Morphological Alterations of the Pyramidal and Stellate Cells of the Visual Cortex in Schizophrenia
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Automated Sorting of Neuronal Trees in Fluorescent Images of Neuronal
www.nature.com/scientificreports OPEN Automated sorting of neuronal trees in fuorescent images of neuronal networks using Received: 29 August 2017 Accepted: 10 April 2018 NeuroTreeTracer Published: xx xx xxxx Cihan Kayasandik1, Pooran Negi1, Fernanda Laezza2, Manos Papadakis1 & Demetrio Labate 1 Fluorescence confocal microscopy has become increasingly more important in neuroscience due to its applications in image-based screening and profling of neurons. Multispectral confocal imaging is useful to simultaneously probe for distribution of multiple analytes over networks of neurons. However, current automated image analysis algorithms are not designed to extract single-neuron arbors in images where neurons are not separated, hampering the ability map fuorescence signals at the single cell level. To overcome this limitation, we introduce NeuroTreeTracer – a novel image processing framework aimed at automatically extracting and sorting single-neuron traces in fuorescent images of multicellular neuronal networks. This method applies directional multiscale flters for automated segmentation of neurons and soma detection, and includes a novel tracing routine that sorts neuronal trees in the image by resolving network connectivity even when neurites appear to intersect. By extracting each neuronal tree, NeuroTreetracer enables to automatically quantify the spatial distribution of analytes of interest in the subcellular compartments of individual neurons. This software is released open-source and freely available with the goal to facilitate applications in neuron screening and profling. Neuronal reconstruction is critical in a variety of neurobiological studies. During the last two decades, a large number of algorithms and sofware toolkits were developed aiming at providing digital reconstruction of neurons from images acquired using bright feld or fuorescent microscopy1. -
Nadder: a Scale-Space Approach for the 3D Analysis of Neuronal Traces
bioRxiv preprint doi: https://doi.org/10.1101/2020.06.01.127035; this version posted December 4, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license. nAdder: A scale-space approach for the 3D analysis of neuronal traces Minh Son Phan1, Katherine Matho2, Emmanuel Beaurepaire1, Jean Livet2, and Anatole Chessel1 1Laboratory for Optics and Biosciences, CNRS, INSERM, Ecole Polytechnique, IP Paris, Palaiseau, France 2Sorbonne Université, INSERM, CNRS, Institut de la Vision, 17 rue Moreau, F-75012 Paris, France December 4, 2020 1 ABSTRACT 2 Tridimensional microscopy and algorithms for automated segmentation and tracing are revolutionizing 3 neuroscience through the generation of growing libraries of neuron reconstructions. Innovative 4 computational methods are needed to analyze these neural traces. In particular, means to analyse the 5 geometric properties of traced neurites along their trajectory have been lacking. Here, we propose 6 a local tridimensional (3D) scale metric derived from differential geometry, which is the distance 7 in micrometers along which a curve is fully 3D as opposed to being embedded in a 2D plane or 1D 8 line. We apply this metric to various neuronal traces ranging from single neurons to whole brain data. 9 By providing a local readout of the geometric complexity, it offers a new mean of describing and 10 comparing axonal and dendritic arbors from individual neurons or the behavior of axonal projections 11 in different brain regions. -
High-Resolution Cortical Imaging
