Adolescent Condom Use and Connectivity in the Social–Planful Brain

Adolescent Condom Use and Connectivity in the Social–Planful Brain Abstract Objective To reduce rates of sexually transmitted infections (STIs) and unwanted pregnancy among adolescents, it is critical to investigate brain connectivity that may underlie adolescents’ sexual health decision-making in the context of intercourse. This study explored relationships between adolescent condom use frequency and the brain’s resting-state functional connectivity, to identify differential patterns of social–affective processing among sexually active youth. Methods In this study, N = 143 sexually active adolescents (68.5% male, Mage = 16.2 years, SD = 1.06) completed magnetic resonance imaging and reported past 3-month frequency of condom use. Resting-state connectivity, seeded on a social region of the brain, the temporoparietal junction (TPJ), was assessed to determine its correspondence with protected sex (condom use). Results Condom use was associated with positive connectivity between the left TPJ and bilateral inferior frontal gyrus (IFG). This relationship was observed in adolescent males only; no connectivity differences were observed with adolescent females. Conclusions This study reflects functional synchrony between nodes of the “social brain,” including the TPJ, and a region of planfulness and control, the IFG. The relationship between these regions suggests that adolescents who have more coordinated systems of communication between these critical components of the brain are more likely to be successful in planning and engaging in safer sexual decision-making; for young males, this differentiated more frequent from less frequent condom use. In turn, interventions designed to reduce STIs/human immunodeficiency virus may benefit from targeting social–planfulness dimensions to help youth implement safer sex behaviors. adolescents, brain connectivity, condom use, HIV risk, MRI, sexual behavior Initiation of sexual activity with partners is developmentally normative for human adolescents in the mid- to late-teen years. For many youth, although sexual behavior can be healthy and propel them toward adaptive adult social roles, engagement in sexual activity also inherently increases risk for unintended, serious health outcomes, including unplanned pregnancy and sexually transmitted infections (STIs) such as the human immunodeficiency virus (HIV; Kann et al., 2016). Thus, safe sexual behavior among adolescents necessitates protective steps, such as condom use, before engaging in episodes of sexual intercourse. To help youth engage in planful sexual behavior, most adolescent prevention programs rely on social cognitive predictors of behavior, such as those based on Social Cognitive Theory, the Theory of Planned Behavior, or the Health Belief Model (Bryan & Feldstein Ewing, 2018). Studies focused on social cognitive dimensions have shown that adolescents who engage in higher rates of protected sex (e.g., consistent condom use) demonstrate greater condom use efficacy, stronger condom use intentions, more positive attitudes toward condoms, and higher self-esteem and optimism (Broaddus & Bryan, 2008; Bryan, Gillman, & Hansen, 2016). Though useful, these programs have been limited to some degree in their ability to explain and modify health behaviors (Bryan & Feldstein Ewing, 2018; Feldstein Ewing et al., 2016). In part, their reliance on rational cognitive theories underemphasizes the importance of social–affective domains of cognition in driving health decisions; these domains are critical in adolescence, when youth are still much in the midst of trying to determine how to navigate novel and salient emotional terrain with sexual/romantic partners (Feldstein Ewing & Bryan, 2015). Moreover, existing programs tend to focus on individual-level social cognitions. However, real-world sexual contexts are dyadic in nature, and they involve joint decision-making about protective efforts that impact someone else’s body in addition to one’s own, often against an instinctual drive to engage in unprotected sex (Feldstein Ewing et al., 2016). As such, the degree to which adolescents engage in planful sexual behavior is likely influenced by social and affective processes in the brain, including social connection and empathy. Emerging brain-based perspectives on sexual decision-making are providing novel insight that can inform how to promote safer sex behavior (Bryan & Feldstein Ewing, 2018). Critically, these neurocognitive insights have the potential to move beyond behavioral reports of sexual decision-making, and to incorporate neural and neurocognitive social–affective and nonconscious processes that likely factor into safer sex decisions (Bryan & Feldstein Ewing, 2018). To date, magnetic resonance imaging (MRI) studies—including functional MRI (fMRI)—on the neural processes of adolescent sexual decision-making are extremely few in number (for reviews, see Feldstein Ewing et al., 2016; Victor & Hariri, 2015). More work at the neural level is needed to disentangle the complex interplay of social, empathic, and affective processes that occur within the brain and which are associated with sexual decisions. This work is youth-relevant, as rapid changes in neurobiological structure and function occur in the window of 14–18 years (Mills, Goddings, Clasen, Giedd, & Blakemore, 2014), the precise time when initiation rates for sexual intercourse see the most dramatic rise over the life span (Kann et al., 2016). The brain can offer especially rich insights into the social–emotional aspects of adolescent sexual decision-making. Mammalian brains are inherently social organs throughout the life span; in adolescence, in particular, the brain experiences heightened attunement to the social environment (Nelson, Jarcho, & Guyer, 2016). This change in adolescence is represented by heightened response magnitude within the “social brain”—a distributed network of regions that reliably co-activate to achieve social and affective functions (Amft et al., 2015). One important region in this still-developing “social processing network” is the temporoparietal junction (TPJ). The TPJ co-activates with other nodes (e.g., precuneus, medial prefrontal cortex [mPFC]) to achieve several aspects of social cognition, such as mentalizing, empathy, and moral reasoning (Li, Mai, & Liu, 2014) – all of which continue to develop through adolescence (Kilford, Garrett, & Blakemore, 2016). Planning decisions around sexual intercourse likely involve many of these same social cognitive processes (e.g., considering someone else’s thoughts in anticipatory interactions before intercourse; positive affect and social connectedness; empathy in protecting another person’s body from STIs), mediated by the TPJ and its functional connections to other social nodes of the brain. As such, it can be expected that differences in the degree to which adolescents engage in modes of protected sex may reflect differences in social brain function, and specifically, differences in connectivity between the TPJ and other social–affective nodes. In the current study, we thus aimed to identify network-level relationships between the brain’s resting-state functional connectivity (rsFC) and a commonly used measure of sexual health behavior—condom use frequency—among adolescents (ages 14–18 years), in key networks of the social–affective brain. rsFC is a method to examine how different regions of the brain co-activate during rest, reflecting correspondence between, and coordinated activity behind cognitive and behavioral processes (Biswal et al., 2010). rsFC represents a useful strategy for measuring social–affective and nonconscious health decision-making processes within the brain (Sheeran et al., 2013) that are difficult to calculate experimentally or through self-report. Following past work on sexual decision-making (Eckstrand et al., 2017; Thayer, Montanaro, Weiland, Callahan, & Bryan, 2014), we focused our analyses on connectivity of the social processing network, and specifically, on connectivity between the TPJ and other social cognitive regions in the adolescent brain. The integration of developmental neuroscience with adolescent sexual risk research is still much emerging (Bryan & Feldstein Ewing, 2018), and we are aware of only one rsFC study on adolescent sexual decision-making, including condom use (Thayer et al., 2014). Thus, the goal of the current study is to extend research informing our understanding of the coordinated neurocognitive mechanisms associated with adolescent sexual health decision-making. Based on past work (Eckstrand et al., 2017; Victor & Hariri, 2015), we hypothesized that condom use frequency would be associated with connectivity between the TPJ and other nodes in the social processing network (Kilford et al., 2016; e.g., precuneus, mPFC; Li et al., 2014). Method Participants All procedures were approved by the participating university institutional review board and with a federal Certificate of Confidentiality. Youth were recruited from justice-related programs in the southwest United States and completed MRI scanning as part of an ongoing study. The parent study was a randomized controlled trial examining two prevention programs to reduce adolescent STI/HIV risk; however, all data presented herein were collected before intervention exposure. To recruit participants, trained research staff introduced the project at various collaborating programs, underscoring the voluntary nature of the study. Written assent was directly obtained from participants. Similar to other adolescent sexual risk studies (Schmiege, Broaddus, Levin, & Bryan, 2009), parent/guardian informed consent was obtained via telephone following youth assent. In terms of inclusion criteria, youth had to be 14–18 years old, fluent in English, and actively participating in a justice-related program. Exclusion criteria included prescription for and/or taking antipsychotics/anticonvulsants, concussions or other head injuries leading to loss of consciousness for >5 min during the past 6 months, and other standard MRI contraindications (e.g., pregnancy; Filbey, McQueeny, DeWitt, & Mishra, 2015). For enrollment, N = 280 youth assented/consented to participate in the study. Of those, N = 249 completed the neuroimaging procedures, including resting state. The remainder did not have a complete resting-state scan (n = 31; e.g., showed MRI contraindications on day of scan). Of those, N = 231 youth had usable resting-state data; 18 were excluded because of imaging data that did not pass quality inspections. From this number, n = 88 were not recently sexually active, or did not have full sexual orientation data. This included a full sample of N =143 adolescents (68.5% male, Mage = 16.2 years (SD = 1.06); see Table I). Table I. Demographic Information on Sample Range Male (n = 98) Female (n = 45) Gender comparison M (SD) M (SD) Age 14–18 16.2 (1.02) 16.0 (1.11) t(141) = 1.46, p = ns Age at first intercourse 7–17 13.2 (1.87) 13.9 (1.29) t(139) = −2.29, p < .05 # of intercourse partnersa 1–15 2.7 (2.70) 1.6 (1.54) t(141) = 2.39, p < .05 Condom use frequencyb 1–5 3.03 (1.54) 3.04 (1.62) t(141) = −0.05, p = ns Race/ethnicity % % χ2 = 6.98, p = ns  Hispanic American 54.1 62.2  Caucasian 14.3 6.7  African-American 5.1 0.0  American Indian/Alaska Native 2.0 4.4  Other 2.0 4.4  More than one race 22.4 22.2 Range Male (n = 98) Female (n = 45) Gender comparison M (SD) M (SD) Age 14–18 16.2 (1.02) 16.0 (1.11) t(141) = 1.46, p = ns Age at first intercourse 7–17 13.2 (1.87) 13.9 (1.29) t(139) = −2.29, p < .05 # of intercourse partnersa 1–15 2.7 (2.70) 1.6 (1.54) t(141) = 2.39, p < .05 Condom use frequencyb 1–5 3.03 (1.54) 3.04 (1.62) t(141) = −0.05, p = ns Race/ethnicity % % χ2 = 6.98, p = ns  Hispanic American 54.1 62.2  Caucasian 14.3 6.7  African-American 5.1 0.0  American Indian/Alaska Native 2.0 4.4  Other 2.0 4.4  More than one race 22.4 22.2 a Past 3-month number of sexual intercourse partners. b Past 3-month condom use frequency: 1 = Never, 2 = Almost never, 3 = Sometimes, 4 = Almost always, 5 = Always. Table I. Demographic Information on Sample Range Male (n = 98) Female (n = 45) Gender comparison M (SD) M (SD) Age 14–18 16.2 (1.02) 16.0 (1.11) t(141) = 1.46, p = ns Age at first intercourse 7–17 13.2 (1.87) 13.9 (1.29) t(139) = −2.29, p < .05 # of intercourse partnersa 1–15 2.7 (2.70) 1.6 (1.54) t(141) = 2.39, p < .05 Condom use frequencyb 1–5 3.03 (1.54) 3.04 (1.62) t(141) = −0.05, p = ns Race/ethnicity % % χ2 = 6.98, p = ns  