After completing the preprocessing, design matrices with two conditions (sexual condition and nonsexual condition) were created for each participant to identify the areas with sexual desire-related activation. Individual first-level analyses of the comparisons of sexual condition minus nonsexual condition were used for a random effects analysis, and mean images were created for each subject. One-sample t-tests on the mean images were used to assess the significant group effects in each group in the contrast images created in the individual analyses. Twosample t-tests were conducted to identify the differences between the two groups for the brain responses in the sexual condition relative to the nonsexual condition. Additionally, correlational analyses were conducted only in the PHB group to determine the regions of activation that correlated with the severity of hypersexuality according to the SAST-R. Because the variance of the questionnaire scores might have been too low to reveal more significant correlations in the control group, correlational analyses were not conducted in the control group. P values less than 0.05 (False Discovery Rate, corrected, cluster size ≥ 20) or 0.001 (uncorrected, cluster size ≥ 20) were considered significant for brain activity as these levels are generally accepted in fMRI studies. All of the coordinates of the activated voxels are shown as MNI coordinates in **Tables 3**, **4**.

The percent signal change was extracted from the Regions of Interest (ROIs) based on the results of the betweengroup and correlation analyses [i.e., bilateral thalamus, right dorsolateral prefrontal cortex (DLPFC), left caudate nucleus, right supramarginal gyrus, and right dorsal anterior cingulate gyrus] with MarsBaR (http://www.sourceforge.net/projects/ marsbar). The ROIs were created by placing a 5-mm sphere around the coordinates reported in **Tables 3**, **4**. In order to examine the temporal characteristics of the hemodynamic responses, the BOLD signal time course was also extracted from the ROIs during the presentation of each sexual stimulus (total of 12 s; 5 s and 7 s thereafter) for all of the participants. The time courses were then averaged across the participants in each group.

As a follow-up test of correlation to calculate the correlation coefficient, the relationships between the scores on the SAST-R and HBI and the percent signal changes in the ROIs based on the results of the correlation analysis (**Table 4**) were analyzed in the PHB group with SPSS 22.

## RESULTS

#### Results of the Psychological Assessments

Of the 20 healthy control subjects, only two reported other emotions besides sexual arousal in response to the three sexual

#### TABLE 2 | Psychological assessment results.

 One participant in the control group reported that two sexual stimuli among the 20 sexual stimuli induced disgust and anger, while the other participant in the control group rated that one sexual picture induced surprise.

An independent t-test indicated no group differences in the dimensions of valence and arousal in response to sexual cues [valence: t(43) = 0.14, p > 0.05, Cohen's d = 0.042; arousal: t(43) = 0.30, p > 0.05, Cohen's d = 0.089].  The intensity of sexual arousal showed that the PHB group experienced more intense sexual arousal than the control group in response to sexually stimulating photos [t(43) = 14.3, p < 0.001, Cohen's d = 4.26]. The results of the psychological assessments are shown in **Table 2**.

#### fMRI Results

In the PHB group, activation was observed in the bilateral middle/inferior frontal gyri [Brodmann area (BA) 9], cuneus/precuneus (BA 7, 18, and 19), striatum, thalamus, and cingulate gyri (BA 24 and 32) in response to sexual stimuli compared with nonsexual stimuli. In the control group, activation was displayed in the bilateral middle/inferior frontal gyri (BA 9), cuneus/precuneus (BA 7, 18, and 19), striatum, thalamus, and left cingulate gyrus (BA 24) (corrected False Discovery Rate, p < 0.05).

In the between-group analysis, the PHB group exhibited greater activation in the right dorsal anterior cingulate cortex (dACC; BA 24 and 32), bilateral thalami, left caudate nucleus, right DLPFC (BA 9, 46), and right supramarginal gyrus (BA 40) relative to the activation in the control group during exposure to sexual stimuli compared with nonsexual stimuli. No brain regions in the control group showed greater activation than in the PHB group. All of the coordinates for the activated voxels are shown as MNI coordinates in **Tables 3**, **4**. **Figure 2** shows the percent signal changes in the control and PHB groups in each experimental condition (that is, sexual and nonsexual conditions) for the selected ROIs, and **Figure 3** displays the mean time series

#### TABLE 3 | Brain regions identified by the group analysis.

*MNI, Montreal Neurological Institute; PHB, Problematic Hypersexual Behavior; BA, Brodmann area.*

*Comparison of brain activation between the two groups (PHB group vs. control group) during the sexual condition compared to the nonsexual condition (corrected False Discovery Rate, p* < *0.05).*

TABLE 4 | Brain regions identified in the correlational analysis in the PHB group during exposure to sexual stimuli.

*PHB, Problematic Hypersexual Behavior; MNI, Montreal Neurological Institute. Brain regions significantly correlated with Sexual Addiction Screening Test-R (SAST–R) scores during the sexual condition in the PHB group (p* < *0.001, uncorrected).*

for each group of the percent signal changes at each time point in the ROIs during the presentation of each sexual stimulus (total of 12 s; 5 and 7 s thereafter) based on the results of the between group analysis.

The correlation analysis of the regions that were related to the SAST-R score demonstrated that the right thalamus and DLPFC (BA 9) were correlated with the SAST-R scores (p < 0.001, uncorrected) in the PHB group during the exposure to sexual stimuli, as shown in **Table 4**. The results of the follow-up analysis showed that the percent signal change that was extracted from the right thalamus and DLPFC correlated significantly with the severity of hypersexuality, as shown in **Figure 4**. The percent signal changes in the right thalamus and right DLPFC correlated positively with the SAST-R scores in the PHB group during exposure to sexual stimuli (right thalamus: r = 0.74, n = 23, p < 0.01; right DLPFC: r = 0.63, n = 23, p < 0.01). In addition, the percent signal changes in the right DLPFC and right thalamus

coordinate; *x* = 56, *y* = 10, *z* = 22) (C) Left caudate nucleus (MNI coordinate; *x* = −38, *y* = −32, *z* = 2) (D) Right supramarginal gyrus (MNI coordinate; *x* = 50, *y* = −42, *z* = 32) (E) Right dorsal anterior cingulate gyrus (MNI coordinate; *x* = 24, *y* = −16, *z* = 34). Results of the comparisons of activation in sexual stimuli minus nonsexual stimuli between the PHB and control groups (*p* < 0.05, False Discovery Rate, corrected). The control group and the PHB group are represented as blue and red, respectively. The y-axis shows the percent signal change and the error bars represents Standard Error of the Mean.

FIGURE 3 | Time course of the hemodynamic responses in each region of interest. (A) Bilateral thalamus (MNI coordinate; *x* = 6, *y* = −36, *z* = 4) (B) Right dorsolateral prefrontal cortex (MNI coordinate; *x* = 56, *y* = 10, *z* = 22) (C) Left caudate nucleus (MNI coordinate; *x* = −38, *y* = −32, *z* = 2) (D) Right supramarginal gyrus (MNI coordinate; *x* = 50, *y* = −42, *z* = 32) (E) Right dorsal anterior cingulate gyrus (MNI coordinate; *x* = 24, *y* = −16, *z* = 34). The y-axis and x-axis display the percent signal change and time (s), respectively, and the error bars represent the Standard Error of the Mean.

were positively related to the HBI scores in the PHB group (right thalamus: r = 0.65, n = 23, p < 0.01; right DLPFC: r = 0.53, n = 23, p < 0.01), as shown in **Figure 4**.

#### DISCUSSION

The present study examined whether there was a difference in the levels of sexual desire between individuals with PHB and healthy controls and, if so, whether this difference was related to functional alterations in the neural substrates of sexual desire in these individuals. As predicted, the PHB group showed significantly heightened levels of sexual desire and altered activation in the PFC and subcortical areas compared to controls. These results suggested that the functional changes in the neural circuitry that mediates cue-induced desire for sexual behavior were similar to those in response to cue presentation in individuals with substance addiction or behavioral addiction (Garavan et al., 2000; Tapert et al., 2003; Crockford et al., 2005; Franklin et al., 2007; Ko et al., 2009; McClernon et al., 2009). Voon et al. (2014) reported abnormal desire and functional changes in regions associated with heightened desire in individuals with compulsive sexual behavior. We replicated and extended these results by investigating the times series of the activation during the total 12 s in the areas associated with sexual desire.

As hypothesized, the analyses of the results of the psychological assessments showed that the PHB group exhibited more frequent sexual desire than the control group during exposure to sexual stimuli, which suggested that this group had a lower threshold for sexual desire. When sexual desire was induced, the PHB group showed a higher intensity of sexual desire than the control group did. This result was consistent with previous findings on individuals with PHB group (Laier et al., 2013; Laier and Brand, 2014; Voon et al., 2014), especially demonstrate that the desire for pornography might play a key role in cybersex addiction.

 The results of the group comparisons of the brain imaging revealed altered activation in the right DLPFC (BA 9) and subcortical regions, including the right dACC (BA 24 and 32), left caudate nucleus, right supramarginal gyrus (BA 40), and right thalamus, and these alterations might be associated with the behavioral characteristics of the PHB group. In addition to brain activation, we examined a time series of the hemodynamic responses in these areas during and after the arousal of sexual desire in these areas.

Among these regions, the left caudate nucleus and right ACC (BA 24 and 32) and the right DLPFC are assumed to be associated with the motivational component of sexual desire. The involvement of the caudate nucleus in motivation and reward processing might account for its response to sexual stimuli (Delgado, 2007). The dorsal striatum is activated during reward anticipation (Delgado, 2007), which possibly reflects the

desire that is associated with such anticipation. In a study of the neural responses associated with pornography consumption, frequent activation as a result of pornography exposure might result in the wearing down and downregulation of the striatum, including the caudate nucleus, in healthy controls (Kühn and Gallinat, 2014). However, in the current study, greater activation was observed in the caudate nucleus in the PHB group, even though the PHB group watched pornography more often. These differences between the results of the present study and those of Kühn and Gallinat (2014) might be explained by the difference in the participants. That is, in contrast to the use of healthy male adults in the previous study, our study was conducted on individuals with PHB. Accumulating evidence suggests that the caudate nucleus is important for stimulusresponse habit learning and the maintenance of addictive behavior (Vanderschuren and Everitt, 2005). The activation of the caudate nucleus in this study might suggest that sexual cue-reactivity is established after repeated exposure to sexual experience.

 Our findings of dACC activation suggest that it has a role in sexual desire, and these results were similar to those of a study on desire-related neural activity in subjects with compulsive sexual behaviors (Voon et al., 2014). In addition, the dACC is known to be important in the initial processing of goaloriented behavior by engaging in conflict monitoring between the urge for behavioral expression and the suppression of that urge (Devinsky et al., 1995; Arnow et al., 2002; Karama et al., 2002; Moulier et al., 2006; Safron et al., 2007). Neuroanatomically, the dACC projects to the DLPFC and parietal lobe (Devinsky et al., 1995; Pizzagalli et al., 2001). In this study, the activation in the dACC in the PHB group might reflect internal conflict between the urge to express sexual impulses as actions and the urge to suppress the impulses due to situational factors during the presentation of sexual stimuli.

The activation of the supramarginal gyrus is associated with increased attention to targets that are perceived as sexual cues (Redouté et al., 2000; Stoléru et al., 2012).  In the current study, the supramarginal activation could reflect the greater attention that was paid by PHB subjects to sexual stimuli and that could result in the higher levels of sexual desire compared with the control group.

Among the regions that were significantly activated in the between group results, the DLPFC and thalamus directly correlated with the severity of sexual addiction in the PHB subjects.   Interestingly, we also found a higher and wider hemodynamic pattern in the thalamus compared with that in controls. This higher and wider hemodynamic response might indicate that sexual arousal was stronger and prolonged in the individuals with PHB.

Similar to the findings of studies on neural activity in individuals with addiction during cue-induced desire, we found altered PFC function in the PHB group. The PFC plays a critical role in future planning and working memory (Bonson et al., 2002). Neuroanatomically, the PFC is interconnected to various areas, including the dACC, caudate nucleus, and parietal lobe (Devinsky et al., 1995; Pizzagalli et al., 2001; Goldman-Rakic and Leung, 2002). Previous studies on addiction have demonstrated that dysfunction of this network, including the PFC, is related to the PFC's regulation of limbic reward regions and its involvement in higher-order executive function, including self-control, salience attribution, and awareness (Goldman-Rakic and Leung, 2002; Feil et al., 2010; Goldstein and Volkow, 2011; Kühn and Gallinat, 2014). In particular, these studies have identified the disrupted function of DLPFC as an impairment in salience attribution, which results in symptoms, such as the abnormally increased sensitivity to an addictive cue as in substance and addicted behaviors and decreased interest to normal-rewarding stimuli (Goldman-Rakic and Leung, 2002; Goldstein and Volkow, 2011). In the current study, the observation of greater DLPFC activation in the PHB group compared to the control group might reflect excessive salience attribution to sexual cues.