High-Resolution Cortical Imaging Alex de Crespigny Ph.D., Ellen Grant M.D., Larry Wald Ph.D., Jean Augustinack Ph.D., Bruce Fischl Ph.D., Helen D’Arceuil Ph.D. Martinos Center, Department of Radiology, Massachusetts General Hospital, Charlestown, MA Structure of the cortex The adult cerebral cortex is a complex convoluted multilayered structure averaging around 2.3mm thick, but thinner (≤ 2mm) in the depths of the sulcal folds and thicker (3-4mm) at the crown of the gyri. It is typically divided into 6 layers depending upon the constituent cell types. On examination a section of the cortex, it is seen to consist of alternating white and gray layers from the surface inward: (1) a thin layer of white substance; (2) a layer of gray substance; (3) a second white layer (outer band of Baillarger or band of Gennari); (4) a second gray layer; (5) a third white layer (inner band of Baillarger); (6) a third gray layer, which rests on the medullary substance of the gyrus (1). Cortical neurons form a highly organized laminar and radial structure with extensive efferents. Long association fibers connect to distant regions of the ipsilateral hemisphere, while short fibers connect to nearby ipsilateral regions. Commissural fibers connect to the cortical regions of the contralateral hemisphere, and projection fibers reach from the cortex into the subcortical structures e.g., corticothalamic/subthalamic projections, etc. High resolution structural MRI of the cortex Conventional diagnostic clinical MRI scans, with in plane resolution of ~1mm and slice thickness up to 5mm can define the gray-white interface of the cerebral cortex but cannot visualize structure within the cortex itself. -
Automated Sorting of Neuronal Trees in Fluorescent Images of Neuronal
Automated sorting of neuronal trees in fluorescent images of neuronal networks using NeuroTreeTracer Cihan Kayasandik1,+, Pooran Negi1,+, Fernanda Laezza2, Manos Papadakis1, and Demetrio Labate1,* 1University of Houston, Department of Mathematics, Houston, Texas, United States of America 2University of Texas Medical Branch, Department of Pharmacology and Toxicology, Galveston, Texas, United States of America *corresponding author: [email protected] +these authors contributed equally to this work ABSTRACT Fluorescence confocal microscopy has become increasingly more important in neuroscience due to its applications in image- based screening and profiling of neurons. Multispectral confocal imaging is useful to simultaneously probe for distribution of multiple analytes over networks of neurons. However, current automated image analysis algorithms are not designed to extract single-neuron arbors in images where neurons are not separated, hampering the ability map fluorescence signals at the single cell level. To overcome this limitation, we introduce NeuroTreeTracer – a novel image processing framework aimed at automatically extracting and sorting single-neuron traces in fluorescent images of multicellular neuronal networks. This method applies directional multiscale filters for automated segmentation of neurons and soma detection, and includes a novel tracing routine that sorts neuronal trees in the image by resolving network connectivity even when neurites appear to intersect. By extracting each neuronal tree, NeuroTreetracer enables to automatically -
Methods to Investigate the Structure and Connectivity of the Nervous System
University of Massachusetts Medical School eScholarship@UMMS GSBS Student Publications Graduate School of Biomedical Sciences 2017-02-16 Methods to investigate the structure and connectivity of the nervous system Donghyung Lee California Institute of Technology Et al. Let us know how access to this document benefits ou.y Follow this and additional works at: https://escholarship.umassmed.edu/gsbs_sp Part of the Neuroscience and Neurobiology Commons Repository Citation Lee D, Huang T, De La Cruz A, Callejas A, Lois C. (2017). Methods to investigate the structure and connectivity of the nervous system. GSBS Student Publications. https://doi.org/10.1080/ 19336934.2017.1295189. Retrieved from https://escholarship.umassmed.edu/gsbs_sp/2000 Creative Commons License This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License. This material is brought to you by eScholarship@UMMS. It has been accepted for inclusion in GSBS Student Publications by an authorized administrator of eScholarship@UMMS. For more information, please contact [email protected]. Fly ISSN: 1933-6934 (Print) 1933-6942 (Online) Journal homepage: http://www.tandfonline.com/loi/kfly20 Methods to investigate the structure and connectivity of the nervous system Donghyung Lee, Ting-Hao Huang, Aubrie De La Cruz, Antuca Callejas & Carlos Lois To cite this article: Donghyung Lee, Ting-Hao Huang, Aubrie De La Cruz, Antuca Callejas & Carlos Lois (2017): Methods to investigate the structure and connectivity of the nervous system, Fly, DOI: 10.1080/19336934.2017.1295189 To link to this article: http://dx.doi.org/10.1080/19336934.2017.1295189 © 2017 The Author(s). Published with license by Taylor & Francis© Donghyung Lee, Ting-Hao Huang, Aubrie De La Cruz, Antuca Callejas, and Carlos Lois Accepted author version posted online: 16 Feb 2017. -
