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Developmental Cognitive Neuroscience 20 (2016) 35–42

Contents lists available at ScienceDirect

Developmental Cognitive Neuroscience

j o ur nal ho me pa ge: ht tp : / /www.e lsev ier .com/ locate /dcn

egative functional coupling between the right fronto-parietal andimbic resting state networks predicts increased self-control and laterubstance use onset in adolescence�

ae-Ho Leea, Eva H. Telzera,b,∗

Department of Psychology, University of Illinois at Urbana-Champaign (UIUC), United StatesBeckman Institute for Advanced Science and Technology, UIUC, United States

r t i c l e i n f o

rticle history:eceived 22 February 2016eceived in revised form 8 June 2016ccepted 16 June 2016vailable online 17 June 2016

eywords:

a b s t r a c t

Recent developmental brain imaging studies have demonstrated that negatively coupled prefrontal-limbic circuitry implicates the maturation of brain development in adolescents. Using resting-statefunctional magnetic resonance imaging (rs-fMRI) and independent component analysis (ICA), the presentstudy examined functional network coupling between prefrontal and limbic systems and links toself-control and substance use onset in adolescents. Results suggest that negative network coupling(anti-correlated temporal dynamics) between the right fronto-parietal and limbic resting state net-

dolescenceisk-taking behavioresting-state fMRI

ndependent component analysis (ICA)ronto-parietal network (FPN)imbic network

works is associated with greater self-control and later substance use onset in adolescents. These findingsincrease our understanding of the developmental importance of prefrontal-limbic circuitry for adolescentsubstance use at the resting-state network level.

© 2016 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-NDlicense (http://creativecommons.org/licenses/by-nc-nd/4.0/).

ntrinsic network connectivity

. Introduction

Adolescence has been characterized as a period of increasedmpulsivity and risky behavior that often leads to the initiation ofealth compromising behaviors such as substance use (Chamberst al., 2003). Indeed, national estimates indicate that rates ofubstance use during adolescence climb steadily from 20 to 70%etween 12 and 17 years of age (Miech et al., 2015; Stagmant al., 2011). Of great concern is that substance use such as alco-ol, cigarette, marijuana and other illicit drugs often increases theisk of dependency, unprotected intercourse, and interpersonaliolence, leading to adverse health outcomes later in life (Verdejo-arcía et al., 2008). Developmental neuroscience research suggests

hat the increased risky behaviors including substance use in ado-escence are due, in part, to imbalanced developmental trajectories

f brain circuits between the subcortical limbic and prefrontal net-orks (Casey et al., 2008; see also Crone et al., 2016), and thus

dolescents exhibit heightened emotional impulsivity and a lack of

� This work was supported by the National Institute of Health (1R01DA039923).∗ Corresponding author at: Department of Psychology, University of Illinois atrbana-Champaign, 603 E. Daniel Street Champaign, IL 61820, United States.

E-mail address: ehtelzer@illinois.edu (E.H. Telzer).

ttp://dx.doi.org/10.1016/j.dcn.2016.06.002878-9293/© 2016 The Authors. Published by Elsevier Ltd. This is an open access article

/).

effective cognitive control (Hare et al., 2008), resulting in greatersubstance use behaviors (Casey and Jones, 2010). Therefore, inves-tigating the neural underpinnings associated with substance useis critical to reduce risk of substance use and increase protectivefactors for adolescents.

A growing body of literature in developmental neuroscienceprovides considerable evidence that functional coupling betweenlimbic and prefrontal systems plays an important role in emotionregulation and risk-taking behavior during adolescence, contribut-ing to substance use (Cservenka et al., 2014; Fareri et al., 2015;Gabard-Durnam et al., 2014; Gee et al., 2013a,b, 2014; Hare et al.,2008; Porter et al., 2015; Qu et al., 2015; van Duijvenvoorde et al.,2016; Weissman et al., 2015). For example, functional MRI (fMRI)studies using task-based indexes of functional brain activationhave demonstrated a developmental shift in functional connec-tivity between limbic and prefrontal circuitry, such that childrenshow positive coupling, which switches to negative coupling byadulthood (Gee et al., 2013a,b, 2014; Hare et al., 2008). Thus, neg-ative functional coupling between these circuitries is an index ofmaturation of neural networks of the brain, representing enhanced

inhibitory projections from frontal to limbic regions. Consistently,a recent longitudinal fMRI study highlighted inverse connectiv-ity between limbic and prefrontal systems by demonstrating thatadolescents who show longitudinal declines in functional cou-

under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.