 These findings indicate that the PHB group might pay excessive attention to sexual stimuli and that it might have an automatic response because the conditional response to sexual stimuli could not be mediated properly. The limitations of the present study were as follows. First, the race of the subjects was Asian.  PHB subjects with cooccurring mental disorders were not enrolled in the present study, thus ensuring the investigation of neural dysfunction based solely on PHB. However, according to a study by Weiss (2004), 28% of males with PHB suffer from major depressive disorder. Taking these factors together limit the generalizability of the study results to the broader universal population. Finally, the two groups may have differed with respect to self-awareness and/or emotional sensitivity due to the treatment of the PHB participants. We tried to decrease the differences between the control and PHB groups by matching for important demographic variables, including age, education level, and handedness, for comparison purposes and by applying strict exclusion criteria, such as the presence of psychiatric disorders and the current use of psychotropic medication, to both groups. Next, we plan to examine how variables that are related to treatment period or treatment type affect the emotional responses, including responses to sexual cues, of individuals with PHB.

Despite these limitations, the results of this study contribute significantly to the literature and have significant implications for future research. We identified specific brain regions that were directly associated with sexual desire and the temporal changes in the activities of these regions among subjects with PHB. Like brain imaging studies on substance and behavior addiction, PHB was related to functional changes in the PFC and subcortical areas, even without the neurotoxicity of drugs. Our results are therefore useful for characterizing the behaviors and associated neural mechanisms of individuals with PHB, and go a step beyond the descriptions of characteristics as in previous studies.

#### FUNDING

This work was supported by the Korea Basic Science Institute (No. E35600) and research fund of 2014 Chungnam National University.

#### ACKNOWLEDGMENTS

The authors would like to thank Korea Basic Science Institute for allowing this study to be conducted at the Department of Human Imaging Center by using 3T MRI scanner (Phillips).

#### SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: http://journal.frontiersin.org/article/10.3389/fnbeh. 2015.00321

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2015 Seok and Sohn. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Impulsivity is Associated with Increased Metabolism in the Fronto-Insular Network in Parkinson's Disease

#### Masoud Tahmasian1,2,3\*, Luisa Rochhausen1 , Franziska Maier 1 , Kim L. Williamson1 , Alexander Drzezga2 , Lars Timmermann1 , Thilo Van Eimeren1,2 and Carsten Eggers 1

1 Department of Neurology, University Hospital of Cologne, Cologne, Germany, 2 Department of Nuclear Medicine, University Hospital of Cologne, Cologne, Germany, 3 Sleep Disorders Research Center, Kermanshah University of Medical Sciences (KUMS), Kermanshah, Iran

Various neuroimaging studies demonstrated that the fronto-insular network is implicated in impulsive behavior. We compared glucose metabolism (as a proxy measure of neural activity) among 24 patients with Parkinson's disease (PD) who presented with low or high levels of impulsivity based on the Barratt Impulsiveness Scale 11 (BIS) scores. Subjects underwent 18-fluorodeoxyglucose positron emission tomography (FDG-PET) and the voxel-wise group difference of FDG-metabolism was analyzed in Statistical Parametric Mapping (SPM8). Subsequently, we performed a partial correlation analysis between the FDG-metabolism and BIS scores, controlling for covariates (i.e., age, sex, severity of disease and levodopa equivalent daily doses). Voxel-wise group comparison revealed higher FDG-metabolism in the orbitofrontal cortex (OFC), anterior cingulate cortex (ACC), and right insula in patients with higher impulsivity scores. Moreover, there was a positive correlation between the FDG-metabolism and BIS scores. Our findings provide evidence that high impulsivity is associated with increased FDG-metabolism within the fronto-insular network in PD.

#### Edited by:

Morten L. Kringelbach, University of Oxford, UK; University of Aarhus, Denmark

#### Reviewed by:

Julie C. Price, University of Pittsburgh, USA Walter Adriani, Istituto Superiore di Sanità, Italy

#### \*Correspondence:

Masoud Tahmasian<EMAIL_ADDRESS>
Received: 28 May 2015 Accepted: 08 November 2015 Published: 30 November 2015

#### Citation:

Tahmasian M, Rochhausen L, Maier F, Williamson KL, Drzezga A, Timmermann L, Van Eimeren T and Eggers C (2015) Impulsivity is Associated with Increased Metabolism in the Fronto-Insular Network in Parkinson's Disease. Front. Behav. Neurosci. 9:317. doi: 10.3389/fnbeh.2015.00317 Keywords: impulsive behaviors, FDG-PET, orbitofrontal cortex, anterior cingulate cortex, insula

## INTRODUCTION

Impulsivity is an umbrella term that covers ''actions that are poorly conceived, prematurely expressed, unduly risky, or inappropriate to the situation and that often result in undesirable outcomes'' (Evenden, 1999). Impulsivity is generally considered as a personality trait that is associated with self-control deficiency and several problematic behaviors such as aggression, risk-seeking behavior, driving violation and also suicide attempt (Owsley et al., 2003; Fineberg et al., 2014; Gvion et al., 2014).  In addition, high impulsivity is a risk factor for impulse control disorders (ICDs) that consist of serious behavioral symptoms such as pathological gambling, compulsive shopping, binge eating and hyper sexuality. Crucially, ICDs may develop due to overstimulation of the mesolimbic system by dopaminergic medication (Lee et al., 2010; Voon et al., 2011a,b; Probst and Van Eimeren, 2013). ICDs are common non-motor symptoms in Parkinson's disease (PD). For example, it has been shown that at least one form of ICDs was found in 13.6% of medicated PD patients (Weintraub et al., 2010). Moreover, the trait of impulsivity might be an important selection criterion for deep brain stimulation (DBS) of the subthalamic nucleus (STN), as DBS of the limbic part of the STN can reduce the activity of the inhibitory networks in PD (Jahanshahi, 2013). Thus, understanding the neural mechanisms of impulsivity in PD may lead to better treatment strategies in future.

Neuroimaging studies demonstrated that the fronto-insular network, including the orbitofrontal cortex (OFC), anterior cingulate cortex (ACC), medial prefrontal cortex (mPFC), dorsolateral prefrontal cortex (dlPFC), and insula, are involved in impulsive behavior in healthy controls (Matsuo et al., 2009a; Cho et al., 2013), patients with neuropsychiatric disorders (Antonucci et al., 2006; Matsuo et al., 2009b; Sebastian et al., 2014; Trost et al., 2014), high risk individuals for psychosis (Lee et al., 2013), and PD patients with ICDs (Cilia et al., 2008, 2011; Van Eimeren et al., 2010; Voon et al., 2011a; Biundo et al., 2015). In this study, we focused on impulsivity rather than ICDs to investigate the neural mechanisms underlying high level of impulsivity as a risk factor for developing ICDs in PD patients, while we controlled for age, gender, severity of disease, and levodopa equivalent daily dose (LEDD) for dopamine agonists. We hypothesized that PD patients with higher level of impulsivity have regional glucose metabolism alterations in the fronto-insular network, particularly in the OFC, ACC, and insula, which have been discussed to be associated with the inhibitory networks and impulsivity behaviors.

## MATERIALS AND METHODS

### Subjects

Twenty-four right-handed patients (mean age 66.29, SD 6.01) with idiopathic PD were recruited from the outpatient clinic of the Department of Neurology, University Hospital of Cologne. The study was approved and registered by the medical ethics board of the University Hospital of Cologne in line with Human Research Committee guidelines. All subjects provided informed consent in accordance with the standard protocol approvals (Nr.10-278). On every subject, medical history and neurological examination were performed. Patients fulfilled criteria for PD based on the United Kingdom Parkinson's Disease Society Brain Bank criteria (Hughes et al., 1992). Evaluation of motor symptoms was assessed with the Unified Parkinson's Disease Rating Scale (UPDRS) Part III (Fahn et al., 1987; Van Hilten et al., 1994) and severity of PD was assessed with Hoehn and Yahr (1967) staging in both the ON- and the OFF-state. The OFF-state reflects withdrawal from dopamine replacement therapy for at least 12 h or from controlled-released drugs, such as dopamine agonists, for at least 72 h. The ON-state was defined as patient's best response to 200 mg of levodopa after the OFF-state. Neuropsychological assessments were acquired in the regular daily medication ONstate. For subsequent analyses, the LEDD of dopamine agonists was calculated according to the guidelines of the German Neurological Society (Diener and Weimar, 2012). In addition, all patients were interviewed in a detailed survey that noted the side of onset and duration of disease since the first diagnoses. Two movement disorders specialists (C.E, L.T) assessed severity of PD. Our exclusion criteria were the following: (i) psychiatric comorbidities including depression (BDI-II score >19; Beck et al., 1996; Kühner et al., 2007), severe cognitive impairment or dementia (Mini Mental State Exam (MMSE) <27; Kessler et al., 2000); (ii) any other severe systemic diseases including cardiovascular diseases or diabetes mellitus; (iii) neurological diseases such as history of head trauma, stroke, brain tumor, epilepsy, or dyskinesia; and (iv) PD patients with diagnosis of ICDs.

### Neuropsychological Assessment

The Barratt Impulsiveness Scale (BIS) is a self-report questionnaire to evaluate impulsivity, which consists of 30 four-point Likert-type items reflecting frequency of occurrence.  The BIS can be divided into three sub-scores including attention, motor, and non-planning impulsiveness. Higher BIS scores reflect higher level of impulsivity (Patton et al., 1995).

### Preprocessing and Analysis of FDG-PET Data

Preprocessing of fluorodeoxyglucose positron emission tomography (FDG-PET) images was carried out using Statistical Parametric Mapping (SPM8) (Wellcome Trust Center for Neuroimaging, London, UK) as described before Drzezga (2009), Eggers et al. (2014) and Tahmasian et al. (2015b). First, the scans were normalized to the standard stereotactical space using the standard PET template and then smoothed using a 6 mm full width at half maximum (FWHM) Gaussian filter.

### FDG-PET Data Acquisition

As described previously (Eggers et al., 2009), a high-resolution 24-detector ring PET scanner (ECAT EXACT HRRT, Siemens CTI, Knoxville, TN, USA) with 207 transaxial image planes and 1.219 mm voxel size was used in this study. Images were acquired with subjects in resting position, with background noise reduced, and with light dimmed. After the injection of 370 MBq of 18F-fluorodeoxyglucose (FDG), cerebral glucose metabolism was measured, reflecting the regional neural activity. Arterialized venous blood sampling allowed absolute quantification for all participants. The imaging was performed in the 3-D mode and was subsequently reconstructed as well as corrected for random artifacts, head motion, attenuation and scatter. The resolution of the reconstructed images was almost isotropic with 2.2 mm FWHM in the center and 2.5 mm FWHM at 10 cm off-axis. The FDG-PET measurement was performed with subjects in their regular medicated state (ON-state) to decrease head motion during scanning and to evaluate neural activity in a similar condition in terms of impulsivity in their daily routine.

#### Statistical Analyses

Following our hypothesis, we divided our patients into two groups based on their BIS scores according to the published standards i.e., patients with higher impulsivity (BIS > 65, n = 8) and lower impulsivity (BIS ≤ 65, n = 16; Voon et al., 2007; Stanford et al., 2009). For group comparisons of demographic and neuropsychiatric data, we carried out two-sample t-tests as a parametric test applied on normally distributed data, and Mann-Whitney U-tests as a nonparametric test for not normally distributed data and also Fisher's exact test for sex difference in the Statistical Package for Social Sciences, version 22 (SPSS). P-values less than 0.05 were considered statistically significant.

For group comparisons of FDG-PET data, a voxel-wise two-sample t-test in SPM8 was performed across the wholebrain, while PET images were normalized by the wholebrain FDG uptake values. The initial uncorrected threshold of 0.001 was applied for group comparison and results were reported as significant at p-value less than 0.05 with familywise error (FWE) correction of the cluster-level. This analysis was controlled for covariates, including age, gender, severity of disease (UPDRS III OFF) and LEDD for dopamine agonists. We chose dopamine agonist LEDD instead of total LEDD because it has been shown that dopamine agonists change the activity of the OFC and rostral cingulate region in PD (Van Eimeren et al., 2009, 2010). Results with total LEDD as a covariate were highly similar (not shown). These analyses yielded a volume-of-interest (VOI) that showed significant metabolic changes in patients with higher impulsivity level compared to patients with lower impulsivity level. We chose the VOI based on significant metabolic changes in the whole brain voxel-wise group comparison to be independent from selection bias of a-priori defined regions. Subsequently, we extracted the absolute averaged FDG-metabolism within the mentioned VOI for each individual subject as applied previously in several neuroimaging studies (Matsuda et al., 2012; Tahmasian et al., 2013, 2015b; Wehrl et al., 2013; Klupp et al., 2014, 2015), then normalized those scores (FDG scores from the VOI divided to global uptake scores) and then performed independent t-test between groups using SPSS.

To detect the association between the FDG-metabolism and impulsivity, we performed a partial correlation analysis between the normalized averaged FDG-metabolism scores of the VOI and the total and sub-scores of BIS across all 24 patients in SPSS, controlling for covariates such as age, gender, severity of disease (UPDRS III OFF) and LEDD for dopamine agonists.

### RESULTS

### Demographic and Neuropsychological Data

Our sample consisted of 24 non-demented, non-depressed, non-ICD PD patients. Demographic information is summarized in **Table 1**. The mean total BIS for the patients with low level of impulsivity was 53.18 (SD 7.60; range 41–63) and for the patients with a high level of impulsivity was 70.37 (SD 4.17; range 65–79). Between groups, there were significant differences on the total and sub-scores of BIS, including attention, motor and non-planning (p < 0.05, **Table 1**). Group comparisons demonstrated no significant differences regarding the severity of disease and severity of motor symptoms. However, there was a significant difference between LEDD calculated only for dopamine agonists as suggested previously (Tomlinson et al., 2010). Hence, we controlled for the effects of dopamine agonists in further analyses. One should note that there was trend towards

TABLE 1 | Demographic and neuropsychological data (a = Mann-Whitney-Test, b = t-test, c = Fisher's exact test, degree of freedom was 22 for all group comparisons, results presented as mean ± SD).

