1. Methods
Participants
Sixty-four right-handed healthy volunteers of German descent were recruited through
university faculties and community facilities. None of the participants fulfilled any criteria of
mental disorders and has no familial history of schizophrenia or affective disorders. The
group of rs1006737 AA homozygotes and AG heterozygotes did not differ from rs1006737
GG homozygotes in age (AA/AG: M(SD) = 29.80 (13.67); GG: M(SD) = 26.09(8.43);
F(1,62) = 1.83, p >.10), sex (AA/AG: 12 male, 21 female; GG: M(SD) = 15 male, 16 female;
χ²(1)= .947; p >.10). The allele frequencies were in Hardy-Weinberg-Equilibrium (Chi2 =
0.19, df = 1, p = 0.77).
All study participants underwent the Structured Clinical Interview for DSM-IV1 to verify
exclusion criteria regarding mental disorders. Exclusion criteria for study participation were:
any criteria of a current or past mental diagnosis, current or past neurological disease, regular
medication or MRI measurement contraindications such as metallic implants, tattoos, cardiac
risk factors and bypasses. Written informed consent was obtained from all subjects before
study participation. The study was approved by the local research ethics committee and
adhered to the Declaration of Helsinki.
DNA extraction and genotyping
Genomic DNA was prepared from whole blood according to standard procedures. rs1006737
was genotyped using a TaqMan 5' nuclease assay. Accuracy was assessed by duplicating 15
% of the original sample, and reproducibility was 100%.
Experimental Paradigm
The reversal learning paradigm used in the present study was adapted from previously used
ones that have been shown to activate a cortico-limbic brain network related to reward and
punishment2,3,4. In comparison to the previous studies, we used playcards instead of abstract
or concrete symbols and monetary reward instead of points in order to realize a real gambling
situation and to foster amygdala activation to reward. In a study comprising 33 young, healthy
adults we could indeed show that our adaptation of the paradigm elicits activation in brain
structures that are associated with the reward and emotional processing5.
Subjects were instructed to choose one of two playing cards displayed on the screen and they
were told that they could learn a rule concerning which card is correct. While sticking to that
rule, they would win in most cases and thus maximise their total gain. Subjects were also
informed that the rule might change from time to time (reversal) and if they were sure of that
change, they should change their behaviour accordingly. Two playing cards from the French
desk (anglo-american) were presented for 3000 ms to the left and right of a fixation cross and
subjects were told to make their choice within these 3000 ms. Subsequently a feedback
concerning the amount of money (ranging from 0.10 to 1.00 €) won or lost, was presented for
2000 ms on the screen. Two inter-trial-intervals, the first jittered between 700 and 3400 ms,
the second jittered between 675 and 2025 ms, were administered to enable precise
desynchronization from the repetition time (TR=2700 ms) and to sufficiently sample the
hemodynamic response function (see Figure 1 for an outline of the experimental design).
In line with previous studies, correct responses were followed by a positive and negative
feedback with a ratio of 80:20². Negative feedback after a correct response is termed
“probabilistic error” and could either lead to a behavioural switch concerning the choice of
the cards or not. False responses (“spontaneous error”) were always followed by negative
feedback. After the change of reward contingencies we discriminate between two events. First
false responses after rule reversal, which are not followed by a behavioural change (preceding
reversal error). Second the last false response after rule reversal followed by behavioural
adaptation, which is termed “behavioural switching” (BSW). Rule reversal occurred randomly
after five, six, seven or eight correct responses but this was not known to the subject. In
addition to experimental trials, affectively neutral trials without winning or losing money
were included as baseline. In these trials, playing cards from the German desk were presented
in the same manner like the ones in the experimental trial, except that a neutral feedback
(“Choice made”) without winning or loosing money was given. Subjects were told in advance
which of the two cards they had to choose in the baseline condition.
On average 40 baseline trials were randomly presented with the constraint of no consecutive
baseline trials and at least one baseline trial every 10th experimental trial. The whole task
comprises 24 rule reversals resulting in an average duration of 36 minutes depending on the
individual performance of the subjects. The task was conducted in 3 separate functional runs.
Across all subjects a mean of 145.05 (6.52) rewarding trials were acquired and a mean sum of
27.85 € (SD = 14.78) won. No differences with respect to these variables were observed
between the genetic groups (F(1,62)< 0.28; p >.10).
MRI Acquisition
All MRI sequences were performed on a 3.0 Tesla whole body scanner (Magnetom Trio,
Siemens Medical Solutions, Erlangen, Germany). For fMRI, 40 gradient-echo T2*-weighted
slices (slice thickness = 2.3 mm) per volume were acquired, using a single shot echo planar
sequence with parallel imaging GRAPPA-technique (acceleration factor 2) and the following
parameters: TR=2700 ms, flip angle=90°, TE=27ms, FOV= 220x220mm², matrix=96x96,
slice gap=0.7mm).
fMRI data analyses
Imaging data were preprocessed and analyzed according to standard procedures using SPM2
(http://www.fil.ion.ucl.ac.uk/spm). Subjects were excluded from fMRI analysis when they
exceeded minimum movement thresholds of 2.5 mm or 2° in any direction.
