Neuroanatomical domain of the foundational model of anatomy ontology

Journal of Biomedical Semantics

James F Brinkley¹  Daniel L Rubin²  Jessica A Turner⁴  Maryann E Martone³  Trond T Nilsen¹  Landon T Detwiler¹  Jose LV Mejino¹  B Nolan Nichols¹ 

[1] University of Washington, Seattle, WA, USA;Stanford University, Stanford, CA, USA;University of California San Diego, San Diego, CA, USA;Mind Research Network, Albuquerque, NM, USA

关键词: mri;    Information retrieval;    Neuroinformatics;    Brain atlas;    Ontology;    Neuroscience;    Neuroanatomy;    Data integration;   

Others  :  806249
DOI  :  10.1186/2041-1480-5-1

 received in 2013-07-04, accepted in 2013-12-24,  发布年份 2014

【 摘 要 】

Background

The diverse set of human brain structure and function analysis methods represents a difficult challenge for reconciling multiple views of neuroanatomical organization. While different views of organization are expected and valid, no widely adopted approach exists to harmonize different brain labeling protocols and terminologies. Our approach uses the natural organizing framework provided by anatomical structure to correlate terminologies commonly used in neuroimaging.

Description

The Foundational Model of Anatomy (FMA) Ontology provides a semantic framework for representing the anatomical entities and relationships that constitute the phenotypic organization of the human body. In this paper we describe recent enhancements to the neuroanatomical content of the FMA that models cytoarchitectural and morphological regions of the cerebral cortex, as well as white matter structure and connectivity. This modeling effort is driven by the need to correlate and reconcile the terms used in neuroanatomical labeling protocols. By providing an ontological framework that harmonizes multiple views of neuroanatomical organization, the FMA provides developers with reusable and computable knowledge for a range of biomedical applications.

Conclusions

A requirement for facilitating the integration of basic and clinical neuroscience data from diverse sources is a well-structured ontology that can incorporate, organize, and associate neuroanatomical data. We applied the ontological framework of the FMA to align the vocabularies used by several human brain atlases, and to encode emerging knowledge about structural connectivity in the brain. We highlighted several use cases of these extensions, including ontology reuse, neuroimaging data annotation, and organizing 3D brain models.

