br Conclusions Neuroimaging studies of developmental and cli
Conclusions Neuroimaging studies of developmental and clinical populations present several challenges and issues for consideration at the subject selection and data acquisition stage. Neuroimaging is expensive, amplifying the importance of obtaining the highest quality data, losing as little data as possible, and generating results that have the highest potential for real-world application. Of course, there are trade-offs that must be faced between the desire to have a well-sampled, deeply characterized dataset and the cost of acquiring such data in terms of both time and money. Funds and resources are not always available to collect multiple T1-weighted scans, 30min of resting state fMRI data, a full battery of clinical and behavioral measures with multiple informants, family studies or thorough family history, etc. Subject factors, such as comfort and time commitment, can limit the extent of the data collection as well. Thus, weighing the many trade-offs can lead to difficult choices. We believe in two principled approaches to these decisions. (1) There is no point in having useless MRI data. Given the high cost involved in neuroimaging and the susceptibility to movement artifact, every attempt to collect useful (which sometimes translates to “enough”) MRI data should be employed. In our experiences with children with TS, collecting two 5-min resting state fMRI scans resulted in retaining only 50% of subjects due to movement, while collecting three to four 7-min scans resulted in retaining 75% of subjects. Collecting more data from each individual yielded significantly less wasted data, which we consider well worth the up-front investment. (2) Omitting phenotyping altogether is, as the saying goes, “penny-wise and pound-foolish.” Tough choices may need to be made with regard to the extent of phenotyping, but some level of subject characterization is necessary in each of the most important phenotypic domains for the mth1 pathway being studied. For instance, several neuroimaging findings in studies of TS have proven to be best interpreted only together with information on comorbid ADHD (Castellanos et al., 1996; Gilbert et al., 2004). Depending on its specific aims, a TS study may require only a brief estimate of current ADHD symptom severity or it may require expert assessment of lifetime ADHD diagnosis, but ignoring ADHD altogether would be hard to justify. Again, it is well worth the up-front investment to characterize the sample in order to obtain more interpretable results.
Introduction A process of neural circuit reorganization begins in late childhood and spans the adolescent transition into young adulthood. Maturation of the frontal cortex is thought to parallel behavioral changes in cognition and decision-making that occur in adolescence (Paus et al., 2008; Somerville and Casey, 2010; Johnson and Wilbrecht, 2011). Cerebral cortex gray matter volume peaks in late childhood and then declines across adolescence (Gogtay et al., 2004), while subcortical structures follow heterogeneous patterns of maturation, with the amygdala increasing in volume and the nucleus accumbens decreasing in volume over adolescence (Ostby et al., 2009; Mills et al., 2014). Human imaging studies also show that functional and structural connectivity matures across peri-adolescence (Fair et al., 2008; Power et al., 2010; Lebel and Beaulieu, 2011), with a general trend of increased connectivity among distant regions with age. Dendritic spines, the sites of most excitatory synapses in the brain, show pruning (defined as a decrease in density) in the frontal cortex across adolescence (Huttenlocher, 1979; Huttenlocher and Dabholkar, 1997; Zuo et al., 2005; Petanjek et al., 2011). MRI technology cannot resolve the precise cellular rewiring underlying the developmental changes in functional and structural measures observed with human imaging techniques. Human imaging studies lack the resolution of individual cells or synapses and cannot identify cell types. Two-photon in vivo microscopy allows chronic imaging of neuronal structures in vivo with submicron resolution in animal models (Denk and Svoboda, 1997; De Paola et al., 2006; Holtmaat et al., 2009; Holtmaat and Svoboda, 2009; Chen et al., 2014). This technique allows us to track the specific wiring of identified circuits across development and with experience.