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Imaging genetics: Adventures in the dopaminergic system Christian Büchel. HBM Barcelona 2010. [email protected] NeuroImage Nord Hamburg University Medical School Eppendorf. Outline. Introductory remarks Hypothesis driven association studies Reward processing Predictions?

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Imaging genetics adventures in the dopaminergic system christian b chel

Imaging genetics: Adventures in the dopaminergic systemChristian Büchel

HBM Barcelona


[email protected]

NeuroImage NordHamburg University Medical School Eppendorf


  • Introductory remarks

  • Hypothesis driven association studies

    • Reward processing

      • Predictions?

      • Genetic influence on predictions

    • Novelty and memory

      • The role of DRD4

  • General remarks

Imaging genetics - Imaging neuroscience meets genetics

  • Commonalities

    • Are interested in interindividual differences

    • Battle the multiple comparisons problem in statistical analysis of their data

  • What a Geneticist might think about Neuroscientists…

    • They have no clue about methodology in genetics (eg never heard of Plink)

    • They don’t care about the heritability of their traits

    • They use ridiciously small sample sizes

    • They stick to boring candidate gene approaches and will never find out anything exciting

  • What a Neuroscientist might think about Geneticists …

    • They have no clue about methodology in neuroimaging

    • They don’t know anything about the brain (i.e. my ground breaking hypotheses)

    • They advocate whole genome approaches that nobody is able to interpret

    • They have no clue about the costs of an MR scan

    • 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

Activation in PFC


  • Simple model : 1-sample t-test

    • Significant deactivation for the whole group in PFC

    • A lot of unexplained interindividual variance

    • Age effects? Gender effects? Personality effects? Genetics effect?

Innate values – sucrose vs quinine

Adapted from K. Berridge





2 x 2 x 2 factorial design:

PROBABILITY (12.5 [26%] – 50% [66%])

MAGNITUDE (1 – 5€)

OUTCOME (win – lose)







time (s)

Anticipation phase: Expected reward magnitude & probability

magnitude 5€ > 1€


y = 3 mm

z = 0 mm



probability high > low

y = 15 mm


Which one would you chose ?

10€ / 70% or 100€ / 50%

EV 7 EV 50

Yacubian et al., J Neuroscience 2006




DAT- COMT interactions

Schott et al., 2006

Bertolino et al., 2006

DAT - COMT interactions

from PFC

  • DAT

    • reuptake of dopamine

    • Variable number of tandem repeats (VNTR) polymorphism (40bp) mainly 9R and 10R

    • 10R

      • Probably higher activity

  • COMT

    • degrades dopamine

    • SN polymorphism (val158met)

    • met158

      • Low enzyme activity

Ventral striatum

Bilder et al., 2004




BOLD signal (a.u.)







Effect of COMT and DAT on predictions

  • Genetic influence on expected value coding during anticipation

Yacubian et al., PNAS 2007

Slope of fMRI response

COMT Val/Val


COMT Val/Val


COMT Val/Met


COMT Val/Met


COMT Met/Met


COMT Met/Met


Sensation seeking

r=-0.77, p<0.05

Inverted u-shape response

“Phasic DA“

Reuter et al., Nature Neuroscience 2005

Some thoughts on …

  • robustness

    • Encourage publication of null results of imaging genetics data (given adequate methodology e.g. sample size etc.)

    • As usual, large n is helpful

    • Consider split half testing (e.g. odd-even samples)

Split half testing

Odd samples

Whole group

Even samples

Yacubian et al., PNAS 2007

Opinions – Sample size

  • “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 = 105

  • Consider stratified sample

Dopamine D4 receptor polymorphisms and novelty

  • Novelty and Dopamine

    • Dopamine activity signals unexpected, salient, motivationally-relevant information

    • mediated via reciprocal dopaminergic projections between hippocampus, ventral striatum and dopaminergic midbrain

  • The role of the Dopamine D4 receptor

    • D4 receptor is preferentially expressed in limbic regions, cortex, basal ganglia and midbrain (SN/VTA)

    • association between novelty seeking and a C to T polymorphism in the DRD4 promoter region (-521C>T; rs1800955) in LD with the exon III VNTR

    • T allele associated with reduced transcription levels of 40%

  • Study:

    • N=46, stratified for rs1800955 (DRD4 -521C>T)

Strange et al., in preparation

Experimental paradigm and behavioural data

  • Behavioural effects

    • Effect only for perceptually salient stimulus (-521C>T)

Strange et al., in preparation

Neuroimaging results

Strange et al., in preparation

Some thoughts on …

  • Candidate gene vs. whole genome approach

    • Interpretability of the results (cf. neuroimaging as a mapping technique vs neuroimaging as a neurophysiology tool)

    • Very strong hypotheses: You can only find what you already know

    • In between approaches (i.e. reducing genetic dimensionality to signal cascades that might be involved in the process (cf. small volume correction in neuroimaging)

    • Both can be interesting

Integrated Project FP 6:Reinforcement-related behaviour in normal brain function and psychopathology

  • Study design

    • Investigate 2000+ 14 years old adolecents across Europe since Dec 2007

    • Predictive Markers for drug abuse

      • Neuropsychology, Behavioural testing, personality assessment, environment assessment

      • Brain function (Reward: MID, Impulsivity: SSRT), Brain structure: T1, DTI

      • Whole genome approach

    • Berlin, Dresden, Dublin, Hamburg, Mannheim, Nottingham, London, Paris

    • Current status: ~1200 volunteers included

Prelim. neuroimaging results: MID task

  • Sample

    • Val158met (rs4680)

    • Focus on homozygotes (Met/Met, Val/Val)

    • n=110 (Met/Met) vs. n=115 (Val/Val)

gain-related effects: conjunction Met/Met & Val/Val

p<0.001, FWE corrected

Outcome–related activation

from PFC

Ventral striatum (peak t=4.86)


Val/Val > Met/Met

p<0.001, uncorrected

Ventral striatum

Bilder et al., 2004

Peters et al., in preparation

Some thoughts on …

  • substructures

    • Imaging genetics: explaining interindividual variance in activation patterns of a certain brain region by a certain marker / genotype

    • 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 50-100 genes)”(from the unknown reviewer)

      • But also to other variables (e.g. age, personality)

    • Again, large n is helpful


  • Combining Imaging and Genetics

    • A very promising approach ( endophenoytpe)

    • As usual there are many pitfalls

    • Field is in a stage of maturation

  • Interpretability

    • Control for substructure

  • Candidate vs whole genome approach

    • Both have their merits (data vs hypothesis driven)

    • Ideally have a large sample to do both

    • Entertain immediate approaches: e.g. signalling cascades

    • GWAS: Cooperation with an advanced functional genetics unit is helpful

  • Sample size

    • Candidate genes: Stratification from a large pool of genotyped volunteers

    • Multi-site data acquisition: Feasible for fMRI and sMRI