MNI standard brain. The rainbow spectrum color scale on the right displays a range of FDOPA V d

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1 Supplementary Figure 1: 1.8 V d (ml g -1 ) 0 Legend: The population mean FDOPA V d image of the 13 subjects at z = 21.0, spatially normalized to the MNI standard brain. The rainbow spectrum color scale on the right displays a range of FDOPA V d between 0 ml g -1 (black) and 1.8 ml g -1 (white). The black arrow indicates the left amygdala, grey arrows indicate bilateral cerebellar cortices used as reference tissue for FDOPA PET kinetic analysis. FDOPA V d can be interpreted as an index of vesicular storage of endogenous dopamine, reflecting the state of readiness for local impulse-dependent dopamine release 9 8

2 Kienast T. et al., Dopamine storage capacity in the amygdala gates processing of aversive stimuli in amygdala and anterior cingulate cortex. Supplementary Material Subjects and screening instruments Thirteen healthy men with a mean age of 43.2 years (SD = 9.5, range years) were included in the study. Subjects with axis I psychiatric disorders according to DSM IV were excluded through the Structured Clinical Interview I and II 1,2. Trait anxiety was measured with Spielberger s State-Trait Anxiety Questionnaire (STAI) 3. Drug abuse was excluded with clinical interviews and urine tests. The study was approved by the local Ethics Committee according to the Declaration of Helsinki and written informed consent was obtained from all participants after the procedures had been fully explained. Methods: PET protocol Positron emission tomography (PET) is a well-established tool to investigate the functional coherency of the dopamine system with affiliated regional or molecular structures in the living human brain (eg 4,5 ). One hour prior to PET with 6-[ 18 F]-fluoro-L-DOPA (FDOPA), subjects were given carbidopa (2.5 mg / kg, p.o.) to minimize extracerebral tracer decarboxylation. Subjects reclined on the scanning bed with eyes closed and their head positioned within the aperture of the Siemens ECAT EXACT PET scanner operating in 3-D mode. To correct for tissue attenuation, transmission scans were acquired using a 68 Ge rod source. A dynamic emission recording, consisting of 28 frames (4 x 1 min, 3 x 2 min, 3 x 3 min, 15 x 5 min 3 x 10 min) lasting 124 minutes was initiated after intravenous administration of 194 MBq FDOPA (SD = 27). Arterial blood samples were collected at intervals during the emission recording and the total radioactivity concentration in plasma samples was measured using a well-counter cross-calibrated to the PET. The fractions of untransformed FDOPA and the main metabolite O-methyl-[ 18 F]-fluoro-L-DOPA (OMFD) were measured by reversed phase HPLC in plasma from blood collected at 5, 10, 15, 20, 30, 45, 60, 90 and 120 1

3 minutes post injection by reversed phase HPLC, and the continuous arterial input function were calculated by bi-exponential fitting of the measured fractions. After normalization of the summation image to a standard MR image (Montreal Neurological Institute), a time-activity curve (TAC) was first obtained for the cerebellum using an anatomical template. The net blood-brain FDOPA clearance (K in app, ml g -1 min -1 ) was calculated in TACs obtained from left and right amygdala templates by linear graphical analysis after subtraction of the total radioactivity measured in the cerebellum, and using frames recorded in the interval min 6,7. While the magnitude of K in app reveals the capacity for dopamine synthesis, it is somewhat underestimated due to the uncorrected and progressive loss of decarboxylated metabolites from brain, and is only imperfectly corrected from the continuously changing proportions of FDOPA and OMFD in the cerebellum 8. We therefore used a novel approach to measure the steady-state cerebral binding of FDOPA (V d, ml g -1 ), as described in detail previously 9. In brief, we first applied a constrained compartmental analysis to calculate the TAC for OMFD in cerebellum, which was then subtracted-frame-wise from the amygdala TACs, thus isolating the amygdala radioactivity due to FDOPA and its decarboxylated metabolites. Then, a multi-linear form of the inlet and outlet steadystate model for FDOPA kinetics was applied to the entire 124 minutes modified amygdala TACs in order to calculate V d, which comprises the sum of the distribution volumes occupied by FDOPA, i.e FDOPA in the brain plasma compartment (V 0 ), unmetabolized FDOPA in brain (V f ), and the trapped [ 18 F]fluorodopamine and other decarboxylated brain metabolites (EDV), itself defined as a ratio of the inherent FDOPA influx to brain (K; ml g -1 min -1 ) to the washout rate constant for the pool of decarboxylated FDOPA metabolites (k loss ; min -1 ). V d can be interpreted as an index of vesicular storage of endogenous dopamine, reflecting the state of readiness for local impulse-dependent dopamine release 9. Methods: fmri protocol 2

