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)