Reconsidering Dual-Systems Models Of

Review

Arrested development? Reconsidering

dual-systems models of brain function

in adolescence and disorders

1 2

Jennifer H. Pfeifer and Nicholas B. Allen

Department of Psychology, University of Oregon, Eugene, OR 97403-1227, USA

Department of Psychological Sciences and Orygen Youth Health Research Centre, University of Melbourne, Victoria 3010,

Australia

The dual-systems model of a ventral affective system, disorders, and highlight results that contradict simple

whose reactivity confers risks and liabilities, and a dual-systems accounts. Speciﬁcally, we focus on evidence

prefrontal control system, whose regulatory capacities that is inconsistent with a uniform portrayal of greater

buffer against these vulnerabilities, is an intuitive ac- ventral affective activity as detrimental to adaptive func-

count that pervades many ﬁelds in the cognitive neu- tional outcomes, and greater prefrontal cortical activity as

rosciences – especially in the study of populations that facilitative of them. We then identify multiple ways in

differ from neurotypical adults, such as adolescents or which progress can be achieved in this ﬁeld, including

individuals with affective or impulse regulation disor- suggestions for future research that incorporate recent

ders. However, recent evidence that is inconsistent with advancements in neuroimaging approaches.

dual-systems models illustrates the complexity of devel-

opmental and clinical variations in brain function. Build- The dual-systems model in adolescent brain-behavior

ing new models to account for this complexity is critical relationships

to progress in these ﬁelds, and will be facilitated by One domain where the dual systems model has dominated

research that emphasizes network-based approaches is the study of normative adolescent brain development.

and maps relationships between structure and function,

An implicit assumption motivating much of this research is

as well as brain and behavior, over time.

that neuroimaging will help explain the signiﬁcant

changes documented in motivated behavior, risk-taking,

The allure of dual-systems models

affect, and peer orientation during adolescence – trends

Over the last decade, a common account of brain function-

that may increase vulnerability to psychopathology or

ing has emerged in the cognitive neurosciences. One sys-

result in a wide range of adverse outcomes, such as drug

tem in this account, the ventral affective system, is

and alcohol abuse, smoking, disordered eating, and health-

commonly portrayed as indexing risks and liabilities (es-

risking behaviors, such as unsafe sex [20–24]. The dual-

pecially those associated with emotional dysregulation) via

systems model suggests that teenagers are predisposed

heightened reactivity in subcortical regions, such as the

towards these new patterns of behavior because of an

amygdala and ventral striatum (VS) [1–4]. A second sys-

imbalance between early maturation of limbic motivation-

tem in this account, the prefrontal cortex (PFC), acts as a

al and emotional systems and slower or later maturation of

counterpoint to the ﬁrst because its responses are depicted

prefrontal cortical control [9,11,15,16,20–22,25–28].

as reﬂecting control and regulation of the ventral affective

Although evidence for the dual-systems model has been

system’s reactions [5–8]. This ‘dual-systems’ account has

elaborated well elsewhere [9,11,20–22,27], it will be brieﬂy

been used to explain patterns in normative neurodevelop-

summarized here. One line of support comes from studies

ment, especially during adolescence [9–11], and also to

of brain function during adolescence. These studies have

account for atypical brain function in disorders of affect

been largely cross-sectional, and smaller in scope than

or impulse regulation (many of which emerge in adoles-

investigations of developmental changes in brain struc-

cence, such as depression and addiction) [12–15]. The

ture, although the use of longitudinal methods and larger

appeal of straightforward this account has aided in formu-

sample sizes has been growing in recent years (reviewed in

lating hypotheses and interpreting results, which has

[29]; see also [30]). Findings relevant to the dual-systems

undoubtedly helped to advance these ﬁelds in years past,

model include multiple reports of heightened subcortical

although advocates note that these models are necessarily

(amygdala or VS) responses in adolescents to affective

both simplistic and speculative [9,11,15–18]. However,

stimuli, including some emotional expressions and certain

such caveats are all too easily overlooked given the pull

phases of reward processing [1–4,31,32]. Other studies

of such a strong meme [19] (Box 1). Therefore, in this

contribute demonstrations of diffuse (non-focal) or dimin-

review we illustrate the complexity of brain function as

ished patterns of activity in PFC during cognitive control

revealed through studies of adolescent development and

tasks, alongside potentially compensatory engagement of

Corresponding author: Pfeifer, J.H. ([email protected]) additional brain regions to achieve control [5–8,33–35].

Review Trends in Cognitive Sciences June 2012, Vol. 16, No. 6

Box 1. Why are dual-systems models so potent? Developmental changes in GM density or cortical thickness

vary signiﬁcantly by region, with extended and more com-

Current dual-systems models likely derive significant power from

plex nonlinear trajectories seen in higher-order association

their foundation in the intuitive appeal of interpreting mental life as

a balancing act between emotions/passions and reason/logic areas (such as dorsolateral PFC or inferior parietal lobule),

[87,88], a dichotomy traceable back to Plato and Aristotle. In which contrast with early peaks and linear declines in

addition, these models may resonate with early theoretical con-

primary sensorimotor areas and other regions with simple

ceptualizations of adolescence as a period biologically inclined to be

laminar structure (such as posterior orbitofrontal cortex,

stormy and stressful [89]. Dual-systems accounts are prominently

subgenual cingulate cortex, and anterior insula) [17,36]. In

featured in cover stories from major international magazines (e.g.,

Time, National Geographic) on topics like ‘what makes teens tick’ addition, WM tracts throughout the brain (including those

[90,91]. There are also many articles dealing with the political and

that connect PFC with the striatum or amygdala) generally

legal ramifications of ‘adolescent brain science’ [19,92,93], and even

exhibit increases in maturational indices from childhood

recent Supreme Court cases dealing with juveniles directly cite

through adolescence – these changes are typically linear

adolescent neuroscience. As written by Justice Kennedy from the

U.S. Supreme Court, citing to amicus briefs from the APA/AMA, with respect to WM volume [37–40] and nonlinear with

