Collectively, these results suggest that maturation of cognitive control may be partly mediated by earlier development of neural systems supporting reactive control and delayed development of systems supporting proactive control. Importantly, the development of these mechanisms is associated with cognitive control in real-life behaviors.

To test the hypothesis that these adolescent behaviors are linked to under-developed proactive control mechanisms, the present study employed a hybrid block/event-related functional Magnetic Resonance Imaging (fMRI) Stroop paradigm combined with self-report questionnaires in a large sample of adolescents and adults, ranging in age from 14 to 25. Compared to adults, adolescents under-activated a set of brain regions implicated in proactive top-down control across task blocks comprised of difficult and easy trials. Moreover, the magnitude of lateral prefrontal activity in adolescents predicted self-report measures of impulse control, foresight, and resistance to peer pressure. Consistent with reactive compensatory mechanisms to reduced proactive control, older adolescents exhibited elevated transient activity in regions implicated in response-related interference resolution.

Funding: This work was supported by the John D. and Catherine T. MacArthur Foundation Research Network on Adolescent Development and Juvenile Justice and an NIMH-funded Interdisciplinary Behavioral Science Center grant (P50 MH079485). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Copyright: © 2011 Andrews-Hanna et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

We predicted that functional maturation of sustained, proactive aspects of control would continue throughout adolescence, and that adolescents might compensate with increased reliance on late-stage reactive mechanisms, particularly for difficult trials requiring response-related interference resolution. As suggested by prior models, immature proactive control might manifest as decreased sustained blood-oxygenated level-dependent (BOLD) activity in lateral prefrontal regions including pDLPFC, whereas reactive response-related compensatory mechanisms might manifest as elevated transient activity in regions such as the ACC and the supplementary motor area (SMA) [5] . Furthermore, if lateral prefrontal regions play a role in proactive top-down control, one might expect that adolescents with increased sustained activity in these regions would report a better ability to control their behavior and plan ahead.

The present study aimed to extend previous adolescent neuroimaging findings by examining the developmental trajectories of both proactive and reactive control and by exploring the neural predictors of individual differences in real-world measures of cognitive function [see 35,36] . To examine the neural structures supporting proactive and reactive aspects of cognitive control, a large sample of adolescents and adults performed a “hybrid” blocked/event-related version of the Color-Word Stroop Task [37] – a classic test of executive function. This hybrid design allowed estimates of more sustained activation averaged across blocks of easy and difficult trials and more transient differences between trial types within a block. In addition, to examine individual differences in real-world behaviors across development, we administered self-report measures of impulsivity, lack of foresight, and susceptibility to peer pressure.

A growing number of studies suggest that adolescents exhibit different patterns of functional activation than adults during tasks requiring cognitive control [14] – [26] , [ reviewed in 3] , [27] – [30] . However, the precise regions that exhibit group differences and the nature of those differences reported in prior literature are somewhat inconsistent, perhaps due to the use of varied tasks, fMRI designs and analysis (i.e. blocked vs. event-related), and group differences in performance across studies. Nevertheless, considering the continued structural and functional maturation of the prefrontal cortex across adolescence [reviewed in 2] , [27] – [34] , one important question is whether there is on-going development of the neural structures that allow one to proactively implement and maintain a task set.

Of relevance to lifespan development, older adults appear to shift from a proactive strategy to a reactive strategy, likely mediated by declining dopamine availability and compromised lateral prefrontal function [11] , [12] . On the other end of the spectrum, young children (age 3) experience difficulties utilizing predictive information and maintaining that information over a few seconds, while older children (age 8) do so more readily [13] .

Recent accounts of cognitive control suggest it can be differentiated into multiple component processes. In the “Cascade-of-Control Model,” we recently distinguished between processes related to the implementation and maintenance of a top-down attentional set from those related to later stages of selection through response selection and evaluation [5] , [6] , [7] . Lateral prefrontal regions including the posterior dorsolateral prefrontal cortex (pDLPFC) are thought to proactively bias attention towards task-relevant goals and representations [8] , while medial prefrontal regions such as the anterior cingulate cortex (ACC) may support more reactive aspects of attentional control, especially late-stage processing including response-related and evaluative aspects of control [9] . Similarly, Braver and colleagues' “Dual Mechanisms of Control Model” refers to “proactive” control as preparatory processes sometimes sustained over the course of the task, and “reactive” control as transient control processes implemented following perception of the stimulus [10] .

