Journal of Counseling Psychology 2014, Vol. 61, No. 4, 521-527

© 2014 American Psychological Association 0022-0167/14/$ 12.00 http://dx.doi.org/10.1037/cou0000033

Neuroscience of Child and Adolescent Health Development Jodene Goldenring Fine and Connie Sung Michigan State University Recent advances in technology and neuroscience have increased our understanding of human neuro­ development. In particular, research on neuroplasticity and psychosocial genomics lends compelling support to a biopsychosocial perspective by elucidating mechanisms through which psychosocial forces and environments shape neurobiology. This article summarizes selected results from recent investigations of neuroplasticity and psychosocial genomics, which demonstrate complex interaction between genes, epigenetic processes, and environmental experience that confers neural growth into adulthood. Coun­ seling psychologists working with children and adolescents need to be familiar with recent literature to be more effective in their work so that they can provide developmentally appropriate counseling services. Social cognitive theory and resilience theory are introduced to illustrate how counseling psychologists can incorporate neuroscience research findings in a counseling context and hypotheses are proposed for future counseling psychology research. Keywords: child, adolescent, brain development, neuroscience, counseling

Advances in pediatric neuroscience over the past decade are beginning to reveal the complex systems involved in neural plas­ ticity. This article introduces selected recent findings regarding multiple influences in child and adolescent neural development. Current thinking on how the brain “remodels” itself from birth to young adulthood is presented, with a special emphasis on adoles­ cent development. The influence of trauma and toxic stress on neurobiology is also reviewed. Throughout, counseling psycholo­ gists are encouraged to think about how this new knowledge can inform their treatment of young clients and caretakers, as well as how scholars of the field might contribute to the important work being done to better understand the connections between observ­ able human behavior and neural development. It was historically thought that the human brain was structurally mature by the end of early childhood (Mann, 1984) and the nature versus nurture debate that dominated the mid-20th century posited unidirectional models of child development that assigned varying degrees of influence independently to genes and environment (Meaney, 2010). It is now thought that a complex interplay of genetic inheritance and environmental experience, mediated by epigenetic processes, shapes the brain from the womb to adult­ hood. The epigenome is a set of chemical compounds, which, in response to environmental stimuli such as nutrition, toxins, or traumatic experiences, directly alter the expression of one’s genes, muting some and activating others. Although the exact specifica­ tions of a model for the epigenome is currently under debate (Riddihough & Zahn, 2010), the importance of its role in adjusting the genome in response to environmental conditions is well ac-

cepted. Moreover, the implications of the epigenetic process be­ come more important as counseling psychologists seek to design interventions capable of yielding lasting changes in complex psy­ chosocial behaviors (Meaney, 2010). Exactly how the epigenome influences brain development is not yet well understood. Much of what we know about the moderation of genetic expression due to environmental variables comes to us from animal models and some adult human studies that combine genotyping, examination of deoxyribonucleaic acid (DNA) methylation differences, and either neuroimaging or postmortem exam­ ination. Longitudinal neuroimaging of typical brain development throughout childhood to adulthood has laid the foundation for better understanding possible points of intervention that may con­ fer maximal inoculation against environmental stressors. Much research is yet needed, and how counseling psychologists might contribute to this work is discussed in this selective review along with ideas for incorporating neuroscience findings into day-to-day counseling intervention work. The focus of the review is neuro­ typical development, with special attention to the adolescent brain and how trauma influences neurological development.

Systematic Remodeling of the Brain The brain comprises many cell types, broadly divided into neurons and glial cells, but researchers speak mostly of gray and white matter. Maturity is recognized as a reduction in the amount of gray matter relative to white matter following an initial wave of gray matter growth (Gogtay et al., 2004). The reduction in gray relative to white matter indicates increasing neural efficiency. Gray matter reduces in volume as unneeded synapses wither. Increasing white matter signals that axons are becoming myelin­ ated, improving the connectivity between neural systems in the brain. Although there is considerable individual variability in the timing of these neural changes (Lenroot & Giedd, 2006), the sequence by which they occur is consistent. The primary systems of the brain develop first, followed by a back-to-front progression that generally follows the phylogeny of the brain, with evolution-

Jodene Goldenring Fine and Connie Sung, Department of Counseling, Educational Psychology, and Special Education (CEPSE), Michigan State University. Correspondence concerning this article should be addressed to Jodene Goldenring Fine, CEPSE, Michigan State University, 620 Farm Lane, 440, Erickson Hall, East Lansing, MI 48824. E-mail: [email protected]