‘developments in psychology and brain science continue to show respect to diffusion parameters [37,39–42]. Many of these

fundamental differences between juvenile and adult minds. . . parts

pathways do not reach 90% of maximum values until after

of the brain involved in behavior control continue to mature through

age 20, particularly those traversing lateral and medial

late adolescence’ ([92], p. 766). Unfortunately, the metaphors used

PFC [37,40]. These varied structural metrics are comple-

in these varied contexts inappropriately suggest that the neurobiol-

ogy of adolescent development and disorders is already quite well mentary and not necessarily robustly correlated [40], sug-

understood, hence constraining future attention and priorities in gesting that GM development is only moderately

science, funding, policy, media, justice, and parenting [19].

informative about WM development and that neither

should probably be heavily extrapolated to predict expected

However, post-hoc interpretations of these patterns repre- functional patterns during adolescence.

sent a debatable issue that has been under increasing In summary, there is substantial evidence that these

scrutiny in recent years (Box 2). In addition, relatively two systems function differentially during adolescence and

few of these fMRI studies directly assess both reactivity to that the PFC exhibits a protracted course of structural

rewards (or other affective stimuli) and regulatory control maturation, although there is somewhat weaker direct

in the same paradigm or sample, or relate brain function to evidence that either pattern impacts real-world behavior,

real-world behaviors (assessed via self/parent reports or despite this connection being frequently inferred or im-

more sophisticated methodologies) that are presumed to be plied. Given the limited empirical demonstrations of links

affected by the interplay between these two systems (but between these neurodevelopmental changes and behavior,

see [1,2,7,28]). especially the lack of studies showing that brain changes

The other main source of support for the dual-systems over time speciﬁcally covary with relevant behavioral

model has been extrapolated from studies of cortical change over time, we suggest that the ﬁeld has sometimes

development that have utilized various metrics of struc- been arguing by analogy – in the sense that conclusions are

tural change in gray matter (GM) or white matter (WM). drawn based on similarities between the pattern of these

Box 2. Updating the interpretive toolkit

A critical overarching factor to consider is that there are new development (or disorders) when individuals are not exhibiting peak

fundamental issues in the cognitive neurosciences that need to be functioning.

resolved, many of which are directly relevant to the way that patterns Although these intuitive ideas have prevailed for years, empirical

of brain function are interpreted. The neurosciences still lack well- reports and reviews are increasingly calling them into question. Several

defined cognitive ontologies – that is, mappings between mental key points were recently raised in this debate [56], foremost among

processes and brain function [56], in part because of the emphasis on them being that focalization, efficiency, and compensatory activity are

old brain-mapping strategies (e,g., ‘where’ does X get processed, primarily ways of describing the observed patterns of data. They create

‘what’ does region Y do). A critical next step is to focus on the the illusion of explanation, but under close scrutiny tend to suffer from

specificity of functional response profiles within regions and decrease circular logic. The focalization hypothesis in particular lacks neurobio-

reliance on reverse inferences [75,94]. Another important direction is logical plausibility [97], statistical tests of it are rare, and informal

to emphasize network-wide patterns rather than activity in isolated observations of this pattern may be spurious consequences of more

regions of interest [95,96]. Without validated cognitive ontologies, we variable spatial normalization in children and adolescents than adults.

are likely to make and perpetuate oversimplified assumptions about Furthermore, new studies have tended not to support the focaliza-

the functions of regions in networks across development (or tion hypothesis. For example, one study examined responses in a

disorders). decision-making task, controlling for important confounding vari-

Three additional familiar concepts that have been relied upon in ables, such as head motion and interindividual variability in anatomy

post hoc interpretations of neuroimaging data, applicable to both and functional organization [98]. Across three strict age bands (13, 17,

development and disorders, warrant reconsideration: focalization, and 21 years), only magnitude increased with age – extent did not

efficiency, and compensatory activity. The focalization hypothesis [33] decrease with age, and there were no regions that went off-line,

argues that, with increasing age, patterns of brain activation mature suggesting a lack of qualitative shifts in the underlying neural

from being diffuse and weak to focal and strong. In other words, networks. Other recent studies have showed patterns of increasing

magnitude (and extent) of activation decrease with development in spatial extent or engagement of a more distributed network with age,

regions considered tangential to the task, and increase (in magnitude such as in response to emotional and cognitive challenge [99]. Finally,

but not extent) in regions considered task-central. The related concept a three-year longitudinal fMRI study of task-switching recently found

of neural efficiency is used to explain decreased responses in task- that there was greater variability over time in the magnitude (but not

central regions with development, whereas compensatory activity extent) of children’s responses across key regions of interest, but not

refers to neural activation that is observed in regions during stages of in those of adolescents or adults [60].

