Larson, R. & Richards, M. H. Daily companionship in late childhood and early adolescence: changing developmental contexts. Child. Dev. 62, 284–300 (1991).
O’Brien, S. F. & Bierman, K. L. Conceptions and perceived influence of peer groups: interviews with preadolescents and adolescents. Child. Dev. 59, 1360–1365 (1988).
Harrell, A. W., Mercer, S. H. & DeRoisier, M. E. Improving the social-behavioral adjustment of adolescents: The effectiveness of a social skills group intervention. J. Child. Fam. Stud. 18, 378–387 (2009).
Gorrese, A. & Ruggieri, R. Peer attachment and self-esteem: A meta-analytic review. Pers. Individ. Dif. 55, 559–568 (2013).
Oldehinkel, A. J., Rosmalen, J. G. M., Veenstra, R., Dijkstra, J. K. & Ormel, J. Being admired or being liked: classroom social status and depressive problems in early adolescent girls and boys. J. Abnorm. Child. Psychol. 35, 417–427 (2007).
Blakemore, S.-J. The social brain in adolescence. Nat. Rev. Neurosci. 9, 267–277 (2008).
Blakemore, S.-J. & Mills, K. L. Is adolescence a sensitive period for sociocultural processing? Annu. Rev. Psychol. 65, 187–207 (2014).
Chein, J., Albert, D., O’Brien, L., Uckert, K. & Steinberg, L. Peers increase adolescent risk taking by enhancing activity in the brain’s reward circuitry. Dev. Sci. 14, F1–F10 (2011).
Knoll, L. J., Magis-Weinberg, L., Speekenbrink, M. & Blakemore, S.-J. Social influence on risk perception during adolescence. Psychol. Sci. 26, 583–592 (2015).
Knoll, L. J., Leung, J. T., Foulkes, L. & Blakemore, S.-J. Age-related differences in social influence on risk perception depend on the direction of influence. J. Adolesc. 60, 53–63 (2017).
Wolf, L. K., Bazargani, N., Kilford, E. J., Dumontheil, I. & Blakemore, S.-J. The audience effect in adolescence depends on who’s looking over your shoulder. J. Adolesc. 43, 5–14 (2015).
Masten, C. L. et al. Neural correlates of social exclusion during adolescence: understanding the distress of peer rejection. Soc. Cogn. Affect. Neurosci. 4, 143–157 (2009).
Sebastian, C., Viding, E., Williams, K. D. & Blakemore, S.-J. Social brain development and the affective consequences of ostracism in adolescence. Brain Cogn. 72, 134–145 (2010).
Dumontheil, I., Apperly, I. A. & Blakemore, S.-J. Online usage of theory of mind continues to develop in late adolescence. Dev. Sci. 13, 331–338 (2010).
Dubois, J. & Adolphs, R. Building a science of individual differences from fMRI. Trends Cogn. Sci. 20, 425–443 (2016).
Rohner, R. P. Toward a conception of culture for cross-cultural psychology. J. Cross Cult. Psychol. 15, 111–138 (1984).
Giedd, J. N. et al. Brain development during childhood and adolescence: a longitudinal MRI study. Nat. Neurosci. 2, 861–863 (1999).
Tamnes, C. K. et al. Development of the cerebral cortex across adolescence: A multisample study of inter-related longitudinal changes in cortical volume, surface area, and thickness. J. Neurosci. 37, 3402–3412 (2017). This study analyzed longitudinal data from 388 individuals aged between 8 and 30 years from four large cohorts in three countries: the United States, the Netherlands and Norway (854 total scans). In all four groups, there were decreases in grey matter volume across the cortex throughout adolescence, with the largest decreases occurring in the prefrontal, parietal and temporal cortices.
Vijayakumar, N. et al. Brain development during adolescence: A mixed-longitudinal investigation of cortical thickness, surface area, and volume. Hum. Brain Mapp. 37, 2027–2038 (2016).
Mills, K. L. et al. Structural brain development between childhood and adulthood: Convergence across four longitudinal samples. Neuroimage 141, 273–281 (2016).
Gilmore, J. H. et al. Longitudinal development of cortical and subcortical gray matter from birth to 2 years. Cereb. Cortex 22, 2478–2485 (2012).