【 授权许可】

   
2014 Nichols et al.; licensee BioMed Central Ltd.

【 图 表 】

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【 参考文献 】

• [1]Jack CR, Bernstein MA, Fox NC, Thompson P, Alexander G, Harvey D, Borowski B, Britson PJ, Whitwell JL, Ward C, Dale AM, Felmlee JP, Gunter JL, Hill DLG, Killiany R, Schuff N, Fox-Bosetti S, Lin C, Studholme C, DeCarli CS, Krueger G, Ward HA, Metzger GJ, Scott KT, Mallozzi R, Blezek D, Levy J, Debbins JP, Fleisher AS, Albert M, et al.: The Alzheimer’s Disease Neuroimaging Initiative (ADNI): MRI methods. J Magn Reson Imaging 2008, 27:685-691.
• [2]Van Essen DC, Ugurbil K, Auerbach E, Barch D, Behrens TEJ, Bucholz R, Chang A, Chen L, Corbetta M, Curtiss SW, Penna Della S, Feinberg D, Glasser MF, Harel N, Heath AC, Larson-Prior L, Marcus D, Michalareas G, Moeller S, Oostenveld R, Petersen SE, Prior F, Schlaggar BL, Smith SM, Snyder AZ, Xu J, Yacoub E: WU-Minn HCP consortium: the human connectome project: a data acquisition perspective. Neuroimage 2012, 62:2222-2231.
• [3]Nooner KB, Colcombe SJ, Tobe RH, Mennes M, Benedict MM, Moreno AL, Panek LJ, Brown S, Zavitz ST, Li Q, Sikka S, Gutman D, Bangaru S, Schlachter RT, Kamiel SM, Anwar AR, Hinz CM, Kaplan MS, Rachlin AB, Adelsberg S, Cheung B, Khanuja R, Yan C, Craddock CC, Calhoun V, Courtney W, King M, Wood D, Cox CL, Kelly AMC, et al.: The NKI-Rockland sample: a model for accelerating the pace of discovery science in psychiatry. Front Neurosci 2012, 6:152.
• [4]Moore EB, Poliakov AV, Lincoln P, Brinkley JF: MindSeer: a portable and extensible tool for visualization of structural and functional neuroimaging data. BMC bioinformatics 2007, 8:389. BioMed Central Full Text
• [5]Riviere D, Régis J, Cointepas Y, Papadopoulos-Orfanos D, Cachia A, Mangin J-F: A freely available Anatomist/BrainVISA package for structural morphometry of the cortical sulci. Neuroimage 2003, 19:19-22.
• [6]Shattuck DW, Leahy RM: BrainSuite: an automated cortical surface identification tool. Med Image Anal 2002, 6:129-142.
• [7]Cox RW: AFNI: what a long strange trip it’s been. Neuroimage 2012, 62:743-747.
• [8]Pieper S, Halle M, Kikinis R: 3D SLICER. In Proceedings of the IEEE International Symposium on Biomedical Imaging. Arlington; 2004:632-635.
• [9]Marcus D, Olsen T, Ramaratnam M, Buckner R: The extensible neuroimaging archive toolkit. Neuroinformatics 2007, 5:11-33.
• [10]Scott A, Courtney W, Wood D, La Garza De R, Lane S, King M, Wang R, Roberts J, Turner JA, Calhoun VD: COINS: an innovative informatics and neuroimaging tool suite built for large heterogeneous datasets. Front Neuroinform 2011, 5:33.
• [11]Van Horn JD, Toga AW: Is it time to re-prioritize neuroimaging databases and digital repositories? Neuroimage 2009, 47:1720-1734.
• [12]Book GA, Anderson BM, Stevens MC, Glahn DC, Assaf M, Pearlson GD: Neuroinformatics Database (NiDB) - a modular, portable database for the storage, analysis, and sharing of neuroimaging data. Neuroinformatics 2013, 11:495-505.
• [13]Ozyurt IB, Keator DB, Wei D, Fennema-Notestine C, Pease KR, Bockholt J, Grethe JS: Federated web-accessible clinical data management within an extensible neuroimaging database. Neuroinformatics 2010, 8:231-249.
• [14]Cox RW: AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. Comput Biomed Res 1996, 29:162-173.
• [15]Dale AM, Fischl B, Sereno MI: Cortical surface-based analysis. I. Segmentation and surface reconstruction. Neuroimage 1999, 9:179-194.
• [16]Jenkinson M, Beckmann CF, Behrens TEJ, Woolrich MW, Smith SM: FSL. Neuroimage 2012, 62:782-790.
