GWP Magazines Business Resource & Lifestyle Issue 28 - Jan/Feb 2010.
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Feb 28, 2010
NEMO data meta-analysis:Application of NEMO analysis
workflow to consortium datasets(redux)
http://nemo.nic.uoregon.edu
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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
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The three pillars of NEMO
• ERP Ontologies• ERP Data• ERP Database & portal
Focus of this All-Hands Meeting
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TODAY
TUTORIAL #2:Decomposition with PCA
TUTORIAL #3:Segmentation with Microstates
TUTORIAL #1:Viewing ERP Data in EEGLAB
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TOMORROW
TUTORIAL #4:Extracting ontology-based attributes
And exporting to text or RDF
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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
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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
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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
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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
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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”
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ERP Patterns
1,000 ms
TIME(in 10s of ms)
SPACE(Scalp Topography)
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Superposition of ERP Patterns
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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?
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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
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TO
P-
DO
WN
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BO
TT
OM
-U
P
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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?
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Combining Top-Down & Bottom-Up
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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
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Simulated ERPs (n=80)
P100
N100
N3
MFN
P300
+NOISE
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BO
TT
OM
-U
P
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Pattern Analysis with PCA & ICA
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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
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BO
TT
OM
-U
P
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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)
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BO
TT
OM
-U
P
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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
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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
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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
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Proposed pipeline for first NEMO meta-
analysis
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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…)