Adolescent Brain Development
Adolescent Brain Development
- Jessica M. BlackJessica M. BlackBoston College
Although it was once widely held that development through toddlerhood was the only significant time of tremendous brain growth, findings from neuroscience have identified adolescence as a second significant period of brain-based changes. Profound modification of brain structure, function, and connectivity, paired with heightened sensitivity to environment, places adolescence both as a heightened period of risk and importantly as a time of tremendous opportunity. These findings are of key relevance for social-work policy and practice, for they speak to the ways in which the adolescent brain both is vulnerable to adverse conditions and remains responsive to positive environmental input such as interventions that support recovery and resilience.
- Children and Adolescents
- Health Care and Illness
- Mental and Behavioral Health
Although it was once widely held that development from the prenatal stage through toddlerhood was the only significant time of tremendous brain growth, findings from neurobiology and neuroscience have identified adolescence as a second significant period of brain-based changes in terms of structure, function, and neural connectivity (Blakemore, 2012b; Casey, Getz, & Galvan, 2008; Fuhrmann, Knoll, & Blakemore, 2015). For the purpose of this entry, adolescence is defined as covering ages 10 to 24 years, approximately anchoring the onset of puberty and the maturation of the brain, with major changes in social roles taking place within this range (Boyce, 2013; Sawyer et al., 2012). It is largely held that the neural connections that remain after the blossoming and pruning during adolescence are the ones most actively used, not necessarily the ones that are most healthy or protective for the developmental trajectory (Fuhrmann et al., 2015; Giedd, 2004). The brain’s growth during this period is primarily experience dependent, and as noted above the brain does not reach maturity until the mid-twenties. The significant changes in the adolescent brain, combined with its ability to adapt connections in response to environmental conditions, positions adolescence both as a heightened period of risk and importantly (and often overlooked) as a time of opportunity because neural networks are not yet fully robust and efficiently connected (Siegel, 2013). Thus it takes less effort (environmental experience) to change the adolescent brain than the adult brain, although brain-based changes are possible throughout the later years of the life course as well (Karatsoreos & McEwen, 2013; Shonkoff, 2011; Shonkoff, Boyce, & McEwen, 2009). These findings are of key relevance and interest for social work research, practice, and policy for they point to the ways in which the adolescent brain is vulnerable to stress and impaired mental health, but also how the adolescent brain remains responsive to positive environmental conditions such as interventions that support recovery and resilience.
The central goal of the following is to introduce social workers to key concepts in adolescent brain development that are both clinically relevant to research and practice and paramount for policy and advocacy work. There is detailed description of how risk factors and processes (such as toxic stress, substance use, sleep deprivation, and isolation) have the potential to negatively influence brain (and psychosocial) development, and also how protective factors and processes (such as positive modeling and social support) may provide important windows of opportunity for adolescents with or without behavioral health diagnosis or risk for diagnosis. Specifically, the opening sections introduce social workers to current advancements in select neuroimaging technologies, methodologies, and concepts, followed by a tour of adolescent brain architecture and function. A discussion of the vulnerability to stress during this stage of development follows, with attention to research from the epigenetic literatures and biomarkers of stress included. The sections then unfold from self to relationships with others, beginning with self-concept and moving to a discussion of social connections and school settings. At this juncture, attention moves to a description of neurobiological risk for impaired mental health during adolescence with the inclusion of sections on psychopathology and substance use. The final sections summarize previously introduced evidence of the ways in which environments can also confer protection and build the adolescent brain to be robust and resilient, with closing comments addressing implications for the field of social work. In sum, the sections below provide social work professionals (scholars, practitioners, and policy makers) with the language and research background to understand adolescent brain development, and with empirical evidence that while this second window of neural growth and change is a period of vulnerability, it is also a time of opportunity that remains unparalleled to any other stage of development in the life course. It is on this exciting note that the current state of brain science research is introduced below.
Current State of Brain Research
It was not until the mid-1990s, with the introduction of functional magnetic resonance imaging (fMRI), that neuroscience research was able to examine activity and anatomy (through structural MRI) of the human brain with the same imaging technology, yet there remained a lack of cross-sectional and longitudinal studies of the adolescent brain for years to come (Casey, Giedd, & Thomas, 2000). Decades of research have shed light on the important components of psychological and social development during adolescence, yet it is the numerous recent findings on the neurobiological underpinnings that seem to provide a robust foundation upon which to elucidate the environment’s conferral of both risk and insulation for adverse outcomes during this stage of development. This triangulation of developmental domains (psychological, social, and biological) fits well with social work’s adherence to the biopsychosocial model of understanding and assessing development. What we once examined strictly behaviorally (e.g., measure of stress through self-report) or observation (e.g., adolescent risk-taking), in many cases can now also be evaluated using sophisticated technologies measuring our brain’s structure, function, neurochemistry, and the genome. Translational neuroscience and neurobiology (connecting research to policy and practice) are growing, and provide important insights into the ways and the timing to enhance well-being in fetal life through adolescence, which can influence later life-course health outcomes (Shonkoff & Garner, 2012). Transdisciplinary research is also on the rise, with scholars in the social sciences partnering with collaborators from branches of biology and medicine. Take, for example, the evidence from the emerging fields of educational neuroscience (the intersection of educational practice and neuroscience research) and social neuroscience (the intersection between social psychology and neuroscience) that practitioners and researchers from very different fields and with little overlap in training or research methodologies can come together effectively to address complex problems and questions.
Although the bridging between social work and neuroscience is still in its infancy, there is great promise for the future synergy between these fields (Matto & Strolin-Goltzman, 2010). However, unlike the fields of education and psychology, where undergraduate and graduate education, professional organizations, and continuing education together facilitate access to neuroscientific findings, the field of social work has met neuroscience research formally only in the most recent of years (Farmer, 2009). For example, it was not until 2015 that the first special interest group (SIG) dedicated to the intersection of neuroscience and social work research was introduced at the annual meeting of the Society for Social Work and Research. That same year was the first for this national conference’s offering of the topical area of Neuroscience within the Cluster of Mental Health for researchers looking for routes to present their findings. In addition, few undergraduate or graduate schools have neuroscience courses specifically designed for social work students. Therefore, the majority of the evidence from brain science cited below comes from fields other than social work; however, every effort is made within each section to translate the meaning and significance for those in the field of social work. Finally, it is accurate to say that social workers have a great deal to learn from neuroscience research, yet at the same time the contributions should be considered along a two-way exchange, with room for collaboration at the fulcrum. Therefore, closing remarks consider future directions for work between these two important and complex fields, with specific considerations for researchers, practitioners, and those in policy. It is important to keep in mind a cautionary note for neuroscientific evidence in adolescent populations (Johnson, Blum, & Giedd, 2009), as these studies rarely point to etiology and very much need the support of social science and professional fields to develop interventions that also pay attention to context, and to the unique and personal journey of each life course. It is on this note that current methods to assess brain structure and function, along with related biomarkers in adolescence are introduced, with limitations of these approaches mentioned as well.
Biomarkers: The Brain and Beyond
Neuroimaging technologies. Recent advancements in neuroimaging technologies and corresponding analytic methods have substantially increased our understanding of brain growth and development, and have also begun to reveal the structure of healthy brain architecture and the brain-associated etiologies of a number of disorders (Bandettini, 2009). Technologies such as near infrared spectroscopy (NIRS), magnetoencephalography (MEG), magnetic resonance imaging (MRI), and electroencephalography (EEG) are noninvasive, and provide critical information on anatomy, neural activity, and timing of activity. Whereas MRI has superior spatial resolution (location within the brain), EEG and MEG have optimal temporal resolution (how long the brain takes to respond), and thus to some extent the technology employed by the study depends on the research question at hand. Additional considerations include funding (MRI and MEG have start-up and running costs significantly greater than NIRS and EEG) and participant age and physical health (EEG and NIRS are more forgiving in terms of participant movement, and have fewer contraindicators such as metal in the body or claustrophobia), which may also influence investigators’ choice of technology(ies).
In order to further elucidate the developmental trajectory of the growing brain, data from multiple time points is needed (Grant, 2012). Due to the relative ease of access, MRI is the most often used technology for brain-based research, in particular in cognitive neuroscience, and can provide valuable information on brain anatomy such as volume and surface area (structural MRI), tissue connections and white matter integrity (diffusion MRI), and brain function (functional MRI) at multiple time points in both typically and atypically developing groups throughout much of the life course, including adolescence (Grant, 2012). We know that the human brain is optimized through efficient and robust connections, and therefore some neuroimaging studies use functional connectivity analysis to examine the integrity and pathways of connectivity when the brain is engaged in an activity (Goldenberg & Galvan, 2015) or when it is at rest (Satterthwaite & Baker, 2015). It is important to recognize that MRI studies (structural and functional) are more often utilized with adolescent populations than with those in the very early stages of the life course, as adolescents understand (and are often excited about) the technology, can follow more complex experimental tasks, and move less in the scanner with training than may infants or very young children. That said, however, special consideration for conducting neuroimaging studies in adolescence is needed. For example, the adolescent brain is significantly more responsive to reward than is the young or adult brain, which has implications for ways in which adolescents are provided incentives (such as money, often standard practice in research) for assenting to participate in studies, and these unintended effects can alter the interpretation of findings, particularly related to investigations of risk and reward in the adolescent brain (Galvan, Van Leijenhorst, & McGlennen, 2012). Neuroimaging has been applied to many different studies of brain development and function in adolescence, including identification and models of conditions and disorders, neuroprognosis (prediction) work, and assessment of treatments and interventions.
