Overview. Multimodal Data Collection. A Role For Data Fusion?: Brain Function (spatiotemporal, task-related) EEG. fmri. Brain Structure (spatial)

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1 Multivariate Methods for Fusion of Multimodal Imaging and Genetic Data Vince D. Calhoun, Ph.D. Executive Science Officer & Director, Image Analysis & MR Research The Mind Research Network Professor, Electrical and Computer Engineering (primary), Psychiatry, Neurosciences, & Computer Science The University of New Mexico 1 Motivation Joint ICA Overview ERP/FMRI Multitask-fMRI CC-ICA/Biomarker Identification mcca smri/fmri/erp mcca+jica DTI+fMRI N-way/CASH Parallel ICA SNPs Epigenetics Conclusions 2 Multimodal Data Collection A Role For Data Fusion?: Brain Function (spatiotemporal, task-related) Brain Structure (spatial) Genetic Data (spatial/chromosomal) EEG EEG EEG Task 1-N Task 1-N T1/T2 SNP fmri fmri fmri DTI Gene Expression lower Temporal Resolution higher lower EEG fmri Spatial Resolution higher Covariates (Age, etc.) Other * 3 4

2 Possible approaches for joint analyses Why Joint/Multimodal? Voxel-based Correlation [Worsely 1998] Straightforward, but difficult to visualize Region-based Interregional correlation [Horwitz, et al, 1984] Structural equation modeling [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 regions 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 et al, 1998, Calhoun et al, 2001] 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 * ( ) 1 = arg max log ( E ; 1) w1 * ( ) 2 = arg max log ( F ; 2) w w p x w w p x w 2 * ( ) ( ) arg max log ( E F w = p x, x ; w) w 5 6 Why Multivariate? Why Independence? { 1 2} = { 1} { 2} ( 1 2) = ( 1) ( 2) E{ h( y1) h( y2) } E{ h( y1) } E{ h( y2) } Uncorrelated: E y y E y E y Independent: p y, y p y p y = Cross validation performance using the top M SNP s selected via two different methods as the basis for an N-factor principle component model. 7 PCA finds directions of maximal variance (using second order statistics) ICA finds directions which maximize independence (using higher order statistics)

3 Why Features? Feature-based ICA What is a feature? 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 between multiple data types at the subject level fmri smri 9 V. D. Calhoun, T. Adali, K. A. Kiehl, R. S. Astur, J. J. Pekar, and G. D. Pearlson, "A Method for Multi-task fmri Data Fusion Applied to Schizophrenia," Hum.Brain Map., vol. 27, pp , 2006 V. D. Calhoun and T. Adali, "Feature-based Fusion of Medical Imaging Data," IEEE Trans. Inf. Tech. in Biomedicine, vol. 13, pp. 1-10, A Family of Multivariate Methods ICA algorithms are based on cost functions which utilize the higher order statistical information J. Sui, T. Adali, Q. Yu, and V. D. Calhoun, "A Review of Multivariate Methods for Multimodal Fusion of Brain Imaging Data," Journal of Neuroscience Methods, vol. 204, pp ,

4 [ x x ] = A [ stask1 stask 2] Task1 Task 2 smri/fmri: fmri/fmri: EEG/fMRI: Generative Model: F1 F2 F1 F2 xi x i = A sc s c Update Equation: Joint ICA { 2 F ( ) ( ) } 1 F 1 T 2 F 2 F 2 T Δ W= η I y u y u W 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): Motivation Joint ICA Overview ERP/FMRI Multitask-fMRI CC-ICA/Biomarker Identification mcca smri/fmri/erp mcca+jica DTI+fMRI N-way/CASH Parallel ICA SNPs Epigenetics Conclusions fmri EEG Joint ERP/fMRI Components 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 15 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,

