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1 Supplementary Online Content Luijten M, Schellekens AF, Kühn S, Machielse MWJ, Sescousse G. Disruption of reward processing in addiction: an image-based meta-analysis of functional magnetic resonance imaging studies. JAMA Psych. doi: /jamapsychiatry emethods. efigure 1. Reward Anticipation Meta-Analytic Results for the Different Subgroups. efigure 2. Reward Outcome Meta-Analytic Results for the Different Subgroups. efigure 3. Binarised Jack-Knife Analyses For Reward Anticipation (A) and Reward Outcome (B). efigure 4. Stability of Whole-Brain Meta-Analytic Results After Excluding the 2 Datasets From van Hell et al. efigure 5. Whole-Brain Meta-Analytic Results in the Ventral Striatum For Reward Anticipation After Excluding the 2 Datasets From van Hell et al. (2010). efigure 6. Stability of Whole-Brain Meta-Analytic Results After Excluding Studies With Partial Brain Coverage (n = 5). efigure 7. Funnel Plots Assessing Publication Bias of fmri Studies For Reward Anticipation (A) And Reward Outcome (B) in the Striatum. etable 1. Task Paradigms and Included Contrasts For Reward Anticipation and Reward Outcome Studies. etable 2. Number of Studies and Participants Involved in Specific Analyses etable 3. Reward Anticipation Meta-Analytic Results Addicted > Controls etable 4. Reward Anticipation Meta-Analytic Results Addicted < Controls etable 5. Reward Outcome Meta-Analytic Results Addicted > Controls etable 6. Reward Outcome Meta-Analytic Results Addicted < Controls ereferences This supplementary material has been provided by the authors to give readers additional information about their work.

2 Supplementary materials Psychiatric comorbidity In order to explore potential confounding effects of psychiatric co-morbidities on brain reward-processing, studies were categorized according to the presence of such co-morbidity among individuals included in the addicted groups. Several studies clearly excluded all cases with psychiatric co-morbidity, using well-validated structured clinical interviews. These were classified as not co-morbid. All other studies, including those that did not systematically assess psychiatric co-morbidity, were classified as co-morbid. There were no studies that specifically examined addicted individuals with psychiatric co-morbidity. Phase of addiction In order to explore potential confounding phase-dependent effects of addiction on brain reward-processing, studies were categorized according to the addiction phase of the individuals included in the addicted groups. Since reporting on the phase of addiction is often limited, studies could only be categorized into active substance use, initial or prolonged abstinence, or a mix of both. Most studies on substance dependence other than nicotine dependence did not report separately on smoking status of the participants, which was therefore not taken into account in categorizing these studies. Studies were thus categorized as follows: active addictive behavior (either gambling or substance use, until or during the day of scanning), initial abstinence (last use >24 hours before scanning, but not >4 weeks), prolonged abstinence (>3 months abstinence), or mixed sample (both active and abstinent users included). If no information on current addictive behavior was available, the sample under survey was considered mixed.

3 efigure 1. Reward anticipation meta-analytic results for the different sub-groups. Those Z-maps show the brain regions showing consistent responses across studies during reward anticipation for substance use disorder individuals and matched healthy controls (left) and gambling disorder individuals and matched healthy controls (right). Functional Z-maps are overlaid on the Colin 27 anatomical template and thresholded at p <.005 and k 10 (equivalent to a corrected p-value of.05, see methods).

4 efigure 2. Reward outcome meta-analytic results for the different sub-groups. Those Z-maps show the brain regions showing consistent responses across studies during reward outcome for substance use disorder individuals and matched healthy controls (left) and gambling disorder individuals and matched healthy controls (right). Functional Z-maps are overlaid on the Colin 27 anatomical template and thresholded at p <.005 and k 10 (equivalent to a corrected p-value of.05, see methods).

5 efigure 3. Binarised jack-knife analyses for reward anticipation (A) and reward outcome (B). Jack-knife analyses consist of systematic repetitions of the meta-analysis of interest after excluding one study at a time. The color bar represents the number of overlapping jackknife analyses at a threshold of p <.005 and k 10 (22 analyses in total for reward anticipation and 23 analyses in total for reward outcome). The results show that the striatal group differences observed during reward anticipation and outcome in the main analyses are observed in virtually all jack-knife analyses. This suggests that our results are highly replicable across many combinations of datasets, and that none of the studies is playing a disproportionate role and driving the results.

6 efigure S4. Stability of whole-brain meta-analytic results after excluding the two datasets from van Hell et al. (2010). Note that excluding the two datasets from van Hell et al. (2010) does not qualitatively affect the main results of our meta-analysis (see comparison with Figure 2 in main text). Note also that the results for gambling addicted individuals remain unchanged after excluding the two datasets from van Hell et al. (2010), since these datasets are substance addiction-related. Functional Z-maps are thresholded at p <.005 and k 10 (equivalent to a corrected p-value of.05).

efigure 5. Whole-brain meta-analytic results in the ventral striatum for reward anticipation after excluding the two datasets from van Hell et al. (2010).

