February 26, 2010 NEMO All-Hands Meeting: Overview of Day 1

Overview Today’s Agenda  Introductions & review of NEMO project aims (GAF)  Overview of data analysis workflow (RMF)  Intro to EEGLAB & NEMO simERP test data (GF/RMF) LUNCH  EEGLAB visualization tutorial  NEMO_Pattern_Decomposition tutorial #1  NEMO_Pattern_Decomposition tutorial #2

(Re)Introductions: Who we are NEMO “Core” (PIs & go-to people) – Dejing Dou (lead PI, CIS — Oregon) – Gwen Frishkoff (co-PI, Psychology — GSU) – Allen Malony (co-I, CIS — Oregon) – Don Tucker (co-I, Psychology — Oregon) – Robert Frank (EEG/ERP Analysis Tools) – Paea LePendu (Ontology Development) – Snezana Nikolic (Ontology Curation) – Jason Sydes (Database & Web Portal) – Haishan Liu (Grad Student, CIS)

(Re)Introductions: Who we are NEMO Consortium – John Connolly & Alex Beaverstone (McMaster U) – Tim Curran & Chris Bird (U Colorado) – Kerry Kilborn & Stephanie Connell (Glasgow U) – Dennis Molfese (U Louisville) – Chuck Perfetti (U Pittsburgh)

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

The three pillars of NEMO ERP Ontologies ERP Data ERP Database & portal

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

TODA Y TUTORIAL #2: Decomposition with PCA TUTORIAL #3: Segmentation with Microstates TUTORIAL #1: Viewing ERP Data in EEGLAB

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

NEMO principles that inform our pattern analysis strategies Current Challenges (motivations) – Tracking what we know Ontologies – Integrating knowledge to achieve high-level understanding of brain–functional mappings Meta-analyses Important Considerations (disiderata) – Stay true to data bottom-up (data-driven methods) – Achieve high-level understanding top-down (hypothesis-driven methods)

Top-down vs. Bottom-up Top-DownBottom-Up PROS Familiar Science-driven (integrative) Formalized Data-driven (robust) CONS Informal Paradigm- affirming? Unfamiliar Study-specific results?

Combining Top-Down & Bottom-Up

TOP-DOWN Traditional approach to bio-ontology dev’t Encode knowledge of concepts (=> classes, relations, & axioms that involve classes & relations) in a formal ontology (e.g., owl/rdf) NEMO owl ontologies being developed & version-tracked on Sourceforge (the main topic of our last meeting)

TOP-DOWN NEMO top-down approach NEMO emphasis on pattern rules/descriptions — way to enforce rigorous definitions Of complex concepts (patterns or “components”) that are central to ERP research

Superposition of ERP Patterns

What do we know about ERP patterns? 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 ?

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

BOTTOM-UP

Two classes of methods for NEMO_ERP_pattern_extractraction Pattern decomposition – Temporal factor analysis (tPCA, tICA) – Spatial factor analysis (sPCA, sICA) – etc. Pattern segmentation (i.e., windowing) – Microstate analysis (5 flavors – Bob will describe)

Decomposition approach PCA, ICA, dipoles etc. multiple methods for principled separation of patterns using factor-analytic approach P100 N100 fP2 P1r/ N3 P1r/ MFN P ms 170ms 200ms 280ms 400ms 600ms

Windowing/segmentation approach P100 N100 fP2 P1r/ N3 P1r/ MFN P ms 170ms 200ms 280ms 400ms 600ms Michel, et al., 2004; Koenig, 1995; Lehmann & Skrandies, 1985 Advantages over factor-analytic/ decomposition methods: Familiarity — Closer to what most ERP researchers do (manually) Less (or at least different!) concerns regarding misallocation of variance Robustness to latency diffs across subjects, conditions

1.Collect ERP data sets with compatible functional attributes 2.Decompose / segment the ERP data into discrete spatio- temporal patterns for analysis & labeling 3.Mark-up patterns within each dataset: labeling of spatial & temporal characteristics (functional labels assigned in step 1) 4.Cluster patterns within data sets 5.Link labeled clusters across data sets 6.Label linked clusters (i.e., establish mappings across patterns from different dataset) Overview Steps in Meta-analysis