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August 9, 2010

August 9, 2010. NEMO Teleconference: Progress summary & Year II planning. http://nemo.nic.uoregon.edu. Overview Agenda. Planning for 2 nd Annual All-Hands meeting Year 2 milestones Experiment Management System (database & portal)

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August 9, 2010

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  1. August 9, 2010 NEMO Teleconference: Progress summary &Year II planning http://nemo.nic.uoregon.edu

  2. Overview Agenda • Planning for 2nd Annual All-Hands meeting • Year 2 milestones • Experiment Management System (database & portal) • Relational (RDF) database & use of ontologies to classify, query data • Encoding ERP patterns and pattern rules in ontology • Next steps for data analysis: • Clustering within and across dataset • Source localization • Milestones to shoot for prior to next AHM

  3. Overview Agenda • Planning for 2nd Annual All-Hands meeting • Year 2 milestones • Experiment Management System (database) • Relational (RDF) database & use of ontologies to classify, query data • Encoding ERP patterns and pattern rules in ontology • Next steps for data analysis: • Clustering within and across dataset • Source localization • Milestones to shoot for prior to next AHM

  4. Overview Agenda • Planning for 2nd Annual All-Hands meeting • Year 2 milestones • Experiment Management System (database & portal) • Relational (RDF) database & use of ontologies to classify, query data • Encoding ERP patterns and pattern rules in ontology • Next steps for data analysis: • Clustering within and across dataset • Source localization • Milestones to shoot for prior to next AHM

  5. The big picture

  6. The big picture

  7. Three areas where partner sites can contribute over next 6-8 weeks • Ontology: Help vet ERP pattern definitions (based on knowledge of literature) • What: Get list of ERP patterns from Gwen & provide feedback (via email/sourceforge tracker) • When: Immediately • Data: Help test and refine data analysis tools • What: Configure SVN software link to sourceforge. Download & test latest NEMO pattern extraction (PCA/ICA/microstate) and metric extraction tools • When: Next week(ish)… Bob to send email when ready • Database: Help test and refine portal interface • What: Get link to latest MI portal mockup from Jason & give feedback (via email/sourceforge tracker). Later: upload actual datasets. • When: Next week(ish)… Jason to send email when ready

  8. Overview Agenda • Planning for 2nd Annual All-Hands meeting • Year 2 milestones • Experiment Management System (database & portal) • Relational (RDF) database & use of ontologies to classify, query data • Encoding ERP patterns and pattern rules in ontology • Next steps for data analysis: • Clustering within and across dataset • Source localization • Milestones to shoot for prior to next AHM

  9. MI Checklist:MIBBI/MINI collaboration

  10. CARMEN Project: Development of MINI Frank Gibson & colleagues

  11. MINEMO: MI for NEMO (Experiment Level I)

  12. MINEMO: MI for NEMO (Experiment Level II)

  13. MINEMO: MI for NEMO (Subject Group I)

  14. MINEMO: MI for NEMO (Subject Group II)

  15. MINEMO: MI for NEMO (Task Level)

  16. MINEMO: MI for NEMO (Stimulus Level I)

  17. MINEMO: MI for NEMO (Stimulus Level II)

  18. MINEMO: MI for NEMO (Response Level I)

  19. MINEMO: MI for NEMO (Contrast Level I)

  20. MINEMO: MI for NEMO (Contrast Level II)

  21. MINEMO: MI for NEMO (EEG Data Level I)

  22. MINEMO: MI for NEMO (EEG Data Level II)

  23. MINEMO: MI (Expt metadata) Portal Interface Note: This is on wish list… • Definition • Concept label & ID • Data Entry instructions • (if not obvious from definition)

  24. Overview Agenda • Planning for 2nd Annual All-Hands meeting • Year 2 milestones • Experiment Management System (database) • Relational (RDF) database & use of ontologies to classify, query data • Encoding ERP patterns and pattern rules in ontology • Next steps for data analysis: • Clustering within and across dataset • Source localization • Milestones to shoot for prior to next AHM

  25. What’s an ontology & what’s it for? “Highly semantically structured” What does this mean & what does it buy us?

  26. The practical goal is to formulate pattern definitions and encode them in ontology. We can then use pattern rules to classify data, and ultimately to revise them based on meta-analysis results Observed Pattern = “N400” iff • Event type is onset of meaningful stimulus (e.g., word) AND • Peak latency is between 300 and 500 ms AND • Scalp region of interest (ROI) is centroparietal AND • Polarity over ROI is negative(>0)

  27. Ontologies for high-level, explicit representation of domain knowledge  theoretical integration • RDF to support principled data classfication & meta-analysis • practical integration

  28. Coding knowledge of patterns in ontology Partial rule for classification of a particular ERP observation as an instance of the N2 class

  29. What is RDF and what is it for? RDF graph (data model)

  30. The big picture

  31. Typical tabular representation of summary ERP data ERP observation (pattern extracted from “raw” ERP data) Peak latency measurement

  32. Annoting ERP data: From Spreadsheets to RDF Pattern Labels Temporal attributes Spatial attributes + Functional attributes = + Concepts coded in OWL  NEMO ontology Data coded in RDF  NEMO database HOW? Robert M. Frank

