Current Research in the UW Structural Informa cs Group
January 2011
James F. Brinkley Shared Vision
• Worldwide network of integrated data and knowledge • Brings relevant informa on to point of need • Fosters transla on Long‐term Goals
• Contribute to shared vision • A structural informa on framework for biomedicine • Use as a basis for organizing biomedical informa on Ra onale
• Structure of the body is the most useful means for organizing biomedical informa on • Most manifesta ons of health and disease are proper es of anatomical en es Primary Informa cs Research Ques ons • How to create a structural informa on framework • How to use this framework as a basis for organizing informa on Approach
• Classify types of informa on • Represent and implement as different modules • Integrate within a distributed framework • Develop applica ons opportunis cally • Applica ons drive methods Current Driving Biological Applica ons
• Anatomy Educa on • Brain mapping • Proteomics • Clinical trials • Craniofacial Malforma ons Research Themes
• Structural framework • Data management • Data integra on • Data visualiza on Projects
• Founda onal Model of Anatomy • Ontology Views • Data management • Data integra on • Visualiza on Founda onal Model of Anatomy
Onard Mejino, Nolan Nichols, Todd Detwiler, Cornelius Rosse Dimensional Anatomy Taxonomy Ontology Anatomical Structure Line (1-D) Volume (3-D) Organ Part Surface (2-D) Anterior Interventricular Organ -is a- Sulcus -is a- Polyhedron region
Right Cardiac Sternocostal bounded by Coronary has Chamber Surface -is a- Sulcus super- object bounded by Infundibulum Inferior margin Right of heart has boundary of subobject Ventricle
Diaphragmatic Coronary Surface bounded by Sulcus
Anatomical Posterior Landmark IV Sulcus
-is a- Apex Crux of heart Point (1-D)
Influence of the FMA Current work on the FMA
• Reorganizing the neuroantomy axis of RadLex • Incorpora ng Lympha cs • Represen ng neural connec vity Re‐organizing the neuroanatomy axis of RadLex
Enabling RadLex with the Foundational Model of Anatomy Ontology to Organize and Integrate Neuro-imaging Data Jose Leonardo V. Mejino Jr. MD1, Landon T. Detwiler MS1, Jessica A. Turner PhD3, Maryann E. Martone PhD4, Daniel L. Rubin MD, MS5 and James F. Brinkley MD, PhD1,2. 1Structural Informatics Group, 2Biomedical and Health Informatics, University of Washington, 3MIND Research Network, 4Department of Neurosciences, University of California San Diego, 5Stanford Center for Bioinformatics Research, Stanford University
Abstract A. FMA B. RadLex Conclusion In this study we empowered RadLex with a We have shown how the ontological robust ontological framework and framework of the FMA explicitly defined additional neuroanatomical content the entities represented by the different derived from a reference ontology, the parcellation and naming schemes and by Foundational Model of Anatomy Ontology doing so it becomes possible to ascertain (FMA)1, with the intent of providing the relationships which correlate these RadLex the facility to correlate the different terms, a prerequisite step for sharing and standards used in annotating neuro- harmonizing data. We have started using radiological image data. It is the objective the ontology to annotate fMRI datasets of this work to promote data sharing, data Figure 2. Class taxonomy of FMA applied to RadLex with strict adherence to IS_A and derive inferences about relationships harmonization and interoperability only relationships between anatomical entities. between the datasets8. between disparate neuro-radiological Materials and Methods labeling systems. Results Enhancing the neuroanatomy ontology of RadLex Enhancement of Anatomy Taxonomy of RadLex. Acknowledgment Supported by NHLBI grant HL08770 and NIBIB involved five major steps (shown in Figure 1): Adoption of the ontological framework of the Introduction contract # HHSN268200800020C. 1.! selecting a reference ontology, the FMA FMA assures a consistent Aristotelian-type Huge amounts of neuro-imaging data are being ontology, a target application ontology, inheritance taxonomy for RadLex (Figure 2). The References produced by different groups and they are RadLex, three neuro-imaging annotation derived ontology provides explicit semantics for 1.!Rosse C, Mejino JLV. 2007. The Foundational recorded based on different, often study terminologies, Talairach Daemon Atlas3, RadLex terms . Model of Anatomy Ontology, In: Burger A, specific, brain parcellation and naming 4 Davidson D, Baldock R (eds). Anatomy FreeSurfer atlas , Anatomical Automatic Enhancement of Neuroanatomy content of FMA. schemes. Using disparate naming conventions 5 Ontologies for Bioinformatics: Principles and Labeling atlas (AAL) and a neuroanatomical Classes and spatio-structural relations were added produces incompatible terms thereby making 6 Practice, London: Springer, pp 59-117. application ontology, NeuroLex , as inputs to in the FMA to accommodate and represent the the correlation of data difficult to achieve. 