Imaging genetics: Adventures in the dopaminergic system Christian Büchel NeuroImage Nord Hamburg University Medical School Eppendorf HBM Barcelona 2010

n Introductory remarks n Hypothesis driven association studies l Reward processing –Predictions? –Genetic influence on predictions l Novelty and memory –The role of DRD4 n General remarks Outline

Imaging genetics - Imaging neuroscience meets genetics n Commonalities n Are interested in interindividual differences n Battle the multiple comparisons problem in statistical analysis of their data

n What a Geneticist might think about Neuroscientists… l They have no clue about methodology in genetics (eg never heard of Plink) l They don’t care about the heritability of their traits l They use ridiciously small sample sizes l They stick to boring candidate gene approaches and will never find out anything exciting

n What a Neuroscientist might think about Geneticists … l They have no clue about methodology in neuroimaging l They don’t know anything about the brain (i.e. my ground breaking hypotheses) l They advocate whole genome approaches that nobody is able to interpret l They have no clue about the costs of an MR scan l Their gold standard is an uncorrected p-value of ~10^-? and think that solves the multiple comparisons problem (havn’t they used FDR before we did?)

Explaining interindividual variance Volunteer n Simple model : 1-sample t-test n Significant deactivation for the whole group in PFC n A lot of unexplained interindividual variance n Age effects? Gender effects? Personality effects? Genetics effect? Activation in PFC

Innate values – sucrose vs quinine Adapted from K. Berridge

Conditioned reward

3 7 choice anticipation outcome 0 time (s) 2 x 2 x 2 factorial design: PROBABILITY (12.5 [26%] – 50% [66%]) MAGNITUDE (1 – 5€) OUTCOME (win – lose) 20€ 15€ 20€ 21€

Anticipation phase: Expected reward magnitude & probability probability high > low y = 15 mm R y=3mm magnitude 5€ > 1€ R z = 0 mm y = 3 mm R Yacubian et al., J Neuroscience 2006 Which one would you chose ? 10€ / 70% or 100€ / 50% EV 7 EV 50

Val/ Val Met/ Met Val/ Met

Schott et al., 2006Bertolino et al., 2006 DAT- COMT interactions

n DAT n reuptake of dopamine n Variable number of tandem repeats (VNTR) polymorphism (40bp) mainly 9R and 10R n 10R l Probably higher activity DAT - COMT interactions n COMT n degrades dopamine n SN polymorphism (val158met) n met158 l Low enzyme activity Ventral striatum Bilder et al., 2004 from PFC

Effect of COMT and DAT on predictions n Genetic influence on expected value coding during anticipation 1€/p-lo1€/p-hi5€/p-lo BOLD signal (a.u.) 5€/p-hi COMT Met/Met Val/Met Val/Val DAT 9R 10R Yacubian et al., PNAS 2007

Inverted u-shape response “Phasic DA“ COMT Met/Met DAT 10R COMT Met/Met DAT 9R COMT Val/Met DAT 10R COMT Val/Met DAT 9R COMT Val/Val DAT 10R COMT Val/Val DAT 9R Slope of fMRI response Sensation seeking ≈ r=-0.77, p<0.05 Reuter et al., Nature Neuroscience 2005

n robustness l Encourage publication of null results of imaging genetics data (given adequate methodology e.g. sample size etc.) l As usual, large n is helpful l Consider split half testing (e.g. odd-even samples) Some thoughts on …

Split half testing Yacubian et al., PNAS 2007 Odd samples Even samples Whole group

n “While the sample size in this study was fairly substantial for an imaging study, it is rather small for a genetics study. The reviewer appreciates the logistical problems and cost of a very large scale imaging x genetics study, and their sample size certainly falls within the scope of others of this type. However, the authors should at least acknowledge the possibility that such studies fall into the complex trait category (looking for an effect of allelic variants in the brain induced by a behavioral paradigm is, by definition, complex) and are therefore subject to the type I error problem that has plagued behavioral genetics research.” (the unknown reviewer) n N = 105 n Consider stratified sample Opinions – Sample size

Dopamine D4 receptor polymorphisms and novelty n Novelty and Dopamine n Dopamine activity signals unexpected, salient, motivationally- relevant information n mediated via reciprocal dopaminergic projections between hippocampus, ventral striatum and dopaminergic midbrain n The role of the Dopamine D4 receptor n D4 receptor is preferentially expressed in limbic regions, cortex, basal ganglia and midbrain (SN/VTA) n association between novelty seeking and a C to T polymorphism in the DRD4 promoter region (-521C>T; rs ) in LD with the exon III VNTR n T allele associated with reduced transcription levels of 40% n Study: n N=46, stratified for rs (DRD4 -521C>T) Strange et al., in preparation

Experimental paradigm and behavioural data n Behavioural effects n Effect only for perceptually salient stimulus (-521C>T) Strange et al., in preparation

Neuroimaging results Strange et al., in preparation

n Candidate gene vs. whole genome approach l Interpretability of the results (cf. neuroimaging as a mapping technique vs neuroimaging as a neurophysiology tool) l Very strong hypotheses: You can only find what you already know l In between approaches (i.e. reducing genetic dimensionality to signal cascades that might be involved in the process (cf. small volume correction in neuroimaging) l Both can be interesting Some thoughts on …

Integrated Project FP 6: Reinforcement-related behaviour in normal brain function and psychopathology n Study design n Investigate years old adolecents across Europe since Dec 2007 n Predictive Markers for drug abuse n Neuropsychology, Behavioural testing, personality assessment, environment assessment n Brain function (Reward: MID, Impulsivity: SSRT), Brain structure: T1, DTI n Whole genome approach n Berlin, Dresden, Dublin, Hamburg, Mannheim, Nottingham, London, Paris n Current status: ~1200 volunteers included

gain-related effects: conjunction Met/Met & Val/Val p<0.001, FWE corrected Prelim. neuroimaging results: MID task n Sample n Val158met (rs4680) n Focus on homozygotes (Met/Met, Val/Val) n n=110 (Met/Met) vs. n=115 (Val/Val)

y=10 Ventral striatum (peak t=4.86) Outcome–related activation Val/Val > Met/Met p<0.001, uncorrected Peters et al., in preparation Ventral striatum Bilder et al., 2004 from PFC

n substructures l Imaging genetics: explaining interindividual variance in activation patterns of a certain brain region by a certain marker / genotype l Make sure that the marker of interest is uncorrelated to –Other markers (e.g. check indicator SNPs on other chromosomes) “Only five genes were analyzed. In order to identify substructures in a study population to rule out type I error from stratification, a more intensive genomic control analysis is necessary (approximately genes)” (from the unknown reviewer) –But also to other variables (e.g. age, personality) l Again, large n is helpful Some thoughts on …

n Combining Imaging and Genetics l A very promising approach (  endophenoytpe) l As usual there are many pitfalls l Field is in a stage of maturation n Interpretability l Control for substructure n Candidate vs whole genome approach l Both have their merits (data vs hypothesis driven) l Ideally have a large sample to do both l Entertain immediate approaches: e.g. signalling cascades l GWAS: Cooperation with an advanced functional genetics unit is helpful n Sample size l Candidate genes: Stratification from a large pool of genotyped volunteers l Multi-site data acquisition: Feasible for fMRI and sMRI Summary