Combining fmri, ERP and SNP data with ICA: Introduction and examples Vince D. Calhoun, Ph.D.

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1 Combining fmri, ERP and SNP data with ICA: Introduction and examples Vince D. Calhoun, Ph.D. Director, Image Analysis & MR Research The Mind Research Network Associate Professor, Electrical and Computer Engineering The University of New Mexico 1

2 Motivation Data Description fmri ERP Genetic Features & Joint Estimation Joint ICA Outline Example 1: ERP/fMRI Example 2: Multielectrode ERP/fMRI Parallel ICA SNP/FMRI SNP/ERP Conclusions EEG EEGEEG Task 1-N Task 1-N T1/T2 SNP Other * fmri fmri fmri DTI Gene Expression 2

3 Multimodal Data Collection Brain Function (spatiotemporal, task-related) EEG EEGEEG Task 1-N Task 1-N fmri fmri fmri Brain Structure (spatial) Genetic Data (spatial/chromosomal) Covariates (Age, etc.) T1/T2 SNP Other * DTI Gene Expression 3

4 A Role For Data Fusion?: lower Temporal Resolution higher lower EEG fmri Spatial Resolution higher 4

5 Motivation Data Description fmri ERP Genetic Features & Joint Estimation Joint ICA Outline Example 1: ERP/fMRI Example 2: Multielectrode ERP/fMRI Parallel ICA SNP/FMRI SNP/ERP Conclusions 5

6 EEG/ERP 6

7 Event-related Potentials (ERPs) _ Voltage + Time EEG 7

8 Genetic Variation ACTTGCATCCG ACTTGTATCCG ACTTGCATCCG ACTTG--TCCG Single Nucleotide Polymorphism (SNP) Insertion/Deletion (Indel) ACTTGGCGCGCTCCG ACTTGGCGC--TCCG ACTTGGC----TCCG Short Tandem Repeat (STR) SNPS SUBJECTS SNP data matrix 8

9 The Link between brain function and genetic coding 9

10 The First Challenge 10

11 A conceptual slide to drive home the point Gene mrna Neuroimaging Protein Cell Cell Interaction Brain Function Behavioral Component Behavioral Disorder 11

12 Motivation Data Description fmri ERP Genetic Features & Joint Estimation Joint ICA Outline Example 1: ERP/fMRI Example 2: Multielectrode ERP/fMRI Parallel ICA SNP/FMRI SNP/ERP Conclusions 12

13 Separate vs. Joint Estimation Linear mixtures with shared mixing parameter In a non-joint analysis, we maximize the likelihood functions for each modality separately Resulting in two unmixing parameters, that then have to somehow be fused together In contrast: for a joint analysis we maximize the joint likelihood function, resulting in a single fused unmixing parameter C C ( E) ( E) ( F) ( F) iv, = ic cv, and iv, = ic cv, c= 1 c= 1 ( E) p ( x ; w) ( F ) p x ; w x as x as 1 ( ) ( ) ( E ) w = arg max log p x ; w * 1 w 1 2 ( ) ( F ) w = arg max log p x ; w * 2 w 2 ( ) ( ) ( E F ) * w = arg max log p x, x ; w w w= 1/ a 13

14 What is a feature? Feature Data Lower dimensional data containing information of interest Examples: An image of activation amplitudes, A gray matter segmentation image, fractional anisotropy image Advantages Less-computationally complex/easier to model Takes advantages of existing analytic approaches Can be used to examine inter-relationships relationships between multiple data types at the subject level fmri smri 14

15 What are the features? Examples: An image of activation amplitudes, A gray matter segmentation image, fractional anisotropy image Advantages Less-computationally complex/easier to model Takes advantages of existing analytic approaches Examines inter-subject covariation of multiple data types Feature Data Modality fmri SB task fmri AOD task smri EEG AOD task Modality fmri DTI smri EEG Core-Feature Recognition related activity Encode-related activity Target-related activity Novel-related activity GM concentration WM concentration CSF concentration Target-related ERP Novel-related ERP Raw data size 260 MB 200 MB 46 MB 25MB Feature size 1.3 MB 10 MB 28 MB.45MB Compression ratio 1:200 1:20 1:1.6 1:56 15