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6 T.-H. Lee, E.H. Telzer / Developmenta

ling between the ventral striatum (VS), a region in the limbicystem, and medial prefrontal cortex (mPFC), a region in therefrontal, executive system, exhibited greater decreases in risk-aking behavior over time (Qu et al., 2015). Tract-tracing studiesn rodents further supports this view by showing that an inversessociation between these two systems is derived from greateruppression of limbic activity via descending projections from therefrontal region, which increasingly emerges over developmentBouwmeester et al., 2002a,b; Cressman et al., 2010; Cunninghamt al., 2002). Resting-state fMRI (rs-fMRI) studies in humans haveurther demonstrated the importance of functional connectivitynd links to adolescent behavior (Fareri et al., 2015; Gabard-urnam et al., 2014; van Duijvenvoorde et al., 2016; Weissmant al., 2015). For example, higher positive functional connectivityetween limbic and prefrontal circuits is associated with hyper-esponsiveness and overvaluation of rewards (Fareri et al., 2015;an Duijvenvoorde et al., 2016), behaviors which contribute toigher frequency of substance-use behavior in adolescents (Caseynd Jones, 2010). Indeed, a recent rs-fMRI study demonstrated thatositive resting-state functional connectivity between the nucleusccumbens (NAcc) and prefrontal regions was related to earlierubstance use (Weissman et al., 2015). Similarly, adolescents with

family history of alcoholism showed less negative connectivityetween NAcc and other executive control regions including the

nferior frontal gyrus and ventrolateral PFC (Cservenka et al., 2014).n sum, these findings suggest that inverse or segregated limbic-refrontal connectivity is related to decreased risky-behaviorsuring adolescence

Given previous evidence indicating developmental maturity ofhe brain can be indexed by the degree of inverse functional cou-ling between limbic and PFC systems, representing the enhancedop-down inhibitory processing of the prefrontal system over theeavier bottom-up signaling from the limbic system (Bouwmeestert al., 2002a,b; Cressman et al., 2010; Cunningham et al., 2002;ee et al., 2013a,b, 2014), the main goal of current study was

o investigate the behavioral outcomes of functional connectivityetween these two systems using rs-fMRI. rs-fMRI can provide aovel framework for investigating the functional systems in the

arge-scale organization of the developing brain in adolescentsUddin et al., 2010), independent of stimulus-induced brain activ-ty usually driven by either experimental demands or participants’ognitive and emotional tendencies. In particular, we sought to pro-ide a novel functional network-level account of adolescent brainonnectivity associated with substance-use behavior by using inde-endent component analysis (ICA) focusing on between-networkoupling. Previous evidence has demonstrated that independentetworks with opposing functions (e.g., top-down inhibitory pro-essing of the prefrontal system versus bottom-up processing of theimbic system) are more likely to show an inverse correlation (i.e.,egative connectivity at between-network level; Fox et al., 2005).ost importantly, this inverse coupling between opposite func-

ional networks increases with age while sub-region connectivitytrength increases in each functional network (i.e., positive connec-ivity at within-network level) as a result of enhanced efficiencyn between- and within-network communication (Stevens et al.,009). Although prior work has found limbic-prefrontal functionalonnectivity plays a role in adolescents’ sensitivity to rewardsnd substance-use behaviors (e.g., van Duijvenvoorde et al., 2016;eissman et al., 2015), the approach they used was seed-based

OI which does not necessarily distinguish between whether theonnectivity metric in certain paired voxels or regions is due toither between- or within network involvement (Xu et al., 2013).

t is difficult to disentangle whether the source of the connec-ivity valence between seed regions (e.g., amygdala or VS) andheir pairwise regions of interest (e.g., mPFC or dorsolateral PFC;lPFC) is derived from either between- or within-network. Given

