http://orcid.org/0000-0001-5643-8210
Figures
Abstract
Background
Novelty-seeking (NS) and impulsive personality traits have been proposed to reflect an interplay between fronto-cortical and limbic systems, including the limbic striatum (LS). Although neuroimaging studies have provided some evidence for this, most are comprised of small samples and many report surprisingly large effects given the challenges of trying to relate a snapshot of brain function or structure to an entity as complex as personality. The current work tested a priori hypotheses about associations between striatal dopamine (DA) release, cortical thickness (CT), and NS in a large sample of healthy adults.
Methods
Fifty-two healthy adults (45M/7F; age: 23.8±4.93) underwent two positron emission tomography scans with [11C]raclopride (specific for striatal DA D2/3 receptors) with or without amphetamine (0.3 mg/kg, p.o.). Structural magnetic resonance image scans were acquired, as were Tridimensional Personality Questionnaire data. Amphetamine-induced changes in [11C]raclopride binding potential values (ΔBPND) were examined in the limbic, sensorimotor (SMS) and associative (AST) striatum. CT measures, adjusted for whole brain volume, were extracted from the dorsolateral sensorimotor and ventromedial/limbic cortices.
Results
BPND values were lower in the amphetamine vs. no-drug sessions, with the largest effect in the LS. When comparing low vs. high LS ΔBPND groups (median split), higher NS2 (impulsiveness) scores were found in the high ΔBPND group. Partial correlations (age and gender as covariates) yielded a negative relation between ASTS ΔBPND and sensorimotor CT; trends for inverse associations existed between ΔBPND values in other striatal regions and frontal CT. In other words, the greater the amphetamine-induced striatal DA response, the thinner the frontal cortex.
Conclusions
These data expand upon previously reported associations between striatal DA release in the LS and both NS related impulsiveness and CT in the largest sample reported to date. The findings add to the plausibility of these associations while suggesting that the effects are likely weaker than has been previously proposed.
Citation: Jaworska N, Cox SM, Casey KF, Boileau I, Cherkasova M, Larcher K, et al. (2017) Is there a relation between novelty seeking, striatal dopamine release and frontal cortical thickness? PLoS ONE 12(3): e0174219. https://doi.org/10.1371/journal.pone.0174219
Editor: Kenji Hashimoto, Chiba Daigaku, JAPAN
Received: January 3, 2017; Accepted: March 5, 2017; Published: March 27, 2017
Copyright: © 2017 Jaworska et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All relevant data are within the paper and its Supporting Information files.
Funding: The authors received no specific funding for this work.
Competing interests: The authors have declared that no competing interests exist.
Introduction
Impulsive novelty seeking (NS) traits have been proposed to reflect individual differences in meso-striatal dopamine (DA) transmission [1],[2] and aspects of cortical morphometry, including grey matter volume and cortical thickness (CT) [3],[4]. The model is compelling. Limbic DA transmission potently influences the incentive salience of rewarding and potentially rewarding (novel) stimuli [5],[6],[7], and cortical projections densely innervate the striatum [8],[9] further modulating the planning of approach and avoidance behaviors [1],[10]. In humans, individual differences in CT, striatal DA D2 receptor availability and striatal DA responses to a drug challenge (e.g., amphetamine) have been reported to account for a substantial proportion of the variance in temperamental features such as NS (Tables 1–3). However, given the noise inherent to both neuroimaging data and personality trait measurements, it seems surprising that a single snapshot of a neurobiological feature can account for so much variation in temperament [11]. Since much of this work has been conducted in small samples, and rarely with the same measure of impulsivity, in the current study, we investigated—for the first time—the relation between NS, CT and amphetamine-induced striatal DA release (via regression analyses) in the largest sample of healthy adults reported to date. Based on the previous studies, we predicted, a priori, that greater DA release would be associated with higher impulsivity-related NS scores and thinner frontal cortices. Additionally, since the association between CT and DA release has only been previously reported once in a smaller cohort [12], we sought to replicate this finding in a substantially larger sample. The same is true for reproducing the association between striatal DA release and NS, which was reported only once previously [13].
Materials and methods
Overview
Data were pooled from five previously reported studies [12],[13],[29],[33],[34], involving healthy adults (N = 52) who underwent two positron emission tomography (PET) [11C]raclopride scans, with and without amphetamine. Structural magnetic resonance image (MRI) scans and Tridimensional Personality Questionnaire (TPQ) data were also acquired [35], with a focus on the relation between neuroimaging variables and NS, including NSTotal scores and the constituent subscales of exploratory excitability (NS1), impulsiveness (NS2), extravagance (NS3) and disorderliness (NS4).
Participants
Participants were healthy men (n = 45) and women (n = 7). Following a telephone screen, volunteers underwent an in-person physical exam, standard laboratory tests and a psychiatric assessment (Structured Clinical Interview for DSM-IV-TR [SCID-IV-TR], non-patient edition [36]). Inclusion criteria were as follows: a) absence of axis I disorder, b) no first-degree relative with a substance use disorder, c) no major medical conditions, d) no current/past neurological issues (e.g., no loss of consciousness for >5 min), e) negative seropositive pregnancy test in females. Volunteers with modest alcohol and infrequent recreational drug use (e.g., hallucinogens, marijuana) were not excluded (see individual papers for further details). All participants provided written informed consent prior to starting the studies, which were carried out in accordance with the Declaration of Helsinki and approved by the Research Ethics Committee of the Montreal Neurological Institute (MNI), McGill University.
Neuroimaging acquisition
PET scans were carried out on two separate days (inter-scan time: 23.6 days±26.9; range: 1–104 days). Participants were asked to abstain for a minimum of 7 h and 24 h from tobacco and alcohol, respectively. Preliminary evidence suggests that there is not an effect of menstrual phase on [11C]raclopride BPND values [37], but females were tested during the follicular phase of their cycle. All participants tested negative on a urine drug screen (Triage Drugs of Abuse Panel, Biosite Diagnostics) prior to each scan.