4 Affective stimuli Several types of visual cues compatible to fmri investigation that have been developed are effectively eliciting emotional states in humans 10,11. We presented negative and neutral pictures from the International Affective Picture System (IAPS) for evoking emotional reaction 12. Each valence category consisted of 18 different pictures. We have previously shown these stimuli to elicit significant fmri activation in the prefrontal and anterior cingulate cortex and in the amygdala 11,13,14. We controlled all pictures for arousal and valence according to the standardized rating procedure described by Bradley and Lang 12. Valence was rated on a scale from 1 (unhappy) to 9 (happy); neutral pictures were rated 5.8 (SD = 1.1) and negative pictures 2.6 (SD = 2.4). Arousal was rated on a scale from 1 (low) to 9 (high); neutral pictures were rated 2.7 (SD = 1.4) and negative pictures 5.4 (SD = 2.9). The order of stimuli was randomized for each subject and passively viewed for 750 ms in an event-related design 11, fmri data acquired at different peristimulus time points were used to calculate the time course of the blood oxygen level dependence (BOLD) signal changes. This was achieved by introducing a random jitter between inter-trial interval and acquisition time, resulting in an equal distribution of data points after each single stimulus. The inter-trial interval was randomized between three and six repetition times (i.e seconds). During the inter-trial interval a fixation cross as fixation condition was presented. Data acquisition We used a 1.5 T clinical whole-body scanner for fmri recodings (Magnetom VISION; Siemens, Erlangen, Germany) equipped with a standard quadrature head coil. Shimming was performed with the automatic Siemens MAP shim. For fmri, 24 slices with 4 mm thickness and 1 mm gap were acquired every 3.3 sec. A standard EPI-Sequence was used with a 3 x 3 x 5 mm voxel size, TR = 3300 ms, TE = 66 ms, α = 90 with an in-plane resolution of 64 x 64 pixels (FOV 220 mm). A morphological 3D T1-weighted magnetization prepared rapid gradient echo (MPRAGE) image data set (1 x 1 x 1 mm³ voxel size, FOV 256 mm, 162 slices, TR = 11.4 ms, TE = 4.4 ms, α = 12 ) 3

5 covering the whole head was acquired for anatomical reference. FMRI slices were oriented axially parallel to the AC-PC line according to Talairach and Tournoux 16. FMRI data analysis Statistical Parametric Mapping (SPM5) was used for data analysis ( 17. The first five T2* images were discarded to allow for T1 equilibration. The remaining first T2* image was used for co-registration of the structural 3D data set. The structural image was spatially normalized to a standard template using a 12-parameter affine transformation with additional nonlinear components and these normalisation parameteres were then applied to the T2* data. The functional data were smoothed with an isotropic Gaussian kernel (12 mm FWHM). For statistical analysis, the different conditions were modeled within the context of the general linear model (negative and neutral pictures) as explanatory variables after convolution with the HRF as well as their first temporal derivative on a voxel-by-voxel basis within SPM5. To detect BOLD response differences elicited by aversive stimuli, the contrast images (signal change of negative versus neutral pictures) of all subjects were included in a second level random effects analysis (P < uncorrected) using a one sample t-test. The relationship between FDOPA trapping (V d ) and the BOLD activation elicited by negative versus neutral stimuli was assessed in a separate SPM analysis by V d as an explanatory variable. Given our hypothesis of amygdala activation during aversive affective stimulation, correction for multiple comparision was performed using small volume correction with an amygdala mask from the WFU-Pickatlas SPM tool 18 with the aal atlas 19. Significance level was set to P < 0.05 FWE corrected for amygdala region of interest (ROI). All other activation are reported at P < uncorrected. All coordinates reported were given according to the atlas of Talairach and Tournoux 16. Psychophysiological interaction To analyze functional connectivity between the amygdala and other brain regions during affective processing, we used psychophysiological interactions (PPI) as implemented in SPM5 20. PPI is 4

