Feb 28, 2010

NEMO data meta-analysis:Application of NEMO analysis

workflow to consortium datasets(redux)

http://nemo.nic.uoregon.edu

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

TODAY

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 1st 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

TO

P-

DO

WN

BO

TT

OM

-U

P

Top-down vs. Bottom-up

Top-Down Bottom-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 datasets2. PCA & ICA methods for spatial & temporal

pattern analysis3. Spatial & temporal metrics for labeling of

discrete patterns4. Revision of pattern rules based on mining of

labeled data

Simulated ERPs (n=80)

P100

N100

N3

MFN

P300

+NOISE

BO

TT

OM

-U

P

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

BO

TT

OM

-U

P

Measure Generation

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)

BO

TT

OM

-U

P

Data mining• Vectors of spatial & temporal attributes as input • Clustering observations patterns (E-M accuracy >97%)• Attribute selection (“Information gain”)

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

Proposed pipeline for first NEMO meta-

analysis

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…)