1 / 13

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

lexine
Download Presentation

February 11, 2011

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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)

More Related