Hispanic American 54.1 62.2  Caucasian 14.3 6.7  African-American 5.1 0.0  American Indian/Alaska Native 2.0 4.4  Other 2.0 4.4  More than one race 22.4 22.2 Range Male (n = 98) Female (n = 45) Gender comparison M (SD) M (SD) Age 14–18 16.2 (1.02) 16.0 (1.11) t(141) = 1.46, p = ns Age at first intercourse 7–17 13.2 (1.87) 13.9 (1.29) t(139) = −2.29, p < .05 # of intercourse partnersa 1–15 2.7 (2.70) 1.6 (1.54) t(141) = 2.39, p < .05 Condom use frequencyb 1–5 3.03 (1.54) 3.04 (1.62) t(141) = −0.05, p = ns Race/ethnicity % % χ2 = 6.98, p = ns  Hispanic American 54.1 62.2  Caucasian 14.3 6.7  African-American 5.1 0.0  American Indian/Alaska Native 2.0 4.4  Other 2.0 4.4  More than one race 22.4 22.2 a Past 3-month number of sexual intercourse partners. b Past 3-month condom use frequency: 1 = Never, 2 = Almost never, 3 = Sometimes, 4 = Almost always, 5 = Always. Measures Sexual Behavior Using established sexual behavior metrics (Bryan, Schmiege, & Magnan, 2012; Schmiege et al., 2009), lifetime sexual activity was assessed by querying whether adolescents had ever engaged in vaginal and/or anal intercourse, defined by “a man putting his penis inside a woman’s vagina or inside someone’s anus (rear end).” Youth also reported the age at which they first engaged in sexual intercourse, as well as number of past 3-month sexual intercourse partners. Condom Use Frequency Via Likert scale, participants reported how often they used condoms during past 3-month sexual intercourse events (1 = Never, 2 = Almost never, 3 = Sometimes, 4 = Almost always, 5 = Always). The frequency distribution was 27.3% (Never), 11.2% (Almost never), 18.2% (Sometimes), 17.5% (Almost always), and 25.9% (Always). Procedure Trained research assistants guided participants through all aspects of the study, including recruitment, administration of questionnaires, and MRI procedures. Participants received $150 for completion of these study components. Resting-State Scan Participants were instructed to lie still in the scanner for 5 min 30 s with their eyes opened and focused on a white fixation cross presented on a black screen through a mirror attached to the scanner head coil. MRI Data Acquisition Scanning was conducted on a Siemens 3.0 T Tim Trio full-body MRI scanner, using a 12-channel phased array head coil. Each brain volume (165 total) consisted of 33 3.5 mm interleaved axial slices, acquired using a T2*-weighted gradient echo planar imaging sequence repetition time (TR) and echo time (TE) (TR/TE = 2,000/29 ms, 75-degree flip angle, 3.8 × 3.8 × 3.5 mm voxels, field of view = 240 mm). In addition, for each participant, a high-resolution sagittally acquired T1-weighted anatomical image (192 slices, 1.00 mm thick) was collected for alignment and normalization of functional images (TR = 2,530 ms, multi-echo TE = 1.64/3.5/5.36/7.22/9.09 ms, 7-degree flip angle, 1.0 mm3 voxels, field of view = 256 mm). Preprocessing Preprocessing was conducted using Analysis of Functional NeuroImaging (AFNI) software (Cox, 1996). Preprocessing steps followed recommended guidelines for rsFC, accounting for the strong impact of head motion on the Blood Oxygenated Level Dependent (BOLD) signal in rsFC (Matthews & Fair, 2015). Preprocessing consisted of removing the first two TRs from the time series to reach steady state, slice timing correction, rigid body motion correction with six motion parameters, spatial normalization to Talairach space, and spatial smoothing with a 4 mm FWHM Gaussian kernel. Functional images were subjected to de-spiking, band-pass filtering >0.1 Hz and <0.08 Hz, and censoring of volumes with head motion >1 mm from the previous volume. The time series of six motion parameters and six first derivative of motion parameters was regressed from the time series data, along with band-pass filtering parameters. Participants (n = 13) with >32 TRs censored (20% of data) because of motion were excluded from analyses. First level analyses All analyses were conducted using AFNI and SPSS V22.0. Based on prior studies of risky sexual behavior and social processing in adolescents (Eckstrand et al., 2017; Thayer et al., 2014), seed regions of interest (ROIs) were created within two nodes of the brain’s social processing network: left TPJ (lTPJ) and right TPJ (rTPJ) (Figure 1a; 8 mm radius; Montreal Neuroimaging Institute MNI coordinates: x = ±48, y = −52, z = 40). The average time course in each ROI was extracted using AFNI’s 3dmaskave and correlated with every voxel in the brain using AFNI’s 3dfim+. These correlation coefficients were transformed using Fisher’s z transformation. More positive values in the resulting z-score for each voxel indicate stronger functional co-activation between the BOLD time course of the ROI and that voxel, while more negative values indicate anticorrelation between the BOLD time courses. To reduce type I errors, Monte Carlo simulations using AFNI’s 3dClustSim, paired with an autocorrelation function (-acf), were used to determine the minimum number of contiguous 3.5 mm voxels per cluster (activated at puncorrected < .05) to yield a threshold of pcorrected < .05: 800 voxels. Figure 1. View largeDownload slide In males, significant positive associations between condom use and rsFC (BOLD % signal change) between left TPJ and the whole brain. (a) Left TPJ seed region; (b, d) significant right and left IFG clusters; (c, e) scatterplots of beta values, denoting connectivity strength, extracted from each IFG cluster. Note. BOLD = Blood Oxygenated Level Dependent; IFG = inferior frontal gyrus; rsFC = resting-state functional connectivity; TPJ = temporoparietal junction. Figure 1. View largeDownload slide In males, significant positive associations between condom use and rsFC (BOLD % signal change) between left TPJ and the whole brain. (a) Left TPJ seed region; (b, d) significant right and left IFG clusters; (c, e) scatterplots of beta values, denoting connectivity strength, extracted from each IFG cluster. Note. BOLD = Blood Oxygenated Level Dependent; IFG = inferior frontal gyrus; rsFC = resting-state functional connectivity; TPJ = temporoparietal junction. Analysis Plan Resting-State Functional Connectivity General linear models were run in AFNI’s 3dMVM program to test for the main effect of condom use frequency on connectivity between seed ROIs and the whole brain. Models were run for each of the two seed ROIs (lTPJ, rTPJ). Participant age, gender, and sexual orientation were added as covariates. Age and Gender as Moderators of rsFC Furthermore, we examined whether condom use-related differences in connectivity were moderated by age and gender. We tested for main and moderated effects of condom use frequency in a single model per ROI, yielding two models. The full model included condom use frequency, age, gender, sexual orientation, a condom use*age interaction term, and a condom use*gender interaction term. Results Sample Characteristics Excluded participants were significantly younger than the final included sample (excluded for scan-related reasons; t(278) = −2.83, p < .01; not sexually active; t(278) = −5.77, p < .001). Of the final sample, males reported younger first-time intercourse (t(139) = −2.29, p < .05) and a higher number of past 3-month sexual partners (t(141) = −2.34, p < .05), but no differences in condom use frequency compared with females (Table I). Age was not associated with past 3-month sexual intercourse or condom use frequency. Resting-State Functional Connectivity Examining the primary hypothesis, when all youth were assessed together, we did not observe a main effect of condom use on TPJ connectivity. Age and Gender as Moderators of rsFC Owing to the exploratory nature of the study, we followed examination of these main effects with standard corrections. Here, for the lTPJ seed, we found a condom use*gender interaction in a right inferior frontal gyrus (rIFG) cluster (ß = .52, SE = .12, p < .001, 95% confidence interval, CI [0.04, 0.12]). Using a more conservative uncorrected cluster threshold of p < .005 to detect smaller clusters of greater magnitude, we observed a second condom use*gender interaction in the contralateral side of the brain—in the left inferior frontal gyrus (lIFG) (ß = .40, SE = .02, p < .001, 95% CI [0.05, 0.13]). The lTPJ did not yield a condom use*age interaction; likewise, no condom use*age or condom use*gender interactions emerged in the rTPJ. The condom use*gender interactions were decomposed by testing for the simple main effect of condom use frequency for each gender. Simple main effects were significant in males only, with no effects observed among young females. In males only, more frequent condom use was associated with stronger, more positive lTPJ-rIFG and lTPJ-lIFG connectivity. Cluster size, peak voxel coordinates, and effect sizes are reported in Table II. For data visualization, significant clusters in the brain are illustrated in Figure 1b (rIFG) and Figure 1d (lIFG). Percent signal change beta values were extracted from the peak voxel coordinates of the significant rIFG and lIFG clusters and plotted as a function of condom use (Figure 1c: rIFG; Figure 1e: lIFG). Table II. Decomposition of Condom Use*Gender Interactions Subgroup: Males (n = 98) Seed ROI FC region # Voxels x y z BA z-score Uncorr. p Corr. p L TPJ R IFG 989 58 20 13 45 4.12 <.05 <.03 L TPJ L IFG 68 −44 16 −1 47 3.63 <.005 <.05 Subgroup: Males (n = 98) Seed ROI FC region # Voxels x y z BA z-score Uncorr. p Corr. p L TPJ R IFG 989 58 20 13 45 4.12 <.05 <.03 L TPJ L IFG 68 −44 16 −1 47 3.63 <.005 <.05 Note: Table displays simple effects of condom use frequency (past 3 months) with rsFC. BA = Brodmann areas; Corr. p = p-threshold corrected for multiple voxel comparisons; FC region = functional connectivity region correlated with seed ROI; IFG = inferior frontal gyrus; L = left; R = right; ROI = region of interest; xyz = peak voxel coordinates reported with MNI coordinate system, TPJ = temporoparietal junction; Uncorr. p = uncorrected p-threshold. Table II. Decomposition of Condom Use*Gender Interactions Subgroup: Males (n = 98) Seed ROI FC region # Voxels x y z BA z-score Uncorr. p Corr. p L TPJ R IFG 989 58 20 13 45 4.12 <.05 <.03 L TPJ L IFG 68 −44 16 −1 47 3.63 <.005 <.05 Subgroup: Males (n = 98) Seed ROI FC region # Voxels x y z BA z-score Uncorr. p Corr. p L TPJ R IFG 989 58 20 13 45 4.12 <.05 <.03 L TPJ L IFG 68 −44 16 −1 47 3.63 <.005 <.05 Note: Table displays simple effects of condom use frequency (past 3 months) with rsFC. BA = Brodmann areas; Corr. p = p-threshold corrected for multiple voxel comparisons; FC region = functional connectivity region correlated with seed ROI; IFG = inferior frontal gyrus; L = left; R = right; ROI = region of interest; xyz = peak voxel coordinates reported with MNI coordinate system, TPJ = temporoparietal junction; Uncorr. p = uncorrected p-threshold. Discussion The primary aim of the current study was to examine associations between adolescent condom use frequency and rsFC in the brain. We hypothesized that social–affective processes, relevant within the adolescent “social brain,” would be associated with protective sexual decision-making (condom use). We examined this by looking at the TPJ and its communication (synchrony) with other nodes relevant to adolescent sexual health decision-making. We expected functional connectivity between the TPJ and other nodes of the social processing network (Kilford et al., 2016; e.g., precuneus, mPFC; Li et al., 2014). However, we found an interesting path of communication between adolescent social (lTPJ) and planfulness (IFG) nodes among adolescent males that was associated with recent condom use frequency. While not completely in line with predictions, it does contribute to the existing literature around social cognitive predictors of behavior. Specifically, studies using Theory of Planned Behavior and Social Cognitive Theory frameworks have identified several cognitive constructs to be key in successful adolescent condom use, including condom use efficacy, condom use intentions, attitudes toward condoms, self-esteem and optimism about the future, and norms around condom use (Broaddus & Bryan, 2008; Bryan et al., 2016; Eggers et al., 2016). Notably, this study suggests that prior constructs integral in understanding adolescent behavior change, including condom use intentions, and of relevance, condom use self-efficacy, could well be associated with key social–planful networks of the developing brain. Of course, adolescent decision-making occurs within the context of the developing brain; yet, developmental