Feb 28, 2010

1. Feb 28, 2010 NEMO data meta-analysis: Application of NEMO analysis workflow to consortium datasets (redux) http://nemo.nic.uoregon.edu

2. Overview of NEMO Project Aims • Design and test procedures for automated & robust ERP pattern analysis and classification • Capture rules, concepts in a formal ERP ontology • Develop ontology-based tools for ERP data markup • Apply ERP analysis tools to consortium datasets • Perform meta-analyses of consortium data • Build data storage & management system

3. The three pillars of NEMO Focus of this All-Hands Meeting • ERP Ontologies • ERP Data • ERP Database & portal

5. TUTORIAL #1:Viewing ERP Data in EEGLAB TUTORIAL #2:Decomposition with PCA TUTORIAL #3:Segmentation with Microstates TODAY

6. TUTORIAL #4:Extracting ontology-based attributes And exporting to text or RDF TOMORROW

7. Overview Steps in Meta-analysis • Collect ERP data sets with compatible functional attributes • Decompose / segment the ERP data into discrete spatio-temporal patterns for analysis & labeling • Mark-up patterns within each dataset: labeling of spatial & temporal characteristics (functional labels assigned in step 1) • Cluster patterns within data sets • Link labeled clusters across data sets • Label linked clusters (i.e., establish mappings across patterns from different dataset)

8. Focus of 1st Annual All-Hands Meeting • Collect ERP data sets with compatible functional attributes • Decompose / segment the ERP data into discrete spatio-temporal patterns for analysis & labeling • Mark-up patterns within each dataset: labeling of spatial & temporal characteristics (functional labels assigned in step 1) • Cluster patterns within data sets • Link labeled clusters across data sets • Label linked clusters (i.e., establish mappings across patterns from different dataset)

9. Overview Steps in Meta-analysis • Collect ERP data sets with compatible functional attributes • Decompose / segment the ERP data into discrete spatio-temporal patterns for analysis & labeling • Mark-up patterns within each dataset: labeling of spatial & temporal characteristics (functional labels assigned in step 1) • Cluster patterns within data sets • Link labeled clusters across data sets • Label linked clusters (i.e., establish mappings across patterns from different dataset)

10. Combining Top-down and Bottom-up Methods for ERP Pattern Classification Gwen Frishkoff University of Pittsburgh Robert Frank, Haishan Liu, & Dejing Dou University of Oregon

11. Human Brain Mapping • Current Challenges • Tracking what we know •  Ontologies • Integrating knowledge to achieve high-level understanding of brain–functional mappings •  Meta-analyses • Important Considerations • Stay true to data (bottom-up) • Achieve high-level understanding (top-down) “Understanding without data is empty. Data without understanding are blind”

12. ERP Patterns 1,000 ms SPACE(Scalp Topography) TIME (in 10s of ms)

13. Superposition of ERP Patterns

14. What do we know? Observed Pattern = “P100” iff • Event type is visual stimulus AND • Peak latency is between 70 and 160 ms AND • Scalp region of interest (ROI) is occipital AND • Polarity over ROI is positive (>0) ? FUNCTION TIME SPACE

15. Why does it matter? • Robust pattern rules would provide a good foundation for– • Development of ERP ontologies • Labeling of ERP data based on pattern rules • Cross-experiment, cross-lab meta-analyses

16. Top-down

17. BOTTOM-UP

18. Top-down vs. Bottom-up

19. Combining Top-Down & Bottom-Up

20. A Case Study • Simulated ERP datasets • PCA & ICA methods for spatial & temporal pattern analysis • Spatial & temporal metrics for labeling of discrete patterns • Revision of pattern rules based on mining of labeled data

21. Simulated ERPs (n=80) P100 N100 N3 MFN P300 + NOISE

22. BOTTOM-UP

23. Pattern Analysis with PCA & ICA

24. ERP pattern analysis ✔ • Temporal PCA (tPCA) • Gives invariant temporal patterns (new bases) • Spatial variability as input to data mining • Spatial ICA (sICA) • Gives invariant spatial patterns (new bases) • Temporal variability as input to data mining • Spatial PCA (sPCA) ✔ X Multiple measures used for evaluation (correlation + L1/L2 norms)

25. BOTTOM-UP

26. Measure Generation Vector attributes = Input to Data mining (clustering & classification) T1 T2 S1 S2 Input to data mining: 32 attribute vectors, defined over 80 “individual” ERPs (observations) CoN ROI ± Centroids CoP A B

27. BOTTOM-UP

28. Data mining • Vectors of spatial & temporal attributes as input • Clustering observations  patterns (E-M accuracy >97%) • Attribute selection (“Information gain”) ± Centroids Peak Latency CoN ✔ CoP • Figure 3. Info gain results for spatial ICA.

29. Revised Rule for the “P100” Pattern = P100v iff • Event type is visual stimulus AND • Peak latency is between 76 and 155 ms AND • Positive centroid is right occipital AND • Negative centroid is left frontal SPACE TIME FUNCTION

30. What we’ve learned • Bottom-up methods result in validation & refinement of top-down pattern rules • Validation of expert selection of temporal concepts (peak latency) • Refinement of expert specification of spatial concepts (± centroids) • Alternative pattern analysis methods (e.g., tPCA & sICA) provide complementary input to bottom-up (data mining) procedures

31. Proposed pipeline for first NEMO meta-analysis

32. Some Preliminary Conclusions • Factor Retention may still be an issue for us collectively to explore • Unrestricted rotation vs. data reduction prior to rotation • For unrestricted path, what number to retain at end (after rotation)? • Also for unrestricted path, how to order factors at end (after rotation) • We agreed to explore these issues, try to decide on final analysis pipeline by some date in near future (TBD…)