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To commence the MoM VII session, “Brain development and ADHD”, Professor Kristine B Walhovd (University of Oslo, Norway) gave a general overview of the principles underlying brain development over the lifespan (Figure).1

Brain development over the lifespan: underlying principles

Brain development over the lifespan: underlying principles

This was followed by a discussion of selected studies from Dr Kerstin Konrad (RWTH Aachen University Hospital, Germany) to consider the question:

How is the ADHD brain wired differently?

Dr Konrad: “[There has been a] shift in the neurobiological conceptualisation of ADHD from a primarily fronto-striatal disorder to a condition characterised by abnormal interplay among several structurally and functionally defined networks.”2

Structural correlates of ADHD

Dr Konrad noted that cortical maturation is delayed in children with ADHD versus controls.3 She then went on to discuss results of a meta-analysis of seven voxel-based morphometry studies, which demonstrated that children/adults with ADHD (n=191) were associated with significantly smaller global grey matter volumes versus healthy controls (n=178; p=0.008), particularly in the lentiform nucleus extending to the caudate. Interestingly, children/adults with ADHD were also associated with significantly larger grey matter volumes in the cingulate cortex (p<0.001).4 Further meta-regression analysis of these data indicated that the proportion of children/adults receiving stimulant medication for ADHD was correlated with increasing grey matter volume in the right caudate, suggesting a potential ‘normalisation’ effect.4

Functional correlates of ADHD

Dr Konrad highlighted the results of a meta-analysis of 55 task-based functional magnetic resonance imaging (MRI) studies, in which the overall distribution of hypo- and hyperactivation was found to significantly differ between networks of the brain for both children (p<0.0001) and adults (p<0.0001) with ADHD:5

Distribution of ADHD-related activation abnormalities5
Children Adults
ADHD-related hypoactivation
  • Ventral attention network (44%)
  • Frontoparietal network (39%)
  • Somatomotor network (9%)
  • Dorsal attention network (8%)
  • Frontoparietal network (97%)
  • Somatomotor network (3%)
ADHD-related hyperactivation
  • Default network (37%)
  • Ventral attention network (23%)
  • Somatomotor network (22%)
  • Dorsal attention network (14%)
  • Visual network (4%)
  • Visual network (41%)
  • Dorsal attention network (33%)
  • Default network (26%)

Number of hypo-/hyperactivated voxels expressed as a percentage of the total number of voxels that significantly differed between those with ADHD (ADHD) versus those without ADHD (control). Number of significant voxels in the comparison control > ADHD: children n=3320, adults n=272; number of significant voxels in the comparison ADHD > control: children n=888, adults n=464.

Functional connectivity and ADHD

Dr Konrad acknowledged that the picture for ADHD-associated brain connectivity abnormalities is unclear; for example, with discordance in fractional anisotropy findings (as an indicator of white matter structure) evident in the literature.6,7 However, research has suggested that stimulant medication (methylphenidate) enhances the activation of fronto-striatal-cerebellar and parieto-temporal regions in children with ADHD, thus ‘normalising’ the dysfunctional connectivity in these regions’ associated attention networks that is implicated in the disorder.8

Clinical implications of neuroimaging findings in ADHD

Potential clinical implications suggested by Dr Konrad that may result from increased understanding of the neurobiological correlates of ADHD included:2

  • Improved understanding of disease mechanism
  • Improved understanding of treatment effects (pharmacological and non-pharmacological)
  • Reconsideration of additional neural network dysfunction
    • e.g. emotional reactivity
  • Brain-based diagnostics
  • Brain-based interventions.

Brain-based diagnostics

Using neuroimaging for individual diagnostic and prognostic classification is a promising future application2

Dr Konrad noted that there has been varying success in studies attempting to achieve diagnostic classification of ADHD via neuroimaging techniques.2

  • In one neuroimaging study, T1-weighted structural MRI scans were analysed from male children with (n=24) and without (n=34) ADHD using a support vector machine. Using white matter images alone yielded a diagnostic predictive accuracy for ADHD of 93%; adding grey matter images did not improve this.9
  • In another study, MRI scans from male adolescents with (n=29) and without (n=29) ADHD were analysed using Gaussian process classification to attempt to characterise disorder-specific grey matter patterns associated with ADHD. Results indicated that it was possible to correctly distinguish those with ADHD from controls with an overall accuracy of 79%.10

In a commentary on Dr Konrad’s talk, Professor David Coghill (University of Dundee, Scotland) also noted that it is important to recognise that there is huge variation in ‘normality’ when considering neurobiological development, and there is therefore the need to put ADHD heterogeneity in context with this.11

  1. Walhovd K. Presented at Meeting of Minds (MoM) VII, 29-30 June 2015, Stockholm, Sweden.
  2. Konrad K. Presented at Meeting of Minds (MoM) VII, 29-30 June 2015, Stockholm, Sweden.
  3. Shaw P, Eckstrand K, Sharp W, et al. Attention-deficit/hyperactivity disorder is characterized by a delay in cortical maturation. PNAS 2007; 104: 19649-19654.
  4. Nakao T, Radua J, Rubia K, et al. Gray matter volume abnormalities in ADHD: voxel-based meta-analysis exploring the effects of age and stimulant medication. Am J Psychiatr 2011; 168: 1154-1163.
  5. Cortese S, Kelly C, Chabernaud C, et al. Towards systems neuroscience of ADHD: a meta-analysis of 55 fMRI studies. Am J Psychiatr 2012; 169: 1038-1055.
  6. Pavuluri MN, Yang S, Kamineno K, et al. Diffusion tensor imaging study of white matter fiber tracts in pediatric bipolar disorder and attention-deficit/hyperactivity disorder. Biol Psychiatr 2009; 65: 586-593.
  7. Silk TJ, Vance A, Rinehart N, et al. White-matter abnormalities in attention deficit hyperactivity disorder: a diffusion tensor imaging study. Hum Brain Mapp 2009; 30: 2757-2765.
  8. Rubia K, Halari R, Cubillo A, et al. Methylphenidate normalises activation and functional connectivity deficits in attention and motivation networks in medication-naïve children with ADHD during a reward continuous performance task. Neuropharmacol 2009; 57: 640-652.
  9. Johnston BA, Mwangi B, Matthews K, et al. Brainstem abnormalities in attention deficit hyperactivity disorder support high accuracy individual diagnostic classification. Hum Brain Mapp 2014; 35: 5179-5189.
  10. Lim L, Marquand A, Cubillo AA, et al. Disorder-specific predictive classification of adolescents with attention deficit hyperactivity disorder (ADHD) relative to autism using structural magnetic resonance imaging. PLOS One 2013; 8: e63660.
  11. Coghill D. Presented at Meeting of Minds (MOM) VII, 29-30 June 2015, Stockholm, Sweden.
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