August 20, 2009 NEMO Year 1: From Theory to Application — Ontology-based analysis of ERP data

Overview Agenda ICBO highlights (5 mins) Logistics (5 mins) ERP pattern analysis methods (20 mins) ERP measure generation (10 mins) Linking measures to ontology (10 mins) Data annotation (deep, ontology-based) (10 mins) Action items highlighted in lime green!

Overview Agenda ICBO highlights (5 mins) Logistics (5 mins) ERP pattern analysis methods (20 mins) ERP measure generation (10 mins) Linking measures to ontology (10 mins) Data annotation (deep, ontology-based) (10 mins) Action items highlighted in lime green!

First International Conference on Biomedical Ontologies (ICBO’09)

First International Conference on Biomedical Ontologies (ICBO’09) High-level issues and "best practices" for onto dev't Tools that may be of use for NEMO Potential collaborations Practical Questions/Issues to resolve

Overview Agenda ICBO highlights (5 mins) Logistics (5 mins) ERP pattern analysis methods (20 mins) ERP measure generation (10 mins) Linking measures to ontology (10 mins) Data annotation (deep, ontology-based) (10 mins) Action items highlighted in lime green!

NEMO “to do” items Identify "point person" at each site who will be responsible for contributing feedback on NEMO wiki and ontologies and for uploading data and testing matlab-based tools for data markup – Please provide name & contact info for this person in an Bookmark NEMO website & explore links under “Collaboration” (more to come next time on how specifically you can contribute)

Overview Agenda ICBO highlights (5 mins) Logistics (5 mins) ERP pattern analysis methods (20 mins) ERP measure generation (10 mins) Linking measures to ontology (10 mins) Data annotation (deep, ontology-based) (10 mins) Action items highlighted in lime green!

ERP Pattern Analysis An embarrassment of riches – A wealth of data – A plethora of methods A lack of integration – How to compare patterns across studies, labs? – How to do valid meta-analyses in ERP research? A need for robust pattern classification – Bottom-up (data-driven) methods – Top-down (science-driven) methods

Ontologies for high- level, explicit representation of domain knowledge  theoretical integration Ontologies to support principled mark-up of data (inc. ERP patterns)  practical integration

NEMO principles that inform our pattern analysis strategies Current Challenges (motivations) – Tracking what we know Ontologies – Integrating knowledge to achieve high-level understanding of brain–functional mappings Meta-analyses Important Considerations (disiderata) – Stay true to data bottom-up (data-driven methods) – Achieve high-level understanding top-down (hypothesis-driven methods)

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

TOP-DOWN Traditional approach to bio-ontology dev’t Encode knowledge of concepts (=> classes, relations, & axioms that involve classes & relations) in a formal ontology (e.g., owl/rdf) NEMO owl ontologies being developed & version-tracked on Sourceforge (the main topic of our last meeting)

TOP-DOWN NEMO top-down approach NEMO emphasis on pattern rules/descriptions — way to enforce rigorous definitions Of complex concepts (patterns or “components”) that are central to ERP research

Superposition of ERP Patterns

What do we know about ERP patterns? 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 a good foundation for–  Development of ERP ontologies  Labeling of ERP data based on pattern rules  Cross-experiment, cross-lab meta-analyses

BOTTOM-UP

Two classes of methods for ERP pattern analysis Pattern decomposition – Temporal factor analysis (tPCA, tICA) – Spatial factor analysis (sPCA, sICA Windowing/segmentation – Microstate analysis (use global field “maps”; compute “global field dissimilarity” between adjacent maps to determine where there are significant shifts in topography Focus today (already implemented & almost ready for YOU to test )

Decomposition approach PCA, ICA, dipoles etc. multiple methods for principled separation of patterns using factor-analytic approach P100 N100 fP2 P1r/ N3 P1r/ MFN P ms 170ms 200ms 280ms 400ms 600ms

Windowing/segmentation approach P100 N100 fP2 P1r/ N3 P1r/ MFN P ms 170ms 200ms 280ms 400ms 600ms Michel, et al., 2004; Koenig, 1995; Lehmann & Skrandies, 1985 Advantages over factor-analytic/ decomposition methods: Familiarity — Closer to what most ERP researchers do (manually) Less (or at least different!) concerns regarding misallocation of variance Robustness to latency diffs across subjects, conditions

What we’ve done (to date…) Implemented sPCA, tPCA, sICA, & microstate analysis Tested & evaluated sPCA, tPCA & sICA (following Dien, Khoe, & Mangun, 2008) using simulated ERP data Explored two different approaches to pattern classification & labeling (the step AFTER decomposition)

1. Classification of Datasets 13+ EEG datasets collected so far (from Tucker/GF, Perfetti/GF, Curran). Waiting on datasets from Molfese, Kilborn, & maybe Dien. [using EGI data only for pilot study] Experimental paradigms/contrasts fall into 3 general categories (some expt yield multiple contrasts):  Semantic priming: prime–target relatedness (6)  Word vs. Nonword recognition (6)  Word/Nonword repetition: episodic memory,  “old/new” comparisons(7) Need studies for a reasonable meta-analysis (GF collecting more data from partners)

1. Data preprocessing 1. filter & segment data 2. detect & reject artifacts 3. interpolate bad channels 4. average across trials w/in subjects 5. manual detection of bad channels 6. interpolate bad channels 7. re-reference montage (PARE) 8. baseline-correct (200ms)

2. Component Analysis Our current practice (NOT set in stone!) - Step 1. Apply eigenvalue decomposition method (eg., tPCA) - Step 2: Rotate ALL latent factors (unrestricted PCA) - Step 3: Retain fairly large number of factors based on log of scree - Step 4: Let ontology-based labeling (next slide) help determine which factors to keep and analyze!