itive Neuroscience 20 (2016) 35–42

the difficulty of evaluating the network level of brain systems(i.e., between- and within network connectivity), ICA holds sev-eral advantages over the seed-based approach in rs-fMRI. First,ICA identifies functional networks distinctly using spatial inde-pendence (Beckmann and Smith, 2004). By taking into accountmultiple simultaneous voxel-by-voxel time-course dynamics, ICAcan decompose data into a linear mixture of spatially independentand temporally coherent course signals that are usually inter-mingled within a given voxel which would be indistinguishablein a conventional seed-based approach, and thus ICA can pro-vide insight into whole-brain functional systems independentlyfor both within- and between-network connectivity without apriori hypotheses for a specific seed-region (Jafri et al., 2008;Joel et al., 2011). Furthermore, ICA requires no a priori speci-fication of non-resting state network relevant signal variabilitysuch as global signal fluctuation and noise such as physiologicaldynamics and head movement that commonly arises in seed-based correlation approach and leads to signal quality changes (e.g.,Murphy et al., 2009; Power et al., 2012 Power et al., 2012) becausean ICA characterizes individual-level spatiotemporal dynamics ofeach brain network by multiple regressions while controlling forthe influence of other networks and sources of variability (e.g.,noise and global signal; Filippini et al., 2009). In addition to theanalytic aspect of ICA described above, ICA is relatively unaf-fected by different temporal sampling rates (see De Luca et al.,2006) thereby increasing flexibility in use of multiple datasets col-lected from multiple sites and scan protocols (e.g., Biswal et al.,2010).

Given the network level approach of using ICA, we were par-ticularly interested in examining between-functional resting-statenetwork (RSN) connectivity between the limbic and right fronto-parietal networks (FPN). The limbic network broadly includesamygdala, VS, primary olfactory, limbic associated cortices (BA28/34/35/36/38), orbitofrontal cortex (OFC), thalamus and basalganglia (BG), and is involved in emotional and autonomic pro-cesses for reward, fear, and anxiety (Janes et al., 2012; Laird et al.,2011; Yeo et al., 2011), indicating that this network represents thebrain system for bottom-up emotional processing. The right FPNcomposes of dlPFC, inferior parietal lobule (IPL), intraparietal sul-cus (IPS) and midcingulate on the right side of brain and is wellknown for its strong involvement in multiple top-down cognitivecontrol processes that require continuous attentional monitoring,response inhibition, and control (Barrós-Loscertales et al., 2011;Corbetta et al., 2008; Dosenbach et al., 2008; Dosenbach et al.,2007; Fair et al., 2009, 2007; Garavan et al., 2008; Vincent et al.,2008; Weissman et al., 2015). Although the FPN exists bilaterally,the left FPN is recruited for higher level language processing suchas comprehension, reading, and explicit working memory with adominance of semantic and phonologic information (Laird et al.,2011), and therefore we focused on the right FPN in the currentstudy.

In sum, the goal of the present study was to provide a between-network level examination of adolescents’ brain connectivity andlinks to self-control and substance use onset. We predicted thatadolescents who show more inverse functional coupling betweenthe right FPN and limbic network (LN) would show higher self-control which would contribute to later substance use onset (e.g.,Weissman et al., 2015). The transition to mid adolescence (e.g.,13–17 years) is a developmental period marked by steep increasesin risky behavior and poor self-control. For instance, lifetime illicitdrug use more than doubles and current drug use (i.e., use withinthe past 30 days) more than triples between ages 13 and 17, such

that by age 17 nearly half of youth have tried a drug at least once(Johnson et al., 2009). Therefore, in the current study, we focusedon mid adolescents ranging from 13 to 17 years.

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. Method

.1. Resting state fMRI dataset

To detect canonical large-scale resting-state network (RSN)ommonly shared across people regardless of age, sex, education,ncome, and ethnicity, we first aggregated all possible rs-fMRIata collected from our lab (i.e., dataset 1 and dataset 2), yielding

total of 79 rs-fMRI scans (Total: Mage = 29.16 years; SD = 14.89;ange = 13–57 years; dataset 1: Mage = 27.92 years; SD = 15.93;ange = 13–52 years; 20 adolescents and 17 adults; dataset 2:

age = 30.32 years; SD = 13.93; range = 14–57 years; 17 adolescentsnd 22 adults). All participants were screened by a phone inter-iew prior to the scan to ensure the participant was not currentlyaking any psychotropic medications and was not diagnosed withny mood disorder. Due to excessive movement (mean frame-ise displacement, FD > 0.5 mm), three data sets were excluded

rom the final analysis (three adolescents; range = 0.83–1.85 mm),ielding a total of 76 resting state datasets possible to use forhe group-level ICA procedure, combined with dual-regressionsee below for analysis details). After the group-level ICA, onlyhe adolescents (age range 13–17 years; N = 37) were selectedrom the total sample to test our primary prediction about thessociation between intrinsic functional network connectivity andubstance use onset in adolescence (Mage = 14.70 years; SD = 1.10;8 females; 20 from dataset 1 and 17 from dataset 2). Malesnd females did not differ in their age, t(35) = −1.31, p = 0.20male: Mage = 14.47 years; SD = 0.90; range = 13–16 years; female:

age = 14.94 years; SD = 1.26; range = 13–17 years). Additional non-arametric test using independent-samples Mann-Whitney U Testonfirmed that the distribution of age was the same across sexategories (p = 0.25).