16 Possible approaches for joint analyses Voxel-based Correlation [Worsely 1998] Straightforward, but difficult to visualize Region-based Interregional correlation [Horwitz,, et al, 1984] Structural equation modeling [McIntosh and Gonzalez [McIntosh and Gonzalez-Lima 1994; Friston et al., 2003, McIntosh & Gonzalez-Lima, 1991, Buchel & Friston, 1997] Multiple regression and extensions [e.g., Kalman filters, Buchel & Friston, 1998] Bayes networks, Dynamic Causal Modeling [Dynamic Causal Modeling, Friston, Penny, et al, 2003] Useful for model testing, does not take into account all brain regionsr Transformation-based A natural set of tools for this problem include those that transform data matrices into a smaller set of modes or components Singular value decomposition [Friston et al., 1993; Friston et al., 1996] Partial Least Squares [McIntosh, Bookstein,, et al, 1996] Canonical Variates Analysis [Strother et al, 1995] Independent Component Analysis [McKeown al., 2002] McKeown et al, 1998, Calhoun et al, 2001, Beckmann, et 16

17 Motivation Data Description fmri ERP Genetic Features & Joint Estimation Joint ICA Outline Example 1: ERP/fMRI Example 2: Multielectrode ERP/fMRI Parallel ICA SNP/FMRI SNP/ERP Conclusions [ xmod1 xmod 2 ] = A [ smod1 smod 2 ] 17

18 Blind Source Separation (ICA) ICA finds directions which maximize independence (using higher order statistics) 18

19 Stationarity 19

20 a Joint ICA Shared Feature Profile b Parallel ICA Distinct but linked Feature Profile participants Coupled feature matrix Feature 1 Feature 2 = Weights * Source matrix Map 1 Map 2 Data1 Initialization (λ 1,λ c1 ) Data2 initialization (λ 2,λ c2 ) Update W 1 based on H 1 Update W 2 based on H 2 voxels/time courses Shared Contribution Matrix Y Stop? N Update W 1 based on Correlation Select the related components Stop? Update W 2 based on Correlation Y STOP 20

21 Joint ICA [ x x ] = A [ s s ] mod1 mod 2 mod1 mod 2 Generative Model: F E F E xi x i = A sc s c Update Equation: { 2 ( ) T T 2 ( ) } F F E E Δ W = η I y u y u W smri/fmri: fmri/fmri: EEG/fMRI: Calhoun VD, Adali T, Giuliani N, Pekar JJ, Pearlson GD, Kiehl KA. (2006): A Method for Multimodal Analysis of Independent Source Differences in Schizophrenia: Combining Gray Matter Structural and Auditory Oddball Functional Data. Hum.Brain Map. 27(1): Calhoun VD, Adali T, Kiehl KA, Astur RS, Pekar JJ, Pearlson GD. (2006): A Method for Multi-task fmri Data Fusion Applied to Schizophrenia. Hum.Brain Map. 27(7): Calhoun VD, Pearlson GD, Kiehl KA. (2006): Neuronal Chronometry of Target Detection: Fusion of Hemodynamic and Eventrelated Potential Data. NeuroImage 30(2):

22 Motivation Data Description fmri ERP Genetic Features & Joint Estimation Joint ICA Outline Example 1: ERP/fMRI Example 2: Multielectrode ERP/fMRI Parallel ICA SNP/FMRI SNP/ERP Conclusions 22

23 fmri EEG Peak of Target -locked fmri signal Preprocess Feature Extraction Preprocess Feature Extraction Position Cz of Target -locked EEG signal Normalize Normalize Subject 1 Subject 2 Subject N jica Subject 1 Subject 2 Subject N Noise Removal Visualization 23

24 Joint ERP/fMRI Components Calhoun, V.D., Pearlson, G.D., and Kiehl, K.A. (2006). Neuronal Chronometry of Target Detection: Fusion of Hemodynamic and Event-related Potential Data. NeuroImage 30,

25 FMRI Snapshots (movie) ERP (temporal) Components: FMRI (spatial) Components: FMRI Image Snapshot: [ ] T= t t 1 [ ] S= s s 1 ( ) = T ( ) M t T S t F Calhoun, V.D., Pearlson, G.D., and Kiehl, K.A. (2006). Neuronal Chronometry of Target Detection: Fusion of Hemodynamic and Event-related Potential Data. NeuroImage 30, N N 25

26 ERP Snapshots = [ 1 N ] [ ] ERP (temporal) Components: T t t FMRI (spatial) Components: S= s s ERP Timecourse Snapshot: 1 N ( ) = T ( ) M v T S v E Calhoun, V.D., Pearlson, G.D., and Kiehl, K.A. (2006). Neuronal Chronometry of Target Detection: Fusion of Hemodynamic and Event-related Potential Data. NeuroImage 30,

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28 Motivation Data Description fmri ERP Genetic Features & Joint Estimation Joint ICA Outline Example 1: ERP/fMRI Example 2: Multielectrode ERP/fMRI Parallel ICA SNP/FMRI SNP/ERP Conclusions 28