6 defined as the change in contribution of one brain area to another with the experimental or psychological context 20. It computes whole-brain connectivity on a voxel-by-voxel basis through a regression equation using the interaction between a seed region (e.g. the amygdala) and a taskspecific effect (e.g. negative versus neutral pictures) as explanatory variable. A significant PPI occurs if there is a significant change in the regression coefficient between the two compared taskspecific conditions (e.g. negative versus neutral pictures). To create the PPI term the time series from the individual amygdala peak voxel was extracted from a 5 mm sphere arround the group peak activation for negative versus neutral pictures at x = 21, y = 1, z = 15 and deconvolved within a Bayesian framework to generate the neuronal signal for the seed region 21. The PPI was then defined as the element-by-element product of the neuronal time series and a vector coding for affectivly negative and neutral pictures. A regression model was set up for each subject in SPM5, which included the following regressors of interest: the time series of the seed region (the physiological variable), the convolved stimulus stick function (the psychological variable: negative and neutral pictures) and the reconvolved interaction term (the psychophysiological variable). Additional regressors of no interest were the convolved stick functions of the remaining affective stimuli. Contrasts images of the PPI term were created for each individual and taken to a second level random effects analysis using a one-sample t-test. We assessed correlations with anxiety measures (STAI-Trait) with a separate model including the anxiety score as an explanatory variable. Supplementary Materials: Results Vd values in distinct brain regions The mean magnitude of FDOPA V d values in the amygdala were substantially higher (left amygdala V d = 1.18 ml g -1, SD = 0.19) compared with the left medial frontal cortex (V d = 0.79 ml g -1, SD = 0.17), bilateral occipital cortex (V d = 0.75 ml g -1, SD = 0.09) or bilateral cerebellum (V d = 0.76 ml g -1, 5

7 SD = 0.14) and lower than in the striatum (e.g. left putamen V d = 3.95 ml g -1, SD = 0.91) (see supplementary figure 1). These data indicate that if the mean magnitude in left putamen is set at 100%, the mean left amygdala V d is 30%, a ratio similar to the relative FDOPA net influx (K app in ; 35%) reported in the literature 22. Specificity of amygdala V d and its correlation to fmri BOLD response The specifity of the observed effects in the amygdala and the dacc was assessed by analysing correlations of FDOPA V d in other regions (e.g. caudate, putamen, medial frontal cortex) with the fmri BOLD response in the amygdala (negative vs. neutral emotional stimuli). FMRI BOLD response in the left and right amygdala was not significantly correlated with FDOPA V d in the bilateral caudate and bilateral putamen and left medial frontal cortex, even at a very liberal threshold of P < 0.01 uncorrected. FDOPA V d in the right medial prefrontal cortex showed a trend towards a positive correlation with fmri activation in the left amygdala at x = 30, y = 6, z = 12 (t = 2.86; P = 0.065, FWE corrected), but this correlation did not reach statistical significance. Location of effects in the amygdala and dacc The maxima for the correlations of FDOPA V d in the left amygdala and dacc (BA 24) appear at slightly different coordinates than the maxima for the fmri task effect (negative versus neutral) in the amygdala and for the connectivity effect in the dacc (BA 24), respectively. However, this difference in maxima is likely an artifact of statistical tresholding. Therefore, we tested whether the amygdala cluster showing the task effect and the dacc (BA 24) cluster displaying the connectivity effect overlap with the clusters showing a correlation with FDOPA V d This was done by restricting the correlation analysis with FDOPA V d to voxels responding to negative vs. neutral stimuli in the amygdala and to those showing a connectivity effect in the dacc (BA24), respectively (tresholded at P < 0.05, uncorrected). As expected, this analysis revealed a FDOPA V d correlation effect in the cluster of amygdala voxels that showed a task effect (negative vs. neutral) with maximum at x = 21, y = 6, z = 13 (t = 6

8 2.18; P = uncorrected). The same was true for the dacc, where we found a correlation with FDOPA V d within those voxels that showed a connectivity effect (maximum at x = 9, y = 10, z = 39; t = 3.58; P = uncorrected). Supplementary Materials: Discussion Localization of ACC BOLD responses As reported above, the ACC area that correlated with amygdala function in the connectivity analysis was dacc. On the basis of the previous literature 23,24, one might have expected the effect to be located somewhat more rostrally within dacc. However, our clusters are still located in the most posterior part of the ACC subregion that receives direct input from the amygdala and that has been implicated in processing of fear and aversive stimuli 23. For example, the peak activation observed in our study is close to the area associated with subjective experience of unpleasantness after pain stimulation 25, while other aspects of negative emotions after pain (e.g. intensity) were correlated with other cingulate areas. Specifity of FDOPA uptake The magnitude of FDOPA V d represents local dopamine regardless of its cellular origin. The dopamine innervation of the amygdala arisies from the ventral tegmental area (VTA; A10 neurons of the dorsal tier in primates), in a pathway projecting to the amygdala among other limbic regions. There is extensive evidence that these neurons are involved in emotion processing In contrast to the VTA the magnocellular part of substatia nigra pars compacta of the ventral tier in primates only sparsely innervates the primate amygdala 29. Whereas most FDOPA metabolism and trapping in striatum occurs in dopamine fibres, serotonin and noradrenaline fibres may contribute to the signal in extrastriatal regions such as the amygdala 22,30. Indeed, dopamine formed from exogenous DOPA is immuno-histochemically detectable within serotonin fibres of rat brain 31. 7

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