.2. Data acquisition

All imaging data were collected using a 3T-Siemens Trio MRIcanner with a 32-channel matrix coil. High-resolution struc-ural images (T1-MPRAGE) were acquired first (repetition time orR = 1.9 s, echo time or TE = 2.3 ms, matrix size = 256 × 256, fieldf view or FOV = 230 mm, flip angle or FA = 90◦, 1 mm isotropicoxel). The resting-state data were acquired from a gradient-cho echo-planar image sequence (dataset 1: 180 vol, 38 slicesith no inter-slice gap, TR = 2 s, matrix = 92 × 92, FOV = 230 mm,

A = 90◦, voxel size 2.5 × 2.5 × 3.3 mm3, 6 min duration; dataset 2:20 vol; 36 slices with no inter-slice gap, TR = 3 s, matrix = 64 × 64,OV = 220 mm, FA = 90◦, voxel size 3.5 × 3.5 × 4.0 mm3, 6 min dura-ion).

.3. Data analysis

For the resting-state data analysis, group-level ICA combinedith dual-regression approach (Beckmann and Smith, 2004;

ilippini et al., 2009) was performed to detect large-scale genericSN including right FPN, limbic network and other sensory relatedetworks across our resting-state data sets (e.g., Laird et al., 2011;obinson et al., 2011; Smith et al., 2009)

ICA was carried out using MELODIC Version 3.14, part of FSLFMRIB’s Software Library, www.fmrib.ox.ac.uk/fsl). Preprocessingrocedure was applied for individual data (128 s high-pass; 6 mmmoothing; masking of non-brain voxels; voxel-wise de-meaning;ormalized voxel-wise variance; normalized into 2mm-MNI-tandard via individual T1-weighted anatomical image). During

he preprocessing, individual level ICA denoising procedure wasonducted to remove artifact signal such as motion and physiooise from the data by using an automated signal classifica-ion toolbox (Tohka et al., 2008; an average of 4.3 components

itive Neuroscience 20 (2016) 35–42 37

(13.7%) were removed from each participant; see supplemen-tary Fig. 1 that indicates representative noise components trainedthe classifier to be automatically regressed out from individualdata; mean FD = 0.05 mm, SD = 0.025 mm, range: 0.02–0.18 mm).Although there are several strategies suggested to rigorouslycorrect for motion-related noise in resting state data, such asspike regression with 24-type of motion parameters (Lemieuxet al., 2007; Satterthwaite et al., 2013) and individual high-motioncontaminated volume scrubbing (Power et al., 2012), several draw-backs of these strategies also exist such that they can introduceoverfitting by the use of a large set of nuisance regressors, linearassumption about motion, and negative influences in the autocor-relation structure of data (see Pruim et al., 2015a,b). Therefore,we applied ICA denoising approach for the current analysis giventhe recent evidence that ICA denoising can effectively enhance thefidelity of data quality in terms of motion control (e.g., Birn et al.,2008; Pruim et al., 2015a,b; Starck et al., 2013). Furthermore, it hasbeen well demonstrated that RSNs estimated by ICA are less proneto artefactual effects from noise such as physiological signal, globalsignal fluctuation and motion due to the ability of ICA to accountfor the existence of such noise effects within additional non-RSNcomponents (for more comprehensive review, please see Cole et al.,2010).

To find the most representative functional networks from ourentire dataset, preprocessed and denoised data were temporallyconcatenated into a single 4D file and applied to group level ICAwith probabilistic principal component analysis (PCA) where thenumber of dimensions was estimated using the Laplace approx-imation to the Bayesian evidence of the model order (Beckmannand Smith, 2004; Minka, 2000), yielding 19 network spatial maps.To distinguish group-level brain networks from artifactual com-ponents (e.g., residual head movement and physiological noise)and to identify canonical resting state networks, all network mapswere spatially cross-correlated with canonical RSN templates (i.e.,template-matching procedure; e.g., Clewett et al., 2014) acquiredfrom previous resting-state studies (Laird et al., 2011; Shirer et al.,2012; Smith et al., 2009). We finally identified 13 intrinsic RSNs(Supplementary Fig. 2) including the right FPN and limbic networks(Fig. 2A; see also Supplementary Fig. 3 and 4). The six remaininggroup level components were considered artifactual (e.g., physio-logical) due to predominant activation in white matter, ventricles,vasculature, or head movement.