521

522

FINE AND SUNG

arily newest systems maturing last (Gogtay et al., 2004). Thus, sensory (e.g., vision) and motor systems are first to mature while areas such as the association cortex, which integrates memory, audio-visual inputs, and object recognition, matures later. The parts of the brain associated with complex decision making, im­ pulse control, error-checking, and judgment mature the latest (Lenroot & Giedd, 2006). It is well established that the early waves of development for sensory systems such as vision are time-limited. In other words, if a perfectly normal nascent vision system is not exposed to light during the first 6 months of life, normal sight will not be achieved at all thereafter (Hubei, Wiesel, & LeVay, 1977). Less is known about the sensitive periods of neural plasticity for more complex developmental processes such as attachment and learning, which require the orchestration of multiple neural regions over time. However, researchers have shown that earlier treatment confers better outcomes in neurobiological disorders such as dyslexia (Peterson & Pennington, 2012) and autism (Granpeesheh, Dixon, Tarbox, Kaplan, & Wilke, 2009), suggesting that more complex functions do have a timing component. Many aspects of environmental influence, both positive and negative, have been shown to affect neural development through a variety of mediators including the hormonal system which acti­ vates the “flight/fight” response via the hypothalamic-pituitaryadrenocortical (HPA) axis (Lupien, McEwen, Gunnar, & Heim, 2009); production of brain-derived neurotropic factor (BDNF), which is associated with neural plasticity; and other molecular markers such as glucocorticoids and serotoninergic receptors that are involved in shaping typical and atypical neural development (Cowansage, LeDoux, & Monfds, 2010; Lesch & Waider, 2012). These mediators are thought to be themselves influenced by a wide variety of environmental variables including exercise (Russo, Murrough, Han, Chamey, & Nestler, 2012), nutrition (Lucassen et al., 2013), and prenatal and postnatal stress (Harris & Seek], 2011; Vaynman, Ying, & Gomez-Pinilla, 2004). These environmental variables are the manipulable factors in neurodevelopment and are therefore important for counseling psychologists and researchers to pay attention to. Molecular biologists are interested in identify­ ing the neural mechanisms by which neural plasticity operates in hope of finding a way of “triggering plasticity machinery” (Hensch, 2005, p. 886) out of the normal sequence. At the same time, counseling psychologists can be more closely delineating the boundaries of natural windows of optimal learning using behav­ ioral observations and sensitivity to training/treatment. Once the keys to triggering neural plasticity are opened, counseling psychol­ ogists will be at the forefront of designing new paradigms for educational and rehabilitative training. The sciences of neuroplasticity and psychosocial genomics may provide new empirical bases for psychotherapeutic interventions. However, there are problems with current methodologies that have hampered the ability to directly link together the genomic, envi­ ronmental, behavioral, and neurobiological fields together in order to understand exactly how the complex interplay between them operates. One of the weakest aspects of this research is that findings have been poorly replicated in neuroimaging studies (Bennett & Miller, 2010; Button et al., 2013), and the clinical utility of neuroimaging in psychiatric and developmental disorders is weak (Linden, 2012). Although neuroimaging has been em­ braced by the popular press, researchers working in the field are

concerned about overinterpretation of findings and the danger of establishing a new phrenology (Karmiloff-Smith, 2010). It is im­ portant to understand that functional magnetic resonance imaging (fMRI) and positron emission tomography (PET) do not directly measure neural activity. The areas that “light up” in the brain on magnetic resonance imaging (MRI) scans are those where blood oxygenation are changing, and their temporal proximity is strik­ ingly lax (up to 7 s). Spatially these areas may be distant from the exact location where neural activity occurs (Pike, 2012). Addi­ tional sources of error include small sample sizes, differences in the technical specifications of MRI hardware, poor experimental designs, and the widely varying methods used for analyses of very complex data (Weyandt, Swentosky, & Gudmundsdottir, 2013). Diffusion tensor imaging (DTI), which provides an indication of white matter development, has shown that maturation of brain system connectivity extends past adolescence through adulthood, but because age proceeds linearly with myelination, age tends to account for most of the behavioral variance in longitudinal studies (e.g., Peters et al., 2014). Reliably tying neuroimaging findings to behavioral indices has become a concern among researchers and is a place where the careful work of counseling psychologists can help to build models of behavioral, environmental and familial influences in neural development. Future directions in neuroimag­ ing will need to tie genetic, epigenetic, and behavioral markers together, suggesting that multidisciplinary work crossing several academic boundaries, including counseling psychology, will need to occur. Despite difficulty in consistently aligning specific indices of brain maturation with behavioral and social attributes, the finding that the human brain is not fully mature until well after adoles­ cence has broad implications for psychological treatment, legal justice, education, and social contexts. Researchers have now identified ways in which the teenage brain differs from adults, particularly in key areas of emotion processing, sensation-seeking and decision making. These findings are making their way into the legal (Ortiz, 2004) and educational policy (Blakemore, 2010) arenas.

The Teenage Brain As we have begun to understand that the process of brain maturation extends well beyond adolescence, research comparing adolescent to adult brains has flourished. Particular focus has been placed on differences in regulation of emotion (Somerville, Jones, & Casey, 2010), risk-taking (Steinberg, 2010), and addiction (Bava & Tapert, 2010). Cognitive maturation continues through the teenage years (Crone, 2009), but there is paradoxically a marked increase in poor decision making, including accidental death, unprotected sex, and experimentation with illegal sub­ stances and alcohol (Eaton et al., 2008). Although various re­ searchers explain this paradox using different language terms (e.g., salience, motivation, emotional regulation, hot vs. cool executive functioning), in essence the findings point to the importance of context. Adolescents may very well know what to do, they how­ ever may not be able to perform according to their own better judgment under certain conditions. Researchers have examined the moderating effects of emotional and social context on decision making in adolescents (Crone, 2009). While teenagers continue to improve performance on cool