5 FMRI Snapshots (movie) Linked EEG/fMRI Results = [ 1 K N ] [ K ] ERP (temporal) Components: T t t FMRI (spatial) Components: S= s s 1 N ( ) ( ) ERP Timecourse Snapshot: M v = T S T v E V. D. Calhoun, L. Wu, K. A. Kiehl, T. Eichele, and G. D. Pearlson, "Aberrant Processing of Deviant Stimuli in Calhoun, V.D., Pearlson, G.D., and Kiehl, K.A. (2006). Neuronal Chronometry of Target Detection: Fusion of Schizophrenia Revealed by Fusion of FMRI and EEG Data," Acta Neuropsychiatria, vol. 22, pp , 2010 Hemodynamic and Event-related Potential Data. NeuroImage 30, Concurrent EEG/fMRI: eyes open vs eyes closed T. Eichele, V. D. Calhoun, M. Moosmann, K. Specht, M. Jongsma, R. Quiroga, H. Nordby, and K. Hugdahl, "Unmixing concurrent EEG-fMRI with parallel independent component analysis," Int. J. Psych., vol. 67, pp , These results suggest that changes in neuronal synchronization as indicated by power fluctuations in highfrequency (>1Hz) EEG rhythms such as posterior alpha are partly mediated by widespread changes in interregional low-frequency (<.1Hz) functional activities detected in fmri. They also indicate that generation of local hemodynamic responses is highly sensitive to global state changes that do not involve changes of mental effort or awareness. L. Wu, T. Eichele, and V. D. Calhoun, "Alpha Hemodynamic Responses in Eyes Open vs. Eyes Closed Resting State EEG-fMRI," in Proc. HBM, Barcelona, Spain,

6 Overview AOD SB-WM Motivation Joint ICA ERP/FMRI Multitask-fMRI CC-ICA/Biomarker Identification Preprocessing Timing/motion correction Spatial normalization Spatial smoothing Feature Extraction GLM Analysis mcca smri/fmri/erp mcca+jica DTI+fMRI N-way/CASH Patients Controls { { Normalize AOD targets Normalize SB-WM recognition Joint Feature Matrix Parallel ICA SNPs Epigenetics Conclusions jica Component Selection Test loading parameters (patients differ from controls) 8 components (MDL) Joint Histograms: fmri Overview Motivation Joint ICA ERP/FMRI Multitask-fMRI CC-ICA/Biomarker Identification mcca smri/fmri/erp mcca+jica DTI+fMRI N-way/CASH Parallel ICA SNPs Epigenetics V. D. Calhoun, T. Adali, K. A. Kiehl, R. S. Astur, J. J. Pekar, and G. D. Pearlson, "A Method for Multi-task fmri Data Fusion Applied to Schizophrenia," Hum.Brain Map., vol. 27, pp , Conclusions 24

7 Evaluating Multiple Features Identifying the best combination Auditory Oddball fmri Task Target-related activity Novel-related activity Sternberg fmri Task Recognitionrelated activity MPRAGE Gray matter segmentation 0.5 khz Standard Encode 10 sec Standard d f g j 1 khz Target Maintain 9 sec Standard Standard Standard Recognition 12 sec tone, sweep, whistle Novel Probes p f n d Standard Kullback-Leibler divergence. ( ) ( ) ( ) ln p s ξ D su = ps ξ d p ( ) ξ u ξ su { SB AOD_N AOD_T GM} 2, R F, F, F, S V. D. Calhoun and T. Adali, "Feature-based Fusion of Medical Imaging Data," IEEE Trans. Inf. Tech. in Biomedicine, vol. 13, pp. 1-10, V. D. Calhoun and T. Adali, "Feature-based Fusion of Medical Imaging Data," IEEE Trans. Inf. Tech. in Biomedicine, vol. 13, pp. 1-10, Optimal Selection of Discriminative Features Joint/Single GLM/ICA Imaging Biomarkers C = E[ln f( y)] + λ Ti 2 J. Sui, T. Adali, G. Pearlson, and V. D. Calhoun, "An ICA-based Method for the Identification of Optimal FMRI Features and Components Using Combined Group-Discriminative Techniques," NeuroImage, vol. 46, pp , D. Kim, J. Sui, S. Rachakonda, T. White, D. S. Manoach, V. P. Clark, B. C. Ho, S. C. Schulz, and V. D. Calhoun, "Identification of imaging biomarkers in schizophrenia: A coefficient-constrained independent component analysis of the Mind multi-site schizophrenia study," Journal of NeuroInformatics, vol. 8, pp ,