Following group-level ICA, dual regression (Filippini et al., 2009)was applied to the adolescent dataset to estimate individual-specific temporal dynamics (i.e., time-series data) and associatedspatial maps based on the group-level ICA network maps. Althoughthe right FPN and limbic networks were our main interests, we per-formed linear model estimation with the full set of group-level ICAspatial maps against the separate individual data sets to extractsubject-specific temporal dynamics for each component map (i.e.,spatial regression) in the absence of influence of other networkdynamics. The estimated individual time-course data were then putinto subsequent linear-model estimation against individual’s dataset to estimate subject-specific spatial maps (i.e., temporal regres-sion). To estimate between-RSN network functional coupling, thecorrelation coefficient between time-series of the right FPN andlimbic networks was finally calculated for each individual adoles-cent (e.g., Damaraju et al., 2010; Jafri et al., 2008).

2.4. Measures for adolescent substance use and self-control

2.4.1. Substance use. We used the Center for Disease Control

and Prevention Youth Risk Behavior Survey (YRBS; Eaton et al.,2006) Questionnaire, which is a common measure of substanceuse. This in-depth questionnaire asks about lifetime substance use(i.e., more than two puffs or sips for the following substance:

38 T.-H. Lee, E.H. Telzer / Developmental Cogn

Table 1Frequency of lifetime drug use in adolescents for the 3 substance use categories.

Smoking Alcohol Marijuana

Age of substance use onset M F M F M FBefore 9 years old 0 0 2 0 0 09–10 years old 0 0 0 0 0 011–12 years old 0 0 0 1 0 013–14 years old 0 2 1 6 2 315–16 years old 1 2 2 5 1 317 years old or older 0 0 1 0 0 0Total (%) 5 (13.5) 18 (48.7) 9 (24.3)

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igarettes, alcohol, marijuana, prescription drugs without a doc-or’s prescription, cocaine and other illegal drugs). Adolescentssed a 7-point scale to indicate the first time they used any ofhese substance (1 = “less than nine years old,” 2 = ”9–10 years old,”

= ”11–12 years old,” 4 = ”13–14 years old” 5 = ”15–16 years old” = ”17 years or older”, 7 = ”never used”). No participants had usedocaine, prescription drugs without a doctor’s prescription, or otherllegal drugs in the current study. We used age of earliest onsetor marijuana, alcohol, or cigarettes as our index of substance use-ehaviors. See Table 1 for descriptives of substance use onset. Twodolescents did not complete the questionnaire.

2.4.2. Self-control. To assess general cognitive control ability,e used the Brief Self-Control Scale (BSCS; Tangney et al., 2004)

o measure dispositional self-regulatory behaviors (e.g., “I am goodt resisting temptation” and “I am able to work effectively towardong-term goals”) using a 7-point scale (1 = “strongly disagree” to

= “strongly agree”). The scale’s internal consistency was � = 0.83.

. Results

Our primary goal was to examine how the right frontoparietal-imbic connectivity is related to self-control and substance-use-nset in adolescents. To this end, we first examined relationshipsetween these variables (i.e., network connectivity, self-control,nd substance-use-onset). To reduce possible impact of non-ormal, skewed distribution of our substance use measure, wemployed a robust method (Pernet et al., 2012) combined withootstrapping resampling (n = 5000).

.1. Association between instrinsic resting state connectivity,elf-control, and substance-use-onset1

We found that the degree of right frontoparietal-limbic func-ional network coupling showed a significant negative correlationith self-control, robust Pearson r (37) = −0.30, p < 0.05, 95%I = [−0.54 −0.06], indicating that adolescents who showed moreegative network connectivity between the right FPN and lim-ic network reported higher self-control (Fig. 1B). In addition, weound a significant negative correlation between age-of-onset forubstance use and frontoparietal-limbic connectivity, robust Spear-an r (35) = −0.36, p < 0.05, 95% CI = [-0.70 −0.10], suggesting that

ge of substance use onset increased with more negatively coupled

etworks (Fig. 1C). An additional group-level comparison by split-ing adolescents into two groups (never-use; n = 17 vs. lifetime-use;