2.!Langlotz CP. RadLex 2006. a new method for the system; entities referenced by the different annotation Current terminologies for neuro-imaging lack indexing online educational materials. 2.! application of the high level class taxonomy of terminologies (Figures 3 and 4). Explicit Radiographics 26:1595–1597. the semantic framework to explicitly declare the the FMA to re-organize the anatomy hierarchy ontological representation therefore allowed for 3.!Talairach, J., Tournoux, P., 1988. Co-planar precise meanings of the terms and therefore 7 of RadLex ; the correlation of the different terms by using stereotaxic atlas of the human brain. Thieme neuro-imaging data and information represented '''''!"#$%&'BC'D8%%&:.4"8='87'=&$%8.=.489"A.:'&=4"4"&>'%&7&%&=A&/';<'/"77&%&=4'4&%9"=8:8#"&>C 3.! enhancement of the neuroanatomy content of FMA properties such as IS_A and PART_OF Medical Publishers, New York. by the terms cannot be readily associated and the FMA to capture the representations that (Figure 5). 4.!Lancaster JL, Woldorff MG, Parsons LM, Liotti applied across different studies. RadLex2 different terminologies intend to use for M, Freitas CS, Rainey L, Kochunov PV, (Radiology Lexicon from RSNA) is a annotating neuroimaging data as well as other Extraction of neuroanatomical “view”, Nickerson D, Mikiten SA, Fox PT. Automated controlled terminology for radiology and seeks forms of neuroscientific data; NeuroFMA, for incoporation into RadLex. Talairach Atlas Labels For Functional Brain to provide the needed semantics for correlating 4.! extraction of the enhanced neuroanatomy View extraction is performed via a procedural Figure 3 Mapping. Human Brain Mapping 10:120-131(2000). the diverse terminologies used for annotating component of the FMA, the NeuroFMA, as an program that is written in JAVA, utilizing the neuro-imaging data. In this work we leveraged Protégé ontology API. Rather than creating a view 5.!N. Tzourio-Mazoyer, B. Landeau, D. ontology “view” for incorporation into Papathanassiou, F. Crivello, O. Etard, N. Delcroix, the Foundational Model of Anatomy Ontology by starting from an empty ontology and then RadLex; Bernard Mazoyer and M. Joliot (January 2002). adding classes, the process starts with a complete (FMA) to re-structure and reinforce the 5.! merging of the extracted NeuroFMA with the "Automated Anatomical Labeling of activations in anatomical domain of RadLex so it can ontologically re-organized RadLex . copy of the FMA and then eliminates everything SPM using a Macroscopic Anatomical incorporate, accommodate and correlate the not required in the NeuroFMA (Figure 6). Parcellation of the MNI MRI single-subject different annotation terminologies. Merging of NeuroFMA into RadLex. brain". NeuroImage 15 (1): 273–289. Technical details for this step are beyond the scope 6.!http://neurolex.org/wiki/Main_Page The approach we describe here serves two 7.!Mejino JLV, Rubin DL, Brinkley JF. FMA- of this presentation. However we found that we practical purposes: RadLex: An application Ontology of Radiological 1)! reference ontologies provide a principled could coalesce classes from the two ontologies in Anatomy derived from the Foundational Model of and robust framework on which to build RadLex. The merging produced “FMA-like” Anatomy Reference Ontology. Proc. AMIA Symp applications specific to a particular field structure to RadLex. A total of 12, 579 classes and 2008:465-469. 33,361 property values were imported into !"#$%&'()'!*+,-./0&1'2%"#345'/&%"6&/'7%89'43&'!*+'2:&745';<'/&:&4"8='2>4%"?&8$4>5'.=/'.//"4"8='87' 8.!Turner JA, Mejino JLV, Brinkley JF, Detwiler 2)! underlying ontological framework :"=?>'.>'>38@=';<'43&'!"#$':"=?'87'Anatomical structure,'@3"A3'@.>'/&:&4&/'7%89'Material facilitates and promotes integration, RadLex from the NeuroFMA. It would have been anatomical entity';$4'.//&/'48'Anatomical entity. LT, Lee HJ, Martone ME and Rubin DL (2010) interoperability and reuse of knowledge very difficult and time-consuming to implement Application of neuroanatomical ontologies for among application ontologies. these changes manually. neuroimaging data annotation. Frontiers in Neuroinformatics 4(10):pp. 1-12. Incorpora ng Lympha cs FMA class taxonomy
Nakagawa et. al. CANCER 1994, Vol. 73, No. 4, 1155‐1162. Represen ng Neural Connec vity
Representing Neural Connectivity in the Foundational Model of Anatomy Ontology BIC Nolan Nichols1, Aaron Perlmutter1, Jose L. V. Mejino Jr. 2, Daniel L. Rubin3, and James F. Brinkley1,2,4 Departments of Medical Education and Biomedical Informatics1, Biological Structure2, Computer Science and Engineering4 University of Washington, Seattle, WA, Stanford Center for Bioinformatics Research, Stanford University3