PET scans were acquired, with and without ingesting a capsule filled with amphetamine (0.3 mg/kg, p.o.) 60 min prior to tracer injection, with a Siemens ECAT HR+ PET scanner (CTI/Siemens, Knoxville, TN, USA; 63 slice coverage; maximum resolution of 4.2 mm; full-width half-maximum [FWHM] in the center of the field of view). In three of the studies, a placebo capsule was administered, while in two studies a baseline scan was obtained (i.e., no placebo capsule; study was used as a covariate, when appropriate, and details are provided in the following sections). Immediately after the transmission scan (for attenuation correction), [11C]raclopride (8–10 mCi) was injected as a bolus into the antecubital vein. Emission data were collected over 60 min in 26 time frames of progressively longer durations.
For anatomical co-registration with PET scans and CT analysis, T1-weighted MRIs were acquired with a 1.5 T Siemens scanner (1 mm slice thickness; TR = 9.7 ms, TE = 4 ms, flip angle = 12°, 256×256 matrix or TR = 22 ms, TE = 9.2 ms, flip angle = 30°, 256 × 256 matrix).
PET analysis
Region of interest (ROI) analysis.
The striatum was divided into 6 functional ROIs, defined on each participant’s MRI (automated segmentation using in-house Automatic Nonlinear Image Matching and Anatomical Labeling [ANIMAL]; ROI-MRI overlap was visually inspected). ROIs were the left/right sensorimotor striatum (SMS; post-commissural putamen), associative striatum (ASTS; pre-commissural dorsal caudate and dorsal putamen, post-commissural caudate) and ventral limbic striatum (LS) [38]. Parametric images were generated by deriving [11C]raclopride BPND values from each ROI using a simplified kinetic model that uses the cerebellum as a reference tissue devoid of DA D2/3 receptors to describe the kinetics of the free and specifically bound ligand [39]. Mean BPND values were extracted from each ROI in the conditions with and without amphetamine. During rest, BPND is proportional to the concentration of available D2/3 receptors; during stimulation, decreases in BPND are proportional to increases in extracellular DA. Amphetamine-induced change in BPND (ΔBPND) was calculated as follows: [(placebo-amphetamine)/placebo]×(100)]. The greater the positive ΔBPND values, the greater the amphetamine-induced DA release [40].
Voxel-wise analysis.
PET images were co-registered to MRIs and parametric [11C]raclopride BPND data were generated at each voxel using a simplified reference tissue model (cerebellum). Images were normalized into standardized (MNI) space. An 8 mm Gaussian smoothing kernel (FWHM) was applied to PET images (2 individuals excluded due to co-registration problems between BPND images/conditions; N = 47). A t-map was created to assess changes in [11C]raclopride BPND between scans with and without amphetamine using a residual t-statistic (BPND baseline>BPND amphetamine) [41]. In this approach, residuals are used to calculate the variance in parameter estimates; this method has been used extensively and validated using Monte Carlo simulations and real PET data [41],[42]. Significant voxels were identified by thresholding the t-map at t>4.1 (p<.05, Bonferroni corrected for multiple comparisons, search volume of the entire striatum).
Cortical thickness (CT) analysis
MRIs were processed using the CIVET 2.0.0 pipeline with CBRAIN (https://cbrain.mcgill.ca/). The pipeline involves: a) Normalizing each image to standardized space (MNI ICBM-152 template). b) Correction of intensity non-uniformity artifacts. c) Tissue type classification. d) Fitting images with a deformable mesh model to extract 2D inner (white/gray matter interface) and outer (pial) cortical surfaces using the CLASP algorithm [43]. This generates CT measurements at 40,962 vertices/hemisphere (distance between outer CSF/gray matter and gray/white matter interfaces). e) Surfaces are registered to the MNI ICBM-152 surface template. f) Reverse linear transformations allow CT estimations in native space. g) CT is calculated at each cortical point using the tlink metric [44]. h) Smoothing with a 20 mm FWHM kernel.
Subsequently, CT vertex values were averaged into 74 regions (37/hemisphere) using the AAL label set [45] [analyzed using SurfStat (http://www.math.mcgill.ca/keith/surfstat/), a toolbox that runs on MATLAB (The MathWorks, Inc., MA, USA; http://www.modelgui.org/mgui-neuro-civet-matlab)]. Further reduction of frontal CT regions was carried out based on precedent connectivity analyses between the striatum and fronto-cortical regions. Specifically, three frontal clusters were created: a) dorsolateral prefrontal cortex (DLPFC: superior frontal gyri, dorsolateral; middle frontal gyri; inferior frontal gyri—triangular part; inferior frontal gyri—opercular part), b) sensorimotor cortex (SM: supplementary motor area; paracentral lobule; pre- & post-central gyri) and c) ventromedial PFC (vmPFC) and limbic cortex (vmPFC/limbic: olfactory cortex; gyrus rectus; middle, superior & inferior frontal gyri—orbital part; anterior cingulate; paracingulate gyri), based on structural and functional connections with the ASTS, SMS and LS, respectively [46]. CT measures were adjusted by total brain volume (regional CT/total brain volume×106, as in Zhou et al., 2014); all CT statistics reflect adjusted CT [47].
