Structural brain network topology underpinning ADHD and response to methylphenidate treatment

Kristi R. Griffiths,Leanne M. Williams,Mayuresh S. Korgaonkar,Taylor A. Braund,Michael R. Kohn,Simon Clarke

doi:10.1038/s41398-021-01278-x

Abstract

Behavioural disturbances in attention deficit hyperactivity disorder (ADHD) are thought to be due to dysfunction of spatially distributed, interconnected neural systems. While there is a fast-growing literature on functional dysconnectivity in ADHD, far less is known about the structural architecture underpinning these disturbances and how it may contribute to ADHD symptomology and treatment prognosis. We applied graph theoretical analyses on diffusion MRI tractography data to produce quantitative measures of global network organisation and local efficiency of network nodes. Support vector machines (SVMs) were used for comparison of multivariate graph measures of 37 children and adolescents with ADHD relative to 26 age and gender matched typically developing children (TDC). We also explored associations between graph measures and functionally-relevant outcomes such as symptom severity and prediction of methylphenidate (MPH) treatment response. We found that multivariate patterns of reduced local efficiency, predominantly in subcortical regions (SC), were able to distinguish between ADHD and TDC groups with 76% accuracy. For treatment prognosis, higher global efficiency, higher local efficiency of the right supramarginal gyrus and multivariate patterns of increased local efficiency across multiple networks at baseline also predicted greater symptom reduction after 6 weeks of MPH treatment. Our findings demonstrate that graph measures of structural topology provide valuable diagnostic and prognostic markers of ADHD, which may aid in mechanistic understanding of this complex disorder.

Highlights

Dysfunction of spatially distributed, interconnected neural systems is thought to be one of the major causes of behavioural disturbances in ADHD1
There were no differences between attention deficit hyperactivity disorder (ADHD) and typically developing children (TDC) groups in global graph measures (global efficiency, t(61) = −0.042, p = 0.673; characteristic path length, t(61) = −0.311, p = 0.757; mean clustering coefficient, t(61) = 1.035, p = 0.258) or univariate measures of nodal local efficiency after correcting for multiple comparisons
Using linear Support vector machines (SVMs) we found a significant model of combination of local efficiency measures to predict ADHD from TDC with 76.2% accuracy (C = 0.218, p = 0.003; see Table 1 for cross-validated model details and Fig. 1A for the most important regional contributors)

Summary

Introduction

Dysfunction of spatially distributed, interconnected neural systems is thought to be one of the major causes of behavioural disturbances in ADHD1. Diffusion-weighted imaging (DWI) is a method widely used to examine microstructural brain properties and white matter connections[5]. Numerous studies have reported alterations in microstructural properties within specific tracts in ADHD, predominantly those linking prefrontal, parietal, cerebellar and SC (e.g.6,7 see[8] for a review). These anomalies have been associated with clinical symptom severity[7] and the improvement of symptoms across development[9], demonstrating the relevance of white matter integrity to core symptoms of ADHD

Methods

Results

Conclusion