Literature DB >> 9410576

NeuroNames Brain Hierarchy.

D M Bowden1, R F Martin.   

Abstract

The NeuroNames Brain Hierarchy is a structured system of neuroanatomical terminology that provides a comprehensive representation of virtually all human and nonhuman primate brain structures that are identifiable either grossly or in Niss1-stained histological sections. This system was devised for computer applications to address deficiencies in the brain terminology presented in Nomina Anatomica. English terms are listed for 783 structures in nine levels of hierarchical ranking. Abbreviations are provided for all superficial and primary volumetric structures. The substructures that constitute the total volume of every superstructure are identified. Superficial features of the brain are clearly distinguished from internal, volumetric brain structures. Structures found solely in either humans or macaques are identified. The purpose of the NeuroNames Brain Hierarchy is to bring greater standardization to the neuroanatomical terminology used by scientific investigators, clinicians, and students. This effort is consistent with the goals of the Unified Medical Language System program of the National Library of Medicine. It is hoped that the systematic construction of the NeuroNames Brain Hierarchy will facilitate use of the most widely accepted definitions of classical neuroanatomy in quantitative computerized neuroimaging applications. It should provide an accurate structural framework against which to reference the many other kinds of neuroanatomical information that are acquired by modern imaging, mapping, and histological labeling techniques.

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Year:  1995        PMID: 9410576     DOI: 10.1006/nimg.1995.1009

Source DB:  PubMed          Journal:  Neuroimage        ISSN: 1053-8119            Impact factor:   6.556


  36 in total

1.  Foundational model of neuroanatomy: implications for the Human Brain Project.

Authors:  R F Martin; J L Mejino; D M Bowden; J F Brinkley; C Rosse
Journal:  Proc AMIA Symp       Date:  2001

2.  Analysis of naming errors during cortical stimulation mapping: implications for models of language representation.

Authors:  David P Corina; Brandon C Loudermilk; Landon Detwiler; Richard F Martin; James F Brinkley; George Ojemann
Journal:  Brain Lang       Date:  2010-05-08       Impact factor: 2.381

3.  Achieving evolvable Web-database bioscience applications using the EAV/CR framework: recent advances.

Authors:  Luis Marenco; Nicholas Tosches; Chiquito Crasto; Gordon Shepherd; Perry L Miller; Prakash M Nadkarni
Journal:  J Am Med Inform Assoc       Date:  2003-06-04       Impact factor: 4.497

4.  Numeric and symbolic knowledge representation of cerebral cortex anatomy: methods and preliminary results.

Authors:  O Dameron; B Gibaud; X Morandi
Journal:  Surg Radiol Anat       Date:  2004-04-30       Impact factor: 1.246

5.  Integrating databases and expert systems for the analysis of brain structures: connections, similarities, and homologies.

Authors:  Mihail Bota; Michael A Arbib
Journal:  Neuroinformatics       Date:  2004

6.  Cytoarchitecture and musculotopic organization of the facial motor nucleus in Cebus apella monkey.

Authors:  J A C Horta-Júnior; O J Tamega; R J Cruz-Rizzolo
Journal:  J Anat       Date:  2004-03       Impact factor: 2.610

7.  NeuroNames 2002.

Authors:  Douglas M Bowden; Mark F Dubach
Journal:  Neuroinformatics       Date:  2003

8.  Tools and approaches for the construction of knowledge models from the neuroscientific literature.

Authors:  Gully A P C Burns; Arshad M Khan; Shahram Ghandeharizadeh; Mark A O'Neill; Yi-Shin Chen
Journal:  Neuroinformatics       Date:  2003

9.  neuroVIISAS: approaching multiscale simulation of the rat connectome.

Authors:  Oliver Schmitt; Peter Eipert
Journal:  Neuroinformatics       Date:  2012-07

10.  Multimodal, multidimensional models of mouse brain.

Authors:  Allan J Mackenzie-Graham; Erh-Fang Lee; Ivo D Dinov; Heng Yuan; Russell E Jacobs; Arthur W Toga
Journal:  Epilepsia       Date:  2007       Impact factor: 5.864

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