variety of clinical manifestations. Due to the complex and multifactorial etiology of chronic disease, designing experiments for proof of causality and the establishment of mechanistic links is nearly impossible in humans. An advantage of using animal models is that both genetic and environmental factors that may influence the course of a particular disease can be controlled. Thus, designing relevant animal models of infection represents a key step in identifying host and pathogen specific mechanisms that contribute to chronic inflammation. Here we describe a mouse model of pathogen-induced chronic inflammation at local and systemic sites following infection with the oral pathogen Porphyromonas gingivalis, a bacterium closely associated with human periodontal disease. Oral infection of specific-pathogen free mice induces a local inflammatory response resulting in destruction of tooth supporting alveolar bone, a hallmark of periodontal disease. In an established mouse model of atherosclerosis, infection with P. gingivalis accelerates inflammatory plaque deposition within the aortic sinus and innominate artery, accompanied by activation of the vascular endothelium, an increased immune cell infiltrate, and elevated expression of inflammatory mediators within lesions. We detail methodologies for the assessment of inflammation at local and systemic sites. The use of transgenic mice and defined bacterial mutants makes this model particularly suitable for identifying both host and microbial factors involved in the initiation, progression, and outcome of disease. Additionally, the model can be used to screen for novel therapeutic strategies, including vaccination and pharmacological intervention.
Immunology, Issue 90, Pathogen-Induced Chronic Inflammation; Porphyromonas gingivalis; Oral Bone Loss; Periodontal Disease; Atherosclerosis; Chronic Inflammation; Host-Pathogen Interaction; microCT; MRI
51556
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Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations
Authors: Fijoy Vadakkumpadan, Hermenegild Arevalo, Natalia A. Trayanova.
Institutions: Johns Hopkins University.
Patient-specific simulations of heart (dys)function aimed at personalizing cardiac therapy are hampered by the absence of in vivo imaging technology for clinically acquiring myocardial fiber orientations. The objective of this project was to develop a methodology to estimate cardiac fiber orientations from in vivo images of patient heart geometries. An accurate representation of ventricular geometry and fiber orientations was reconstructed, respectively, from high-resolution ex vivo structural magnetic resonance (MR) and diffusion tensor (DT) MR images of a normal human heart, referred to as the atlas. Ventricular geometry of a patient heart was extracted, via semiautomatic segmentation, from an in vivo computed tomography (CT) image. Using image transformation algorithms, the atlas ventricular geometry was deformed to match that of the patient. Finally, the deformation field was applied to the atlas fiber orientations to obtain an estimate of patient fiber orientations. The accuracy of the fiber estimates was assessed using six normal and three failing canine hearts. The mean absolute difference between inclination angles of acquired and estimated fiber orientations was 15.4 °. Computational simulations of ventricular activation maps and pseudo-ECGs in sinus rhythm and ventricular tachycardia indicated that there are no significant differences between estimated and acquired fiber orientations at a clinically observable level.The new insights obtained from the project will pave the way for the development of patient-specific models of the heart that can aid physicians in personalized diagnosis and decisions regarding electrophysiological interventions.
Bioengineering, Issue 71, Biomedical Engineering, Medicine, Anatomy, Physiology, Cardiology, Myocytes, Cardiac, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, MRI, Diffusion Magnetic Resonance Imaging, Cardiac Electrophysiology, computerized simulation (general), mathematical modeling (systems analysis), Cardiomyocyte, biomedical image processing, patient-specific modeling, Electrophysiology, simulation
50125
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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
4375
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Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
Authors: Phoebe Spetsieris, Yilong Ma, Shichun Peng, Ji Hyun Ko, Vijay Dhawan, Chris C. Tang, David Eidelberg.
Institutions: The Feinstein Institute for Medical Research.