Wierenga, L. et al. Typical development of basal ganglia, hippocampus, amygdala and cerebellum from age 7 to 24. Neuroimage 96, 67–72 (2014).
Tamnes, C. K., Bos, M. G. N., van de Kamp, F. C., Peters, S. & Crone, E. A. Longitudinal development of hippocampal subregions from childhood to adulthood. Preprint at bioRxiv https://doi.org/10.1101/186270 (2017). This paper assessed the structural development of subregions within the hippocampus. Data were from a large accelerated longitudinal study (
n = 270, 678 scans) of 8- to 28-year-olds. The study found heterogeneity of trajectories across region, with some showing early volume increases and others showing nonlinear decreases in volume.
Mills, K. L., Goddings, A.-L., Clasen, L. S., Giedd, J. N. & Blakemore, S.-J. The developmental mismatch in structural brain maturation during adolescence. Dev. Neurosci. 36, 147–160 (2014).
Somerville, L. H. Searching for signatures of brain maturity: What are we searching for? Neuron 92, 1164–1167 (2016).
Crone, E. A., van Duijvenvoorde, A. C. K. & Peper, J. S. Annual Research Review: Neural contributions to risk-taking in adolescence–developmental changes and individual differences. J. Child. Psychol. Psychiatry 57, 353–368 (2016).
Simmonds, D. J., Hallquist, M. N. & Luna, B. Protracted development of executive and mnemonic brain systems underlying working memory in adolescence: A longitudinal fMRI study. Neuroimage 157, 695–704 (2017).
Crone, E. A. & Elzinga, B. M. Changing brains: how longitudinal functional magnetic resonance imaging studies can inform us about cognitive and social-affective growth trajectories. Wiley Interdiscip. Rev. Cogn. Sci. 6, 53–63 (2015).
Herting, M. M., Gautam, P., Chen, Z., Mezher, A. & Vetter, N. C. Test-retest reliability of longitudinal task-based fMRI: Implications for developmental studies. Dev. Cogn. Neurosci. https://doi.org/10.1016/j.dcn.2017.07.001 (2017).
King, K.M. et al. Longitudinal modeling in developmental neuroimaging research: Common challenges, and solutions from developmental psychology. Dev. Cogn. Neurosci. https://doi.org/10.1016/j.dcn.2017.11.009 (2017). This provides an overview of issues involved in conducting longitudinal structural and functional studies to measure brain development across age. Suggested analytical approaches are demonstrated on simulated data, and the underlying code is available for other researchers to access.
Sherman, L., Steinberg, L. & Chein, J. Connecting brain responsivity and real-world risk taking: Strengths and limitations of current methodological approaches. Dev. Cogn. Neurosci. https://doi.org/10.1016/j.dcn.2017.05.007 (2017).
Ordaz, S. J., Foran, W., Velanova, K. & Luna, B. Longitudinal growth curves of brain function underlying inhibitory control through adolescence. J. Neurosci. 33, 18109–18124 (2013).
Hackman, D. A. & Farah, M. J. Socioeconomic status and the developing brain. Trends Cogn. Sci. 13, 65–73 (2009).
Farah, M. J. The neuroscience of socioeconomic status: Correlates, causes, and consequences. Neuron 96, 56–71 (2017).
Noble, K. G. et al. Family income, parental education and brain structure in children and adolescents. Nat. Neurosci. 18, 773–778 (2015). This cross-sectional study shows an association between SES and cortical surface area across age. Data were from a cohort of 1,099 individuals aged 3 to 20 years old. There was a significant interaction between SES, age and surface area, highlighting the importance of including SES in studies investigating the development of brain structure.
Noble, K. G., Houston, S. M., Kan, E. & Sowell, E. R. Neural correlates of socioeconomic status in the developing human brain. Dev. Sci. 15, 516–527 (2012).
Muscatell, K. A. et al. Social status modulates neural activity in the mentalizing network. Neuroimage 60, 1771–1777 (2012).
Tompkins, V., Logan, J. A. R., Blosser, D. F. & Duffy, K. Child language and parent discipline mediate the relation between family income and false belief understanding. J. Exp. Child. Psychol. 158, 1–18 (2017).
