Attention deficit hyperactivity disorder

Abstract

Over the last two decades, there have been numerous technical and methodological advances available to clinicians and researchers to better understand attention deficit hyperactivity disorder (ADHD) and its etiology. Despite the growing body of literature investigating the disorder's pathophysiology, ADHD remains a complex psychiatric disorder to characterize. This chapter will briefly review the literature on ADHD, with a focus on its history, the current genetic insights, neurophysiologic theories, and the use of neuroimaging to further understand the etiology. We address some of the major concerns that remain unclear about ADHD, including subtype instability, heterogeneity, and the underlying neural correlates that define the disorder. We highlight that the field of ADHD is rapidly evolving; the descriptions provided here will hopefully provide a sturdy foundation for which to build and improve our understanding of the disorder.

Fig. 1

Using multivariate pattern analysis to compare the two most common subtypes of ADHD (ADHD-Inattentive and ADHD-Combined), this report showed relatively strong classification for single subjects (Fair et al. 2012b). Up to 77.0 % accuracy was attained for ADHD-C compared to typically developing controls (TDC), and up to 80.8 % accuracy for ADHD-I compared to TDC. Note that the features, or connections, that contributed most strongly to these predictions showed distributed patterns of atypical connectivity relative to TDC, measured by “differential” node strength (nodes with many connections that differentiated groups). Node strength for ADHD-C versus TDC shows strong differentiation in regions (a) somewhat different from those found in ADHD-I versus TDC (b). c Comparisons between the subtypes show similar trends. Node colors: red, default; blue, cerebellum; yellow, fronto-parietal; black, cingulo-opercular; green, occipital; cyan, sensorimotor

Fig. 2

In a previous report, community detection was used to identify subgroups in typically developing controls (TDC) and ADHD child samples (Fair et al. 2012a). a Four unique subgroups (i.e., cognitive profiles) were identified in TDC and community structure is depicted by correlation matrices shown in (b). Darker colors on the grid show lower correlations between subjects, while lighter colors reveal positive correlations between subjects. Identified communities are outlined in white. c Applying the community detection algorithm to the ADHD cohort independently shows similar findings as in (a), with correlation matrices presented in (d). The authors highlight that, based on neuropsychological performance, TDC can be classified into distinct subgroups with high precision and the heterogeneity in individuals with ADHD may be “nested” in this normal variation