= 18; two-sample t-test with unequal sample size and variance)

1 To examine possible effects of sex, we also conducted the same analysis whileontrolling for sex, and found identical relationships with statistical significance;ight FPN and self-control: r (37) = −0.28, p < 0.05, 95% CI after bootstrapping = [-.52 −0.01]; FPNR and substance use: r (35) = −0.40, p < 0.05, 95% CI = [-0.68 −0.002];ubstance use and self-control: r (35) = −0.42, p < 0.05, 95% CI = [0.19 0.64]. Based onhese results, we did not control for sex in the rest of the analyses.

itive Neuroscience 20 (2016) 35–42

further confirmed that negatively coupled right frontoparietal-limbic network-connectivity is related to adolescents’ substanceuse such that the group that never used substances showed sig-nificant anti-correlated functional connectivity between the rightFPN and limbic network compared to the group who had usedsubstances, t (33) = −2.26, p < 0.05, 95% CI = [-0.29 −0.02] (Fig. 1D).Finally, self-control showed a significant positive correlation withsubstance use onset (i.e., higher self-control associated with latersubstance use onset), robust Spearman r (35) = 0.44, p < 0.05, 95%CI = [0.19 0.64] (Fig. 1E).

In addition to the right FPN, we subsequently repeated the sameanalyses focusing on the left FPN to rule out the different function-ality between right- and left FPNs. We did not find any significantrelationships between the degree of functional network coupling(i.e., left frontroparietal − limbic network connectivity), substanceuse onset (95% CI = [−0.56 0.10]) or self-control (95% CI = [−0.450.08]), confirming the different functional involvement of eachfrontoparietal network in the brain.

3.2. Mediation analyses

Given the results implying that all pairwise correlationsbetween frontoparietal-limbic connectivity, self-control andsubstance-use-onset showed statistically meaningful relation-ships with each other, we tested regression models by usingthe mediation analysis described by Preacher and Hayes (2008).Our main interest of the regression model was to test whetherfrontoparietal-limbic connectivity (i.e., independent variable) wasassociated with the degree of substance use (i.e., outcome) throughself-control (i.e., mediator; model 1). The magnitude and the sig-nificance of the effect was calculated using 5000 bootstrappingresampling and a bias-corrected confidence interval.

Model 1 showed a significant indirect effect of frontoparietal-limbic connectivity on substance use through self-control (indirecteffect: B = −2.89, SE = 1.24, p < 0.05, 95% CI [−1.98 −0.04]; directeffect: B = −0.50, SE = 0.22, p < 0.05, 95% CI = [−0.94 −0.01], Fig. 1F).To rule out the possibility that early substance-use onset altersdevelopmental trajectory of between-network coupling, we alsotested another regression model by using substance-use onsetas the independent variable, frontoparietal-limbic connectivity asoutcome and self-control as mediator (model 2). Model 2 was notstatistically significant (indirect effect; 95% CI = [−0.22 0.02]).

4. Discussion

The goal of the present study was to examine the neural cor-relates underlying adolescents’ substance use and self-control byfocusing on two resting-state intrinsic networks, the right FPNand limbic network. We successfully replicated and extended pre-vious findings regarding resting state connectivity patterns andsubstance-use behavior in adolescents (Weissman et al., 2015) bydemonstrating that more negatively coupled right frontoparietal-limbic connectivity is associated with higher self-control abilityand later substance use onset in adolescence. Our findings supportthe idea that negative functional coupling between the top-downfrontal system and bottom-up limbic system could be an index ofdevelopmental brain maturation during adolescence. Importantly,the current study provides evidence for the relationship betweentop-down control enhancement and suppression of bottom-upactivity in adolescent risky behavior at the “between-networklevel” by adopting an ICA approach. Together, the current study

increases our understanding of adolescent neural network connec-tivity and its relation to self-control and substance use behavior.

