Feb 28, 2010 NEMO data meta-analysis: Application of NEMO analysis workflow to consortium datasets (redux)

Overview of NEMO Project Aims Design and test procedures for automated & robust ERP pattern analysis and classification Capture rules, concepts in a formal ERP ontology Develop ontology-based tools for ERP data markup Apply ERP analysis tools to consortium datasets Perform meta-analyses of consortium data Build data storage & management system

The three pillars of NEMO ERP Ontologies ERP Data ERP Database & portal Focus of this All-Hands Meeting Focus of this All-Hands Meeting

TODA Y TUTORIAL #2: Decomposition with PCA TUTORIAL #3: Segmentation with Microstates TUTORIAL #1: Viewing ERP Data in EEGLAB

TOMORROW TUTORIAL #4: Extracting ontology-based attributes And exporting to text or RDF

1.Collect ERP data sets with compatible functional attributes 2.Decompose / segment the ERP data into discrete spatio- temporal patterns for analysis & labeling 3.Mark-up patterns within each dataset: labeling of spatial & temporal characteristics (functional labels assigned in step 1) 4.Cluster patterns within data sets 5.Link labeled clusters across data sets 6.Label linked clusters (i.e., establish mappings across patterns from different dataset) Overview Steps in Meta-analysis

1.Collect ERP data sets with compatible functional attributes 2.Decompose / segment the ERP data into discrete spatio- temporal patterns for analysis & labeling 3.Mark-up patterns within each dataset: labeling of spatial & temporal characteristics (functional labels assigned in step 1) 4.Cluster patterns within data sets 5.Link labeled clusters across data sets 6.Label linked clusters (i.e., establish mappings across patterns from different dataset) Focus of 1 st Annual All-Hands Meeting

1.Collect ERP data sets with compatible functional attributes 2.Decompose / segment the ERP data into discrete spatio- temporal patterns for analysis & labeling 3.Mark-up patterns within each dataset: labeling of spatial & temporal characteristics (functional labels assigned in step 1) 4.Cluster patterns within data sets 5.Link labeled clusters across data sets 6.Label linked clusters (i.e., establish mappings across patterns from different dataset) Overview Steps in Meta-analysis

Combining Top-down and Bottom- up Methods for ERP Pattern Classification Gwen Frishkoff University of Pittsburgh Robert Frank, Haishan Liu, & Dejing Dou University of Oregon

Human Brain Mapping Current Challenges – Tracking what we know  Ontologies – Integrating knowledge to achieve high-level understanding of brain–functional mappings  Meta-analyses Important Considerations – Stay true to data (bottom-up) – Achieve high-level understanding (top-down) “Understanding without data is empty. Data without understanding are blind”

ERP Patterns 1,000 ms TIME (in 10s of ms) SPACE (Scalp Topography)

Superposition of ERP Patterns

What do we know? Observed Pattern = “P100” iff  Event type is visual stimulus AND  Peak latency is between 70 and 160 ms AND  Scalp region of interest (ROI) is occipital AND  Polarity over ROI is positive (>0) FUNCTION TIME SPACE ?

Why does it matter? Robust pattern rules would provide a good foundation for–  Development of ERP ontologies  Labeling of ERP data based on pattern rules  Cross-experiment, cross-lab meta-analyses

TOP-DOWN

BOTTOM-UP

Top-down vs. Bottom-up Top-DownBottom-Up PROS Familiar Science-driven (integrative) Formalized Data-driven (robust) CONS Informal Paradigm- affirming? Unfamiliar Study-specific results?

Combining Top-Down & Bottom-Up

A Case Study 1.Simulated ERP datasets 2.PCA & ICA methods for spatial & temporal pattern analysis 3.Spatial & temporal metrics for labeling of discrete patterns 4.Revision of pattern rules based on mining of labeled data

Simulated ERPs (n=80) P100 N100 N3 MFN P300 + NOISE

BOTTOM-UP

Pattern Analysis with PCA & ICA

ERP pattern analysis Temporal PCA (tPCA) – Gives invariant temporal patterns (new bases) – Spatial variability as input to data mining Spatial ICA (sICA) – Gives invariant spatial patterns (new bases) – Temporal variability as input to data mining Spatial PCA (sPCA) ✔ ✔ Multiple measures used for evaluation (correlation + L1/L2 norms) X

BOTTOM-UP

Measure Generation A B T1 T2 S1 S2 Vector attributes = Input to Data mining (clustering & classification) CoP CoN ROI± Centroids Input to data mining: 32 attribute vectors, defined over 80 “individual” ERPs (observations)

BOTTOM-UP

Data mining Vectors of spatial & temporal attributes as input Clustering observations  patterns (E-M accuracy >97%) Attribute selection (“Information gain”) Figure 3. Info gain results for spatial ICA. CoP CoN ✔ ± Centroids Peak Latency

Revised Rule for the “P100” Pattern = P100v iff  Event type is visual stimulus AND  Peak latency is between 76 and 155 ms AND  Positive centroid is right occipital AND  Negative centroid is left frontal SPACE TIME FUNCTION

What we’ve learned Bottom-up methods result in validation & refinement of top-down pattern rules  Validation of expert selection of temporal concepts (peak latency)  Refinement of expert specification of spatial concepts (± centroids) Alternative pattern analysis methods (e.g., tPCA & sICA) provide complementary input to bottom- up (data mining) procedures

Simulated ERP Patterns “P100”“N100”“N3”“MFN”“P300”

Applying NEMO Functional Ontology Angela Laird BrainMap Jessica Turner BIRNlex (now part of Neurolex) CogPO

Adoption of BrainMap Classification Schema (modified for NEMO specifics*) Each experiment dataset classified based on: Subject contrasts — for first phase of NEMO, focus on healthy young monolingual adults (no contrasts) Condition contrasts — Which are defined with respect to stimulus, response, and instruction attributes * See NEMO_functional.low for details

Agreed to identify more auditory ERP data sets Agreed to restructure this hierarchy: primed as a special case of ”activated” (primed = within WM/activation span  SOA<10 sec AND prime-targ lag <3 ?

Datasets for NEMO Metaanalysis #1

Proposed pipeline for first NEMO meta- analysis

LP1 dataset (semantic contrast)– tPCA results (reconstructed ERP) projected into data space

LP1 dataset ( Lexicality Contrast )– tPCA results (reconstructed ERP) projected into data space

Following are some selected factors from LP1 tPCA results to illustrate some possible issues with factor ordering & retention after rotation…

LP1 dataset (tPCA Fac 1)– Lexicality Contrast

LP1 dataset (tPCA Fac 2)– Lexicality Contrast

Some Preliminary Conclusions Factor Retention may still be an issue for us collectively to explore – Unrestricted rotation vs. data reduction prior to rotation – For unrestricted path, what number to retain at end (after rotation)? – Also for unrestricted path, how to order factors at end (after rotation) We agreed to explore these issues, try to decide on final analysis pipeline by some date in near future (TBD…)