323

Review Trends in Cognitive Sciences June 2012, Vol. 16, No. 6

brain changes (including their hypothesized neuropsycho- affective system. But what does that immaturity translate

logical implications) and folk psychologies of adolescent into at the level of function – more or less activity? As noted

development [19]. above, many empirical studies and reviews note that in

tasks where cognitive control is required, activations in

Evidence of complexity in adolescent brain-behavior PFC increase with age [33–35]. Yet, others report decreases

relationships in PFC responses with age [8,44,45] or interesting non-

In this section we examine recent data that may constitute linear patterns – less or more activity in adolescence than

exceptions to dual-systems accounts of neurotypical adoles- either adulthood or childhood [25,52,53]. Overall, the liter-

cent development. This includes empirical studies contra- ature on neurodevelopmental underpinnings of cognitive

dicting the patterns of brain function during adolescence control shows bidirectional, complex patterns – both

described above, reports suggesting that occasionally the increases and decreases in PFC activations that vary across

supposed roles of particular brain regions as predisposing subregions and tasks – that should be interpreted with

towards functional versus dysfunctional outcomes are re- caution [8,54–56]. For example, a recent study examining

versed (with subcortical reactivity appearing protective or inhibitory control in an antisaccade task found that tran-

PFC responses seeming to confer vulnerabilities), and pat- sient activations in PFC related to inhibitory control during

terns in structural development that counter dual-systems a given saccade decrease with age, whereas sustained

predictions. Throughout, we remain mindful of recent ﬁnd- set-related activations in PFC increase [6]. Such complex

ings (discussed in Box 2) establishing the limited evidence patterns are difﬁcult to reconcile, as both developmental

for focalization effects across development, the potential patterns are routinely considered evidence of ‘mature’ brain

pitfalls of interpreting patterns across development or dis- function, which can make the literature appear ‘inconsistent

orders by redescribing them as compensatory or inefﬁcient, or opportunistic’ ([8], p.109; see also [56]).

and our limited understanding or acknowledgement of se- Complexities are also beginning to emerge in the litera-

lectivity (or lack thereof) in brain function. ture on adolescent structural development. In one study,

We begin with the ventral affective system, focusing on contrary to dual-systems predictions, increased WM ma-

the VS and amygdala. First, many researchers observe turity was associated with increased engagement in risky

either hypoactivity or no differences between adolescents behaviors, adjusting for known age-related increases in

and adults in VS responses to some reward conditions or both measures [57]. Another recent investigation that

paradigms [1,3,43]. Similarly, recent developmental fMRI mapped structure-behavior changes through adolescence

studies of emotion reactivity and regulation have not using a cohort-sequential design observed that VS (speciﬁ-

consistently found heightened responses in amygdala dur- cally, nucleus accumbens) actually volumetrically in-

ing adolescence [31,32,44,45]. Second, studies indicate the creased over a two-year period across three age groups

VS neither responds solely to rewards [46] nor always ranging in initial age from 9-23 years, and that this was

biases adolescents towards maladaptive behavior. For ex- correlated with increased drive in the behavioral approach

ample, a recent longitudinal study of the transition from system (BAS [58]). Initial levels of VS and medial OFC

childhood to adolescence found increases in VS response to volumes, on the other hand, were correlated with increases

emotional facial expressions were associated with in BAS reward responsiveness [59]. The effects of individ-

increases in resistance to peer inﬂuence and decreases ual differences in volumes across these regions on facets of

in risky behavior [31]. Although initially counterintuitive, reward sensitivity did not vary by age group. This study is

this makes sense when considering that peers are external notable for addressing the lack of data linking longitudinal

regulators of behavior – for most typically-developing teen- change in behavioral indices of reward sensitivity to rele-

agers, sensitivity to peers will guide them towards largely vant brain structure (as noted above). At the same time, it

positive outcomes. In another recent study, greater VS also reveals complexities that may be inconsistent with

response during social exclusion was associated with great- dual-systems models. Speciﬁcally, the VS exhibited a clear

er reductions in negative affect following social exclusion nonlinear trajectory with a peak in 13-17 year-olds (deﬁned

during adolescence [47]. Both of these studies are therefore as late-adolescents), rather than the linear patterns of

consistent with ﬁndings that VS activity may index effec- volumetric decreases and peak functional responses typi-

tive emotion regulation [48] and with the more general cally cited as evidence of early ‘maturity’ in the ventral

proposition that some forms of affective variability may be affective system [9,11,20–22,27]. In addition, there was no

associated with positive adjustment [49]. Two further evidence for age-related variation in the magnitude of

studies observed that diminished striatal (and heightened correlations between VS or medial OFC structure and

prefrontal) responses to reward anticipation and outcome reward sensitivity.

were associated with reports of lower daily positive affect It should also be noted in closing this section that other

and higher depression in both typically-developing and key factors besides development are known to inﬂuence

depressed adolescents [50,51]. Taken together, these stud- patterns of activity across prefrontal and subcortical

ies suggest that occasionally the ventral affective system is regions. These include (i) task performance [60], (ii) task

not hyperreactive during adolescence and that heightened design, including transient-event versus sustained-set

responses are sometimes associated with adaptive out- paradigms [6] or different baseline and timing conditions

comes. [28,61], and (iii) various data analysis techniques. For

The case of PFC function in adolescent development is example, researchers recently discovered that some func-

perhaps even more complex. The dual-systems model tional connectivity analyses interpreted as demonstrating

posits that the PFC is still immature relative to the ventral developmental increases in long-range connections during

324

Review Trends in Cognitive Sciences June 2012, Vol. 16, No. 6

rest (i.e., independent from any task) were spurious effects teenage years is mediated by protracted maturation of the

of greater motion in younger participants [62,63], which prefrontal regulatory system when compared to the brain

were eliminated with appropriate controls for motion-in- networks associated with sensitivity to affective and social

duced artifacts. Similar effects could be at work in the stimuli [16,73]. Such proposals utilize an explanatory

empirical studies that show developmental increases in framework to understand the developmental epidemiology

task-dependent functional connectivity between prefrontal of depression that is very similar to the dual-systems

and subcortical regions [2,31]. models used to explain the broader range of affective

and behavioral changes observed in adolescence, such as

Complex brain-behavior relationships in depression and with respect to risk taking and drug use. However there is

its development a fundamental difference between these latter behaviors

Although dual-systems models are prevalent in proposals and depression in that, although risk taking and substance

about the neural foundations of many clinical disorders, we use become more common during adolescence, they decline

focus here on major depressive disorder (MDD). This condi- during early adulthood, when regulatory brain systems

tion is frequently described as resulting from heightened have usually reached adult levels of maturity [11,27]. The

limbic reactivity (especially in the amygdala) that over- developmental epidemiology of depression, on the other

whelms PFC control capacities, and/or deﬁcient top-down hand, shows that, although increased rates of depression

control (particularly by medial PFC) over limbic regions start in adolescence, they persist though adulthood – well