Recent advances in neuroimaging research have increased our understanding of the development and decline of cognitive ability across the human lifespan. Despite such progress, the neural underpinnings of transition periods within the lifespan are less well-established. Adolescence represents a neurobiological transition period sometimes marked by impulsivity, lack of foresight, poor decision-making, elevated emotional reactivity, and sensation-seeking behavior [reviewed in 1] , [2] , [3] . Some of these behavioral characteristics have been attributed to deficits in cognitive control, which has been described as the ability to “override or augment reflexive and habitual reactions in order to orchestrate behavior in accord with [one's] intentions” [4, p 59] .

Materials and Methods

Participants Participants under the age of 18 were recruited from after-school programs, community centers, and through fliers on local bus routes in metropolitan Denver. Adult participants were recruited via flyers in communities similar to those where youth were recruited, including bulletin boards for custodial and maintenance staff at hospitals and at community colleges, grocery stores, churches, and local bus routes. Advertisements were also placed in local newspapers and email lists. Before study enrollment, participants were screened to exclude those who were left-handed, those who learned English as a non-native language, those who were pregnant or trying to become pregnant, and those with a history of psychiatric or neurological disorders, drug abuse, head trauma, claustrophobia, metallic implants or other MRI contraindications. Informed consent was obtained in writing from all participants of age to provide consent (≥18), and from the legal guardians or parents of all participants below the age of consent (<18). Additionally, all minors signed an additional “assent” form explaining the procedures of the study. All procedures were approved by the Colorado Multiple Institutional Review Board and participants were reimbursed with cash. A total of 41 adolescent individuals (ages 14–17) and 43 adult individuals (ages 18–25) met the above criteria and participated in the study. However, 7 adolescent and 7 adult participants either failed to complete the study or produced unusable imaging data due to scanner artifacts and/or excessive movement (>2 mm linear displacement). Additionally, 1 adolescent and 2 adults were excluded for failing to respond on a considerable number of trials, while 1 adolescent and 1 adult qualified as outliers for their respective groups (>2.5 SD from the group mean) on overall accuracy and were excluded from subsequent analyses. No participants were considered outliers on overall response time (RT). After eliminating participants based on the above criteria, 32 adolescents (15.6 yr, 14–17, 53.1% male) and 33 adults (21.9 yr, 18–25, 45.5% male) yielded useable imaging and behavioral data and were analyzed in the present manuscript. Demographic information is outlined in Table 1. Additionally, participants were administered a written two-subtest version of the Wechsler Abbreviated Scale of Intelligence (WASI; Psychological Corporation, 1999), which has been previously normed for use by participants aged 6–89. The two-subtest version includes the Vocabulary subtest and a Matrix Reasoning subtest. Scores from the separate subtests were combined into a full-scale IQ (FSIQ) measure. As outlined in Table 1, adolescent and adult groups were matched on gender and parental education, but adults exhibited significantly higher FSIQ scores compared to adolescents (t = 2.33, p = 0.022). PPT PowerPoint slide

PowerPoint slide PNG larger image

larger image TIFF original image Download: Table 1. Participant demographics. https://doi.org/10.1371/journal.pone.0021598.t001

