february 11, 2011
DESCRIPTION
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 - PowerPoint PPT PresentationTRANSCRIPT
February 11, 2011
Overview of All-Hands Meeting Agenda
Gwen Frishkoff
http://nemo.nic.uoregon.edu
NEMO NIH Annual All-Hands Meeting 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
2/11/11
TODAY
NEMO NIH Annual All-Hands Meeting 3
NEMO processing pipeline
2/11/11
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 their spatial, temporal & functional characteristics– ERP Metric Extraction
Meta-Analysis Extracted ERP pattern labeling Extracted ERP pattern clustering Protocol incorporates and integrates:
ERP pattern extractionERP metric extraction/RDF generationNEMO Data Base (NEMO Portal / NEMO FTP Server)NEMO Knowledge Base (NEMO Ontology/Query Engine)
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 their spatial, temporal & functional characteristics– ERP Metric Extraction
Meta-Analysis Extracted ERP pattern labeling Extracted ERP pattern clustering Protocol incorporates and integrates:
ERP pattern extractionERP metric extraction/RDF generationNEMO 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: LexicalityPseudowords – 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 wordsParadigm 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
http://brainmapping.unige.ch/Functionalmicrostatesegmentation.htm
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)