tazarotene Here we use T weighted EPI T to characterize age

Here we use T2*-weighted EPI (T2*) to characterize age-related differences in the neurophysiology of the human adolescent striatum in vivo using a multivariate pattern analysis approach. Specifically we use spatial patterns of striatal T2* to generate highly significant age predictions from both task-related and resting state T2*-weighted EPI (fMRI) acquisitions, demonstrating the strong and robust relationship between this measure and development. Furthermore, we identify the ventral striatum, a central hub of dopamine reward pathways hypothesized to underlie adolescent risk-taking (Blum et al., 2000; Casey et al., 2008; Spear, 2000), as a critical component of adolescent striatal maturation. This work highlights the dynamic nature of normative adolescent striatal development, informing models of the maturation of motivational systems during adolescence.
Materials and methods
Results
Discussion
Conclusion Our results provide in vivo evidence of continued neurophysiological maturation of striatal regions throughout human adolescence. Our findings and the nature of the T2* signal suggest that age related differences in striatal neurophysiology are most strongly influenced by differences in tissue–iron concentration (Aoki et al., 1989; Chavhan et al., 2009, Daugherty and Raz, 2013; He and Yablonskiy, 2009; Langkammer et al., 2010; Schenck, 2003). Given the contribution of this tissue property to tazarotene function, including dopamine function, and the role of the striatum in learning, motivation, and reward processing, protracted maturation of the striatum as indexed by T2* may strongly contribute to known developmental changes in behavior and brain function through adolescence.
Authors’ contributions
Conflicts of interest
Acknowledgments The project described was supported by grant number 5R01 MH080243 from the National Library of Medicine, National Institutes of Health. The contents of this report are solely the responsibility of the authors and do not necessarily represent the official views of the National Library of Medicine or NIH, DHHS.
Introduction Sensory function involves the neuronal filtering of a signal of interest from competing sources of stimulation, often occurring within the same sensory domain. This filtering can be guided by selective attention, which plays a dynamic gatekeeping role by modulating neural responses to sensory input to bring about awareness of the most behaviorally-relevant environmental elements and the suppression of others. While both cellular approaches in animal models and far-field recordings in humans yield insights into how neural activity can be modified by selective attention, we cannot yet model all components involved in this filtering process. Influences of life factors such as maturation and sensory enrichment on attention\'s underlying biology provide additional factors that must be incorporated into a reliable model (e.g., Booth et al., 2003; Coch et al., 2005; Patston et al., 2007; Stevens et al., 2009; Strait and Kraus, 2011a; Strait et al., 2014a). Most commonly, electrophysiological studies of selective attention, at both single-cell and population levels, have considered averaged sensory-evoked activity, comparing averaged responses comprising hundreds of trials to attended and concurrently ignored inputs. This approach emphasizes those aspects of the response that occur consistently but limits the assessment of attention\'s effects on aspects of the response that vary from trial to trial. Consideration of response variability in itself may provide insights into how the brain responds to differing sensory demands (Reich et al., 1997; Steinmetz et al., 2000), maturational changes (Gogtay et al., 2004; Li et al., 2001), and neuromodulatory influences (Jacob et al., 2013)—moving us toward a more comprehensive model of the attentive brain. We previously assessed the variability of scalp-recorded auditory-evoked activity during a selective attention task in adults and reported that, across the scalp, evoked responses to attended speech demonstrate less between-trial variability than responses to ignored speech (Strait and Kraus, 2011a). A reduction in response variability with attention had previously been reported in other domains, such as the somatosensory (Steinmetz et al., 2000) and visual systems (Fries et al., 2001, 2008), and more recently within auditory cortex during an interval discrimination task (Abolafia et al., 2013). Rather than “turning up the volume” of neural responses to attended input by increasing the size of the recruited neural population, selective attention fine-tunes the encoding of a target signal by synchronizing brain activity and reducing its variability over time, effectively increasing its signal-to-noise ratio.