Abbreviations: BIS, barratt impulsiveness scale; LEDD, levodopa equivalent daily dose; MMSE, mini–mental state examination; PD, Parkinson's disease; SD, standard deviation; UPDRS, unified Parkinson's disease rating scale.

a significant group difference regarding the LEDD-total and duration of disease since diagnosis.

### PD Patients with Higher Impulsivity Level Revealed Increased Glucose Metabolism in the Fronto-Insular Network

Voxel-wise two-sample t-test between groups demonstrated higher metabolism within the fronto-insular network including the OFC, medial-frontal gyrus, ACC and insula, mainly on the right hemisphere (based on Automated Anatomical Labeling atlas; Tzourio-Mazoyer et al., 2002) in subjects with higher impulsivity level compared to individuals with lower level of impulsivity (p < 0.05, FWE corrected; **Figure 1A**, **Table 2**). Moreover, patients with higher impulsivity revealed higher averaged FDG-metabolism using the group difference VOIs determined by SPM8 (mean ± SD = 2.28 ± 0.14 vs. 1.97 ± 0.10; **Figure 1B**). On the other hand, PD patients with higher level of impulsivity had significant decreased FDG-metabolism in the superior parietal gyrus and occipital cortex compared to PD patients with lower level of impulsivity (p < 0.05, FWE corrected, not shown).

### Association between Impulsivity and Glucose Metabolism in the Fronto-Insular Network

We assessed the link between FDG-metabolism of the frontoinsular network and BIS scores. Results showed a positive correlation between the averaged FDG-metabolism and total BIS scores (r = 0.761, p < 0.001) across all patients (**Figure 2A**). Furthermore, significant positive correlations were found between the FDG-metabolism and BIS sub-score for attention (r = 0.646, p < 0.05), motor (r = 0.506, p < 0.05), and non-planning impulsivity (r = 0.670, p < 0.001; **Figures 2B–D**).

## DISCUSSION

To assess the neural correlates of impulsivity in PD patients, we compared glucose metabolism of patients with higher impulsivity level and patients with lower impulsivity level. We found that patients with higher impulsivity level showed increased glucose metabolism within the fronto-insular network including the OFC, medial frontal gyrus, ACC, and right insula (**Figure 1**). Moreover, our findings demonstrated positive correlations between the averaged FDG-metabolism of those regions and BIS scores (i.e., total and sub-score for attention, motor, and nonplanning impulsivity) across all patients (**Figure 2**). These results provide further evidence that higher impulsivity is linked with altered function of the fronto-insular network. Our findings are in line with previous reports indicating that high impulsivity is associated with structural and functional changes of regions associated with reward-related decision making and impulse control behavior including the OFC and ACC in healthy controls and individuals at ultra-high risk for psychosis (Horn et al., 2003; Brown et al., 2006; Cilia et al., 2008; Matsuo et al., 2009a; Cho et al., 2013; Lee et al., 2013). PD patients with higher level of impulsivity also demonstrated lower FDG-metabolism in the superior parietal gyrus and occipital cortex compared to other group. Based on our hypothesis we did not expect changes in these parieto-occipital regions. These findings might

TABLE 2 | Voxel-wise group comparison t-test demonstrated increased metabolism in PD patients with higher impulsivity compared to PD patients with lower impulsivity.

Abbreviations: FWE, family-wise error; MNI, Montreal Neurological Institute.

be additionally due to the increased FDG-metabolism in the fronto-insular network a secondary compensatory change of network activity.

Its worthy to note that FDG-PET imaging provides a quantitative measurement of regional metabolism within the synaptic terminals of the neuron-astrocyte functional unit. In detail, after injection of FDG, its tissue uptake increases in the active region, which correlates with the local metabolism of brain tissue. Hence, increase of glucose uptake provides indirect evidence of higher synaptic metabolism in a particular region (Lucignani and Nobili, 2010).

### Neural Correlates of Impulsivity within the Fronto-Insular Network

Distinct brain regions are responsible for processing of rewardrelated learning, goal-directed actions, decision-making and the formation of habits (Schultz et al., 2000; Torregrossa et al., 2008). Among them, the OFC is involved in sensory and emotional integration, encoding the affective value of reinforcers and evaluation of the expected rewards/punishments of a decision. Therefore, the OFC has an important role in adaptive decisionmaking, guiding behavior, judgments, and behavioral regulation (Kringelbach, 2005; Torregrossa et al., 2008; Schoenbaum et al., 2011). Animal studies revealed that OFC lesions result in failure to assess the value of an outcome under changing conditions, improper inhibition of motor responses, devalue the reinforce, increase in habitual responding and increased premature responses (Torregrossa et al., 2008). Thus, it seems that activity of the OFC is essential for proper impulse control. Moreover, it has been shown that the association of the lateral PFC to both aggression and attentional impulsivity depends on OFC contribution (Gansler et al., 2011). In a functional neuroimaging (fMRI) study, Horn et al. (2003) applied

FIGURE 2 | Positive correlation between the averaged FDG-metabolism within the group comparison volume-of-interest (i.e., the fronto-insular network) extracted for each subject and the total BIS scores (A), attention BIS score (B), motor BIS score (C), non-planning BIS score (D). Partial correlations were performed across all 24 PD patients with additional covariates such as age, sex, severity of disease and LEDD for dopamine agonists; ∗p < 0.05). BIS, Barratt Impulsiveness Scale; LEDD, levodopa equivalent daily dose.

Go/No-Go task. This task is often used to assess a participant's ability to sustain attention and inhibit responses. The authors demonstrated a significant activation in the anterior lateral OFC during the inhibition task. They showed that subjects with higher impulsivity activated the right inferior frontal gyrus, posterior lateral OFC and anterior insula (Horn et al., 2003). Similarly, our results support the idea that hyperactivity of the OFC is essential for proper inhibition in PD patients with high impulsivity.

The OFC has reciprocal connections with the ACC, which is involved in executive functions such as attention, inhibition and emotion regulation (Devinsky et al., 1995; Banks et al., 2007; Rushworth et al., 2007). Another fMRI study demonstrated that activation of several regions, including the ACC, was positively correlated with impulsivity level during inhibitory control paradigms, suggesting a regulatory role of ACC in modulating impulsive behaviors (Brown et al., 2006). Recently, Kerr et al. (2015) revealed that impulsivity was linked with higher activation of the ACC and amygdala during anticipation of the primary reward. In addition, impulsivity was negatively associated with functional connectivity between the ACC and amygdala (Kerr et al., 2015). Wilbertz et al. (2014) highlighted a link between urgency as an impulsivity subdomain and a network including the inferior frontal gyrus, anterior insula and dorsal ACC. Another Go/No-Go fMRI study revealed that subjects with internet gaming addiction had hyperactivity during No-Go trials in several brain regions including the left superior medial frontal gyrus, right ACC, right superior/middle frontal gyrus. Interestingly, activation of the superior medial frontal gyrus was positively associated with BIS-11, suggesting an association between impulsivity and impaired prefrontal impulse inhibition (Ding et al., 2014). Moreover, it has been reported that self-control and successful inhibition of impulsive behaviors, particularly motor impulsivity, and reactive aggression depends on the anterior insula activity (Dambacher et al., 2015).

Several studies highlighted the relationship between gray matter volume changes and the BIS scores in healthy controls and subjects at ultra-high risk for psychosis (Matsuo et al., 2009a; Cho et al., 2013; Lee et al., 2013). For example, Cho et al. (2013) found positive correlations between volume of mPFC, dlPFC, OFC, ACC and total, non-planning, and attention/cognitive BIS scores but not with motor impulsivity. Churchwell and Yurgelun-Todd (2013) found a positive linear association between anterior insula thickness and non-planning imulsivity, and both of them had negative correlations with age. Similarly, it has been demonstrated that patients with major depressive disorder, alcoholism, posttraumatic stress disorder, attention-deficit/hyperactivity disorder, antisocial personality disorder or bipolar disorder showed positive correlations between the left, right, and total OFC gray matter volume and BIS motor impulsivity scores and aggression (Antonucci et al., 2006). Our results are similar to these findings, as we found significant positive associations between the FDGmetabolism of the fronto-insular network and the total, nonplanning, attention and motor impulsivity. On the other hand, Matsuo et al. (2009a) demonstrated that gray matter volumes of the bilateral OFC and left ACC were negatively correlated with the total BIS scores. More specifically, they found negative associations between the right OFC volume and non-planning impulsivity, and between the left OFC volume and motor impulsivity.

In the right fronto-insular cortex and anterior limbic area of the human brain there are large bipolar neurons so-called ''von Economo neurons'', particularly in the ACC and the anterior insula. These neurons are involved in empathy, social awareness, and self-control and their numbers are reduced in several neuropsychiatric disorders including fronto-temporal dementia, schizophrenia, bipolar disorder, addiction and ICDs (Allman et al., 2011a,b; Kim et al., 2012).

Taken together, the above-mentioned studies showed that the fronto-insular network is critically involved in impulsiveness. With regards to our findings of the increased metabolism within this network, it is possible to speculate that subjects with higher impulsivity scores need this network to be more active in order to inhibit their impulses, compared to subjects with lower impulsivity.

### Impulsivity vs. Impulse Control Disorders

Although impulsivity is a natural behavior that can be controlled by inhibitory mechanisms in healthy individuals, it can be considered as a risk factor for ICDs in patients with PD (Cilia and Van Eimeren, 2011; Probst and Van Eimeren, 2013). Patients with ICDs such as pathological gambling, hypersexuality, and kleptomania have more compulsive characteristics resulting in failure to resist aggressive impulses (Weiss and Marsh, 2012; Fineberg et al., 2014). Although the neural systems for regulating impulsive, compulsive, and habitual behaviors have an overlapping regional pathophysiology (i.e., activation of the OFC), impulsivity and subdomains of ICDs have different pathophysiological mechanisms (Torregrossa et al., 2008; Leeman and Potenza, 2012). Accordingly, one should be aware that impulsivity and ICDs are conceptually and pathophysiologically distinct.

A recent study demonstrated that PD patients with ICDs had cortical thinning in fronto-striatal circuitry including the right superior OFC, left rostral middle frontal, bilateral caudal middle frontal region, corpus callosum, right accumbens, as well as an increase in the left amygdala. Moreover, they found a positive correlation between severity of impulsive symptoms and cortical thickness of left rostral middle frontal, inferior parietal, and supramarginal regions (Biundo et al., 2015).

Several fMRI studies revealed that dopamine agonist therapy mediates the ability of PD patients to control their impulses and may lead to high impulsivity and ICDs (Cilia et al., 2008; Van Eimeren et al., 2009, 2010; Ray and Strafella, 2010; Voon et al., 2011a; Weiss and Marsh, 2012; Napier et al., 2015). For example, it has been suggested that PD patients with pathological gambling have an ACC-striatal disconnection and also a hyperactivity in the OFC, hippocampus, amygdala, insula, and ventral pallidum, possibly associated with a drug-induced overstimulation of relatively preserved reward-related neuronal systems (Cilia et al., 2008, 2011). Van Eimeren et al. (2010) demonstrated that in the lateral OFC, rostral cingulate zone, amygdala, and external pallidum, healthy controls had higher activity in response to dopamine agonist, while PD patients with pathological gambling showed a significant DA-induced reduction of activity. To control for the influence of dopamine agonists on the suggested relationship (Tahmasian et al., 2015a), we applied a partial correlation approach, which accounts separately for influences of medication (LEDD for dopamine agonists) on local FDGmetabolism. Furthermore, the results were also independent from age and sex.

Due to our results, we assume that higher activity of fronto-insular network is necessary in patients with higher impulsivity. In particular, patients with higher impulsivity level have self-control deficiency and tendency to do problematic risky behaviors (Owsley et al., 2003; Fineberg et al., 2014; Gvion et al., 2014). Thus, they need to inhibit their impulses more than subjects with lower impulsivity level. As mentioned above, patients with lesions or atrophy in the OFC and ACC show more impulsive, antisocial, and risky behaviors (Winstanley et al., 2004; Berlin et al., 2005; Matsuo et al., 2009a,b; Kerr et al., 2015). Taken together, we suggest that the observed increased activity in the inhibitory network (Van Eimeren et al., 2010) within the fronto-insular regions is necessary to allow an active inhibition of risk-related impulsive behaviors, particularly in patients with higher impulsivity (for review, see Seguin, 2004; Crews and Boettiger, 2009; Perry et al., 2011). One should note that our subjects had higher impulsivity and not ICDs diagnosis. Hence, future studies should systematically compare subjects with different levels of impulsivity vs. healthy subjects and patients with ICDs to provide explicit proof of this hypothesis.