neuroscience and health psychology literatures do not often intersect. This study suggests that critical advances in adolescent sexual decision-making would benefit from inclusion of the perspective of the brain—particularly social and planfulness regions—that are likely key components of the adolescent decision-making process. In terms of the implication of the TPJ as a key region of the adolescent brain, the TPJ is highly relevant in the processing of social cues, which operate at an elevated level during this developmental period. Concretely, studies suggest that the adolescent brain is acutely attuned to social cues, even at nonconscious levels (Kilford et al., 2016). Relevant to this article, connections between the TPJ and IFG have been implicated in the capacity to estimate another’s social or affective experience (Li et al., 2014). This process has been termed “mentalizing” in the developmental neuroscience literature, and it is more specifically referred to as “empathy” in the developmental, clinical, and health literatures. Enhanced capacity for anticipating and connecting with another individual’s experience is a fundamental part of human connectedness, and among adolescents, it is a critical piece of their often nascent navigation of romantic partnerships (e.g., Ewing & Bryan, 2015). Of relevance, girls have historically reported sexual behavior as a method to advance relationship development (Raiford, Seth, & Diclemente, 2013). This study suggests that among boys, those who have a stronger cognitive network in the domain of empathy (TPJ-IFG) may be better able to “tune in” to the needs of their romantic/sexual partner, which in turn, may enhance their capacity to adeptly engage in successful condom use. Resting-state connectivity has been useful for identification of global, stable traits related to social–affective processing (Geerligs, Rubinov, Cam-CAN, & Henson, 2015). While it can be tempting to ascribe more activation or connectedness with “good” versus “bad” behavior, and similarly, to potentially try to intuit whether these data suggest potential “trait” versus “state” behaviors, it is important to note that imaging still remains a developing field, and we simply do not have the capacity to make those distinctions (Feldstein Ewing, Sakhardande, & Blakemore, 2014). Yet these data do suggest future critical routes of inquiry around individual differences in the neural coordination of social cognitive processing, and subsequent health behavior. Complementing recent functional task findings in this age group (Eckstrand et al., 2017), the co-activation of lTPJ and IFG is intriguing. The IFG is commonly viewed as a cognitive planfulness region where cognition, emotion, and interoceptive awareness integrate (Adolfi et al., 2017). Notably, connectivity between IFG and posterior regions, including TPJ, is shown to increase across development to improve cognitive control (Marek, Hwang, Foran, Hallquist, & Luna, 2015). While it is somewhat surprising that the TPJ did not correlate with other social–affective processing nodes (e.g., precuneus, mPFC), we suggest the data herein reflect that the degree to which adolescents engage in condom use may vary less as a function of low-level social–affective processes within nodes of the social brain; rather, it may vary more as a function of coordination between social and cognitive control networks that engage in higher-level anticipatory emotional processing, such as empathy and mentalizing. The findings in this study were centralized within the lTPJ. This is interesting, as the rTPJ is more heavily implicated in spatial orientation. In contrast, the lTPJ is more heavily associated with language processes (Amft et al., 2015), including adolescent social peer interaction and communication (Kilford et al., 2016). We speculate that the specificity of the results to lTPJ suggests that differences in condom use frequency may be related to adolescents’ ability to communicate with sexual partners around condom use. Moreover, lTPJ functional connections with prefrontal cortex, specifically IFG, have been linked with cognitive perspective taking (Baumgartner, Götte, Gügler, & Fehr, 2012; Kilford et al., 2016). Thus, specificity to lTPJ-IFG connectivity is well in line with the interpretation that the capacity for mentalizing/empathy may play a role in the condom use equation, rather than other forms of related, but potentially, irrelevant social cognitive processes (e.g., spatial orientation). Nevertheless, more research is needed to assess the discriminability of TPJ-condom use associations to sexual health decision-making, rather than to broader social cognitive processes. Condom use–functional connectivity associations were moderated by gender. We did not observe anticipated relationships for young females. This may reflect gender differences in the pacing of not just hormonal development but also of neural maturation and connectivity (Scheinost et al., 2015). Recent research has highlighted the lower level of variance observed for girls developmentally, many of whom are already fairly advanced in pubertal development at the ages enrolled within this study (Feldstein Ewing et al., 2018); this may have impacted capacity to detect network relationships for young girls in the current study. Another element in this equation is that networks relevant for empathy and mentalizing may be more salient for young boys rather than girls, given that for young girls, the decision to use (or not use) condoms occurs ultimately on someone else’s body. Thus, the cognitive equation relevant to driving successful condom use may include different paths and salient neurocognitive processes (Feldstein Ewing & Bryan, 2015; Feldstein Ewing et al., 2016). It is unknown whether more positive TPJ-IFG connectivity in relation to greater condom use is reflective of more mature brain function. Generally, positive connectivity represents synchrony in the brain—regions of the brain successfully working together in tandem to achieve a unitary function—while negative connectivity represents opposing or complementary functions between target networks (Biswal et al., 2010). As the brain matures well into adolescence (completing for young males around age 25 years), more specialization of function occurs, whereby there is stronger positive connectivity within specific functional networks of the brain (Fair et al., 2007, 2008). Ultimately, longitudinal studies are needed to disentangle the effects of brain connectivity, age, and gender on decisions to use protection during intercourse. Limitations First, we did not collect data on whether condoms were used for disease prevention, pregnancy prevention, or both. Future studies will continue to examine the complex personal and situational factors motivating decisions to use condoms, including from the perspective of the dyad, and relevant underlying neural mechanisms. Second, many other forms of sexual behavior, such as oral sex, carry some degree of physical health risk (Kann et al., 2016). However, because intercourse events represent the strongest risks for pregnancy and STIs, we chose to constrain analyses to health decision-making around sexual intercourse. Third, the absence of observed associations in young females may have been driven by low power (only 45 females vs. 98 males), and potentially a high proportion of the sample identifying as bisexual; however, given the congruence of this sample with other adolescents in the community, we believe that this group was representative of the larger community from which it was drawn, who continue to have high rates of sexual intercourse necessitating condom use (Bryan et al., 2018). Fourth, it could be that the observed functional connectivity differences are not specific to adolescent sexual decision-making. It is indeed the case that, among adolescents, the TPJ and IFG are identified as part of a multifunctional social cognitive network that undergoes significant development during adolescence (Kilford et al., 2016); in particular, they have been associated with affect regulation in the context of monitoring peer social feedback (He et al., 2018). While we were only able to assess the relevance of this network to sexual decision-making, future work will continue to disaggregate the impact of this network on other domains of adolescent decision-making. This will help inform the specificity of this network to sexual decision-making (only), or its potentially more global impact on and implications for the broader domain of adolescent health. At the same time, it is important to identify that what is unique about adolescents’ movement into romantic and sexual decision-making is that this behavior falls within a domain of evolutionarily adaptive behavior that humans are preprogrammed to engage in via neurobiological maturation (Giedd, 2015), and which is healthy and appropriate (Feldstein Ewing et al., 2016). Indeed, it is important to also note the numerous positive psychosocial outcomes that can come from sexual engagement, including pleasure and intimacy, during a developmental period when youth are beginning to learn about their own and others’ bodies and to invest in romantic relationships (Harden, 2014). Here, adolescent sexual behavior is viewed not as inherently risky but as a healthy and normative behavior, albeit one which can yield undesired outcomes (e.g., pregnancy, STIs) if requisite preventive steps are not taken. From this perspective, it is possible that adolescents with less lTPJ-IFG connectivity and lower condom use frequency do not represent a qualitatively distinct risk endophenotype. Fifth, a high percentage of participants in our sample of youth reported lifetime engagement in sexual intercourse (75.7%). In fact, U.S. justice-involved youth represent a critical and large higher-risk group that is often missed in developmental neuroscience research on health decision-making (Bryan et al., 2016; Feldstein Ewing et al., 2016). Furthermore, justice-involved youth—predominantly Hispanic males in many U.S. cities—are closer in psychosocial function to the majority youth population than previously believed; in reality, they may merely fall on the riskier side of a normal distribution of behavior (Skeem, Scott, & Mulvey, 2014). Clinical and research data do not indicate qualitative distinctions between justice-involved youth and their peers (Feldstein Ewing, Montanaro, Gaume, Caetano, & Bryan, 2015), and in this respect, the current study represents an endeavor to overcome an important disparity in representative sampling. Clinical Implications The coupling of condom use decisions in adolescents within the “social-planful brain” suggests that interventions designed to reduce rates of unwanted pregnancy and STIs may find use in targeting cognitive and affective processes—such as empathy and mentalizing—relevant in adolescent sexual decision-making. Furthermore, these targets may be especially salient for young males. Identification of neural targets involved both in motivating unprotected sex decisions and in harnessing condom use decisions has the potential to inform clinicians about which intervention strategies may work best in which populations, and via which neurocognitive processes. At the same time, adolescent interventions that integrate dyadic elements are also gaining traction as promising “next-level” adjunctive approaches to improving HIV-risk reduction efficacy in this age group. Dyadic interventions in adolescent populations have already shown efficacy in reducing rates of HIV-risk behavior (Tobin, Kuramoto, Davey-Rothwell, & Latkin, 2011). The current study represents a potential step forward, as it provides preliminary insights into the neurocognitive mechanisms by which prevention interventions may impact condom use decisions in a context that is dyadic and rich in social–affective complexity. Acknowledgment The authors would like to thank Dustin Truitt for assistance on this project. Funding This work was supported by the National Institute of Nursing Research at the National Institutes of Health (grant number 1R01NR013332-01 to S.F.E. and A.B.). Conflicts of interest: None declared. References Adolfi F. , Couto B. , Richter F. , Decety J. , Lopez J. , Sigman M. , Manes F. , Ibáñez A. ( 2017 ). Convergence of interoception, emotion, and social cognition: A twofold fMRI meta-analysis and lesion approach . Cortex , 88 , 124 – 142 . https://doi.org/10.1016/j.cortex.2016.12.019 Google Scholar CrossRef Search ADS PubMed Amft M. , Bzdok