Complimentary biomarkers. Although neuroimaging has significantly advanced our understanding of the anatomy and activity of the human brain, there are additional biomarkers that can be complementary to understanding the landscape of adolescent brain development. These biomarkers represent a sample of what current biological research examines, first in animal (often rodent) models and then in humans. For example, much of what is known about the negative effects of prolonged and heightened stress response activation, described in more detail in an upcoming section (see below, “Mental Health”), was discovered first at a neurobiological level in animal models (McEwen, 2012; Sapolsky, 2004). When working in interdisciplinary collaborations, it may be the case that a cognitive neuroscientist interested in exploring the neural underpinnings of adolescent mental health is interested in brain function related to recall for negative versus positive stimuli, and thus may utilize MRI to gather both structural and functional information about the brain, perhaps by comparing groups with and without family risk for psychopathology. The examination of the central nervous system (which includes the brain) can be complemented by measures of the peripheral nervous system (including the somatic and autonomic systems), as the two systems are intimately linked because experience in the environment through activation of the peripheral nervous system sends back critical information to be processed and acted upon by the central nervous system (Buchanan & Tranel, 2009). It is also possible to add into the experimental design measures of psychophysiology (oftentimes with significantly reduced cost compared to multimodal neuroimaging) within the autonomic nervous system, a division of the peripheral nervous system, including the sympathetic and parasympathetic systems, to assess heart rate and cardiovascular responses, respiration, muscle activity, and eye responses (such as blinks and movements) using eye tracking devices (Hutchison, 2013; Vaughn, DeLisi, & Matto, 2014). In addition, studies with adolescent participants gather measures of the stress hormone cortisol (through blood, urine, or hair), which exhibits neurotoxic effects to the brain when sustained and unremitting stress responses persist (Malter Cohen, Tottenham, & Casey, 2013; McEwen & Gianaros, 2010; Shonkoff & Garner, 2012). Recent work focused on health, aging, and disease onset in the animal and human literatures has also included measures of telomere length (the protective caps much like shoelace ends that keep our chromosomes from fraying) and telomerase activity (the enzyme that rebuilds telomeres) as it seems toxic stress accelerates the fraying, which can promote cell senescence (the cessation of cell division), which can have negative effects on health outcomes (Epel, 2012; Epel et al., 2004; Epel et al., 2006). Finally, although a systematic review of the genetics literatures is beyond the scope of the present review, it has been widely recognized that our genomes are not our destiny. In fact, very few conditions can be attributed to our genome alone, but rather are associated with the dyanmic exchange of genetic endowment, behavior, and environment. Referred to as the diathesis-stress model, it is now better understood that risk for negative outcomes such as psychopathology may not appear (or may be delayed or minimized) based on how one’s environment affects gene expression (the epigenome), in particular, on the extent to which environment mitigates or enhances stress (such as the caregiving environment) (Vaughn et al., 2014; Yang et al., 2013). In sum, measures of psychophysiology, cortisol, telomeres, and the epigenome can be combined with multimodal neuroimaging or used as standalone measures in studies with adolescents.
Contributions from neuroscience show that the brain is an organ of adaptation, built to respond to environmental demands (Applegate & Shapiro, 2005; Badenoch, 2008; Davidson & McEwen, 2012). Rather than filtering environmental input to determine whether the influences are health promoting or detrimental, the brain responds immediately (and often in long-lasting ways), rather than with foresight as to what might be brain enhancing in the long term. The sensitivity of the brain to respond to experience is referred to as neuroplasticity, and our understanding that the brain is most plastic through adolescence (Fuhrmann et al., 2015) is of particular relevance to social workers who provide therapeutic intervention seeking positive and sustained behavioral changes, for it is important to recognize that change is possible at the neurobiological level and that any behavioral change is in fact a brain-based one (Cozolino, 2014, 2016; Montgomery, 2013; Siegel, 2013). Building robust, resilient, and nurturing brains depends on the bridge between brain plasticity and skilled social workers’ facilitation of access to strong and supportive internal and external environments for their adolescent clients and families. Healthy brain architecture (described further in the following section, “Tour of Brain Architecture and Activity”) requires adequate amounts of neurons (brain cells) and connections (synapses) between neurons that communicate to one another through electrical signals transmitted down their dendrites that release neurotransmitters (chemical signals) into the synaptic cleft, to be received and interpreted by the neighboring neuron. The more the synaptic cleft is in use, the heightened likelihood that new synapses will form (synaptogenesis), much like adding a new bus stop on a very popular downtown line. Like humans, individual neurons cannot exist on their own, and therefore it is paramount that strong connections are formed between neurons so that neural matrices can grow and thrive in the adolescent brain (Cozolino, 2014). This process suggests that the brain operates during this stage of development on the “use it or lose it principle,” such that neuronal connections that are active are the ones that will remain, and those that are less recruited have a heightened possibility of being pruned in adolescence (Badenoch, 2008).
Although most functional neurons are born (neurogenesis) well before adolescence (primarily during gestation), there is evidence that new neurons can be created even into adulthood, such as in the hippocampus, which subserves memory and emotional processing (Galvan & Bredesen, 2007; Galvan, Greenberg, & Jin, 2006; Kempermann, Song, & Gage, 2015; Ming & Song, 2011). These findings provide social workers with evidence of the potential for even our most foundational biology to change over the life course with environmental input, fitting well with the major social-work perspectives, life course (each journey as connected, unique, and nonlinear) (Hutchison, 2013) and strengths (never placing a ceiling on clients’ capacity to change and grow in positive and resource-rich environments) (Saleebey, 2008). There are three forms of neuroplasticity: experience-expectant, experience-dependent, and experience-independent, where synapses within neural connections can either be gained or lost (Kolb & Gibb, 2014). Experience-expectant plasticity shows how brain systems need specific (and time-appropriate) input during development, such as visual and auditory stimulation to develop those systems robustly in infancy and the first few years of life. Some may refer to these developmental windows as critical periods, when experience is required to develop systems (Ben-Ari, 2015; Kolb & Gibb, 2014). Similar to experience-dependent plasticity, experience-independent plasticity also emphasizes a developmental process, yet rather than our genome prescribing each neuronal connection, instead it overproduces connections and sends them to a general location; however, the exact areas or layers where axons terminate is based more on spontaneous firing with nearby cells, and therefore is independent of others cells. In this way connections in specific (but not completely genetically prescribed) locations become strengthened, while those not connected to the firing eventually wither and die (Kolb & Gibb, 2014). However, by adolescence the emphasis of plasticity is experience-dependent, as many neural networks (systems) are already present and therefore experience can increase or decrease the numbers of synapses and the strengths of connections among already present neurons (Holtmaat & Svoboda, 2009). In this way, adolescence can be conceptualized as a sensitive period because it represents a second wave (separate from the early years of life) of synaptic overproduction and pruning when the brain is maximally responsive to environmental input, whether positive or negative (Fuhrmann et al., 2015). This form of plasticity represents tremendous opportunity in the adolescent brain (and therefore for behavior), yet plasticity can be associated with maladaptive outcomes (fewer neural connections with less robust networks) if the environment is deprived of health-advancing resources such as strong caregiving and social supports, buffers to toxic (unremitting) stress, adequate sleep, and proper nutrition (Evans, 2004; Kim et al., 2013; Kolb & Gibb, 2014; Schriber & Guyer, 2015; Shonkoff et al., 2009; Shonkoff & Garner, 2012). Every experience can alter brain and behavior (Kolb & Gibb, 2014), and even though the temporal nature of the effects may differ, adolescents’ social synapses (the space where social exchange takes place) (Cozolino, 2014) are key areas for social workers to attend to, for these synapses are associated with ways in which neural synapses communicate, weakening or strengthening brain architecture and function over time. Given that there are approximately 1.8 billion individuals between the ages of 10 and 24, it is helpful for social workers to recognize this period of time as an important opportunity for growth and change, expanding their understanding that plasticity is not restricted to the first five years of development alone (Boyce, 2013; Pringle et al., 2016; Sawyer et al., 2012). Fifteen years ago there was just a handful of studies examining the neural underpinnings of adolescent development, but now there are thousands of papers published, which have very much informed our recognition that the brain has not yet reached maturity, and in fact grows in both linear and nonlinear ways during this stage (Blakemore, 2012b; Boyce, 2013; Casey, Jones, & Somerville, 2011; Fuhrmann et al., 2015). These findings are discussed in more detail in the following section.
Tour of Brain Architecture and Activity
By age six, the brain is 90% of its adult size, yet the ways in which the neurons connect and the strength of the neuronal matrices are still very much under construction in adolescence (Casey, Getz, et al., 2008). Thus, the focus of this change during adolescence is not so much in the weight of the brain, but rather in terms of its structure and function (described below). On a cautionary note, it is important to realize that at this point in neuroscience research, the direct relationship between structural and functional development is speculative at best, and likely a very complex process (Blakemore, 2012b). To begin, brain structure includes the interconnections between and among gray matter (cell bodies) and white matter (myelinated axons), and both undergo tremendous change with implications for behavior during this stage of development (discussed in the section below, “Function”). Although neuroscientists examine a wide range of regions and circuits within the brain, many studies that serve to investigate the neural circuitry of risk and reward seeking, psychopathology, goal setting, and the social brain tend to focus on key cortical (outer) structures (sometimes referred to as the thinking part of the brain) and subcortical structures (deeper in the brain and more evolutionarily conserved than the cortex) (Casey, Getz, et al., 2008; Casey & Jones, 2010; Fuhrmann et al., 2015; Galvan, Hare, Voss, Glover, & Casey, 2007; Mills, Lalonde, Clasen, Giedd, & Blakemore, 2014; Sawyer et al., 2012; Siegel, 2013). These subcortical structures include the limbic system, which is important for basic emotions and drives, experience of reward, fight or flight responses, and memory (Cozolino, 2014; Vaughn et al., 2014). Whereas many areas of the limbic system such as the amygdala (fight or flight) are online and fully functional early in development (by the eighth month of gestation), the cortex (sometimes referred to as the neocortex) grows substantially in adolescence (Cozolino, 2014; Mills, Goddings, Clasen, Giedd, & Blakemore, 2014).
Specifically, changes in gray matter volume actually follow an inverted U-shaped pattern such that there is an increase through childhood, a peak reached in adolescence (around the time of puberty onset), and a pruning into adulthood (Blakemore, 2012b; Casey, Getz, et al., 2008; Giedd et al., 2009; Sawyer et al., 2012; Thompson et al., 2001). Groundbreaking work using MRI to scan children and adolescents aged four to 22 years old every two years showed this peak being reached about the age of 12, and earlier for girls than for boys (Giedd, 2004; Giedd et al., 1999). It is important to recognize that studies employing MRI to examine cortical thickness show the changes in gray matter volume are region specific, with the sensory and motor cortex maturing (i.e., pruning gray matter so loss means maturation) earlier than the parts of the frontal and temporal lobes (important for executive functioning and language and memory) (Blakemore, 2012b). An MRI study comparing cortical changes between children and adolescents showed significant acceleration of gray matter loss between these two stages of development in the dorsal prefrontal cortex (important for but not exclusively responsible for executive functions) and the parietal cortex (sensory and motor) (Sowell, Delis, Stiles, & Jernigan, 2001). In fact the gray matter loss continued up to the threshold of late adulthood (Sowell et al., 2003). This gray matter loss (which amounts to 7–10% of gray matter volume), helps to improve efficiency of communication, but it also means this can be a time of increased cognitive and emotional disorganization, for the overproduction (exuberance) of dendrites and synapses means there is an opportunity for billions more neural connections in the brain, connections that need time to organize, reorganize, and wither if unused (Badenoch, 2008). When adolescents cannot find words to describe their emotions or have trouble with planning, it may just represent the navigation through a neural construction zone than defiance or deliberate disorganization. The reason for this gray matter loss is debated by scholars, with some suggesting that the findings indicate that there is significant proliferation of synapses at the onset of puberty followed by region-specific reductions that preserve the most-used connections (Giedd et al., 1999; Sowell et al., 2001), while others positing that it may be due to increases in white matter volume (such as intracortical myelination) that is picked up on MRI scans more readily than aspects of gray matter (Giorgio et al., 2010; Perrin et al., 2008).