[64]. However, the empirical evidence in adult and adoles- past the period when prefrontal regulatory mechanisms

cent samples is equivocal. Although many studies have have presumably matured [74].

shown patterns of hyperactivity in the amygdala in This observation constrains the hypotheses that can

depressed samples [65,66], a recent study demonstrated explain the epidemiology of depression in neurodevelop-

amygdala hyperactivity in response to rewards amongst mental terms. According to an alternative hypothesis [74],

depressed samples, which ﬁts less well with the idea that depression does not represent a failure of prefrontal regu-

amygdala hyperactivity is responsible for the negative affect lation. Instead, vulnerability to depression may actually be

associated with depression [67]. Yet, many other studies increased by the development of prefrontally mediated

have found no signiﬁcant differences in amygdala reactivity capacities, such as the adolescent’s enhanced ability to

between depressed individuals and various other sub- anticipate abstract and distal rewards (or punishments;

groups, including healthy controls [68–70]. There is similar Figure 1). This model predicts that the PFC, rather than

disagreement about patterns of activity in PFC. One recent exclusively functioning in ways such that maturation con-

review of brain function in depression [71] concluded that strains vulnerability to depression, may in part also func-

depression is typiﬁed by hypoactivation in dorsolateral PFC tion in ways such that its development is permissive of

(including dorsal ACC), as well as hyperactivation in ven- increased rates of depression in vulnerable individuals.

tromedial PFC (including subgenual ACC). This distinction Consistent with this proposition, as noted above, greater

is accompanied by the premise that dorsolateral regions are depressive symptoms and lower daily positive affect have

associated with adaptive processes, such as cognitive con- been associated with heightened PFC responses in adoles-

trol, executive function, and emotion regulation, whereas cence [50,51]. This does not imply that PFC development is

ventromedial regions either generate negative emotions or pathogenic per se, but rather that, along with important

maladaptively heighten self-reﬂection and self-awareness. new capacities and abilities, it ushers in a new ‘adult’ suite

However, this recasting of roles is also subject to disagree- of problems for the developing individual to confront.

ment, as other researchers have suggested that ventrome-

dial PFC plays a central role in the downregulation of New directions

amygdala responses [2,72]. In order to move beyond the limitations of dual-systems

Furthermore, it has been conjectured that the increased models, new conceptual and methodological tools will be

vulnerability to depressive disorders observed during the needed. Here we highlight three relevant strategies that

TRENDS in Cognitive Sciences

Figure 1. Dark sides of PFC function. This cartoon aptly illustrates how development of PFC function during adolescence may increase vulnerability to depression via the

ability to anticipate abstract and distal rewards or punishments, or set goals and realize disappointment at one’s failure to meet them. This Dilbert cartoon by Scott Adams is

reprinted with permission from Universal Uclick.

325

Review Trends in Cognitive Sciences June 2012, Vol. 16, No. 6

we believe have the potential to better deal with the signiﬁcant advances in our understanding may be possible.

complexities of brain function in adolescent development More generally, studies of these types should reduce the

and clinical disorders of affect and impulse regulation. One frequency of analogical reasoning about trajectories in

approach that is gaining momentum is to switch the level development or disorders based on piecemeal studies of

of analysis from isolated regions to coordinated networks. structure, function, or behavior in isolation.

Another approach encourages greater collaboration across Finally, embracing the new developments in neuroim-

historically independent methodological approaches in the aging approaches described above should not mean aban-

cognitive neurosciences (i.e., mapping brain structure doning well established methods in developmental and

versus function). A ﬁnal useful direction is to foster greater clinical sciences. Many important conceptual and theoret-

integration of theories and empirical ﬁndings from devel- ical advances have resulted from more traditional meth-

opmental and clinical research conducted outside the cog- ods of inquiry in psychology, ranging from observational

nitive neurosciences. data collection to interviews. It is time to ensure that

Early fMRI studies primarily constituted basic map- neuroimaging investigations connect better with develop-

pings of functions, pursuing the question of ‘where’ to mental or clinical data and theories [55]. Just as neuros-

localize a given process in the brain. It is now clear that cientists believe the fruits of their research will help

simple one-to-one mappings between mental processes and constrain, reﬁne, or refute theories of developmental

brain regions are rare – instead, entire networks of regions and clinical variations in affect and behavior [13,86],

interactively support a given function [75]. Many impor- such an arrangement would be most beneﬁcial if it were

tant insights about networks have emerged from research- bidirectional.

ers investigating structural and task-independent

functional connectivity between brain regions. For exam- Concluding remarks

ple, studies of low-frequency oscillations in the BOLD Given all the counterevidence and caveats summarized

signal during ‘rest’ helped to identify multiple networks above, it seems clear that caution is warranted before

whose response proﬁles were coordinated in absence of applying dual-systems models to adolescent development

tasks [76]. These functional networks map on to the struc- and disorders. This is not because dual-systems models are

tural connections between regions illuminated by techni- fundamentally ﬂawed. Rather, overreliance on these mod-

ques such as diffusion tensor imaging [77] and there are els for post hoc interpretations of patterns in functional or

similarities between resting-state and structural esti- structural data will tend to reify them, repeating the

mates of regional connectivity across development [78] history of other strong memes in neurodevelopment and

and disorders [79], although the two methodologies are disorders discussed in Box 2. In addition, the functions and

not completely redundant [80,81]. A related trend is the response proﬁles exhibited within and between regions

classiﬁcation of mental states or group membership based addressed in these models are numerous and complex,

on multivoxel pattern analysis (MVPA) techniques, which and their relationships with real-world behavior do not

utilize machine-based learning algorithms to analyze result in a consistent pattern of risks conferred by only one

multivariate data in a manner that is typically more system or region, and protections conferred only by others.

