1. On Brain Atlases and GIS Infrastructure for spatial data integration: across scales, species and projects Ilya Zaslavsky SDSC, BIRN-CC, Mouse-BIRN

2. Outline Atlas functionality Spatial data integration –Mouse brain atlas work –Spatial information infrastructure –Uses of ontology in spatial data integration: Ontology-based feature identification Ontology-based location exchange –Research portals for spatial data integration How BIRN atlas work influenced other cyberinfrastructure projects –STARE (ophthalmology) –GEON, CUAHSI, NIEHS Hurricane Response portal

3. What is an atlas?  a set of 2D images or a 3D volume in a specified coordinate system, with anatomic feature delineations, and a set of spatially registered annotations  What we need is more than an atlas: A gateway to large distributed databases of images, volumes, segmentations, other spatially-registered data Collection of spatial data sources Collection of spatial data registries, services and workflows:  image registration, segmentation, spatial selection, spatial analysis, integration of spatial data Collection of viewers and annotation tools with authenticated access  And APIs to make them all work together

4. SMaRT: Spatial Markup and Rendering Tool

5. Spatial Queries on Brain Structures, including 3D queries The Smart Atlas: Spatial Integration over Distributed Data WebStart from BIRN portal

6. Spatial integration of distributed multiscale data The new version of the S.M.A.R.T. Brain Atlas is a MapObjects-Java based Web application for ontology- aware discovery and integration of distributed multiscale brain data registered to the common stereotaxic coordinate system.

7. GIS Challenges  Multiple collections of brain images/ atlases, but lack of interoperability across them At the database and catalog levels At the interface level  Queries such as:  “which atlas sources provide images for user-selected area of the brain at a given resolution”, or  “display image segmentations available in one atlas over a newly obtained image stored in a different location on the grid”, or  “compare protein distributions in a given area of the brain available at several atlas sources” cannot be answered without a dedicated infrastructure that supports spatial integration of brain data  Heterogeneity in: image formats – conventions – spatial registration – access, retrieval and display mechanisms The SMART Atlas is the prototype infrastructure to answer this challenge

8. Spatial sources and requests 1: getSources ({slice_plane_params, 2Dshape}, sp_rel) 2: getFragment(URI,plane_params,2Dshape,image_dims)

9. Interesting Issues

10. Use of Ontologies to Link Features Structures on slices color coded by relationships contained in the UMLS SMART Atlas uses Unified Medical Language System (UMLS) to query across multiple data sources and explore spatial relationships across brain slices in different coordinate systems (eg, across species)

11. Use of ontologies to delineate anatomic features 1 2 3 4 Color shows number of labels assigned to each polygon

12. Special Cases: 4  ns: nigrostriatal bundle  al: ansa lenticularis, C0152352  mfb: medial forebrain bundle, C0025055  MCLH: magnocellular nucleus of the lateral hypothalamus  LH: lateral hypothalamic area, C0020654  Capital letters designate nuclei (typically) AND dotted lines typically show nuclei  Fiber tracks: small letters and solid lines  mfb and LH are siblings in UMLS; a bundle of neurons (mfb) “runs through” a nucleus (LH)  the polygon is assigned “LH”, with “mfb” as a secondary (fiber bundle) designation  Same picture with “al” vs “MCLH”

13. MBAT Programmers Meeting From MBAT to WOMBAT Web-based Ontology-based

14. Architecture Basic data type abstraction (or physical databases) Sources Gene expression 2D images 2D vector segmentations 3D volumes 4+D volumes (FMRI) Time series Phenotype/behavioral surfaces Methods over the abstract representation – Shall have common signatures getExpressed(gene, feature) translate (coords) convert (formats) registries Clients: MBAT etc. Data integration tools

15. Interoperability desiderata Read data from other atlases/databases API for data retrieval and transformation Find relevant data in other atlases/ databases API for atlas catalogs View the region of interest in another atlas API for atlas state exchange MBAT Drexel’s NT UCLA’s Shiva Smart Atlas Gensat Allen Brain Atlas CCDB, others Spatial registry

16. Exchanging location information across scales and coordinate systems  Directly: By space, using Stereotaxic coordinates  Formal coordinate system definitions  Coordinate system translation services (incl. between image and stareotaxic)  Spatial registration of images and segmentations By anatomic feature names, via UMLS + Bonfire + Neuronames + …  Ontological registration  Semantic translation services  Indirectly By verbal description of spatial locations relative to well- defined features (a collection of location rules) In relative coordinates; by matching derived indices: histograms, shape indexes, etc.

17. Agreed-upon ROI description and state exchange protocol STATE_INIT:: user pwd 10009 STATE_SUBMIT 10009 smart m1,n1,p1 m2,n2,p2 x1,y1,z1 x2,y2,z2... m1,n1,p1 m2,n2,p2 x1,y1,z1 x2,y2,z2... success STATE_RETRIEVE 10009 shiva smart m1,n1,p1 m2,n2,p2 x1,y1,z1 x2,y2,z2... m1,n1,p1 m2,n2,p2 x1,y1,z1 x2,y2,z2... + Java API

18. Integration across clients: ROI exchange API in action INITIALIZE SERVICE SUBMIT STATE RETRIEVE STATE

19. Position Synchronization

20. Accessing spatial registration sources from each viewer  Image registration workflow Ability to plug in image registration/warping tools from LONI, Drexel, UCSD, Ohio  Spatial registry at BIRN-CC: Oracle Spatial database; contains:  Service URI (SRB:path, or ArcIMS:URL,service,layer)  Coordinate system of the layer  Plane to which the image or markup belongs: a,b,c,d wrt stereotaxic (dorsal/ventral to bregma; anterior/posterior to bregma; lateral to midline)  Positioning of image within the plane (6 elements of TFW)  Image contour or feature extent as SDO  Signal type, and link to additional signal metadata  Additionally: to simplify search for non-oblique slices: slice types, associated numeric constant for visualization: thumbnail paths

21. Image Registration Portlet Automatic generation of spatially-registered image services

22. Image Registration Portlet: Spatial Registry

23. Impact

24. ESRI Health Sciences Brochure Presented at several GIS conferences: keynote at International Health GIS, 2004; Part of ESRI’s President’s address at GIS User Conference (2003), other…

26. The STARE (Structural Analysis of the Retina) project, UCSD Foveal Coordinate System Background Coordinate System Veins Arteries Optic nerve Fovea Cotton-Wool Hemorrhages

27. SAN DIEGO SUPERCOMPUTER CENTER, UCSD SciR&D BIRN Grid architecture in other projects CI is about RE-USING data and research resources !!

28. Drinking water surface intakes and TRI facilities Now at: http://www-apps.niehs.nih.gov/katrina/

29. TRI facilities within 3 miles of user-entered address

30. TRI facilities within a mile of drinking water intakes

31. Environmental justice applications: social and demographic variables overlaid with environmental effects

32. Data download and metadata are available for all spatial layers