February 11, 2011 Overview of All-Hands Meeting Agenda Gwen Frishkoff

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Presentation transcript:

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  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 2/11/111NEMO NIH Annual All-Hands Meeting TODAY

NEMO processing pipeline 2/11/11NEMO NIH Annual All-Hands Meeting2

NEMO Information Processing Pipeline ERP 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 their spatial, 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)

NEMO Information Processing Pipeline ERP 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 their spatial, 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)

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

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?

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

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

Alternative method for decomposition Michel, et al., 2004; Koenig, 1995; Lehmann & Skrandies, 1985

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

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?

Statistical Analyses  TANOVA  AACH (Clustering)