Meta Analysis First Steps Data Analysis Metric Generation

Meta Analysis First Steps Data Analysis, Metric Generation and Extracted Pattern Annotation

Project Goals for Meta-Analysis • Subgoal #1: Complete first statistical meta-analysis of ERP patterns from NEMO consortium datasets – Target first paper submission by May 2009 • Subgoal #2: Compare pattern mappings from different meta-analyses & establish functionally relevant links between patterns – Lexical, semantic, & memory-related ERP – Establish meaningful pattern classes, hierarchies based on meta analyses results

Meta Summary of ERP Data Meta Analysis Analyze Mark-up Label Cluster Link Label Linked Clusters Publish (rinse and repeat)

Meta Analysis Steps • Obtain ERP data sets with compatible functional constraints – NEMO consortium data • Decompose / segment the ERP data into discrete spatio-temporal patterns – PCA / ICA / Microstate Segmentation • Mark-up patterns with their categorical, functional and spatio-temporal characteristics – NEMOautolabel • Label patterns • Cluster patterns within data sets • Link labeled clusters across data sets • Label linked clusters • Publish

Datasets for Meta-analysis #1

Datasets for Meta-analysis #2 & #3

Techniques for Decomposing / Segmenting ERP Data Into Discrete Spatio-Temporal Patterns • Component Separation – PCA: Principal Components Analysis • Established protocol with supporting literature – Dien, Frishkoff, Kayser & Tenke • Applied to 9 consortium data sets from 3 separate labs – ICA: Independent Components Analysis • Established protocol with supporting literature, though less extensive than PCA ERP research – Makeig et al • Mixed results / interpretation difficulties w. r. t. consortium data • Automated Windowing / Microstate Segmentation • Established protocol with supporting literature – Lehman, Koenig, Murray • In progress: currently adding to NEMOautolabel

Microstate Segmentation Overview Simulated Microstates • Simulated data set of 4 distinct, finite duration, topographies (microstates). Note topographies are partially overlapping

Microstate Segmentation Overview Microstate Boundaries • Microstate border probability function (MSBPF): Quantifies probability of topographic change as a function of time

Overview of PCA • Temporal PCA – Variables: Time samples – Observations: Channel waveforms across conditons + subjects – Relationship matrix quantifies temporal correlations Basis of approach for decomposing and statistically quantifying NEMO consortium data • Spatial PCA – – Variables: Channel locations Observations: Spatial topographies across conditons + subjects Relationship matrix quantifies spatial correlations Problematic due to high spatial overlap of patterns from volume conduction Not used due to concerns of misallocation of variance / factor splitting

PCA Decomposition Protocol for Analysis of Consortium Data • Dien PCA Toolbox / ERP Toolkit • Read. Seg. Raw / PCAto. Raw to import from and export to EGI segmented simple-binary files • Temporal PCA algorithms – – – Covariance relationship matrix Kaiser factor loading normalization Retain all factors prior to any and all rotations Varimax rotation followed by Promax relaxation Statistically analyze 25 post-rotation factors, sorted in order of decreasing projected variance (based on Fac. Var)

PCAto. Raw / ERP PCA Toolbox • PCAto. Raw run-time parameters • PCAto. Raw invokes Dien ERP PCA Toolbox

PCAto. Raw / ERP PCA Toolbox • Npraw data PCA decomposition summary

PCA Decomposition Protocol Factor Retention – Part I • Pre-rotation factor retention – “The problem of the number of components” • Scree test – Linear scale may underestimate factor retention • Parallel test – Compare scree of experimental data to scree of random data of equal dimensions • Full pre-rotation retention – Kayser & Tenke proposal – Factor retention, pre-rotation, affects both explained variance and rotation outcome – Full pre-rotation retention eliminates effect of retention subjectivity on rotation outcome

PCA Decomposition Protocol Factor Retention – Part 2 • Post-rotation factor retention – Determine number of retained components for adequate reconstruction of scalp recorded ERP • Retained components represent majority of ERP variance – Factors are sorted on Fac. Var, the fraction of data variance accounted for by each individual factor – Default post-rotation sort order of ERP PCA Toolkit – Statistical analysis, via NEMOautolabel, performed on retained factors – Flag retained factors with “robust” variance or high relative Global Field Power – Flag retained factors containing spatiotemporal characteristics of target patterns