7 efigure 5. Whole-brain meta-analytic results in the ventral striatum for reward anticipation after excluding the two datasets from van Hell et al. (2010). Excluding the two datasets from van Hell et al. (2010) revealed an increased response in the ventral striatum during reward anticipation in individuals with addictive behaviours compared with healthy controls. This seems to be due to these two datasets presenting relatively large effect sizes in the opposite direction in the ventral striatum, as can be seen from in the forest plot which reports mean variance of effect sizes for group differences estimated from individual studies (note that in contrast to the whole-brain results, effect sizes for the two datasets from van Hell et al. (2010) were incorporated in the forest plot in order to illustrate their impact on the ventral striatal results). Note however that this result is not observed separately in individuals with substance addiction, and observed only weakly and unilaterally in individuals with gambling addiction. In addition, a close examination of the functional clusters shows that the peaks are not located in the striatum itself, and that the activations observed in the ventral part of the striatum are in fact the tips of more ventral/posterior clusters spanning the dorsal amygdala and posterior orbitofrontal cortex. We thus think this is a potentially interesting but fragile result that will need further investigation in future meta-analyses, in order to disentangle whether it is a false positive or a real effect that only surfaces weakly in the present analysis because of e.g. methodological issues known to affect this region in individual studies (low sensitivity due to susceptibility artefacts, less accurate normalization ). Functional Z- maps are thresholded at p <.005 and k 10 (equivalent to a corrected p-value of.05).

8 efigure 6. Stability of whole-brain meta-analytic results after excluding studies with partial brain coverage (n = 5). Note that excluding these studies does not qualitatively affect the main results our metaanalysis (see comparison with Figure 2 in main text). Note also that the results for gambling addicted individuals remain unchanged after excluding partial brain coverage studies, since all the excluded studies are substance addiction studies. Functional Z-maps are thresholded at p <.005 and k 10 (equivalent to a corrected p-value of.05).

9 efigure S7. Funnel plots assessing publication bias of fmri studies for reward anticipation (A) and reward outcome (B) in the striatum. These funnel plots represent variance as a function of mean effect sizes of group differences extracted for all studies in the striatum (data are the same as in Figures 2 and 3). Funnel plots show no evidence of publication bias for reward anticipation (normal distribution over the funnel), and an indication of weak publication bias for reward outcome (slight left orientation of the distribution over the funnel). Compared with the original effect size observed in our meta-analysis, the effect size estimated after correction for publication bias based on Duval and Tweedie s trim and fill analyses showed no differences for the reward anticipation phase (estimated corrected effect size:.201, 95CI: ). For reward outcome a slightly smaller estimated effect size was found after correction for publication bias (adding the red dots representing missing studies) compared with the original effect size observed in our meta-analysis (estimated corrected effect size: -.149, 95CI: ). Nonetheless, the corrected effect size for reward outcome was still significant, indicating that if missing studies had been published, our results for reward outcome would still have been the same, except for a slightly lower effect size.

10 etable 1. Task paradigms and included contrasts for monetary reward anticipation and monetary reward outcome studies Study Task Reward anticipation Reward outcome Balodis et al. (2012) (A1 phase) win Beck et al. (2009) win Bjork et al. (2008) (single trials and first response of doubleresponse trials) win (single response trials) Bjork et al. (2012) (high and low reward magnitude combined) Higher order contrast between reward notification and hit notification Bustamante et al. (2014) (high and low reward magnitude combined) Choi et al. (2012) Cousijn et al. (2012) Iowa gambling task Outcome win > outcome loss Fauth- Bühler et al. (2014) Instrumental motivation task Parametric modulation reward anticipation Parametric modulation outcome Filbey et al. (2013) (all reward magnitudes) *

11 Goldstein et al. (2007) Hägele et al. (2015) Jansma et al. (2013) Forced choice monetary reward task (high reward magnitude) ** win (high and low reward magnitude combined) win (high reward magnitude) ** Jia et al. (2011) win Martin et al. (2014) Reward prediction task win > anticipation monetary loss Outcome expected win > outcome expected loss Miedl et al. (2010) Blackjack game Outcome win > outcome loss Nestor et al. (2010) win Patel et al. (2013) win > implicit baseline (high and low reward magnitude combined Outcome win > implicit baseline (high and low reward magnitude combined) Romanczuk- Seiferth et al. (2015) win > anticipation no win * win * Rose et al. (2014) win > anticipation no win ** win ** De Ruiter et al. (2009) Probabilistic reversal learning task Outcome win > implicit baseline Sescousse et al. (2013) Incentive win

12 Van Hell et al. (2010) Van Holst et al. (2012) Guessing task Anticipation 70% chance monetary win > anticipation 30% chance monetary win (high and low reward magnitude combined) win Outcome win > outcome loss Van Holst et al. (2014) Guessing task Anticipation 70% chance monetary win > anticipation 30% chance monetary win (high and low reward magnitude combined) Outcome win > outcome loss Yip et al. (2014) * The data in the paper was analyzed including several covariates (e.g., age). We received data without the covariates. ** The original study included a pharmacological manipulation. We received data from the placebo condition