Task Paradigm Participants completed a hybrid blocked/event-related version of the Stroop Color-Word task [37]. This paradigm has been shown to be particularly sensitive to individual differences within a population as well as distinguishing control from clinical groups [38], [39]. In addition to fixation trials, three trial types were included in the task paradigm: congruent, incongruent, and neutral. On congruent trials, the color of the ink was consistent with the semantic content of the word (e.g. “red” in red ink). On incongruent trials, the ink color and word meaning were inconsistent (e.g. “red” in green ink). On neutral trials, a non-color word was presented in a colored font (e.g. “bond” in blue ink). Neutral words were matched with incongruent and congruent words for word length. Participants were instructed to identify the ink color of each word using one of four buttons on button boxes held in his/her left and right hands. On each trial, the word appeared for 1500 ms, followed by 500 ms of fixation between trials. Participants completed three Stroop task runs while scanned with fMRI. Each run comprised four, 24 s fixation (F) blocks interleaved with nine, 24 s task blocks. Three task blocks were grouped into triads and each triad consisted of a congruent (C), incongruent (I), and neutral (N) block, first-order counterbalanced across triads and participants. For example, the order of one run might be F-CIN-F-INC-F-NCI-F. Half of the trials in each block consisted of stimuli that were specific to that block (i.e. incongruent (i), congruent (c), neutral (n)) and the remaining half of the trials consisted of neutral stimuli that appeared across all blocks. The trial types within blocks were pseudo-randomly ordered such that no more than two trials of the same type could appear in a row. The inclusion of block-general neutral words within each block allows one to examine transient changes in attentional control (i.e., within a block). In addition, the presence of these neutral words minimizes any potential habituation effects that might occur in the incongruent and congruent blocks and ensures that within the congruent block participants do not “cheat” and adopt a strategy of reading the word. Hence, within the congruent blocks, six congruent trials (c) were mixed with six block-general neutral trials (n c ) to allow for comparisons between trial types within blocks. Similarly, within incongruent blocks, six incongruent trials (i) were mixed with six block-general neutral trials (n i ). Neutral blocks consisted of 12 block-general neutral trials (n n ) and 12 neutral trials that were specific to the neutral block (n). In total, participants completed 324 task trials, with 54 trials corresponding to each trial type.

Self-Report Questionnaires Outside the MRI scanner, participants completed a variety of self-report questionnaires as part of a larger, ongoing study. These questionnaires asked participants to rate the degree to which several cognitive, social, and emotional characteristics were accurate representations of their own personalities and behaviors. All questionnaires were completed on a computer in a quiet testing room. The Weinberger Adjustment Inventory [WAI; 40] was administered to assess the degree to which participants were able to control their impulses and suppress aggressive behaviors. The Impulse Control subscale, in which we were most interested, was comprised of 8 items (e.g. “I do things without giving them enough thought”). Participants rated the self-descriptiveness of these items using a 1–5 Likert scale (1 = false, 2 = somewhat false, 3 = not sure, 4 = somewhat true, 5 = true), with some items being reverse scored. The suppression of aggression subscale consisted of 7 items (e.g. “If someone tries to hurt me, I make sure I get even with them”) to which participants responded using the same scale. Participants also completed the Future Orientation Questionnaire [41], a 15-item questionnaire that assesses the degree to which individuals plan ahead, anticipate future consequences, and think about the future. Items consisted of two opposing statements separated by the word “BUT.” Participants selected the statement that they believe best fit their own personality. They then quantified whether the chosen statement was “very true” or “sort of true.” The 15-items were divided into three separate, 5-item subscales: Planning Ahead (e.g. “Some people think that planning things out in advance takes all the fun out of things” BUT “Other people think that things work out better if they are planned out in advance”), Anticipation of Future Consequences (e.g. “Some people have trouble imagining how things might play out over time” BUT “Other people are usually pretty good at seeing in advance how one thing can lead to another”), and Time Perspective (e.g. “Some people would rather be happy today than take their chances on what the future may bring” BUT “Other people will give up their happiness now so that they can get what they want in the future”). Scores ranged from 1–4, where 1 = least future orientation and 4 = most future orientation. The 10-item Resistance to Peer Influence questionnaire [42] was administered to assess cognitive control, particularly in social situations. Similar to the Future Orientation questionnaire, participants selected one of two opposing statements that they believe best fit their own personality (e.g. “Some people would do something that they knew was wrong just to stay on their friends' good side” BUT “Other people would not do something they knew was wrong just to say on their friends' good side”). Next, participants quantified whether the chosen statement was “very true” or “sort of true.” Scores ranged from 1–4 where 1 = least resistance to peer influence and 4 = most resistance to peer influence. Finally, participants completed the 19-item Sensation Seeking Scale [43]. Seven of the 19 items assessed the degree to which participants engage in unplanned and impulsive behaviors (e.g. “I hardly ever spend much time on the details of planning ahead”). Since we were interested in these questions for the purpose of the present study, we averaged the seven items into an Impulsivity and Lack of Foresight subscale. The 12 remaining items, which were not of interest for the present study, generally assessed the degree to which participants engage in thrill-seeking behavior (e.g. “I like doing things just for the thrill of it”). Participants indicated whether each item was “true” or “false.” Higher scores indicate greater impulsivity/lack of foresight and thrill-seeking behavior. Because planning and foresight (which are often negatively correlated with impulsivity) [44], represent key characteristics of “proactive” control, we hypothesized that sustained patterns of prefrontal activity during the Stroop Task would predict self-report measures of planning ability and impulse control, particularly in the adolescent group. Additionally, we reasoned that the neural underpinnings of proactive control might extend to the social domain, particularly the ability to resist the influence of peer pressure. Prior studies have found positive relationships between resistance to peer influence and impulse control in adolescents [45], as well as positive relationships with the strength of task-related functional connectivity between prefrontal cortex and regions involved in action observation [35]. To examine these possibilities, we selected scores from the Weinberger Adjustment Inventory Impulse Control subscale, the Planning Ahead subscale of the Future Orientation questionnaire, the Impulsivity/Lack of Foresight subscale of the Sensation Seeking Scale, and the Resistance to Peer Influence questionnaire. Principal Components Factor Analyses on these measures (after controlling for the effect of age) resulted in a single significant factor explaining 52% of the variance in participants' scores (eigenvalue = 2.09). However, while the measures relating to impulse control and planning ability loaded heavily on the single factor (factor loadings for all three measures >0.75), “Resistance to Peer Influence” loaded on this factor to a smaller degree (factor loading = 0.30). These results suggest that the control of social behavior may be governed somewhat by other factors. Based on the extraction of a single significant factor, we converted each self-report measure to z-scores separately for each group (i.e. adolescents, adults), reverse-scored the Impulsivity/Lack of Foresight measure, and averaged the z-scores across measures to create a cognitive/social control composite. Higher scores are indicative of greater cognitive/social control. Then, for purposes of comparing scores across adolescent and adult groups, we computed z-scores using the mean of the entire group.