consistent with a network-based approach, although this Acknowledgment of our limitations is gradually growing,

methodology is still rare in developmental and clinical particularly the lack of selective structure-function map-

populations [82]. Investigating and conceptualizing brain pings and brain-behavior mappings [18,56,75], as well as

function in terms of real-time multi-region networks will the contradictory use of neuroscience and other psycholog-

help mitigate the tendency to parcel human brain function ical research to argue both for and against adolescent

in a phrenological manner, by illuminating how these capacities for mature behavior [18], the tendency to misin-

regions work together to solve challenges such as regulating terpret differences as deﬁciencies [11], the oversimpliﬁca-

impulsive behavior or experiencing healthy levels of affect. tion of structure-function mappings between subcortical

Next, signiﬁcant gains are likely to result from pairing structures and approach or avoidance motivation [9], and

methodologies that in the past have proceeded indepen- the lack of appreciation for the dynamic interplay between

dently, as it may allow researchers to ‘triangulate’ concepts limbic and cognitive systems during adolescence [9,11,17].

and questions of interest. This approach would include Nevertheless, this is a very important message to convey

studies mapping relations not only between brain struc- and to focus our research efforts on remedying. Indeed,

ture and function as mentioned above, but also, critically, given that the dual-systems interpretations of adolescent,

between behavior and brain structure or function (or both). clinical, and forensic behavior have been so enthusiasti-

However, as noted above, there is as yet relatively little cally adopted by the legal, educational, and journalistic

empirical research that directly links developmental consumers of behavioral science (Box 1), it is especially

changes in adolescent brain function or structure to devel- important that the developmental and clinical cognitive

opmental changes in behavior, cognition, or affect [55] – neurosciences do not simply become co-opted into another

and a particular paucity of truly longitudinal studies that long-standing meme about human mental life and func-

have tracked changes in brain and behavior or clinical tioning being deﬁned by a battle between reason and

symptomatology over time within individuals across ado- regulation, on the one hand, versus the emotions, on the

lescence (with notable recent exceptions) [31,59,60,83–85]. other. Rather, scientiﬁc research should rigorously hew to

To the extent that these structure-function, structure- its vital role to not only conﬁrm our prior conceptions, but

behavior, or function-behavior studies can be coordinated also to shock and surprise, no matter how counterintuitive

in large-scale studies of development or disorders [30], or complex the answers may be (Box 3).

326

Review Trends in Cognitive Sciences June 2012, Vol. 16, No. 6

7 Andrews-Hanna, J.R. et al. (2011) Cognitive control in adolescence:

Box 3. Questions for future research

neural underpinnings and relation to self-report behaviors. PLoS

What are the consequences of the two best documented ONE 6, e21598

developmental changes in brain structure, cortical thinning and 8 Luna, B. et al. (2010) What has fMRI told us about the development of

increased white matter maturity (as reflected in volume, density, cognitive control through adolescence? Brain Cogn. 72, 101–113

diffusion parameters), for brain function and relevant behavior? 9 Casey, B.J. et al. (2011) Braking and accelerating of the adolescent

We expect direct measurement of behavior ‘outside the scanner,’ brain. J. Res. Adolesc. 21, 21–33

using observational methodologies or other types of verifiable 10 Spear, L.P. (2011) Rewards, aversions and affect in adolescence:

and more ecologically valid data, to become essential. Emerging convergences across laboratory animal and human data.

Given that neuroimaging research has tended to support the Dev. Cogn. Neurosci. 1, 390–403

existence of significant sex differences in the neurobiological 11 Steinberg, L. (2010) A behavioral scientist looks at the science of

bases of affective functioning [100] and that many of the notable adolescent brain development. Brain Cogn. 72, 160–164

sex differences in rates of mental disorders also emerge during 12 Bechara, A. (2005) Decision making, impulse control and loss of

adolescence [101], to what extent does differential brain develop- willpower to resist drugs: a neurocognitive perspective. Nat.

ment mediate these effects? A focus on aspects of structural or Neurosci. 8, 1458–1463

functional development that are affected by gonadal hormones 13 Carver, C.S. et al. (2008) Serotonergic function, two-mode models of

will be of central importance. Additionally, very little is known self-regulation, and vulnerability to depression: what depression has

about how other facets of pubertal development that are known to in common with impulsive aggression. Psychol. Bull. 134, 912–943

widely impact adolescent outcomes (such as timing and velocity 14 Casey, B.J. and Jones, R.M. (2010) Neurobiology of the adolescent

of pubertal changes [102]) impact brain development and brain- brain and behavior: implications for substance use disorders. J. Am.

behavior relationships. Acad. Child Adolesc. Psychiatry 49, 1189–1201 quiz 1285

In terms of clinical disorders where affect is a strong feature of the 15 Gladwin, T.E. et al. (2011) Addiction, adolescence, and the integration

symptomatology, how can we achieve a more nuanced under- of control and motivation. Dev. Cogn. Neurosci. 1, 364–376

standing of the relations between different phenomena (e.g., 16 Ernst, M. et al. (2009) Neurobiology of the development of motivated

affective temperament, emotional responses, and mood), as well behaviors in adolescence: a window into a neural systems model.

as the way in which they interact, both during development and in Pharmacol. Biochem. Behav. 93, 199–211

the context of mental disorders? A better understanding of core 17 Giedd, J.N. and Rapoport, J.L. (2010) Structural MRI of pediatric

affect [103] and emotional convergence (or lack thereof [104]) brain development: what have we learned and where are we going?