29 fmri/snp connection fmri component: specific brain regions with common independent brain functionality. SNP component: a linearly weighted group of SNPs functioning together. Relationship assumption If an association of SNPs partially define a certain brain function in specific brain regions, Then, the linked fmri and SNP components should share a similar pattern of existence across subjects. fmri components SNP components Expression pattern Subject Subject 25 Expression pattern 29

30 Parallel ICA structure Data1: (fmri) Data2: (SNP) X1 X2 A1 W1 S1 S2 W2 A2 MAX : {H(Y1 Y1) ) + H(Y2 Y2) ) }, <Infomax< Infomax> Subject to: arg max g{w1,w2 ŝ1, 1,ŝ22 }, Cov(a,a ) 2 2 1i 2j g( )=Correlation(A 1,A 2) = Var(a 1i ) Var(a 2j ) 30

31 Dynamic parallel ICA optimization Dynamically constrained components: X1 ŝ1 Adaptive constraint strength: Effects on Entropy H(.) X2 ŝ2 31

32 DATA1 A1 Simulation data DATA2 A2 True component: 8/8; True correlation:0.8 Data1 Data2 Parallel ICA Regular ICA connection connection True Correlation Parallel ICA correlation Regular ICA correlation

33 Parallel ICA 1.independent components/factors of each modality 2.Inter Inter-relationship relationship between modalities The algorithm is based on classic ICA,, aiming to extract independent sources and enhance the connections between modalities. 33

34 Motivation Data Description fmri ERP Genetic Features & Joint Estimation Joint ICA Outline Example 1: ERP/fMRI Example 2: Multielectrode ERP/fMRI Parallel ICA SNP/FMRI SNP/ERP Conclusions 34

35 Example 1: SNP/fMRI Fusion Data Description: 20 Sz & 43 Healthy controls (Caucasian) fmri: one image per subject (Target activation in Auditory Oddball task SNP: one array per subject (384 SNP genotypes - -> > 367 SNPs) Control vs Patient p<0.001 SNP Z score Gene Rs AADC: aromatic L-amino L acid decarboxylase Rs ADRA2A: alpha-2a adrenergic receptor gene Rs CHRNA7: alpha 7 nicotinic cholinergic receptor Rs DISC1: disrupted in schizophrenia 1 Rs CHAT: choline acetyltransferase Rs CHRNA7: cholinergic receptor, nicotinic, alpha 7 R SCARB1: scavenger receptor class B, member 1 Rs GNAO1: guanine nucleotide binding protein (G protein), alpha activating activity polypeptide O Rs APOC3: apolipoprotein C-III Rs CHRM3: muscarinic-3 3 cholinergic receptor J. Liu, G. D. Pearlson, A. Windemuth, G. Ruano, N. I. Perrone-Bizzozero, and V. D. Calhoun, "Combining Fmri and Snp Data to Investigate Connections between Brain Function and Genetics Using Parallel Ica," Hum.Brain Map., In Press. 35

36 Subject loading associated with linked SNP and fmri components SNP 20 SZ 43 HC fmri 36

37 Sample size vs Number of SNPs Data collected at UCI 37

38 Motivation Data Description fmri ERP Genetic Features & Joint Estimation Joint ICA Outline Example 1: ERP/fMRI Example 2: Multielectrode ERP/fMRI Parallel ICA SNP/FMRI SNP/ERP Conclusions 38

39 Example 2: Genetics and P3 ERP generation Subjects: 41 healthy subjects(24 female, 17 male) EEG collected during AOD task, target/novel ERPs extracted Blood sample collected, genotyped 384 SNPs from 222 genes 6 physiological systems. 39

40 40 ADRA2A ADRA2A APOE APOE ABCB1 ABCB1 TH TH ABCB1 ABCB1 MDH1 MDH1 GNAO1 GNAO1 ADRA2A ADRA2A rs rs rs7412 rs7412 rs rs rs rs rs rs rs rs rs rs rs rs Genes Genes SNPs SNPs 0.55 Target Stimuli Novel Stimuli ADRA2A ADRA2A APOE APOE TH TH MDH1 MDH1 ABCB1 ABCB1 APOE APOE PIK3C3 PIK3C3 PIK3C3 PIK3C3 ADRA2A ADRA2A rs rs rs7412 rs7412 rs rs rs rs rs rs rs rs rs rs rs rs rs rs Genes Genes SNPs SNPs 0.47

41 Pathway Analysis 41

42 Fusion ICA Toolbox (FIT) 250+ unique downloads Funded by NIH 1 R01 EB

43 Thank You! 43

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