MRI Data Acquisition Scanning was performed on a 3T GE Signa scanner (Milwaukee, WI), with a standard 4-channel head coil. Three-dimensional, high resolution, T1-weighted IR-SPGR anatomical images were acquired using the following parameters: repetition time (TR) = 9.61 ms, echo time (TE) = 2.0 ms, inversion time (TI) = 500 ms, field-of-view (FOV) = 220 mm, matrix size = 256×256, in-plane resolution = 0.87 mm×0.87 mm, slice thickness = 1.7 mm, 124 coronal slices. Additionally, T2*-weighted gradient echo, echo-planar functional images (with ramp sampling) were acquired using the following parameters: TR = 2000 ms, TE = 32 ms, flip angle = 77°, FOV = 220 mm, matrix size = 64×64, in-plane resolution = 3.44 mm×3.44 mm, slice thickness = 4 mm, 29 interleaved slices acquired parallel to the AC-PC line, 163 volumes. Stimuli were programmed using E-Prime software (Psychology Software Tools, Inc) and were viewed through MRI-compatible goggles. Participants were given earplugs to dampen scanner noise and an air pillow was inflated around each participant's head to minimize head movement. Participants held a four-button fiber-optic button box in each hand and responded to each trial with one of two buttons per hand colored in either red, green, blue, or yellow ink.

