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Nucleus- and Cell-Specific Gene Expression in Monkey Thalamus
Nucleus- and cell-specific gene expression in monkey thalamus Karl D. Murray, Prabhakara V. Choudary, and Edward G. Jones* Center for Neuroscience and Department of Psychiatry and Behavioral Sciences, University of California, Davis, CA 95616 Contributed by Edward G. Jones, December 6, 2006 (sent for review November 16, 2006) Nuclei of the mammalian thalamus are aggregations of neurons adult monkeys. By comparing expression profiles of thalamus with unique architectures and input–output connections, yet the and cerebral cortex, we identified genes not hitherto known to molecular determinants of their organizational specificity remain be expressed in thalamus. Further profiling of microdissected unknown. By comparing expression profiles of thalamus and nuclei identified more comprehensive sets of genes with nucleus- cerebral cortex in adult rhesus monkeys, we identified transcripts specific expression. Confirmation of gene expression by RT- that are unique to dorsal thalamus or to individual nuclei within it. PCR, in situ hybridization histochemistry, and/or immunohisto- Real-time quantitative PCR and in situ hybridization analyses con- chemistry revealed remarkable cell- and nucleus-specific firmed the findings. Expression profiling of individual nuclei mi- patterns of expression. Functionally, the genes are related to crodissected from the dorsal thalamus revealed additional subsets development and cell–cell interactions, implying their involve- of nucleus-specific genes. Functional annotation using Gene On- ment in the establishment and maintenance of thalamic nuclear tology (GO) vocabulary and Ingenuity Pathways Analysis revealed and cellular specificity. overrepresentation of GO categories related to development, mor- phogenesis, cell–cell interactions, and extracellular matrix within Results the thalamus- and nucleus-specific genes, many involved in the Genes specific to thalamus were identified by comparing expres- Wnt signaling pathway. -
UNIVERSITY of CALIFORNIA, SAN DIEGO Substructure Within The
UNIVERSITY OF CALIFORNIA, SAN DIEGO Substructure within the Dorsal Lateral Geniculate Nucleus of the Pigmented Rat A dissertation submitted in partial satisfaction of the requirements for the degree Doctor of Philosophy in Neurosciences by Claire B. Discenza Committee in charge: Professor Pamela Reinagel, Chair Professor Timothy Genter, Co-Chair Professor Serge Belongie Professor Edward Calloway Professor Maryann Martone 2011 Copyright Claire B. Discenza, 2011 All rights reserved. The dissertation of Claire B. Discenza is approved, and it is acceptable in quality and form for publication on microfilm and electronically: Co-Chair Chair University of California, San Diego 2011 iii DEDICATION To Skip Lizárd iv TABLE OF CONTENTS Signature Page . iii Dedication . iv TableofContents................................. v List of Figures . vii List of Tables . viii List of Abbreviations . ix Acknowledgements . x Vita........................................ xi Abstract of the Dissertation . xii Chapter 1 Introduction . 1 1.1 Basic organization of the mammalian dorsal lateral genic- ulate nucleus of the thalamus . 1 1.2 Anatomical clues to functional organization . 4 1.3 The rat dLGN . 5 1.4 Conclusion . 8 1.5 References.......................... 8 Chapter 2 Dorsal Lateral Geniculate Substructure in the Long-Evans Rat: a Cholera Toxin B Subunit Study . 15 2.1 Abstract . 15 2.2 Introduction . 16 2.3 Methods . 19 2.3.1 Subjects . 19 2.3.2 Intra-ocular injections . 20 2.3.3 Perfusion and histology . 20 2.3.4 Imaging . 22 2.3.5 DLGN tracing and 3D reconstructions . 22 2.3.6 Volume calculation . 22 2.3.7 Analysis of the segregation of RGC termination zones......................... 23 2.3.8 Stereoptics and cytoarchitectural analysis . -
Supporting Information Modha and Singh SI Text Transpose of Themselves, Brain Regions with Different Acronyms but the Largest Previous Network