would help constrain future neurobiological models of affective Neuron 67, 728–734

disorders [105], as current models wherein reactions by ventral 18 Johnson, S.B. et al. (2009) Adolescent maturity and the brain: the

affective systems are poorly regulated by PFC control systems promise and pitfalls of neuroscience research in adolescent health

may not even address the primary affective phenomena under- policy. J. Adolesc. Health 45, 216–221

lying these disorders (which may involve deficits as well as 19 Payne, M.A. (2012) ‘All Gas and No Brakes!’: Helpful Metaphor or

excesses in affective reactivity, for example). Harmful Stereotype?. J. Adol. Res. 27, 3–17

How do environments influence neurobiological development, 20 Casey, B.J. et al. (2008) The adolescent brain. Ann. N.Y. Acad. Sci.

and subsequent risk for disorders? For example, early life 1124, 111–126

experiences such as institutionalization, child abuse, and stress 21 Casey, B.J. et al. (2008) The adolescent brain. Dev. Rev. 28, 62–77

affect brain development in ways that likely confer vulnerabilities 22 Casey, B.J. et al. (2010) Adolescence: what do transmission,

[106–108], but how do neurodevelopment and environmental transition, and translation have to do with it? Neuron 67, 749–760

effects interact over the lifespan? Such investigations offer hope 23 Paus, T. et al. (2008) Why do many psychiatric disorders emerge

by informing prevention approaches grounded in neurobiology during adolescence? Nat. Rev. Neurosci. 9, 947–957

and will ultimately provide a more sophisticated understanding of 24 Steinberg, L. (2005) Cognitive and affective development in

neural plasticity and which modifiable environmental risk factors adolescence. Trends Cogn. Sci. 9, 69–74

offer the most promising means of intervention to encourage the 25 Geier, C.F. and Luna, B. (2009) The maturation of incentive

development of healthy brains and individuals. processing and cognitive control. Pharmacol. Biochem. Behav. 93,

212–221

26 Somerville, L.H. and Casey, B.J. (2010) Developmental neurobiology

Acknowledgements of cognitive control and motivational systems. Curr. Opin. Neurobiol.

20, 236–241

Special thanks are due to Lauren Kahn, Will Moore, and the joint

27 Steinberg, L. (2008) A social neuroscience perspective on adolescent

Developmental Social Neuroscience (DSN), Social and Affective

risk-taking. Dev. Rev. 28, 78–106

Neuroscience (SAN), and Social Psychoneuroendocrinology (SPEL)

28 Galvan, A. (2010) Adolescent development of the reward system.

laboratories at the University of Oregon for their assistance with and

Front. Hum. Neurosci. 4, 6

feedback on the manuscript. The authors would also like to thank the

29 Pfeifer, J.H. and Blakemore, S.J. (2012) Adolescent social cognitive

reviewers for their helpful suggestions and insights.

and affective neuroscience: past, present, and future. Soc. Cogn.

Affect. Neurosci. 7, 1–10

References 30 Schumann, G. et al. (2010) The IMAGEN study: reinforcement-

1 Geier, C.F. et al. (2010) Immaturities in reward processing and its related behaviour in normal brain function and psychopathology.

inﬂuence on inhibitory control in adolescence. Cereb. Cortex 20, 1613– Mol. Psychiatry 15, 1128–1139

1629 31 Pfeifer, J.H. et al. (2011) Entering adolescence: resistance to peer

2 Hare, T.A. et al. (2008) Biological substrates of emotional reactivity inﬂuence, risky behavior, and neural changes in emotion reactivity.

and regulation in adolescence during an emotional go-nogo task. Biol. Neuron 69, 1029–1036

Psychiatry 63, 927–934 32 Vasa, R.A. et al. (2011) Enhanced right amygdala activity in

3 Van Leijenhorst, L. et al. (2010) What motivates the adolescent? Brain adolescents during encoding of positively-valenced pictures. Dev.

regions mediating reward sensitivity across adolescence. Cereb. Cogn. Neurosci. 1, 88–99

Cortex 20, 61–69 33 Durston, S. et al. (2006) A shift from diffuse to focal cortical activity

4 Guyer, A.E. et al. (2008) A developmental examination of amygdala with development. Dev. Sci. 9, 1–8

response to facial expressions. J. Cogn. Neurosci. 20, 1565–1582 34 Bunge, S.A. and Wright, S.B. (2007) Neurodevelopmental changes in

5 Crone, E.A. (2009) Executive functions in adolescence: inferences from working memory and cognitive control. Curr. Opin. Neurobiol. 17,

brain and behavior. Dev. Sci. 12, 825–830 243–250

6 Velanova, K. et al. (2009) The maturation of task set-related activation 35 Eshel, N. et al. (2007) Neural substrates of choice selection in adults

supports late developmental improvements in inhibitory control. and adolescents: development of the ventrolateral prefrontal and

J. Neurosci. 29, 12558–12567 anterior cingulate cortices. Neuropsychologia 45, 1270–1279

327

Review Trends in Cognitive Sciences June 2012, Vol. 16, No. 6

36 Shaw, P. et al. (2008) Neurodevelopmental trajectories of the human 64 Savitz, J. and Drevets, W.C. (2009) Bipolar and major depressive

cerebral cortex. J. Neurosci. 28, 3586–3594 disorder: neuroimaging the developmental-degenerative divide.