8 Overview Multi-set Canonical Correlation Analysis Motivation Joint ICA ERP/FMRI Multitask-fMRI CC-ICA/Biomarker Identification mcca smri/fmri/erp mcca+jica DTI+fMRI N-way/CASH Parallel ICA SNPs Epigenetics Conclusions 29 N. Correa, T. Adali, Y. Li, and V. D. Calhoun, "Canonical Correlation Analysis for Data Fusion and Group Inferences: Examining applications of medical imaging data," IEEE Signal Proc. Magazine, vol. 27, pp , Three-way MCCA Results. Overview fmri (Target) smri (Gray Matter) N. Correa, T. Adali, Y. Li, and V. D. Calhoun, "Canonical Correlation Analysis for Data Fusion and Group Inferences: Examining applications of medical imaging data," IEEE Signal Proc. Magazine, vol. 27, pp , Motivation Joint ICA ERP/FMRI Multitask-fMRI smri/fmri CC-ICA/Biomarker Identification mcca smri/fmri/erp mcca+jica DTI+fMRI N-way/CASH Parallel ICA SNPs Epigenetics Conclusions 32

9 mcca and jica are complementary approaches mcca+jica By Combining the two approaches we can: Identify both feature-common and feature-distinct connection Make full use of the cross-information of two data sets, in order to separate sources more accurately. Use mcca to link features in the two datasets, then use jica to decomposes the spatial maps Addresses limitation of both mcca & jica 33 J. Sui, G. D. Pearlson, T. Adali, K. A. Kiehl, A. Caprihan, J. Liu, J. Yamamoto, and V. D. Calhoun, "Discriminating Schizophrenia and Bipolar Disorder by Fusing FMRI and DTI in A Multimodal CCA+Joint ICA Based Model," NeuroImage, vol. 57, pp , Comparison of jica, mcca, & mcca+jica Two-way fusion of fmri/dti in BP & SZ Two modalities FMRI : Auditory oddball (target) contrast maps (AOD) DTI : Fractional anisotropy (FA) Three groups including participants 54 schizophrenia (SZ, age 37±12, 22 females) 48 bipolar disorder (BP, age 37±14, 26 females ) 62 healthy controls (HC, age 38±17, 30 females) mean FA maps mean AOD maps J. Sui, G. D. Pearlson, T. Adali, K. A. Kiehl, A. Caprihan, J. Liu, J. Yamamoto, and V. D. Calhoun, "Discriminating Schizophrenia and Bipolar Disorder by Fusing FMRI and DTI in A Multimodal CCA+Joint ICA Based Model," NeuroImage, vol. 57, pp , J. Sui, G. D. Pearlson, T. Adali, K. A. Kiehl, A. Caprihan, J. Liu, J. Yamamoto, and V. D. Calhoun, "Discriminating Schizophrenia and Bipolar Disorder by Fusing FMRI and DTI in A Multimodal CCA+Joint ICA Based Model," NeuroImage, vol. 57, pp ,

10 Results from Schizophrenia, Bipolar, and Healthy Individuals Default Mode FMRI A1 DTI A2 A1 HC A2 HC A1 SZ A1 BP A2 SZ A2 BP Abbreviations : F: frontal lobe P: parietal lobe T: temporal lobe O: occipital lobe If the two ICs have the same frame color in two modalities, they are joint ICs J. Sui, G. D. Pearlson, T. Adali, K. A. Kiehl, A. Caprihan, J. Liu, J. Yamamoto, and V. D. Calhoun, "Discriminating Schizophrenia and Bipolar Disorder by Fusing FMRI and DTI in A Multimodal CCA+Joint ICA Based Model," NeuroImage, vol. 57, pp , SLF: Superior longitudinal fasciculus CGC: Cingulum CST: Corticospinal tract UF: Uncinate fasciculus IFO: Inferior fronto-occipital fasciculus ILF: Inferior longitudinal fasciculus ATR: Anterior thalamic radiation. 38 Variation of functional spatial maps Extension of mcca+jica to N-way fusion Back-reconstruction used to estimate group specific maps X 1 fmri HC x SZ A 1 BP C 1 Contrast Map X 2 X 3 DTI smri Preprocessing->Feature HC SZ A 2 BP Fractional Anisotropy M HC SZ N Sbj A BP 3 Gray Matter Multiset-CCA x x C 2 C 3 Joint ICA Concatenation C 1 C 2 C 3 S 1 S 2 S 3 Independent sources T E( Ai, Aj ) = diag([ ri, ri..., ri ]) i, j [1,2,3] (1) (2) ( M), j, j, j M M Ι Λ1,2 Λ1,3 T Λ A= [ A1, A2, A3], E( A A) = Λ2,1 Ι Λ2,3, Λ 3,1 Λ3,2 Ι = E( AA) = diag([ r, r,... r ]) T (1) (2) ( M) T i, j i j i, j i, j i, j i, j [1,2,3], M is No. of Component Correlation of mixing matrices after multiset CCA J. Sui, G. D. Pearlson, T. Adali, K. A. Kiehl, A. Caprihan, J. Liu, J. Yamamoto, and V. D. Calhoun, "Discriminating Schizophrenia and Bipolar Disorder by Fusing FMRI and DTI in A Multimodal CCA+Joint ICA Based Model," NeuroImage, vol. 57, pp , J. Sui, H. He, Q. Yu, T. Adali, G. D. Pearlson, and V. D. Calhoun, "A Novel N-Way Brain Imaging Data Fusion Model And Its Application to Schizophrenia," in Proc. HBM, Beijing, China,