The finding of significant gains in white matter volume from childhood into adolescence (and well into adulthood and beyond) is consistently demonstrated in MRI studies, in particular in the frontal and parietal cortices, in the integrity of myelin (the insulation for axons), and in the increased organization of white matter tracts that connect brain regions (Blakemore, 2012b). Myelination (which is seen in MRI scans as white matter volume) significantly increases the speed through which electrical signals are transmitted through axons (100 times or more when an axon is myelinated) (Purves et al., 2001), so a more myelinated brain allows for faster communication (Giedd, 2004; Lebel et al., 2012). Increased myelination, however, takes place first in the limbic (emotion and drive) region and then the prefrontal, meaning that the adolescent brain is able to process emotional information very quickly, yet at the same time does not have a fully myelinated prefrontal cortex to also increase the speed of the central executive, which may interrupt or at least pause an emotional response with a reasoned, goal-directed one (Stiles & Jernigan, 2010). In sum, findings from structural MRI studies demonstrate that the structures (regions) of the brain that are involved in decision making, impulse control, language, memory, and reason have developed before puberty, yet as described in more detail in the next section, these regions are not yet fully efficient and balanced with subcortical (limbic) activation, and therefore for some, adolescence seems a paradoxical period of increased cognitive and social maturation partnered with increased incidence of risk taking and negative health outcomes.
The balance between the prefrontal cortex (the executive or decision maker of the brain) and the limbic system (processing emotional information) becomes more robust as the healthy adolescent brain matures into young adulthood (Blakemore, 2012b; Blakemore & Choudhury, 2006; Casey, Getz, et al., 2008). As mentioned above, a paradox emerged as investigators began to compare brain and behavior of childhood, adolescence, and adulthood, for adolescents are neurodevelopmentally (and psychosocially) more mature than their younger counterparts, yet engage in behaviors that could have negative consequences at significantly higher rates, in particular in emotionally charged (rather than cognitively cool) settings (Hutchison, 2013; Siegel, 2013; Vaughn et al., 2014). Many investigators have attributed this increased risk of adverse health behaviors and outcomes (such as substance use, suicide, pregnancy, and premature death due to accidents) to the underdevelopment of the prefrontal cortex (the brain’s CEO involved in decision making, planning, and impulse control) alone (Blakemore & Choudhury, 2006; Mills, Goddings, et al., 2014; Mills, Lalonde, et al., 2014; Sawyer et al., 2012; Siegel, 2013). Yet in actuality the prefrontal cortex is more mature than in childhood, so there must be another explanation for these nonlinear changes seen in behavior in adolescence (Casey, Getz, et al., 2008; Galvan et al., 2006). Functional MRI studies have helped to elucidate this developmental paradox, as it seems adolescents’ heightened responsiveness to reward and emotional contexts in social settings, when impulse control is still under development, is due to the differential temporal (timing) nature of development, whereby the more mature bottom-up limbic system is the more mature driver and the top-down cognitive control system (the frontal cortex) remains the passenger (Casey et al., 2011; Casey, Getz, et al., 2008; Casey, Jones, & Hare, 2008; Galvan et al., 2007). Parallel to the operation of a student-driver vehicle, over time the frontal cortex can take over the breaking and acceleration, and in time can become a copilot of emotion regulation and decision making with the subcortical system. The development of this balance is a key neurobiological assignment in adolescence, with those who are prone to emotional reactivity (Casey, Getz, et al., 2008) or who do not have mature adult brain mentors to help sculpt these connections (Blakemore, 2012a; Cozolino, 2014; Montgomery, 2013; Schriber & Guyer, 2015; Siegel, 2013) at greater risk for poor outcomes. Further, as demonstrated by fMRI studies, compared to the child and to the adult brain, the neural signatures of the adolescent brain position it as a time of increased reception to reward (measured in MRI scans with games where participants can win money, and even monitoring sugar intake during fMRI) (Blakemore & Robbins, 2012; Casey et al., 2011; Casey, Getz, et al., 2008; Casey et al., 2000; Casey, Jones, et al., 2008; Galvan et al., 2007; Siegel, 2013), increasing risk for maladaptation with the presence of poor goodness of fit for adolescents’ health-promoting developmental needs. It seems that the adolescent brain actually has less free-floating dopamine (a neurotransmitter implicated in motivation, pleasure, and reward) than does the adult brain, and also has more excitatory than inhibitory receptors than it will when fully mature, yet when an experience actually reaches threshold in the adolescent brain to be perceived as rewarding (such as evidenced by heightened activation in the dopamingeric reward circuitry including the nucleus accumbens), more dopamine is expressed than in the adult brain (Casey, Getz, et al., 2008; Casey, Jones, et al., 2008; Galvan et al., 2006; Jacobus & Tapert, 2013; Siegel, 2013). These findings suggest that the adolescent brain is particularly receptive to reward, and large rather than small or moderate incentives at that, and that this circuitry is more mature than is the prefrontal cortex (which actually shows reduced activation when the reward circuitry is activated) (Casey, Getz, et al., 2008; Galvan et al., 2006; Spicer et al., 2007).
Neurodevelopmentally, this appears to be a stage unlike those that anchor it, whereby dopamine is released only for the most salient and robust of rewards (Casey, Jones, et al., 2008), which can also be considered a unique opportunity for adolescents to really become passionate about and connected to particularly rewarding experiences that do not confer heightened risk to development. However, given that the adolescent brain is very much under construction, and not yet fully mature, it may not have the developmental history to withstand stressors the way an adult brain may (Tzanoulinou & Sandi, 2016). It is important to remember that the brain is an organ of adaptation, and the subcortical systems such as the fight-or-flight response are meant to be life-preserving and therefore are vigilant to stressors that signal danger. Briefly, the stress response system, which includes the hypothalamus-pituitary-adrenal axis (HPA axis), was designed to be activated on a short-term basis to preserve life (imagine the need for speed rather than digestion if trying to outrun a hungry bear), yet the HPA axis does not distinguish between psychological and physical stressors, and a prolonged stress response can be neurotoxic to the brain (perpetuating the release of the stress hormone cortisol, which can kill neurons in the hippocampus and thereby reduce its volume) and physical health (increasing the rate of shortening of telomeres, which protect our chromosomes from fraying, and which is implicated in disease processes) (Epel et al., 2004; Epel et al., 2006; Malter Cohen et al., 2013; McEwen, 2012; Sapolsky, 2004; Shalev et al., 2013; Shonkoff et al., 2009; Shonkoff & Garner, 2012). Adolescents are increasing their independence, in both cognitive and social domains, from their primary caregivers, yet their brains and their coping systems are not yet fully mature and capable of handling stressors in the same way that an adult brain (without a trauma history) may be able to do. Although a systematic review of the neurobiology of stress is beyond the scope of this review, it is important for social workers to recognize that the negative effects seen in chronic stress (discussed below) can (1) take place due to psychological stressors and not solely physical ones, (2) include stressors such as boredom, sleep deprivation, and loneliness beyond those typically conceptualized as inducing a neurobiological stress response, and (3) cause damage (temporarily or long-term) to the brain’s structure and function, directly through the neurotoxic effects of cortisol on the hippocampus. In sum, the findings reviewed in this section have implications for increased perceptions of boredom and engagement with risk taking that can be particularly harmful, but also for positive and health-promoting behaviors that can build rewarding senses of the self and connection with others. Each of these topics is reviewed in more detail below, beginning with the development of self and the importance of social connections during this stage of development.
Humans have social brains; we are wired to connect and to build our brains through social interaction (Cozolino, 2014). From infancy into adulthood the combination of a secure sense of self and secure attachment relationships is associated with mental health outcomes than an insecure sense of self and insecure attachment styles (anxious, avoidant, or disorganized) (Anda et al., 2006; Blakely & Dziadosz, 2015; Gillath, Bunge, Shaver, Wendelken, & Mikulincer, 2005; Serra et al., 2015). It seems that secure attachment styles actually reduce the brain’s stress response when activated, whereas an insecure attachment to others (such as a parent) is actually a source of stress rather than a mechanism for managing and therefore diminishing the stress response (Cozolino, 2010, 2014; Hughes & Baylin, 2012; Smith, Woodhouse, Clark, & Skowron, 2015). Attachment styles are not hardwired, nor are they stable across the life course, but rather are malleable and can be positively redirected through appropriate intervention from infancy (in particular, related to addressing caregivers’ insecure attachment profiles) and into adulthood (Blakely & Dziadosz, 2015; Cozolino, 2016; Hamilton, 2000; Suchman, Decoste, Rosenberger, & McMahon, 2012; Taylor, Rietzschel, Danquah, & Berry, 2015; Vachon, 2016; Zhang & Labouvie-Vief, 2004).