37 Lebel, C. (2008) Microstructural maturation of the human brain from Neurosci. Biobehav. Rev. 33, 699–771

childhood to adulthood. Neuroimage 40, 1044–1055 65 Dichter, G.S. et al. (2009) Affective context interferes with cognitive

38 Lenroot, R.K. et al. (2007) Sexual dimorphism of brain developmental control in unipolar depression: an fMRI investigation. J. Affect.

trajectories during childhood and adolescence. Neuroimage 36, Disord. 114, 131–142

1065–1073 66 Yang, T.T. et al. (2010) Adolescents with major depression

39 Ostby, Y. et al. (2009) Heterogeneity in subcortical brain development: demonstrate increased amygdala activation. J. Am. Acad. Child

a structural magnetic resonance imaging study of brain maturation Adolesc. Psychiatry 49, 42–51

from 8 to 30 years. J. Neurosci. 29, 11772–11782 67 Davey, C.G. et al. (2011) Increased Amygdala Response to Positive

40 Tamnes, C.K. et al. (2010) Brain maturation in adolescence and young Social Feedback in Young People with Major Depressive Disorder.

adulthood: regional age-related changes in cortical thickness and Biol. Psychiatry 69, 734–741

white matter volume and microstructure. Cereb. Cortex 20, 534–548 68 Brotman, M.A. et al. (2010) Amygdala activation during emotion

41 Asato, M.R. (2010) White matter development in adolescence: a DTI processing of neutral faces in children with severe mood

study. Cereb. Cortex 20, 2122–2131 dysregulation versus ADHD or bipolar disorder. Am. J. Psychiatry

42 Giorgio, A. et al. (2008) Changes in white matter microstructure 167, 61–69

during adolescence. Neuroimage 39, 52–61 69 Demenescu, L.R. et al. (2011) Neural correlates of perception of

43 Bjork, J.M. (2010) Adolescents, adults and rewards: comparing emotional facial expressions in out-patients with mild-to-moderate

motivational neurocircuitry recruitment using fMRI. PLoS ONE 5, depression and anxiety. A multicenter fMRI study. Psychol. Med. 41,

e11440 2253–2264

44 Pitskel, N.B. et al. (2011) How grossed out are you? The neural bases 70 Townsend, J.D. et al. (2010) fMRI activation in the amygdala and the

of emotion regulation from childhood to adolescence. Dev. Cogn. orbitofrontal cortex in unmedicated subjects with major depressive

Neurosci. 1, 324–337 disorder. Psychiatry Res. 183, 209–217

45 McRae, K. et al. (2012) The development of emotion regulation: an 71 Koenigs, M. and Grafman, J. (2009) The functional neuroanatomy of

fMRI study of cognitive reappraisal in children, adolescents and depression: distinct roles for ventromedial and dorsolateral prefrontal

young adults. Soc. Cogn. Affect. Neurosci. 7, 11–22 cortex. Behav. Brain Res. 201, 239–243

46 Levita, L. et al. (2009) The bivalent side of the nucleus accumbens. 72 Lieberman, M.D. et al. (2007) Putting feelings into words: affect

Neuroimage 44, 1178–1187 labeling disrupts amygdala activity in response to affective stimuli.

47 Masten, C.L. et al. (2009) Neural correlates of social exclusion during Psychol. Sci. 18, 421–428

adolescence: understanding the distress of peer rejection. Soc. Cogn. 73 Nelson, E.E. et al. (2005) The social re-orientation of adolescence: A

Affect. Neurosci. 4, 143–157 neuroscience perspective on the process and its relation to

48 Wager, T.D. et al. (2008) Prefrontal-subcortical pathways mediating psychopathology. Psychol. Med. 35, 163–174

successful emotion regulation. Neuron 59, 1037–1050 74 Davey, C.G. et al. (2008) The emergence of depression in adolescence:

49 Kuppens, P. et al. (2010) Emotional inertia and psychological development of the prefrontal cortex and the representation of

maladjustment. Psychol. Sci. 21, 984–991 reward. Neurosci. Biobehav. Rev. 32, 1–19

50 Forbes, E.E. et al. (2009) Altered striatal activation predicting real- 75 Poldrack, R.A. (2011) Inferring mental states from neuroimaging

world positive affect in adolescent major depressive disorder. Am. J. data: from reverse inference to large-scale decoding. Neuron 72,

Psychiatry 166, 64–73 692–697

51 Forbes, E.E. et al. (2010) Healthy adolescents’ neural response to 76 Dosenbach, N.U. et al. (2008) A dual-networks architecture of top-

reward: associations with puberty, positive affect, and depressive down control. Trends Cogn. Sci. 12, 99–105

symptoms. J. Am. Acad. Child Adolesc. Psychiatry 49, 162–72.e1–5 77 Greicius, M.D. et al. (2009) Resting-state functional connectivity

52 Somerville, L.H. (2011) Frontostriatal maturation predicts cognitive reﬂects structural connectivity in the default mode network. Cereb.

control failure to appetitive cues in adolescents. J. Cogn. Neurosci. 23, Cortex 19, 72–78

2123–2134 78 Fair, D.A. et al. (2008) The maturing architecture of the brain’s default

53 Scherf, K.S. et al. (2006) Brain basis of developmental change in network. Proc. Natl. Acad. Sci. U.S.A. 105, 4028–4032

visuospatial working memory. J. Cogn. Neurosci. 18, 1045–1058 79 Uddin, L.Q. et al. (2010) Typical and atypical development of

54 Astle, D.E. and Scerif, G. (2009) Using developmental cognitive functional human brain networks: insights from resting-state

neuroscience to study behavioral and attentional control. Dev. FMRI. Front. Syst. Neurosci. 4, 21

Psychobiol. 51, 107–118 80 Honey, C.J. et al. (2009) Predicting human resting-state functional

55 Crone, E.A. and Richard Ridderinkhof, K. (2011) The developing connectivity from structural connectivity. Proc. Natl. Acad. Sci.

brain: from theory to neuroimaging and back. Dev. Cogn. Neurosci. U.S.A. 106, 2035–2040

1, 101–109 81 Damoiseaux, J.S. and Greicius, M.D. (2009) Greater than the sum of

56 Poldrack, R.A. (2010) Mapping mental function to brain structure: how its parts: a review of studies combining structural connectivity and

can cognitive neuroimaging succeed? Persp. Psychol. Sci. 5, 753–761 resting-state functional connectivity. Brain Struct. Funct. 213,

57 Berns, G.S. et al. (2009) Adolescent Engagement in Dangerous 525–533

Behaviors Is Associated with Increased White Matter Maturity of 82 Dosenbach, N.U. et al. (2010) Prediction of individual brain maturity

Frontal Cortex. PLoS ONE 4, e6773 using fMRI. Science 329, 1358–1361

58 Gray, J.A. (1991) Fear, panic, and anxiety: what’s in a name? Psychol. 83 Finn, A.S. (2010) Longitudinal evidence for functional specialization

Inquiry 2, 77–78 of the neural circuit supporting working memory in the human brain.