NEUROSCIENCE OF CHILD AND ADOLESCENT

executive functioning tasks, their ability to exert cognitive control during tasks involving high-stakes rewards, or hot executive func­ tioning tasks, may lag (Zelazo & Carlson, 2012). Cool tasks are those without salient reward or punishment. Examples are recall­ ing and transforming numbers, discovering sorting rules, or inhib­ iting automatic responses in Stroop-like tasks. In contrast, hot tasks are those that require balancing risks and potential rewards in high-stakes situations, for example, earning a high amount of money, but enduring a higher frequency of losing money if a high-stakes approach is taken. Using a hot gambling task, Huizenga, Crone, and Jansen (2007) reported that young children choose a guessing strategy that avoids punishment and adults inhibit temptation to respond to immediate gratification in favor of long-term gain. Adult-like long-term strategies were not observed until late in adolescence or early adulthood. Prencipe et al. (2011) found linear age-related improvement in young adolescents on cool executive functioning tasks, but improvements on hot tasks improved more gradually and later in development. Neuroimaging evidence suggests that the relative maturation of two distinct neural systems, one for reward and the other for cognitive control, may account for adolescence as a time of im­ proved cognitive growth but reduced ability to manage affective and social influences in decision making (Van Leijenhorst et al., 2010). Dopeminergic reward-processing (ventral striatum) regions increase in sensitivity (Blakemore & Robbins, 2012) in the ab­ sence of fully mature and interconnected prefrontal areas that are needed to exert the top-down pressure required for delaying sat­ isfaction to reach long-term gain (Casey, Jones, & Hare, 2008). However, the “dual systems” (Steinberg, 2010) models of adoles­ cent behavior, which suggest separable and independently func­ tioning executive control and emotion/motivation systems, ignore the interconnectedness among the neural circuitries supporting them (Luciana, 2013). The two systems may be interdependent such that perturbations or experience in one may influence the development of the other, providing opportunity for environmental shaping through either system. Work in this area might be espe­ cially important in the prevention, or at least the mitigation, of psychopathologies such as schizophrenia and bipolar that emerge during the reorganization of the frontotemporal neural systems (Brent, Thermenos, Keshavan, & Seidman, 2013; Hajek et al., 2013). Reward system sensitivity may be one of the most important points of interventions for counseling psychologists in part be­ cause it has been found to be mediated by social factors. Among the most salient variables in reward processing for adolescents is peer interaction. For example, presence of peers has been shown to be important in addictive behaviors among teenagers (O’Brien, Albert, Chein, & Steinberg, 2011). There is considerable evidence that adolescents can learn to adopt adaptive behaviors and avoid maladaptive behaviors. For example, one of the best predictors of individual and aggregate drug use is the perception that drugs are dangerous to one’s health (Bachman, Johnston, & O’Malley, 1998). Social cognitive theory (SCT), developed by Bandura (1986), emphasizes the interaction among personal factors, behav­ ior, and social environment and suggests that self-efficacy, out­ come expectancy, and facilitators and impediments can be applied to further develop theoretically based intervention models and empirically supported strategies (e.g., skills training) in an effort to promote behavioral change and, by extension, biological change.

523

Specific skills such as modeling, mastery learning, social persua­ sion, and self-regulation are often used in education and interven­ tion programs to promote self-efficacy in learning and behavioral change. Thus, counseling psychologists are well-poised to help examine how such interventions shape neural functioning and behavioral outcomes.

The Neuroplastic Brain: Trauma Neural plasticity is essential for humans to respond to everchanging environmental demands (Fuchs, Flugge, & Czeh, 2006), and when typical environmental pressures meet typical neural systems, the result is adaptive learning and adjustment (Obradovic, Bush, Stamperdahl, Adler, & Boyce, 2010). There are times, however, when unexpectedly salient or severe pressures are ex­ erted, and, coupled with biological predisposition, the brain re­ sponds with shaping neural circuitry that, while adaptive in the moment, may lead to maladaptive functioning later. In some cases, an injury may be so intense that the brain is unable to adequately adapt to subsequent typical experiences, as has been seen in children bom with extremely low birth weight (Volpe, 2009). Disruption to early neural systems, whether from early birth, trauma to the brain from injury (e.g., concussion, chemotherapy), or severe psychosocial trauma, can result in downstream conse­ quences affecting development years distant from the initial event. The timing of these events is salient to the outcomes, since the brain has sensitive periods when the neural systems in the brain are especially likely to be shaped by the environment (Tottenham & Sheridan, 2010). For example, a brain that has mature language functioning will suffer less from diffuse injury, such as chemo­ therapy, than one that is in the process of organizing the neural circuitry for language. Insult to the brain following birth can come as a physical or psychological blow. Recently researchers have searched for the neural consequences of emotional trauma and neglect in children, following evidence of neural differences in adults with posttraumatic stress disorder (PTSD; Shin, Rauch, & Pitman, 2006; Vil­ larreal et al., 2002). Several studies have found smaller hippocam­ pal volumes in adults who were abused as children (Andersen et al., 2008; Teicher, Anderson, & Polcari, 2012), but not all studies agree (Pederson et al., 2004). In children, similar reports of smaller hippocampi have been published (Carrion, Weems, & Reiss, 2007), but others have failed to reproduce similar results (De Beilis, Hall, Boring, Frustaci, & Moritz, 2001; Tupler & De Beilis, 2006). In a meta-analysis of hippocampal volumes in both children and adults, Woon and Hedges (2008) found that while children do not show reduced volumes, adults do. They speculate that these changes may not occur until adulthood as a downstream event, but because the data are not longitudinal, such conclusions are tenu­ ous. Longitudinal research is needed to investigate these working hypotheses. In humans, we cannot experimentally apply stress or trauma in order to better understand how neural changes occur and whether the systems are plastic enough to normalize with treatment. One fascinating rodent study indicates that changes to the hippocampus transmitted to offspring prenatally via maternal trauma can be modified in early development. Lemaire, Lamarque, Le Moal, Piazza, and Abrous (2006) applied stress to rat mothers who then gave birth to pups having lifelong smaller volumes of the hip-