Attachment theory remains applicable throughout the life course, and even though adolescents as gaining independence, a secure attachment style with caregivers aids development, and an insecure early history of relationships can be offset by the development of attuned and stable attachment relationships with siblings, peers, or mentors, or a strong therapeutic relationship with a clinician in adolescence (Blakely & Dziadosz, 2015; Coutinho, Silva, & Decety, 2014; Cozolino, 2010, 2014; Hughes & Baylin, 2012; Schriber & Guyer, 2015; Siegel, 2013). Compared to children, adolescents have a more robustly developed social brain, with increased capacity to mentalize (e.g., theory of mind), to engage in more complex peer interactions, to empathize and reason abstractly and hypothetically, and to be susceptible to the social context (Blakemore, 2012a; Hutchison, 2013; Schriber & Guyer, 2015; Steinberg & Morris, 2001; Vaughn et al., 2014). Current theoretical frameworks suggest that adolescence is a time of neurobiological susceptibility to social context, whereby positive social environments such as parental warmth and positive modeling promote optimal transitions and trajectories into adulthood, but negative environments, such as those with chronic stressors and without social buffering, can promote maladaptive pathways and psychopathology, with negative effects leading into adulthood (Cozolino, 2014; Fuhrmann et al., 2015; Schriber & Guyer, 2015; Siegel, 2013), and with some individuals being more susceptible to environment and therefore the possible consequences than others (Ellis, Boyce, Belsky, Bakermans-Kranenburg, & van Ijzendoorn, 2011). Developmentally, adolescents are distancing themselves, at least in part and often temporarily within particular life domains, from their most proximal caregivers through the expansion of their social convoys (social networks that are primarily peer-based) and increasing social responsibilities and expectations, yet more than ever they require support and stability from caregivers and mentors who themselves may believe adolescence is a time for adults to step back (Arnett, 1999; Bronson & Merryman, 2009; Greene, 2008; Siegel, 2013). The overarching theme within this section is that adolescents need: (1) opportunities to discover their multiple selves through health-promoting and rewarding routes, (2) mature, resource-filled adult brains (parents, teachers, neighbors, and mentors) to build well-balanced brains of their own, (3) pathways that are conducive to the building of this self-discovery and these attuned relationships, and (4) environments that prevent or reduce chronic stress.
Erik Erikson described the stage of adolescent psychosocial development as a period of prolonged stress, a central crisis driven by need to resolve the question, who am I? (Greene, 2008). Although other scholars have debated how much adolescence truly is a time of “storm and stress” (a phrase coined by the child psychologist G. Stanley Hall in 1904), as most adolescents fair well during this stage (Arnett, 1999), how the self is conceptualized and engages in the social world has an increased chance of being under biopsychosocial construction during this window. As mentioned, adolescents are better able to reason abstractly and hypothetically, and therefore to attempt to answer the question of self-identity in multiple ways and in different domains. For adolescents who may have experienced a childhood of constricted and negative perceptions of the self and other, this new period of construction of the social brain may be an important opportunity to revisit earlier assessments of the self and relationships with others (Badenoch, 2008; Blakely & Dziadosz, 2015; Cozolino, 2014; Montgomery, 2013; Siegel, 2013). The social brain includes the regions involved in social cognition, which support processes of recognizing and evaluating mental states of the self and other, including feelings and desires, traits, behaviors, and intentions (Blakemore, den Ouden, Choudhury, & Frith, 2007; Frith & Frith, 2007; Sebastian, Viding, Williams, & Blakemore, 2010). These perceptions of self and others can be channeled via verbal or nonverbal expression, and received consciously or subconsciously (Cozolino, 2014). Neuroimaging studies demonstrate that many regions are recruited in the social brain depending on the cognitive task. These regions include, but are not limited to, the amygdala, the medial prefrontal cortex (mPFC), the temporo-parietal junction, and the anterior insula (for more detail, see Cozolino, 2014; Frith & Frith, 2007; Sebastian et al., 2010). Neuroimaging studies that measure neural responses to thinking about others’ intentions and to knowledge about the self indicate different patterns for pre-adolescents and adolescents than for adults. Specifically, the younger group seems to rely heavily on the prefrontal cortex, including the mPFC, (which may reflect greater self-reflection), than the adults who activated the lateral temporal cortex (indicating retrieval of memory) (Blakemore et al., 2007; Pfeifer, Lieberman, & Dapretto, 2007; Sebastian et al., 2010). Findings suggest a developmental shift in the pattern of self-reflectivity, whereby, when asked about the self versus other, adolescents need to activate the more anterior regions involved with online active processing of “is this like me?,” whereas adults recruit posterior regions more involved in memory storage, “this is like me” (Pfeifer et al., 2007; Pfeifer & Peake, 2012; Sebastian et al., 2010). These findings fit well with the psychosocial literature demonstrating that adolescence is a time of increased ability to take perspectives and to consider a wider social context beyond the self, and also that sense of self (self-concept) is under development. Knowing that even neurobiologically adolescence is an opportunity to engage in more self-reflection, and that the way one answers questions about the self does not seem to draw upon “stored” knowledge in the same way as it does for adults, suggests that the social brain is in a rare developmental window of increased cognitive maturity matched with still plastic approaches to answering the question “who am I”? Socially, adolescents are sensitive and susceptible to their social environments, and as indicated from MRI study findings discussed below, perhaps even more so than adults.
The brain is built, not born, constructed from a genetic blueprint malleable in expression and therefore structure and function. Unlike a computer’s hardware that remains static and preprogrammed, the brain’s cells (neurons) grow connections (or lose them) in very dynamic ways throughout the life course, and their connections (as described earlier regarding experience-dependent plasticity) depend on experience, and much of that is social. Peers have a great influence on one another during adolescence in many domains of life (Hutchison, 2013; Vaughn et al., 2014). Emotional competence is a foundation for social competence, which is required for the development and sustainment of peer relationships (Hutchison, 2013). Studies of emotion regulation and resistance to peer influence show that compared to adults, adolescents demonstrate more activity in the prefrontal regions when they are required to suppress and emotion (sadness), with self-regulation of emotion appearing to rely on the lateral and medial PFC (Levesque et al., 2003; Levesque et al., 2004). Recall that the PFC is the center of the executive system, and is also involved in self-reflection regarding questions of knowledge about the self. During this stage of development, compared to adulthood, when needing to actively cope (in this case suppress) sadness, adolescents call on the central executive for support, as it is a system still coming into balance with the subcortical limbic system. The balance between these two systems is not automated, but seems to be more a matter of attention moving to whichever region of the brain is screaming loudest (which seems to be the subcortical limbic system) (Siegel, 2013). Further, the adolescents who had higher resistance to peer influence (a behavioral measure) showed greater functional connectivity between the brain regions when viewing angry stimuli in the MRI scanner (Sebastian et al., 2010). Relatedly, adults may have the perception that adolescents are independent and capable of making their own choices in many areas of their lives (Siegel, 2013), yet an fMRI study also showed that in an in-scanner task, where they were told their preferences on a particular “game” may be randomly displayed to all other adolescents playing the in-scanner game, adolescents’ brains lit up in a way that demonstrated distress and danger (even to the possibility, as in reality their choices were never displayed) (Bronson & Merryman, 2009). The social setting of peer culture is a complex one, and even though adolescents are building knowledge of the self and considering multiple alternatives, the mere possibility of having their selections displayed to peer strangers signaled danger.
These and other findings demonstrate that adolescents respond often with emotion before cognition, and that the social context needs to be perceived as safe and socially supportive. Peer groups can be protective for adolescent development or can confer risk. One special consideration for the possibility of peer influence, negative or positive, is founded on the neurobiological literature on risk and reward seeking in adolescence. An fMRI study asked both adults and adolescents about ways they should act under certain possibly aversive or dangerous experiences, such as biting down on a light bulb (Bronson & Merryman, 2009). The brain scans of adults demonstrated automatic responses of aversion and distress, whereas the adolescents’ scans showed recruitment of the prefrontal cortex (thinking the scenario through) before later coming to the same conclusion in terms of their answers (No!) that the adults had seemingly automatically. The results suggest that adults are capable of both feeling and thinking abstractly, whereas adolescents are capable of the latter but have difficulty feeling abstractly (Bronson & Merryman, 2009). Adults are aided by feelings, such as the sense that a particular course of action may not be an optimal one to follow. It seems that during this developmental window of adolescence this process is still under construction and, as research suggests, seems to unravel more in emotionally hot climates (such as in peer settings where peer pressure may be an issue) than in cognitively cool climates (such as research lab settings or speaking with parents about particular topics) (Hutchison, 2013; Vaughn et al., 2014). For example, girls who displayed more cognitive control (as indicated by brain scans that showed increased recruitment of the bilateral dorsolateral PFC) were less sensitive to relational aggression than those showing less cognitive control (Baird, Silver, & Veague, 2010). Therefore, environments and social relationships that support the development of this bridge between cognition and emotion are paramount, for, given the extra time the adolescent brain needs to reason through decision making that may have negative health outcomes, extra attention should be paid to fostering climates that create safe (but also rewarding) spaces for adolescents to learn from peers and experience strong mentoring and modeling from adults.
There are many experiences that the brain finds rewarding, from positive social connections, to feeling understood, to the experience of humor (Eisenberger & Cole, 2012; Morelli, Torre, & Eisenberger, 2014; Vrticka, Black, Neely, Walter Shelly, & Reiss, 2013). The engaged brain is a motivated one. In fact, findings from neuroimaging studies suggest that the brain responds to boredom as though it has been exposed to a stressor (Mathiak, Klasen, Zvyagintsev, Weber, & Mathiak, 2013; Merrifield & Danckert, 2014). The adolescent brain is primed to be active and engaged, looking for rewarding experiences. Reward does not have to pose risk to health any more than all risk should be considered aversive. Many middle and high schools are turning to new models to support optimal development in adolescence, including programs that focus specifically on building curriculum and climates that emphasize positive social and emotional learning (Benningfield, Potter, & Bostic, 2015; Coutinho et al., 2014; Elias & Weissberg, 2000; Hill, 2001; Payton et al., 2000), and building bridges to community and career, and employing adult professionals as mentors through high school apprenticeship programs to connect young people to possible questions and careers that are of great personal and practical interest, and where teamwork is paramount (Hill, 2001; Mekinda, 2012). At the same point, however, healthy social, cognitive, and emotional functioning requires that adequate levels of nutrition, exercise, and sleep are met in adolescence, with findings suggesting that most adolescents are not able to meet any of the basic levels in those three domains (Hutchison, 2013; Sawyer et al., 2012).