Data Processing and Statistical Analysis fMRI Preprocessing. To prepare the data for statistical analyses, a series of image preprocessing steps were performed using FSL tools (FMRIB, Oxford, UK, www.fmrib.ox.ac.uk). The first 7 volumes were discarded to ensure scanner intensity stabilization, followed by motion correction using a rigid-body translation and rotation algorithm (MCFLIRT) and extraction of brain tissue (BET). Next, the three functional runs were concatenated. Within FMRIB Easy Analysis Tool (FEAT), the 4D concatenated images were corrected for differences in slice timing, were spatially smoothed using an 8 mm full-width half-maximum (FWHM) Gaussian kernel, and were pre-whitened with FMRIB's Improved Linear Model (FILM). Run constants and linear trends were modeled within each GLM to account for differences in overall intensity between runs and to remove low-frequency scanner drift. Modeling Blocked Effects. As described previously, the hybrid block/event-related task paradigm was designed such that block effects and event-related effects would be modeled within separate GLMs. To examine block-effects, three separate regressors (one for each block type: congruent (C), incongruent (I), neutral (N)) were modeled by convolving a double-gamma response function with the onsets of each initial correct trial in a string of correct trials as an epoch. Additionally, three separate regressors were modeled to account for incorrect (error) trials within each block type. In order to ensure that blocked effects were independent of these error trials, each blocked regressor was orthogonalized with respect to the corresponding error regressor. As described in more detail in the results, contrasts of interest include each of the three block types compared to fixation (i.e. C-F, I-F, N-F), I-N (i.e. the Stroop interference effect), C-N (i.e. the Stroop facilitation effect), and I-C (i.e., a measure of cognitive control in the face of conflict). Modeling Event-Related Effects. To explore event-related effects, seven regressors corresponding to separate trial types were modeled in a single GLM: incongruent trials (i), neutral trials within incongruent blocks (n i ), congruent trials (c), neutral trials within congruent blocks (n c ), neutral trials (n), neutral trials within neutral blocks (n n ), and incorrect trials (e). For each regressor, a double-gamma response function was convolved with the onset of each trial. The contrast of i - n i trials (i.e. the trial-related Stroop interference effect) was explored for the purposes of the present study since this contrast reflects the greatest difference in transient demand on executive control. Statistical Analyses. FMRIB's Improved Linear Model (FILM) was used to separately compute the blocked and event-related GLMs for individual participants. Each participant's data was registered to the Montreal Neurological Institute (MNI) stereotaxic template using FMRIB's Linear Image Registration Tool (FLIRT) two-step process. Higher-level, group analyses for each contrast of interest (i.e. I block parameter estimate – N block parameter estimate) were computed using FMRIB's Local Analysis of Mixed Effects (FLAME), which models the within-subject variance using fixed-effects analyses and the between-subject variances using random-effects analyses. Within these higher-level GLMs, covariates of non-interest included each participant's fullscale IQ, overall error rate, and overall non-response rate. Thus, one can be reasonably confident that observed effects of age are not driven by individual differences in IQ or error rate. Within FLAME, group difference analyses (i.e. adults vs. adolescents; 16–17 yr olds vs. 14–15 yr olds) for each contrast of interest were computed using two-sample t-tests. Higher-level whole-brain correlation analyses between fMRI Stroop interference estimates (parameter estimates for the contrasts of I-N blocks and i-n i trials) and composite scores reflecting self-report measures of cognitive/social control were performed using FSL's robust regression to minimize the impact of outliers [46]. Whole-brain correlation analyses were performed separately for the adolescent and adult groups, and age was entered as a covariate of non-interest in each GLM. To determine appropriate voxel-wise and cluster-wise statistical thresholds for functional images, Monte Carlo simulations were performed using the AlphaSim algorithm [47]. As demonstrated by the algorithm, clusters of activation were considered significant if they exceeded a voxel-wise threshold of p<0.005 (two-tailed) and a cluster size of 103 contiguous voxels. The peak x,y,z coordinate in MNI space was extracted from each significant cluster and listed in fMRI tables, as well as the number of voxels comprising each cluster and the z-statistic corresponding to the adolescent, adult, and group difference maps separately. In some cases, a significant cluster comprised a large number of voxels and spanned distant brain regions. In such cases, the larger cluster was subjected to increasingly stricter voxel-wise thresholds and increasingly smaller cluster-wise thresholds (in accordance with AlphaSim) until it partitioned into smaller clusters. The peak coordinates from these smaller clusters are listed in the table. The anatomical description of each significant cluster was classified primarily using the Harvard-Oxford Probabilistic Structural Atlas. If a smaller cluster spanned multiple regions, all regions are listed (e.g. IFG/MFG). We omit the reporting of Brodmann areas in statistical tables since Brodmann areas can vary substantially across atlases and are often determined from a single individual as opposed to probabilistic brain template [see 48]. Behavioral data reported in the results and Table 2 was calculated after removing subjects that fell above or below 2.5 SD of the group mean. Unless otherwise-noted, the significance of statistical tests (e.g. paired t-tests, independent samples t-tests, correlation analyses) were calculated using two-tails. PPT PowerPoint slide

PowerPoint slide PNG larger image

larger image TIFF original image Download: Table 2. Behavioral performance. https://doi.org/10.1371/journal.pone.0021598.t002