D. , Laird A. R. , Fox P. T. , Schilbach L. , Eickhoff S. B. ( 2015 ). Definition and characterization of an extended social-affective default network . Brain Structure and Function , 220 , 1031 – 1049 . https://doi.org/10.1007/s00429-013-0698-0 Google Scholar CrossRef Search ADS PubMed Baumgartner T. , Götte L. , Gügler R. , Fehr E. ( 2012 ). The mentalizing network orchestrates the impact of parochial altruism on social norm enforcement . Human Brain Mapping , 33 , 1452 – 1469 . https://doi.org/10.1002/hbm.21298 Google Scholar CrossRef Search ADS PubMed Biswal B. B. , Mennes M. , Zuo X. N. , Gohel S. , Kelly C. , Smith S. M. , Beckmann C. F. , Adelstein J. S. , Buckner R. L. , Colcombe S. , Dogonowski A. M. , Ernst M. , Fair D. , Hampson M. , Hoptman M. J. , Hyde J. S. , Kiviniemi V. J. , Kötter R. , Li S. J. , Lin C. P. , Lowe M. J. , Mackay C. , Madden D. J. , Madsen K. H. , Margulies D. S. , Mayberg H. S. , McMahon K. , Monk C. S. , Mostofsky S. H. , Nagel B. J. , Pekar J. J. , Peltier S. J. , Petersen S. E. , Riedl V. , Rombouts S. A. , Rypma B. , Schlaggar B. L. , Schmidt S. , Seidler R. D. , Siegle G. J. , Sorg C. , Teng G. J. , Veijola J. , Villringer A. , Walter M. , Wang L. , Weng X. C. , Whitfield-Gabrieli S. , Williamson P. , Windischberger C. , Zang Y. F. , Zhang H. Y. , Castellanos F. X. , Milham M. P. ( 2010 ). Toward discovery science of human brain function . Proceedings of the National Academy of Sciences of the United States of America , 107 , 4734 – 4739 . https://doi.org/10.1073/pnas.0911855107 Google Scholar CrossRef Search ADS PubMed Broaddus M. R. , Bryan A. ( 2008 ). Consistent condom use among juvenile detainees: The role of individual differences, social bonding, and health beliefs . AIDS and Behavior , 12 , 59 – 67 . https://doi.org/10.1007/s10461-007-9260-5 Google Scholar CrossRef Search ADS PubMed Bryan A. D. , Feldstein Ewing S. W. ( 2018 ). Sex and the brain: Empirical intersection of neurocognition and sexual behavior. Introduction to the special section on neuroscience and sexual behavior . Annals of Behavioral Medicine , 52 , 353 – 355 . https://doi: 10.1093/abm/kax070 Google Scholar PubMed Bryan A. D. , Gillman A. S. , Hansen N. S. ( 2016 ). Changing the context is important and necessary, but not sufficient, for reducing adolescent risky sexual behavior: A reply to steinberg (2015) . Perspectives on Psychological Science , 11 , 535 – 538 . https://doi.org/10.1177/1745691616638093 Google Scholar CrossRef Search ADS PubMed Bryan A. D. , Magnan R. E. , Gillman A. S. , Yeater E. A. , Feldstein Ewing S. W. , Kong A. S. , Schmiege S. J. ( 2018 ). Effect of including alcohol and cannabis content in a sexual risk-reduction intervention on the incidence of sexually transmitted infections in adolescents . JAMA Pediatrics , 172 , e175621 . https://doi.org/10.1001/jamapediatrics.2017.5621 Google Scholar CrossRef Search ADS PubMed Bryan A. D. , Schmiege S. J. , Magnan R. E. ( 2012 ). Marijuana use and risky sexual behavior among high-risk adolescents: Trajectories, risk factors, and event-level relationships . Developmental Psychology , 48 , 1429 – 1442 . https://doi.org/10.1037/a0027547 Google Scholar CrossRef Search ADS PubMed Cox R. W. ( 1996 ). AFNI: Software for analysis and visualization of functional magnetic resonance neuroimages . Computers and Biomedical Research , 29 , 162 – 173 . Retrieved from https://www.ncbi.nlm.nih.gov/pubmed/8812068 Google Scholar CrossRef Search ADS PubMed Eckstrand K. L. , Choukas-Bradley S. , Mohanty A. , Cross M. , Allen N. B. , Silk J. S. , Jones N. P. , Forbes E. E. ( 2017 ). Heightened activity in social reward networks is associated with adolescents’ risky sexual behaviors . Developmental Cognitive Neuroscience , 27 , 1 – 9 . https://doi.org/10.1016/j.dcn.2017.07.004 Google Scholar CrossRef Search ADS PubMed Eggers S. M. , Aarø L. E. , Bos A. E. , Mathews C. , Kaaya S. F. , Onya H. , de Vries H. ( 2016 ). Sociocognitive predictors of condom use and intentions among adolescents in three Sub-Saharan sites . Archives of Sexual Behavior , 45 , 353 – 365 . https://doi.org/10.1007/s10508-015-0525-1 Google Scholar CrossRef Search ADS PubMed Feldstein Ewing S. W. , Bryan A. D. ( 2015 ). A question of love and trust? The role of relationship factors in adolescent sexual decision making . Journal of Developmental and Behavioral Pediatrics , 36 , 628 – 634 . https://doi.org/10.1097/DBP.0000000000000190 Google Scholar CrossRef Search ADS PubMed Feldstein Ewing S. W. , Sakhardande A. , Blakemore S. J. ( 2014 ). The effect of alcohol consumption on the adolescent brain: A systematic review of MRI and fMRI studies of alcohol-using youth . Neuroimage: Clinical , 5 , 420 – 437 . https://doi.org/10.1016/j.nicl.2014.06.011 Google Scholar CrossRef Search ADS PubMed Fair D. A. , Cohen A. L. , Dosenbach N. U. , Church J. A. , Miezin F. M. , Barch D. M. , Raichle M. E. , Petersen S. E. , Schlaggar B. L. ( 2008 ). The maturing architecture of the brain’s default network . Proceedings of the National Academy of Sciences of the United States of America , 105 , 4028 – 4032 . https://doi.org/10.1073/pnas.0800376105 Google Scholar CrossRef Search ADS PubMed Fair D. A. , Dosenbach N. U. , Church J. A. , Cohen A. L. , Brahmbhatt S. , Miezin F. M. , Barch D. M. , Raichle M. E. , Petersen S. E. , Schlaggar B. L. ( 2007 ). Development of distinct control networks through segregation and integration . Proceedings of the National Academy of Sciences of the United States of America , 104 , 13507 – 13512 . https://doi.org/10.1073/pnas.0705843104 Google Scholar CrossRef Search ADS PubMed Feldstein Ewing S. W. , Hudson K. A. , Caouette J. D. , Mayer A. R. , Thayer R. E. , Ryman S. G. , Bryan A. D. ( 2018 ). Sexual risk-taking and subcortical brain volume in adolescence . Annals of Behavioral Medicine , 52 , 393 – 405 . https://doi.org/10.1093/abm/kax027 Google Scholar PubMed Feldstein Ewing S. W. , Montanaro E. A. , Gaume J. , Caetano R. , Bryan A. D. ( 2015 ). Measurement invariance of alcohol instruments with Hispanic youth . Addictive Behaviors , 46 , 113 – 120 . https://doi.org/10.1016/j.addbeh.2015.03.004 Google Scholar CrossRef Search ADS PubMed Feldstein Ewing S. W. , Ryman S. R. , Gillman A. , Weiland B. J. , Thayer R. E. , Bryan A. D. ( 2016 ). Developmental cognitive neuroscience of adolescent sexual risk and alcohol use . AIDS and Beahvior , 20(Suppl 1) , S97 – S108 . Google Scholar CrossRef Search ADS Filbey F. M. , McQueeny T. , DeWitt S. J. , Mishra V. ( 2015 ). Preliminary findings demonstrating latent effects of early adolescent marijuana use onset on cortical architecture . Developmental Cognitive Neuroscience , 16 , 16 – 22 . Google Scholar CrossRef Search ADS PubMed Geerligs L. , Rubinov M. Cam-CAN Henson R. N. ( 2015 ). State and trait components of functional connectivity: Individual differences vary with mental state . Journal of Neuroscience , 35 , 13949 – 13961 . https://doi.org/10.1523/JNEUROSCI.1324-15.2015 Google Scholar CrossRef Search ADS PubMed Giedd J. N. ( 2015 ). The amazing teen brain . Scientific American , 312 , 32 – 37 . https://doi.org/10.1038/scientificamerican0615-32 Google Scholar CrossRef Search ADS PubMed Harden K. P. ( 2014 ). A sex-positive framework for research on adolescent sexuality . Perspectives on Psychological Science , 9 , 455 – 469 . https://doi.org/10.1177/1745691614535934 Google Scholar CrossRef Search ADS PubMed He Z. , Lin Y. , Xia L. , Liu Z. , Zhang D. , Elliot R. ( 2018 ). Critical role of the right VLPFC in emotional regulation of social exclusion: A tDCS study . Social Cognitive and Affective Neuroscience , in press. https://doi.org/doi: 10.1093/scan/nsy026 Kann L. , McManus T. , Harris W. A. , Shanklin S. L. , Flint K. H. , Hawkins J. , Queen B. , Lowry R. , Olsen E. O. , Chyen D. , Whittle L. , Thornton J. , Lim C. , Yamakawa Y. , Brener N. , Zaza S. ( 2016 ). Youth risk behavior surveillance—United States, 2015 . Morbidity and Mortality Weekly Report , 65 , 1 – 174 . https://doi.org/10.15585/mmwr.ss6506a1 Google Scholar PubMed Kilford E. J. , Garrett E. , Blakemore S. J. ( 2016 ). The development of social cognition in adolescence: An integrated perspective . Neuroscience and Biobehavioral Reviews , 70 , 106 – 120 . https://doi.org/10.1016/j.neubiorev.2016.08.016 Google Scholar CrossRef Search ADS PubMed Li W. , Mai X. , Liu C. ( 2014 ). The default mode network and social understanding of others: What do brain connectivity studies tell us . Frontiers in Human Neuroscience , 8 , 74 . https://doi.org/10.3389/fnhum.2014.00074 Google Scholar PubMed Marek S. , Hwang K. , Foran W. , Hallquist M. N. , Luna B. ( 2015 ). The contribution of network organization and integration to the development of cognitive control . PLoS Biology , 13 , e1002328 . https://doi.org/10.1371/journal.pbio.1002328 Google Scholar CrossRef Search ADS PubMed Matthews M. , Fair D. A. ( 2015 ). Functional brain connectivity and child psychopathology - Overview and methodological considerations for investigators new to the field . Journal of Child Psychology and Psychiatry , 56 , 400 – 414 . Google Scholar CrossRef Search ADS PubMed Mills K. L. , Goddings A. L. , Clasen L. S. , Giedd J. N. , Blakemore S. J. ( 2014 ). The developmental mismatch in structural brain maturation during adolescence . Developmental Neuroscience , 36 , 147 – 160 . https://doi.org/10.1159/000362328 Google Scholar CrossRef Search ADS PubMed Nelson E. E. , Jarcho J. M. , Guyer A. E. ( 2016 ). Social re-orientation and brain development: An expanded and updated view . Developmental Cognitive Neuroscience , 17 , 118 – 127 . https://doi.org/10.1016/j.dcn.2015.12.008 Google Scholar CrossRef Search ADS PubMed Power J. D. , Barnes K. A. , Snyder A. Z. , Schlaggar B. L. , Petersen S. E. ( 2012 ). Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion . Neuroimage , 59 , 2142 – 2154 . https://doi.org/10.1016/j.neuroimage.2011.10.018 Google Scholar CrossRef Search ADS PubMed Raiford J. L. , Seth P. , Diclemente R. J. ( 2013 ). What girls won’t do for love: Human immunodeficiency virus/sexually transmitted infections risk among young african-american women driven by a relationship imperative . Journal of Adolescent Health , 52 , 566 – 571 . https://doi.org/10.1016/j.jadohealth.2012.09.006 Google Scholar CrossRef Search ADS PubMed Scheinost D. , Finn E. S. , Tokoglu F. , Shen X. , Papademetris X. , Hampson M. , Constable R. T. ( 2015 ). Sex differences in normal age trajectories of functional brain networks . Human Brain Mapping , 36 , 1524 – 1535 . https://doi.org/10.1002/hbm.22720 Google Scholar CrossRef Search ADS PubMed Schmiege S. J. , Broaddus M. R. , Levin M. E. , Bryan A. D. ( 2009 ). Randomized trial of group interventions to reduce HIV/STD risk and change theoretical mediators among detained adolescents . Journal of Consulting and Clinical Psychology , 77 , 38 – 50 . Retrieved from 10.1037/a0014513 Google Scholar CrossRef Search ADS PubMed Sheeran P. , Gollwitzer P. M. , Bargh J. A. ( 2013 ). Nonconscious processes and health . Health Psychology , 32 , 460 – 473 . Google Scholar CrossRef Search ADS PubMed Skeem J. L. , Scott E. , Mulvey E. P. ( 2014 ). Justice policy reform for high-risk juveniles: Using science to achieve large-scale crime reduction . Annual Review of Clinical Psychology , 10 , 709 – 739 . https://doi.org/10.1146/annurev-clinpsy-032813-153707 Google Scholar CrossRef Search ADS PubMed Thayer R. E. , Montanaro E. , Weiland B. J. , Callahan T. J. , Bryan A. D. ( 2014 ). Exploring the relationship of functional network connectivity to latent trajectories of alcohol use and risky sex . Current HIV Research , 12 , 293 – 300 . Google Scholar CrossRef Search ADS PubMed Tobin K. E. , Kuramoto S. J. , Davey-Rothwell M. A. , Latkin C. A. ( 2011 ). The STEP into action study: A peer-based, personal risk network-focused HIV prevention intervention with injection drug users in Baltimore, Maryland . Addiction , 106 , 366 – 375 . https://doi.org/10.1111/j.1360-0443.2010.03146.x Google Scholar CrossRef Search ADS PubMed Victor E. C. , Hariri A. R. ( 2015 ). A neuroscience perspective on sexual risk behavior in adolescence and emerging adulthood . Development and Psychopathology , 28 , 471 – 487 . https://doi.org/10.1017/S0954579415001042 Google Scholar CrossRef Search ADS PubMed © The Author(s) 2018. Published by Oxford University Press on behalf of the Society of Pediatric Psychology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices) http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Pediatric Psychology Oxford University Press