Although a systematic review of these areas of development is beyond the scope of the present review, when working with adolescents each domain should be assessed, for each can comprise functioning in many domains. For example, sleep deprivation may contribute to the misdiagnosis of ADHD or depression (Hutchison, 2013; Soffer-Dudek, Sadeh, Dahl, & Rosenblat-Stein, 2011), increase risk taking due to imbalances between cognitive and affective brain systems (Telzer, Fuligni, Lieberman, & Galvan, 2013), facilitate substance use and dependence to stay awake in the day or aid sleep at night (Hutchison, 2013), alter brain development in the form of less white matter integrity (affecting the efficiency of neural communication) (Telzer, Goldenberg, Fuligni, Lieberman, & Galvan, 2015), impair memory and educational performance (Carskadon, 2011b), increase cortisol (the stress hormone) reactivity (Mrug, Tyson, Turan, & Granger, 2016), impair the processing and interpretation of emotional information (Soffer-Dudek et al., 2011) and increase attention to and memory for negative rather than positive information (Bronson & Merryman, 2009), and disregulate hormones signally satiety and hunger (increasing the desire to eat) (Bronson & Merryman, 2009). Adolescents who present with impaired memory or heightened attention to the negative may in fact be sleep-deprived. Findings suggest that adolescents are the most sleep-deprived population, needing an average of 9.25 hours per night while receiving several hours less than that on average (Carskadon, 2011a; Hutchison, 2013), indicating that parents should be more aware of the times their adolescents are going to bed (largely set by the youth themselves) (Bronson & Merryman, 2009), while motivating some school districts to delay start times in middle and high schools (with very positive academic along with social-emotional benefits) to address the shifts in circadian rhythms that lead adolescents to feel awake later into the night and sleepy in the morning (American Academy of Pediatrics, 2014; Carskadon, 2011a, 2011b).
Social settings can also pose risk for adolescents who feel isolated, and isolation (the subjective experience of feeling lonely and not the objective measure of social contacts) contributes to poor mental health (such as depression), physical health (such as cardiovascular disease), and premature death (even beyond mortality rates for obesity or smoking) (Cacioppo et al., 2002; Cacioppo, Hughes, Waite, Hawkley, & Thisted, 2006; Cacioppo et al., 2015; Cacioppo, Capitanio, & Cacioppo, 2014; Hawkley & Cacioppo, 2010). There are many reasons adolescents may feel lonely, and it is therefore all the more important for adults (such as caregivers) during this stage of development to be attentive to their social and emotional needs and connections. At a time when parents may feel it is time to back off and let their children take the proverbial wheel, a back-and-forth copiloting rich with attunement, empathy, and respect is needed, for this developmental window seems to provide an optimal chance for parents and significant adults to contribute toward adolescents’ trajectories of independent living and improved health outcomes (Siegel, 2013). If therapeutic interventions are required at this stage, they may focus on improving parent-offspring attachment relationships (Taylor et al., 2015; Vachon, 2016). In the next section, special attention is paid to adolescents with risk for impaired mental health and substance use, with a focus on the relationships between neurobiological underpinnings and psychosocial contexts that support or truncate routes to positive adaptation.
As demonstrated above, adolescence is a time of substantial brain-based changes, with neuroimaging research suggesting that either imbalances or alterations to the timing of these occurrences increase the risk for psychopathology (Keshavan, Giedd, Lau, Lewis, & Paus, 2014). Although stress was mentioned previously, it returns here as an important topic in adolescent mental health. Scholars suggest that biopsychosocial factors (from environment to epigenetics to hormones) can affect stress reactivity and therefore increase the risk for some, but not all, adolescents to develop a psychiatric disorder (Keshavan et al., 2014). There are many aspects of adolescents’ social environment, such as inadequate sleep and exposure to victimization (in one or more life circumstances), and genetic risk, such as family history of psychopathology, that when paired with out-of-balance limbic versus cortical recruitment may contribute to a heightened attention to negative affect and stressors, a disregulated (either over- or underactive) stress response through the HPA axis, and less adaptive coping and related resources to reduce a stress response (Buss et al., 2012; Casey, Getz, et al., 2008; Casey, Jones, et al., 2008; Chen, Burley, & Gotlib, 2012; Cozolino, 2013, 2014; Gotlib et al., 2010; McEwen, 2012; Sapolsky, 2004; Shonkoff & Garner, 2012; Waugh, Muhtadie, Thompson, Joormann, & Gotlib, 2012; Weder et al., 2014). Heightened stress reactivity and environments that continue to perpetuate stress responses can be cyclical and contribute to maladaptive pathways. Recently neuroimaging studies have examined some of the neural correlates of depression, finding that untreated depression is associated with smaller hippocampal volume (recall that cortisol is neurotoxic at high levels) in adults (Sheline, Gado, & Kraemer, 2003) and adolescents (Chen, Hamilton, & Gotlib, 2010), and that adolescent girls with risk for depression show heightened physiological arousal (stress responses) (Waugh et al., 2012), less reward activation (Gotlib et al., 2010), heightened chances of interpreting ambiguous information as negative (Dearing & Gotlib, 2009), and poorer sleep quality (Chen et al., 2012). However, it is important to realize that the brain can learn how to reappraise negative or neutral experiences as positive, which may help confer some protection for adolescents at risk of major depression or anxiety disorders based on family history (Foland-Ross, Cooney, Joormann, Henry, & Gotlib, 2014; Miller, Hamilton, Sacchet, & Gotlib, 2015).
Structural neuroimaging studies have also helped to shed light on neurobiological markers of other psychiatric disorders with onset in childhood. Findings suggest that in child-onset schizophrenia, gray matter loss seems to be accelerated, following the same posterior-to-anterior pattern demonstrated in healthy adolescent development, yet at an earlier period developmentally, a finding in line with MRI findings of adult-onset schizophrenia demonstrating significant cortical gray matter loss that could not be explained by the overgrowth of white matter (which itself shows delay in growth in schizophrenia) (Gogtay & Thompson, 2010). The use of neuroimaging technologies, combined with standard psychosocial assessments, may hold great promise for the field of mental health, for the use of biomarkers may support early detection (and the young brain is more responsive to intervention and needs less of it than the mature adult brain), and combined with epigenetics at some point in the future may support a personalized approach to treatment (Keshavan et al., 2014). Early intervention and positive social supports are paramount, for mental illness bears social stigma, which can contribute to feelings of isolation and stress in an already at-risk group of youth (Mueller, Callanan, & Greenwood, 2015; Mukolo, Heflinger, & Wallston, 2010). Adolescents who meet the diagnostic criteria for a psychiatric disorder are also significantly more likely than those who do not to meet criteria for substance use disorder (Deas & Brown, 2006), a topic pursued in the following section.
As described in the previous section, even very small changes in the young brain’s neurodevelopmental trajectory (e.g., brain structure and myelination) may have neurobehavioral and social-emotional implications (Casey & Jones, 2010; Keshavan et al., 2014). Brain development can be affected by exposure to drugs and abuse of alcohol, even for adolescents who were exposed to substances in utero (Irner, 2012), which can disrupt healthy neurocognitive functioning and can set up a maladaptive trajectory into adulthood (Blakemore, 2013; Jacobus & Tapert, 2013; Jager & Ramsey, 2008; Lisdahl, Gilbart, Wright, & Shollenbarger, 2013; Luciana & Feldstein Ewing, 2015). Recall that adolescence is a developmental window of increased receptivity for experiences (whether behaviors, substances, or activities) that the brain experiences as maximally, not simply minimally or moderately, rewarding. Adolescence is a time of increased dopamine release when the brain and body are engaged in experiences perceived to be rewarding, and this response is greater than what is seen in children and adults (Casey, Getz, et al., 2008; Galvan et al., 2007; Galvan et al., 2006). In fact, dopamine is implicated in all addictive behaviors and substances (Jacobus & Tapert, 2013). Given that adolescents are more likely to be driven to experiment and have access to settings for experimentation, the adolescent years seem to be a time of particular susceptibility to addiction, including to drugs and alcohol (Casey & Jones, 2010; Luciana & Feldstein Ewing, 2015). New experiences perceived as rewarding are likely met with a strong release of dopamine, which contributes to the addictive cycle, for dopamine drops as substances such as alcohol wear off, leading to a drop in dopamine and an increased need for a greater release of dopamine in the future due to thresholding effects (Casey & Jones, 2010; Jacobus & Tapert, 2013; Luciana & Feldstein Ewing, 2015; Suchman et al., 2012). It should also be noted that foods with a high glycemic index, such as processed foods, can also become part of an addictive cycle, for their intake raises blood sugar, which contributes to a rise in dopamine levels and therefore activates the reward circuitry (Jacobus & Tapert, 2013). Recall that sleep deprivation also affects the hormone that regulates patterns of hunger, so a sleep-deprived adolescent may also be more susceptible to this pattern of eating, depending on the nutrition of the foods available, while at the same time at greater risk for misuse of substances to address a disrupted sleep-wake cycle (Bronson & Merryman, 2009; Hutchison, 2013). Moreover, although empirical evidence research with the adolescent population is lacking, studies of adults suggest that addiction to substances such as drugs and alcohol can interrupt the brain’s reward circuitry that responds to close social connections and attachment, such that the dopaminergic activation experienced during intake of substances takes over and the potentially rewarding experiences drawn from close social ties is no longer a viable match (Suchman et al., 2012). We know the social brain is sensitive to the social environment during this stage of development, yet extra support and resources may be needed for substance misusing and abusing adolescents, who may truly benefit from interventions using social connections, yet whose brains are responding more to the cycle of substance addiction. However, as described in the following section, this stage of development does offer many routes to healthy development even when initial trajectories appear maladaptive.
Conclusion and Implications
As previously introduced, the brain is an organ of adaptation, meant to protect survival and therefore respond behaviorally to environmental input. New technologies combined with standard behavioral assessments have aided scientific understanding of the neurodevelopmental aspects that are associated with healthy development, and those that confer greater risk. Use of multimodal neuroimaging to understand adolescent behavior and development has become increasingly widespread, with fMRI becoming the leading tool to see the human brain in action over the past two decades (Huettel, Song, & McCarthy, 2009). Translating neuroimaging findings into advancements in both policy and practice is still in its infancy; however, interdisciplinary research has fueled the discovery of links between neurodevelopment and environment, and has begun to apply those findings to clinical practice (Applegate & Shapiro, 2005; Cozolino, 2010; Montgomery, 2013) and to educational settings with adolescents (Cozolino, 2013; Sala & Anderson, 2012). These findings are of particular relevance to social workers who engage in policy and practice work to design, enhance, and promote healthy contexts for biopsychosocial development.
