1. July 17, 2009 NEMO Year 1: Overview & Planning http://nemo.nic.uoregon.edu

2. Overview Agenda Introductions & go-to people (7 mins) Scheduling regular teleconferences (3 mins) Review of project aims (15 mins) Contributing to NEMO -- overview (10 mins) (website, wiki, database) Overview of current ontologies (25 mins) Overview of tools for labeling data (next time) Action items highlighted in lime green!

3. Overview Agenda Introductions & go-to people (7 mins) Scheduling regular teleconferences (3 mins) Review of project aims (15 mins) Contributing to NEMO -- overview (10 mins) (website, wiki, database) Overview of current ontologies (25 mins) Overview of tools for labeling data (next time) Action items highlighted in lime green!

4. Introductions: Who we are (1/3) NEMO “Core” (PIs & go-to people) – Dejing Dou (lead PI, CIS) – Gwen Frishkoff (co-PI, Psychology) – Allen Malony (co-I, CIS) – Don Tucker (co-I, Psychology) – Paea LePendu* (Ontology Development) – Robert Frank* (EEG/ERP Analysis Tools) – Jason Sydes* (Database & Wed Portal) – Haishan Liu (Grad Student, CIS) Matt Cranor & Charlotte Wise (Grants Admin)

5. Introductions: Who we are (2/3) NEMO Consortium – John Connolly (McMaster U) – Tim Curran (U Colorado) – Joe Dien (U Maryland) – Kerry Kilborn (Glasgow U) – Dennis Molfese (U Louisville) – Chuck Perfetti (U Pittsburgh) Please send link to your website to Jason (jasonsydes@gmail.com)

6. Introductions: Who we are (3/3) External collaborators (NEMO ontologies & database development; integration with other projects in BO community) – Jessica Turner (fBIRN & “CogPO” project) – Angela Laird (BrainMap & “CogPO” project) – Maryann Martone (NIF -- www.neuinfo.org) – Jeff Grethe & Scott Makeig (“HeadIT” project) – Folks at OBOF (http://www.obofoundry.org/)? – Folks at NCBO (http://bioontology.org/)?

7. Overview Agenda Introductions & go-to people (7 mins) Scheduling regular teleconferences (3 mins) Review of project aims (15 mins) Contributing to NEMO -- overview (10 mins) (website, wiki, database) Overview of current ontologies (25 mins) Overview of tools for labeling data (next time)

8. Regular Meetings Schedule using Doodle http://www.doodle.com/ Once monthly? Gwen to propose dates & times on Doodle for next month’s meeting later today Please respond to Doodle email (click on link and check available days & times)

9. Overview Agenda Introductions & go-to people (7 mins) Scheduling regular teleconferences (3 mins) Review of project aims (15 mins) Contributing to NEMO -- overview (10 mins) (website, wiki, database) Overview of current ontologies (25 mins) Overview of tools for labeling data (next time)

10. Overview of Project Aims 1. Design and test procedures for automated ERP pattern analysis and classification (*) – “top-down” initial definitions of pattern rules, concepts (hypotheses) – “bottom-up” data mining for pattern validation & refinement 2. Capture rules, concepts in a formal ERP ontology (TODAY) 3. Develop ontology-based tools for ERP data markup (*) 4. Apply ERP analysis tools to consortium datasets (*) 5. Perform meta-analyses of consortium data (*) 6. Build relational database to store ontology-based annotations and to support complex reasoning over annotated data  “ontology database” 7. Build data storage & management system  “EEG database” (*) Proposed focus of next month’s meeting

11. The three pillars of NEMO Ontologies (TODAY) Ontology-based analysis tools (next time?) Ontology database & portal

12. Overview Agenda Introductions & go-to people (7 mins) Scheduling regular teleconferences (3 mins) Review of project aims (15 mins) Contributing to NEMO -- overview (10 mins) (website, wiki, database) Overview of current ontologies (25 mins) Overview of tools for labeling data (next time)