Functional scans were realigned to the first scan of each run as reference and slice-timed
afterwards. fMRI data were normalized into the Talairach and Tournoux6 stereotaxic space25
using MNI templates. During normalization images were resampled every 3mm using sinc
interpolation and were smoothed with a 9x9x9 mm Gaussian kernel to decrease spatial noise.
We performed individual statistical analyses, using a General Linear Model to model blood
oxygen level dependent (BOLD) signal changes to the different conditions. To remove
variations in signal due to movement artifacts, movement parameters calculated during the
realignment were included in the model as parameters of no interest. For each subject, one
image for each of the above outlined conditions was calculated. For the purpose of the present
study, one contrast, i.e. “reward – baseline” was calculated to assess BOLD response changes
associated with reward. These individual statistical parametric maps were entered in two types
of second-level, random-effects analyses: (1) a two-sample t-test in order to assess
differences in the neural response to reward between carriers of at least one CACNA1C risk
allele (AA/AG) and those participants not carrying any risk allele (GG) and (2) a regression
analyses with AA homozygotes (coded as 3), AG heterozygotes (coded as 2) and GG
homozygotes (coded as 1) in order to assess a gene-dose effect.
T statistics were normalized to z scores to provide a statistical measure which is independent
of the sample size.
Significant clusters of activation were defined with a height threshold of p <.05 (FWEcorrected) within predefined regions of interest (ROI) which included brain structures
consistently associated with reward processing7,8,,9, i.e. the orbitofrontal cortex, the anterior
cingulate cortex, the nucleus accumbens and the amygdala. ROI masks were derived from the
automated anatomical labelling atlas in WFU PickAtlas v2.010,11.
2. Results
The above described regression analyses yielded a very similar result than the two-sample ttest that is a positive correlation between the genotype and increased amygdala activity in
response to reward (t = 4.38; z = 4.07; p <.05 (FEW-corrected within ROI); k = 40 voxels; see
Figure 2). This correlation signifies that the more risk alleles the higher the amygdala activity
in response to reward and therefore supports a gene-dose effect.
3. References
1. First, M.B., Gibbon, M., Spitzer, R.L. & Williams, J.B.W. (1997). Structured Clinical
Interview for DSM-IV Axis I Disroders (SCID-I). American Psychiatric Publishing, Inc.,
Arlington.
2. Remijnse, P.L., Nielen, M.M.A., Uylings, H.B.M. & Veltman, D.J. (2005). Neural
correlate of a reversal learning task with an affectively neutral baseline: An event-related
fMRI study. Neuroimage, 26, 609-618.
3. Jocham, G., Klein, T.A., Neumann, J., von Cramon, D.Y., Reuter, M. & Ullsperger, M.
(2009). Dopamine DRD2 polymorphism alters reversal learning and associated neural
activity. J Neurosci, 29, 3695-3704.
4. Cools, R., Clark, L., Owen, A.M. & Robbins, T.W. (2002). Defining the neural
mechanisms of probabilistic reversal learning using event-related functional magnetic
resonance imaging. J Neurosci, 22, 4563-4567.
5. Linke, J., Kirsch, P., King, A.V., Gass, A., Hennerici, M.G., Bongers, A., & Wessa, M.
(2009). Motivational orientation modulates the neural response to reward. Submitted for
Publication.
6. Talairach, J. & Tournoux, P. (1988). Co-planar stereotaxic atlas of the human brain.
Thieme Medical Publishers, New York.
7. O'Doherty, J., Kringelbach, M.L., Rolls, E.T., Hornak, J. & Andrews, C. (2001). Abstract
reward and punishment representation in the human orbitofrontal cortex. Nature
Neurosci, 4, 95-102.
8. Kirsch, P., Schienle, A., Stark, R., Sammer, G., Blecker, C., Walter, B., Ott, U., Burkart,
J. & Vaitl, D. (2003). Anticipation of reward in a nonaversive differential conditioning
paradigm and the brain reward system: an event-related fMRI study. Neuroimage, 20,
1086-1095.
9. McClure, S.M., York, M.K. & Montague, P.R. (2004). The neural substrates of reward
processing in humans: the modern role of FMRI. Neuroscientist, 10, 260-268.
10. Tzourio-Mazoyer, N., Landeau, B., Papathanassiou, D., Crivello, F., Etard, O., Delcroix,
N., Mazoyer, B. & Joliot, M. (2002) Automated anatomical labeling of activations in SPM
using a macroscopic anatomical parcellation of the MNI MRI single-subject brain.
Neuroimage, 15, 273-289.
11. Maldjian, J.A., Laurienti, P.J., Kraft, R.A. & Burdette, J.H. (2003) An automated method
for neuroanatomic and cytoarchitectonic atlas-based interrogation of fMRI data sets.
Neuroimage, 19, 1233-1239.
4. Figures
Figure 1. Schematic outline of the trial structure of experimental and baseline trials
throughout the experiment
Figure 2. Positive correlation between genotype (AA homozygotes: N = 7; AG
heterozygotes: N = 26; GG homozygotes: N = 31) and regional activation in the amygdala (x
= 33; y = -3; z = -24) in response to reward.