The adolescent brain is sensitive to the environment, which can confer protection, as seen in studies assessing positive social support, interventions that address and reduce toxic stress, and provide opportunities for self-discovery and reward (Cozolino, 2014; Davidson & McEwen, 2012; Human et al., 2014; Montgomery, 2013; Siegel, 2013; Whittle et al., 2014). On the other hand, and sometimes simultaneously, adolescents’ genetic endowment combined with an adverse environment (or settings that confer multiple risk factors) and behavior can increase chances of maladaptive trajectories into young adulthood and beyond (Casey & Jones, 2010; Foland-Ross, Kircanski, & Gotlib, 2014; Keshavan et al., 2014; Sapolsky, 2004; Shonkoff et al., 2009; Shonkoff & Garner, 2012). However, given that the adolescent brain is more sensitive to vulnerabilities than the adult brain, it is also more resilient, and by providing resources at the proximal and more distal levels, social workers can significantly contribute to the promotion of positive routes to adolescent neurobiological and psychosocial well-being. Social-work researchers may be particularly interested in future work in collaboration with the field of neuroscience, for further investigations of the relationships between brain structure and function, and between biological and psychosocial development with an emphasis on the life course and environment (both built and psychosocial), are needed. Specifically, future work may evaluate development prospectively and at multiple time points, building knowledge of the important interactions among a number of contributing factors (such as neurobiology and environment) to adolescents’ developmental outcomes and transitions to adulthood. Compared to standard behavioral evaluation of psychosocial development, in vivo (within those living) brain science is still in its infancy, and therefore the established field of social work that centers on supporting positive change through all ecological systems is in the exciting position of advancing our understanding of the multiple pathways that support and sustain healthy biopsychosocial development in and beyond adolescence.
- American Academy of Pediatrics. (2014). School start times for adolescents. Pediatrics, 134(3), 642–649.
- Anda, R. F., Felitti, V. J., Bremner, J. D., Walker, J. D., Whitfield, C., Perry, B. D., Giles, W. H. (2006). The enduring effects of abuse and related adverse experiences in childhood. A convergence of evidence from neurobiology and epidemiology. European Archives of Psychiatry and Clinical Neuroscience, 256(3), 174–186.
- Applegate, J. S., & Shapiro, J. R. (2005). Neurobiology for clinical social work: Theory and practice. New York: W. W. Norton.
- Arnett, J. J. (1999). Adolescent storm and stress, reconsidered. American Psychologist, 54(5), 317–326.
- Badenoch, B. (2008). Being a brain-wise therapist: A practical guide to interpersonal neurobiology. New York: W. W. Norton.
- Baird, A. A., Silver, S. H., & Veague, H. B. (2010). Cognitive control reduces sensitivity to relational aggression among adolescent girls. Social Neuroscience, 5(5–6), 519–532.
- Bandettini, P. A. (2009). What’s new in neuroimaging methods? Annals of the New York Academy of Sciences, 1156, 260–293.
- Ben-Ari, Y. (2015). Is birth a critical period in the pathogenesis of autism spectrum disorders? Nature Reviews Neuroscience, 16(8), 498–505.
- Benningfield, M. M., Potter, M. P., & Bostic, J. Q. (2015). Educational impacts of the social and emotional brain. Child and Adolescent Psychiatric Clinics of North America, 24(2), 261–275.
- Blakely, T. J., & Dziadosz, G. M. (2015). Application of attachment theory in clinical social work. Health and Social Work, 40(4), 283–289.
- Blakemore, S. J. (2012a). Development of the social brain in adolescence. Journal of the Royal Society of Medicine, 105(3), 111–116.
- Blakemore, S. J. (2012b). Imaging brain development: The adolescent brain. Neuroimage, 61(2), 397–406.
- Blakemore, S. J. (2013). Teenage kicks: Cannabis and the adolescent brain. Lancet, 381(9870), 888–889.
- Blakemore, S. J., & Choudhury, S. (2006). Development of the adolescent brain: implications for executive function and social cognition. Journal of Child Psychology and Psychiatry, 47(3–4), 296–312.
- Blakemore, S.-J., den Ouden, H., Choudhury, S., & Frith, C. (2007). Adolescent development of the neural circuitry for thinking about intentions. Social Cognitive and Affective Neuroscience, 2(2), 130–139.
- Blakemore, S. J., & Robbins, T. W. (2012). Decision-making in the adolescent brain. Nature Neuroscience, 15(9), 1184–1191.
- Boyce, N. (2013). Sarah-Jayne Blakemore: Rethinking the adolescent brain. The Lancet, 382(9902), 1395.
- Bronson, P., & Merryman, A. (2009). NurtureShock: New thinking about children (1st ed.). New York: Twelve.
- Buchanan, T. W., & Tranel, D. (2009). Central and peripheral nervous system interactions: From mind to brain to body. International Journal of Psychophysiology, 72(1), 1–4.
- Buss, C., Davis, E. P., Shahbaba, B., Pruessner, J. C., Head, K., & Sandman, C. A. (2012). Maternal cortisol over the course of pregnancy and subsequent child amygdala and hippocampus volumes and affective problems. Proceedings of the National Academy of Science of the United States of America, 109(20), E1312–1319.
- Cacioppo, J. T., Hawkley, L. C., Crawford, L. E., Ernst, J. M., Burleson, M. H., Kowalewski, R. B., Berntson, G. G. (2002). Loneliness and health: Potential mechanisms. Psychosomatic Medicine, 64(3), 407–417.
- Cacioppo, J. T., Hughes, M. E., Waite, L. J., Hawkley, L. C., & Thisted, R. A. (2006). Loneliness as a specific risk factor for depressive symptoms: Cross-sectional and longitudinal analyses. Psychology and Aging, 21(1), 140–151.
- Cacioppo, S., Bangee, M., Balogh, S., Cardenas-Iniguez, C., Qualter, P., & Cacioppo, J. T. (2015). Loneliness and implicit attention to social threat: A high-performance electrical neuroimaging study. Cognitive Neuroscience, 7(1–4), 138–159.
- Cacioppo, S., Capitanio, J. P., & Cacioppo, J. T. (2014). Toward a neurology of loneliness. Psychological Bulletin, 140(6), 1464–1504.
- Carskadon, M. A. (2011a). Sleep in adolescents: The perfect storm. Pediatric Clinics of North America, 58(3), 637–647.
- Carskadon, M. A. (2011b). Sleep’s effects on cognition and learning in adolescence. Progress in Brain Research, 190, 137–143.
- Casey, B. J., Getz, S., & Galvan, A. (2008). The adolescent brain. Development Review, 28(1), 62–77.
- Casey, B. J., Giedd, J. N., & Thomas, K. M. (2000). Structural and functional brain development and its relation to cognitive development. Biological Psychology, 54(1–3), 241–257.
- Casey, B. J., & Jones, R. M. (2010). Neurobiology of the adolescent brain and behavior: implications for substance use disorders. Journal of the American Academy of Child and Adolescent Psychiatry, 49(12), 1189–1201; quiz 1285.
- Casey, B. J., Jones, R. M., & Hare, T. A. (2008). The adolescent brain. Annals of the New York Academy of Sciences, 1124, 111–126.
- Casey, B., Jones, R. M., & Somerville, L. H. (2011). Braking and accelerating of the adolescent brain. Journal of Research on Adolescence, 21(1), 21–33.
- Chen, M. C., Burley, H. W., & Gotlib, I. H. (2012). Reduced sleep quality in healthy girls at risk for depression. Journal of Sleep Research, 21(1), 68–72.
- Chen, M. C., Hamilton, J. P., & Gotlib, I. H. (2010). Decreased hippocampal volume in healthy girls at risk of depression. Archives of General Psychiatry, 67(3), 270–276.
- Coutinho, J. F., Silva, P. O., & Decety, J. (2014). Neurosciences, empathy, and healthy interpersonal relationships: recent findings and implications for counseling psychology. Journal Counseling Psychology, 61(4), 541–548.
- Cozolino, L. J. (2010). The neuroscience of psychotherapy: Healing the social brain (2d ed.). New York: W. W. Norton.
- Cozolino, L. J. (2013). The social neuroscience of education: Optimizing attachment and learning in the classroom. New York: W. W. Norton.
- Cozolino, L. J. (2014). The neuroscience of human relationships: Attachment and the developing social brain (2d ed.). New York: W. W. Norton.
- Cozolino, L. J. (2016). Why therapy works: Using our minds to change our brains (1st ed.). New York: W. W. Norton.
- Davidson, R. J., & McEwen, B. S. (2012). Social influences on neuroplasticity: Stress and interventions to promote well-being. Nature Neuroscience, 15(5), 689–695.
- Dearing, K. F., & Gotlib, I. H. (2009). Interpretation of ambiguous information in girls at risk for depression. Journal of Abnormal Child Psychology, 37(1), 79–91.
- Deas, D., & Brown, E. S. (2006). Adolescent substance abuse and psychiatric comorbidities. Journal of Clinical Psychiatry, 67(7), e02.
- Eisenberger, N. I., & Cole, S. W. (2012). Social neuroscience and health: Neurophysiological mechanisms linking social ties with physical health. Nature Neuroscience, 15(5), 669–674.
- Elias, M. J., & Weissberg, R. P. (2000). Primary prevention: Educational approaches to enhance social and emotional learning. Journal of School Health, 70(5), 186–190.
- Ellis, B. J., Boyce, W. T., Belsky, J., Bakermans-Kranenburg, M. J., & van Ijzendoorn, M. H. (2011). Differential susceptibility to the environment: An evolutionary-neurodevelopmental theory. Development and Psychopathology, 23(1), 7–28.
- Epel, E. (2012). How “reversible” is telomeric aging? Cancer Prevention Research (Phila), 5(10), 1163–1168.
- Epel, E. S., Blackburn, E. H., Lin, J., Dhabhar, F. S., Adler, N. E., Morrow, J. D., & Cawthon, R. M. (2004). Accelerated telomere shortening in response to life stress. Proceedings of the National Academy of Sciences of the United States of America, 101(49), 17312–17315.
- Epel, E. S., Lin, J., Wilhelm, F. H., Wolkowitz, O. M., Cawthon, R., Adler, N. E., Blackburn, E. H. (2006). Cell aging in relation to stress arousal and cardiovascular disease risk factors. Psychoneuroendocrinology, 31(3), 277–287.
- Evans, G. W. (2004). The environment of childhood poverty. American Psychologist, 59(2), 77–92.