The rs-fMRI reflects the stability and integrity of connectionsin functional networks (Cole et al., 2010). Across development

T.-H. Lee, E.H. Telzer / Developmental Cognitive Neuroscience 20 (2016) 35–42 39

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ig. 1. Resting-state networks of interest in the current study (A), and the effect of fC-D), the relationship between self-control and substance use (E) and mediation pesampling (n = 5000).

nd into adulthood, negative connectivity increases as neuraletworks become more specialized and as the influence of top-own regulatory networks become more mature and gain controlver bottom-up affective, subcortical networks (Fox et al., 2005).mportantly, a simulation of neural activity at rest coupled withmpirically-measured structural connectivity shows that such neg-tive connectivity is biologically meaningful and may representomplex patterns between regions within a network (Cabral et al.,011). Thus, the segregation into negatively valenced connec-ivity between the fronto-parietal and limbic networks amongon-substance users in the current study may therefore reflect

ncreasing sophistication of functional coupling between these net-orks as the brain matures throughout adolescence. Our findings

re consistent with prior resting-state studies. For instance, Weiss-an and colleagues (2015) used seed-based resting-state analyses

nd found that earlier substance use onset was associated withreater positive coupling between the bilateral nucleus accum-ens and regions of the FPN. Similarly, Cservenka and colleagues2014) used seed-based resting state analyses and found that ado-escents at risk for substance use (i.e., history of family alcoholism)howed less negative connectivity between the nucleus accumbensnd the ventrolateral PFC (vlPFC). Our results extend this worknd suggest that negatively coupled resting-state frontoparietal-imbic connectivity is a protective factor in terms of substance use-nset, whereas positive connectivity may be a risk factor for earlierubstance use onset and later addiction.

In the current study, we focused on the right FPN. The rightPN helps shift attentional focus and maintain the focused pro-essing from disruptions when cognitive resources are allocated to

nal coupling of these two networks in adolescent’s self-control (B), substances use). Error bars denote the standard error term. *p < 0.05 at 95% CI after bootstrapping

a certain task. (Barrós-Loscertales et al., 2011; Corbetta et al., 2008;Dosenbach et al., 2008, 2007; Fair et al., 2009, 2007; Garavan et al.,2008; Vincent et al., 2008). That is, the right FPN contributes tothe general cognitive regulatory process that requires continuousattentional monitoring, response inhibition, and control. Indeed,previous studies have shown that adults with impaired right FPNhave a deficit in cognitive tasks such as the Stroop and Go/No-Gotask requiring executive function of inhibition and control (Barrós-Loscertales et al., 2011; Garavan et al., 2008). Although FPN existsbilaterally in the brain, right and left networks are well known torepresent different cognitive functions. Indeed, when we repeatedall the analyses for exploratory purpose with the left FPN to confirmthe lateralized function, no statistically significant relationshipsbetween variables were found.

Our findings are consistent with previous findings from resting-state (Fareri et al., 2015; van Duijvenvoorde et al., 2016; Weissmanet al., 2015) and task-based fMRI studies (Gee et al., 2013a,b,2014; Qu et al., 2015) in terms of the beneficial effect of negativelimbic-prefrontal connectivity on adolescents’ developing brainand behavior. However, it should be noted that there is also con-tradictory evidence from both task-based (Christakou et al., 2011)and resting-based studies (Gabard-Durnam et al., 2014). For exam-ple, Christakou et al. (2011) found that limbic-prefrontal positiveconnectivity was associated with enhanced impulse control using adelayed discounting decision task. Similarly, Gabard-Durnam et al.(2014) have implicated that resting-state connectivity between

amygdala and mPFC increases with age (i.e., stronger positiveconnectivity). These seemingly contradictory findings are not unex-pected. First, as noted by He (2013), the evoked- and intrinsic neural

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0 T.-H. Lee, E.H. Telzer / Developmenta

onnectivity interact in unpredictable ways, and the valence of con-ectivity is possible to be reversed between them. Furthermore,pontaneous BOLD signal can be evoked differently by differentognitive demands of task-based approaches, which can alter thealence of connectivity (He, 2013) associated with the narrowedower distribution across frequencies (Baria et al., 2013). Sec-nd, resting-state connectivity can be differentiated dependingn the analytic approach (seed vs. ICA) or the connectivity met-ic (within- and between network). While independent networksith opposing functions are more likely to show more negative

alence of connectivity (i.e., between-network level; Fox et al.,005), sub-region connectivity strength in each functional networkan increase regardless of its connectivity direction (i.e., valence)t the same time as a result of enhanced efficiency in between-nd within-network communication. For example, previous evi-ence has demonstrated that inverse coupling between oppositeunctional networks increases with age, whereas sub-region con-ectivity strength within given networks increases (Stevens et al.,009). Therefore, the reversed valence but increased connectiv-

ty strength (magnitude) between amygdala-mPFC connectivity inhe study of Gabard-Durnam et al. (2014) might be due to theithin-network observation rather than between-network given

he nature of seed-based approach which cannot distinguish con-ectivity metrics (i.e., within- and between networks; Xu et al.,013). Disentangling the sources of this difference in connectiv-

ty valence during task-based and rs-fMRI with the considerationf connectivity metrics is an important future direction to furthernderstand brain connectivity patterns and links to substance use.