Statistics
Unless stated otherwise, means, standard deviations (SD) and partial eta-squares (η2partial: effect sizes) are presented. To characterize the effect of amphetamine-induced DA release, repeated-measures analysis of covariance (rmANOCVA; study as covariate since preliminary analyses revealed a main effect of study on BP values) was carried out on BPND values with drug condition (no amphetamine, amphetamine) and striatal ROI (LS, ASTS, SMS) as within-subject factors. Since no hemisphere effects were seen in preliminary analyses, it was not included as a factor. rmANOVAs were carried out on ΔBPND values, with striatal ROI as the within-subject factor (study was not used as a between-subject factor since preliminary analyses yielded no main effect of study on ΔBPND values). Finally, we carried out a median split of ΔBPND in the LS, yielding high vs. low amphetamine responders, and univariate ANCOVAs tested for group (high vs. low responders) differences in NS scores (age as covariate). Since NS and other impulsivity traits generally decrease as adults grow older, age was used as a covariate [48] (although our sample was relatively young, we noted an inverse correlation between NS3 and age: r = -.33, N = 52, p = .017).
Partial correlations were carried out between BPND values per drug condition in each striatal ROI (6 total) and NS scores (5 total: 4 subscales and NSTotal; age and study controlled for), only p<.005 results were reported (i.e., .05/11 = .0045). Partial correlations (age controlled for) were also carried out between ΔBPND in each striatal ROI (3 total) and NS scores (5 total); only p<.006 results were reported (i.e., .05/8 = .0063).
rmANCOVAs were carried out on CT measures with frontal region (DLPFC, SM, vmPFC/limbic) as the within-subject factor; age and gender were used as covariates [49]; preliminary analyses indicated no hemispheric effects, thus, it was not included as a factor). Study was not used as a factor as preliminary analyses yielded no effect of study on CT. Partial correlations (controlling for age and gender) were carried out between CT measures in the frontal regions (averaged across hemispheres) and NS scores (5 total); only correlations with p<.006 were reported (i.e., .05/8 = .006).
Partial correlations (controlling for age, gender and study) were also carried out between frontal CT values (3 regions) and BPND measures (3 ROIs) in each drug condition (amphetamine, baseline/placebo); correlations between CT values and ΔBPND were also carried out (controlling for age and gender). Significance was set at p = .006 (i.e., .05/8) and p = .008 (i.e., .05/6), respectively.
Finally, we carried out stepwise multiple regressions to assess the influence of CT measures (3 regions) and NS scores (5 total) on ΔBPND in each striatal ROI. Age and gender were also included as predictor variables (stepwise). Multicollinearity was assessed using the variance inflation factor (VIF; <1.5 deemed acceptable); autocorrelations in the residuals were assessed using the Durbin Watson statistics (acceptable: 1.5-2.5). The significance of the ANOVA was p<.005 (i.e., p<.05/11 factors). Unstandardized beta coefficients are presented.
Results
Participant characteristics
Detailed participant characteristics can be found elsewhere [12],[13],[29],[33],[34]. CT data were available from all 52 participants (18–42 yr, Table 4); BPND/ΔBPND data were available from 49 participants (3 PET scans could not be used because of image quality/technical issues).
BPND & ΔBPND results
No sphericity violations existed, and there was no relation between inter-scan time duration (days) and ΔBPND. A main effect of study existed for BPND [F(1,44) = 3.55, p = .04, η2partial = .24]; study was used as a covariate in BPND value analyses. The rmANCOVA of BPND values yielded main effects of drug condition [F(1,44) = 6.44, p = .02], reflecting lower BPND values on the sessions with vs. without amphetamine (2.45±.40 vs. 2.55±.41, η2partial = .13), and ROI [F(2,88) = 55.29, p<.001, η2partial = .56], reflecting a gradation of BPND values from LS to ASTS [ps<.001; LS: 2.25±.40, ASTS: 2.52±.40, SMS: 2.73±.51]. In this case, lower BPND values indicated greater extracellular DA in the synapse (i.e., inverse relation between BPND values and DA). A drug condition×ROI interaction was also evident [F(2,88) = 3.73, p = .028, η2partial = .08], reflecting significant effects of amphetamine in the LS (p<.001, placebo: 2.33±.38, amphetamine: 2.17±.36) and ASTS (p = .045; placebo: 2.56±.38, amphetamine: 2.48±.36) but not SMS (placebo: 2.71±.46, amphetamine: 2.67±.47; Fig 1).
Mean binding potentials (BPND ± standard error of the mean) in the limbic, associative and sensorimotor striatum with (grey) and without (white) amphetamine (***p<.001, *p<.05).
The rmANOVA assessing ΔBPND values yielded a main effect of ROI [F(2,96) = 7.84, p = .001, η2partial = .14, study was not used as a covariate as there was no study effect on ΔBPND]. Follow-up comparisons indicated that ΔBPND values in the LS were larger than those in the ASTS (p = .017) and SMS (p = .001; Fig 2). Voxel-wise analysis confirmed reduced BPND following amphetamine administration in four clusters, with the greatest effect in the right LS (Table 5; Fig 3).
Amphetamine-induced changes (%) in binding potentials (ΔBPND ± standard error of the mean) in the limbic, associative and sensorimotor striatum. More positive ΔBPND values reflect greater amphetamine-induced dopamine release (*p<.05 and ***p = .001, respectively).
Voxel-wise t-map of [11C]raclopride binding potential (BPND) illustrating reduced BPND (i.e., greater dopamine release) following amphetamine vs. baseline (t>4.1, p<.05 corrected). No significant voxels were noted outside the striatum.
The univariate ANCOVA (age as a covariate) yielded a trend for a difference between the low vs. high LS ΔBPND groups on NS2 (impulsiveness) [F(1,46) = 3.20, p = .07, η2partial = .07; based on our a priori hypothesis: p = .035], with higher scores in the high LS ΔBPND group (3.63±1.88; low: 2.68±1.80; Fig 4).
Boxplots of impulsiveness scores (NS2) in high and low dopamine (DA) responders reflecting the extent of binding potential changes (ΔBPND) in the limbic striatum (LS) to amphetamine (based on a median-split).