524

FINE AND SUNG

pocampus than pups bom to unstressed mothers. However, early increased handling of prenatally stressed pups after birth (enrich­ ment) normalized their hippocampal volumes compared to pups who did not receive increased handling. The authors concluded that “beneficial environments during critical periods or adversity in early life during critical periods of development can remodel the neuronal circuitry throughout the lifespan” (Lemaire et al., 2006, p. 791). There is something of a possible chicken and egg problem with regard to human vulnerability to stress. Gilbertson et al. (2002) examined this by comparing monzygotic twins. One of each pair was a combat veteran exposed to trauma and the other had no combat exposure. Hippocampal volume emerged as a risk factor for, not a cause of PTSD. The authors rejected the theory of hippocampal atrophy but note that smaller hippocampal volumes may predispose one to increased psychopathology. However, given the previous work suggesting that trauma early in develop­ ment can influence neurogeneisis of the hippocampus, it may be that smaller hippocampi indicate early stress and trauma that, in turn, confers vulnerability in adulthood. The mechanism by which toxic stress influences early brain development is hypothesized to be a neurotoxic inflammatory hormonal response. It arises from activation of the HPA axis, which produces elevated and prolonged glucocorticoid levels thought to disrupt neural systems that are in a period of sensitivity (Shonkoff et al., 2012). Most vulnerable are the glucocorticoid receptors of brain regions associated with emotional learning, self-regulation, reward processing, and executive control, such as hippocampus, amygdala, and prefrontal cortex. Reductions in vol­ ume of these areas have been observed in maltreated children (De Brito et al., 2013), depressed adults reporting childhood toxic stress (Frodl et al., 2010), and children who experienced early institutionalization (Tottenham et al., 2010). Gene X Environment interactions have also been observed, suggesting that specific genetic vulnerabilities may mediate the effect of toxic stress on psychological outcomes (Chen, Li, & McGue, 2013; Frodl et al.,

2010). Individual differences in response to toxic stress and trauma have been documented, providing an opening for counseling psy­ chologists to consider which factors confer resilience in the face of severe environmental adversity in childhood. Why do some chil­ dren thrive despite early toxic stress? Researchers have shown that normalization of brain functioning can be observed when inter­ vention occurs early on. Institutionalized children placed in foster care before the age of 20 months show better attachment security (Almas et al., 2012) and normalization of electroencephalography (EEG) alpha activity (Vanderwert, Marshall, Nelson, Zeanah, & Fox, 2010) at age 8, compared to children removed to foster care after 2 years of age. Although early intervention is virtually uni­ versally recognized, very few empirically based interventions are documented with attention to (a) how timing influences outcomes, (b) the mechanisms by which interventions change neural devel­ opment, and (c) which individuals are most likely to benefit based on genetic characteristics. One leading researcher of child development has called for the adoption of a biodevelopmental framework for understanding dis­ parities in health, learning, and behavior that could be used to guide research and policy (Shonkoff, 2010). This framework sug­ gests that much of the adult disease observed and previously

thought to originate as products of adult behaviors are better viewed as processes originating in childhood, or even in prenatal development (Shonkoff, Boyce, & McEwen, 2009). “Biological embedding” refers to “the epigenetic pathways by which adversity is transmuted into lasting alterations in disease risk” (Shonkoff et al., 2009, p. 2254), when an early systemic response to adversity is adaptive in the moment but confers a threat to functioning in the long run. This type of embedding is thought to be most potent during periods of special sensitivity to specific types of environ­ mental stimuli during neural development. For counseling psychologists working within a biodevelopmen­ tal framework, resilience theory may provide an interesting ap­ proach to ameliorating the downstream effects of maladaptive functioning. Empirical support for the association between resil­ ience and psychosocial adjustment has begun to emerge in the literature (Fergus & Zimmerman, 2005). Although no universal definition of resilience has been established, resilience is fre­ quently defined as a dynamic process that encompasses both a behavioral and psychological manifestation of positive adaptation within the context of significant adversity (Todd & Worell, 2000). Resilience theory focuses on understanding healthy development in spite of risk exposure and seeks to identify both risks and promotive factors that either help bring about a positive outcome or reduce/avoid a negative outcome. The promotive factors that can help youth avoid the negative effects of risks maybe either assets residing within the individual (e.g., competence, coping skills, and self-efficacy) or resources external to the individual (e.g., parental support, adult mentoring, and community organiza­ tions). Modem resilience theory embraces an ecological context, moving away from a static, within-individual conceptualization (Fergus & Zimmerman, 2005) that is consistent with the multisys­ tem interactions being revealed at work in neural development. The concept of resilience and its associated evidence suggest several implications for prevention and intervention. A key idea is that interventions may need to focus on developing assets and resources for children exposed to risk (Yates, Egeland, & Sroufe, 2003), instead of the more traditional approach of focusing on risk amelioration. The usual practice is to list deficits that predispose, enable, and reinforce some negative behaviors. A resilience ap­ proach, however, emphasizes assets and resources as the focus for change. Internal assets that may be particularly critical to develop include social skills for relating to peers, self-efficacy for healthpromoting behavior, academic skills, and participation in extracur­ ricular and community activities. Botvin and Griffin (2002) have suggested that skills building for life in general, such as develop­ ment of generic social and problem-solving skills, can be just as important as building skills for risk avoidance. Moreover, building generalizable skills and engaging in practices of them may help to develop the neural circuitry for top-down control and self­ regulation critical to performing adequately in adulthood. External resources that may be developed include opportunities for adult mentorship (Zimmerman, 2010), parenting skills (Kumpfer & Al­ varado, 2003), and provision of health-promoting settings for adolescents (Hanlon, Bateman, Simon, O’Grady, & Carswell, 2002). Another key idea is that, because of the multidimensional nature of resilience, interventions that cut across behaviors may be most effective. Interventions that focus solely on substance use avoidance in adolescence, for example, may be too narrowly focused to alter the entire context of influences in a young person’s

NEUROSCIENCE OF CHILD AND ADOLESCENT

life. Thus, a resilience model may be one way to address the need for theory-driven evidence-based interventions aimed at promoting healthy brain development in children and adolescents.