Symeonidou, I., Dumontheil, I., Chow, W.-Y. & Breheny, R. Development of online use of theory of mind during adolescence: An eye-tracking study. J. Exp. Child. Psychol. 149, 81–97 (2016).
Mills, K. L., Dumontheil, I., Speekenbrink, M. & Blakemore, S.-J. Multitasking during social interactions in adolescence and early adulthood. R. Soc. Open. Sci. 2, 150117 (2015).
Abrams, D., Weick, M., Thomas, D., Colbe, H. & Franklin, K. M. On-line ostracism affects children differently from adolescents and adults. Br. J. Dev. Psychol. 29, 110–123 (2011).
Sebastian, C. L. et al. Developmental influences on the neural bases of responses to social rejection: implications of social neuroscience for education. Neuroimage 57, 686–694 (2011).
Vijayakumar, N., Cheng, T. W. & Pfeifer, J. H. Neural correlates of social exclusion across ages: A coordinate-based meta-analysis of functional MRI studies. Neuroimage 153, 359–368 (2017).
Cascio, C. N., O’Donnell, M. B., Simons-Morton, B. G., Bingham, C. R. & Falk, E. B. Cultural context moderates neural pathways to social influence. Cult. Brain 5, 50–70 (2017).
Choudhury, S. Culturing the adolescent brain: what can neuroscience learn from anthropology? Soc. Cogn. Affect. Neurosci. 5, 159–167 (2010).
Steinberg, L. et al. Around the world, adolescence is a time of heightened sensation seeking and immature self-regulation. Dev. Sci. https://doi.org/10.1111/desc.12532 (2017).
Duell, N. et al. Age patterns in risk taking across the world. J. Youth Adolesc. https://doi.org/10.1007/s10964-017-0752-y (2017).
Miller, J. G. & Kinsbourne, M. Culture and neuroscience in developmental psychology: Contributions and challenges. Child. Dev. Perspect. 6, 35–41 (2012).
Telzer, E. H., Masten, C. L., Berkman, E. T., Lieberman, M. D. & Fuligni, A. J. Gaining while giving: an fMRI study of the rewards of family assistance among white and Latino youth. Soc. Neurosci. 5, 508–518 (2010). This is one of the few fMRI studies to compare neural activity in adolescents of different cultures, in this case White and Latino Americans. When winning money for their families, Latino participants showed more activation in brain regions that have been implicated in reward processing. The paper demonstrates that individual differences in culture can be associated with different patterns of neural activity.
Telzer, E. H. & Fuligni, A. J. Daily family assistance and the psychological well-being of adolescents from Latin American, Asian, and European backgrounds. Dev. Psychol. 45, 1177–1189 (2009).
Fuligni, A. J., Tseng, V. & Lam, M. Attitudes toward family obligations among American adolescents with Asian, Latin American, and European backgrounds. Child. Dev. 70, 1030–1044 (1999).
Telzer, E. H., Fuligni, A. J., Lieberman, M. D. & Galván, A. Meaningful family relationships: neurocognitive buffers of adolescent risk taking. J. Cogn. Neurosci. 25, 374–387 (2013).
Steinberg, L. & Monahan, K. C. Age differences in resistance to peer influence. Dev. Psychol. 43, 1531–1543 (2007).
Loke, A. Y. & Mak, Y. W. Family process and peer influences on substance use by adolescents. Int. J. Environ. Res. Public Health 10, 3868–3885 (2013).
D’Amico, E. J. & McCarthy, D. M. Escalation and initiation of younger adolescents’ substance use: the impact of perceived peer use. J. Adolesc. Health 39, 481–487 (2006).
Unger, J. B. et al. Peer influences and access to cigarettes as correlates of adolescent smoking: a cross-cultural comparison of Wuhan, China, and California. Prev. Med. 34, 476–484 (2002).
Headen, S. W., Bauman, K. E., Deane, G. D. & Koch, G. G. Are the correlates of cigarette smoking initiation different for black and white adolescents? Am. J. Public Health 81, 854–858 (1991).
Landrine, H., Richardson, J. L., Klonoff, E. A. & Flay, B. Cultural diversity in the predictors of adolescent cigarette smoking: the relative influence of peers. J. Behav. Med. 17, 331–346 (1994).