### LIMITATIONS

Our study has several limitations: (i) albeit the BIS scale is the most common tool to assess of impulsivity, it is a selfreport and subjective questionnaire. Thus, it is not an objective

### REFERENCES

assessment of impulsivity; (ii) it should be noted that our findings may be limited to moderate impulsivity level of our subjects and can not be generalized to ICDs in PD patients; (iii) the observed difference in glucose metabolism might be due to underlying mechanism including the different levels of von Economo neurons in the fronto-insular network, receptor availability or genetic difference across subjects but these data were not available for us to correct for their influence. In particular, gray matter volume difference is probably one of the most important confounding factors, but we did not have structural MRI data from these subjects to perform atrophy correction; and (iv) our sample size was rather small, particularly in patients with higher impulsivity level, we also did not include healthy control subjects or PD patients with ICDs. Future studies with larger sample size should consider atrophy correction and systematically compare healthy controls and PD patients with and without ICDs.

### CONCLUSION

In summary, the current study provides evidence that PD patients with higher impulsivity level have increased glucose metabolism within the fronto-insular network compared to PD patients with lower impulsivity level. The data are consistent with several structural and fMRI studies, suggesting that the activity of fronto-insular network is essential for proper impulse inhibition, particularly in PD patients with higher impulsivity. Our findings shed new light on the neural correlates of impulsivity in PD.

### ACKNOWLEDGMENTS

This study was supported by the German Research Foundation (KFO 219, TP10). We wish to thank all patients for their participation in the study. Furthermore, we thank the staff of the Departments of Neurology, University Hospital of Cologne and also Max Planck institute for Metabolism Research for their help in recruitment and data collection.

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**Conflict of Interest Statement**: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2015 Tahmasian, Rochhausen, Maier, Williamson, Drzezga, Timmermann, Van Eimeren and Eggers. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution and reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Hoarders Only Discount Consumables and Are More Patient for Money

#### *Brian D. Vickers, Stephanie D. Preston\*, Richard Gonzalez and Andrea M. Angott†*

*Department of Psychology, University of Michigan, Ann Arbor, MI, USA*

Individuals with hoarding disorder (HD) excessively acquire and retain goods while also exhibiting characteristics of impulsivity and addiction. However, HD individuals do not always perform impulsively in experiments, they do not appear interested in money, and they exhibit many features of risk-aversion and future-planning. To examine impulsivity in HD, we compared validated community participants high and low in hoarding tendencies on questionnaire measures of hoarding and impulsivity as well as a standard experimental measure of impulsivity (intertemporal discounting) that was modified to compare decisions about money, pens, and snacks. Common discounting effects were replicated. Compared to the low hoarding group, the high hoarding group was more impatient for consumables (pens and snacks) but they were more patient for money. This increased patience for money in high hoarding individuals is in contrast to all other studies on discounting in disordered populations, but consistent with the phenomenology of HD. HD does not appear to be driven by a fundamental inability to wait, but rather a specific, potent desire for consumable rewards.

#### *Edited by:*

*Matthias Brand, University of Duisburg-Essen, Germany*

#### *Reviewed by:*

*Astrid Müller, Hannover Medical School, Germany David F. Tolin, Institute of Living, USA*

> *\*Correspondence: Stephanie D. Preston<EMAIL_ADDRESS>
#### *†Present address:*

*Andrea M. Angott, SciMed Solutions, Durham, NC, USA*

> *Received: 25 September 2015 Accepted: 08 February 2016 Published: 04 March 2016*

#### *Citation:*

*Vickers BD, Preston SD, Gonzalez R and Angott AM (2016) Hoarders Only Discount Consumables and Are More Patient for Money. Front. Behav. Neurosci. 10:30. doi: 10.3389/fnbeh.2016.00030*

Keywords: hoarding disorder, discounting, impulsivity, addiction, consumption

## INTRODUCTION

Individuals with hoarding disorder (HD) are characterized by their excessive acquisition and retention of goods with limited or no value, leading to significantly cluttered living spaces that cannot be used for their original purpose, and significant associated distress and life impairment (Frost and Hartl, 1996). HD now occupies its own diagnostic category in the DSM-V under the OCD and related disorders section (American Psychological Association [APA], 2013), but it is often comorbid with other impulsive–compulsive disorders, including OCD, compulsive buying, gambling, and trichotillomania (Samuels et al., 2002; Frost et al., 2011), leading some to characterize it as one of an extended family of compulsive–impulsive spectrum disorders (e.g., see McElroy et al., 1995). HD also shares many features with addiction, which is also considered by many to be a disorder of impulsivity (reviewed in Bickel and Marsch, 2001; Perry and Carroll, 2008; Odum, 2011). Like addiction, HD involves prioritizing rewarding items over other important life priorities like safety, shelter, and social relationships; it is associated with neural changes in the mesolimbocortical system (reviewed in Wang et al., 2012); and it is difficult to remit even if there are some promising treatment options (Tolin et al., 2015). Thus, despite now occupying its own diagnostic category, transdiagnostic approaches need to continue to understand the role of impulsivity in HD (e.g., Tolin and Villavicencio, 2011; Rasmussen et al., 2013; Timpano et al., 2013).

There is empirical evidence that HD individuals are more impulsive than comparison individuals (comparison groups range across studies from typical community samples to clinical groups with and without hoarding symptoms). However, the evidence is more consistent when individual differences measures are used than when experimental tasks are used. This inconsistency likely reflects the fact that there are multiple, distinct constructs associated with impulsivity including risk seeking, present-focus, response inhibition, loss of control, and delay discounting (see Perry and Carroll, 2008). HD individuals scored more highly on multiple impulsive action and inattention individual difference survey measures (Grisham et al., 2007; Tolin and Villavicencio, 2011; Timpano et al., 2013). They were also more impulsive on a signal detection task despite performing the task more slowly (Grisham et al., 2007) and they had poorer response inhibition on this task, but this appears to be because the HD participants were older than the control participants (Rasmussen et al., 2013). In one study, HD individuals preferred the larger, more immediate reward on the Iowa Gambling Task (Lawrence et al., 2006), but they performed like controls in two other studies using the same task (Grisham et al., 2007; Tolin and Villavicencio, 2011). Thus, evidence for impulsivity in HD is more questionable to date for experimental data, and research has yet to compare HD individuals to controls on a common experimental measure of impulsivity in behavioral economics the intertemporal discounting task (ITD).

Beyond experimental inconsistencies linking HD to impulsivity, multiple facts question the assumption that HD individuals do have a domain-general problem with impulsivity. HD individuals do not impulsively acquire or keep money and, in fact, they use money especially frugally to obtain goods (Samuels et al., 2008; Frost et al., 2009). The documented paranoid personality traits in HD and their excessive fear of events like home break-ins (Samuels et al., 2008), as well as their indecision and perfectionism (Frost and Gross, 1993), are also characteristics that are more associated with risk-aversion and a future-focus that is unlike that associated with addiction or trait impulsivity (Loewenstein et al., 2001). The goal of hoarding behavior *per se* can also be construed as fundamentally risk-avoidant and future-oriented, as individuals acquire and protect resources that they think they may need later (Frost and Hartl, 1996)—much like the way food-storing animals create, maintain, and protect hoards of food to prepare for future scarcities (Preston and Jacobs, 2001, 2005; Preston, 2013). The hoarding in HD also results in significant interpersonal conflict, discomfort, and difficulty using living spaces (Frost et al., 2000), which could be construed as a short term pain that is being suffered in order to provide a long-term benefit or protection from risk—again unlike impulsivity. Thus, from a global perspective, hoarding behavior actually bears many hallmarks of extreme patience, at least regarding the ultimate goal of the behavior. HD individuals may simply report fears about future needs to mask an irrational impulse to acquire; however, it is also possible that their impulsiveness is a proximate mechanism by which HD individuals achieve a genuine, ultimate goal to provide for the future.

Taken together, impulsiveness in HD needs to be examined empirically, across domains, particularly using the standard, accepted laboratory task for measuring impulsivity in the addiction literature—the ITD. On a typical ITD trial, participants are asked to make a forced choice between accepting a smaller quantity of a reward sooner (e.g., \$5 today) vs. a larger quantity of the reward later (e.g., \$12 in a month). The exact quantities of the reward sooner vs. later and the precise latency that participants have to wait for each option are systematically altered over many otherwise identical trials so that a "discounting rate" can be calculated per person, over time, representing the degree to which they are susceptible to prefer a smaller amount when delivered sooner—the operationalization of impulsivity.

The current study compared individuals with high and low hoarding tendencies on their intertemporal discounting for goods, food, and money. Most hoarding studies only examine the degree to which individuals acquire or fail to discard material goods; however, we included food because we were interested in the degree to which hoarding reflects an evolved, adaptive food-storing instinct shared with other species (Preston, 2013). We also included money not only because it is the typical unit of reward for ITD tasks in behavioral economics, but also because we hypothesized that high hoarding participants would not be uniformly impulsive, based on anecdotal reports and case studies suggesting that HD individuals are not interested in money *per se*. It is also useful for the large ITD literature to realize that not all individuals are highly motivated by money, which is almost always the only reward provided or compared in behavioral economic studies. Based on our evolutionary view and the phenomenological reports of HD, we predicted that high hoarding individuals would be impulsive for goods and food (pens and snacks), but not money.

### MATERIALS AND METHODS

### Participants

In order to ensure a broad distribution of hoarding tendencies, participants were recruited using two different advertisements for a decision making study, one that specifically asked for participants who would consider themselves "packrats" and another that did not specify. Of the 38 participants, 27 were community members of any adult age who were compensated \$10 for participation (16 high hoarding from the packrat advertisement and 11 low hoarding from the unspecified advertisement). The remaining 11 participants were recruited and compensated with course credit through the university psychology pool (three high hoarding, eight low hoarding). All participants were female, which is common in HD studies (e.g., Frost and Gross, 1993) and practical, given a strong bias for females to respond to community advertisements looking for "packrats" (hereafter referred to as the "high hoarding" group when not discussing the advertisement itself).

This was a study of individual differences rather than the clinical diagnostic category of HD. As such, participants were not given an official clinical interview or diagnosis and they are not referred to here as hoarders or individuals Vickers et al. Discounting in Hoarders

with HD. Instead, we placed participants into high and low hoarding groups based on their response to the advertisements and a validated clinical instrument with high specificity and sensitivity for detecting hoarding with scores *>*14 on the Hoarding Rating Scale (HRS) (Tolin et al., 2010). The HRS is typically administered in a clinical setting using an interview format, but we have adapted it into an easy-toadminister self-report questionnaire that reliably demonstrates individual differences in the non-clinical population, which also correlate with other individual difference measures that are commonly elevated in HD and with a longer published, validated hoarding questionnaires [i.e., the Savings Inventory-Revised (SI-R); Frost et al., 2004] (see, Wang et al., 2012). To ensure a clear distinction between low and high hoarding participants, rather than just split participants by the advertisement they responded to or whether their HRS scores were above or below of 14, we allowed participants to be placed into the high hoarding group regardless of which advertisement they responded to if they had an HRS scores *>* 19 (median = 27.00, mean = 26.74, *SD* = 4.96; *n* = 19, mean age = 35.05, *SD* = 17.42) and we allowed participants to be placed into the low hoarding group, regardless of which ad they responded to, if they had an HRS score *<* 13 (median = 4.00, mean = 4.53, *SD* = 4.03; *n* = 19, mean age = 48.21, *SD* = 15.20). Participants with HRS scores between 13 and 19 were excluded. The two groups did differ statistically in age, *t*(36) = 2.48, *p* = 0.018, η2 p = 0.146, but in the direction opposite to the predicted confound, as usually HD individuals are older but our high hoarding individuals were younger than our low hoarding individuals. Age was used as a factor in analysis (below) and did not influence the results. High and low hoarding participants did not differ statistically on income, based on results from the Happiness Spending Inventory (Dunn et al., 2008) that we administered for another study completed by the same participants, *t*(36) = 1.04, *p* = 0.306.

Participants were consented and tested individually in the laboratory using MediaLab (Jarvis, 2006) on a Dell desktop PC. All participants were consented and debriefed in writing and in person; all procedures were approved by the Institutional Review Board of the University of Michigan.

### Intertemporal Discounting Task

The ITD task used in this study consisted of three blocks (order randomized) of intertemporal choices about money, pens, or snacks. Units were equilibrated across domains as money was listed in whole dollars and pen and snack choices were worth approximately one dollar each. Participants selected their favorite pen or snack from an array, and all choices thereafter used their chosen item to ensure task interest (e.g., Easy Touch or Precise Gel pens; M&M or Oreo snacks). Each block consisted of 45 choices between a smaller reward (x1) sooner (t1) or a larger reward (x2) later (t2) (see **Table 1**). Reward quantities (0–85) and delays (now to 135 days from now) varied across trials.

Before each block participants would select their most preferred stimulus for that domain (e.g., Easy Touch vs. Precise Gel pens in the pen domain). Then, each trial would show them a choice between a smaller amount of their selected item sooner or a larger amount of that same item later, such as 3 Easy Touch pens today or 12 Easy Touch pens in 19 days. The task was self-paced including the length of the break between each of the three blocks. The task within each domain included nine trials used in a prior ITD study of addiction (Kirby et al., 1999), plus 36 more trials that we added to measure responses to smaller and larger quantities and shorter and longer delays than was previously included (described in full below), and to systematically investigate different aspects of the choice attributes. This led to a total of 135 trials for each participant.