59 Urosˇevic´, S. et al. (2012) Longitudinal changes in behavioral approach J. Neurosci. 30, 11062–11067

system sensitivity and brain structures involved in reward processing 84 Moore, W.E. et al. (2012) Facing puberty: associations between

during adolescence. Dev. Psychol. http://dx.doi.org/10.1037/a0027502 pubertal development and neural responses to affective facial

60 Koolschijn, P.C. et al. (2011) A three-year longitudinal functional displays. Soc. Cogn. Affect. Neurosci. 7, 35–43

magnetic resonance imaging study of performance monitoring and 85 Shaw, D.J. et al. (2012) Development of the action observation

test-retest reliability from childhood to early adulthood. J. Neurosci. network during early adolescence: a longitudinal study. Soc. Cogn.

31, 4204–4212 Affect. Neurosci. 7, 64–80

61 Geier, C. and Luna, B. (2009) The maturation of incentive processing 86 Heatherton, T.F. and Wagner, D.D. (2011) Cognitive neuroscience of

and cognitive control. Pharmacol. Biochem. Behav. 93, 212–221 self-regulation failure. Trends Cogn. Sci. 15, 132–139

62 Power, J.D. et al. (2012) Spurious but systematic correlations in 87 Solomon, R.C. (2004) Thinking about Feeling: Contemporary

functional connectivity MRI networks arise from subject motion. Philosophers on Emotions, Oxford University Press

Neuroimage 59, 2142–2154 88 Hofmann, W. et al. (2009) Impulse and self-control from a dual-

63 Van Dijk, K.R.A. et al. (2012) The inﬂuence of head motion on intrinsic systems perspective. Persp. Psychol. Sci. 4, 162–176

functional connectivity MRI. Neuroimage 59, 431–438 89 Hall, G.S. (1904) In Adolescence (Vols. I and II), Appleton

328

Review Trends in Cognitive Sciences June 2012, Vol. 16, No. 6

90 Wallis, C. and Dell, K. (2004) What makes teens tick. Time Magazine 100 Whittle, S. et al. (2011) Sex differences in the neural correlates of

163, 56–65 emotion: evidence from neuroimaging. Biol. Psychol. 87, 319–333

91 Dobbs, D. (2011) Beautiful brains. Natl. Geographic 220 101 Kessler, R.C. et al. (2012) Prevalence, persistence, and

92 Maroney, A. (2010) The false promise of adolescent brain science in sociodemographic correlates of DSM-IV disorders in the National

juvenile justice. Notre Dame Law Rev. 85, 89–176 Comorbidity Survey Replication Adolescent Supplement. Arch.

93 Jennings, J.C. (2010) Juvenile justice, Sullivan, and Graham: how the Gen. Psychiatry 69, 372–380

Supreme Court’s decision will change the neuroscience debate. Duke 102 Mendle, J. et al. (2010) Development’s tortoise and hare: pubertal

L. Tech. Rev. 6–19 timing, pubertal tempo, and depressive symptoms in boys and girls.

94 Poldrack, R.A. (2006) Can cognitive processes be inferred from Dev. Psychol. 46, 1341–1353

neuroimaging data? Trends Cogn. Sci. 10, 59–63 103 Barrett, L.F. and Bliss-Moreau, E. (2009) Affect as a Psychological

95 Castellanos, F.X. and Proal, E. (2012) Large-scale brain systems in Primitive. Adv. Exp. Soc. Psychol. 41, 167–218

ADHD: beyond the prefrontal-striatal model. Trends Cogn. Sci. 16, 104 Mauss, I.B. and Robinson, M.D. (2009) Measures of emotion: a review.

17–26 Cogn. Emot. 23, 209–237

96 Poldrack, R.A. et al. (2009) Decoding the large-scale structure of brain 105 Lindquist, K.A. et al. (2012) The brain basis of emotion: a meta-

function by classifying mental states across individuals. Psychol. Sci. analytic review. Behav. Brain Sci. 35, 121–143

20, 1364–1372 106 McGowan, P.O. et al. (2009) Epigenetic regulation of the

97 Brown, T.T. et al. (2006) Does human functional brain organization glucocorticoid receptor in human brain associates with childhood

shift from diffuse to focal with development? Dev. Sci. 9, 9–11 abuse. Nat. Neurosci. 12, 342–348

98 Keulers, E.H. et al. (2011) Developmental changes between ages 13 107 Spinelli, S. (2009) Early-life stress induces long-term morphologic

and 21 years in the extent and magnitude of the BOLD response changes in primate brain. Arch. Gen. Psychiatry 66, 658–665

during decision making. Neuroimage 54, 1442–1454 108 Tottenham, N. et al. (2010) Prolonged institutional rearing is

99 Strang, N.M. et al. (2011) Developmental changes in adolescents’ associated with atypically large amygdala volume and difﬁculties

neural response to challenge. Dev. Cogn. Neurosci. 1, 560–569 in emotion regulation. Dev. Sci. 13, 46–61

329