The scaled subprofile model (SSM)1-4 is a multivariate PCA-based algorithm that identifies major sources of variation in patient and control group brain image data while rejecting lesser components (Figure 1). Applied directly to voxel-by-voxel covariance data of steady-state multimodality images, an entire group image set can be reduced to a few significant linearly independent covariance patterns and corresponding subject scores. Each pattern, termed a group invariant subprofile (GIS), is an orthogonal principal component that represents a spatially distributed network of functionally interrelated brain regions. Large global mean scalar effects that can obscure smaller network-specific contributions are removed by the inherent logarithmic conversion and mean centering of the data2,5,6. Subjects express each of these patterns to a variable degree represented by a simple scalar score that can correlate with independent clinical or psychometric descriptors7,8. Using logistic regression analysis of subject scores (i.e. pattern expression values), linear coefficients can be derived to combine multiple principal components into single disease-related spatial covariance patterns, i.e. composite networks with improved discrimination of patients from healthy control subjects5,6. Cross-validation within the derivation set can be performed using bootstrap resampling techniques9. Forward validation is easily confirmed by direct score evaluation of the derived patterns in prospective datasets10. Once validated, disease-related patterns can be used to score individual patients with respect to a fixed reference sample, often the set of healthy subjects that was used (with the disease group) in the original pattern derivation11. These standardized values can in turn be used to assist in differential diagnosis12,13 and to assess disease progression and treatment effects at the network level7,14-16. We present an example of the application of this methodology to FDG PET data of Parkinson's Disease patients and normal controls using our in-house software to derive a characteristic covariance pattern biomarker of disease.
Medicine, Issue 76, Neurobiology, Neuroscience, Anatomy, Physiology, Molecular Biology, Basal Ganglia Diseases, Parkinsonian Disorders, Parkinson Disease, Movement Disorders, Neurodegenerative Diseases, PCA, SSM, PET, imaging biomarkers, functional brain imaging, multivariate spatial covariance analysis, global normalization, differential diagnosis, PD, brain, imaging, clinical techniques
50319
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Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy
Authors: Zulfi Haneef, Agatha Lenartowicz, Hsiang J. Yeh, Jerome Engel Jr., John M. Stern.
Institutions: Baylor College of Medicine, Michael E. DeBakey VA Medical Center, University of California, Los Angeles, University of California, Los Angeles.
Functional connectivity MRI (fcMRI) is an fMRI method that examines the connectivity of different brain areas based on the correlation of BOLD signal fluctuations over time. Temporal Lobe Epilepsy (TLE) is the most common type of adult epilepsy and involves multiple brain networks. The default mode network (DMN) is involved in conscious, resting state cognition and is thought to be affected in TLE where seizures cause impairment of consciousness. The DMN in epilepsy was examined using seed based fcMRI. The anterior and posterior hubs of the DMN were used as seeds in this analysis. The results show a disconnection between the anterior and posterior hubs of the DMN in TLE during the basal state. In addition, increased DMN connectivity to other brain regions in left TLE along with decreased connectivity in right TLE is revealed. The analysis demonstrates how seed-based fcMRI can be used to probe cerebral networks in brain disorders such as TLE.
Medicine, Issue 90, Default Mode Network (DMN), Temporal Lobe Epilepsy (TLE), fMRI, MRI, functional connectivity MRI (fcMRI), blood oxygenation level dependent (BOLD)
51442
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Cortical Source Analysis of High-Density EEG Recordings in Children
Authors: Joe Bathelt, Helen O'Reilly, Michelle de Haan.
Institutions: UCL Institute of Child Health, University College London.
EEG is traditionally described as a neuroimaging technique with high temporal and low spatial resolution. Recent advances in biophysical modelling and signal processing make it possible to exploit information from other imaging modalities like structural MRI that provide high spatial resolution to overcome this constraint1. This is especially useful for investigations that require high resolution in the temporal as well as spatial domain. In addition, due to the easy application and low cost of EEG recordings, EEG is often the method of choice when working with populations, such as young children, that do not tolerate functional MRI scans well. However, in order to investigate which neural substrates are involved, anatomical information from structural MRI is still needed. Most EEG analysis packages work with standard head models that are based on adult anatomy. The accuracy of these models when used for children is limited2, because the composition and spatial configuration of head tissues changes dramatically over development3.  In the present paper, we provide an overview of our recent work in utilizing head models based on individual structural MRI scans or age specific head models to reconstruct the cortical generators of high density EEG. This article describes how EEG recordings are acquired, processed, and analyzed with pediatric populations at the London Baby Lab, including laboratory setup, task design, EEG preprocessing, MRI processing, and EEG channel level and source analysis. 
Behavior, Issue 88, EEG, electroencephalogram, development, source analysis, pediatric, minimum-norm estimation, cognitive neuroscience, event-related potentials 
51705
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Analysis of Tubular Membrane Networks in Cardiac Myocytes from Atria and Ventricles
Authors: Eva Wagner, Sören Brandenburg, Tobias Kohl, Stephan E. Lehnart.
Institutions: Heart Research Center Goettingen, University Medical Center Goettingen, German Center for Cardiovascular Research (DZHK) partner site Goettingen, University of Maryland School of Medicine.