Additional trial types were included to expand upon results in four ways. A set of trials at small x1, x2 values was included to test whether discounting effects are present even for relatively small amounts. Another set of trials systematically varied time and quantity to more precisely estimate how changes in time and quantity influence choice. The final two types of trials investigated whether high or low hoarding participants were more likely to violate assumptions of typical discounting models. Exponential discounting assumes a steadily decreasing likelihood of choosing the larger later (LL) option as the time to acquire it increases at a rate proportional to 1 1+*t* , where *t* is time. Power utility is based on the fact that people value increasing amounts less (i.e., diminishing utility); power utility requires that if amounts are both multiplied by a constant, then choices should not change (see **Table 2** for a summary of the specific trials). Because the present study used amounts in three different domains (money, pens, snacks) it is important to distinguish whether patterns of choice are due to changes in the utility of the amount or to changes in the discounting of time. Different discounting patterns could arise when comparing, say, choices for money and choices for pens, not because participants discount time differentially in those two domains but because they value increments in the amounts differentially. The ability to separate utility from discounting comes from our design where we systematically manipulate elements of the amounts and time in each trial.

### Psychopathological Symptoms

After the ITD, participants completed psychopathology inventories that could be correlated with differences in impulsivity emerging from the task, including the HRS, SI-R, Obsessive Compulsive Inventory-Revised (OCI-R; Foa et al., 2002), Barratt Impulsivity Scale (BIS; Patton and Stanford, 1995), Beck Anxiety Inventory (BAI; Beck et al., 1988a), and Beck Depression Inventory II (BDI; Beck et al., 1988b). All survey measures were administered after the ITD task to avoid priming participants with issues related to their psychopathology before the task.

### Analysis

To assess overall discounting, the more impulsive, SS responses were recoded as 0 and the more patient, LL responses were recoded as 1. Results are presented in terms of the proportion of LL choices, representing the degree of patience or willingness to wait longer for the larger reward. But as is common in the behavioral economics literature, any time a participant or group is referred to as having a higher or steeper discount rate, that

#### TABLE 1 | Trials in the Intertemporal Discounting Task (ITD), sorted by trial type.

*Options denoted as in the text with x1 representing the smaller amount, t1 the sooner time, x2 the larger amount, and t2 the later time; all choices were presented as pairs of (x*1*, t*1 *vs. x*2*, t*2*). Original Kirby items were taken from Kirby et al. (1999). Trials with very small quantities offered now or after a delay were added to their set as were trials that offered their moderate reward amounts with a smaller or a larger delay than they used. New items were added to test specific functional forms and to separate delay from amount. The separate subsections of analysis are presented in the final four columns, with numbers depicting which trials (rows) were relevant for that comparison.*

#### TABLE 2 | Subcategories of trials added to examine different attributes of discounting.

*Each subcategory is listed below with the number of trials per domain, which variables were manipulated vs. held constant, and the attribute of interest. x1, smaller amount, x2, larger amount, t1, sooner time, t2, later time.*

means that they value the more patient LL option less in favor of the more impulsive SS option.

All contrasts used the following model except where noted: a logistic curve was fit using a logistic hierarchical linear model (HLM) including fixed factors for group (low hoarding = 0; high hoarding = 1), domain (money, pen, snack), their interaction, and a random factor for each participant's intercept. Planned contrasts compared discounting money vs. consumables (pens and snacks together) (2, −1, −1) and pens vs. snacks (0, 1, −1). All discounting results remained after separately controlling for age or participant population (community vs. psychology pool), but a few small shifts in result thresholds are noted below under individual difference correlations.

### RESULTS

Analyses are organized by comparing overall discounting rates first, followed by the specific tests for the degree to which choices were influenced by delay and quantity, followed by tests to examine behavioral consistency with both exponential and power law forms of discounting (defined below).

### Overall Discounting

Overall rates of discounting were not different in the two groups, χ2(1) = 1.35, *p* = 0.25, but they did differ by domain, χ2(2) = 7.97, *p* = 0.021, and the two interacted, χ2(2) = 105.76, *p <* 0.001. Money was discounted less than consumables overall, odds ratio (*OR*) = 1.10, *z* = 2.76, *p* = 0.006, and the difference in discounting money compared to pens and snacks was 61% larger in high hoarding than low hoarding participants, money vs. consumables contrast by group: *OR* = 1.61, *z* = 10.17, *p <* 0.001. This interaction was due to high hoarding individuals discounting pens and snacks more steeply than low hoarders and money less steeply than low hoarders, and low hoarders discounting all three domains at similar levels. The contrast between pens and snacks was also marginally larger in high than low hoarders, *OR* = 0.86, *z* = −1.78, *p* = 0.075, with high hoarders responding more impulsively for pens than for snacks, *OR* = 0.85, *z* = −2.81, *p* = 0.005, and low hoarders again treating them similarly, *OR* = 0.98, *z* = −0.35, *p* = 0.729 (see **Figure 1A**). These main effects and interactions were consistent across the subsets of trial types reported below and so are not re-presented in each subsection.

### Effect of the Later Reward Amount with a Fixed Early Reward and Delay

This subset of trials examined the effect on choice of increasing the larger amount (x2 = 2, 4, 6, 8, 10) while fixing the smaller amount (x1 = 1) and fixing the delay periods (t1 = 0, t2 = 16 days). The logistic HLM with a centered x2 parameter found that people generally waited longer as the amounts increased, x2 effect: *OR* = 1.33, *z* = 4.95, *p <* 0.001, and this effect did not differ between high and low hoarding individuals, *OR* = 1.03, *z* = 0.37, *p* = 0.711. As with the overall discounting effects above, as later quantities increased people were still more patient for money than for consumables, domain effect: χ2(2) = 7.30, *p* = 0.026; money vs. consumables: *OR* = 1.24, *z* = 2.00, *p* = 0.046, and waited marginally longer for more pens than for more snacks, *OR* = 0.71, *z* = −1.86, *p* = 0.063. At these low reward values, there was no omnibus group difference between money and consumables, group by money vs. consumables: *OR* = 1.22, *z* = 1.29, *p* = 0.196, but high hoarding individuals were more impulsive for snacks than pens while low hoarding individuals were more impulsive for pens than snacks, group by pens vs. snacks: *OR* = 2.13, *z* = 2.97, *p* = 0.003. These slopes across values of x2 did not significantly interact by domain and group, x2 by domain by group interaction, χ2(2) = 0.70, *p* = 0.704. Thus, small and increasing quantities of the larger, later reward (holding the three terms x1, t1, and t2 fixed) reveals no difference in impulsivity for money compared to consumables as x2 increases, and at these small amounts high hoarding individuals actually wait longer to acquire more pens than snacks compared to controls (see **Figure 1B**).

### Effect of the Delay and Moderate Reward Amounts with a Fixed Initial Time (Now)

A second subset of trials compared choices between nine original trials (Kirby et al., 1999) to two identical types that varied t2 to be earlier or later than the original one. The initial time was fixed to deliver immediate reward (t1 = 0), and the early

participants made consistent SS choices are in the lighter shade and the number of times participants made consistent LL choices are in the darker shade.

and later reward were moderately sized and fixed within sets of three choices (e.g., x1 = 24, x2 = 35) (see **Table 1**). HLM analyses compared the effect of the delay within sets of three trials (original t2, earlier t2, later t2) and the effect of the total amount of reward across the nine sets of three trials. A parameter was added to the HLM for t2, ordering each of the nine triplets by their mean x1, x2 amounts to assess quantity effects with a centered linear "quantity contrast." This analysis complements the previous one by changing which variable was manipulated while leaving the others constant. Previously we only varied the second quantity and this time we only varied the length of the larger delay interval while holding the other three parameters fixed.

As the t2 delay increased, participants generally became increasingly impatient for money compared to consumables, t2 by money vs. consumables: *OR* = 0.99, *z* = −4.47, *p <* 0.001, and more impatient for pens than snacks, t2 by domain omnibus: χ2(2) = 26.85, *p <* 0.001; t2 by pens vs. snacks: *OR* = 0.994, *z* = −2.54, *p* = 0.011. But looking separately at each group, only low hoarding individuals showed this increasing impatience with longer delays for money and pens, t2 by group by domain: χ2(2) = 11.77, *p* = 0.003; t2 by domain in high hoarding individuals: χ2(2) = 0.37, *p* = 0.831; t2 by domain in low hoarding individuals: χ2(2) = 27.03, *p <* 0.001; t2 by money vs. consumables in low hoarding individuals: *OR* = 0.993, *z* = −4.48, *p <* 0.001; t2 by pen vs. snack in low hoarding individuals: *OR* = 0.994, *z* = −2.55, *p* = 0.011. There were no effects of the amount of moderate reward or any other effects in this model, χ2s *<* 2.55, *p*s *>* 0.115 (see **Figure 2**). Therefore, low hoarding individuals had high discount rates for money compared to consumables (i.e., would only choose LL after short delays for money) but high hoarding individuals had similar discount rates for both money and consumables as t2 increases.

### Effects of Adding a Constant to Both Times: Testing Exponential Discounting

Theories of exponential discounting hold that people's discounting rates decline exponentially as time increases, and the rate of discounting only depends on the difference in time between the two points. Exponential discounting assumes that with fixed quantities, delaying both t1 and t2 by the same constant will not alter choices. To assess the fit of exponential curves, crosstabs were created in the form (x1, t1 vs. x2, t2) compared to (x1, t1 + *k* vs. x2, t2 + *k*), where *k* is a constant.

The number of preference reversals were summed over the three trial pairs (i.e., 0, 1, 2, or 3 preference reversals) and compared with a Poisson HLM, but this time using the number of preference reversals as the dependent variable. The original trials involved moderate reward amounts (19–75 items) offered immediately (0 days) or after a moderate to large delay (21– 93 days), and to these trials a corresponding constant of 15, 20, or 32 days was added to both the early and the later option.

Overall, choices were consistent across groups and domains, χ2(1)s *<* 0.348, *p*s *>* 0.50. Within each domain, there was also no difference in the number of consistent compared to inconsistent choices between the low and high hoarding groups, *z*s *<* 0.20, *p*s *>* 0.60. In line with the high hoarding group's general consummatory impulsiveness, high hoarding individuals predominantly made consistently impulsive choices for pens and snacks (about eight times more often SS– SS than LL–LL) but chose more often to be consistently patient for money (50% more LL–LL than SS–SS). The low hoarding individuals, instead, were biased to be somewhat impulsive for all three domains, choosing SS–SS about 50% more often than LL–LL for pens, snacks, and money (see **Table 3**; **Figure 1C**). Thus, both low and high hoarding groups were consistent with assumptions of exponential discounting, but high hoarding individuals had many more SS–SS consistent choices for pens and snacks compared to low hoarding individuals, as was the case in the overall discounting rates.

### Effects of Multiplying a Constant by Both Amounts: Testing the Power Utility Function

The power utility function can produce effects that may be similar to discounting. If people have different utilities for, say, money and pens, then we may incorrectly attribute the difference to discounting rather than utility. Power utility assumes that when both times in the same trial are multiplied by a constant, *k*, the preferences will not change.

We compared trials that were identical except for a constant *k* multiplier that was applied to both smaller (x1) and larger (x2) reward amounts; e.g., (x1, t1 vs. x2, t2) compared to (*k* • x1, t1 vs. *k* • x2, t2). These trials all involved larger quantities of items in both smaller and larger positions (i.e., from 32 to 90) offered at two delays that were both displaced in time and never immediate (32 vs. 93 days or 29 vs. 90 days). This set of trials included one pair and one triplet. The trial pair used *k* = 1.38 with x1, x2 pairs of (32, 44) and (48, 66). The triplet used *k* = 1.36 with x1, x2 pairs of (44, 60), (55, 75), and (66, 90). Again, the Poisson test was used to compare the number of preference reversals for each domain (0, 1, 2, or 3 of the possible 3 reversals from SS to LL or from LL to SS). Supporting the power utility function, choices on these trials were consistent in both low and high hoarding groups, across domains, χ2(1) *<* 1.96, *p*s *>* 0.373. Within each

TABLE 3 | Cross-tabulations by group and domain for tests of exponential discounting, with a constant added to both times with (x1, t1 vs. x2, t2) compared to (x1, t1 **+** k vs. x2, t2**+**k).

*Each 2* × *2 Table is based on three trials per participant (each 2* × *2 table has 57 entries).*

TABLE 4 | Cross-tabulations by group and domain for tests of the power utility function, with a constant added to both amounts with (x1, t1 vs. x2, t2) compared to (k **•** x1, t1 vs. k **•** x2, t2).

*Consistent preferences are demonstrated by higher values in SS/SS and LL/LL cells (diagonals in quadrants one and three), indicating that participants decided similarly in the original decision and the k shifted version. This trial type included one pair and one triplet but to avoid overlapping choices from the triplet only the pair is presented here.*

domain, there was also no difference in the number of preference reversals between low and high hoarding groups, *z*s *<* |1.20|, *p*s *>* 0.250. The nature of the consistent choices in this set of trials was similar to that of the prior section, with high hoarding individuals being overwhelmingly, consistently impatient for pens and snacks (SS–SS, with actually no LL–LL choices for high hoarding individuals deciding about pens), but more often consistently patient for money. Conversely, the low hoarding group was biased to be consistent in all three domains by being

two to three times more impatient than patient (see **Table 4**; **Figure 1D**).