11 Pair-wise correlation of mixing coefficients IC No.!Group! HC! SZ! Group Differences HC vs SZ Number (sex) 116!(44!female)!! 97!!(24!female)!! Imaging: fmri, smri, DTI Site effects regressed out fmri-dti fmri-smri DTI-sMRI Simulation Framework for Data Fusion Copulas: a form of bivariate (or multivariate) distributions with uniformly distributed marginals Sampling from a bivariate copula leads to two sets of values (each uniformly distributed) which are associated in the way described by the specific copula Using the probability integral (or inverse cumulative distribution, icdf) we can assign any desired marginal distribution R. Silva and V. D. Calhoun, "Validating Divergence as a Tool for Assessment of Group Differences in a JICA Fusion Framework," in Proc. HBM, Quebec City, Canada, Motivation Joint ICA Overview ERP/FMRI Multitask-fMRI CC-ICA/Biomarker Identification mcca smri/fmri/erp mcca+jica DTI+fMRI N-way/CASH Parallel ICA SNPs Epigenetics Conclusions 44

12 Genetic Information Data1: (fmri) Parallel ICA: Two Goals Data2: (SNP) Genetic: single nucleotide polymorphism (SNP) X1 X2 Genetic: copy number variation (CNV) Identify Hidden Features A1 W1 W2 A2 Identify Hidden Features deletion insertion S1 S2 Epigenetic: methylation Identify Linked Features 45 MAX : {H(Y1) + H(Y2) }, <Infomax> 2 Cov(a 1i,a 2j) 2 Subject to: arg max g{w1,w2 ŝ1,ŝ2 }, g( )=Correlation(A 1,A 2) = Var(a 1i ) Var(a 2j ) J. Liu, O. Demirci, and V. D. Calhoun, "A Parallel Independent Component Analysis Approach to Investigate Genomic Influence on Brain Function," IEEE Signal Proc. Letters, vol. 15, pp , Simulation Simulation: Designed to provide a more complete understanding of Parallel ICA while applied to genomic SNP array studies. We specified the parameters for each component and input them into PLINK, an open-source whole genome association analysis toolset [ Conditions: sample size effect. case to control ratio, SNP array size effect, case-related SNP s vs. total SNP s, odds ratio, connection strength between genotype and phenotype effects Initial Proof of Concept: SNP/fMRI Fusion Data Description: 20 Sz & 43 Healthy controls fmri: one image per subject (Target activation in AOD task) SNP: one array per subject (384 SNP genotypes - -> 367 SNPs) Control vs Patient p<0.001 Simulation results suggest that parallel ICA, in general, is able to extract more accurately the components and connections than a correlation test, in particular for weak linkages. Results also indicate that the ratio of sample size to SNP size should be at least However, when the data have a low odds ratio or cases vs. controls ratio, the correlation test provides results reliably, though with lower accuracy. Liu, J, et al, IEEE Bioinformatics and Biomedicine. 2008: Philadelphia, PA. 47 SNP Z score Gene Rs AADC: aromatic L-amino 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 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., vol. 30, pp ,

