br Conflict of interest statement br Acknowledgements This

Conflict of interest statement
Acknowledgements This research was supported by operating grants from the Natural Sciences and Engineering Research Council of Canada (NSERC) and the Canadian Institutes of Health Research (CIHR) to DA. Furthermore, additional support was provided by the Ontario Graduate Scholarship program to SV as well as the Canada Research Chairs (CRC) Program to DA.
Introduction Adolescent behavior is characterized by increases in sensation-seeking that can lead to maladaptive risk-taking, resulting in increased likelihood of death or serious injury (Eaton et al., 2006). Thus, there is an impetus to understand the neurodevelopmental changes in the motivational system that may contribute to this behavioral profile. The striatum is of particular interest in this context because of its involvement in motivation and reward processing as well as learning, motor control, and cognition (Haber and Knutson, 2010; McClure et al., 2003; Middleton and Strick, 2000; Vo et al., 2011). Rodent and non-human primate models provide evidence indicating continued striatal synaptogenesis in early adolescence, peaks in dopamine receptor fasudil hcl and dopamine projections from the striatum to prefrontal cortex, and synaptic pruning in late adolescence (Crews et al., 2007; Kalsbeek et al., 1988; Rosenberg and Lewis, 1995; Tarazi et al., 1998; Teicher et al., 1995). This line of evidence has led to the hypothesis that similar neurophysiological changes are occurring in adolescent humans (Casey et al., 2008; Spear, 2000). Initial functional magnetic resonance imaging (fMRI) studies have found compelling evidence suggesting peak sensitivity of the adolescent striatum to reward stimuli relative to adults and children (Ernst et al., 2005; Galvan et al., 2006, 2007; Geier et al., 2010; Leijenhorst et al., 2010; Padmanabhan et al., 2011), though this finding has not been consistent (Bjork et al., 2004; Eshel et al., 2007) and likely depends on the reward context investigated (Crone and Dahl, 2012). For example, recent work has suggested that striatal reactivity to reward anticipation increases into adulthood while reactivity to reward receipt decreases (Hoogendam et al., 2013). Currently there is a lack of in vivo measures with which to assess age-related differences in human striatal neurophysiology which limits our ability to understand neural mechanisms underlying differences in adolescent striatal function. Understanding the development of striatal neurophysiology is of particular significance given that abnormal striatal neurophysiology and function are implicated in a range of neuropsychological disorders that emerge during childhood and adolescence (Bradshaw and Sheppard, 2000; Chambers et al., 2003). An improved understanding of normative neurophysiological maturation of the striatum can thus inform models of normal and abnormal adolescent behavior. Tissue–iron concentration is predominant in the striatum (Haacke et al., 2005; Schenck, 2003) and has been found to support dopamine D2 receptor and dopamine transporter (DAT) densities in studies of iron deficiency, ADHD, and restless leg syndrome, which are related to abnormalities in DA processing, (Adisetiyo et al., 2014; Connor et al., 2009; Erikson et al., 2000; Wiesinger et al., 2007), as well as the function and regulation of dopamine neurons (Beard, 2003; Jellen et al., 2013). As such, differences in striatal tissue iron concentration, which can be measured using MRI, can potentially serve as an indicator of dopaminergic differences in adolescence. Tissue–iron is paramagnetic and thus strongly influences the T2*-weighted MRI signal (Langkammer et al., 2010, 2012; Schenck, 2003), which can be non-invasively collected in vivo throughout the lifespan (Aquino et al., 2009; Haacke et al., 2005; Wang et al., 2012). The influence of iron on the T2* signal has been used to quantify iron in a variety of MR measures, including susceptibility weighted imaging (SWI) (Haacke et al., 2004), R2* (Haacke et al., 2010), and R2′ (Sedlacik et al., 2014). In this study, we make use of a large T2*-weighted echo-planar imaging (EPI) dataset, most akin to SWI. Initial studies have used similar data in conjunction with multivariate pattern analysis to investigate the striatal processes underlying learning (Vo et al., 2011).