Future Directions Molecular biologists, geneticists, and neuroscientists are seek­ ing understand the complex transactions between genes, environ­ ment, and the mediating epigenome that result in pathologies of mental and physical health. It is virtually indisputable that brain development includes periods of special sensitivity and opportu­ nity for growth from the womb to early adulthood. Yet little is known about the timing of higher order neural shaping periods beyond those of basic sensory systems, how resilience is con­ ferred, how best to identify those at special risk for maladaptation, and how diversity of culture might influence these processes. Counseling psychologists willing to invest intellectual capital in learning the language of neuroscience can provide very important guidance as the field develops from basic bench science to trans­ lational science meant to address atypical and maladaptive func­ tioning quite possibly seeded years before symptoms become apparent. Counseling psychologists can bring a fully ecological perspective, provide expertise on empirically validated interven­ tions, and play an important role in the communication of psychoeducational neuroscience news to parents, teachers, and chil­ dren.

References Almas, A. N., Degnan, K. A., Radulescu, A., Nelson, C. A., Zeanah, C. H., & Fox, N. A. (2012). Effects of early intervention and the moderating effects of brain activity on institutionalized children’s social skills at age 8. Proceedings o f the National Academy o f Sciences o f the United States o f A m erica, /09(S uppl. 2), 17228-17231. doi:10.1073/pnas .1121256109 Andersen, S., Tomada, A., Vincow, E., Valente, E., Polcari, A., & Teicher, M. (2008). Preliminary evidence for sensitive periods in the effect of childhood sexual abuse on regional brain development. Journal o f Neu­ ropsychiatry and Clinical Neurosciences, 20, 292-301. doi: 10.1176/appi .neuropsych.20.3.292 Bachman, J. G., Johnston, L. D., & O ’Malley, P. M. (1998). Explaining recent increases in students’ marijuana use: Impacts of perceived risks and disapproval, 1976 through 1996. American Journal o f Public Health, 88, 887-892. doi:10.2105/AJPH.88.6.887 Bandura, A. (1986). Social foundations o f thought and action: A social cognitive theory. Englewood Cliffs, NJ: Prentice Hall. Bava, S., & Tapert, S. F. (2010). Adolescent brain development and the risk for alcohol and other drug problems. Neuropsychology Review, 20, 398-413. doi: 10.1007/s 11065-010-9146-6 Bennett, C. M., & Miller, M. B. (2010). How reliable are the results from functional magnetic resonance imaging? Annals o f the New York Acad­ emy o f Sciences, 1191, 133-155. doi:10.11 ll/j.l749-6632.2010.05446.x Blakemore, S. J. (2010). The developing social brain: Implications for education. Neuron, 65, 744-747. doi:10.1016/j.neuron.2010.03.004 Blakemore, S. J., & Robbins, T. W. (2012). Decision-making in the adolescent brain. Nature Neuroscience, 15, 1184-1191. doi:10.1038/nn .3177 Botvin, G. J., & Griffin, K. W. (2002). Life skills training as a primary prevention approach for adolescent drug abuse and other problem be­ haviors. International Journal o f Emergency Mental Health, 4, 41-47. Brent, B. K., Thermenos, H. W„ Keshavan, M. S., & Seidman, L. J. (2013). Gray matter alterations in schizophrenia high-risk youth and early-onset