13 etable 2. Number of studies and participants involved in specific analyses Analyses Reward Anticipation Reward Outcome N Studies N Participant s N Studies HC versus Addicted individuals HC: 505 AI: 526 N Participant s 20 1,3,4,6,8-17,19,21-25 HC: 492 AI: 506 HC versus substance users ,7-13,15,16,18-20 HC: 351 AI: ,4,8-13,15,16,19,21-23,25 HC: 296 AI: 330 HC versus Gamblers 6 1,5,6,13,14,17 HC: 171 AI: ,6,13,14,17,24,25 HC: 187 AI: 176 HC: Healthy controls AI: Addicted individuals

14 etable 3. Reward anticipation meta-analytic results Addicted > Controls Brain region Hemisphere MNI coordinates SDM-Z X Y Z Brainstem Right Putamen / Pallidum Right Anterior medial Right 4-18 temporal lobe / Amygdala Amygdala Left Lateral orbital Left / inferior frontal Superior frontal Left Superior frontal Right/Left Inferiolateral Left parietal lobe Superior parietal Left Middle temporal Left Posterior temporal Left lobe Posterior temporal Left lobe Cuneus Right Lingual Right Lateral occipital Right lobe Lateral occipital Left lobe Cerebellum Left Cerebellum Right Cerebellum Right Cerebellum Left Cerebellum Left Cerebellum Left Threshold: voxel-wise p <.005 and k 10. Anatomical localization of functional clusters was performed based on a probabilistic atlas (Hammers et al., 2003).

15 etable 4. Reward anticipation meta-analytic results Addicted < Controls Brain region Hemisphere MNI coordinates SDM-Z X Y Z Striatum (anterior middle caudate) Left Right Striatum (posterior dorsal Left caudate) Right Thalamus Left Thalamus Right Anterior cingulate (mpfc) Right Anterior cingulate Right Anterior cingulate Right Posterior cingulate Right Posterior cingulate Left Inferior frontal Left Middle frontal Right Precentral Right Precentral Left Precentral Right/Left Postcentral Left Postcentral Right Inferiolateral parietal lobe Right Inferiolateral parietal lobe Right Inferiolateral parietal lobe Left Superior parietal Right Posterior parietal lobe Right Posterior temporal lobe Left Posterior temporal lobe Left Cuneus Right/Left Lingual / cuneus Left Lateral occipital lobe Right White matter Left Corpus callosum / ventrical Left Threshold: voxel-wise p <.005 and k 10. Anatomical localization of functional clusters was performed based on a probabilistic atlas (Hammers et al., 2003).

16 etable 5. Reward outcome meta-analytic results Addicted > Controls Brain region Hemisphere MNI coordinates SDM-Z X Y Z Ventral Left striatum (ventral caudate) Ventral Right striatum (ventral putamen / nucleus accumbens) Ventral Left striatum (ventral putamen) Striatum Left (lateral putamen) Insula Right Insula Right Thalamus Left Anterior Right orbital Posterior Left orbital Posterior Left cingulate Precentral Left Precentral Left Postcentral Left Postcentral Right Superior Right/Left frontal Inferior Right frontal Middle frontal Left

17 Superior frontal Right Left Right Superior frontal Inferiolateral Right parietal lobe Inferiolateral Right parietal lobe Inferiolateral Right parietal lobe Inferiolateral Right parietal lobe Posterior Right temtopral lobe / Lateral occipital lobe Posterior Left temporal lobe Posterior Left temporal lobe Lingual Right Lateral Right occipital lobe Threshold: voxel-wise p <.005 and k 10. Anatomical localization of functional clusters was performed based on a probabilistic atlas (Hammers et al., 2003).

18 etable 6. Reward outcome meta-analytic results Addicted < Controls Brain region Hemisphere MNI coordinates SDM-Z X Y Z Dorsal striatum (dorsal caudate) Left Anterior cingulate Left Hippocampus Left Precentral Left Right Precentral Left Postcentral Left Superior frontal Left Inferor temporal Right Left Posterior temporal lobe Right Posterior temporal lobe Left Posterior temporal lobe Right Superior temporal Left Lateral occipital lobe Left Lateral occipital lobe Left Lateral occipital lobe Left Lingual Left White matter / Lateral ventricle Right Ventricle Left White matter Left Threshold: voxel-wise p <.005 and k 10. Anatomical localization of functional clusters was performed based on a probabilistic atlas (Hammers et al., 2003).

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Resistance to forgetting associated with hippocampus-mediated. reactivation during new learning

Resistance to Forgetting 1 Resistance to forgetting associated with hippocampus-mediated reactivation during new learning Brice A. Kuhl, Arpeet T. Shah, Sarah DuBrow, & Anthony D. Wagner Resistance to