#### Individual Differences Measures

As a complementary analysis to the group-level analysis above, we also investigated whether continuous, individuallevel variation in the degree to which participants exhibited hoarding, impulsivity, and psychopathological tendencies correlated with their main outcome variable from the discounting analysis reported above. For each person we correlated their individual difference data across all of the scales with the degree to which they were more patient for money than consumables by subtracting their pooled LL choices for both pens and snacks from their percent of LL choices for money. This produced a variable ranging from +1 (100% choices for money and 0% choices for pens and snacks) to −1 (0% choices for money and 100% LL choices for pens and snacks), with the zero point representing similar proportions for money and consumables. Consistent with the group-level effects above—where high hoarding individuals were more patient for money than consumables this behavior was also significantly correlated with the continuous, individual difference measures across the whole sample including all hoarding scales (HRS, SI-R Clutter, SI-R Difficulty Discarding, SI-R Acquisition, OCI-R Hoarding), *r*s(36) *>* 0.33, *p*s ≤ 0.05, η2 ps *>* 0.10, and the BIS Attentional Impulsivity subscale, *r*(36) = 0.40, *p* = 0.012, η2 p = 0.16 (see **Table 5**). Even after controlling for age and population (community vs. or psychology pool) all results remained except that scores from the prescreening administration of the HRS became marginal in both cases and the correlations with SI-R acquisition dropped to the marginal level, *t*s(35) *>* 1.77, *p*s *<* 0.09, η2 p s *>* 0.08.

### DISCUSSION

Using a modified version of a standard impulsivity task from behavioral economics, we found reliable evidence across multiple measures and analysis strategies that people with problematic hoarding tendencies are actually more patient than people with low hoarding tendencies for money, but they are indeed less patient for consumable goods, particularly pens. The high hoarding group's greater patience for money suggests that they do not have a domain-general problem with impulsivity as has been suggested for addiction (e.g., Odum, 2011), and that they do possess the cognitive capacity to save or wait for larger reward.

To our knowledge, this is the first time a disordered population has demonstrated greater patience for money compared to controls, or to have shown discounting rates for money that are inversely correlated with discounting for consumable rewards. Extensive prior research finds that impaired populations—including those addicted to heroin, cocaine, alcohol, cigarettes, food and gambling—discount their drug of choice more steeply than money while also discounting money more steeply than controls, even compared to ex-users (see reviews in Bickel and Marsch, 2001; Perry and Carroll, 2008). The fact that drugs are usually discounted more steeply than money is attributed to the more direct impact of consumables on the biological reward system, with many studies finding greater discounting for consumables like food, candy, or beer compared to money, even in nondisordered populations (Odum and Rainaud, 2003; Odum

et al., 2006; Estle et al., 2007). We replicated this overall effect of greater discounting for consumables, but also found that participants with low tendencies to hoard treated the three domains more similarly to one another. However, the group with high hoarding tendencies discounted money less steeply and discounted consumables more steeply than controls. Thus, domain-specific discounting rates may be more powerful in populations with a focused desire. This differential treatment of money by individuals with high hoarding tendencies does accord with their real-world apparent lack of interest in making and accumulating money and their frugal use of it to obtain desired goods (Frost et al., 2009).

Our results also suggest that the delay impacts choice more than the quantity, particularly for rewards that are offered "now." The participants with high hoarding tendencies were particularly prone to impulsively obtain pens—even more so than snacks—again attesting to their domain-specific interest in goods *per se*. This is particularly interesting in light of prior studies across domains that presumed that drugs of abuse and food are discounted more steeply than money because they can be consumed (and, thus, activate the biological reward system more strongly). For the participants with high hoarding tendencies, pens are their desired item of choice and they appear to strongly drive the choice system despite not being literal consummatory reward. However, paradoxically, the individuals with high hoarding tendencies did prefer a single snack delivered immediately over more snacks delivered later while being willing to wait for a delay from now to accumulate more than one pen; they also generally preferred multiple pens immediately over more pens later. Such reversals perhaps make sense if you either consider that only single snacks can be consumed immediately (and thus have a greater appeal to people with hoarding tendencies only in cases when t1 = 0) or if wanting to accumulate material goods takes a non-linear function that eventually curves downward, whereby more pens is better but there are limits to how many you could need or how many in the short term could satisfy the desire. Regardless, these complexities attest to the need for future ITD work to include a broad range of relevant units and delays to capture important biologically-relevant stages of the process and units of interest (see also Odum et al., 2006).

The particular impatience of high hoarding individuals for immediate consumables suggests that the "incentive salience" (cf., Berridge and Robinson, 2003) of goods is what makes the items difficult for these people to resist (see an overview of the biological mechanisms for cross-domain reward in Preston et al., 2014). Similarly, in a prior study, neural activity in the nucleus accumbens—the region associated with the motivation to acquire rewarding drugs—increased during the acquisition of goods to the extent that participants reported real-world problems with hoarding (Wang et al., 2012). This rewarding property of goods for people with HD, but not for money, has been underappreciated—with most theories focusing on their indecision or fear of making mistakes (e.g., see Frost and Hartl, 1996). Perhaps fear can potentiate

*High hoarding individuals discounted money less and consumables (pens and snacks) more than low hoarding individuals, so individual differences measures were correlated with the difference score of the % larger-later (LL) decisions for money minus pens and snacks. All correlations have df* = *36. HRS, Hoarding Rating Scale. SIR, Savings Inventory Revised. OCI-R, Obsessive Compulsive Inventory, Revised. BAI, Beck Anxiety Inventory. BDI, Beck Depression Inventory. BIS, Behavioral Impulsivity Scale. STAIT, State Trait Anxiety Inventory, State.* ∗*p < 0.05, t p < 0.10.*

a proximate motivation toward goods to alleviate future uncertainty, to provide comfort in the absence of rewarding social bonds, or to provide literal, physical protection (Preston, 2013).

Multiple facts suggest that our task and results are valid. Unlike in some other experimental tests of HD and impulsivity, we did not find null effects and we replicated most standard discounting effects, including greater patience for increasing later reward and greater impatience for immediate, early reward and for consumables over money. We also replicated our results from the two group comparison of high vs. low hoarding individuals with continuously-varying individual difference correlations across the whole sample; these correlations also replicate a prior study that found greater attentional impulsivity in HD (Tolin and Villavicencio, 2011). Our results also remained similar after controlling for age and recruitment method (community or psychology pool). Moreover, our results affirmed the assumptions of exponential discounting and the power utility function, particularly in low hoarding participants. The individuals with high hoarding tendencies did tend to be consistent for consumables when a constant was added to both delays (which does not violate exponential discounting), but only because they preferred smaller amounts of pens or snacks offered immediately while preferring the larger quantity when the two time periods were shifted away from "now," which is consistent with the known potency of immediately available reward.

In addition to replicating many common findings in the delay discounting literature, our task and trial design used an enhanced version of a standard set of delay discounting items (Kirby et al., 1999) with items that allowed us to test for specific forms of discounting. For instance, exponential discounting requires that if a person chooses (x1, t1) over (x2, t2) then they should choose (x1, t1 + *k*) over (x2, t2 + *k*). Evidence of preference reversals across such a pair rejects exponential discounting without requiring that we estimate parameter values (see Krantz et al., 1971, for an explanation of this approach). This approach allows us to determine whether exponential discounting holds across money, pens, and snacks, and whether it holds equally in the two groups. We opted not to perform non-linear parameter estimation because it would be difficult to compare across three domains. For example, if we fit a hyperbolic discounting function and observed differences across the three domains in the discounting parameter, we would not be able to determine whether the different parameters actually resulted from differences in discounting across the domains or from a confounding difference in utility (e.g., because a pack of cookies really isn't worth an exact dollar or means something different when you have 5 packs or 35 packs compared to 1). The approach adopted in this study tailored the trial types to study different aspects of the decision while making fewer assumptions about the domains and using statistically powered but simple tests that allow us to compare groups and domains. A more careful simultaneous assessment of discounting vs. utility functions across domains is beyond the scope of this project, but should also be investigated.

Despite the novelty and consistency of our results, there are some limitations. Our choices were not incentivized with real rewards and our high hoarding group was identified through self-report, which was followed up with a validated clinical questionnaire rather than a structured clinical interview. However, ITD procedures are known to produce similar results whether the rewards are hypothetical or real (Johnson and Bickel, 2002; Madden et al., 2003) and hoarding should be thought of as a continuously varying individual difference and not just a present or absent psychopathology (reviewed in Preston et al., 2009; Preston, 2013). One must also not assume that greater attentional impulsivity and ITD discounting for goods should be extended to other forms of impulsivity that we did not measure, or other aspects of HD such as discarding problems. In clinical samples up to 60% of hoarding patients meet the criteria for compulsive buying (e.g., Frost et al., 2002), so it is possible that this impulsiveness for consumable reward explains the compulsive buying and acquisition of free items in HD, but we need to determine empirically which if any other forms of impulsiveness are disordered in HD. A large, systematic study that compares all forms of impulsivity within participants and across groups and domains is still needed.

A trans-diagnostic, symptom-based approach to HD that generously includes both empirically validated tasks and individual difference measures can help us understand the underlying problems that promote HD. This approach can also help us understand the potential link between HD and other related phenomena like impulse-control disorders and addiction, which is currently poorly understood and likely prevents us from being maximally effective in treating HD.

### AUTHOR CONTRIBUTIONS

SP developed the study concept. SP, RG, and AA designed the study. SP and AA oversaw subject recruitment and testing. BV performed data analysis under the supervision of SP and RG. SP

#### REFERENCES

and BV drafted the paper, and all authors provided revisions and approved the final version of the paper for submission.

#### ACKNOWLEDGMENTS

Research was supported by a grant from the University of Michigan Office for the Vice President for Research (OVPR) to SP. Thanks to Jenny Porter, Jacqueline Rau, David Chester, Aleah Burson, Chris Boffi, and Lauren Oglevee for assistance.

from a case-control study. *Behav. Res. Ther.* 40, 517–528. doi: 10.1016/S0005- 7967(01)00026-2

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Copyright © 2016 Vickers, Preston, Gonzalez and Angott. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

# Anticipatory pleasure predicts effective connectivity in the mesolimbic system

*Zhi Li1,2, Chao Yan3, Wei-Zhen Xie4, Ke Li5, Ya-Wei Zeng5, Zhen Jin5, Eric F. C. Cheung6 and Raymond C. K. Chan1\**

*1 Neuropsychology and Applied Cognitive Neuroscience Laboratory, Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China, 2 The University of Chinese Academy of Sciences, Beijing, China, 3 Key Laboratory of Brain Functional Genomics (MOE and STCSM), School of Psychology and Cognitive Science, East China Normal University, Shanghai, China, 4 Department of Psychology, University of California, Riverside, CA, USA, 5 MRI Center, Hospital 306 of PLA, Beijing, China, 6 Castle Peak Hospital, Hong Kong Special Administrative Region, Tuen Mun, China*

Convergent evidence suggests the important role of the mesolimbic pathway in anticipating monetary rewards. However, the underlying mechanism of how the sub-regions interact with each other is still not clearly understood. Using dynamic causal modeling, we constructed a reward-related network for anticipating monetary reward using the Monetary Incentive Delay Task. Twenty-six healthy adolescents (Female/Male = 11/15; age = 18.69 ± 1.35 years; education = 12 ± 1.58 years) participated in the present study. The best-fit network involved the right substantia nigra/ventral tegmental area (SN/VTA), the right nucleus accumbens (NAcc) and the right thalamus, which were all activated during anticipation of monetary gain and loss. The SN/VTA directly activates the NAcc and the thalamus. More importantly, monetary gain modulated the connectivity from the SN/VTA to the NAcc and this was significantly correlated with subjective anticipatory pleasure (*r* = 0.649, *p <* 0.001). Our findings suggest that activity in the mesolimbic pathway during the anticipation of monetary reward could to some extent be predicted by subjective anticipatory pleasure.