Unger, J. B. et al. Ethnic variation in peer influences on adolescent smoking. Nicotine Tob. Res. 3, 167–176 (2001).
Welborn, B. L. et al. Neural mechanisms of social influence in adolescence. Soc. Cogn. Affect. Neurosci. 11, 100–109 (2016).
Lamblin, M., Murawski, C., Whittle, S. & Fornito, A. Social connectedness, mental health and the adolescent brain. Neurosci. Biobehav. Rev. 80, 57–68 (2017).
Arseneault, L., Bowes, L. & Shakoor, S. Bullying victimization in youths and mental health problems: ‘much ado about nothing’? Psychol. Med. 40, 717–729 (2010).
Copeland, W. E., Wolke, D., Angold, A. & Costello, E. J. Adult psychiatric outcomes of bullying and being bullied by peers in childhood and adolescence. JAMA Psychiatry 70, 419–426 (2013).
Takizawa, R., Maughan, B. & Arseneault, L. Adult health outcomes of childhood bullying victimization: evidence from a five-decade longitudinal British birth cohort. Am. J. Psychiatry 171, 777–784 (2014).
Singham, T. et al. Concurrent and longitudinal contribution of exposure to bullying in childhood to mental health: The role of vulnerability and resilience. JAMA Psychiatry 74, 1112–1119 (2017).
van Harmelen, A.-L. et al. Adolescent friendships predict later resilient functioning across psychosocial domains in a healthy community cohort. Psychol. Med. 47, 2312–2322 (2017).
Will, G.-J., van Lier, P. A. C., Crone, E. A. & Güroğlu, B. Chronic childhood peer rejection is associated with heightened neural responses to social exclusion during adolescence. J. Abnorm. Child. Psychol. 44, 43–55 (2016).
Telzer, E. H., Miernicki, M. E. & Rudolph, K. D. Chronic peer victimization heightens neural sensitivity to risk taking. Dev. Psychopathol. 10, 1–14 (2017). This fMRI study compared adolescents with a history of chronic peer victimization to those with no history of being victimized. The participants with a history of victimization took more risks in a risk-taking task and also showed heightened activation in a number of regions during the task, showing how individual differences in peer environment are associated with behavioral and neural differences.
Falk, E. B. et al. Neural responses to exclusion predict susceptibility to social influence. J. Adolesc. Health 54(Suppl), S22–S31 (2014).
Peake, S. J., Dishion, T. J., Stormshak, E. A., Moore, W. E. & Pfeifer, J. H. Risk-taking and social exclusion in adolescence: neural mechanisms underlying peer influences on decision-making. Neuroimage 82, 23–34 (2013).
Rudolph, K. D., Miernicki, M. E., Troop-Gordon, W., Davis, M. M. & Telzer, E. H. Adding insult to injury: neural sensitivity to social exclusion is associated with internalizing symptoms in chronically peer-victimized girls. Soc. Cogn. Affect. Neurosci. 11, 829–842 (2016).
Lansford, J. E., Criss, M. M., Pettit, G. S., Dodge, K. A. & Bates, J. E. Friendship quality, peer group affiliation, and peer antisocial behavior as moderators of the link between negative parenting and adolescent externalizing behavior. J. Res. Adolesc. 13, 161–184 (2003).
Telzer, E. H., Fuligni, A. J., Lieberman, M. D., Miernicki, M. E. & Galván, A. The quality of adolescents’ peer relationships modulates neural sensitivity to risk taking. Soc. Cogn. Affect. Neurosci. 10, 389–398 (2015).
Schriber, R. A. & Guyer, A. E. Adolescent neurobiological susceptibility to social context. Dev. Cogn. Neurosci. 19, 1–18 (2016).
Caouette, J. D. & Guyer, A. E. Gaining insight into adolescent vulnerability for social anxiety from developmental cognitive neuroscience. Dev. Cogn. Neurosci. 8, 65–76 (2014).
Darling, N. & Steinberg, L. Parenting style as context: An integrative model. Psychol. Bull. 113, 487–496 (1993).
Kerr, M., Stattin, H. & Özdemir, M. Perceived parenting style and adolescent adjustment: revisiting directions of effects and the role of parental knowledge. Dev. Psychol. 48, 1540–1553 (2012).