In cardiac myocytes a complex network of membrane tubules - the transverse-axial tubule system (TATS) - controls deep intracellular signaling functions. While the outer surface membrane and associated TATS membrane components appear to be continuous, there are substantial differences in lipid and protein content. In ventricular myocytes (VMs), certain TATS components are highly abundant contributing to rectilinear tubule networks and regular branching 3D architectures. It is thought that peripheral TATS components propagate action potentials from the cell surface to thousands of remote intracellular sarcoendoplasmic reticulum (SER) membrane contact domains, thereby activating intracellular Ca2+ release units (CRUs). In contrast to VMs, the organization and functional role of TATS membranes in atrial myocytes (AMs) is significantly different and much less understood. Taken together, quantitative structural characterization of TATS membrane networks in healthy and diseased myocytes is an essential prerequisite towards better understanding of functional plasticity and pathophysiological reorganization. Here, we present a strategic combination of protocols for direct quantitative analysis of TATS membrane networks in living VMs and AMs. For this, we accompany primary cell isolations of mouse VMs and/or AMs with critical quality control steps and direct membrane staining protocols for fluorescence imaging of TATS membranes. Using an optimized workflow for confocal or superresolution TATS image processing, binarized and skeletonized data are generated for quantitative analysis of the TATS network and its components. Unlike previously published indirect regional aggregate image analysis strategies, our protocols enable direct characterization of specific components and derive complex physiological properties of TATS membrane networks in living myocytes with high throughput and open access software tools. In summary, the combined protocol strategy can be readily applied for quantitative TATS network studies during physiological myocyte adaptation or disease changes, comparison of different cardiac or skeletal muscle cell types, phenotyping of transgenic models, and pharmacological or therapeutic interventions.
Bioengineering, Issue 92, cardiac myocyte, atria, ventricle, heart, primary cell isolation, fluorescence microscopy, membrane tubule, transverse-axial tubule system, image analysis, image processing, T-tubule, collagenase
51823
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Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
Authors: Rangaraj M. Rangayyan, Shantanu Banik, J.E. Leo Desautels.
Institutions: University of Calgary , University of Calgary .
We demonstrate methods for the detection of architectural distortion in prior mammograms of interval-cancer cases based on analysis of the orientation of breast tissue patterns in mammograms. We hypothesize that architectural distortion modifies the normal orientation of breast tissue patterns in mammographic images before the formation of masses or tumors. In the initial steps of our methods, the oriented structures in a given mammogram are analyzed using Gabor filters and phase portraits to detect node-like sites of radiating or intersecting tissue patterns. Each detected site is then characterized using the node value, fractal dimension, and a measure of angular dispersion specifically designed to represent spiculating patterns associated with architectural distortion. Our methods were tested with a database of 106 prior mammograms of 56 interval-cancer cases and 52 mammograms of 13 normal cases using the features developed for the characterization of architectural distortion, pattern classification via quadratic discriminant analysis, and validation with the leave-one-patient out procedure. According to the results of free-response receiver operating characteristic analysis, our methods have demonstrated the capability to detect architectural distortion in prior mammograms, taken 15 months (on the average) before clinical diagnosis of breast cancer, with a sensitivity of 80% at about five false positives per patient.
Medicine, Issue 78, Anatomy, Physiology, Cancer Biology, angular spread, architectural distortion, breast cancer, Computer-Assisted Diagnosis, computer-aided diagnosis (CAD), entropy, fractional Brownian motion, fractal dimension, Gabor filters, Image Processing, Medical Informatics, node map, oriented texture, Pattern Recognition, phase portraits, prior mammograms, spectral analysis
50341
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Segmentation and Measurement of Fat Volumes in Murine Obesity Models Using X-ray Computed Tomography
Authors: Todd A. Sasser, Sarah E. Chapman, Shengting Li, Caroline Hudson, Sean P. Orton, Justin M. Diener, Seth T. Gammon, Carlos Correcher, W. Matthew Leevy.
Institutions: Carestream Molecular Imaging , University of Notre Dame , University of Notre Dame , Oncovision, GEM-Imaging S.A..
