February 26, 2010 NEMO All-Hands Meeting: Overview of Day 1
http://nemo.nic.uoregon.edu
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Overview Todays 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
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(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)
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(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)
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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
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The three pillars of NEMO ERP Ontologies ERP Data ERP Database
& portal
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The three pillars of NEMO ERP Ontologies ERP Data ERP Database
& portal Focus of this All-Hands Meeting Focus of this
All-Hands Meeting
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TODA Y TUTORIAL #2: Decomposition with PCA TUTORIAL #3:
Segmentation with Microstates TUTORIAL #1: Viewing ERP Data in
EEGLAB
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TOMORROW TUTORIAL #4: Extracting ontology-based attributes And
exporting to text or RDF
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NEMO principles that inform our pattern analysis strategies
Current Challenges (motivations) Tracking what we know Ontologies
Integrating knowledge to achieve high-level understanding of
brainfunctional 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 Traditional approach to bio-ontology devt 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)
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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
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Superposition of ERP Patterns
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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 ?
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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
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BOTTOM-UP
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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)
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Decomposition approach PCA, ICA, dipoles etc. multiple methods
for principled separation of patterns using factor-analytic
approach P100 N100 fP2 P1r/ N3 P1r/ MFN P300 100ms 170ms 200ms
280ms 400ms 600ms
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Windowing/segmentation approach P100 N100 fP2 P1r/ N3 P1r/ MFN
P300 100ms 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
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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