In recent years, there has been growing enthusiasm that functional magnetic resonance imaging (MRI) could achieve clinical utility for a broad range of neuropsychiatric disorders. However, several barriers remain. For example, the acquisition of large-scale datasets capable of clarifying the marked heterogeneity that exists in psychiatric illnesses will need to be realized. In addition, there continues to be a need for the development of image processing and analysis methods capable of separating signal from artifact. As a prototypical hyperkinetic disorder, and movement-related artifact being a significant confound in functional imaging studies, ADHD offers a unique challenge. As part of the ADHD-200 Global Competition and this special edition of Frontiers, the ADHD-200 Consortium demonstrates the utility of an aggregate dataset pooled across five institutions in addressing these challenges. The work aimed to (1) examine the impact of emerging techniques for controlling for “micro-movements,” and (2) provide novel insights into the neural correlates of ADHD subtypes. Using support vector machine (SVM)-based multivariate pattern analysis (MVPA) we show that functional connectivity patterns in individuals are capable of differentiating the two most prominent ADHD subtypes. The application of graph-theory revealed that the Combined (ADHD-C) and Inattentive (ADHD-I) subtypes demonstrated some overlapping (particularly sensorimotor systems), but unique patterns of atypical connectivity. For ADHD-C, atypical connectivity was prominent in midline default network components, as well as insular cortex; in contrast, the ADHD-I group exhibited atypical patterns within the dlPFC regions and cerebellum. Systematic motion-related artifact was noted, and highlighted the need for stringent motion correction. Findings reported were robust to the specific motion correction strategy employed. These data suggest that resting-state functional connectivity MRI (rs-fcMRI) data can be used to characterize individual patients with ADHD and to identify neural distinctions underlying the clinical heterogeneity of ADHD.

The nature of immature reward processing and the influence of rewards on basic elements of cognitive control during adolescence are currently not well understood. Here, during functional magnetic resonance imaging, healthy adolescents and adults performed a modified antisaccade task in which trial-by-trial reward contingencies were manipulated. The use of a novel fast, event-related design enabled developmental differences in brain function underlying temporally distinct stages of reward processing and response inhibition to be assessed. Reward trials compared with neutral trials resulted in faster correct inhibitory responses across ages and in fewer inhibitory errors in adolescents. During reward trials, the blood oxygen level–dependent signal was attenuated in the ventral striatum in adolescents during cue assessment, then overactive during response preparation, suggesting limitations during adolescence in reward assessment and heightened reactivity in anticipation of reward compared with adults. Importantly, heightened activity in the frontal cortex along the precentral sulcus was also observed in adolescents during reward-trial response preparation, suggesting reward modulation of oculomotor control regions supporting correct inhibitory responding. Collectively, this work characterizes specific immaturities in adolescent brain systems that support reward processing and describes the influence of reward on inhibitory control. In sum, our findings suggest mechanisms that may underlie adolescents’ vulnerability to poor decision-making and risk-taking behavior.

SUMMARY The parietal lobe has long been viewed as a collection of architectonic and functional subdivisions. Though much parietal research has focused on mechanisms of visuospatial attention and control-related processes, more recent functional neuroimaging studies of memory retrieval have reported greater activity in left lateral parietal cortex (LLPC) when items are correctly identified as previously studied (“old”) vs. unstudied (“new”). These studies have suggested functional divisions within LLPC that may provide distinct contributions towards recognition memory judgments. Here, we define regions within LLPC by developing a novel parcellation scheme that integrates data from resting state functional connectivity MRI (rsfcMRI) and functional MRI (fMRI). This combined approach results in a six-fold parcellation of LLPC based on the presence (or absence) of memory retrieval-related activity, dissociations in the profile of task-evoked timecourses, and membership in large-scale brain networks. This parcellation should serve as a roadmap for future investigations aimed at understanding LLPC function.