13 Larger scale study of schizophrenia Schizophrenia patients and healthy controls MCIC data: Boston, Iowa, Minnesota and New Mexico Genome-wide 1M SNP data - [biallelic coding (AA, AB, or BB] fmri sensorimotor task- Block design motor response to auditory stimulation SNP data Subject control: heterozygosity, nearest neighbor, 2nd degree or closer relatives, duplication SNP control: missing genotyping ratio, minor allele frequency, Hardy-Weinberg equilibrium, linkage disequilibrium, etc. Population stratification correction: using PCA Coding (0 for AA, 1 for AB, and 2 for BB ) fmri data SPM preprocessing (alignment, normalization, filter, GLM) and contrast image Outlier subject excluded Datasets: 208 subjects withsnp ( SNPs) and fmri data (52322 voxels) Resulting Linked Component fmri component number = 8, SNP component number = 5 One pair of linked components is identified, with p-value passing Bonferroni correction fmri%component% index% SNP%component% index% r fmri4snp% P4value% 1! 3! ! 3.49E001! 2! 2! 0.042! 5.48E001! 3! 1! 0.099! 1.51E001! 4! 2! ! 4.54E002! 5! 1! 0.141! 4.16E002! 6! 5! ! 6.44E002! 7! 1! 0.178! 9.95E003! 8% 4% 0.282% 3.39E405% Bootstrap: Multiple runs of parallel-ica with 5157 randomly selected SNPs. The median correlation was 0.16 J. Chen, V. D. Calhoun, G. D. Pearlson, S. Ehrlich, J. Turner, B. C. Ho, T. Wassink, A. Michael, and J. Liu, "Multifaceted genomic risk for brain function in schizophrenia," NeuroImage, in press 49 J. Chen, V. D. Calhoun, G. D. Pearlson, S. Ehrlich, J. Turner, B. C. Ho, T. Wassink, A. Michael, and J. Liu, "Multifaceted genomic risk for brain function in schizophrenia," NeuroImage, in press 50 Identified fmri component Summary of SNP Results Conduct pathway analysis and functional annotation clustering based on identified 94 genes IPA (Ingenuity Pathway Analysis) identifies Schizophrenia of humans as one of the top biofunctions, involving 11 genes IPA also identifies a number of significant canonical pathways, four of which are related to neurotransmitter signaling David s Bioinformatics Resource reports the most significant cluster to be functionally related to synapse. A cluster annotated as cell projection is also identified Disease%and%disorder% Gene% p4value% Brain%region% Brodmann%area% Volume% Max%z4score% Postcentral!Gyrus! 1,!2,!3,!5,!7,!40,!43! 9.7/4.4! 6.23(42,032,60)/5.70(039,032,62)! Precentral!Gyrus! 4,!6! 8.2/1.7! 6.03(42,012,56)/4.40(059,012,42)! Inferior!Parietal!Lobule! 40! 3.0/0.5! 6.03(45,035,57)/4.88(045,032,57)! Medial!Frontal!Gyrus! :6,!32! 1.0/1.6! 4.82(3,03,53)/5.27(0,03,53)! Superior!Temporal!Gyrus! 21,!22,!38! 1.4/0.5! 3.24(62,012,1)/2.93(050,03,05)! Schizophrenia!of!humans! BDNF,!COMT,!DISC1,!DRD3,!ERBB4,!GAD1,!! GRIN2B,!HTR7,!NOTCH4,!NRG1,!NRG3! 6.49E009! NeurotransmiIer%signaling%pathway% Gene% p4value% GABA!receptor!signaling! GABRA4,!GABRG3,!GAD1! 2.13E003! Dopamine!receptor!signaling! COMT,!DRD3,!PPP2R2C! 7.66E003! Neuregulin!signaling! NRG1,!NRG3,!ERBB4! 1.15E002! Glutamate!receptor!signaling! GRIN2B,!GRID2! 3.74E002! FuncNonal%annotaNon%%cluster% Gene% p4value% Synapse! Cell!projecYon! GABRA4,!GABRG3,!GAD1,!GRIN2B,!GRID2,!ERBB4,! SHC4,!OTOF,!PSD3,!CTBP2! DRD3,!GAD1,!GRIN2B,!MYCBP2,!DNAH11,!WNT2,!! ESR1,!CDH13,!ALCAM,!MYO5A! 5.20E003! 9.70E002! J. Chen, V. D. Calhoun, G. D. Pearlson, S. Ehrlich, J. Turner, B. C. Ho, T. Wassink, A. Michael, and J. Liu, "Multifaceted genomic risk for brain function in schizophrenia," NeuroImage, in press 51 J. Chen, V. D. Calhoun, G. D. Pearlson, S. Ehrlich, J. Turner, B. C. Ho, T. Wassink, A. Michael, and J. Liu, "Multifaceted genomic risk for brain function in schizophrenia," NeuroImage, in press 52

