Collating and curating neuroanatomical nomenclatures: principles and use of the Brain Architecture Knowledge Management System (BAMS)

Terms used to describe nervous system parts and their interconnections are rife with synonyms, partial correspondences, and even homonyms, making effective scientific communication unnecessarily difficult. To address this problem a new Topological Relations schema for the Relations module of BAMS (B...

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Main Authors: Mihail Bota, Larry W Swanson
Format: Article
Language:English
Published: Frontiers Media S.A. 2010-03-01
Series:Frontiers in Neuroinformatics
Subjects:
Online Access:http://journal.frontiersin.org/Journal/10.3389/fninf.2010.00003/full
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spelling doaj-76a5cd67d3c5421d9893fb113d3b4c6c2020-11-25T00:04:14ZengFrontiers Media S.A.Frontiers in Neuroinformatics1662-51962010-03-01410.3389/fninf.2010.00003801Collating and curating neuroanatomical nomenclatures: principles and use of the Brain Architecture Knowledge Management System (BAMS)Mihail Bota0Larry W Swanson1University of Southern CaliforniaUniversity of Southern CaliforniaTerms used to describe nervous system parts and their interconnections are rife with synonyms, partial correspondences, and even homonyms, making effective scientific communication unnecessarily difficult. To address this problem a new Topological Relations schema for the Relations module of BAMS (Brain Architecture Knowledge Management System) was created. It includes a representation of the qualitative spatial relations between nervous system parts defined in different neuroanatomical nomenclatures or atlases and is general enough to record data and metadata from the literature, regardless of description level or species. Based on this foundation a Projections Translations inference engine was developed for the BAMS interface that automatically translates neuroanatomical projection (axonal inputs and outputs) reports across nomenclatures from translated information. To make BAMS more useful to the neuroscience community three things were done. First, we implemented a simple schema for validation of the translated neuroanatomical projections. Second, more than 1000 topological relations between brain gray matter regions for the rat were inserted, along with associated details. Finally, a case study was performed to enter all historical or legacy published information about terminology related to one relatively complex gray matter region of the rat. The bed nuclei of the stria terminalis (BST) were chosen and 21 different nomenclatures from 1923 to present were collated, along with 284 terms for parts (gray matter differentiations), 360 qualitative topological relations between parts, and more than 7,000 details about spatial relations between parts, all of which was annotated with appropriate metadata. This information was used to construct a graphical “knowledge map” of relations used in the literature to describe subdivisions of the rat BST.http://journal.frontiersin.org/Journal/10.3389/fninf.2010.00003/fullData Miningdatabasesneuroanatomical projectionsNeuroanatomyneuroinformatics
collection DOAJ
language English
format Article
sources DOAJ
author Mihail Bota
Larry W Swanson
spellingShingle Mihail Bota
Larry W Swanson
Collating and curating neuroanatomical nomenclatures: principles and use of the Brain Architecture Knowledge Management System (BAMS)
Frontiers in Neuroinformatics
Data Mining
databases
neuroanatomical projections
Neuroanatomy
neuroinformatics
author_facet Mihail Bota
Larry W Swanson
author_sort Mihail Bota
title Collating and curating neuroanatomical nomenclatures: principles and use of the Brain Architecture Knowledge Management System (BAMS)
title_short Collating and curating neuroanatomical nomenclatures: principles and use of the Brain Architecture Knowledge Management System (BAMS)
title_full Collating and curating neuroanatomical nomenclatures: principles and use of the Brain Architecture Knowledge Management System (BAMS)
title_fullStr Collating and curating neuroanatomical nomenclatures: principles and use of the Brain Architecture Knowledge Management System (BAMS)
title_full_unstemmed Collating and curating neuroanatomical nomenclatures: principles and use of the Brain Architecture Knowledge Management System (BAMS)
title_sort collating and curating neuroanatomical nomenclatures: principles and use of the brain architecture knowledge management system (bams)
publisher Frontiers Media S.A.
series Frontiers in Neuroinformatics
issn 1662-5196
publishDate 2010-03-01
description Terms used to describe nervous system parts and their interconnections are rife with synonyms, partial correspondences, and even homonyms, making effective scientific communication unnecessarily difficult. To address this problem a new Topological Relations schema for the Relations module of BAMS (Brain Architecture Knowledge Management System) was created. It includes a representation of the qualitative spatial relations between nervous system parts defined in different neuroanatomical nomenclatures or atlases and is general enough to record data and metadata from the literature, regardless of description level or species. Based on this foundation a Projections Translations inference engine was developed for the BAMS interface that automatically translates neuroanatomical projection (axonal inputs and outputs) reports across nomenclatures from translated information. To make BAMS more useful to the neuroscience community three things were done. First, we implemented a simple schema for validation of the translated neuroanatomical projections. Second, more than 1000 topological relations between brain gray matter regions for the rat were inserted, along with associated details. Finally, a case study was performed to enter all historical or legacy published information about terminology related to one relatively complex gray matter region of the rat. The bed nuclei of the stria terminalis (BST) were chosen and 21 different nomenclatures from 1923 to present were collated, along with 284 terms for parts (gray matter differentiations), 360 qualitative topological relations between parts, and more than 7,000 details about spatial relations between parts, all of which was annotated with appropriate metadata. This information was used to construct a graphical “knowledge map” of relations used in the literature to describe subdivisions of the rat BST.
topic Data Mining
databases
neuroanatomical projections
Neuroanatomy
neuroinformatics
url http://journal.frontiersin.org/Journal/10.3389/fninf.2010.00003/full
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