purchase (RS)-CPP br Social contexts and the adolescent brai

Social contexts and the adolescent brain
Future directions and conclusions Drawing from prevailing models of adolescent neurodevelopment and a growing neuroimaging literature on the interrelations among social contexts, functional and structural properties of the brain, and developmental outcomes, we have proposed from this review of the literature a framework of adolescent neurobiological sensitivity to social context (Figs. 1 and 2). Neurobiological susceptibility models (Ellis et al., 2011) focus on how endogenous, biological factors confer some individuals, relative to others, with greater susceptibility to environmental influences. However, the vast majority of empirical work guided by these theoretical frameworks has not incorporated direct measures of the purchase (RS)-CPP as a source of neurobiological moderating factors. Nor has the available neuroimaging literature tended to use neurobiological susceptibility frameworks for interpreting brain function/structure as moderators of social-contextual influences on outcomes (but see Yap et al., 2008, and Whittle et al., 2011, for exceptions). We found some possible illustrations in adolescence of neural characteristics that moderated family or peer influences in a for-better or for-worse fashion. For brain structure, this included volume of the amygdala with possible gender differences in the directionality of effects (Whittle et al., 2008; Yap et al., 2008), decreased leftward asymmetric ACC volume in males (Whittle et al., 2008; Yap et al., 2008), and larger hippocampi in females (Whittle et al., 2011). For brain function, the subACC and dACC (Masten et al., 2009), VS (Guyer et al., 2006a,b; Guyer et al., 2012a,b; Guyer et al., 2015; Telzer et al., 2013a,b; Telzer et al., 2014b), TPJ (Falk et al., 2014; Peake et al., 2013), and vlPFC (Guyer et al., 2015) showed sensitivity to peer or parenting cues and contexts and/or were linked to competencies or vulnerabilities aligned with the bivalent outcomes expected by neurobiological susceptibility models. All of these regions fall under the auspices of the social-affective and cognitive-regulatory systems outlined in models of adolescent neurodevelopment reviewed above.
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Acknowledgments
Introduction Research using electroencephalography (EEG) as a measure of neurophysiology during infancy and early childhood has increased in recent years and studies have utilized EEG techniques to examine both generalities and individual differences in early cognitive development. In typically developing children there is a developmental decrease in EEG power of low-frequency rhythms (e.g., delta and theta) and an increase in high-frequency rhythms (e.g., beta and gamma) across age (Matousek and Petersen, 1973; Harmony et al., 1990). Relative to typically developing children, children with learning or attention disorders often demonstrate higher levels of low-frequency power and lower levels of high-frequency power (Barry et al., 2003). This atypical EEG profile has also been found in children who were previously institutionalized (Marshall et al., 2004) and children growing up in economically disadvantaged environments (Harmony et al., 1990; Otero et al., 2003; Tomalski et al., 2013). Growing up in a socioeconomically disadvantaged environment is associated with substantially worse health and impaired psychological, cognitive, and emotional development throughout the lifespan (McLoyd, 1998; Bradley and Corwyn, 2002; Adler and Rehkopf, 2008). Childhood socioeconomic status (SES), typically characterized by parental educational attainment, family income and parental occupation (McLoyd, 1998), is strongly associated with later cognitive development and academic achievement (Bradley et al., 2001; Brooks-Gunn and Duncan, 1997; Evans, 2004; Hoff, 2003; McLoyd, 1998). In contrast to investigations examining associations between childhood SES and general intelligence or global measures of cognitive development, a number of more recent studies have adopted a cognitive neuroscience approach to understanding SES differences in cognition (Hackman and Farah, 2009; Raizada and Kishiyama, 2010; Brito and Noble, 2014). These studies measured associations between SES and specific neurocognitive systems and have reported SES-related differences in child language (Farah et al., 2006; Noble et al., 2007, 2005), memory (Farah et al., 2006; Noble et al., 2007, 2005), and executive functions (Farah et al., 2006; Kishiyama et al., 2009; Lipina et al., 2005; Stevens et al., 2009).