  33. Annotating Data in RDF • Data Annotation • The process of marking up or “tagging” data with meaningful symbols; tags may come from ontology linked to a URI • URI (Uniform Resource Identifier) • A compact sequence of characters that identifies an abstract or physical resource (typically located on the Web) • RDF (Resource Description Framework) • RDF is a directed, labeled graph (data model) for representing information (typically on the Web) *See Glossary (http://www.seiservices.com/nida/1014080/ReadingRoom.aspx)

  34. The “RDF Triple” • In RDF form: <001> <type> <NEMO_0000093> • Subject – Predicate – Object • In natural language: • The data represented in row A is an instance of (“type” = “is a”) some ERP pattern. • That is, measurements (cells) are “about” ERP patterns (rows). • In graph form:

  35. RDF Triple #2 • In RDF form: <002> <type> <NEMO_0745000> • Subject – Predicate – Object • In natural language = • The data represented in cell Z (row A, column 1) is an instance of (“is a”) a peak latency temporal measurement (i.e., the time at which the pattern is of maximal amplitude)

  36. RDF Triple #3 • This graph represents an assertion, expressed in RDF = • <001> <is_peak_latency_measurement_of> <002> • The data represented in cell Z is a temporal property of the ERP pattern represented in row A

  37. RECALL: Pattern definition is encoded in the ontology (not in RDF data rep!) HOW?

  38. An ERP pattern rule (temporal criterion) as it appears in Protégé Protégé rendering OWL/RDF rendering

  39. RECALL: The goal is to formulate pattern definitions, use them to classify data, and ultimately to revise them based on meta-analysis results Observed Pattern = “N400” iff • Event type is onset of meaningful stimulus (e.g., word) AND • Peak latency is between 300 and 500 ms AND • Scalp region of interest (ROI) is centroparietal AND • Polarity over ROI is negative(>0)

  40. In RDF terms… This is the inference that we want to make

  41. Linking data to ontology — Step 1:A worked example (Formulating rule) First, we write the ERP pattern rule as follows: IF(1) 001 type ERP_spatiotemporal pattern • and (2) 002 type peak_latency_measurement_datum • and (3) 002 is_peak_latency_measurement_of 001, • and (4) 002 has_numeric_value X, • and (5) 500 >= X >= 300 (X has datatype decimal) (in reality, there are spatial, temporal, & functional criteria…) THEN(6) 001 type N400_pattern

  42. Linking data to ontology — Step 2:Formulating rule as SPARQL query Next, we convert the rule to a SPARQL query by replacing natural language terms with corresponding URI (tags) from NEMO ontology • type  rdf:type • ERP_spatiotemporal_pattern NEMO_0000093 • peak_latency_measurement  NEMO_0745000 • is_measurement_of  NEMO_9278000 • has_numeric_value  NEMO_7943000

  43. Linking data to ontology — Step 3:Executing the SPARQL query Finally, we load Virtuoso’sSPARQL interface http://nemo.nic.uoregon.edu:8890/sparql & then cut and paste the query into the Query textbox and click Run Query. …. And Virtuoso returns the following results (for ex): As a result, we can deduce that ERP observations 0002, 0003, 0004, & 0006 are N400 pattern instances… QED 

  44. Overview Agenda • Planning for 2nd Annual All-Hands meeting • Year 2 milestones • Experiment Management System (database) • Relational (RDF) database & use of ontologies to classify, query data • Encoding ERP patterns and pattern rules in ontology • Next steps for data analysis: • Clustering within and across dataset • Source localization • Milestones to shoot for prior to next AHM

  45. Need to vet ERP pattern rules in ontology!

  46. Overview Agenda • Planning for 2nd Annual All-Hands meeting • Year 2 milestones • Experiment Management System (database) • Relational (RDF) database & use of ontologies to classify, query data • Encoding ERP patterns and pattern rules in ontology • Next steps for data analysis: • Clustering within and across dataset • Source localization • Milestones to shoot for prior to next AHM

  47. Overview Agenda • Planning for 2nd Annual All-Hands meeting • Year 2 milestones • Experiment Management System (database) • Relational (RDF) database & use of ontologies to classify, query data • Encoding ERP patterns and pattern rules in ontology • Next steps for data analysis: • Clustering within and across dataset • Source localization • Milestones to shoot for prior to next AHM

  48. What next? Commitments from individual sites; task assignments • Ontology: Help vet ERP pattern definitions (based on knowledge of literature) • What: Get list of ERP patterns from Gwen & provide feedback (via email/sourceforge tracker) • When: Immediately • Data: Help test and refine data analysis tools • What: Configure SVN software link to sourceforge. Download & test latest NEMO pattern extraction (PCA/ICA/microstate) and metric extraction tools • When: Next week(ish)… Bob to send email when ready • Database: Help test and refine portal interface • What: Get link to latest MI portal mockup from Jason & give feedback (via email/sourceforge tracker). Later: upload actual datasets. • When: Next week(ish)… Jason to send email when ready

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