1.! Summary 3. Example of a Connectivity Relation 5. Sub-property Hierarchy and Definitions Sub-property Hierarchy Term Definition •! Our current effort focuses on representing neural connectivity relationships between Connected_to Structural anatomical property which holds between each gray and white matter structures in the Foundational Model of Anatomy Ontology (FMA)1. Connectivity Hierarchy anatomical structure of type A and some anatomical structure of type B such that each structure shares some connected_to part of its bona fide anatomical surface with that of the •! The FMA contains a number of terms that imply either structural or functional - attached_to* other. - innervates Continuous_with Connected_to property which holds between each connectivity, such as sends_output_to or receives_input_from, but the semantics of their - projects_to* anatomical entity of type A and some anatomical entity of type B such that there is no bona fide boundary between structural connectivity relationships were not yet made formally explicit. - receives_attachment_from* their contiguous constitutional parts. - innervated_by Synapse_with Connected_to property where there is apposition between •! To formalize structural connectivity relationships in the FMA we developed a set of - receives_projection _from* the presynaptic membrane of a neurite of one neuron and - continuous_with* the postsynaptic membrane of one or more neurites of definitions to disambiguate and clarify the terminologies describing the types of - has_projection* another neuron or a region of a muscle cell or a gland cell and some form of neurotransmission is evident between connectivity relationships that exist between gray and white matter structures at different - projects_from* them. levels of granularity. f - synapses_with* Projects_to* Attached_to property where individual axons comprising a - has pathway fiber tract originating from one or more brain regions - has efferent pathway synapse_with neurites or somas of a collection of neurons •! Scales of connectivity relations range from long-range association, commissural, and - sends_output_to* located in one or more other brain regions. This relation projection fibers at the mesoscopic scale to synaptic junctions at the microscopic scale. - has afferent pathway may be synonymous with ‘terminates_in’. - receives_input_from* Projects_from* Continuous_with property where individual axons •! This work focused on generating a representation of connectivity at the mesoscopic comprising a fiber tract are parts of a collection of neurons located in one or more brain regions. This relation may be scale, which aims to facilitate the integration of annotated open-access neuroimaging Figure 3. Connectivity properties arranged synonymous with ‘originates_from’. datasets - including structural MRI (sMRI), functional MRI (fMRI) and diffusion tensor in a hierarchy in the Spatial Association Sends_output_to* Efferent pathway property consisting of relations where A imaging (DTI). Network (SAn) of the FMA (above). has_projection B and B projects_to C, and where neuro- Definitions for properties implemented in mission is sent from A to C. the FMA* (right). Receives_input_from* Afferent pathway property consisting of relations where A receives_projection_from B and B projects_from C, and Figure 1. Demonstration of FMA white matter and gray matter classes that relate to the where neurotransmission is received by A from C. JHU DTI-81 atlas probability distribution of the left superior longitudinal fasciculus.
2. Methodology 4. Relate Tract and Region Based Atlases 6. Future Work •! The FMA is a reference ontology for the domain of anatomy that symbolically represents the phenotypic Anatomical Knowledge •! Continue developing the FMA representation of connectivity at the mesoscopic scale and organization of the human body at all levels of granularity. begin implementation of additional connectivity relations at finer levels of granularity. FMA •! In this study we applied FMA principles to the represent structural connectivity properties of gray matter Brain Region Atlases •! Develop a more rich representation of the Brodmann Area parcellation schema by and white matter neural structures using the principle of Anatomical Structural Abstraction (ASA). Brodmann area 6 of left inferior frontal gyrus Part_of explicitly defining cytoarchitectonic, regional, and long-range connectivity properties. Gray matter of left inferior frontal gyrus Talairach Left Cerebrum.Frontal Lobe. •! Connectivity, in addition to Location and Orientation, is one of the three components of the ASA Spatial Left inferior frontal gyrus Inferior Frontal Gyrus.Gray Left frontal lobe Association Network (SAn). We focused on explicitly representing connectivity properties between white Matter.Brodmann area 6 •! Demonstrate