Extraction of Protein Profiles from Primary Neurons Using Active
Journal of Neuroscience Methods 225 (2014) 1–12 Contents lists available at ScienceDirect Journal of Neuroscience Methods jo urnal homepage: www.elsevier.com/locate/jneumeth Extraction of protein profiles from primary neurons using active ଝ contour models and wavelets a,∗ b a a Danny Misiak , Stefan Posch , Marcell Lederer , Claudia Reinke , a b Stefan Hüttelmaier , Birgit Möller a Institute of Molecular Medicine, Martin Luther University Halle-Wittenberg, Heinrich-Damerow-Str. 1, 06120 Halle, Germany b Institute of Computer Science, Martin Luther University Halle-Wittenberg, Von-Seckendorff-Platz 1, 06099 Halle, Germany h i g h l i g h t s • Extraction of neuron areas using active contours with a new distance based energy. • Location of structural neuron parts by a wavelet based approach. • Automatic extraction of spatial and quantitative data about distributions of proteins. • Extraction of various morphological parameters, in a fully automated manner. • Extraction of a distinctive profile for the Zipcode binding protein (ZBP1/IGF2BP1). a r t i c l e i n f o a b s t r a c t Article history: The function of complex networks in the nervous system relies on the proper formation of neuronal Received 26 October 2013 contacts and their remodeling. To decipher the molecular mechanisms underlying these processes, it is Received in revised form essential to establish unbiased automated tools allowing the correlation of neurite morphology and the 18 December 2013 subcellular distribution of molecules by quantitative means. Accepted 19 December 2013 We developed NeuronAnalyzer2D, a plugin for ImageJ, which allows the extraction of neuronal cell morphologies from two dimensional high resolution images, and in particular their correlation with Keywords: protein profiles determined by indirect immunostaining of primary neurons. -
15 Telencephalon: Neocortex
15 Telencephalon: Neocortex Introduction.........................491 – Dendritic Clusters, Axonal Bundles and Radial Cell Cords As (Possible) Sulcal Pattern ........................498 Constituents of Neocortical Minicolumns . 582 Structural and Functional Subdivision – Microcircuitry of Neocortical Columns .... 586 ofNeocortex.........................498 – Neocortical Columns and Modules: – Structural Subdivision 1: Cytoarchitecture . 498 A Critical Commentary ............... 586 – Structural Subdivision 2: Myeloarchitecture . 506 Comparative Aspects ................... 591 – Structural Subdivision 3: Myelogenesis ....510 Synopsis of Main Neocortical Regions ...... 592 – Structural Subdivision 4: Connectivity .....510 – Introduction....................... 592 – Functional Subdivision ................516 – Association and Commissural Connections . 592 – Structural and Functional Subdivision: – Functional and Structural Asymmetry Overview .........................528 of the Two Hemispheres .............. 599 – Structural and Functional Localization – Occipital Lobe ..................... 600 in the Neocortex: Current Research – Parietal Lobe ...................... 605 and Perspectives ....................530 – TemporalLobe..................... 611 Neocortical Afferents ...................536 – Limbic Lobe and Paralimbic Belt ........ 617 Neocortical Neurons – FrontalLobe ...................... 620 and Their Synaptic Relationships ..........544 – Insula........................... 649 – IntroductoryNote...................544 – Typical Pyramidal -
Target-Specific Contrast Agents for Magnetic Resonance Microscopy