Adolescent Condom Use and Connectivity in the Social–Planful Brain

Loading next page...
 
/lp/ou_press/adolescent-condom-use-and-connectivity-in-the-social-planful-brain-5Gr0ZqnKyU
Publisher
Oxford University Press
Copyright
© The Author(s) 2018. Published by Oxford University Press on behalf of the Society of Pediatric Psychology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com
ISSN
0146-8693
eISSN
1465-735X
D.O.I.
10.1093/jpepsy/jsy025
Publisher site
See Article on Publisher Site

Abstract

Abstract Objective To reduce rates of sexually transmitted infections (STIs) and unwanted pregnancy among adolescents, it is critical to investigate brain connectivity that may underlie adolescents’ sexual health decision-making in the context of intercourse. This study explored relationships between adolescent condom use frequency and the brain’s resting-state functional connectivity, to identify differential patterns of social–affective processing among sexually active youth. Methods In this study, N = 143 sexually active adolescents (68.5% male, Mage = 16.2 years, SD = 1.06) completed magnetic resonance imaging and reported past 3-month frequency of condom use. Resting-state connectivity, seeded on a social region of the brain, the temporoparietal junction (TPJ), was assessed to determine its correspondence with protected sex (condom use). Results Condom use was associated with positive connectivity between the left TPJ and bilateral inferior frontal gyrus (IFG). This relationship was observed in adolescent males only; no connectivity differences were observed with adolescent females. Conclusions This study reflects functional synchrony between nodes of the “social brain,” including the TPJ, and a region of planfulness and control, the IFG. The relationship between these regions suggests that adolescents who have more coordinated systems of communication between these critical components of the brain are more likely to be successful in planning and engaging in safer sexual decision-making; for young males, this differentiated more frequent from less frequent condom use. In turn, interventions designed to reduce STIs/human immunodeficiency virus may benefit from targeting social–planfulness dimensions to help youth implement safer sex behaviors. adolescents, brain connectivity, condom use, HIV risk, MRI, sexual behavior Initiation of sexual activity with partners is developmentally normative for human adolescents in the mid- to late-teen years. For many youth, although sexual behavior can be healthy and propel them toward adaptive adult social roles, engagement in sexual activity also inherently increases risk for unintended, serious health outcomes, including unplanned pregnancy and sexually transmitted infections (STIs) such as the human immunodeficiency virus (HIV; Kann et al., 2016). Thus, safe sexual behavior among adolescents necessitates protective steps, such as condom use, before engaging in episodes of sexual intercourse. To help youth engage in planful sexual behavior, most adolescent prevention programs rely on social cognitive predictors of behavior, such as those based on Social Cognitive Theory, the Theory of Planned Behavior, or the Health Belief Model (Bryan & Feldstein Ewing, 2018). Studies focused on social cognitive dimensions have shown that adolescents who engage in higher rates of protected sex (e.g., consistent condom use) demonstrate greater condom use efficacy, stronger condom use intentions, more positive attitudes toward condoms, and higher self-esteem and optimism (Broaddus & Bryan, 2008; Bryan, Gillman, & Hansen, 2016). Though useful, these programs have been limited to some degree in their ability to explain and modify health behaviors (Bryan & Feldstein Ewing, 2018; Feldstein Ewing et al., 2016). In part, their reliance on rational cognitive theories underemphasizes the importance of social–affective domains of cognition in driving health decisions; these domains are critical in adolescence, when youth are still much in the midst of trying to determine how to navigate novel and salient emotional terrain with sexual/romantic partners (Feldstein Ewing & Bryan, 2015). Moreover, existing programs tend to focus on individual-level social cognitions. However, real-world sexual contexts are dyadic in nature, and they involve joint decision-making about protective efforts that impact someone else’s body in addition to one’s own, often against an instinctual drive to engage in unprotected sex (Feldstein Ewing et al., 2016). As such, the degree to which adolescents engage in planful sexual behavior is likely influenced by social and affective processes in the brain, including social connection and empathy. Emerging brain-based perspectives on sexual decision-making are providing novel insight that can inform how to promote safer sex behavior (Bryan & Feldstein Ewing, 2018). Critically, these neurocognitive insights have the potential to move beyond behavioral reports of sexual decision-making, and to incorporate neural and neurocognitive social–affective and nonconscious processes that likely factor into safer sex decisions (Bryan & Feldstein Ewing, 2018). To date, magnetic resonance imaging (MRI) studies—including functional MRI (fMRI)—on the neural processes of adolescent sexual decision-making are extremely few in number (for reviews, see Feldstein Ewing et al., 2016; Victor & Hariri, 2015). More work at the neural level is needed to disentangle the complex interplay of social, empathic, and affective processes that occur within the brain and which are associated with sexual decisions. This work is youth-relevant, as rapid changes in neurobiological structure and function occur in the window of 14–18 years (Mills, Goddings, Clasen, Giedd, & Blakemore, 2014), the precise time when initiation rates for sexual intercourse see the most dramatic rise over the life span (Kann et al., 2016). The brain can offer especially rich insights into the social–emotional aspects of adolescent sexual decision-making. Mammalian brains are inherently social organs throughout the life span; in adolescence, in particular, the brain experiences heightened attunement to the social environment (Nelson, Jarcho, & Guyer, 2016). This change in adolescence is represented by heightened response magnitude within the “social brain”—a distributed network of regions that reliably co-activate to achieve social and affective functions (Amft et al., 2015). One important region in this still-developing “social processing network” is the temporoparietal junction (TPJ). The TPJ co-activates with other nodes (e.g., precuneus, medial prefrontal cortex [mPFC]) to achieve several aspects of social cognition, such as mentalizing, empathy, and moral reasoning (Li, Mai, & Liu, 2014) – all of which continue to develop through adolescence (Kilford, Garrett, & Blakemore, 2016). Planning decisions around sexual intercourse likely involve many of these same social cognitive processes (e.g., considering someone else’s thoughts in anticipatory interactions before intercourse; positive affect and social connectedness; empathy in protecting another person’s body from STIs), mediated by the TPJ and its functional connections to other social nodes of the brain. As such, it can be expected that differences in the degree to which adolescents engage in modes of protected sex may reflect differences in social brain function, and specifically, differences in connectivity between the TPJ and other social–affective nodes. In the current study, we thus aimed to identify network-level relationships between the brain’s resting-state functional connectivity (rsFC) and a commonly used measure of sexual health behavior—condom use frequency—among adolescents (ages 14–18 years), in key networks of the social–affective brain. rsFC is a method to examine how different regions of the brain co-activate during rest, reflecting correspondence between, and coordinated activity behind cognitive and behavioral processes (Biswal et al., 2010). rsFC represents a useful strategy for measuring social–affective and nonconscious health decision-making processes within the brain (Sheeran et al., 2013) that are difficult to calculate experimentally or through self-report. Following past work on sexual decision-making (Eckstrand et al., 2017; Thayer, Montanaro, Weiland, Callahan, & Bryan, 2014), we focused our analyses on connectivity of the social processing network, and specifically, on connectivity between the TPJ and other social cognitive regions in the adolescent brain. The integration of developmental neuroscience with adolescent sexual risk research is still much emerging (Bryan & Feldstein Ewing, 2018), and we are aware of only one rsFC study on adolescent sexual decision-making, including condom use (Thayer et al., 2014). Thus, the goal of the current study is to extend research informing our understanding of the coordinated neurocognitive mechanisms associated with adolescent sexual health decision-making. Based on past work (Eckstrand et al., 2017; Victor & Hariri, 2015), we hypothesized that condom use frequency would be associated with connectivity between the TPJ and other nodes in the social processing network (Kilford et al., 2016; e.g., precuneus, mPFC; Li et al., 2014). Method Participants All procedures were approved by the participating university institutional review board and with a federal Certificate of Confidentiality. Youth were recruited from justice-related programs in the southwest United States and completed MRI scanning as part of an ongoing study. The parent study was a randomized controlled trial examining two prevention programs to reduce adolescent STI/HIV risk; however, all data presented herein were collected before intervention exposure. To recruit participants, trained research staff introduced the project at various collaborating programs, underscoring the voluntary nature of the study. Written assent was directly obtained from participants. Similar to other adolescent sexual risk studies (Schmiege, Broaddus, Levin, & Bryan, 2009), parent/guardian informed consent was obtained via telephone following youth assent. In terms of inclusion criteria, youth had to be 14–18 years old, fluent in English, and actively participating in a justice-related program. Exclusion criteria included prescription for and/or taking antipsychotics/anticonvulsants, concussions or other head injuries leading to loss of consciousness for >5 min during the past 6 months, and other standard MRI contraindications (e.g., pregnancy; Filbey, McQueeny, DeWitt, & Mishra, 2015). For enrollment, N = 280 youth assented/consented to participate in the study. Of those, N = 249 completed the neuroimaging procedures, including resting state. The remainder did not have a complete resting-state scan (n = 31; e.g., showed MRI contraindications on day of scan). Of those, N = 231 youth had usable resting-state data; 18 were excluded because of imaging data that did not pass quality inspections. From this number, n = 88 were not recently sexually active, or did not have full sexual orientation data. This included a full sample of N =143 adolescents (68.5% male, Mage = 16.2 years (SD = 1.06); see Table I). Table I. Demographic Information on Sample Range Male (n = 98) Female (n = 45) Gender comparison M (SD) M (SD) Age 14–18 16.2 (1.02) 16.0 (1.11) t(141) = 1.46, p = ns Age at first intercourse 7–17 13.2 (1.87) 13.9 (1.29) t(139) = −2.29, p < .05 # of intercourse partnersa 1–15 2.7 (2.70) 1.6 (1.54) t(141) = 2.39, p < .05 Condom use frequencyb 1–5 3.03 (1.54) 3.04 (1.62) t(141) = −0.05, p = ns Race/ethnicity % % χ2 = 6.98, p = ns  Hispanic American 54.1 62.2  Caucasian 14.3 6.7  African-American 5.1 0.0  American Indian/Alaska Native 2.0 4.4  Other 2.0 4.4  More than one race 22.4 22.2 Range Male (n = 98) Female (n = 45) Gender comparison M (SD) M (SD) Age 14–18 16.2 (1.02) 16.0 (1.11) t(141) = 1.46, p = ns Age at first intercourse 7–17 13.2 (1.87) 13.9 (1.29) t(139) = −2.29, p < .05 # of intercourse partnersa 1–15 2.7 (2.70) 1.6 (1.54) t(141) = 2.39, p < .05 Condom use frequencyb 1–5 3.03 (1.54) 3.04 (1.62) t(141) = −0.05, p = ns Race/ethnicity % % χ2 = 6.98, p = ns  Hispanic American 54.1 62.2  Caucasian 14.3 6.7  African-American 5.1 0.0  American Indian/Alaska Native 2.0 4.4  Other 2.0 4.4  More than one race 22.4 22.2 a Past 3-month number of sexual intercourse partners. b Past 3-month condom use frequency: 1 = Never, 2 = Almost never, 3 = Sometimes, 4 = Almost always, 5 = Always. Table I. Demographic Information on Sample Range Male (n = 98) Female (n = 45) Gender comparison M (SD) M (SD) Age 14–18 16.2 (1.02) 16.0 (1.11) t(141) = 1.46, p = ns Age at first intercourse 7–17 13.2 (1.87) 13.9 (1.29) t(139) = −2.29, p < .05 # of intercourse partnersa 1–15 2.7 (2.70) 1.6 (1.54) t(141) = 