Whole-brain structural topology in adult attention-deficit/hyperactivity disorder: Preserved global – disturbed local network organization

Abstract

Prior studies demonstrate altered organization of functional brain networks in attention-deficit/hyperactivity disorder (ADHD). However, the structural underpinnings of these functional disturbances are poorly understood. In the current study, we applied a graph-theoretic approach to whole-brain diffusion magnetic resonance imaging data to investigate the organization of structural brain networks in adults with ADHD and unaffected controls using deterministic fiber tractography. Groups did not differ in terms of global network metrics — small-worldness, global efficiency and clustering coefficient. However, there were widespread ADHD-related effects at the nodal level in relation to local efficiency and clustering. The affected nodes included superior occipital, supramarginal, superior temporal, inferior parietal, angular and inferior frontal gyri, as well as putamen, thalamus and posterior cerebellum. Lower local efficiency of left superior temporal and supramarginal gyri was associated with higher ADHD symptom scores. Also greater local clustering of right putamen and lower local clustering of left supramarginal gyrus correlated with ADHD symptom severity. Overall, the findings indicate preserved global but altered local network organization in adult ADHD implicating regions underpinning putative ADHD-related neuropsychological deficits.

An Extension of the Procedural Deficit Hypothesis from Developmental Language Disorders to Mathematical Disability

“…In brief, any neurodevelopmental disorder involving abnormalities of brain structures underlying procedural memory could be comorbid with MD, with the likelihood of comorbidity depending to what extent the same (portions of) structures are affected in both disorders. Indeed, at least DCD and ADHD are promising candidates, since both are associated with abnormalities of procedural memory structures ( Krain and Castellanos, 2006 ; Kashiwagi and Tamai, 2013 ; Peters et al, 2013 ; Sidlauskaite et al, 2015 ), and both have been linked to math difficulties ( Kaufmann and Nuerk, 2008 ; Gomez et al, 2015 ).…”

Attention Performance Measured by Attention Network Test Is Correlated with Global and Regional Efficiency of Structural Brain Networks

“…Diffusion tractography methods using diffusion tensor imaging (DTI) provide an opportunity for investigating the brain anatomical connectivity in vivo , which enables us to reveal the basic topological features of the large-scale structural network across the entire brain. In previous neuroimaging investigations, structural brain networks obtained from tractography have been successfully applied to healthy subjects ( Gong et al, 2009 ; Sun et al, 2015 ), as well as diseased population, such as ADHD ( Cao et al, 2013 ; Sidlauskaite et al, 2015 ), Schizophrenia ( van den Heuvel et al, 2010 ) and Alzheimer’s Disease (AD) ( Daianu et al, 2015 ).…”

BrainNET: Inference of brain network topology using Machine Learning

“…The B-corr method, however, which is the combination of correlation and BrainNET, showed significant changes between all groups similar to BrainNET. Previous studies have shown that ADHD is often associated with changes in functional organization of the brain [18,21]. BrainNET analysis of ADHD data supports the notion that functional organization of brain changes in ADHD, and it was effective in identifying the subtle changes in the ADHD subjects.…”

“…ADHD is one of the most common neurodevelopmental disorders in children with significant socioeconomic and psychological effects [17,18] and is very difficult to diagnose [19]. ADHD has widespread but often subtle alterations in multiple brain regions affecting brain function [20,21] [19,22] [23][24][25]. Neuro Bureau, a collaborative neuroscience forum, has released fully processed open source fMRI data "ADHD-200 preprocessed" from several sites [26,27] providing an ideal dataset to test the BrainNET model and compare its performance with standard Pearson correlation.…”