14 A. smri component A (group difference) smri/snp Structural deficits in brain regions consistently implicated in previous schizophrenia reports, including frontal and temporal lobes and thalamus were related to SNPs from 16 genes, several previously associated with schizophrenia risk and/or involved in normal CNS development, including AKT, PI3k, SLC6A4, DRD2, CHRM2 and ADORA2A. Genetics and P3 ERP generation Subjects: 41 healthy subjects (24 female, 17 male) EEG collected during AOD task, target/novel ERPs extracted B. smri component B (linked, but no group difference) K. Jagannathan, V. D. Calhoun, J. Gelernter, M. Stevens, J. Liu, F. Bolognani, A. Windemuth, G. Ruano, and G. D. Pearlson, "Genetic associations of brain structural networks in schizophrenia: a preliminary study using parallel ICA," Biological Psychiatry, vol. 68, pp , Blood sample collected, genotyped 384 SNPs from 222 genes 6 physiological systems. J. Liu, K. A. Kiehl, G. D. Pearlson, N. I. Perrone-Bizzozero, and V. D. Calhoun, "Genetic Determinants of Target and Novelty Processing," NeuroImage, vol. 46, pp , ERP Topography & SNP Associations Pathway Analysis Target Stimuli 0.55 SNPs rs rs7412 rs rs rs rs rs rs Genes ADRA2A APOE ABCB1 TH ABCB1 MDH1 GNAO1 ADRA2A Novel Stimuli 0.47 SNPs rs rs7412 rs rs rs rs rs rs rs Genes ADRA2A APOE TH MDH1 ABCB1 APOE PIK3C3 PIK3C3 ADRA2A J. Liu, K. A. Kiehl, G. D. Pearlson, N. I. Perrone-Bizzozero, and V. D. Calhoun, "Genetic Determinants of Target and Novelty Processing," NeuroImage, vol. 46, pp , J. Liu, K. A. Kiehl, G. D. Pearlson, N. I. Perrone-Bizzozero, and V. D. Calhoun, "Genetic Determinants of Target and Novelty Processing," NeuroImage, vol. 46, pp ,

15 Classification with SNP & fmri Overview Subjects fmri data Gene data 20 Patients 20 Healthy Controls Auditory Oddball Task 367 SNPs SVM Classification fmri + Gene fmri Gene Motivation Joint ICA ERP/FMRI Multitask-fMRI CC-ICA/Biomarker Identification mcca Dataset Number of Training Subjects Number of Testing Subjects smri/fmri/erp mcca+jica DTI+fMRI N-way/CASH Parallel ICA SNPs Epigenetics H. Yang, J. Liu, J. Sui, G. Pearlson, and V. D. Calhoun, "A Hybrid Machine Learning Method for Fusing fmri and Genetic Data to Classify Schizophrenia," Frontiers in Human Neuroscience, vol. 4, pp. 1-9, Conclusions 58 Methylation Sex Correction Methylation Sex Correction Sex difference: Genomic ~27,000 sites from 23 pairs of chromosomes 130 subjects (heavy drinker 33 females, 97 males, age 31.3!9.7) Goal: Association with gender, age, BMI, alcohol use, cigarette use, marijuana use, depression, stress, etc. Results: Methylation correlation Gender Age* BMI* Max_drinks* MJ_use* J. Liu, M. Morgon, K. Hutchison, E. Claus, and V. D. Calhoun, "A Study of the Influence of Sex on Genome Wide Methylation," PLoS ONE, vol. 5, pp. 1-8, *: after sex effect correction J. Liu, M. Morgon, K. Hutchison, E. Claus, and V. D. Calhoun, "A Study of the Influence of Sex on Genome Wide Methylation," PLoS ONE, vol. 5, pp. 1-8,

16 Ancestry effect on methylation CNV and substance abuse Effect of double deletion at 22q on alcohol use disorder severity and cue-elicited BOLD response in the precuneus J. Liu, K. Hutchison, M. Morgan, N. I. Perrone-Bizzozero, J. Sui, and V. D. Calhoun, "Identification of Genetic and Epigenetic Factors Contributing to Population Structure," PLoS ONE, vol. 5, pp. 1-8, R. A. Yeo et al PLoS ONE, vol. 6, R. A. Yeo et al, Biological Psychiatry, In Press. J. Liu et al. Plos One, Under Review. 62 Software Left Hemisphere Visual Stimuli Onset unique downloads Funded by: 1R01EB unique downloads Funded by: R01EB

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