#### *Edited by:*

*Israel Liberzon, University of Michigan, USA*

#### *Reviewed by:*

*Brian Knutson, Stanford University, USA Arash Javanbakht, University of Michigan, USA*

#### *\*Correspondence:*

*Raymond C. K. Chan, Neuropsychology and Applied Cognitive Neuroscience Laboratory, Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Room 526, South Block, 16 Lincui Road, Beijing 100101, China<EMAIL_ADDRESS>
> *Received: 01 June 2015 Accepted: 30 July 2015 Published: 12 August 2015*

#### *Citation:*

*Li Z, Yan C, Xie W-Z, Li K, Zeng Y-W, Jin Z, Cheung EFC and Chan RCK (2015) Anticipatory pleasure predicts effective connectivity in the mesolimbic system. Front. Behav. Neurosci. 9:217. doi: 10.3389/fnbeh.2015.00217* Keywords: hedonic capacity, monetary reward, dynamic causal modeling, anticipatory pleasure

## Introduction

Deficits in hedonic capacity, namely anhedonia, are often found in patients with schizophrenia, bipolar disorder, major depression, substance addiction, anxiety, and eating disorders (Shankman et al., 2014). Traditional symptom-based psychiatric diagnosis may not be able to capture these underlying features that cut across diagnostic entities. The recently proposed Research Domain Criteria (RDoC) aims to address this problem (Cuthbert, 2014; Cuthbert and Workgrp, 2014). The RDoC suggests researchers to focus on elemental cognitive and emotional functions, such as hedonic capacity, using various approaches ranging from behavioral performance, through brain circuits, to genes (NIMH, 2008). The Monetary Incentive Delay (MID) task (Knutson et al., 2000) and the Temporal Experience of Pleasure Scale (TEPS; Gard et al., 2006) have been suggested as appropriate instruments to examine the two components of hedonic experience, namely anticipatory and consummatory pleasure. Converging evidence suggests that the nucleus accumbens (NAcc) is a vital hedonic hotspot in anticipatory pleasure (Kringelbach and Berridge, 2009; Berridge and Kringelbach, 2013). Using the MID task, activation of the NAcc has been observed during anticipation of secondary rewards (Knutson et al., 2000, 2001). Similar results have been reported in anticipation of primary rewards, such as sucrose solution (O'Doherty et al., 2002) and social rewards (Kohls et al., 2013). The NAcc appears to play a key role in integrating information from the midbrain, the limbic system and the frontal cortex to facilitate appropriate choice and goal-directed behavior (Camara et al., 2009). The substantia nigra/ventral tegmental area (SN/VTA) also plays an important role in reward processing (Horvitz, 2000; Duezel et al., 2009). Anticipation of primary and secondary rewards, taste (O'Doherty et al., 2002), money (Breiter et al., 2001), and happy faces (Aharon et al., 2001) activates the SN/VTA. Lastly, the thalamus also plays a role in hedonic experience. Previous studies have reported activation of the thalamus in anticipation of rewards (Knutson et al., 2000, 2001; Knutson and Greer, 2008). The thalamus integrates messages from the emotional, cognitive, and motor cortices and relays information to the frontal cortex to formulate goal-directed behavior (Haber and Calzavara, 2009). In addition, the thalamus also appears to be important in retrospective and prospective coding for predicted reward (Komura et al., 2001). The NAccnigra-thalamic circuit is involved in the regulatory function of the thalamus in reward processing (Montaron et al., 1996). Our study (Chan et al., in press), adopting the modified MID task, showed that anticipation of monetary gain activated the NAcc, the globus pallidus and the thalamus, whereas anticipation of monetary loss activated the NAcc, the thalamus and the SN/VTA. These findings further support the important roles of the SN/VTA, the NAcc and the thalamus during anticipation of rewards.

Although the functional connectivity between the NAcc, the SN/VTA and the thalamus have been a focus of recent studies in hedonic capacity (Camara et al., 2009; Haber and Calzavara, 2009; Cauda et al., 2011), to the best of our knowledge, few studies had examined the relationships between these three regions, especially the interaction between the SN/VTA and the NAcc during anticipation of rewards. Some studies have used dynamic causal modeling (DCM) to investigate reward related circuits (Alexander and Brown, 2010; Veldhuizen et al., 2011; Gonen et al., 2012; Cho et al., 2013; Yu et al., 2013). Using DCM, Veldhuizen et al. (2011) found that the anterior insular represents breaches of taste identity by receiving afferent connectivity from the ventral striatum and the inferior parietal cortex. Furthermore, by reciprocal connectivity from the amygdala, the ventral striatum plays a role in anticipating the attractability of human faces (Yu et al., 2013). Gonen et al. (2012) found that the VTA and the NAcc are related to the behavioral activation system and the NAcc represents the reward by cooperating with the dorsal medial prefrontal cortex. In addition, brain activity in the substantia nigra was found to be capable of predicting dopamine release in the NAcc during the anticipation of rewards (Schott et al., 2008). These findings suggest the vital role of the VS, especially the NAcc, in the reward circuit. In addition, the SN/VTA may also be engaged during the anticipation of rewards. However, the causal relationship between the SN/VTA and the NAcc is still unknown. In the present study, we constructed nine dynamic causal models between the SN/VTA, the NAcc and the thalamus, which contained reciprocal pathways between the SN/VTA and the NAcc, and between the NAcc and the thalamus and a non-reciprocal pathway from the SN/VTA to the thalamus (**Figure 1**). Taking into account the complexity of the models, the connectivity from the thalamus to the SN/VTA was excluded from consideration because this anatomical connectivity is not as clear-cut as the other five. Moreover, we measured the subjective anticipatory and consummatory pleasure of participants and correlated them with the parameters of the best-fit model.

Given these and our previous work using the MID task, we examined the reward-related network for anticipating monetary reward. We hypothesized that (1) the NAcc, the SN/VTA and the thalamus would all be activated during anticipation of monetary gain and loss; (2) the SN/VTA would exert a direct effect on the NAcc whereas the thalamus would integrate information from the SN/VTA and the NAcc.

### Materials and Methods

#### Participants

Twenty-six (11 females) healthy right-handed adolescents with a mean age of 18.6 years (sd = 1.35), a mean duration of education of 12 years (sd = 1.58) and a mean IQ estimate of 95.38 (sd = 13.56) [estimated by the shot-form of the Chinese version of the Wechsler Adult Intelligence Scale-Revised (WAIS-R; Gong, 1992)] were recruited from the community. Exclusion criteria included: a personal or family history of mental illness, a history of head injury, and a history of substance abuse. The study was approved by the Ethics Committee of the Institute of Psychology, the Chinese Academy of Sciences. Written informed consents were obtained from all participants. Each participant was recompensed with 100 CNY (China yuan) and the monetary rewards they acquired in the MID task after the completion of the study.

#### Temporal Experience Pleasure Scale

The TEPS was designed to measure anticipatory and consummatory pleasure (Gard et al., 2006). We used the Chinese version of the TEPS which consists of 10 items with a six-point Likert scale measuring anticipatory pleasure and 10 items measuring consummatory pleasure (Chan et al., 2012). The items measuring anticipatory pleasure capture the pleasure experienced during the anticipation of positive events, such as "When I hear about a new movie starring my favorite actor, I can't wait to see it," whereas the items measuring consummatory pleasure capture the pleasure experienced during the consummation of positive events, such as "A hot cup of coffee or tea on a cold morning is very satisfying to me." Higher score on the TEPS indicates higher hedonic capacity. Both the original and the Chinese version of the TEPS have been shown to have satisfactory reliability and validity (Gard et al., 2006; Chan et al., 2012).

#### Monetary Incentive Delay Task (MID)

In this study, we used an abbreviated version of the MID task developed by Chan et al. (in press). A cue lasting 250 ms was

presented on a projection screen which was reflected in a small mirror fixed on the head coil of the scanner, followed by the first interval and then a blue cross that the participants were asked to quickly hit by pressing a button. Then the second interval was presented which was followed by the monetary stimuli. The cues consisted of a triangle signifying the gain condition, a square signifying the loss condition and a circle signifying the neutral condition. In the gain condition, participants could gain five monetary points if the blue cross was hit. In the loss condition, participants would lose five monetary points if the blue cross was not hit. In the neutral condition, participants would gain or lose nothing whether the blue cross was hit or not. The duration of intervals were randomized to avoid participants from anticipating the blue cross and to maintain the duration of each trial at 12 s. In addition, the duration of the blue cross was jittered around 300 ms according to the performance of each participant to maintain the accuracy at about 66%. Participants were asked to perform two runs of the task. Each run contained 30 trials, 10 gain conditions, 10 loss conditions, 10 neutral conditions, and a blank screen lasting for 8 s presented in the first instance for a dummy scan. The order of the trials was pseudorandom across

participants and different between the two runs. Participants were told that the final remuneration would be 100 CNY plus the monetary points they obtained in the task. The average hit rate, the reaction time by condition and the earnings are presented in Supplementary Table S1.

#### Functional MRI Data Collection

Imaging data were collected in a Siemens 3T Trio scanner with a 32-channel head coil at the MRI Centre of the 306 Hospital in Beijing. T2∗-weighted echo planner ingredient sequence (TR = 2000 ms; TE = 30 ms; FOV = 210 mm; slices = 32; flip angle = 90◦; image matrix = 64 × 64; voxel dimensions = 3.3 mm × 3.3 mm × 4 mm) was applied to acquire functional brain images. Then a high resolution T1 structure image was obtained with the sequence: TR = 2300 ms; TE = 3 ms; FOV = 256 mm; flip angle = 9◦; image matrix = 256 × 256; voxel dimensions = 1 mm × 1 mm × 1 mm.

#### Functional MRI Data Processing

All the fMRI data were analyzed with the free software Statistical Parameter Mapping 8 (SPM8, Wellcome Trust Centre for Neuroimaging, London, UK). Before pre-processing, the first four dummy scans were discarded. After slice timing correction, images were realigned to the twentieth slice of each TR. Then the mean EPI image was normalized to the single person template of the Montreal Neurological Institute. Finally all the images were smoothed with a Gaussian kernel of 5 mm full-width halfmaximum.

Based on the canonical haemodynamic response function (HRF), only the three anticipatory events: the monetary gain, the monetary loss, and the neutral condition, were included in the general linear modeling. Besides, the six parameters of head movement generated in the realignment were included in the modeling as covariates. The contrast 'gain – neutral' was designed to examine brain activities in response to monetary gain, and the contrast 'loss – neutral' was designed to examine brain activities in response to monetary loss. The contrast 'all – neutral' referred to the general effect of monetary stimuli and was used in the DCM. The contrasts of each participant were included in a onesample *t*-test which was set in the second-level analysis of the SPM8.

Since we aimed to examine the function of the SN/VTA, the NAcc, and the thalamus and their interaction during the anticipation of monetary stimuli, we analyzed brain activation with pre-defined regions of interest (ROI). The ROIs of the NAcc and the thalamus were selected from the Harvard-Oxford subcortical structure atlas. The ROI of the SN/VTA was adopted from a very high resolution subcortical probabilistic atlas which was quantified with a 7T structure MRI (Keuken et al., 2014). The three ROIs, were masked on the contrast 'gain – neutral' and the contrast 'loss – neutral' respectively. Small volume correction (SVC) within an 8-mm radius sphere was applied. The statistical threshold was set as familiar-wise-error (FWE) correction with *p <* 0.05.

#### Dynamic Causal Modeling

Before the procedure, the time courses of the SN/VTA, the NAcc, and the thalamus were extracted from the contrast 'all – neutral' of all participants. The ROIs were defined as the overlaps between the masks used in the ROI analysis with an 8-mm radius sphere centered around the peak points activated in the SN/VTA, the NAcc, and the thalamus, respectively. The statistical threshold was set as uncorrected *p <* 0.05.

We constructed nine dynamic causal models. The complete model, Model 1, contained reciprocal connectivity between the SN/VTA and the NAcc, reciprocal connectivity between the NAcc and the thalamus, and a non-reciprocal connectivity from the SN/VTA to the thalamus. From Model 1, one or two connectivity was subtracted in different ways to form eight other models (**Figure 1**). The right SN/VTA, the right NAcc, and the right thalamus were included in the model for their stronger activations than their left- sided counterparts (**Table 1**). Using the SPM8, a HRF was constructed, which contained the event 'all,' 'gain,' and 'loss'. The event 'all' was defined as an input at the SN/VTA which is axiomatically considered a dopamine-rich region projecting to the terminals of the NAcc (Duezel et al., 2009; Haber and Knutson, 2010; Cauda et al., 2011). For the exploratory aim of this study and the unclear TABLE 1 | Analysis of regions of interest (ROI) during the anticipation to monetary rewards.

*The threshold was set as p < 0.05 with family-wise error (FWE) correction, small volume correction (SVC).*

effect of valence to the connectivity of the reward circuit, the event 'gain' and 'loss' were, respectively, defined as perturbations to all the intrinsic connectivity, or the edges, of the models. A random-effect analysis of Bayesian Model Selection (BMS) was applied to identify the best-fit model with the highest exceedance probability (Friston et al., 2003; Stephan et al., 2010). Then the endogenous and perturbed parameters of the bestfit model were imported into the Predictive Analytics Software 18.0 (PASW 18.0) for significance testing. Bonferroni correction was applied to correct for multiple comparison. Finally, all the parameters were correlated with the total, the anticipatory and the consummatory subscale scores of the TEPS using Pearson Correlation.

### Results

#### Regions of Interest Analysis

The bilateral NAcc, the bilateral SN/VTA and the bilateral thalamus were all significantly activated to the contrasts 'gain – neutral,' 'loss – neutral,' and 'all – neutral' (**Table 1**).

#### Dynamic Causal Modeling

The BMS identified Model 8 as the best-fit model with the highest exceedance probability during the anticipation of monetary stimuli (**Figures 2** and **3**). The endogenous connectivity of Model 8 contained two causal pathways from the SN/VTA to the NAcc

and to the thalamus, while the NAcc had a causal effect on the thalamus. Endogenous parameters of the pathway from the SN/VTA to the NAcc [*t*(25) = 10.96, *p <* 0.001] and to the thalamus [*t*(25) = 8.42, *p <* 0.001] were significant, while that from the NAcc to the thalamus was not [*t*(25) = 1.27, *p* = 0.217] (**Table 2**).

As for the modulation parameters caused by external experimental stimuli in the Model 8, both monetary 'gain' and 'loss' modulated the connectivity from the SN/VTA to the NAcc, from the SN/VTA to the thalamus and from the NAcc to the thalamus (**Table 2**).