13. NEMO Central nemo.nic.uoregon.edu

14. Contributing to NEMO NEMO central – http://nemo.nic.uoregon.edu NEMO ftp site (EEG database) – ftp://nemo.nic.uoregon.edu/EEG_Experiments NEMO sourceforge (ontologies) – http://nemoontologies.svn.sourceforge.net/viewvc/ nemoontologies/current/ NEMO listserve (to note ontology “bugs” and feature requests) – http://sourceforge.net/mail/?group_id=263320 NEMO wiki (discussion) – coming soon…

15. Overview Agenda Introductions & go-to people (7 mins) Scheduling regular teleconferences (3 mins) Review of project aims (15 mins) Contributing to NEMO -- overview (10 mins) (website, wiki, database) Overview of current ontologies (25 mins) Overview of tools for labeling data (next time)

16.  Why (what problem are we trying to solve?)  What (what IS an ontology anyway, and how can it help address this problem?)  How (ERP ontology design and implementation methods in NEMO) NEMO ontology development

17. Why are there so few statistical meta-analyses in ERP research? The Problem

18. Complexity of Data LATENT PATTERNSMEASURED DATA Superposition

19. Embarrassment of Riches

20. 410 ms 450 ms 330 ms Peak latency 410 ms Loose Semantics! Will the “real” N400 please step forward? Sample Database Query: Show me all the N400 patterns in the database.

21. Putative “N400”- labeled patterns Parietal N400 ≠ ≠ Frontal N400 Parietal P600

22. What’s an ontology and how does it help us address the lack of integration in ERP research?

23. Knowledge  Semantically structured (Taxonomy, CMap, Ontology,…) Information  Syntactically structured (Tables, XML, RDF,…) Data  Minimally structured or unstructured Ontologies to support VALID pooling of ERP patterns across datasets  theoretical integration

24. Why ontologies in particular? Rich, explicit, computable semantics…. But takes time to build!

25. How we’re going to build ontologies for NEMO […and apply them to real data – next time] FIRST RELEASE OF ONTOLOGIES IN AUGUST (DON’T BOTHER TO COMMENT ON OLD VERSIONS…)

26. NEMO ontology design principles (following OBO “best practices”) 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 (upper & mid-level) concepts 3. Validate definitions of complex concepts using 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

27. Factoring the ERP domain 1 sec TIMESPACE FUNCTION  Modulation of pattern features (time, space, amplitude) under different experiment conditions

28. ERP spatial subdomain 1 sec TIMESPACE FUNCTION  Modulation of ERP pattern features under different experiment conditions

29. International 10-10 EEG Electrode Locations Fz ITT electrode location Fz (medial frontal) Fz ITT electrode location Fz (medial frontal)

30. Scalp surface “regions of interest”

31. NEMO Spatial Ontology BFO (Basic Formal Ontology)  UPPER BFO (Basic Formal Ontology)  UPPER FMA (Foundational Model of Anatomy ontology)  MIDLEVEL FMA (Foundational Model of Anatomy ontology)  MIDLEVEL SNAP

32. ERP temporal subdomain 1 sec TIMESPACE FUNCTION  Modulation of ERP pattern features under different experiment conditions

33. Early ( “exogenous” ) vs. Late ( “endogenous” ) ERP processes ~0-150 ms after event (e.g., stimulus onset) 501 ms or more after event (e.g., stimulus onset) ~151-500 after event (e.g., stimulus onset) EARLY LATE MID-LATENCY

34. NEMO Temporal Ontology SPAN

35. ERP functional subdomain 1 sec TIMESPACE FUNCTION  Modulation of ERP pattern features under different experiment conditions

36. NEMO Functional Ontology Angela Laird BrainMap Jessica Turner BIRNlex (now part of Neurolex) CogPO http://brainmap.org/scribe/index.html

37. Reconsistituting the ERP domain… 1 sec TIMESPACE FUNCTION  Modulation of ERP pattern features under different experiment conditions