2.39, p < .05 Condom use frequencyb 1–5 3.03 (1.54) 3.04 (1.62) t(141) = −0.05, p = ns Race/ethnicity % % χ2 = 6.98, p = ns  Hispanic American 54.1 62.2  Caucasian 14.3 6.7  African-American 5.1 0.0  American Indian/Alaska Native 2.0 4.4  Other 2.0 4.4  More than one race 22.4 22.2 Range Male (n = 98) Female (n = 45) Gender comparison M (SD) M (SD) Age 14–18 16.2 (1.02) 16.0 (1.11) t(141) = 1.46, p = ns Age at first intercourse 7–17 13.2 (1.87) 13.9 (1.29) t(139) = −2.29, p < .05 # of intercourse partnersa 1–15 2.7 (2.70) 1.6 (1.54) t(141) = 2.39, p < .05 Condom use frequencyb 1–5 3.03 (1.54) 3.04 (1.62) t(141) = −0.05, p = ns Race/ethnicity % % χ2 = 6.98, p = ns  Hispanic American 54.1 62.2  Caucasian 14.3 6.7  African-American 5.1 0.0  American Indian/Alaska Native 2.0 4.4  Other 2.0 4.4  More than one race 22.4 22.2 a Past 3-month number of sexual intercourse partners. b Past 3-month condom use frequency: 1 = Never, 2 = Almost never, 3 = Sometimes, 4 = Almost always, 5 = Always. Measures Sexual Behavior Using established sexual behavior metrics (Bryan, Schmiege, & Magnan, 2012; Schmiege et al., 2009), lifetime sexual activity was assessed by querying whether adolescents had ever engaged in vaginal and/or anal intercourse, defined by “a man putting his penis inside a woman’s vagina or inside someone’s anus (rear end).” Youth also reported the age at which they first engaged in sexual intercourse, as well as number of past 3-month sexual intercourse partners. Condom Use Frequency Via Likert scale, participants reported how often they used condoms during past 3-month sexual intercourse events (1 = Never, 2 = Almost never, 3 = Sometimes, 4 = Almost always, 5 = Always). The frequency distribution was 27.3% (Never), 11.2% (Almost never), 18.2% (Sometimes), 17.5% (Almost always), and 25.9% (Always). Procedure Trained research assistants guided participants through all aspects of the study, including recruitment, administration of questionnaires, and MRI procedures. Participants received $150 for completion of these study components. Resting-State Scan Participants were instructed to lie still in the scanner for 5 min 30 s with their eyes opened and focused on a white fixation cross presented on a black screen through a mirror attached to the scanner head coil. MRI Data Acquisition Scanning was conducted on a Siemens 3.0 T Tim Trio full-body MRI scanner, using a 12-channel phased array head coil. Each brain volume (165 total) consisted of 33 3.5 mm interleaved axial slices, acquired using a T2*-weighted gradient echo planar imaging sequence repetition time (TR) and echo time (TE) (TR/TE = 2,000/29 ms, 75-degree flip angle, 3.8 × 3.8 × 3.5 mm voxels, field of view = 240 mm). In addition, for each participant, a high-resolution sagittally acquired T1-weighted anatomical image (192 slices, 1.00 mm thick) was collected for alignment and normalization of functional images (TR = 2,530 ms, multi-echo TE = 1.64/3.5/5.36/7.22/9.09 ms, 7-degree flip angle, 1.0 mm3 voxels, field of view = 256 mm). Preprocessing Preprocessing was conducted using Analysis of Functional NeuroImaging (AFNI) software (Cox, 1996). Preprocessing steps followed recommended guidelines for rsFC, accounting for the strong impact of head motion on the Blood Oxygenated Level Dependent (BOLD) signal in rsFC (Matthews & Fair, 2015). Preprocessing consisted of removing the first two TRs from the time series to reach steady state, slice timing correction, rigid body motion correction with six motion parameters, spatial normalization to Talairach space, and spatial smoothing with a 4 mm FWHM Gaussian kernel. Functional images were subjected to de-spiking, band-pass filtering >0.1 Hz and <0.08 Hz, and censoring of volumes with head motion >1 mm from the previous volume. The time series of six motion parameters and six first derivative of motion parameters was regressed from the time series data, along with band-pass filtering parameters. Participants (n = 13) with >32 TRs censored (20% of data) because of motion were excluded from analyses. First level analyses All analyses were conducted using AFNI and SPSS V22.0. Based on prior studies of risky sexual behavior and social processing in adolescents (Eckstrand et al., 2017; Thayer et al., 2014), seed regions of interest (ROIs) were created within two nodes of the brain’s social processing network: left TPJ (lTPJ) and right TPJ (rTPJ) (Figure 1a; 8 mm radius; Montreal Neuroimaging Institute MNI coordinates: x = ±48, y = −52, z = 40). The average time course in each ROI was extracted using AFNI’s 3dmaskave and correlated with every voxel in the brain using AFNI’s 3dfim+. These correlation coefficients were transformed using Fisher’s z transformation. More positive values in the resulting z-score for each voxel indicate stronger functional co-activation between the BOLD time course of the ROI and that voxel, while more negative values indicate anticorrelation between the BOLD time courses. To reduce type I errors, Monte Carlo simulations using AFNI’s 3dClustSim, paired with an autocorrelation function (-acf), were used to determine the minimum number of contiguous 3.5 mm voxels per cluster (activated at puncorrected < .05) to yield a threshold of pcorrected < .05: 800 voxels. Figure 1. View largeDownload slide In males, significant positive associations between condom use and rsFC (BOLD % signal change) between left TPJ and the whole brain. (a) Left TPJ seed region; (b, d) significant right and left IFG clusters; (c, e) scatterplots of beta values, denoting connectivity strength, extracted from each IFG cluster. Note. BOLD = Blood Oxygenated Level Dependent; IFG = inferior frontal gyrus; rsFC = resting-state functional connectivity; TPJ = temporoparietal junction. Figure 1. View largeDownload slide In males, significant positive associations between condom use and rsFC (BOLD % signal change) between left TPJ and the whole brain. (a) Left TPJ seed region; (b, d) significant right and left IFG clusters; (c, e) scatterplots of beta values, denoting connectivity strength, extracted from each IFG cluster. Note. BOLD = Blood Oxygenated Level Dependent; IFG = inferior frontal gyrus; rsFC = resting-state functional connectivity; TPJ = temporoparietal junction. Analysis Plan Resting-State Functional Connectivity General linear models were run in AFNI’s 3dMVM program to test for the main effect of condom use frequency on connectivity between seed ROIs and the whole brain. Models were run for each of the two seed ROIs (lTPJ, rTPJ). Participant age, gender, and sexual orientation were added as covariates. Age and Gender as Moderators of rsFC Furthermore, we examined whether condom use-related differences in connectivity were moderated by age and gender. We tested for main and moderated effects of condom use frequency in a single model per ROI, yielding two models. The full model included condom use frequency, age, gender, sexual orientation, a condom use*age interaction term, and a condom use*gender interaction term. Results Sample Characteristics Excluded participants were significantly younger than the final included sample (excluded for scan-related reasons; t(278) = −2.83, p < .01; not sexually active; t(278) = −5.77, p < .001). Of the final sample, males reported younger first-time intercourse (t(139) = −2.29, p < .05) and a higher number of past 3-month sexual partners (t(141) = −2.34, p < .05), but no differences in condom use frequency compared with females (Table I). Age was not associated with past 3-month sexual intercourse or condom use frequency. Resting-State Functional Connectivity Examining the primary hypothesis, when all youth were assessed together, we did not observe a main effect of condom use on TPJ connectivity. Age and Gender as Moderators of rsFC Owing to the exploratory nature of the study, we followed examination of these main effects with standard corrections. Here, for the lTPJ seed, we found a condom use*gender interaction in a right inferior frontal gyrus (rIFG) cluster (ß = .52, SE = .12, p < .001, 95% confidence interval, CI [0.04, 0.12]). Using a more conservative uncorrected cluster threshold of p < .005 to detect smaller clusters of greater magnitude, we observed a second condom use*gender interaction in the contralateral side of the brain—in the left inferior frontal gyrus (lIFG) (ß = .40, SE = .02, p < .001, 95% CI [0.05, 0.13]). The lTPJ did not yield a condom use*age interaction; likewise, no condom use*age or condom use*gender interactions emerged in the rTPJ. The condom use*gender interactions were decomposed by testing for the simple main effect of condom use frequency for each gender. Simple main effects were significant in males only, with no effects observed among young females. In males only, more frequent condom use was associated with stronger, more positive lTPJ-rIFG and lTPJ-lIFG connectivity. Cluster size, peak voxel coordinates, and effect sizes are reported in Table II. For data visualization, significant clusters in the brain are illustrated in Figure 1b (rIFG) and Figure 1d (lIFG). Percent signal change beta values were extracted from the peak voxel coordinates of the significant rIFG and lIFG clusters and plotted as a function of condom use (Figure 1c: rIFG; Figure 1e: lIFG). Table II. Decomposition of Condom Use*Gender Interactions Subgroup: Males (n = 98) Seed ROI FC region # Voxels x y z BA z-score Uncorr. p Corr. p L TPJ R IFG 989 58 20 13 45 4.12 <.05 <.03 L TPJ L IFG 68 −44 16 −1 47 3.63 <.005 <.05 Subgroup: Males (n = 98) Seed ROI FC region # Voxels x y z BA z-score Uncorr. p Corr. p L TPJ R IFG 989 58 20 13 45 4.12 <.05 <.03 L TPJ L IFG 68 −44 16 −1 47 3.63 <.005 <.05 Note: Table displays simple effects of condom use frequency (past 3 months) with rsFC. BA = Brodmann areas; Corr. p = p-threshold corrected for multiple voxel comparisons; FC region = functional connectivity region correlated with seed ROI; IFG = inferior frontal gyrus; L = left; R = right; ROI = region of interest; xyz = peak voxel coordinates reported with MNI coordinate system, TPJ = temporoparietal junction; Uncorr. p = uncorrected p-threshold. Table II. Decomposition of Condom Use*Gender Interactions Subgroup: Males (n = 98) Seed ROI FC region # Voxels x y z BA z-score Uncorr. p Corr. p L TPJ R IFG 989 58 20 13 45 4.12 <.05 <.03 L TPJ L IFG 68 −44 16 −1 47 3.63 <.005 <.05 Subgroup: Males (n = 98) Seed ROI FC region # Voxels x y z BA z-score Uncorr. p Corr. p L TPJ R IFG 989 58 20 13 45 4.12 <.05 <.03 L TPJ L IFG 68 −44 16 −1 47 3.63 <.005 <.05 Note: Table displays simple effects of condom use frequency (past 3 months) with rsFC. BA = Brodmann areas; Corr. p = p-threshold corrected for multiple voxel comparisons; FC region = functional connectivity region correlated with seed ROI; IFG = inferior frontal gyrus; L = left; R = right; ROI = region of interest; xyz = peak voxel coordinates reported with MNI coordinate system, TPJ = temporoparietal junction; Uncorr. p = uncorrected p-threshold. Discussion The primary aim of the current study was to examine associations between adolescent condom use frequency and rsFC in the brain. We hypothesized that social–affective processes, relevant within the adolescent “social brain,” would be associated with protective sexual decision-making (condom use). We examined this by looking at the TPJ and its communication (synchrony) with other nodes relevant to adolescent sexual health decision-making. We expected functional connectivity between the TPJ and other nodes of the social processing network (Kilford et al., 2016; e.g., precuneus, mPFC; Li et al., 2014). However, we found an interesting path of communication between adolescent social (lTPJ) and planfulness (IFG) nodes among adolescent males that was associated with recent condom use frequency. While not completely in line with predictions, it does contribute to the existing literature around social cognitive predictors of behavior. Specifically, studies using Theory of Planned Behavior and Social Cognitive Theory frameworks have identified several cognitive constructs to be key in successful adolescent condom use, including condom use efficacy, condom use intentions, attitudes toward condoms, self-esteem and optimism about the future, and norms around condom use (Broaddus & Bryan, 2008; Bryan et al., 2016; Eggers et al., 2016). Notably, this study suggests that prior constructs integral in understanding adolescent behavior change, including condom use intentions, and of relevance, condom use self-efficacy, could well be associated with key social–planful networks of the developing brain. Of course, adolescent decision-making occurs within the context of the developing brain; yet, developmental neuroscience and health psychology literatures do not often intersect. This study suggests that critical