- Farmer, R. L. (2009). Neuroscience and social work practice: The missing link. Los Angeles: SAGE.
- Foland-Ross, L. C., Cooney, R. E., Joormann, J., Henry, M. L., & Gotlib, I. H. (2014). Recalling happy memories in remitted depression: a neuroimaging investigation of the repair of sad mood. Cognitive, Affective, & Behavioral Neuroscience, 14(2), 818–826.
- Foland-Ross, L. C., Kircanski, K., & Gotlib, I. H. (2014). Coping with having a depressed mother: The role of stress and coping in hypothalamic-pituitary-adrenal axis dysfunction in girls at familial risk for major depression. Development and Psychopathology, 26(4, Pt 2), 1401–1409.
- Frith, C. D., & Frith, U. (2007). Social cognition in humans. Current Biology, 17(16), R724–732.
- Fuhrmann, D., Knoll, L. J., & Blakemore, S. J. (2015). Adolescence as a sensitive period of brain development. Trends in Cognitive Sciences, 19(10), 558–566.
- Galvan, V., & Bredesen, D. E. (2007). Neurogenesis in the adult brain: implications for Alzheimer’s disease. CNS Neurological Disorders—Drug Targets, 6(5), 303–310.
- Galvan, V., Greenberg, D. A., & Jin, K. (2006). The role of vascular endothelial growth factor in neurogenesis in adult brain. Mini Reviews in Medicinal Chemistry, 6(6), 667–669.
- Galvan, A., Hare, T. A., Parra, C. E., Penn, J., Voss, H., Glover, G., & Casey, B. J. (2006). Earlier development of the accumbens relative to orbitofrontal cortex might underlie risk-taking behavior in adolescents. Journal of Neuroscience, 26(25), 6885–6892.
- Galvan, A., Hare, T., Voss, H., Glover, G., & Casey, B. J. (2007). Risk-taking and the adolescent brain: Who is at risk? Developmental Science, 10(2), F8–F14.
- Galvan, A., Van Leijenhorst, L., & McGlennen, K. M. (2012). Considerations for imaging the adolescent brain. Development Cognitive Neuroscience, 2(3), 293–302.
- Giedd, J. N. (2004). Structural magnetic resonance imaging of the adolescent brain. Annals of the New York Academy of Sciences, 1021, 77–85.
- Giedd, J. N., Blumenthal, J., Jeffries, N. O., Castellanos, F. X., Liu, H., Zijdenbos, A., & Rapoport, J. L. (1999). Brain development during childhood and adolescence: A longitudinal MRI study. Nature Neuroscience, 2(10), 861–863.
- Giedd, J. N., Lalonde, F. M., Celano, M. J., White, S. L., Wallace, G. L., Lee, N. R., & Lenroot, R. K. (2009). Anatomical brain magnetic resonance imaging of typically developing children and adolescents. Journal of the American Academy of Child and Adolescent Psychiatry, 48(5), 465–470.
- Gillath, O., Bunge, S. A., Shaver, P. R., Wendelken, C., & Mikulincer, M. (2005). Attachment-style differences in the ability to suppress negative thoughts: exploring the neural correlates. Neuroimage, 28(4), 835–847.
- Giorgio, A., Watkins, K. E., Chadwick, M., James, S., Winmill, L., Douaud, G., James, A. C. (2010). Longitudinal changes in grey and white matter during adolescence. NeuroImage, 49(1), 94–103.
- Gogtay, N., & Thompson, P. M. (2010). Mapping gray matter development: Implications for typical development and vulnerability to psychopathology. Brain and Cognition, 72(1), 6–15.
- Goldenberg, D., & Galvan, A. (2015). The use of functional and effective connectivity techniques to understand the developing brain. Development Cognitive Neuroscience, 12, 155–164.
- Gotlib, I. H., Hamilton, J. P., Cooney, R. E., Singh, M. K., Henry, M. L., & Joormann, J. (2010). Neural processing of reward and loss in girls at risk for major depression. Archives of General Psychiatry, 67(4), 380–387.
- Grant, P. E. (2012). Evolution of pediatric neuroimaging and application of cutting-edge techniques. In A. A. Benasich, & R. H. Fitch (Eds.), Developmental dyslexia: Early precursors, neurobehavioral markers, and biological substrates (pp. 227–240). Baltimore, MD: Paul H. Brookes.
- Greene, R. R. (2008). Psychosocial theory comprehensive handbook of social work and social welfare. John Wiley.
- Hamilton, C. E. (2000). Continuity and discontinuity of attachment from infancy through adolescence. Child Development, 71(3), 690–694.
- Hawkley, L. C., & Cacioppo, J. T. (2010). Loneliness matters: A theoretical and empirical review of consequences and mechanisms. Annals of Behavioral Medicine, 40(2), 218–227.
- Hill, P. T. (2001). High schools and development of healthy young people. Adolescent Medicine, 12(3), 459–470.
- Holtmaat, A., & Svoboda, K. (2009). Experience-dependent structural synaptic plasticity in the mammalian brain. Nature Reviews Neuroscience, 10(9), 647–658.
- Huettel, S. A., Song, A. W., & McCarthy, G. (2009). Functional magnetic resonance imaging (2d ed.). Sunderland, MA: Sinauer Associates.
- Hughes, D. A., & Baylin, J. F. (2012). Brain-based parenting: the neuroscience of caregiving for healthy attachment. New York: W. W. Norton.
- Human, L. J., Chan, M., DeLongis, A., Roy, L., Miller, G. E., & Chen, E. (2014). Parental accuracy regarding adolescent daily experiences: Relationships with adolescent psychological adjustment and inflammatory regulation. Psychosomatic Medicine, 76(8), 603–610.
- Hutchison, E. D. (2013). Essentials of human behavior: integrating person, environment, and the life course. Los Angeles: SAGE.
- Irner, T. B. (2012). Substance exposure in utero and developmental consequences in adolescence: A systematic review. Child Neuropsychology, 18(6), 521–549.
- Jacobus, J., & Tapert, S. F. (2013). Neurotoxic effects of alcohol in adolescence. Annual Review of Clinical Psychology, 9, 703–721.
- Jager, G., & Ramsey, N. F. (2008). Long-term consequences of adolescent cannabis exposure on the development of cognition, brain structure and function: An overview of animal and human research. Current Drug Abuse Reviews, 1(2), 114–123.
- Johnson, S. B., Blum, R. W., & Giedd, J. N. (2009). Adolescent maturity and the brain: The promise and pitfalls of neuroscience research in adolescent health policy. Journal of Adolescent Health, 45(3), 216–221.
- Karatsoreos, I. N., & McEwen, B. S. (2013). Annual Research Review: The neurobiology and physiology of resilience and adaptation across the life course. Journal of Child Psychology and Psychiatry, 54(4), 337–347.
- Kempermann, G., Song, H., & Gage, F. H. (2015). Neurogenesis in the adult hippocampus. Cold Spring Harbor Perspectives in Biology, 7(9), a018812.
- Keshavan, M. S., Giedd, J., Lau, J. Y., Lewis, D. A., & Paus, T. (2014). Changes in the adolescent brain and the pathophysiology of psychotic disorders. Lancet Psychiatry, 1(7), 549–558.
- Kim, P., Evans, G. W., Angstadt, M., Ho, S. S., Sripada, C. S., Swain, J. E., Phan, K. L. (2013). Effects of childhood poverty and chronic stress on emotion regulatory brain function in adulthood. Proceedings of the National Academy of Science of the United States of America, 110(46), 18442–18447.
- Kolb, B., & Gibb, R. (2014). Searching for the principles of brain plasticity and behavior. Cortex, 58, 251–260.
- Lebel, C., Gee, M., Camicioli, R., Wieler, M., Martin, W., & Beaulieu, C. (2012). Diffusion tensor imaging of white matter tract evolution over the lifespan. Neuroimage, 60(1), 340–352.
- Levesque, J., Eugene, F., Joanette, Y., Paquette, V., Mensour, B., Beaudoin, G., & Beauregard, M. (2003). Neural circuitry underlying voluntary suppression of sadness. Biological Psychiatry, 53(6), 502–510.
- Levesque, J., Joanette, Y., Mensour, B., Beaudoin, G., Leroux, J. M., Bourgouin, P., & Beauregard, M. (2004). Neural basis of emotional self-regulation in childhood. Neuroscience, 129(2), 361–369.
- Lisdahl, K. M., Gilbart, E. R., Wright, N. E., & Shollenbarger, S. (2013). Dare to delay? The impacts of adolescent alcohol and marijuana use onset on cognition, brain structure, and function. Frontiers in Psychiatry, 4, 53.
- Luciana, M., & Feldstein Ewing, S. W. (2015). Introduction to the special issue: substance use and the adolescent brain: Developmental impacts, interventions, and longitudinal outcomes. Developmental Cognitive Neuroscience, 16, 1–4.
- Malter Cohen, M., Tottenham, N., & Casey, B. J. (2013). Translational developmental studies of stress on brain and behavior: Implications for adolescent mental health and illness?. Neuroscience, 249, 53–62.
- Mathiak, K. A., Klasen, M., Zvyagintsev, M., Weber, R., & Mathiak, K. (2013). Neural networks underlying affective states in a multimodal virtual environment: Contributions to boredom. Frontiers in Human Neuroscience, 7.
- Matto, H. C., & Strolin-Goltzman, J. (2010). Integrating social neuroscience and social work: Innovations for advancing practice-based research. Social Work, 55(2), 147–156.
- McEwen, B. S. (2012). Brain on stress: How the social environment gets under the skin. Proceedings of the National Academy of Science of the United States of America, 109 Supplement 2, 17180–17185.
- McEwen, B. S., & Gianaros, P. J. (2010). Central role of the brain in stress and adaptation: links to socioeconomic status, health, and disease. Annals of the New York Academy of Sciences, 1186, 190–222.
- Mekinda, M. A. (2012). Support for career development in youth: program models and evaluations. New Directions for Youth Development, 2012(134), 45–54, 48.
- Merrifield, C., & Danckert, J. (2014). Characterizing the psychophysiological signature of boredom. Experimental Brain Research, 232(2), 481–491.
- Miller, C. H., Hamilton, J. P., Sacchet, M. D., & Gotlib, I. H. (2015). Meta-analysis of functional neuroimaging of major depressive disorder in youth. JAMA Psychiatry, 72(10), 1045–1053.