NeuroImage 46 (2009) 382–393 Contents lists available at ScienceDirect NeuroImage journal homepage: www.elsevier.com/locate/ynimg Target-specific contrast agents for magnetic resonance microscopy Megan L. Blackwell a,b,⁎, Christian T. Farrar a,d, Bruce Fischl a,c,d, Bruce R. Rosen a,d a Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, USA b Harvard-MIT Division of Health Sciences and Technology, Cambridge, MA, USA c Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA, USA d Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA article info abstract Article history: High-resolution ex vivo magnetic resonance (MR) imaging can be used to delineate prominent architectonic Received 2 July 2007 features in the human brain, but increased contrast is required to visualize more subtle distinctions. To aid Revised 5 December 2008 MR sensitivity to cell density and myelination, we have begun the development of target-specific Accepted 15 January 2009 paramagnetic contrast agents. This work details the first application of luxol fast blue (LFB), an optical Available online 30 January 2009 stain for myelin, as a white matter-selective MR contrast agent for human ex vivo brain tissue. Formalin-fixed human visual cortex was imaged with an isotropic resolution between 80 and 150 μm at 4.7 and 14 T before and after en bloc staining with LFB. Longitudinal (R1) and transverse (R2) relaxation rates in LFB-stained tissue increased proportionally with myelination at both field strengths. Changes in R1 resulted in larger contrast-to-noise ratios (CNR), per unit time, on T1-weighted images between more myelinated cortical layers (IV–VI) and adjacent, superficial layers (I–III) at both field strengths. -
Direct Visualization of the Perforant Pathway in the Human Brain with Ex Vivo Diffusion Tensor Imaging
ORIGINAL RESEARCH ARTICLE published: 28 May 2010 HUMAN NEUROSCIENCE doi: 10.3389/fnhum.2010.00042 Direct visualization of the perforant pathway in the human brain with ex vivo diffusion tensor imaging Jean C. Augustinack1*, Karl Helmer1, Kristen E. Huber1, Sita Kakunoori1, Lilla Zöllei1,2 and Bruce Fischl1,2 1 Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA 2 Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA Edited by: Ex vivo magnetic resonance imaging yields high resolution images that reveal detailed cerebral Andreas Jeromin, Banyan Biomarkers, anatomy and explicit cytoarchitecture in the cerebral cortex, subcortical structures, and white USA matter in the human brain. Our data illustrate neuroanatomical correlates of limbic circuitry with Reviewed by: Konstantinos Arfanakis, Illinois Institute high resolution images at high field. In this report, we have studied ex vivo medial temporal of Technology, USA lobe samples in high resolution structural MRI and high resolution diffusion MRI. Structural and James Gee, University of Pennsylvania, diffusion MRIs were registered to each other and to histological sections stained for myelin for USA validation of the perforant pathway. We demonstrate probability maps and fiber tracking from Christopher Kroenke, Oregon Health and Science University, USA diffusion tensor data that allows the direct visualization of the perforant pathway. Although it *Correspondence: is not possible to validate the DTI data with invasive measures, results described here provide Jean Augustinack, Athinoula A. an additional line of evidence of the perforant pathway trajectory in the human brain and that Martinos Center for Biomedical the perforant pathway may cross the hippocampal sulcus. -
A Scale-Space Approach for 3D Neuronal Traces Analysis
bioRxiv preprint doi: https://doi.org/10.1101/2020.06.01.127035; this version posted June 1, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license. A SCALE-SPACE APPROACH FOR 3D NEURONAL TRACES ANALYSIS Minh Son Phan1, Katherine Matho2, Jean Livet2, Emmanuel Beaurepaire1, and Anatole Chessel1 1Laboratory for Optics and Biosciences, CNRS, INSERM, Ecole Polytechnique, IP Paris, Palaiseau, France 2Sorbonne Université, Institut de la Vison, INSERM, CNRS, Paris, France June 1, 2020 ABSTRACT 1 The advent of large-scale microscopy along with advances in automated image analysis algorithms 2 is currently revolutionizing neuroscience. These approaches result in rapidly increasing libraries 3 of neuron reconstructions requiring innovative computational methods to draw biological insight 4 from. Here, we propose a framework from differential geometry based on scale-space representation 5 to extract a quantitative structural readout of neurite traces seen as tridimensional (3D) curves 6 within the anatomical space. We define and propose algorithms to compute a multiscale hierarchical 7 decomposition of traced neurites according to their intrinsic dimensionality, from which we deduce 8 a local 3D scale, i.e. the scale in microns at which the curve is fully 3D as opposed to being 9 embedded in a 2D plane or a 1D line. We applied our scale-space analysis to recently published 10 data including zebrafish whole brain traces to demonstrate the importance of the computed local 3D 11 scale for description and comparison at the single arbor levels and as a local spatialized information 12 characterizing axons populations at the whole brain level. -
