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February 11, 2011

http://nemo.nic.uoregon.edu. February 11, 2011. Overview of All-Hands Meeting Agenda Gwen Frishkoff. Summary of Agenda. Day 1 : Data Analysis New NEMO decomposition ( Exercise #1 : tsPCA) New NEMO segmentation ( Exercise #2 : MSA) Day 2 : Database & Ontology

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February 11, 2011

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  1. http://nemo.nic.uoregon.edu February 11, 2011 Overview of All-Hands Meeting Agenda Gwen Frishkoff

  2. Summary of Agenda • Day 1: Data Analysis • New NEMO decomposition (Exercise #1: tsPCA) • New NEMO segmentation (Exercise #2: MSA) • Day 2: Database & Ontology • New NEMO portal (Exercise #3: metadata entry) • New Metric & RDF Generation (Exercise #4) • Ontology-based analysis (Exercise #5: classification of data in Protégé) • Day 3: Meta-analysis • Within-experiment stats • Between-experiment stats TODAY NEMO NIH Annual All-Hands Meeting

  3. NEMO processing pipeline NEMO NIH Annual All-Hands Meeting

  4. NEMO Information Processing PipelineERP Pattern Extraction, Identification and Labeling • Obtain ERP data sets with compatible functional constraints • NEMO consortium data • Decompose / segment ERP data into discrete spatio-temporal patterns • ERP Pattern Decomposition / ERP Pattern Segmentation • Mark-up patterns with theirspatial, temporal & functional characteristics • ERP Metric Extraction • Meta-Analysis • Extracted ERP pattern labeling • Extracted ERP pattern clustering • Protocol incorporates and integrates: • ERP pattern extraction • ERP metric extraction/RDF generation • NEMO Data Base (NEMO Portal / NEMO FTP Server) • NEMO Knowledge Base (NEMO Ontology/Query Engine)

  5. NEMO Information Processing PipelineERP Pattern Extraction, Identification and Labeling • Obtain ERP data sets with compatible functional constraints • NEMO consortium data • Decompose / segment ERP data into discrete spatio-temporal patterns • ERP Pattern Decomposition / ERP Pattern Segmentation • Mark-up patterns with theirspatial, temporal & functional characteristics • ERP Metric Extraction • Meta-Analysis • Extracted ERP pattern labeling • Extracted ERP pattern clustering • Protocol incorporates and integrates: • ERP pattern extraction • ERP metric extraction/RDF generation • NEMO Data Base (NEMO Portal / NEMO FTP Server) • NEMO Knowledge Base (NEMO Ontology/Query Engine)

  6. Target Meta-Analyses • Meta-Analysis #1: Semantic Priming • Unrelated – Related Words (Visual) • Meta-Analysis #2: Lexicality • Pseudowords – Words (Visual) • Meta-Analysis #3: Episodic Memory/Repetition (Words) • Old/Repeated – New/Unrepeated Words

  7. Meta-Analysis Goals • Proof of Concept — It is possible to label ERP patterns from different experiments, labs using a coherent framework • New Discoveries & Hypothesis Testing — Comparison of frontal negativities across exeriments will help to address basic questions • Is N3 always modulated by semantic priming? (cf. LIFG controversy) • Are MFN and N4 distinct physiogical & functional components? • Do pseudowords always elicit greater MFN compared with real words?

  8. Coding of Function Adaptation of BrainMap taxonomy (Laird, et al., 2005) • Fixed across datasets: • Stimulus: visually presented words • Paradigm class: lexical/semantic discrimination • ERP pattern analysis (2D centroid based segmentation) • Variable across datasets: • EEG acquisition (e.g., #electrodes) • Stimulus timing (e.g., prime–target SOA) • Task instructions: lexical vs. semantic decision

  9. Meta-Analysis #1:Semantic (Unrelated – Related)

  10. Alternative method for decomposition http://brainmapping.unige.ch/Functionalmicrostatesegmentation.htm Michel, et al., 2004; Koenig, 1995; Lehmann & Skrandies, 1985

  11. Meta-Analysis #2: Lexical (Pseudoword– Word)

  12. Labeling discrete patterns • Two basic methods • Top-down (expert/rule-driven) • Bottom-up (data-driven) • Pros & Cons to both  need to combine • What’s the right mix?

  13. Statistical Analyses • TANOVA • AACH (Clustering)

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