#### Correlation between Subjective Pleasure Experience and Modeling Parameters

The mean total TEPS score of the participants was 78.18 ± 12.72, while the mean anticipatory and consummatory subscale scores were 41.78 ± 6.9 and 36.4 ± 7.39, respectively. We found

TABLE 2 | Parameters of endogenous connections and modulation of the Model 8.

*SN/VTA, Substantia nigra/ventral tegmental area; Tha, Thalamus; NAc, Nucleus accumbens;* ∗∗*p < 0.01.*

FIGURE 3 | Best-fit dynamic causal model. Model 8 is the best model in fitting the time series of the SN/VTA, the NAcc, and the thalamus. In Model 8, the SN/VTA projects two causal pathways to the NAcc and the thalamus, whereas the NAcc exerts a causal influence on the thalamus.

FIGURE 4 | Correlation between the effect of monetary gain on the mesolimbic pathway and anticipatory pleasure experience. Modulation of monetary gain on the causal connectivity from the SN/VTA to the NAcc positively correlates with anticipatory pleasure experience. The Pearson correlation coefficient remains significant after Bonferroni correction.

significant positive correlation between the modulation by 'gain' events on the nigrostriatal pathway from the SN/VTA to the NAcc with anticipatory subscale score (*r* = 0.649, *p <* 0.001) and total score on the TEPS (*r* = 0.555, *p* = 0.003). The former correlation remained significant after Bonferroni correction (**Figure 4**).

#### Discussion

In the present study, using the MID Task, we observed activation of the SN/VTA, the NAcc, and the thalamus in healthy adolescents during anticipation of both monetary gain and loss. We found that the SN/VTA projects two causal pathways to the NAcc and the thalamus. Importantly, the causal connection from the SN/VTA to the NAcc was strengthened by the anticipation of monetary gain. This modulation was also positively correlated with subjective pleasure ratings, especially anticipatory pleasure.

Consistent with results from previous fMRI studies (Knutson et al., 2000, 2001; Breiter et al., 2001; Zink et al., 2004), the NAcc is activated during anticipation of both reward and punishment in the present study. Earlier research in animals highlighted the role of the NAcc in reward anticipation, namely the "wanting" component of reward processing (Berridge and Robinson, 1998; Berridge, 2003). However, other animal studies had stressed the role of the NAcc in reward learning because local dopamine release was associated with novel stimuli and predictive cues indicating forthcoming reward or punishment (Schultz et al., 1997; Schultz, 2007). In human studies, the NAcc is conceptualized as a hedonic hotspot important in pleasure processing (Knutson and Greer, 2008; Kringelbach and Berridge, 2009). Knutson and Greer (2008) reviewed fMRI studies employing the MID task and suggested that the NAcc is involved in anticipating positive events and is associated with subjective pleasure and approaching behavior. Electrical stimulation of the NAcc in rodents and deep brain stimulation of the NAcc in humans both promote approaching behavior (Kringelbach and Berridge, 2009; Berridge and Kringelbach, 2013). Although a previous study had demonstrated that anticipation of reward rather than punishment activated the NAcc (Sabatinelli et al., 2007), activation of the NAcc had also been reported in anticipation of aversive stimuli in another study (Zink et al., 2004). Our findings lend support to the important role of NAcc in the processing of both salient information and pleasure during anticipation of rewards.

The role of the SN/VTA and the thalamus in reward processing is less controversial compared to the function of the NAcc discussed above. O'Doherty et al. (2002) found that the SN/VTA is activated during anticipation of glucose, whereas its role in anticipation of secondary rewards such as monetary stimuli is less clear. While Knutson et al. (2000, 2001) did not observe activation of the SN/VTA during anticipation of monetary stimuli, Breiter et al. (2001) identified activation in the VTA during anticipation of monetary rewards which was similar to our findings. Moreover, the VTA has been found to be activated in response to beautiful faces, which suggested that the midbrain may play an important role in positive social reward (Aharon et al., 2001). Activation of the SN/VTA to both monetary gain and loss identified in this study stresses the role of SN/VTA in reward processing.

Activation of the thalamus during anticipation of monetary gain and loss in the present study is consistent with results from a previous review (Knutson and Greer, 2008). While the thalamus is regarded as a center for information gathering and integration (Haber and Calzavara, 2009; Kohls et al., 2013), a previous study of electrical recording in rats suggested that the thalamus is also involved in retrospective and prospective coding to reward (Komura et al., 2001). In addition, the thalamus appears to be responsive to salient sensory information during the anticipation of incentives (Cho et al., 2013).

We also found a causal pathway from the SN/VTA to the NAcc during anticipation of monetary stimuli. To the best of our knowledge, few studies have investigated the causal relationship between the midbrain and the VS. A previous study on functional connectivity has reported that spontaneous activation of the NAcc correlated with reward-related brain circuits including the orbitofrontal cortex, the globus pallidus, the thalamus, the midbrain, the amygdala and the insular (Cauda et al., 2011). In addition, connectivity between the mesolimbic system and cortical areas appears to be altered in developmental conditions (Camara et al., 2009). The close relationship between mesolimbic connection and reward processing may be related to local dopaminergic metabolism. Schott et al. (2008) found that activation of the SN/VTA is associated with dopamine release in the NAcc. Moreover, Knutson and Gibbs (2007) identified that dopamine release in the NAcc activates postsynaptic D1 receptors which further induces activation in the NAcc during anticipation of reward. Both studies in functional connectivity and neurotransmitter stressed the role of mesolimbic connection in anticipation of positive events and pleasure, but the causal direction between the midbrain and the VS in this process is not clear. The causal connectivity from the SN/VTA to the NAcc identified in the present study is not only consistent with previous functional connectivity studies, but also provides new information regarding causal relationships in the mesolimbic pathway. Moreover, our findings support the role of the thalamus in integrating information from emotional, cognitive and motor cortical and subcortical areas to facilitate approaching and goal-directed behaviors (Haber and Calzavara, 2009; Haber and Knutson, 2010). In the best-fit model of our study, the SN/VTA projects an excitatory pathway to the thalamus, whereas the NAcc projects an inhibitory pathway to the thalamus. In another previous study which investigated brain circuits during anticipation of monetary incentives using DCM, Cho et al. (2013) found that the thalamus modulated the NAcc through the thalamus-to-NAcc and the thalamus-to-insula-to-NAcc connections which was different from our findings. We believe that the different findings may be related to the choice of the driven regions adopted. In the present study, the SN/VTA was chosen as the driven region, which is upstream to the dopaminergic complex in the NAcc (Schott et al., 2008; Duezel et al., 2009), whereas the thalamus was chosen as the driven region by Cho et al. (2013). However, a connection from the NAcc to the thalamus was also identified in Cho et al.'s (2013) study, which is similar to our findings. We found that both monetary 'gain' and 'loss' induced perturbation in all three connectivities in Model 8, namely the SN/VTA-to-thalamus connectivity, the SN/VTA-to-NAcc connectivity, and the NAccto-thalamus connectivity. In contrast to the study by Cho et al. (2013), which found different patterns of perturbation between monetary 'gain' and 'loss' in the thalamus-insula-NAcc circuit, our findings revealed a similar pattern regardless of valence in the SN/VTA-NAcc-thalamus network. The ROIs adopted and the driving ROI chosen may both cause the difference between the present and previous findings. The SN/VTA adopted here appears to play an elementary role in reward processing which may not be sensitive to the valence of the reward.

More interestingly, the modulation by monetary gain on the connectivity from the SN/VTA to the NAcc was correlated with anticipatory pleasure experience in the present study. The MID task and the TEPS are tools suggested by the RDoC for measuring anticipatory and consummatory pleasure in fMRI and behavioral paradigms, respectively, (NIMH, 2008), but few studies have investigated the underlying neural mechanism of the two instruments. People with schizophrenia spectrum disorders report dampened subjective anticipatory pleasure, while their consummatory pleasure is relatively preserved (Kring and Caponigro, 2010; Kring et al., 2011). In addition, previous studies have identified that people with schizophrenia showed reduced activation in the NAcc when anticipating monetary stimuli (Juckel et al., 2006, 2012; Walter et al., 2009). These results suggest that the two instruments seem to capture similar underlying neural mechanisms and our findings corroborated this in healthy adolescents. The connection from the SN/VTA to the NAcc in adolescents who reported higher anticipatory pleasure is more easily perturbed by positive events. This phenomenon may reflect inherent dopaminergic metabolism in the mesolimbic system and this hypothesis merits further research.

This study has several limitations. First, we only focused on the causal network in the mesolimbic system and did not investigate other important reward-related circuits such as the mesocortical system. The second limitation is that the adopted dopaminergic midbrain area is the SN rather than the VTA, but the boundary between the SN and the VTA in human is difficult to distinguish (Duezel et al., 2009). Furthermore, laterality was not taken into consideration in this study. We only focused on the right hemisphere which showed higher activation than the

### References

left. Finally, only nine models were tested in this study. Future identification of other relevant connectivity or models is needed.

#### Conclusion

Anticipation of both monetary gain and loss activated the NAcc and other reward-related areas, namely the SN/VTA and the thalamus. The SN/VTA projects causal pathways to the NAcc and the thalamus, while the thalamus integrates information from the SN/VTA and the NAcc. Anticipatory pleasure appears to predict the susceptibility of causal connection from the SN/VTA to the NAcc to positive events. The present findings also lend support to the applicability of the RDoC in research.

### Acknowledgment

This study was supported by a grant from the "Strategic Priority Research Program (B)" of the Chinese Academy of Sciences (XDB02030002), the National Science Fund China (81088001, 91132701), the Beijing Training Project for Leading Talents in S&T (Z151100000315020).

### Supplementary Material

The Supplementary Material for this article can be found online at: http://journal*.*frontiersin*.*org/article/10*.*3389/fnbeh*.* 2015*.*00217

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Copyright © 2015 Li, Yan, Xie, Li, Zeng, Jin, Cheung and Chan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

# Delay discounting without decision-making: medial prefrontal cortex and amygdala activations reflect immediacy processing and correlate with impulsivity and anxious-depressive traits

Vera U. Ludwig1,2† , Corinna Nüsser 3,4† , Thomas Goschke4 , Dina Wittfoth-Schardt 5,6 , Corinde E. Wiers 1,2 , Susanne Erk 1 , Björn H. Schott 1,7\*‡ and Henrik Walter 1,2‡

1 Division of Mind and Brain Research, Department of Psychiatry and Psychotherapy, Charité Universitätsmedizin Berlin, Berlin, Germany, 2 Berlin School of Mind and Brain, Humboldt Universität zu Berlin, Berlin, Germany, 3 Division of Medical Psychology, University of Bonn, Bonn, Germany, 4 Department of Psychology, Technische Universität Dresden, Dresden, Germany, 5 Institute of Neuroradiology, Hannover Medical School, Hannover, Germany, 6 NICA—NeuroImaging and Clinical Applications, Hannover, Germany, 7 Leibniz-Institute for Neurobiology, Magdeburg, Germany

#### Edited by:

Raymond C. K. Chan, Chinese Academy of Sciences, China

#### Reviewed by:

Hongbin Wang, Texas A&M University Health Science Center, USA Xiaochu Zhang, University of Science and Technology of China, China

> \*Correspondence: Björn H. Schott<EMAIL_ADDRESS>
†These authors have contributed equally to this work. ‡These authors share senior authorship.

Received: 02 July 2015 Accepted: 04 October 2015 Published: 29 October 2015

#### Citation:

Ludwig VU, Nüsser C, Goschke T, Wittfoth-Schardt D, Wiers CE, Erk S, Schott BH and Walter H (2015) Delay discounting without decision-making: medial prefrontal cortex and amygdala activations reflect immediacy processing and correlate with impulsivity and anxious-depressive traits. Front. Behav. Neurosci. 9:280. doi: 10.3389/fnbeh.2015.00280

Humans value rewards less when these are delivered in the future as opposed to immediately, a phenomenon referred to as delay discounting. While delay discounting has been studied during the anticipation of rewards and in the context of intertemporal decision-making, little is known about its neural correlates in the outcome phase (during reward delivery) and their relation to personality. Personality traits that have been associated with increased delay discounting include impulsivity and, potentially, anxious-depressive traits. Here we performed functional magnetic resonance imaging (fMRI) in 72 healthy participants while they carried out a monetary incentive delay (MID) task with a delay manipulation. In sixty percent of the experimental trials, participants won rewards that differed in magnitude (0.05e, 0.50e or 1e) and delay until delivery (immediately, 10 days, or 100 days). A factor analysis on questionnaires yielded two factors reflecting Impulsivity and Anxiety/Depression, which we used to examine potential relationships between personality and delay discounting. When winning a reward, medial prefrontal cortex (mPFC) activation was higher for immediate compared to delayed rewards. Moreover, amygdala activation correlated with reward magnitude for immediate but not for delayed rewards. Amygdala activation to winning immediate rewards was higher in more impulsive participants, while mPFC activation to winning immediate rewards was higher in more anxious-depressed participants. Our results uncover neural correlates of delay discounting during reward delivery, and suggest that impulsivity and subclinical anxious-depressive traits are related to stronger neural responses for winning immediate relative to delayed rewards.

Keywords: reward, delay discounting, impulsivity, individual differences, fMRI, amygdala, prefrontal cortex

## INTRODUCTION