• [17]Friston KJ, Holmes AP, Worsley KJ, Poline JP, Frith CD, Frackowiak RS: Statistical parametric maps in functional imaging: a general linear approach. Hum Brain Mapp 1994, 2:189-210.
• [18]Van Essen DC, Dierker DL: Surface-based and probabilistic atlases of primate cerebral cortex. Neuron 2007, 56:209-225.
• [19]Talairach J, Tournoux P: Co-Planar Stereotaxic Atlas of the Human Brain: 3-Dimensional Proportional System: an Approach to Cerebral Imaging. New York: Thieme Medical Publishers, Inc; 1988.
• [20]Tzourio-Mazoyer N, Landeau B, Papathanassiou D, Crivello F, Etard O, Delcroix N, Mazoyer B, Joliot M: Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage 2002, 15:273-289.
• [21]Evans AC, Collins DL, Mills SR, Brown ED, Kelly RL, Peters TM: 3D statistical neuroanatomical models from 305 MRI volumes. In Proceedings of the Nuclear Science Symposium and Medical Imaging Conference: 31 Oct-6 Nov 1993. Edited by Lowell Klaisner. San Francisco: IEEE Conference Record; 1993:1813-1817.
• [22]Shattuck DW, Mirza M, Adisetiyo V, Hojatkashani C, Salamon G, Narr KL, Poldrack RA, Bilder RM, Toga AW: Construction of a 3D probabilistic atlas of human cortical structures. Neuroimage 2008, 39:1064-1080.
• [23]Caviness VS, Meyer J, Makris N, Kennedy DN: MRI-based topographic parcellation of human neocortex: an anatomically specified method with estimate of reliability. J Cogn Neurosci 1996, 8:566-587.
• [24]Klein A, Tourville J: 101 labeled brain images and a consistent human cortical labeling protocol. Front Neurosci 2012, 6:171.
• [25]Bohland JW, Bokil H, Allen CB, Mitra PP: The brain atlas concordance problem: quantitative comparison of anatomical parcellations. PLoS ONE 2009, 4:e7200.
• [26]Wakana S, Jiang H, Nagae-Poetscher LM, van Zijl PCM, Mori S: Fiber tract-based atlas of human white matter anatomy. Radiology 2004, 230:77-87.
• [27]Oishi K, Zilles K, Amunts K, Faria A, Jiang H, Li X, Akhter K, Hua K, Woods R, Toga AW: Human brain white matter atlas: identification and assignment of common anatomical structures in superficial white matter. Neuroimage 2008, 43:447-457.
• [28]Lancaster JL, Woldorff MG, Parsons LM, Liotti M, Freitas CS, Rainey L, Kochunov PV, Nickerson D, Mikiten SA, Fox PT: Automated Talairach atlas labels for functional brain mapping. Hum Brain Mapp 2000, 10:120-131.
• [29]Desikan RS, Ségonne F, Fischl B, Quinn BT, Dickerson BC, Blacker D, Buckner RL, Dale AM, Maguire RP, Hyman BT, Albert MS, Killiany RJ: An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage 2006, 31:968-980.
• [30]Fischl B, van der Kouwe A, Destrieux C, Halgren E, Ségonne F, Salat DH, Busa E, Seidman LJ, Goldstein J, Kennedy D, Caviness V, Makris N, Rosen B, Dale AM: Automatically parcellating the human cerebral cortex. Cereb Cortex 2004, 14:11-22.
• [31]Ashburner J, Friston KJ: Nonlinear spatial normalization using basis functions. Hum Brain Mapp 1999, 7:254-266.
• [32]Bug WJ, Ascoli GA, Grethe JS, Gupta A, Fennema-Notestine C, Laird AR, Larson SD, Rubin D, Shepherd GM, Turner JA, Martone ME: The NIFSTD and BIRNLex vocabularies: building comprehensive ontologies for neuroscience. Neuroinformatics 2008, 6:175-194.
• [33]Imam FT, Larson SD, Bandrowski A, Grethe JS, Gupta A, Martone ME: Development and use of ontologies inside the neuroscience information framework: a practical approach. Front Genet 2012, 3:111.
• [34]Bowden DM, Martin RF: NeuroNames brain hierarchy. Neuroimage 1995, 2:63-83.
• [35]Bowden DM, Dubach MF: NeuroNames 2002. Neuroinformatics 2003, 1:43-59.
• [36]Bowden DM, Song E, Kosheleva J, Dubach MF: NeuroNames: an ontology for the BrainInfo portal to neuroscience on the web. Neuroinformatics 2012, 10:97-114.