- 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. Development Neuroscience, 36(3–4), 147–160.
- Mills, K. L., Lalonde, F., Clasen, L. S., Giedd, J. N., & Blakemore, S. J. (2014). Developmental changes in the structure of the social brain in late childhood and adolescence. Social Cognitive and Affect Neuroscience, 9(1), 123–131.
- Ming, G. L., & Song, H. (2011). Adult neurogenesis in the mammalian brain: Significant answers and significant questions. Neuron, 70(4), 687–702.
- Montgomery, A. (2013). Neurobiology essentials for clinicians: What every therapist needs to know (1st ed.). New York: W. W. Norton.
- Morelli, S. A., Torre, J. B., & Eisenberger, N. I. (2014). The neural bases of feeling understood and not understood. Social Cognitive and Affective Neuroscience, 9(12), 1890–1896.
- Mrug, S., Tyson, A., Turan, B., & Granger, D. A. (2016). Sleep problems predict cortisol reactivity to stress in urban adolescents. Physiology and Behavior, 155, 95–101.
- Mueller, J., Callanan, M. M., & Greenwood, K. (2015). Communications to children about mental illness and their role in stigma development: An integrative review. Journal of Mental Health, 25(1), 1–9.
- Mukolo, A., Heflinger, C. A., & Wallston, K. A. (2010). The stigma of childhood mental disorders: a conceptual framework. Journal of the American Academy of Child and Adolescent Psychiatry, 49(2), 92–103.
- Payton, J. W., Wardlaw, D. M., Graczyk, P. A., Bloodworth, M. R., Tompsett, C. J., & Weissberg, R. P. (2000). Social and emotional learning: A framework for promoting mental health and reducing risk behavior in children and youth. Journal of School Health, 70(5), 179–185.
- Perrin, J. S., Herve, P. Y., Leonard, G., Perron, M., Pike, G. B., Pitiot, A., Paus, T. (2008). Growth of white matter in the adolescent brain: Role of testosterone and androgen receptor. Journal of Neuroscience, 28(38), 9519–9524.
- Pfeifer, J. H., Lieberman, M. D., & Dapretto, M. (2007). “I know you are but what am I?!”: Neural bases of self- and social knowledge retrieval in children and adults. Journal of Cognitive Neuroscience, 19(8), 1323–1337.
- Pfeifer, J. H., & Peake, S. J. (2012). Self-development: Integrating cognitive, socioemotional, and neuroimaging perspectives. Development Cognitive Neuroscience, 2(1), 55–69.
- Pringle, J., Mills, K., McAteer, J., Jepson, R., Hogg, E., Anand, N., & Blakemore, S. J. (2016). A systematic review of adolescent physiological development and its relationship with health-related behaviour: A protocol. Systematic Review, 5(1), 3.
- Purves, D., Augustine, G. J., Fitzpatrick, D., Katz, L. C., LaMantia, A.‑S., McNamara, J. O., & Williams, S. M. (Eds.). (2001). Neuroscience (2d ed.). Sunderland, MA: Sinauer Associates.
- Sala, S. D., & Anderson, M. (Eds.) (2012). Neuroscience in education: The good, the bad, and the ugly. Oxford: Oxford University Press.
- Saleebey, D. (2008). The strengths perspective: Putting possibility and hope to work in our practice. In B. W. White (Ed.), Comprehensive handbook of social work and social welfare: The profession of social work (pp. 123–142). Hoboken, NJ: John Wiley.
- Sapolsky, R. M. (2004). Why zebras don’t get ulcers (3d ed.). New York: St. Martin’s.
- Satterthwaite, T. D., & Baker, J. T. (2015). How can studies of resting-state functional connectivity help us understand psychosis as a disorder of brain development? Current Opinion in Neurobiology, 30, 85–91.
- Sawyer, S. M., Afifi, R. A., Bearinger, L. H., Blakemore, S. J., Dick, B., Ezeh, A. C., & Patton, G. C. (2012). Adolescence: A foundation for future health. Lancet, 379(9826), 1630–1640.
- Schriber, R. A., & Guyer, A. E. (2015). Adolescent neurobiological susceptibility to social context. Developmental Cognitive Neuroscience, 19, 1–18.
- Sebastian, C., Viding, E., Williams, K. D., & Blakemore, S. J. (2010). Social brain development and the affective consequences of ostracism in adolescence. Brain and Cognition, 72(1), 134–145.
- Serra, M., De Pisapia, N., Rigo, P., Papinutto, N., Jager, J., Bornstein, M. H., & Venuti, P. (2015). Secure attachment status is associated with white matter integrity in healthy young adults. Neuroreport, 26(18), 1106–1111.
- Shalev, I., Entringer, S., Wadhwa, P. D., Wolkowitz, O. M., Puterman, E., Lin, J., & Epel, E. S. (2013). Stress and telomere biology: A lifespan perspective. Psychoneuroendocrinology, 38(9), 1835–1842.
- Sheline, Y. I., Gado, M. H., & Kraemer, H. C. (2003). Untreated depression and hippocampal volume loss. American Journal of Psychiatry, 160(8), 1516–1518.
- Shonkoff, J. P. (2011). Protecting brains, not simply stimulating minds. Science, 333(6045), 982–983.
- Shonkoff, J. P., Boyce, W. T., & McEwen, B. S. (2009). Neuroscience, molecular biology, and the childhood roots of health disparities: Building a new framework for health promotion and disease prevention. JAMA, 301(21), 2252–2259.
- Shonkoff, J. P., & Garner, A. S. (2012). The lifelong effects of early childhood adversity and toxic stress. Pediatrics, 129(1), e232–246.
- Siegel, D. J. (2013). Brainstorm: The power and purpose of the teenage brain. New York: Jeremy P. Tarcher/Penguin.
- Smith, J. D., Woodhouse, S. S., Clark, C. A., & Skowron, E. A. (2015). Attachment status and mother-preschooler parasympathetic response to the strange situation procedure. Biological Psychology, 114, 39–48.
- Soffer-Dudek, N., Sadeh, A., Dahl, R. E., & Rosenblat-Stein, S. (2011). Poor sleep quality predicts deficient emotion information processing over time in early adolescence. Sleep, 34(11), 1499–1508.
- Sowell, E. R., Delis, D., Stiles, J., & Jernigan, T. L. (2001). Improved memory functioning and frontal lobe maturation between childhood and adolescence: A structural MRI study. Journal of the International Neuropsychological Society, 7(3), 312–322.
- Sowell, E. R., Peterson, B. S., Thompson, P. M., Welcome, S. E., Henkenius, A. L., & Toga, A. W. (2003). Mapping cortical change across the human life span. Nature Neuroscience, 6(3), 309–315.
- Spicer, J., Galvan, A., Hare, T. A., Voss, H., Glover, G., & Casey, B. (2007). Sensitivity of the nucleus accumbens to violations in expectation of reward. Neuroimage, 34(1), 455–461.
- Steinberg, L., & Morris, A. S. (2001). Adolescent development. Annual Review of Psychology, 52, 83–110.
- Stiles, J., & Jernigan, T. L. (2010). The basics of brain development. Neuropsychology Review, 20(4), 327–348.
- Suchman, N. E., Decoste, C., Rosenberger, P., & McMahon, T. J. (2012). Attachment-based interventions for substance-using mothers: A preliminary test of the proposed mechanisms of change. Infant Mental Health Journal, 33(4), 360–371.
- Taylor, P. J., Rietzschel, J., Danquah, A., & Berry, K. (2015). The role of attachment style, attachment to therapist, and working alliance in response to psychological therapy. Psychology and Psychotherapy, 88(3), 240–253.
- Telzer, E. H., Fuligni, A. J., Lieberman, M. D., & Galvan, A. (2013). The effects of poor quality sleep on brain function and risk taking in adolescence. NeuroImage, 71, 275–283.
- Telzer, E. H., Goldenberg, D., Fuligni, A. J., Lieberman, M. D., & Galvan, A. (2015). Sleep variability in adolescence is associated with altered brain development. Development Cognitive Neuroscience, 14, 16–22.
- Thompson, P. M., Vidal, C., Giedd, J. N., Gochman, P., Blumenthal, J., Nicolson, R., & Rapoport, J. L. (2001). Mapping adolescent brain change reveals dynamic wave of accelerated gray matter loss in very early-onset schizophrenia. Proceedings of the National Academy of Science of the United States of America, 98(20), 11650–11655.
- Tzanoulinou, S., & Sandi, C. (2016). The Programming of the Social Brain by Stress During Childhood and Adolescence: From Rodents to Humans. Current Topics in Behavioral Neuroscience.
- Vachon, M. L. (2016). Targeted intervention for family and professional caregivers: Attachment, empathy, and compassion. Palliative Medicine, 30(2), 101–103.
- Vaughn, M. G., DeLisi, M., & Matto, H. C. (2014). Human behavior: A cell to society approach. Hoboken, NJ: John Wiley.
- Vrticka, P., Black, J. M., Neely, M., Walter Shelly, E., & Reiss, A. L. (2013). Humor processing in children: Influence of temperament, age and IQ. Neuropsychologia, 51(13), 2799–2811.
- Waugh, C. E., Muhtadie, L., Thompson, R. J., Joormann, J., & Gotlib, I. H. (2012). Affective and physiological responses to stress in girls at elevated risk for depression. Development and Psychopathology, 24(2), 661–675.
- Weder, N., Zhang, H., Jensen, K., Yang, B. Z., Simen, A., Jackowski, A., & Kaufman, J. (2014). Child abuse, depression, and methylation in genes involved with stress, neural plasticity, and brain circuitry. Journal of the American Academy of Child and Adolescent Psychiatry, 53(4), 417–424.e415.
- Whittle, S., Simmons, J. G., Dennison, M., Vijayakumar, N., Schwartz, O., Yap, M. B., & Allen, N. B. (2014). Positive parenting predicts the development of adolescent brain structure: A longitudinal study. Development Cognitive Neuroscience, 8, 7–17.
- Yang, B. Z., Zhang, H., Ge, W., Weder, N., Douglas-Palumberi, H., Perepletchikova, F., Kaufman, J. (2013). Child abuse and epigenetic mechanisms of disease risk. American Journal of Preventive Medicine, 44(2), 101–107.
- Zhang, F., & Labouvie-Vief, G. (2004). Stability and fluctuation in adult attachment style over a 6-year period. Attachment and Human Development, 6(4), 419–437.