525

schizophrenia: A review of structural MRI findings. Child and Adoles­ cent Psychiatric Clinics o f North America, 22, 689-714. doi: 10.1016/j .chc.2013.06.003 Button, K. S., Ionnidis, J. P. A., Mokrysz, C., Nosek, B. A., Flint, J., Robinson, E. S. J., & Runafo, M. R. (2013). Power failure: Why small sample size undermines the reliability of neuroscience. Nature Reviews Neuroscience, 14, 365-376. doi:10.1038/nrn3475 Carrion, V. G., Weems, C. F., & Reiss, A. L. (2007). Stress predicts brain changes in children: A pilot longitudinal study on youth stress, posttraumatic stress disorder, and the hippocampus. Pediatrics, 119, 509-516. doi: 10.1542/peds.2006-2028 Casey, B., Jones, R. M., & Hare, T. A. (2008). The adolescent brain. Annals o f the New York Academy o f Sciences, 1124, 111-126. doi: 10.1196/annals. 1440.010 Chen, J., Li, X., & McGue, M. (2013). The interacting effect of the BDNF Val66Met polymorphism and stressful life events on adolescent depres­ sion is not an artifact of gene-environment correlation: Evidence from a longitudinal twin study. Journal o f Child Psychology and Psychiatry, 54, 1066-1073. doi: 10.I l l 1/jcpp. 12099 Cowansage, K. K., LeDoux, J. E., & Monfils, M. H. (2010). Brain-derived neurotrophic factor: A dynamic gatekeeper of neural plasticity. Current Molecular Pharmacology, 3, 12-29. Crone, E. A. (2009). Executive functions in adolescence: Inferences from brain and behavior. Developmental Science, 12, 825-830. doi: 10.1111/j .1467-7687.2009.00918.x De Beilis, M. D., Hall, J., Boring, A. M., Frustaci, K., & Moritz, G. (2001). A pilot longitudinal study of hippocampal volumes in pediatric maltreatment-related posttraumatic stress disorder. Biological Psychia­ try, 50, 305-309. doi: 10.1016/S0006-3223(01)01105-2 De Brito, S. A., Viding, E., Sebastian, C. L., Kelly, P. A., Mechelli, A., Maris, H., & McCrory, E. J. (2013). Reduced orbitofrontal and temporal grey matter in a community sample of maltreated children. Journal o f Child Psychology and Psychiatry, 54, 105-112. doi: 10. I l l l/j.14697610.2012.02597.x Eaton, D. K., Kann, L., Kinchen, S., Shanklin, S., Ross, J., Hawkins, J . , . . . Wechsler, H. (2008). Youth risk behavior surveillance—United States, 2007. Morbidity and Mortality Weekly Report Surveillance Summaries, 57, 1-131. Fergus, S., & Zimmerman, M. A. (2005). Adolescent resilience: A frame­ work for understanding healthy development in the face of risk. Annual Review o f Public Health, 26, 399-419. doi: 10.1146/annurev.publhealth .26.021304.144357 Frodl, T., Reinhold, E., Koutsouleris, N., Donohoe, G., Bondy, B„ Reiser, M., . . . Meisenzahl, E. M. (2010). Childhood stress, serotonin trans­ porter gene and brain structures in major depression. Neuropsychophar­ macology, 35, 1383-1390. doi:10.1038/npp.2010.8 Fuchs, E., Flugge, G., & Czeh, B. (2006). Remodeling of neuronal net­ works by stress. Front Bioscience, 11, 2746-2758. doi: 10.2741/2004 Gilbertson, M. W., Shenton, M. E., Ciszewski, A., Kasai, K., Lasko, N. B., Orr, S. P., & Pitman, R. K. (2002). Smaller hippocampal volume predicts pathologic vulnerability to psychological trauma. Nature Neu­ roscience, 5, 1242-1247. doi:10.1038/nn958 Gogtay, N., Giedd, J. N., Lusk, L., Hayashi, K. M., Greenstein, D., Vaituzis, A. C., . . . Thompson, P. M. (2004). Dynamic mapping of human cortical development during childhood through early adulthood. Proceedings o f the National Academy o f Sciences o f the United States o f America, 101, 8174-8179. doi:10.1073/pnas,0402680101 Granpeesheh, D., Dixon, D. R., Tarbox, J., Kaplan, A. M., & Wilke, A. E. (2009). The effects of age and treatment intensity on behavioral inter­ vention outcomes for children with autism spectrum disorders. Research in Autism Spectrum Disorders, 3, 1014-1022. doi: 10.1016/j.rasd.2009 .06.007 Hajek, T., Cullis, J., Novak, T., Kopecek, M., Blagdon, R., Propper, L . , . . . Alda, M. (2013). Brain structural signature of familial predisposition for