• [37]Rosse C, Mejino JLV: A reference ontology for biomedical informatics: the foundational model of anatomy. J Biomed Inform 2003, 36:478-500.
• [38]Menzel C: Reference ontologies -- application ontologies: either/or or both/and? In Proceedings of the KI2003 Workshop on Reference Ontologies and Application Ontologies: 16 September 2003. Edited by Grenon P, Menzel C, Smith B. Hamburg: CEUR Workshow Proceedings; 2003:2.
• [39]Shaw M, Detwiler LT, Brinkley JF, Suciu D: Generating application ontologies from reference ontologies. In Proceedings of the AMIA Annual Symposium: 8-12 November 2008. Washington: AMIA Annual Symposium Proceedings Archive; 2008:672-676.
• [40]Langlotz CP: RadLex: a new method for indexing online educational materials. Radiographics 2006, 26:1595-1597.
• [41]Mejino JLV, Rubin DL, Brinkley JF: FMA-RadLex: an application ontology of radiological anatomy derived from the foundational model of anatomy reference ontology. In Proceedings of the AMIA Annual Symposium: 8-12 November 2008. Washington: AMIA Annual Symposium Proceedings Archive; 2008:465-469.
• [42]Noy NF, Sintek M, Decker S, Crubézy M, Fergerson RW, Musen MA: Creating semantic web contents with protege-2000. Intelligent Systems, IEEE 2001, 16:60-71.
• [43]Smith B, Ashburner M, Rosse C, Bard J, Bug W, Ceusters W, Goldberg LJ, Eilbeck K, Ireland A, Mungall CJ: The OBO Foundry: coordinated evolution of ontologies to support biomedical data integration. Nat Biotechnol 2007, 25:1251-1255.
• [44]Noy NF, Rubin DL: Translating the foundational model of anatomy into OWL. Web Semant 2008, 6:133-136.
• [45]Golbreich C, Grosjean J, Darmoni SJ: The foundational model of anatomy in OWL 2 and its use. Artif Intell Med 2013, 57:119-132.
• [46]Mejino JL, Detwiler LT, Turner JA, Martone ME, Rubin DL, Brinkley JF: Enabling RadLex with the Foundational Model of Anatomy Ontology to Organize and Integrate Neuro-imaging Data. Washington, D.C: Proceedings of the Annual Symposium of the American Medical Informatics Association; 2010:1171.
• [47]Turner JA, Mejino JLV, Brinkley JF, Detwiler LT, Lee HJ, Martone ME, Rubin DL: Application of neuroanatomical ontologies for neuroimaging data annotation. Front Neuroinform 2010, 4: . doi:10.3389/fninf.2010.00010
• [48]Sporns O, Tononi G, Kötter R: The human connectome: a structural description of the human brain. PLoS Comput Biol 2005, 1:245-251.
• [49]Hagmann P, Cammoun L, Gigandet X, Gerhard S, Grant PE, Wedeen V, Meuli R, Thiran J-P, Honey CJ, Sporns O: MR connectomics: principles and challenges. J Neurosci Methods 2010, 194:34-45.
• [50]Smith B, Ceusters W, Klagges B, Köhler J, Kumar A, Lomax J, Mungall C, Neuhaus F, Rector AL, Rosse C: Relations in biomedical ontologies. Genome Biol 2005, 6:R46. BioMed Central Full Text
• [51]Van Essen DC: A population-average, landmark-and surface-based (PALS) atlas of human cerebral cortex. Neuroimage 2005, 28:635-662.
• [52]Brinkley JF, Detwiler LT, Structural Informatics Group: A query integrator and manager for the query web. J Biomed Inform 2012, 45:975-991.
• [53]Prud'hommeaux E, Seaborne A: SPARQL Query Language for RDF. [http://www.w3.org/TR/rdf-sparql-query/ webcite]
• [54]Manola F, Miller E: RDF Primer. [http://www.w3.org/TR/rdf-primer/ webcite]
• [55]Lancaster JL, Rainey LH, Summerlin JL, Freitas CS, Fox PT, Evans AC, Toga AW, Mazziotta JC: Automated labeling of the human brain: a preliminary report on the development and evaluation of a forward-transform method. Hum Brain Mapp 1997, 5:238-242.