Modality-Spanning Deficits in Attention-Deficit/Hyperactivity Disorder in Functional Networks, Gray Matter, and White Matter

Abstract

Previous neuroimaging investigations in attention-deficit/hyperactivity disorder (ADHD) have separately identified distributed structural and functional deficits, but interconnections between these deficits have not been explored. To unite these modalities in a common model, we used joint independent component analysis, a multivariate, multimodal method that identifies cohesive components that span modalities. Based on recent network models of ADHD, we hypothesized that altered relationships between large-scale networks, in particular, default mode network (DMN) and task-positive networks (TPNs), would co-occur with structural abnormalities in cognitive regulation regions. For 756 human participants in the ADHD-200 sample, we produced gray and white matter volume maps with voxel-based morphometry, as well as whole-brain functional connectomes. Joint independent component analysis was performed, and the resulting transmodal components were tested for differential expression in ADHD versus healthy controls. Four components showed greater expression in ADHD. Consistent with our a priori hypothesis, we observed reduced DMN-TPN segregation co-occurring with structural abnormalities in dorsolateral prefrontal cortex and anterior cingulate cortex, two important cognitive control regions. We also observed altered intranetwork connectivity in DMN, dorsal attention network, and visual network, with co-occurring distributed structural deficits. There was strong evidence of spatial correspondence across modalities: For all four components, the impact of the respective component on gray matter at a region strongly predicted the impact on functional connectivity at that region. Overall, our results demonstrate that ADHD involves multiple, cohesive modality spanning deficits, each one of which exhibits strong spatial overlap in the pattern of structural and functional alterations.

Disrupted functional connectivity of cerebellar default network areas in attention‐deficit/hyperactivity disorder

“…Advanced rs-fMRI analysis methods, such as graph theory and dynamic FC have recently been applied to give insight to global-level spontaneous network abnormalities in ADHD (Di Martino et al, 2013; Ou et al, 2014; Wang et al, 2009). Furthermore, multivariate approaches combining structural and functional changes have given insight into the pathophysiology of ADHD (Kessler et al, 2014) and could be further applied to understanding cerebro-cerebellar network abnormalities and their effects on behavior. Additionally, it was recently shown that targeted non-invasive CerDMN stimulation with TMS leads to changes in FC with and within cortical networks (Halko et al, 2014) supporting further investigation of the effects of CerDMN neurostimulation on inattention and related behaviors.…”

Measuring electrophysiological connectivity by power envelope correlation: a technical review on MEG methods

“…These networks have been shown to be core to the function of the human brain. Moreover, they are perturbed in a number of different diseases (Schnitzler and Gross, 2005;Kessler et al, 2014;Friston, 1998;Palaniyappan and Liddle, 2012); for example an important hypothesis underlying symptoms of schizophrenia is one of dysconnectivity between regions, and recent work has shown that the salience network (a commonly observed network of functional connectivity in fMRI which incorporates bilateral insula and cingulate cortices)…”

“…This is just one of a large number of observations implicating abnormal network structure or function in diseases ranging from developmental disorders (Haneef et al, 2014;Kessler et al, 2014;Maccotta et al, 2013;Tomasi and Volkow, 2012) to neurodegeneration (Allen et al, 2007;Hacker et al, 2012;Grady et al, 2001;Wang et al, 2007;Hawellek et al, 2011;Leavitt et al, 2014).…”

Optimising experimental design for MEG resting state functional connectivity measurement

“…Furthermore, the excellent temporal precision afforded by MEG allows estimation of dynamic changes in network structure Brookes et al, 2014;O'Neill et al, 2015a). The importance of characterising electrophysiological connectivity is growing, with numerous demonstrations that connections are perturbed in pathologies Friston, 1998;Guggisberg et al, 2008;Kessler et al, 2014;Palaniyappan and Liddle, 2012;Schnitzler and Gross, 2005;Stufflebeam et al, 2011;Tewarie et al, 2014;van Dellen et al, 2012). However, demonstrations have typically been made on large subject groups, with little attention paid to the accuracy of measurements within individual subjects.…”