Correlations between BPND/ΔBPND & NS scores
No significant partial correlations (significance was set at p<.005/006) existed between NS scores and ΔBPND values.
CT measures & correlations with NS scores
A rmANCOVA (age and gender as covariates) yielded a main effect of region [F(2,98) = 4.05, p = .02, η2partial = .08], with cortical thickness greatest in the limbic region and thinnest in the SM region (ps<.001). Study was not used as a covariate since preliminary analyses yielded no main effect of study on CT. No significant partial correlations between CT and NS scores emerged (gender and age were used as covariates, significance set at p<.006). When age and gender were not controlled for, exploratory Spearman’s correlations indicated that there was an inverse correlation between thickness in the vmPFC/limbic cortex and NS3 scores (extravagance; rho = -.38, p = .006, N = 52).
Correlations between CT & BPND/ΔBPND
Partial correlations (age and gender controlled for) yielded a negative relation between ASTS ΔBPND and CT in the SM region (r = -.38, df = 45, p = .008; Fig 5); a trend for an inverse relation also existed between LS ΔBPND and CT in the SM region (r = -.36, p = .013) and between ASTS ΔBPND and CT in the DLPFC (r = -.35, p = .015). For each association, the thinner the cortex, the greater the amphetamine-induced DA response. No significant associations between baseline BPND values and CT were observed (age, gender and study controlled for).
Correlation between the residuals from regressing change in binding potentials (% ΔBPND) in the associative striatum on age and gender, and the residuals from regressing adjusted sensorimotor cortical thickness on age and gender.
Multiple regressions
No autocorrelation or multicollinearity violations were noted. Stepwise multiple regression analysis yielded no regressions at the p<.005 level. LS ΔBPND was weakly associated with CT in the SM cortex (F[1,37] = 5.88, p = .019; R2 = .11, adjusted R2 = .09; predicted LS ΔBPND = 56.84+[-20.70][SM CT]). Similarly, ASTS ΔBPND scores were modestly associated with CT in the SM cortex (F[1,47] = 7.21, p = .01; R2 = .13, adjusted R2 = .12; predicted SMS ΔBPND = 46.22+[-17.86][SM CT]).
Discussion
In the current study, we examined the relation between NS, frontal CT and striatal DA responses to an amphetamine challenge in a large sample of young healthy adults. The primary findings were that the greatest amphetamine-induced changes in BPND occurred in the ventral limbic striatum (LS). Individuals with high vs. low LS ΔBPND responses (high vs. low drug responders) had higher impulsiveness (NS2) scores. Partial correlations indicated that greater amphetamine-induced striatal DA responses are associated with thinner frontal cortices.
As expected, BPND values decreased following amphetamine administration. Thanks to the greater statistical power afforded by the large sample size, this study was able to convincingly demonstrate regional differences in the magnitude of this effect, with larger ΔBPND responses in the LS vs. other striatal regions, consistent with smaller PET studies in humans [13],[34],[46], [50],[51] and microdialysis studies in laboratory animals [52]. Via our voxel-wise analysis, we also confirmed that, with the d-amphetamine dose and route of administration used, there are no statistically significant changes in [11C]raclopride binding in voxels outside the striatum.
The mechanism accounting for the different magnitude of DA responses within striatal sub-regions requires more study, but limbic striatal inputs from the ventral tegmental area (VTA) and dorsal substantial nigra (SN) express fewer D2 autoreceptors and DA transporters (DAT) than projections to other striatal regions [53]. Decreased D2 autoreceptor expression may be associated with greater amphetamine-induced DA release in the LS (vs. other striatal regions) [28],[54], though the effect of fewer DATs is less clear [38],[55]. The LS also sends non-reciprocal GABAergic projections to the ASTS, as such, ASTS activity is regulated by LS GABAergic input (similarly, the ASTS appears to regulate the SMS via GABA projections) [38],[55]. Finally, the LS has extensive connections with the orbitofrontal cortex (OFC), vmPFC, and aspects of the anterior cingulate cortex (ACC) [46] as well as the amygdala and hippocampus [55]. Together, these systems play a pivotal role in the initiation and inhibition of approach toward rewarding and potentially rewarding stimuli [56].
The ΔBPND values in the current study are smaller than what has been reported previously with smaller samples [12],[13],[29],[33],[34]. This likely reflects that, in the current study, we used an automated PET analysis approach (i.e., an in-house PET analysis pipeline, involving few manual data corrections specifically with respect to co-registration between structural MRI and BP images; the pipeline employs an iterative co-registration process) that integrates Turku PET centre tools (http://www.turkupetcentre.net/)) for ROI analysis, which relies on traditional non-linear fitting to estimate Simplified Reference Tissue Model [SRTM] parameters. This approach is different from those used by the individual studies, which calculated SRTM parameters for ROI analysis using a basis function [39]. Perhaps most importantly, the automated pipeline employed ROIs that were larger than those used in some of the other studies. As a result, the correction for multiple comparisons was greater. Finally, we applied double-erosion (2 voxels), which may have excluded peak activation in the inferior and ventral-most aspects of the LS. These methodological differences may have influenced our BP and ΔBPND ROI values. However, given the large sample and different methods used previously, the automated pipeline improved the objectivity of the analyses, and the use of more stringent significance detection approaches in the ROI analyses minimized false positives.