3. Component Labeling NEXT MAJOR CHALLENGE: How to tune pattern rules (particularly TI-max begin and end) to fit each individual dataset. Data mining on results from different component analyses? (Note mining of tPCA data won’t help to refine temporal criteria.)

4. Meta-analysis (next milestone!!) Apply pattern decomposition & labeling to NEMO consortium datasets Identify one experimental contrast for each analysis Compute Effect Size (ES) estimates for each study Run mixed effects analysis: test homogeneity of variance across studies if rejected, then test effects of variables that differ across studies, laboratories (e.g., nature of stimuli, task, subjects)

ERP Meta-analysis goals 1.Demonstrate working NEMO consortium 2.Demonstrate application of BrainMap-like taxonomy for classification of functional (experimental) contrasts. 3.Show that ERP component analysis, measure generation, and component labeling tools can be used on a large scale 4.** Show that combination of bottom-up and top-down methods for refining pattern rules can be used to tune rules for detecting target ERP patterns across different datasets 5.** Show that we can (semi-)automatically indentify analogous patterns across datasets (follows from 4), enabling us to carry out statistical meta-analyses ** harder problems to discuss…

A Case Study with real data (CIN’07 paper) 1.Real 128-channel ERP data 2.Temporal PCA used for pattern analysis 3.Spatial & temporal metrics for labeling of discrete patterns 4.Revision of pattern rules based on mining of labeled data

Example: Rule for “P100” For any n, FA n = PT 1 iff – temp criterion #1: 70ms > TI-max (FA n ) < 170ms AND – spat criterion #1 : SP-r (FA n, SP(PT 1 )) >.7 AND – func criterion #1: EVENT (FA n ) = stimon AND – func criterion #2: MODAL (EV) = visual AND

Example of output [1] values for summary measures (for one subject, one/six expt conditions)

Example of output [2] Matches to spatial, temporal & functional criteria for one subject & one/six experimental conditions

Summary results for Rule #1

A Case Study with simulated ERPs (HBM’08 tak) 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

Simulated ERP Datasets (in DipSim) Dipole Simulator (P. Berg)

Patrick Berg’s Dipole Simulator Simulated ERP data: Creating individual ERPs Random jitter in intensity NO temporal jitter NO spatial jitter

BOTTOM-UP

Pattern Analysis with PCA & ICA (Decomposition approach)

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

New inputs to NEMO PATTERN DEFINITIONS (Revised) “P100”1.70 ms < TI-max ≤ 140 ms 2. ROI = Occipital 3. IN-mean (ROI) > 0 “N100”1.141 ms < TI-max ≤ 220 ms 2. ROI = Occipital 3. IN-mean (ROI) < 0 “N3c”1.221 ms < TI-max ≤ 260 ms 2. ROI = Anterior Temporal 3. IN-mean (ROI) < 0 “MFN”1.261 ms < TI-max ≤ 400 ms 2. ROI = Mid Frontal 3. IN-mean (ROI) < 0 “P300”1.401 ms < TI-max ≤ 600 ms 2. ROI = Parietal 3. IN-mean (ROI) > 0 SPATIALTEMPORAL

What we’ve learned (so far…) 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

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

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

Alternative Spatial Metrics Scalp (ROI) “regions-of-intrest” Positive and negative “centroids” (topographic source & sink) CPOS CNEG

TPCA “Grand Average” Results Misallocation when there is high temporal, but low spatial correlation

SICA “Grand Average” Results Misallocation when there is high temporal and high spatial correlation

Labeling data Matches to pattern rules for one subject & one experiment condition

NeuroElectroMagnetic Ontologies (NEMO) 1. Statistical measure generation (basic concepts)  Temporal: peak latency, duration, onset of pattern  Spatial: electrode location, scalp “region,” ±centroid  Functional: subject, stimulus, task & measurement attributes 2. Labeling data (ERP patterns) w/ basic concepts 3. Analysis & mining of labeled data to derive pattern rules (complex concepts) data-driven 4. Coding of concepts in data-driven ontology

Overview Agenda ICBO highlights (5 mins) Logistics (5 mins) ERP pattern analysis methods (20 mins) ERP measure generation (10 mins) Linking measures to ontology (10 mins) Data annotation (deep, ontology-based) (10 mins) Action items highlighted in lime green!

BOTTOM-UP

Statistical Measure Generation Temporal – Peak latency – Duration (cf. spectral measures) Spatial (topographic) – Scalp regions-of-Interest (ROI) – Positive & negative centroids Functional (experimental) – Concepts borrowed from BrainMap (Laird et al.) where possible

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)

Overview Agenda ICBO highlights (5 mins) Logistics (5 mins) ERP pattern analysis methods (20 mins) ERP measure generation (10 mins) Linking measures to ontology (10 mins) Data annotation (deep, ontology-based) (10 mins) Action items highlighted in lime green!

Automated ontology-based labeling of ERP data Pattern Labels Functional attributes Temporal attributes Spatial attributes =++ Robert M. Frank Concepts encoded in NEMO_Data.owl

NEMO Data Ontology: Where ontology meets epistemology Ontology for Biological Investigations (OBI) & Information Artifact Ontology (IAO) Ontology for Biological Investigations (OBI) & Information Artifact Ontology (IAO)

Overview Agenda ICBO highlights (5 mins) Logistics (5 mins) ERP pattern analysis methods (20 mins) ERP measure generation (10 mins) Linking measures to ontology (10 mins) Data annotation (deep, ontology-based) (10 mins) Action items highlighted in lime green!