July 17, 2009 NEMO Year 1: Overview & Planning

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!

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!

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)

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

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 -- – Jeff Grethe & Scott Makeig (“HeadIT” project) – Folks at OBOF ( – Folks at NCBO (

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)

Regular Meetings Schedule using Doodle Once monthly? Gwen to propose dates & times on Doodle for next month’s meeting later today Please respond to Doodle (click on link and check available days & times)

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)

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

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

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)

NEMO Central nemo.nic.uoregon.edu

Contributing to NEMO NEMO central – NEMO ftp site (EEG database) – ftp://nemo.nic.uoregon.edu/EEG_Experiments NEMO sourceforge (ontologies) – nemoontologies/current/ NEMO listserve (to note ontology “bugs” and feature requests) – NEMO wiki (discussion) – coming soon…

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)

 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

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

Complexity of Data LATENT PATTERNSMEASURED DATA Superposition

Embarrassment of Riches

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.

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

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

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

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

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…)

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

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

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

International EEG Electrode Locations Fz ITT electrode location Fz (medial frontal) Fz ITT electrode location Fz (medial frontal)

Scalp surface “regions of interest”

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

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

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) ~ after event (e.g., stimulus onset) EARLY LATE MID-LATENCY

NEMO Temporal Ontology SPAN

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

NEMO Functional Ontology Angela Laird BrainMap Jessica Turner BIRNlex (now part of Neurolex) CogPO

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

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

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

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

Protégé Software for Ontology Development

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)

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: Steffen StaabRaphael VolzWWW

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

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

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

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

Data and Ontology 48 School_IDNameAgeTitleRanking D. Dou36Dr.Assistant Professor P. 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

Ontology-based Annotation: the links 49 School_IDNameAgeTitleRanking D. Dou36Dr.Assistant Professor P. 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

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

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