Consistent with our a priori hypothesis [28],[13],[30] high vs. low amphetamine responders (i.e., those with greater DA release to amphetamine) exhibited higher NS2 (impulsiveness) scores. That this effect was subtle should not be surprising. There is inherent variability in neuroimaging indices, and a snapshot of a single aspect of function in a particular neural system (or morphometry) is unlikely to capture even an unvarying trait with minimal noise [11]. Measures of externalizing traits suffer from the same limitations. Moreover, any one measure of personality is unlikely to encapsulate the aspect most closely related to the neuroimaging metric being assessed; indeed, different aspects of externalizing traits may reflect various aspects of meso-striatal DA system function [57]. Nevertheless, the consistency with which measures of striatal DA transmission have been found to be associated with impulsive/externalizing traits (Table 2), along with the current study, suggests that individuals with higher impulsiveness scores are characterized by a more pronounced DA response to an amphetamine challenge. Based on the extensive connections with cortical regions implicated in emotion regulation [46], greater LS DA responses may facilitate more disinhibitory, impulsive-like behaviours to novelty and rewards.
The most novel aspect of this study was our attempt to relate striatal DA activity, frontal CT and NS traits in a large, healthy population. Inconsistent with our hypotheses, however, regression analyses indicated that the combination of NS and CT did not yield a stronger statistical prediction of striatal DA responses, as compared to either variable alone (i.e., although CT was associated with ΔBPND, inclusion of NS did not improve the model). We found that greater DA responses in the ASTS were correlated with thinner sensorimotor cortices; similar tendencies existed between LS DA response and sensorimotor CT as well as between ASTS DA response and DLPFC CT. This is consistent with previous work by our group in a smaller sample, which was included in the current study [12]. To our knowledge, no other published data exist regarding the relation between striatal DA activity and cortical morphometry in healthy individuals. However, disease states characterized by striatal DA dysfunction, including Parkinson’s disease [58],[59], schizophrenia [60] and substance use disorders [61],[62], are typically associated with widespread cortical thinning as well as volume loss of the grey matter. Although brain morphometric changes in such disorders can be accounted for by numerous factors, and are certainly not solely related to striatal DA activity modifications, these findings suggest, at least to a certain extent, a relation between cortex morphometry and striatal DA activity.
Previous work found that OFC metabolism was inversely associated with drug-induced striatal DA release in controls [63], consistent with the idea that the OFC plays an integral role in reward valuation by way of LS activity regulation. Additionally, thicker PFCs are generally associated with improved cognition [64]. As such, a thicker PFC may index functional integrity, including more effective engagement of regulatory processes in the presence of rewards and other salient cues. Given the significant overlap between cortico-striatal loops, it is perhaps not surprising that we did not see specific correlations between ΔBPND in distinct striatal regions (ASTS, LS, SMS) and CT in corresponding fronto-cortical regions.
Conclusion & limitations
This is the first known study to simultaneously assess in vivo striatal DA release in relation to CT and NS in humans. This noted, the results should be considered in light of the following. First, the no drug condition contained a placebo in some studies but not all. However, there were no differences in ΔBPND between studies with vs. without a placebo capsule, and study was used as a covariate in the statistical analyses when appropriate. Second, although our sample size is large for a PET study, it is relatively small for CT assessments, and this may have decreased the probability of identifying a correlation between CT and NS. Large populations are generally required to account for the maturational variability that occurs throughout late adolescence and early adulthood (i.e. the age group we examined) [65]. Further, even in large healthy populations, the association between externalizing traits (and personality traits in general) and brain morphometric features is generally subtle [3],[4]. Third, CT can be adjusted by total brain volume (TBV)/intracranial volume (ICV), grey matter volume or not at all. In the current study, we adjusted CT by TBV, following the recommendations of Zhou et al., 2014 [47]. By not including TBV as a covariate or control variable (in the partial correlations), the degrees of freedom were affected. We compensated for the potential influence of this decision on significance by setting stringent p-value thresholds for the CT analyses. In comparison, significant associations were not observed when using absolute CT values or CT values with TBV added as a covariate. It is plausible that the addition of TBV as a covariate decreased our power to detect effects, or that adjusted CT values are more sensitive in revealing a relation between striatal DA responses and cortical morphometry. Indeed, there is little consensus as to whether TBV/ICV should [66] or should not [4],[67],[68] be included as a covariate in CT analyses in the context of substance use research. More broadly, there is limited agreement as to how CT should be analyzed (i.e., absolute vs. corrected values). Thus, such methodological differences must be kept in mind when comparing and replicating future research. Further, we focused on TPQ-assessed NS, however, different externalizing measures may lead to different results, and should be investigated in future work. Finally, associations between NS, striatal DA release and CT may be nonlinear, and non-linear statistical approaches might reveal more complex associations. However, in our sample, exploratory non-linear correlations were not significant, and the current results strengthen the evidence for associations between striatal DA release, NS related impulsiveness and frontal cortical thickness in the largest known sample to date.
Supporting information
S1 Table. Excel table containing the dataset used in the analyses outlined in this manuscript.
https://doi.org/10.1371/journal.pone.0174219.s001
(XLSX)
Author Contributions
- Conceptualization: NJ ML.
- Data curation: NJ SMC KL.
- Formal analysis: NJ SMC KL.
- Funding acquisition: ML.
- Investigation: MC KFC IB ML.
- Methodology: NJ SMC KL.
- Project administration: AD CB ML SMC KFC MC IB.
- Resources: KL.
- Software: NJ SMC KL.
- Supervision: ML AD CB.
- Validation: NJ SMC.
- Visualization: NJ SMC.
- Writing – original draft: NJ SMC ML.
- Writing – review & editing: NJ SMC KFC MC ML.
References
- 1. Depue RA, Collins PF. Neurobiology of the structure of personality: dopamine, facilitation of incentive motivation, and extraversion. Behav Brain Sci. 1999;22: 491–517; discussion 518–69. pmid:11301519
- 2. Ikemoto S, Panksepp J. The role of nucleus accumbens dopamine in motivated behavior: a unifying interpretation with special reference to reward-seeking. Brain Res Brain Res Rev. 1999;31: 6–41. pmid:10611493
- 3. Schilling C, Kuhn S, Paus T, Romanowski A, Banaschewski T, Barbot A, et al. Cortical thickness of superior frontal cortex predicts impulsiveness and perceptual reasoning in adolescence. Mol Psychiatry. 2013;18: 624–630. pmid:22665261
- 4. Holmes AJ, Hollinshead MO, Roffman JL, Smoller JW, Buckner RL. Individual differences in cognitive control circuit anatomy link sensation seeking, impulsivity, and substance use. J Neurosci. 2016;36: 4038–4049. pmid:27053210
- 5.