Kim-Spoon, J., Maciejewski, D., Lee, J., Deater-Deckard, K. & King-Casas, B. Longitudinal associations among family environment, neural cognitive control, and social competence among adolescents. Dev. Cogn. Neurosci. 26, 69–76 (2017).
Harper, J. M., Padilla-Walker, L. M. & Jensen, A. C. Do siblings matter independent of both parents and friends? Sympathy as a mediator between sibling relationship quality and adolescent outcomes. J. Res. Adolesc. 26, 101–114 (2016).
Bonell, C. et al. Initiating change locally in bullying and aggression through the school environment (INCLUSIVE): a pilot randomised controlled trial. Health Technol. Assess. 19, 1–109, vii–viii (2015).
Luengo Kanacri, B. P. et al. Longitudinal relations among positivity, perceived positive school climate, and prosocial behavior in Colombian adolescents. Child. Dev. 88, 1100–1114 (2017).
Goddings, A.-L. et al. The influence of puberty on subcortical brain development. Neuroimage 88, 242–251 (2014).
Herting, M. M. & Sowell, E. R. Puberty and structural brain development in humans. Front. Neuroendocrinol. 44, 122–137 (2017).
Motta-Mena, N. V. & Scherf, K. S. Pubertal development shapes perception of complex facial expressions. Dev. Sci. 20, e12451 (2017).
Craig, W. et al. A cross-national profile of bullying and victimization among adolescents in 40 countries. Int. J. Public Health 54(Suppl 2), 216–224 (2009).
Tippett, N. & Wolke, D. Socioeconomic status and bullying: a meta-analysis. Am. J. Public Health 104, e48–e59 (2014).
Williams, D. R., Priest, N. & Anderson, N. B. Understanding associations among race, socioeconomic status, and health: Patterns and prospects. Health Psychol. 35, 407–411 (2016).
Shanahan, M. J. & Hofer, S. M. Social context in gene-environment interactions: retrospect and prospect. J. Gerontol. B Psychol. Sci. Soc. Sci. 60, 65–76 (2005).
Byrd, A. L. & Manuck, S. B. MAOA, childhood maltreatment, and antisocial behavior: meta-analysis of a gene-environment interaction. Biol. Psychiatry 75, 9–17 (2014).
McCrory, E., De Brito, S. A. & Viding, E. Research review: the neurobiology and genetics of maltreatment and adversity. J. Child. Psychol. Psychiatry 51, 1079–1095 (2010).
Knafo, A. & Jaffee, S. R. Gene-environment correlation in developmental psychopathology. Dev. Psychopathol. 25, 1–6 (2013).
Kaufmann, T. et al. Delayed stabilization and individualization in connectome development are related to psychiatric disorders. Nat. Neurosci. 20, 513–515 (2017).
Rosenberg, M. D. et al. A neuromarker of sustained attention from whole-brain functional connectivity. Nat. Neurosci. 19, 165–171 (2016).
Van Essen, D. C. et al. The WU-Minn Human Connectome Project: an overview. Neuroimage 80, 62–79 (2013).
Volkow, N. D. et al. The conception of the ABCD study: From substance use to a broad NIH collaboration. Dev. Cogn. Neurosci. https://doi.org/10.1016/j.dcn.2017.10.002 (2017).
Madhyastha, T. et al. Current methods and limitations for longitudinal fMRI analysis across development. Dev. Cogn. Neurosci. https://doi.org/10.1016/j.dcn.2017.11.006 (2017).
Mills, K. L. & Tamnes, C. K. Methods and considerations for longitudinal structural brain imaging analysis across development. Dev. Cogn. Neurosci. 9, 172–190 (2014).
Kievit, R.A. et al. Developmental cognitive neuroscience using Latent Change Score models: A tutorial and applications. Dev. Cogn. Neurosci. https://doi.org/10.1016/j.dcn.2017.11.007 (2017).
Wierenga, L.M., Sexton, J.A., Laake, P., Giedd, J.N. & Tamnes, C.K. A key characteristic of sex differences in the developing brain: Greater variability in brain structure of boys than girls. Cereb. Cortex https://doi.org/10.1093/cercor/bhx154 (2017).