526

FINE AND SUNG

bipolar disorder: Replicable evidence for involvement of the right infe­ rior frontal gyrus. Biological Psychiatry, 73, 144-152. doi: 10.1016/j .biopsych.2012.06.015 Hanlon, T. E., Bateman, R. W., Simon, B. D., O’Grady, K. E„ & Carswell, S. B. (2002). An early community-based intervention for the prevention of substance abuse and other delinquent behavior. Journal o f Youth and Adolescence, 31, 459-471. doi:10.1023/A:1020215204844 Harris, A., & Seckl, J. (2011). Glucocorticoids, prenatal stress and the programming of disease. Hormones and Behavior, 59, 279-289. doi: 10.1016/j.yhbeh.2010.06.007 Hensch, T. K. (2005). Critical period plasticity in local cortical circuits. Nature Reviews Neuroscience, 6, 877-888. doi:10.1038/nml787 Hubei, D. H., Wiesel, T. N., & LeVay, S. (1977). Plasticity of ocular dominance columns in monkey striate cortex. Philosophical Transac­ tions o f the Royal Society o f London. Series B, Biological Sciences, 278, 377-409. doi: 10.1098/rstb. 1977.0050 Huizenga, H. M., Crone, E. A., & Jansen, B. J. (2007). Decision-making in healthy children, adolescents and adults explained by the use of increas­ ingly complex proportional reasoning rules. Developmental Science, 10, 814-825. doi: 10.1111/j. 1467-7687.2007,00621.x Karmiloff-Smith, A. (2010). Neuroimaging of the developing brain: Tak­ ing “developing” seriously. Human Brain Mapping, 31, 934-941. doi: 10.1002/hbm.21074 Kumpfer, K. L., & Alvarado, R. (2003). Family-strengthening approaches for the prevention of youth problem behaviors. American Psychologist, 58, 457-465. doi:10.1037/0003-066X.58.6-7.457 Lemaire, V., Lamarque, S., Le Moal, M., Piazza, P. V., & Abrous, D. N. (2006). Postnatal stimulation of the pups counteracts prenatal stressinduced deficits in hippocampal neurogenesis. Biological Psychiatry, 59, 786-792. doi:10.1016/j.biopsych.2005.11.009 Lenroot, R. K., & Giedd, J. N. (2006). Brain development in children and adolescents: Insights from anatomical magnetic resonance imaging. Neuroscience and Biobehavioral Reviews, 30, 718-729. doi: 10.1016/j .neubiorev.2006.06.001 Lesch, K. P., & Waider, J. (2012). Serotonin in the modulation of neural plasticity and networks: Implications for neurodevelopmental disorders. Neuron, 76, 175-191. doi:!0.1016/j.neuron.2012.09.013 Linden, D. E. (2012). The challenges and promise of neuroimaging in psychiatry. Neuron, 73, 8-22. doi:10.1016/j.neuron.2011.12.014 Lucassen, P. J., Naninck, E. F„ van Goudoever, J. B., Fitzsimons, C., Joels, M., & Korosi, A. (2013). Perinatal programming of adult hippocampal structure and function; emerging roles of stress, nutrition and epigenetics. Trends in Neurosciences, 36, 621-631. doi: 10.1016/j.tins.2013.08 .002 Luciana, M. (2013). Adolescent brain development in normality and psy­ chopathology. Development and Psychopathology, 2, 1325-1345. doi: 10.1017/S0954579413000643 Lupien, S. J., McEwen, B. S., Gunnar, M. R., & Heim, C. (2009). Effects of stress throughout the lifespan on the brain, behaviour and cognition. Nature Reviews Neuroscience, 10, 434-445. doi:10.1038/nm2639 Mann, M. D. (1984). The growth of the brain and skull in children. Developmental Brain Research, 315, 169-178. doi: 10.1016/01653806(84)90152-4 Meaney, J. M. (2010). Epigenetics and the biological definition of Gene X Environment interactions. Child Development, 81, 41-79. doi: 10.1111/j . 1467-8624.2009.01381.x Obradovic, J., Bush, N. R„ Stamperdahl, J., Adler, N. E., & Boyce, W. T. (2010). Biological sensitivity to context: The interactive effects of stress reactivity and family adversity on socioemotional behavior and school readiness. Child Development, 81, 270-289. doi: 10.1111/j. 1467-8624 ,2009.01394.x O’Brien, L., Albert, D., Chein, J., & Steinberg, L. (2011). Adolescents prefer more immediate rewards when in the presence of their peers.

Journal o f Research on Adolescence, 21, 747-753. doi: 10.1111/j. 15327795.2011.00738.x Ortiz, A. (2004). Adolescent brain development and legal culpability. Washington, DC: Juvenile Justice Center. Pederson, C. L., Maurer, S. H., Kaminski, P. L., Zander, K. A., Peters, C. M„ Stokes-Crowe, L. A., & Osborn, R. E. (2004). Hippocampal volume and memory performance in a community-based sample of women with posttraumatic stress disorder secondary to child abuse. Journal o f Traumatic Stress, 17, 3 7 -4 0 . doi:10.1023/B:JO TS .0000014674.84517.46 Peters, B. D„ Ikuta, T., DeRosse, P., John, M., Burdick, K. E., Gruner, P., . . . Malhotra, A. K. (2014). Age-related differences in white matter tract microstructure are associated with cognitive performance from child­ hood to adulthood. Biological Psychiatry, 75, 248-256. doi: 10.1016/j .biopsych.2013.05.020 Peterson, R. L., & Pennington, B. F. (2012). Developmental dyslexia. The Lancet, 379, 1997-2007. doi: 10.1016/S0140-6736( 12)60198-6 Pike, G. B. (2012). Quantitative functional MRI: Concepts, issues and future challenges. Neuroim age, 62, 1234-1240. doi: 10.1016/j .neuroimage.2011.10.046 Prencipe, A., Kesek, A., Cohen, J., Lamm, C., Lewis, M. D., & Zelazo, P. D. (2011). Development of hot and cool executive function during the transition to adolescence. Journal o f Experimental Child Psychology, 108, 621-637. doi: 10.1016/j.jecp.2010.09.008 Riddihough, G., & Zahn, L. M. (2010). What is epigenetics? Science, 330, 611. doi: 10.1126/science.330.6004.611 Russo, S. J., Murrough, J. W., Han, M. H., Chamey, D. S., & Nestler, E. J. (2012). Neurobiology of resilience. Nature Neuroscience, 15, 14751484. doi:10.1038/nn.3234 Shin, L. M„ Rauch, S. L., & Pitman, R. K. (2006). Amygdala, medial prefrontal cortex, and hippocampal function in PTSD. Annals o f the New York Academy o f Sciences, 1071, 67-79. doi:10.1196/annals. 1364.007 Shonkoff, J. P. (2010). Building a new biodevelopmental framework to guide the future of early childhood policy. Child Development, 81, 357-367. doi: 10.1111/j. 1467-8624.2009.01399.x Shonkoff, J. P., Boyce, W. T., & McEwen, B. S. (2009). Neuroscience, molecular biology, and the childhood roots of health disparities: Build­ ing a new framework for health promotion and disease prevention. Journal o f the American Medical Association, 301, 2252-2259. doi: 10.100 l/jama.2009.754 Shonkoff, J. P., Gamer, A. S., Siegel, B. S., Dobbins, M. I., Earls, M. F., McGuinn, L., . . . Wood, D. L. (2012). The lifelong effects of early childhood adversity and toxic stress. Pediatrics, 129, e232-e246. doi: 10.1542/peds.2011-2663 Somerville, L. H., Jones, R. M., & Casey, B. (2010). A time of change: Behavioral and neural correlates of adolescent sensitivity to appetitive and aversive environmental cues. Brain and Cognition, 72, 124-133. doi:10.1016/j.bandc.2009.07.003 Steinberg, L. (2010). A behavioral scientist looks at the science of adoles­ cent brain development. Brain and Cognition, 72, 160-164. doi: 10.1016/j.bandc.2009.11.003 Teicher, M. H., Anderson, C. M., & Polcari, A. (2012). Childhood mal­ treatment is associated with reduced volume in the hippocampal sub­ fields CA3, dentate gyrus, and subiculum. Proceedings o f the National Academy o f Sciences o f the United States o f America, 109, E563-E572. doi:10.1073/pnas.l 115396109 Todd, J. L., & Worell, J. (2000). Resilience in low-income, employed, African American women. Psychology o f Women Quarterly, 24, 119128. doi: 10.111 l/j,1471-6402.2000.tb00192.x Tottenham, N„ Hare, T. A., Quinn, B. T., McCarry, T. W., Nurse, M., Gilhooly, T., . . . Eigsti, I. M. (2010). Prolonged institutional rearing is associated with atypically large amygdala volume and difficulties in emotion regulation. Developmental Science, 13, 46 -6 1 . doi: 10.1111/j .1467-7687.2009.00852.x