Leyton M. The neurobiology of desire: Dopamine and the regulation of mood and motivational states in humans. In: Kringelbach ML, Berridge KC, editors. Pleasures of the brain: Oxford University Press; 2010. pp. 222–243.
- 6. Berridge KC, Robinson TE. Parsing reward. Trends Neurosci. 2003;26: 507–513. pmid:12948663
- 7. Schultz W. Dopamine reward prediction error coding. Dialogues Clin Neurosci. 2016;18: 23–32. pmid:27069377
- 8. Morris LS, Kundu P, Dowell N, Mechelmans DJ, Favre P, Irvine MA, et al. Fronto-striatal organization: Defining functional and microstructural substrates of behavioural flexibility. Cortex. 2016;74: 118–133. pmid:26673945
- 9. Choi EY, Yeo BT, Buckner RL. The organization of the human striatum estimated by intrinsic functional connectivity. J Neurophysiol. 2012;108: 2242–2263. pmid:22832566
- 10. Aupperle RL, Melrose AJ, Francisco A, Paulus MP, Stein MB. Neural substrates of approach-avoidance conflict decision-making. Hum Brain Mapp. 2015;36: 449–462. pmid:25224633
- 11. Vul E, Harris C, Winkielman P, Pashler H. Puzzlingly High Correlations in fMRI Studies of Emotion, Personality, and Social Cognition. Perspect Psychol Sci. 2009;4: 274–290. pmid:26158964
- 12. Casey KF, Cherkasova MV, Larcher K, Evans AC, Baker GB, Dagher A, et al. Individual differences in frontal cortical thickness correlate with the d-amphetamine-induced striatal dopamine response in humans. J Neurosci. 2013;33: 15285–15294. pmid:24048857
- 13. Leyton M, Boileau I, Benkelfat C, Diksic M, Baker G, Dagher A. Amphetamine-induced increases in extracellular dopamine, drug wanting, and novelty seeking: a PET/[11C]raclopride study in healthy men. Neuropsychopharmacology. 2002;27: 1027–1035. pmid:12464459
- 14. Lee B, London ED, Poldrack RA, Farahi J, Nacca A, Monterosso JR, et al. Striatal dopamine d2/d3 receptor availability is reduced in methamphetamine dependence and is linked to impulsivity. J Neurosci. 2009;29: 14734–14740. pmid:19940168
- 15. Gjedde A, Kumakura Y, Cumming P, Linnet J, Moller A. Inverted-U-shaped correlation between dopamine receptor availability in striatum and sensation seeking. Proc Natl Acad Sci U S A. 2010;107: 3870–3875. pmid:20133675
- 16. Reeves SJ, Polling C, Stokes PR, Lappin JM, Shotbolt PP, Mehta MA, et al. Limbic striatal dopamine D2/3 receptor availability is associated with non-planning impulsivity in healthy adults after exclusion of potential dissimulators. Psychiatry Res. 2012;202: 60–64. pmid:22595510
- 17. Boileau I, Payer D, Chugani B, Lobo D, Behzadi A, Rusjan PM, et al. The D2/3 dopamine receptor in pathological gambling: a positron emission tomography study with [11C]-(+)-propyl-hexahydro-naphtho-oxazin and [11C]raclopride. Addiction. 2013;108: 953–963. pmid:23167711
- 18. Kim JH, Son YD, Kim HK, Lee SY, Kim YB, Cho ZH. Dopamine D(2/3) receptor availability and human cognitive impulsivity: a high-resolution positron emission tomography imaging study with [(1)(1)C]raclopride. Acta Neuropsychiatr. 2014;26: 35–42. pmid:25142098
- 19. Robertson CL, Ishibashi K, Mandelkern MA, Brown AK, Ghahremani DG, Sabb F, et al. Striatal D1- and D2-type dopamine receptors are linked to motor response inhibition in human subjects. J Neurosci. 2015;35: 5990–5997. pmid:25878272
- 20. Oberlin BG, Albrecht DS, Herring CM, Walters JW, Hile KL, Kareken DA, et al. Monetary discounting and ventral striatal dopamine receptor availability in nontreatment-seeking alcoholics and social drinkers. Psychopharmacology (Berl). 2015;232: 2207–2216.
- 21. Lawrence AD, Brooks DJ. Ventral striatal dopamine synthesis capacity is associated with individual differences in behavioral disinhibition. Front Behav Neurosci. 2014;8: 86. pmid:24672449
- 22. Ishii T, Sawamoto N, Tabu H, Kawashima H, Okada T, Togashi K, et al. Altered striatal circuits underlie characteristic personality traits in Parkinson's disease. J Neurol. 2016;263: 1828–1839. pmid:27334907
- 23. Smith CT, Wallace DL, Dang LC, Aarts E, Jagust WJ, D'Esposito M, et al. Modulation of impulsivity and reward sensitivity in intertemporal choice by striatal and midbrain dopamine synthesis in healthy adults. J Neurophysiol. 2016;115: 1146–1156. pmid:26683066
- 24. Zald DH, Cowan RL, Riccardi P, Baldwin RM, Ansari MS, Li R, et al. Midbrain dopamine receptor availability is inversely associated with novelty-seeking traits in humans. J Neurosci. 2008;28: 14372–14378. pmid:19118170
- 25. Savage SW, Zald DH, Cowan RL, Volkow ND, Marks-Shulman PA, Kessler RM, et al. Regulation of novelty seeking by midbrain dopamine D2/D3 signaling and ghrelin is altered in obesity. Obesity (Silver Spring). 2014;22: 1452–1457.