PCAto. Raw / ERP PCA Toolbox NPraw. raw Test Dataset Results • PCAto. Raw output files: –. log: –. mat: –. fig: –. raw: Summary run statistics MATLAB workspace variables Pre- and post-rotation factor scree plots Factor loadings projected back to channel space § One set for each conditon / cell § Grand average (_G. raw) or subject-specific (_S##. raw)

PCAto. Raw / ERP PCA Toolbox NPraw. raw Test Dataset Results • Examine NPraw_G. raw factor waveforms, in scalp-surface space, at each channel across conditions (Topo. Plot Mode) Factor 1 waveforms (0 -900 ms; 0. 1 uv/mm). Note condition effects / factor separation at centropariertal and anterior ventral sites

PCAto. Raw / ERP PCA Toolbox NPraw. raw Test Dataset Results • Examine NPraw_G. raw factor topographies, in scalp-surface space, at peak intensity across conditions (Topo. Map Mode) Factor 1 scalp-surface topographies, 600 ms post-stimulus, for the 4 NPraw experimental conditions (L to R): Con. Final, Con. Mid, Incon. Final, Incon. Mid

NEMOautolabel Marking up ERP Components / Microstates: NEMO_data • • Mark-up observed patterns (components / microstates) with user-specified information on the experimental procedure and subject group Each mark-up element (NEMOautolabel) has a unique NEMOautolabel ID and will map to a corresponding element in the NEMO ontology ERP_Comp. Analysis. Method Cond_Stan Expt. ID EEG_Montage Event_Modality Sess. ID Event_Type Cell. No Stim_Type Cell. Label ERP_Obs. ID Subject_Group ERP_Observed. Pattern NEMOautolabel_Name NEMOautolabel_Def NEMOautolabel_ID NEMOlex_Name NEMOlex_ID Expt. ID represents "experiment ID" and specifies the experimental procedure and subject group. AL: 0000003 experiment_id NM: 0000059

NEMOautolabel Marking up ERP Components / Microstates: NEMO_data • • Mark-up observed patterns (components / microstates) with their temporal characteristics MATLAB-based functions extract temporal metrics for each condition, subject and component / microstate – Data driven – Harnesses expert-knowledge: Domain experts specify the temporal characteristics of interest Ti_Max_round TI_Begin TI_End TI_Dur_round NEMOautolabel_Name NEMOautolabel_Def NEMOautolabel_ID NEMOlex_Name NEMOlex_ID Ti_Max specifies for each temporal component the time point of its peak absolute intensity, in milliseconds. AL: 0000019 ERP_pattern_peak_latency NM: 0000047

NEMOautolabel Marking up ERP Components / Microstates: NEMO_data • • Mark-up observed patterns (components / microstates) with their spatial characteristics MATLAB-based functions extract spatial metrics for each condition, subject and component / microstate – Data driven – Harnesses expert-knowledge: Domain experts specify the spatial characteristics of interest COP_X 2 d Lat. Index_Threshold EGICh_COP COP_Y 2 d Laterality_COP ITTCh_COP CON_X 2 d Lat. Index_COP ROI_COP CON_Y 2 d ROInolat_COP EGICh_CON COP_X 3 d Laterality_CON ITTCh_CON COP_Y 3 d Lat. Index _CON ROI_CON COP_Z 3 d ROInolat_CON CON_X 3 d CON_Y 3 d CON_Z 3 d NEMOautolabel_Name NEMOautolabel_Def NEMOautolabel_ID NEMOlex_Name NEMOlex_ID ITTCh_COP International 10 -10 electrode location closest to the component pair's center-ofpositivity xy-coordinate pair (COP_X 2 d, COP_Y 2 d), in L 2 norm, on a montage-specific 2 -D flat map of scalp-surface electrode locations. AL: 0000036 TBA