NEUROSCIENCE OF CHILD AND ADOLESCENT Tottenham, N., & Sheridan, M. A. (2010). A review of adversity, the amygdala and the hippocampus: A consideration of developmental tim­ ing. Frontiers in Human Neuroscience, 3, 68. doi:10.3389/neuro.09.068 .2009 Tupler, L. A., & De Beilis, M. D. (2006). Segmented hippocampal volume in children and adolescents with posttraumatic stress disorder. Biological Psychiatry, 59, 523-529. doi:10.1016/j.biopsych.2005.08.007 Vanderwert, R. E., Marshall, P. J., Nelson, C. A., Ill, Zeanah, C. H., & Fox, N. A. (2010). Timing of intervention affects brain electrical activity in children exposed to severe psychosocial neglect. PLoS ONE, 5, el 1415. doi:10.1371/joumal.pone.0011415 Van Leijenhorst, L., Moor, B. G., Op de Macks, Z. A., Rombouts, S. A., Westenberg, P. M., & Crone, E. A. (2010). Adolescent risky decision­ making: Neurocognitive development of reward and control regions. Neuroimage, 51, 345-355. doi:10.1016/j.neuroimage.2010.02.038 Vaynman, S., Ying, Z., & Gomez-Pinilla, F. (2004). Hippocampal BDNF mediates the efficacy of exercise on synaptic plasticity and cognition. European Journal o f Neuroscience, 20, 2580-2590. doi: 10.1111/j. 14609568.2004.03720.x Villarreal, G., Hamilton, D. A., Petropoulos, H., Driscoll, I., Rowland, L. M., Griego, J. A., . . . Brooks, W. M. (2002). Reduced hippocampal volume and total white matter volume in posttraumatic stress disorder. Biological Psychiatry, 52, 119-125. doi: 10.1016/S0006-3223(02) 01359-8 Volpe, J. J. (2009). Brain injury in premature infants: A complex amalgam of destructive and developmental disturbances. The Lancet Neurology, 8, 110-124. doi: 10.1016/S 1474-4422(08)70294-1

527

Weyandt, L., Swentosky, A., & Gudmundsdottir, B. G. (2013). Neuroim­ aging and ADHD: FMRI, PET, DTI findings, and methodological lim­ itations. D evelopm ental Neuropsychology, 38, 211-225. doi: 10.1080/87565641.2013.783833 Woon, F. L., & Hedges, D. W. (2008). Hippocampal and amygdala volumes in children and adults with childhood maltreatment-related posttraumatic stress disorder: A meta-analysis. Hippocampus, 18, 7 2 9 736. doi: 10.1002/hipo,20437 Yates, T. M., Egeland, B., & Sroufe, L. A. (2003). Rethinking resilience: A developmental process perspective. In S. S. Luthar (Ed.), Resilience and vulnerability (pp. 243-266). Cambridge, England: Cambridge Uni­ versity Press, doi: 10.1017/CBO9780511615788.012 Zelazo, P. D., & Carlson, S. M. (2012). Hot and cool executive function in childhood and adolescence: Development and plasticity. Child Devel­ opment Perspectives, 6, 354-360. doi: 10.1111/j. 1750-8606.2012 ,00246.x Zimmerman, M. A. (2010). Natural mentors, mental health, and risk behaviors: A longitudinal analysis of African American adolescents transitioning into adulthood. American Journal o f Community Psychol­ ogy, 46, 36-48. doi: 10.1007/s 10464-010-9325-x

Received December 14, 2013 Revision received April 20, 2014 Accepted April 21, 2014 ■

E-Mail Notification of Your Latest Issue Online! Would you like to know when the next issue of your favorite APA journal will be available online? This service is now available to you. Sign up at http://notify.apa.org/ and you will be notified by e-mail when issues of interest to you become available!

Copyright of Journal of Counseling Psychology is the property of American Psychological Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use.

Neuroscience of child and adolescent health development.

Recent advances in technology and neuroscience have increased our understanding of human neurodevelopment. In particular, research on neuroplasticity ...
5MB Sizes 3 Downloads 6 Views