38. NEMO ERP Ontology 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

39. PATTERNDEFINITIONS (Revised) “P100”1.70 ms < TI-max ≤ 140 ms 2. ROI = Occipital 3. IN-mean (ROI) > 0 “N100”1.141 ms < TI-max ≤ 220 ms 2. ROI = Occipital 3. IN-mean (ROI) < 0 “N3c”1.221 ms < TI-max ≤ 260 ms 2. ROI = Anterior Temporal 3. IN-mean (ROI) < 0 “MFN”1.261 ms < TI-max ≤ 400 ms 2. ROI = Mid Frontal 3. IN-mean (ROI) < 0 “P300”1.401 ms < TI-max ≤ 600 ms 2. ROI = Parietal 3. IN-mean (ROI) > 0 SPATIALTEMPORAL

40. Cycles of pattern definition, validation, & refinement (MORE ON THIS NEXT TIME…) Frishkoff, Frank, et al., 2007

41. Protégé Software for Ontology Development

42. Overview Agenda Introductions & go-to people (7 mins) Scheduling regular teleconferences (3 mins) Review of project aims (15 mins) Contributing to NEMO -- overview (10 mins) (website, wiki, database) Overview of current ontologies (25 mins) Overview of RDF/OWL annotation (Dejing Dou)

43. An Introduction for Annotation Annotation and Markup – HTML – XML/RDF/OWL Ontology-based Annotation – Ontologies and Data Tables. – Links of Data and Ontological Concepts – Applications Reference: Siegfried Handschuh, Steffen Staab, Raphael Volz: On deep annotation. WWW 2003: 431-438Steffen StaabRaphael VolzWWW 2003 43

44. Annotation and Markup The idea of Annotation or Markup came from WWW. HTML, Hypertext Markup Language, is still a well-used markup language. For example, your personal homepage are very possibly written in HTML: Dejing Dou’s Homepage …. The tags (annotators) (e.g., title, body..) are well defined and computer can process and display the text, images …in preferred places, color and font size. 44

45. XML/RDF/OWL The XML, eXtensible Markup Language, lets users self-define new tags: Dejing Dou Assistant Professor Paea Lependu …. I defined those new tags (faculty, name, ranking…) but computer do not know the meaning or the semantics of them. Using similar syntax, RDF (Resource Definition Framework) and OWL (Web Ontology Language) allow users to define the semantics of tags as ontologies. 45

46. A Simple Ontology of University 46 People Faculty Staff Student Assistant Prof. Associate Prof. Full Prof. String Name Graduate Student Undergraduate Is_a String title Number age

47. Sample Data on the People 47 School_IDNameAgeTitleRanking 950499879D. Dou36Dr.Assistant Professor 950699887P. LePendu34 Graduate Student ……………

48. Data and Ontology 48 School_IDNameAgeTitleRanking 950499879D. Dou36Dr.Assistant Professor 950699887P. LePendu34 Graduate Student …………… People Faculty Staff Student Assistant Prof. Associate Prof. Full Prof. String Graduate Student Undergraduate Is_a String title Number age Nam e

49. Ontology-based Annotation: the links 49 School_IDNameAgeTitleRanking 950499879D. Dou36Dr.Assistant Professor 950699887P. LePendu34 Graduate Student …………… People Faculty Staff Student Assistant Prof. Associate Prof. Full Prof. String Nam e Graduate Student Undergraduate Is_a String title Number age

50. Results In RDF/OWL Computer can process it automatically: Dejing Dou 36 Dr. Paea Lependu 34 … 50

51. What we can do? Search – Example: return all data rows related to faculty (i.e., all data of assistant, associate and full professors will be returned.) Query – Examples: Give the average age of assistant and associate professors only? What are the difference of age range between faculty and students? In NEMO, we will develop ontology-based tools to automatically answer: Return all PCA factors related to “P100” and “N100” only (Search) What are the difference of range of time latency between Lab A and Lab B’s “P100” patterns in the same paradigm X ? (Query) 51