Obesity is associated with increased morbidity and mortality as well as reduced metrics in quality of life.1 Both environmental and genetic factors are associated with obesity, though the precise underlying mechanisms that contribute to the disease are currently being delineated.2,3 Several small animal models of obesity have been developed and are employed in a variety of studies.4 A critical component to these experiments involves the collection of regional and/or total animal fat content data under varied conditions. Traditional experimental methods available for measuring fat content in small animal models of obesity include invasive (e.g. ex vivo measurement of fat deposits) and non-invasive (e.g. Dual Energy X-ray Absorptiometry (DEXA), or Magnetic Resonance (MR)) protocols, each of which presents relative trade-offs. Current invasive methods for measuring fat content may provide details for organ and region specific fat distribution, but sacrificing the subjects will preclude longitudinal assessments. Conversely, current non-invasive strategies provide limited details for organ and region specific fat distribution, but enable valuable longitudinal assessment. With the advent of dedicated small animal X-ray computed tomography (CT) systems and customized analytical procedures, both organ and region specific analysis of fat distribution and longitudinal profiling may be possible. Recent reports have validated the use of CT for in vivo longitudinal imaging of adiposity in living mice.5,6 Here we provide a modified method that allows for fat/total volume measurement, analysis and visualization utilizing the Carestream Molecular Imaging Albira CT system in conjunction with PMOD and Volview software packages.
Medicine, Issue 62, X-ray computed tomography (CT), image analysis, in vivo, obesity, metabolic disorders
3680
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A Technique for Serial Collection of Cerebrospinal Fluid from the Cisterna Magna in Mouse
Authors: Li Liu, Karen Duff.
Institutions: Columbia University.
Alzheimer's disease (AD) is a progressive neurodegenerative disease that is pathologically characterized by extracellular deposition of β-amyloid peptide (Aβ) and intraneuronal accumulation of hyperphosphorylated tau protein. Because cerebrospinal fluid (CSF) is in direct contact with the extracellular space of the brain, it provides a reflection of the biochemical changes in the brain in response to pathological processes. CSF from AD patients shows a decrease in the 42 amino-acid form of Aβ (Aβ42), and increases in total tau and hyperphosphorylated tau, though the mechanisms responsible for these changes are still not fully understood. Transgenic (Tg) mouse models of AD provide an excellent opportunity to investigate how and why Aβ or tau levels in CSF change as the disease progresses. Here, we demonstrate a refined cisterna magna puncture technique for CSF sampling from the mouse. This extremely gentle sampling technique allows serial CSF samples to be obtained from the same mouse at 2-3 month intervals which greatly minimizes the confounding effect of between-mouse variability in Aβ or tau levels, making it possible to detect subtle alterations over time. In combination with Aβ and tau ELISA, this technique will be useful for studies designed to investigate the relationship between the levels of CSF Aβ42 and tau, and their metabolism in the brain in AD mouse models. Studies in Tg mice could provide important validation as to the potential of CSF Aβ or tau levels to be used as biological markers for monitoring disease progression, and to monitor the effect of therapeutic interventions. As the mice can be sacrificed and the brains can be examined for biochemical or histological changes, the mechanisms underlying the CSF changes can be better assessed. These data are likely to be informative for interpretation of human AD CSF changes.
Neuroscience, Issue 21, Cerebrospinal fluid, Alzheimer's disease, Transgenic mouse, β-amyloid, tau
960
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Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
Authors: Wenan Chen, Ashwin Belle, Charles Cockrell, Kevin R. Ward, Kayvan Najarian.
Institutions: Virginia Commonwealth University, Virginia Commonwealth University Reanimation Engineering Science (VCURES) Center, Virginia Commonwealth University, Virginia Commonwealth University, Virginia Commonwealth University.
In this paper we present an automated system based mainly on the computed tomography (CT) images consisting of two main components: the midline shift estimation and intracranial pressure (ICP) pre-screening system. To estimate the midline shift, first an estimation of the ideal midline is performed based on the symmetry of the skull and anatomical features in the brain CT scan. Then, segmentation of the ventricles from the CT scan is performed and used as a guide for the identification of the actual midline through shape matching. These processes mimic the measuring process by physicians and have shown promising results in the evaluation. In the second component, more features are extracted related to ICP, such as the texture information, blood amount from CT scans and other recorded features, such as age, injury severity score to estimate the ICP are also incorporated. Machine learning techniques including feature selection and classification, such as Support Vector Machines (SVMs), are employed to build the prediction model using RapidMiner. The evaluation of the prediction shows potential usefulness of the model. The estimated ideal midline shift and predicted ICP levels may be used as a fast pre-screening step for physicians to make decisions, so as to recommend for or against invasive ICP monitoring.
Medicine, Issue 74, Biomedical Engineering, Molecular Biology, Neurobiology, Biophysics, Physiology, Anatomy, Brain CT Image Processing, CT, Midline Shift, Intracranial Pressure Pre-screening, Gaussian Mixture Model, Shape Matching, Machine Learning, traumatic brain injury, TBI, imaging, clinical techniques
3871
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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
1988
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