1. OHBM Morning Workshop – June 20, 2009 Neurocognitive ontologies: Methods for sharing and integration of human brain data Neural ElectroMagnetic Ontologies (NEMO): An ontological framework for sharing and integration of ERP data Gwen A. Frishkoff, Ph.D. Language Imaging Lab Medical College of Wisconsin

2.  Challenges to integration of ERP data  ERP ontology design and implementation  Ontology-based analysis of ERP data  Future directions and a call for community involvement

3.  Challenges to integration of ERP data  ERP ontology design and implementation  Ontology-based analysis of ERP data  Future directions and a call for community involvement

4. Event-Related Potentials (ERP) Tried and true method for noninvasive brain functional mapping Millisecond temporal resolution Direct measure neuronal activity Portable and inexpensive Recent innovations give new windows into rich, multi-dimensional patterns – More spatial info (high-density EEG) – More temporal & spectral info (JTF, etc.) – Multimodal integration & joint recordings of EEG and fMRI 1 sec

5. Why are there so few statistical meta-analyses in ERP research?

6. An embarrassment of riches

7. 410 ms 450 ms 330 ms Peak latency 410 ms A lack of standardization (need for a controlled vocabulary) Will the “real” N400 please step forward? Database Query: Show me all the N400 patterns in the database.

8. Putative “N400”- labeled patterns Parietal N400 ≠ ≠ Frontal N400 Parietal P600 A Need for Integration

9. Knowledge  Semantically structured (Taxonomy, CMap, Ontology,…) Information  Syntactically structured (Tables, XML, RDF,…) Data  Minimally structured or unstructured Ontologies for high- level, explicit representation of domain knowledge  theoretical integration Ontologies to support principled mark-up of data for meta-analysis  practical integration

10. Neural ElectroMagnetic Ontologies  A set of formal (OWL) ontologies for representation of ERP domain concepts  A suite of tools for ontology-based annotation and analysis of ERP data  A database that includes publicly available, annotated data from our NEMO ERP consortium to demonstrate application of ontology for ERP meta-analysis of results in studies of language

11.  Challenges to integration of ERP data  ERP ontology design and implementation  Ontology-based analysis of ERP data  Future directions and a call for community involvement

12. NEMO ontology design principles 1. Factor the domain to generate modular (“orthogonal”) ontologies that can be reused, integrated for other projects 2. Reuse existing ontologies (esp. foundational concepts) to define basic (low-level) concepts 3. Validate definitions of high-level concepts in bottom- up (data-driven) as well as top-down (knowledge- driven) methods 4. Collaborate with a community of experts in collaborative design, testing of ontologies

13. #1. Factoring the ERP domain 1 sec TIMESPACE FUNCTION  Modulation of ERP pattern features under different experiment conditions

14. #2. Reuse of low-level concepts BFO (Basic Formal Ontology) BFO (Basic Formal Ontology) FMA (Foundational Model of Anatomy ontology) FMA (Foundational Model of Anatomy ontology)

15. #3. Validation of high-level concepts Observed Pattern = “P100” iff  Event type is stimulus AND FUNCTIONAL  Peak latency is between 70 and 140 ms AND TEMPORAL  Scalp region of interest (ROI) is occipital AND SPATIAL  Polarity over ROI is positive (>0) FUNCTION TIME SPACE

16. Cycles of pattern definition, validation, & refinement

17. #4. Community Engagement NEMO ERP Consortium Dennis Molfese (U. Louisville) Charles Perfetti (U. Pittsburgh) Tim Curran (U. Colorado) Joseph Dien (U. Michigan) John Connolly (McMaster U.) Kerry Kilborn (Glasgow U.)

18.  Challenges to integration of ERP data  ERP ontology design and implementation  Ontology-based analysis of ERP data  Future directions and a call for community involvement

19. Ontology-based labeling of data Pattern Labels Functional attributes Temporal attributes Spatial attributes =++

20. NEMO Database and Portal www. nemo.nic.uoregon.edu Allen D. Malony

21. NEMO Database  Raw and transformed EEG and ERP data  Metadata (incl. data provenance, cognitive paradigm attributes)  Summary measures representing spatial, temporal (or spectral), and “functional” (contrast) information for each ERP pattern of interest)

22. Querying the data: OntoEngine Dejing Dou

23.  Challenges to integration of ERP data  ERP ontology design and implementation  Ontology-based analysis of ERP data  Future directions and a call for community involvement

24. Ongoing & Future Work  Refinement of first-generation NEMO ontologies (v1.0 targeted for release in July 2009)  Representation of ERP patterns in “source” (anatomical) space  Ontology-based meta-analyses of ERP data in studies of language comprehension  Outreach to neuroimaging community; call for participation in NEMO consortium

25. Funding from the National Institutes of Health (NIBIB), R01-EB007684 (Dou, Frishkoff, Malony & Tucker) NEMO Ontology Task Force Robert M. Frank (NIC) Dejing Dou (CIS) Paea LePendu (CIS) Haishan Liu (CIS) Allen Malony (NIC, CIS) Don Tucker (NIC, Psych) Acknowledgments www.nemo.nic.uoregon.edu NEMO ERP Consortium Tim Curran (U. Colorado) Dennis Molfese (U. Louisville) John Connolly (McMaster U.) Kerry Kilborn (Glasgow U.) Joe Dien (U. Michigan) Special thanks to: Jessica Turner (UCI) Angela Laird (UTHSC) Scott Makeig & Jeff Grethe (UCSD)