Staging of Alzheimer Disease-Associated Neurowbrillary Pathology Using Parayn Sections and Immunocytochemistry
Acta Neuropathol (2006) 112:389–404 DOI 10.1007/s00401-006-0127-z METHODS REPORT Staging of Alzheimer disease-associated neuroWbrillary pathology using paraYn sections and immunocytochemistry Heiko Braak · Irina AlafuzoV · Thomas Arzberger · Hans Kretzschmar · Kelly Del Tredici Received: 8 June 2006 / Revised: 21 July 2006 / Accepted: 21 July 2006 / Published online: 12 August 2006 © Springer-Verlag 2006 Abstract Assessment of Alzheimer’s disease (AD)- revised here by adapting tissue selection and process- related neuroWbrillary pathology requires a procedure ing to the needs of paraYn-embedded sections (5–15 m) that permits a suYcient diVerentiation between initial, and by introducing a robust immunoreaction (AT8) for intermediate, and late stages. The gradual deposition hyperphosphorylated tau protein that can be processed of a hyperphosphorylated tau protein within select on an automated basis. It is anticipated that this neuronal types in speciWc nuclei or areas is central to revised methodological protocol will enable a more the disease process. The staging of AD-related neuroW- uniform application of the staging procedure. brillary pathology originally described in 1991 was per- formed on unconventionally thick sections (100 m) Keywords Alzheimer’s disease · NeuroWbrillary using a modern silver technique and reXected the pro- changes · Immunocytochemistry · gress of the disease process based chieXy on the topo- Hyperphosphorylated tau protein · Neuropathologic graphic expansion of the lesions. To better meet the staging · Pretangles demands of routine laboratories this procedure is Introduction This study was made possible by funding from the German Research Council (Deutsche Forschungsgemeinschaft) and BrainNet Europe II (European Commission LSHM-CT-2004- The development of intraneuronal lesions at selec- 503039). -
MITOCW | MIT9 14S09 Lec36-Mp3
MITOCW | MIT9_14S09_lec36-mp3 The following content is provided under a Creative Commons license. Your support will help MIT OpenCourseWare continue to offer high quality educational resources for free. To make a donation or view additional materials from hundreds of MIT courses, visit MIT OpenCourseWare at ocw.mit.edu. PROFESSOR: So this class, we will talk about some of the cell types in the neocortex and the way they're connected. We'll talk about different regions of neocortex, that is actually after it had experienced, in evolution, a lot of expansion, and we're just going to deal with mammalian cortex in small animals, like rodents, and human being. And then we'll talk a little bit about some of the major fibers in and out of the membrain. We've talked about those already, but I want to review them to make sure that they're in your mind. And then we'll get, at least, to an introduction of thalamocortical connections, which again, you've had some because when we talked about visual system, we talked about the genicular body rejection in the visual area, primary visual cortex, the auditory cortex. We talked about medial geniculate body, somatosensory has been talked about several times. We talked about the ventral nucleus. All right. So what are the two most commonly encountered classes in the cortical cells? The most characteristic cell type in the neocortex is the pyramidal cell, and this is what they look like. This is Brodel's simplification of the pyramidal cell that shows their major characteristics, a cell body with a sort of pyramidal shape in an apical dendrite that goes up towards the surface, and many of these cells send their apical dendrite all the way up to the surface where they arborize right up in layer one.