- 26. Riccardi P, Zald D, Li R, Park S, Ansari MS, Dawant B, et al. Sex differences in amphetamine-induced displacement of [(18)F]fallypride in striatal and extrastriatal regions: a PET study. Am J Psychiatry. 2006;163: 1639–1641. pmid:16946193
- 27. Oswald LM, Wong DF, Zhou Y, Kumar A, Brasic J, Alexander M, et al. Impulsivity and chronic stress are associated with amphetamine-induced striatal dopamine release. Neuroimage. 2007;36: 153–166. pmid:17433881
- 28. Buckholtz JW, Treadway MT, Cowan RL, Woodward ND, Li R, Ansari MS, et al. Dopaminergic network differences in human impulsivity. Science. 2010;329: 532. pmid:20671181
- 29. Cherkasova MV, Faridi N, Casey KF, O'Driscoll GA, Hechtman L, Joober R, et al. Amphetamine-induced dopamine release and neurocognitive function in treatment-naive adults with ADHD. Neuropsychopharmacology. 2014;39: 1498–1507. pmid:24378745
- 30. Oswald LM, Wand GS, Wong DF, Brown CH, Kuwabara H, Brasic JR. Risky decision-making and ventral striatal dopamine responses to amphetamine: a positron emission tomography [(11)C]raclopride study in healthy adults. Neuroimage. 2015;113: 26–36. pmid:25795343
- 31. Schilling C, Kuhn S, Romanowski A, Schubert F, Kathmann N, Gallinat J. Cortical thickness correlates with impulsiveness in healthy adults. Neuroimage. 2012;59: 824–830. pmid:21827861
- 32. Jiang Y, Guo X, Zhang J, Gao J, Wang X, Situ W, et al. Abnormalities of cortical structures in adolescent-onset conduct disorder. Psychol Med. 2015;45: 3467–3479. pmid:26189512
- 33. Boileau I, Dagher A, Leyton M, Welfeld K, Booij L, Diksic M, et al. Conditioned dopamine release in humans: a positron emission tomography [11C]raclopride study with amphetamine. J Neurosci. 2007;27: 3998–4003. pmid:17428975
- 34. Boileau I, Dagher A, Leyton M, Gunn RN, Baker GB, Diksic M, et al. Modeling sensitization to stimulants in humans: an [11C]raclopride/positron emission tomography study in healthy men. Arch Gen Psychiatry. 2006;63: 1386–1395. pmid:17146013
- 35. Cloninger CR, Svrakic DM, Przybeck TR. A psychobiological model of temperament and character. Arch Gen Psychiatry. 1993;50: 975–990. pmid:8250684
- 36.
First MB, Spitzer RI, Gibbon M, editors. Axis I Disorders. New York, USA: New York State Psychiatric Institute; 1995.
- 37. Nordstrom AL, Olsson H, Halldin C. A PET study of D2 dopamine receptor density at different phases of the menstrual cycle. Psychiatry Res. 1998;83: 1–6. pmid:9754700
- 38. Martinez D, Slifstein M, Broft A, Mawlawi O, Hwang DR, Huang Y, et al. Imaging human mesolimbic dopamine transmission with positron emission tomography. Part II: amphetamine-induced dopamine release in the functional subdivisions of the striatum. J Cereb Blood Flow Metab. 2003;23: 285–300. pmid:12621304
- 39. Gunn RN, Lammertsma AA, Hume SP, Cunningham VJ. Parametric imaging of ligand-receptor binding in PET using a simplified reference region model. Neuroimage. 1997;6: 279–287. pmid:9417971
- 40. Laruelle M. Imaging synaptic neurotransmission with in vivo binding competition techniques: a critical review. J Cereb Blood Flow Metab. 2000;20: 423–451. pmid:10724107
- 41. Aston JA, Gunn RN, Worsley KJ, Ma Y, Evans AC, Dagher A. A statistical method for the analysis of positron emission tomography neuroreceptor ligand data. Neuroimage. 2000;12: 245–256. pmid:10944407
- 42. Ko JH, Reilhac A, Ray N, Rusjan P, Bloomfield P, Pellecchia G, et al. Analysis of variance in neuroreceptor ligand imaging studies. PLoS One. 2011;6: e23298. pmid:21858062
- 43. Kim JS, Singh V, Lee JK, Lerch J, Ad-Dab'bagh Y, MacDonald D, et al. Automated 3-D extraction and evaluation of the inner and outer cortical surfaces using a Laplacian map and partial volume effect classification. Neuroimage. 2005;27: 210–221. pmid:15896981
- 44. Lerch JP, Evans AC. Cortical thickness analysis examined through power analysis and a population simulation. Neuroimage. 2005;24: 163–173. pmid:15588607
- 45. Tzourio-Mazoyer N, Landeau B, Papathanassiou D, Crivello F, Etard O, Delcroix N, et al. Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage. 2002;15: 273–289. pmid:11771995
- 46. Tziortzi AC, Haber SN, Searle GE, Tsoumpas C, Long CJ, Shotbolt P, et al. Connectivity-based functional analysis of dopamine release in the striatum using diffusion-weighted MRI and positron emission tomography. Cereb Cortex. 2014;24: 1165–1177. pmid:23283687
- 47. Zhou Q, Goryawala M, Cabrerizo M, Barker W, Duara R, Adjouadi M. Significance of normalization on anatomical MRI measures in predicting Alzheimer's disease. ScientificWorldJournal. 2014;2014: 541802. pmid:24550710
- 48. Josefsson K, Jokela M, Cloninger CR, Hintsanen M, Salo J, Hintsa T, et al. Maturity and change in personality: developmental trends of temperament and character in adulthood. Dev Psychopathol. 2013;25: 713–727. pmid:23880387