the utility of connectivity relations in the FMA for knowledge discovery by Part_of matter and gray matter neural entities at the mesoscopic scale. Left brodmann area 6 integrating region-based annotations from sMRI and fMRI datasets with tract-based annotations from DTI datasets. •! Gray Matter Properties Projects_to (Connectivity Relation) FreeSurfer - has_projection and projects_from are inverse properties between "structure of origin" and Ctx-lh-inferiorfrontal •! Determine how new knowledge about neural connectivity from the human connectome the fibers it sends out. Left superior longitudinal fasciculus proper Ctx-lh-G_frontal_inferior Part of project and related structural and functional connectivity research can can be incorporated - ’Brodmann area 39 of inferior parietal lobule' has_projection ’Superior longitudinal fasciculus Left superior longitudinal fasciculus proper’ into the FMA. Acknowledgement - ’Superior longitudinal fasciculus proper' projects_from 'Brodmann area 6 AAL Atlas of inferior parietal lobule’ Frontal Inferior_Left Supported by NHLBI grant HL08770 and NIBIB contract # HHSN268200800020C. Fiber Tract Atlas •!White Matter Properties References - receives_projection and projects_to are inverse properties between "structure of termination (target)” JHU DTI 81 Atlas 1.! Rosse C and Mejino JLV. (2007) The Foundational Model of Anatomy Ontology, in Burger A, Davidson D and Baldock R. Eds. Superior Longitudinal Fasciculus L Anatomy Ontologies for Bioinformatics: Principles and Practice, pages pp. 59-117. Springer. and the fibers it receives. 2.! Detwiler LT, Suciu, D, Franklin JD, Moore EB, Poliakov AV, Lee ES, Corina DP, Ojemann GA and Brinkley, JF. (2009) Distributed - 'Dorsal segment of superior longitudinal fasciculus' projects_to 'Brodmann area 6 of superior XQuery-based integration and visualization of multimodality data: Application to brain mapping. Frontiers in Neuroinformatics. frontal gyrus' 3.! Turner JA, Mejino JLV, Brinkley JF, Detwiler LT, Lee HJ, Martone ME, and Rubin DL. (2010) Application of neuroanatomical ontologies Figure 2. Labels are used to annotate white matter structures in the JHU-DTI-81 fiber tract for neuroimaging data annotation. Frontiers in Neuroinformatics. - 'Brodmann area 6 of superior frontal gyrus' receives_projection from ‘Superior longitudinal 4.! Wakana S, Jiang H, Nagae-Poetscher LM, van Zijl PCM, Mori S. (2004) Fiber tract-based atlas of human white matter anatomy. fasciculus proper' atlas and grey matter structures in atlases based on brain regions. The FMA can be used to Radiology. reconcile the different parcellation schemas.. AMIA 2010 Annual Symposium Washington, DC November 13-17, 2010 Planned work on the FMA
• Convert to OWL 2 • Use as a basis for applica on ontologies • Use as a basis for reorganizing other ontologies Projects
• Founda onal Model of Anatomy • Ontology Views • Data management • Data integra on • Visualiza on Ontology Views over the Seman c Web
Todd Detwiler, Marianne Shaw, Onard Mejino, Dan Suciu, Linda Shapiro, John Gennari, Dan Cook, Nicola Dell, Natasha Noy, Daniel Rubin, Mark Musen Mo va on
• Reference ontologies like the FMA are (or will be) too large for prac cal use • How can reference ontologies be made prac cal for applica ons? Approach
• Applica on ontologies as views over one or more reference ontologies • A view is a query that defines a formal transforma on from one or more source ontologies to a target applica on ontology SparQL Extensions: vSparQL Marianne Shaw, Todd Detwiler and Dan Suciu • Gleen Regular path library • Subqueries • Recursive Queries • Skolem func ons vSparQL Subquery
Subquery vSparQL Recursive Query
Base case
Recursive step vSparQL Recursive Query Results
Graphical User Interface over vSPARQL
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• Op miza on of query execu on • Evaluate on use cases • Examine other query languages • View Query Manager Projects
• Founda onal Model of Anatomy • Ontology Views • Data management • Data integra on • Visualiza on Data Management
Joshua Franklin, Xenia Hertzenberg, Ron Shaker, Nolan Nichols Driving applica ons
• Ins tute of Transla onal Health Sciences (ITHS) • UW Neuroproteomics Center • UW Interdisciplinary Brain Imaging Center (IBIC) ITHS
RedCap Electronic Data Capture Rlab Freezer Management
RedCap/Rlab “Baby Shower” with Stevens Lab caTissue at ITHS UW Neuroproteomics Center XNAT at IBIC Data management challenges/future work • Build versus buy • Incorpora ng common terminology for later data integra on • Reducing the me to develop custom applica ons How to link together
? Projects
• Founda onal Model of Anatomy • Ontology Views • Data management • Data integra on • Visualiza on Data Integra on
Todd Detwiler, Joshua Franklin, Ron Shaker Lightweight Distributed Queries
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