References PCA Dien, J. (1998). Addressing misallocation of variance in principal components analysis of event-related potentials. Brain Topogr, 11(1), 43 -55. Dien, J. , & Frishkoff, G. A. (2005). Introduction to principal components analysis of event-related potentials. In T. Handy (Ed. ), Event-Related Potentials: A Methods Handbook. (pp. 189 -208). Cambridge, MA: MIT Press. Dien, J. , Beal, D. J. , & Berg, P. (2005). Optimizing principal components analysis of event-related potentials: matrix type, factor loading weighting, extraction, and rotations. Clin Neurophysiol, 116(8), 1808 -1825. Dien, J. (2006). Progressing towards a consensus on PCA of ERPs. Clin Neurophysiol, 117(3), 699 -702; author reply 703 -697. Dien, J. , Khoe, W. , & Mangun, G. R. (2007). Evaluation of PCA and ICA of simulated ERPs: Promax vs. Infomax rotations. Hum Brain Mapp, 28(8), 742 -763. Dien, J. (2009). Evaluating two-step PCA of ERP data with Geomin, Infomax, Oblimin, Promax, and Varimax rotations. Psychophysiology. Kayser, J. , & Tenke, C. E. (2003). Optimizing PCA methodology for ERP component identification and measurement: theoretical rationale and empirical evaluation. Clin Neurophysiol, 114(12), 2307 -2325. Kayser, J. , & Tenke, C. E. (2005). Trusting in or breaking with convention: towards a renaissance of principal components analysis in electrophysiology. Clin Neurophysiol, 116(8), 1747 -1753.

References ICA Dien, J. , Khoe, W. , & Mangun, G. R. (2007). Evaluation of PCA and ICA of simulated ERPs: Promax vs. Infomax rotations. Hum Brain Mapp, 28(8), 742 -763. Microstate Analysis Michel, C. M. , Murray, M. M. , Lantz, G. , Gonzalez, S. , Spinelli, L. , & Grave de Peralta, R. (2004). EEG source imaging. Clin Neurophysiol, 115(10), 2195 -2222. Murray, M. M. , Brunet, D. , & Michel, C. M. (2008). Topographic ERP analyses: a step-by-step tutorial review. Brain Topogr, 20(4), 249 -264. Koenig, T. , Kochi, K. , & Lehmann, D. (1998). Event-related electric microstates of the brain differ between words with visual and abstract meaning. Electroencephalogr Clin Neurophysiol, 106(6), 535 -546. Koenig, T. , & Lehmann, D. (1996). Microstates in language-related brain potential maps show noun-verb differences. Brain Lang, 53(2), 169 -182. Lehman, D. , & Skrandies, W. (1985). Spatial analysis of evoked potentials in man - A review. Progress in Neurobiology, 23, 227 -250. Pizzagalli, D. , Lehmann, D. , Koenig, T. , Regard, M. , & Pascual-Marqui, R. D. (2000). Face-elicited ERPs and affective attitude: brain electric microstate and tomography analyses. Clin Neurophysiol, 111(3), 521 -531.

References Annotating functional attributes Fox, P. T. , Laird, A. R. , Fox, S. P. , Fox, P. M. , Uecker, A. M. , Crank, M. , et al. (2005). Brain. Map taxonomy of experimental design: description and evaluation. Hum Brain Mapp, 25(1), 185 -198. Spatial & temporal metric generation Handy, T. (2005). Basic Principles of ERP Quantification. In T. Handy (Ed. ), Event-Related Potentials: A Methods Handbook (pp. 33– 56). Cambridge, MA: MIT Press. Luck, S. (2005). An Introduction to the Event-Related Potential Technique Boston, MA: The MIT Press. Otten, L. J. , & Rugg, M. D. (2005). Interpreting Event-Related Brain Potentials. In T. Handy (Ed. ), Event. Related Potentials: A Methods Handbook (pp. 3– 16). Cambridge, MA: MIT Press. Picton, T. W. , Bentin, S. , Berg, P. , Donchin, E. , Hillyard, S. A. , Johnson, R. , Jr. , et al. (2000). Guidelines for using human event-related potentials to study cognition: recording standards and publication criteria. Psychophysiology, 37(2), 127 -152.