- 49. Storsve AB, Fjell AM, Tamnes CK, Westlye LT, Overbye K, Aasland HW, et al. Differential longitudinal changes in cortical thickness, surface area and volume across the adult life span: regions of accelerating and decelerating change. J Neurosci. 2014;34: 8488–8498. pmid:24948804
- 50. Cox SM, Benkelfat C, Dagher A, Delaney JS, Durand F, McKenzie SA, et al. Striatal dopamine responses to intranasal cocaine self-administration in humans. Biol Psychiatry. 2009;65: 846–850. pmid:19249751
- 51. Drevets WC, Gautier C, Price JC, Kupfer DJ, Kinahan PE, Grace AA, et al. Amphetamine-induced dopamine release in human ventral striatum correlates with euphoria. Biol Psychiatry. 2001;49: 81–96. pmid:11164755
- 52. Di Chiara G, Imperato A. Drugs abused by humans preferentially increase synaptic dopamine concentrations in the mesolimbic system of freely moving rats. Proc Natl Acad Sci U S A. 1988;85: 5274–5278. pmid:2899326
- 53. Hurd YL, Pristupa ZB, Herman MM, Niznik HB, Kleinman JE. The dopamine transporter and dopamine D2 receptor messenger RNAs are differentially expressed in limbic- and motor-related subpopulations of human mesencephalic neurons. Neuroscience. 1994;63: 357–362. pmid:7891851
- 54. Milella MS, Fotros A, Gravel P, Casey KF, Larcher K, Verhaeghe JA, et al. Cocaine cue-induced dopamine release in the human prefrontal cortex. J Psychiatry Neurosci. 2016;41: 322–330. pmid:26900792
- 55.
Haber SN. Neuroanatomy of Reward: A View from the Ventral Striatum. In: Gottfried JA, editor. Neurobiology of Sensation and Reward. Boca Raton, FL, USA: CRC Press/Taylor & Francis; 2011.
- 56. Lappin JM, Reeves SJ, Mehta MA, Egerton A, Coulson M, Grasby PM. Dopamine release in the human striatum: motor and cognitive tasks revisited. J Cereb Blood Flow Metab. 2009;29: 554–564. pmid:19088741
- 57. D'Amour-Horvat V, Leyton M. Impulsive actions and choices in laboratory animals and humans: effects of high vs. low dopamine states produced by systemic treatments given to neurologically intact subjects. Front Behav Neurosci. 2014;8: 432. pmid:25566001
- 58. Pereira JB, Ibarretxe-Bilbao N, Marti MJ, Compta Y, Junque C, Bargallo N, et al. Assessment of cortical degeneration in patients with Parkinson's disease by voxel-based morphometry, cortical folding, and cortical thickness. Hum Brain Mapp. 2012;33: 2521–2534. pmid:21898679
- 59. Shao N, Yang J, Shang H. Voxelwise meta-analysis of gray matter anomalies in Parkinson variant of multiple system atrophy and Parkinson's disease using anatomic likelihood estimation. Neurosci Lett. 2015;587: 79–86. pmid:25484255
- 60. Rimol LM, Nesvag R, Hagler DJ Jr, Bergmann O, Fennema-Notestine C, Hartberg CB, et al. Cortical volume, surface area, and thickness in schizophrenia and bipolar disorder. Biol Psychiatry. 2012;71: 552–560. pmid:22281121
- 61. Fortier CB, Leritz EC, Salat DH, Venne JR, Maksimovskiy AL, Williams V, et al. Reduced cortical thickness in abstinent alcoholics and association with alcoholic behavior. Alcohol Clin Exp Res. 2011;35: 2193–2201. pmid:21919920
- 62. Durazzo TC, Tosun D, Buckley S, Gazdzinski S, Mon A, Fryer SL, et al. Cortical thickness, surface area, and volume of the brain reward system in alcohol dependence: relationships to relapse and extended abstinence. Alcohol Clin Exp Res. 2011;35: 1187–1200. pmid:21410483
- 63. Volkow ND, Wang GJ, Telang F, Fowler JS, Logan J, Jayne M, et al. Profound decreases in dopamine release in striatum in detoxified alcoholics: possible orbitofrontal involvement. J Neurosci. 2007;27: 12700–12706. pmid:18003850
- 64. Yuan P, Raz N. Prefrontal cortex and executive functions in healthy adults: a meta-analysis of structural neuroimaging studies. Neurosci Biobehav Rev. 2014;42: 180–192. pmid:24568942
- 65. Vijayakumar N, Allen NB, Youssef G, Dennison M, Yucel M, Simmons JG, et al. Brain development during adolescence: A mixed-longitudinal investigation of cortical thickness, surface area, and volume. Hum Brain Mapp. 2016;37: 2027–2038. pmid:26946457
- 66. Jacobus J, Squeglia LM, Meruelo AD, Castro N, Brumback T, Giedd JN, et al. Cortical thickness in adolescent marijuana and alcohol users: A three-year prospective study from adolescence to young adulthood. Dev Cogn Neurosci. 2015;16: 101–109. pmid:25953106
- 67. Boulos PK, Dalwani MS, Tanabe J, Mikulich-Gilbertson SK, Banich MT, Crowley TJ, et al. Brain cortical thickness differences in adolescent females with substance use disorders. PLoS One. 2016;11: e0152983. pmid:27049765
- 68. Lange EH, Nerland S, Jorgensen KN, Morch-Johnsen L, Nesvag R, Hartberg CB, et al. Alcohol use is associated with thinner cerebral cortex and larger ventricles in schizophrenia, bipolar disorder and healthy controls. Psychol Med. 2016: 1–14.