advances in adolescent sexual decision-making would benefit from inclusion of the perspective of the brain—particularly social and planfulness regions—that are likely key components of the adolescent decision-making process. In terms of the implication of the TPJ as a key region of the adolescent brain, the TPJ is highly relevant in the processing of social cues, which operate at an elevated level during this developmental period. Concretely, studies suggest that the adolescent brain is acutely attuned to social cues, even at nonconscious levels (Kilford et al., 2016). Relevant to this article, connections between the TPJ and IFG have been implicated in the capacity to estimate another’s social or affective experience (Li et al., 2014). This process has been termed “mentalizing” in the developmental neuroscience literature, and it is more specifically referred to as “empathy” in the developmental, clinical, and health literatures. Enhanced capacity for anticipating and connecting with another individual’s experience is a fundamental part of human connectedness, and among adolescents, it is a critical piece of their often nascent navigation of romantic partnerships (e.g., Ewing & Bryan, 2015). Of relevance, girls have historically reported sexual behavior as a method to advance relationship development (Raiford, Seth, & Diclemente, 2013). This study suggests that among boys, those who have a stronger cognitive network in the domain of empathy (TPJ-IFG) may be better able to “tune in” to the needs of their romantic/sexual partner, which in turn, may enhance their capacity to adeptly engage in successful condom use. Resting-state connectivity has been useful for identification of global, stable traits related to social–affective processing (Geerligs, Rubinov, Cam-CAN, & Henson, 2015). While it can be tempting to ascribe more activation or connectedness with “good” versus “bad” behavior, and similarly, to potentially try to intuit whether these data suggest potential “trait” versus “state” behaviors, it is important to note that imaging still remains a developing field, and we simply do not have the capacity to make those distinctions (Feldstein Ewing, Sakhardande, & Blakemore, 2014). Yet these data do suggest future critical routes of inquiry around individual differences in the neural coordination of social cognitive processing, and subsequent health behavior. Complementing recent functional task findings in this age group (Eckstrand et al., 2017), the co-activation of lTPJ and IFG is intriguing. The IFG is commonly viewed as a cognitive planfulness region where cognition, emotion, and interoceptive awareness integrate (Adolfi et al., 2017). Notably, connectivity between IFG and posterior regions, including TPJ, is shown to increase across development to improve cognitive control (Marek, Hwang, Foran, Hallquist, & Luna, 2015). While it is somewhat surprising that the TPJ did not correlate with other social–affective processing nodes (e.g., precuneus, mPFC), we suggest the data herein reflect that the degree to which adolescents engage in condom use may vary less as a function of low-level social–affective processes within nodes of the social brain; rather, it may vary more as a function of coordination between social and cognitive control networks that engage in higher-level anticipatory emotional processing, such as empathy and mentalizing. The findings in this study were centralized within the lTPJ. This is interesting, as the rTPJ is more heavily implicated in spatial orientation. In contrast, the lTPJ is more heavily associated with language processes (Amft et al., 2015), including adolescent social peer interaction and communication (Kilford et al., 2016). We speculate that the specificity of the results to lTPJ suggests that differences in condom use frequency may be related to adolescents’ ability to communicate with sexual partners around condom use. Moreover, lTPJ functional connections with prefrontal cortex, specifically IFG, have been linked with cognitive perspective taking (Baumgartner, Götte, Gügler, & Fehr, 2012; Kilford et al., 2016). Thus, specificity to lTPJ-IFG connectivity is well in line with the interpretation that the capacity for mentalizing/empathy may play a role in the condom use equation, rather than other forms of related, but potentially, irrelevant social cognitive processes (e.g., spatial orientation). Nevertheless, more research is needed to assess the discriminability of TPJ-condom use associations to sexual health decision-making, rather than to broader social cognitive processes. Condom use–functional connectivity associations were moderated by gender. We did not observe anticipated relationships for young females. This may reflect gender differences in the pacing of not just hormonal development but also of neural maturation and connectivity (Scheinost et al., 2015). Recent research has highlighted the lower level of variance observed for girls developmentally, many of whom are already fairly advanced in pubertal development at the ages enrolled within this study (Feldstein Ewing et al., 2018); this may have impacted capacity to detect network relationships for young girls in the current study. Another element in this equation is that networks relevant for empathy and mentalizing may be more salient for young boys rather than girls, given that for young girls, the decision to use (or not use) condoms occurs ultimately on someone else’s body. Thus, the cognitive equation relevant to driving successful condom use may include different paths and salient neurocognitive processes (Feldstein Ewing & Bryan, 2015; Feldstein Ewing et al., 2016). It is unknown whether more positive TPJ-IFG connectivity in relation to greater condom use is reflective of more mature brain function. Generally, positive connectivity represents synchrony in the brain—regions of the brain successfully working together in tandem to achieve a unitary function—while negative connectivity represents opposing or complementary functions between target networks (Biswal et al., 2010). As the brain matures well into adolescence (completing for young males around age 25 years), more specialization of function occurs, whereby there is stronger positive connectivity within specific functional networks of the brain (Fair et al., 2007, 2008). Ultimately, longitudinal studies are needed to disentangle the effects of brain connectivity, age, and gender on decisions to use protection during intercourse. Limitations First, we did not collect data on whether condoms were used for disease prevention, pregnancy prevention, or both. Future studies will continue to examine the complex personal and situational factors motivating decisions to use condoms, including from the perspective of the dyad, and relevant underlying neural mechanisms. Second, many other forms of sexual behavior, such as oral sex, carry some degree of physical health risk (Kann et al., 2016). However, because intercourse events represent the strongest risks for pregnancy and STIs, we chose to constrain analyses to health decision-making around sexual intercourse. Third, the absence of observed associations in young females may have been driven by low power (only 45 females vs. 98 males), and potentially a high proportion of the sample identifying as bisexual; however, given the congruence of this sample with other adolescents in the community, we believe that this group was representative of the larger community from which it was drawn, who continue to have high rates of sexual intercourse necessitating condom use (Bryan et al., 2018). Fourth, it could be that the observed functional connectivity differences are not specific to adolescent sexual decision-making. It is indeed the case that, among adolescents, the TPJ and IFG are identified as part of a multifunctional social cognitive network that undergoes significant development during adolescence (Kilford et al., 2016); in particular, they have been associated with affect regulation in the context of monitoring peer social feedback (He et al., 2018). While we were only able to assess the relevance of this network to sexual decision-making, future work will continue to disaggregate the impact of this network on other domains of adolescent decision-making. This will help inform the specificity of this network to sexual decision-making (only), or its potentially more global impact on and implications for the broader domain of adolescent health. At the same time, it is important to identify that what is unique about adolescents’ movement into romantic and sexual decision-making is that this behavior falls within a domain of evolutionarily adaptive behavior that humans are preprogrammed to engage in via neurobiological maturation (Giedd, 2015), and which is healthy and appropriate (Feldstein Ewing et al., 2016). Indeed, it is important to also note the numerous positive psychosocial outcomes that can come from sexual engagement, including pleasure and intimacy, during a developmental period when youth are beginning to learn about their own and others’ bodies and to invest in romantic relationships (Harden, 2014). Here, adolescent sexual behavior is viewed not as inherently risky but as a healthy and normative behavior, albeit one which can yield undesired outcomes (e.g., pregnancy, STIs) if requisite preventive steps are not taken. From this perspective, it is possible that adolescents with less lTPJ-IFG connectivity and lower condom use frequency do not represent a qualitatively distinct risk endophenotype. Fifth, a high percentage of participants in our sample of youth reported lifetime engagement in sexual intercourse (75.7%). In fact, U.S. justice-involved youth represent a critical and large higher-risk group that is often missed in developmental neuroscience research on health decision-making (Bryan et al., 2016; Feldstein Ewing et al., 2016). Furthermore, justice-involved youth—predominantly Hispanic males in many U.S. cities—are closer in psychosocial function to the majority youth population than previously believed; in reality, they may merely fall on the riskier side of a normal distribution of behavior (Skeem, Scott, & Mulvey, 2014). Clinical and research data do not indicate qualitative distinctions between justice-involved youth and their peers (Feldstein Ewing, Montanaro, Gaume, Caetano, & Bryan, 2015), and in this respect, the current study represents an endeavor to overcome an important disparity in representative sampling. Clinical Implications The coupling of condom use decisions in adolescents within the “social-planful brain” suggests that interventions designed to reduce rates of unwanted pregnancy and STIs may find use in targeting cognitive and affective processes—such as empathy and mentalizing—relevant in adolescent sexual decision-making. Furthermore, these targets may be especially salient for young males. Identification of neural targets involved both in motivating unprotected sex decisions and in harnessing condom use decisions has the potential to inform clinicians about which intervention strategies may work best in which populations, and via which neurocognitive processes. At the same time, adolescent interventions that integrate dyadic elements are also gaining traction as promising “next-level” adjunctive approaches to improving HIV-risk reduction efficacy in this age group. Dyadic interventions in adolescent populations have already shown efficacy in reducing rates of HIV-risk behavior (Tobin, Kuramoto, Davey-Rothwell, & Latkin, 2011). The current study represents a potential step forward, as it provides preliminary insights into the neurocognitive mechanisms by which prevention interventions may impact condom use decisions in a context that is dyadic and rich in social–affective complexity. Acknowledgment The authors would like to thank Dustin Truitt for assistance on this project. Funding This work was supported by the National Institute of Nursing Research at the National Institutes of Health (grant number 1R01NR013332-01 to S.F.E. and A.B.). Conflicts of interest: None declared. References Adolfi F. , Couto B. , Richter F. , Decety J. , Lopez J. , Sigman M. , Manes F. , Ibáñez A. ( 2017 ). Convergence of interoception, emotion, and social cognition: A twofold fMRI meta-analysis and lesion approach . Cortex , 88 , 124 – 142 . https://doi.org/10.1016/j.cortex.2016.12.019 Google Scholar CrossRef Search ADS PubMed Amft M. , Bzdok D. , Laird A. R. , Fox P. T. , Schilbach L. , Eickhoff S. B. ( 2015 ). Definition and characterization of an extended social-affective default network . Brain Structure and Function , 220 , 1031 – 1049 . https://doi.org/10.1007/s00429-013-0698-0 Google Scholar CrossRef Search ADS PubMed Baumgartner T. , Götte L. , Gügler R. , Fehr E. ( 2012 ). The mentalizing