Supporting Information Modha and Singh SI Text transpose of themselves, brain regions with different acronyms but The Largest Previous Network. The largest previous network of the with the same full name, typographical errors, incorrect case, miss- macaque brain consists of 95 vertices and 2,402 edges [1], where the ing special characters, existence of brain regions without mapping network models brain regions as vertices and the presence of long dis- or connectivity relations, incorrect L and I relations, self-loops in L tance connections as directed edges between them. This network is relation, missing relations, and so on. displayed in Figure S1 and should be compared to our network in Fig- ure 1 of the main text. It can be seen that the largest previous network Constructing a Network and a Hierarchical Brain Map. Let us rep- completely misses corticosubcortical and subcortico-subcortical long resent mapping relations I, L, O, E, C and the connectivity rela- distance connections and has significant gaps even amongst cortico- tion CNZ as two-dimensional binary matrices. The rows as well as cortical long distance connections. Two-thirds of our data has never columns correspond to various brain regions. The (i; j)-th entry of even been previously compiled, let alone analyzed and understood. I relation is 1 if there exists an identity relation between the brain regions corresponding to row i and column j, and zero otherwise. CoCoMac Database. We briefly summarize the CoCoMac database, Similarly, for L, O, E, C matrices the (i; j)-th entry is 1 if there ex- and refer the reader to the original publications for more details ists the associated relation between the brain regions corresponding [2, 3, 4, 5, 6]. -
The Brain As a Multi-Layered Map. Scales and Reference Points for Pattern Recognition in Neuroimaging
European Journal of Geography Volume 8, Number 1:6 - 31, February 2017 ©Association of European Geographers THE BRAIN AS A MULTI-LAYERED MAP. SCALES AND REFERENCE POINTS FOR PATTERN RECOGNITION IN NEUROIMAGING Margarita Zaleshina Moscow Institute of Physics and Technology, Moscow, Russia [email protected] Alexander Zaleshin Moscow Institute of Physics and Technology, Moscow, Russia [email protected] Abstract In this paper, we provide an overview of brain mapping in neuroscience and describe the application of spatial data processing techniques to represent the brain as a multi-layered map.Anatomical reference points (landmarks) are determined from the topological properties of the brain, including the shapes of sulci, gyri, and fissures. Functional reference points are calculated by measured parameters of brain activity. Linking experimental results with spatial and temporal reference points is a necessary step for performing a comparative analysis of heterogeneous data regarding brain structures and activity. Using reference points helps define coordinate systems and scales, highlight points of interest and regions of interest, create templates, and classify data. The paper shows that spatial analysis is a convenient approach to pattern recognition in neuroimaging. We also discuss the role of extrinsic behavior landmark stimuli and intrinsic brain structural elements such as place cells and grid cells in navigation tasks. Keywords: Brain mapping, neuroimaging, pattern recognition, positioning systems in the brain. 1. INTRODUCTION The brain can be represented as a spatially distributed multi-scale and multi-level structure in which continuous dynamic processes occur. Two main blocks of spatial data processing tasks in neuroscience are shown in Figure 1. Studies of brain mapping generally focus on addressing the question, “What is the space of the brain?” (Figure 1A). -
Neuroinformatics: a Spotlight on Various Databases and the Importance of Their Integration Erica Sequeira and Saraswathy Nagendran*
Original Article Neuroinformatics: A Spotlight on Various Databases and the Importance of Their Integration Erica Sequeira and Saraswathy Nagendran* SVKM`s NMIMS Shobhaben Pratapbhai Patel School of Pharmacy and Technology Management, Vile Parle (W), Mumbai, India ABSTRACT Brain is one of the most complex organs of the body and thus the challenge in neuroscience would seem the effort of understanding the complex structure, function, and development of the nervous system in healthy as well as diseased condition. Such understanding requires the integration of huge amounts of heterogeneous and complex data collected at various levels of investigation. Neuroinformatics combines neuroscience with information science/technology and deals with the creation and maintenance of web accessible databases that will be required to achieve such integration. There is significant interest amongst neuroscientists in sharing neuroscience data and analytical tools which provides the opportunity to differently re- Address for analyze previously collected data and encourage new neuroscience Correspondence interpretations that facilitates further development. However, information is usually stored in various databases, managed by SVKM`s NMIMS heterogeneous database management systems or files, spreadsheets Shobhaben Pratapbhai etc. which results into inaccuracy and inconsistencies in data Patel School of acquisition and processing, lack of coordination resulting in Pharmacy and duplication of efforts and of resources. Thus an integration of Technology databases