No abstract

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 collected findings suggest the craving-related activation of a network of limbic, paralimbic, and striatal brain regions, including structures involved in stimulus-reward association (amygdala), incentive motivation (subcallosal gyrus/nucleus accumbens), and anticipation (anterior cingulate cortex).

Heightened distractibility in participants with ADHD as indexed by increased reaction time (RT) variability has been hypothesized to be due to a failure to sufficiently suppress activation in the default attention network during cognitively demanding situations. The present study utilized fMRI to examine the relationship between intra-individual variability (IIV) in task RT and suppression of BOLD response in regions of the default network, using a working memory paradigm and two levels of control tasks. IIV was calculated separately for thirteen healthy control and twelve children with ADHD, Combined Type. Children with ADHD displayed significantly more RT variability than controls. Neural measures showed that although both groups displayed a pattern of increasing deactivation of the medial prefrontal cortex (PFC) with increasing task difficulty, the ADHD group was significantly less deactive than controls. Correlations between IIV and brain activation suggested that greater variability was associated with a failure to deactivate ventromedial PFC with increasing task difficulty. T-tests on brain activation between participants with ADHD with low versus high IIV implicated a similar region so that high variability was associated with greater activity in this region. These data provide support for the theory that increased distractibility in at least some participants with ADHD may be due to an inability to sufficiently suppress activity in the default attention network in response to increasing task difficulty.

Background Previous research has documented that childhood behavioral disturbances predict lower scores on academic tests and curtail educational attainment. It is unknown which types of childhood behavioral problems are most likely to predict these outcomes. Methods An ethnically diverse cohort was assessed at age 6 for behavioral problems and IQ, and at age 17 for academic achievement in math and reading. Of the original cohort of 823 children, 693 (84%) had complete data. Multiple regressions were used to estimate associations of attention, internalizing and externalizing problems at age 6 with math and reading achievement at age 17, adjusting for IQ and indicators of family socioeconomic status. Results Adjusting for IQ, inner city community and maternal education and marital status, teacher ratings of attention, internalizing behavior and externalizing problems at age 6 significantly predict math and reading achievement at age 17. When types of problems are examined simultaneously, attention problems predict math and reading achievement with little attenuation, while the influence of externalizing and internalizing problems is materially reduced and not significant. Standardized coefficients representing the adjusted associations of attention problems and IQ at age 6 with achievement scores at age 17 were −0.12 (p<.001) and 0.55 (p<.001) respectively for math and −0.10 (p=.002) and 0.48 (p<.001) respectively for reading. Conclusion Interventions targeting attention problems at school entry should be tested as a potential avenue for improving educational achievement.