One other important issue should be noted is that the degree ofegative coupling reported previously largely varies. For example,ome studies have shown the negative shift from positive to nega-ive values (Gee et al., 2013a,b, 2014), whereas other studies haveound relatively reduced or decreased positive connectivity (or nearero) (Cservenka et al., 2014; Fareri et al., 2015; van Duijvenvoordet al., 2016; Weissman et al., 2015). In our case, we used the termsnverse, negative shift and anti-correlation interchangeably androadly to describe the maturation of brain development in termsf functional connectivity. However, given the nature of the cor-elative approach in functional connectivity, the metric per se isot necessarily straightforward in terms of positive and negativealue from the absolute zero point (Hutchison et al., 2013) becauseunctional connectivity is more about how much neural signals inertain voxels (or networks) fluctuate together (i.e., positive cor-elation) or reversely (i.e., negative-, inverse- or anti correlation)ith neural signals in other voxels (or networks).

Our analyses focused on the limbic network connectivity withhe right FPN and its influence on self-control in adolescents. How-ver, other studies have also suggested that functional couplingetween other networks such as default-mode network (DMN),entral executive network (CEN) and saliency network (SN) plays aole in higher-order attention and control processes in the brain. Forxample, the SN is involved in the detection of salient stimuli andan initiate the attentional control system of the brain by decreas-ng the connectivity between DMN and CEN transiently (for aomprehensive review, see Uddin et al., 2010). That is, the moment-y-moment connectivity changes between different networks playn importance role in enhancement for cognitive and attentionalontrol in the human brain system. Therefore, further investigationf transient connectivity switching between networks in terms ofttentional control with a link to adolescents’ risk-taking behav-or will also increase our understanding of adolescents’ developingrain.

Some limitations should be considered in interpreting theurrent findings. First, we cannot determine whether the func-ional connectivity patterns preceded and contributed to substancese-behaviors and cognitive control, or vice versa given our cor-

itive Neuroscience 20 (2016) 35–42

relational analysis and cross-sectional sample. However, it isplausible to interpret that less risky behavior is a result of increasedprefrontal inhibitory process in the limbic system (i.e., inversefunctional coupling), and our regression model using mediationsupported this pathway (i.e., model 1). In our other model usingsubstance-use-onset as the independent variable to predict func-tional connectivity or self-control, we failed to find any mediationeffects, increasing confidence that the observed less substance usewas derived from the effect of inverse functional coupling betweentop-down and bottom-up networks combined with heightenedself-control, and not vice versa. Also, because we focused on adoles-cents using a cross-sectional approach, and adults’ substance-usebehavior was not collected in the current study, it is not clearwhether the current findings may vary across larger developmentalgroups and how resting state connectivity patterns change devel-opmentally to predict substance use behaviors. Second, we onlyused self-report measures of adolescents’ behavior, and thus ourassessment might be a suboptimal method to measure self-controland risky behavior compared to experimental based assessmentsin the laboratory such as the balloon analog risk task (e.g., Quet al., 2015) and Go-NoGo task (e.g., McCormick et al., 2016), whichmeasure risk-taking behavior and cognitive control, respectively.Future research might employ experimental-based assessments ofself-control and risk taking.

In summary, consistent with previous development studies(Fareri et al., 2015; Gee et al., 2013a, 2014; Qu et al., 2015; vanDuijvenvoorde et al., 2016; Weissman et al., 2015), we showed thatthe negatively coupled top-down control system (i.e., right fronto-parietal executive network) and bottom-up emotional system (i.e.,limbic network) plays an important role in adolescent substanceuse. Importantly, the present study provides supporting evidencethat inverse coupling between right FPN and limbic network couldbe an index of developmental maturation in the brain by showingthat adolescents exhibited greater self-control and later substanceuse onset with the degree of inverse coupling at the intrinsic brainnetwork level.

Appendix A. Supplementary data

Supplementary data associated with this article can be found, inthe online version, at http://dx.doi.org/10.1016/j.dcn.2016.06.002.

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