An independent components and functional connectivity analysis of resting state fMRI data points to neural network dysregulation in adult ADHD.Hum Brain Mapp. 2014 Apr; 35(4):1261-72.HB
Spontaneous fluctuations can be measured in the brain that reflect dissociable functional networks oscillating at synchronized frequencies, such as the default mode network (DMN). In contrast to its diametrically opposed task-positive counterpart, the DMN predominantly signals during a state of rest, and inappropriate regulation of this network has been associated with inattention, a core characteristic of attention-deficit/hyperactivity disorder (ADHD). To examine whether abnormalities can be identified in the DMN component of patients with ADHD, we applied an independent components analysis to resting state functional magnetic resonance imaging data acquired from 22 male medication-naïve adults with ADHD and 23 neurotypical individuals. We observed a stronger coherence of the left dorsolateral prefrontal cortex (dlPFC) with the DMN component in patients with ADHD which correlated with measures of selective attention. The increased left dlPFC-DMN coherence also surfaced in a whole-brain replication analysis involving an independent sample of 9 medication-naïve adult patients and 9 controls. In addition, a post hoc seed-to-voxel functional connectivity analysis using the dlPFC as a seed region to further examine this region's suggested connectivity differences uncovered a higher temporal coherence with various other neural networks and confirmed a reduced anticorrelation with the DMN. These results point to a more diffuse connectivity between functional networks in patients with ADHD. Moreover, our findings suggest that state-inappropriate neural activity in ADHD is not confined to DMN intrusion during attention-demanding contexts, but also surfaces as an insufficient suppression of dlPFC signaling in relation to DMN activity during rest. Together with previous findings, these results point to a general dysfunction in the orthogonality of functional networks.