network orchestrates the impact of parochial altruism on social norm enforcement . Human Brain Mapping , 33 , 1452 – 1469 . https://doi.org/10.1002/hbm.21298 Google Scholar CrossRef Search ADS PubMed Biswal B. B. , Mennes M. , Zuo X. N. , Gohel S. , Kelly C. , Smith S. M. , Beckmann C. F. , Adelstein J. S. , Buckner R. L. , Colcombe S. , Dogonowski A. M. , Ernst M. , Fair D. , Hampson M. , Hoptman M. J. , Hyde J. S. , Kiviniemi V. J. , Kötter R. , Li S. J. , Lin C. P. , Lowe M. J. , Mackay C. , Madden D. J. , Madsen K. H. , Margulies D. S. , Mayberg H. S. , McMahon K. , Monk C. S. , Mostofsky S. H. , Nagel B. J. , Pekar J. J. , Peltier S. J. , Petersen S. E. , Riedl V. , Rombouts S. A. , Rypma B. , Schlaggar B. L. , Schmidt S. , Seidler R. D. , Siegle G. J. , Sorg C. , Teng G. J. , Veijola J. , Villringer A. , Walter M. , Wang L. , Weng X. C. , Whitfield-Gabrieli S. , Williamson P. , Windischberger C. , Zang Y. F. , Zhang H. Y. , Castellanos F. X. , Milham M. P. ( 2010 ). Toward discovery science of human brain function . Proceedings of the National Academy of Sciences of the United States of America , 107 , 4734 – 4739 . https://doi.org/10.1073/pnas.0911855107 Google Scholar CrossRef Search ADS PubMed Broaddus M. R. , Bryan A. ( 2008 ). Consistent condom use among juvenile detainees: The role of individual differences, social bonding, and health beliefs . AIDS and Behavior , 12 , 59 – 67 . https://doi.org/10.1007/s10461-007-9260-5 Google Scholar CrossRef Search ADS PubMed Bryan A. D. , Feldstein Ewing S. W. ( 2018 ). Sex and the brain: Empirical intersection of neurocognition and sexual behavior. Introduction to the special section on neuroscience and sexual behavior . Annals of Behavioral Medicine , 52 , 353 – 355 . https://doi: 10.1093/abm/kax070 Google Scholar PubMed Bryan A. D. , Gillman A. S. , Hansen N. S. ( 2016 ). Changing the context is important and necessary, but not sufficient, for reducing adolescent risky sexual behavior: A reply to steinberg (2015) . Perspectives on Psychological Science , 11 , 535 – 538 . https://doi.org/10.1177/1745691616638093 Google Scholar CrossRef Search ADS PubMed Bryan A. D. , Magnan R. E. , Gillman A. S. , Yeater E. A. , Feldstein Ewing S. W. , Kong A. S. , Schmiege S. J. ( 2018 ). Effect of including alcohol and cannabis content in a sexual risk-reduction intervention on the incidence of sexually transmitted infections in adolescents . JAMA Pediatrics , 172 , e175621 . https://doi.org/10.1001/jamapediatrics.2017.5621 Google Scholar CrossRef Search ADS PubMed Bryan A. D. , Schmiege S. J. , Magnan R. E. ( 2012 ). Marijuana use and risky sexual behavior among high-risk adolescents: Trajectories, risk factors, and event-level relationships . Developmental Psychology , 48 , 1429 – 1442 . https://doi.org/10.1037/a0027547 Google Scholar CrossRef Search ADS PubMed Cox R. W. ( 1996 ). AFNI: Software for analysis and visualization of functional magnetic resonance neuroimages . Computers and Biomedical Research , 29 , 162 – 173 . Retrieved from https://www.ncbi.nlm.nih.gov/pubmed/8812068 Google Scholar CrossRef Search ADS PubMed Eckstrand K. L. , Choukas-Bradley S. , Mohanty A. , Cross M. , Allen N. B. , Silk J. S. , Jones N. P. , Forbes E. E. ( 2017 ). Heightened activity in social reward networks is associated with adolescents’ risky sexual behaviors . Developmental Cognitive Neuroscience , 27 , 1 – 9 . https://doi.org/10.1016/j.dcn.2017.07.004 Google Scholar CrossRef Search ADS PubMed Eggers S. M. , Aarø L. E. , Bos A. E. , Mathews C. , Kaaya S. F. , Onya H. , de Vries H. ( 2016 ). Sociocognitive predictors of condom use and intentions among adolescents in three Sub-Saharan sites . Archives of Sexual Behavior , 45 , 353 – 365 . https://doi.org/10.1007/s10508-015-0525-1 Google Scholar CrossRef Search ADS PubMed Feldstein Ewing S. W. , Bryan A. D. ( 2015 ). A question of love and trust? The role of relationship factors in adolescent sexual decision making . Journal of Developmental and Behavioral Pediatrics , 36 , 628 – 634 . https://doi.org/10.1097/DBP.0000000000000190 Google Scholar CrossRef Search ADS PubMed Feldstein Ewing S. W. , Sakhardande A. , Blakemore S. J. ( 2014 ). The effect of alcohol consumption on the adolescent brain: A systematic review of MRI and fMRI studies of alcohol-using youth . Neuroimage: Clinical , 5 , 420 – 437 . https://doi.org/10.1016/j.nicl.2014.06.011 Google Scholar CrossRef Search ADS PubMed Fair D. A. , Cohen A. L. , Dosenbach N. U. , Church J. A. , Miezin F. M. , Barch D. M. , Raichle M. E. , Petersen S. E. , Schlaggar B. L. ( 2008 ). The maturing architecture of the brain’s default network . Proceedings of the National Academy of Sciences of the United States of America , 105 , 4028 – 4032 . https://doi.org/10.1073/pnas.0800376105 Google Scholar CrossRef Search ADS PubMed Fair D. A. , Dosenbach N. U. , Church J. A. , Cohen A. L. , Brahmbhatt S. , Miezin F. M. , Barch D. M. , Raichle M. E. , Petersen S. E. , Schlaggar B. L. ( 2007 ). Development of distinct control networks through segregation and integration . Proceedings of the National Academy of Sciences of the United States of America , 104 , 13507 – 13512 . https://doi.org/10.1073/pnas.0705843104 Google Scholar CrossRef Search ADS PubMed Feldstein Ewing S. W. , Hudson K. A. , Caouette J. D. , Mayer A. R. , Thayer R. E. , Ryman S. G. , Bryan A. D. ( 2018 ). Sexual risk-taking and subcortical brain volume in adolescence . Annals of Behavioral Medicine , 52 , 393 – 405 . https://doi.org/10.1093/abm/kax027 Google Scholar PubMed Feldstein Ewing S. W. , Montanaro E. A. , Gaume J. , Caetano R. , Bryan A. D. ( 2015 ). Measurement invariance of alcohol instruments with Hispanic youth . Addictive Behaviors , 46 , 113 – 120 . https://doi.org/10.1016/j.addbeh.2015.03.004 Google Scholar CrossRef Search ADS PubMed Feldstein Ewing S. W. , Ryman S. R. , Gillman A. , Weiland B. J. , Thayer R. E. , Bryan A. D. ( 2016 ). Developmental cognitive neuroscience of adolescent sexual risk and alcohol use . AIDS and Beahvior , 20(Suppl 1) , S97 – S108 . Google Scholar CrossRef Search ADS Filbey F. M. , McQueeny T. , DeWitt S. J. , Mishra V. ( 2015 ). Preliminary findings demonstrating latent effects of early adolescent marijuana use onset on cortical architecture . Developmental Cognitive Neuroscience , 16 , 16 – 22 . Google Scholar CrossRef Search ADS PubMed Geerligs L. , Rubinov M. Cam-CAN Henson R. N. ( 2015 ). State and trait components of functional connectivity: Individual differences vary with mental state . Journal of Neuroscience , 35 , 13949 – 13961 . https://doi.org/10.1523/JNEUROSCI.1324-15.2015 Google Scholar CrossRef Search ADS PubMed Giedd J. N. ( 2015 ). The amazing teen brain . Scientific American , 312 , 32 – 37 . https://doi.org/10.1038/scientificamerican0615-32 Google Scholar CrossRef Search ADS PubMed Harden K. P. ( 2014 ). A sex-positive framework for research on adolescent sexuality . Perspectives on Psychological Science , 9 , 455 – 469 . https://doi.org/10.1177/1745691614535934 Google Scholar CrossRef Search ADS PubMed He Z. , Lin Y. , Xia L. , Liu Z. , Zhang D. , Elliot R. ( 2018 ). Critical role of the right VLPFC in emotional regulation of social exclusion: A tDCS study . Social Cognitive and Affective Neuroscience , in press. https://doi.org/doi: 10.1093/scan/nsy026 Kann L. , McManus T. , Harris W. A. , Shanklin S. L. , Flint K. H. , Hawkins J. , Queen B. , Lowry R. , Olsen E. O. , Chyen D. , Whittle L. , Thornton J. , Lim C. , Yamakawa Y. , Brener N. , Zaza S. ( 2016 ). Youth risk behavior surveillance—United States, 2015 . Morbidity and Mortality Weekly Report , 65 , 1 – 174 . https://doi.org/10.15585/mmwr.ss6506a1 Google Scholar PubMed Kilford E. J. , Garrett E. , Blakemore S. J. ( 2016 ). The development of social cognition in adolescence: An integrated perspective . Neuroscience and Biobehavioral Reviews , 70 , 106 – 120 . https://doi.org/10.1016/j.neubiorev.2016.08.016 Google Scholar CrossRef Search ADS PubMed Li W. , Mai X. , Liu C. ( 2014 ). The default mode network and social understanding of others: What do brain connectivity studies tell us . Frontiers in Human Neuroscience , 8 , 74 . https://doi.org/10.3389/fnhum.2014.00074 Google Scholar PubMed Marek S. , Hwang K. , Foran W. , Hallquist M. N. , Luna B. ( 2015 ). The contribution of network organization and integration to the development of cognitive control . PLoS Biology , 13 , e1002328 . https://doi.org/10.1371/journal.pbio.1002328 Google Scholar CrossRef Search ADS PubMed Matthews M. , Fair D. A. ( 2015 ). Functional brain connectivity and child psychopathology - Overview and methodological considerations for investigators new to the field . Journal of Child Psychology and Psychiatry , 56 , 400 – 414 . Google Scholar CrossRef Search ADS PubMed Mills K. L. , Goddings A. L. , Clasen L. S. , Giedd J. N. , Blakemore S. J. ( 2014 ). The developmental mismatch in structural brain maturation during adolescence . Developmental Neuroscience , 36 , 147 – 160 . https://doi.org/10.1159/000362328 Google Scholar CrossRef Search ADS PubMed Nelson E. E. , Jarcho J. M. , Guyer A. E. ( 2016 ). Social re-orientation and brain development: An expanded and updated view . Developmental Cognitive Neuroscience , 17 , 118 – 127 . https://doi.org/10.1016/j.dcn.2015.12.008 Google Scholar CrossRef Search ADS PubMed Power J. D. , Barnes K. A. , Snyder A. Z. , Schlaggar B. L. , Petersen S. E. ( 2012 ). Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion . Neuroimage , 59 , 2142 – 2154 . https://doi.org/10.1016/j.neuroimage.2011.10.018 Google Scholar CrossRef Search ADS PubMed Raiford J. L. , Seth P. , Diclemente R. J. ( 2013 ). What girls won’t do for love: Human immunodeficiency virus/sexually transmitted infections risk among young african-american women driven by a relationship imperative . Journal of Adolescent Health , 52 , 566 – 571 . https://doi.org/10.1016/j.jadohealth.2012.09.006 Google Scholar CrossRef Search ADS PubMed Scheinost D. , Finn E. S. , Tokoglu F. , Shen X. , Papademetris X. , Hampson M. , Constable R. T. ( 2015 ). Sex differences in normal age trajectories of functional brain networks . Human Brain Mapping , 36 , 1524 – 1535 . https://doi.org/10.1002/hbm.22720 Google Scholar CrossRef Search ADS PubMed Schmiege S. J. , Broaddus M. R. , Levin M. E. , Bryan A. D. ( 2009 ). Randomized trial of group interventions to reduce HIV/STD risk and change theoretical mediators among detained adolescents . Journal of Consulting and Clinical Psychology , 77 , 38 – 50 . Retrieved from 10.1037/a0014513 Google Scholar CrossRef Search ADS PubMed Sheeran P. , Gollwitzer P. M. , Bargh J. A. ( 2013 ). Nonconscious processes and health . Health Psychology , 32 , 460 – 473 . Google Scholar CrossRef Search ADS PubMed Skeem J. L. , Scott E. , Mulvey E. P. ( 2014 ). Justice policy reform for high-risk juveniles: Using science to achieve large-scale crime reduction . Annual Review of Clinical Psychology , 10 , 709 – 739 . https://doi.org/10.1146/annurev-clinpsy-032813-153707 Google Scholar CrossRef Search ADS PubMed Thayer R. E. , Montanaro E. , Weiland B. J. , Callahan T. J. , Bryan A. D. ( 2014 ). Exploring the relationship of functional network connectivity to latent trajectories of alcohol use and risky sex . Current HIV Research , 12 , 293 – 300 . Google Scholar CrossRef Search ADS PubMed Tobin K. E. , Kuramoto S. J. , Davey-Rothwell M. A. , Latkin C. A. ( 2011 ). The STEP into action study: A peer-based, personal risk network-focused HIV prevention intervention with injection drug users in Baltimore, Maryland . Addiction , 106 , 366 – 375 . https://doi.org/10.1111/j.1360-0443.2010.03146.x Google Scholar CrossRef Search ADS PubMed Victor E. C. , Hariri A. R. ( 2015 ). A neuroscience perspective on sexual risk behavior in adolescence and emerging adulthood . Development and Psychopathology , 28 , 471 – 487 . https://doi.org/10.1017/S0954579415001042 Google Scholar CrossRef Search ADS PubMed © The Author(s) 2018. Published by Oxford University Press on behalf of the Society of Pediatric Psychology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices)

Journal

Journal of Pediatric PsychologyOxford University Press

Published: May 14, 2018

There are no references for this article.

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Search

Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly

Organize

Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.

Access

Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve

Freelancer

DeepDyve

Pro

Price

FREE

$49/month
$360/year

Save searches from
Google Scholar,
PubMed

Create lists to
organize your research

Export lists, citations

Read DeepDyve articles

Abstract access only

Unlimited access to over
18 million full-text articles

Print

20 pages / month

PDF Discount

20% off