Brain imaging in ADHD - specificity and clinical translation
(la conferencia se expondrá en castellano)
Prof Katya Rubia
Affiliations: Department of Child and Adolescent Psychiatry, King´s College London, Institute of Psychiatry, UK
I will present our recent meta-analyses on structural, functional and biochemical brain abnormalities in ADHD patients, evidence for specificity of these abnormalities relative to other child psychiatric disorders, and discuss clinical translation of neuroimaging in ADHD.
Meta-analyses of region of interest (ROI) structural imaging studies have shown consistent abnormalities in ADHD in the grey matter of frontal, striatal and cerebellar regions (Valera et al., 2007). However, ROI studies are biased towards apriori hypothesised brain regions. Our meta-analysis of whole-brain structural MRI studies shows that ADHD children have most consistent grey matter abnormalities exclusively in the basal ganglia (Nakao et al., 2011). Furthermore, there was an age effect, with studies including children showing the most pronounced deficits, while studies in adults showed no differences in the basal ganglia structure relative to healthy controls (Nakao et al., 2011). The findings of basal ganglia structure deficits are parallel to our meta-analysis of positron emission tomography (PET) studies where we showed consistently reduced striatal dopamine transporter levels in medication-naïve ADHD patients (Fusar-Poli et al., 2012). This could suggest that ADHD patients have lower levels of dopamine in the basal ganglia which may possibly be related to their structural abnormalities in this region. Our meta-analyses of functional MRI studies show cognitive domain-specific abnormalities in dissociated fronto-striatal and fronto-cerebellar networks mediating inhibition, attention and timing functions as well as problems to deactivate the default mode network, both together impairing performance. Our studies comparing between disorders suggest that inferior prefrontal dysfunction as well as some basal ganglia and cerebellar abnormalities in ADHD children may be disorder-specific relative to other child psychiatric disorders, such as conduct disorder (Rubia, 2011), obsessive-compulsive disorder (Rubia, 2011) and autism (Christakou et al., 2013, Chantiluke et al., 2014).
Clinical translation of neuroimaging in ADHD is only just emerging. For neuroimaging to be clinically useful it needs to help with individual diagnostic or prognostic classification. Traditional fMRI analyses are based on group statistics with small effect sizes for group difference findings which has made clinical applications for individual diagnostic classification difficult. Multivariate pattern recognition analyses (MVPA) find spatial and functional imaging patterns that discriminate between groups and can generalise this learned classification to individual subjects and hence provide individual diagnostic classification. We have shown that it is possible to disorder-specifically classify individual ADHD patients with up to 80% accuracy based on structural and functional imaging data (Lim et al., 2013, Hart et al., 2014a,b). Whilst imaging-based classification algorithms are unlikely to replace clinical assessment and diagnosis, they may be a useful objective, automated, and reliable complementary diagnostic tool that could reduce variability in clinical practice and, ultimately, help to improve diagnostic accuracy or revise clinical diagnosis through biomarker classification of uncertain diagnostic cases. Furthermore, these methods are likely to be more useful for prognostic rather than diagnostic classification, such as predicting the disease progression and/or adult outcome of ADHD or medication response, given that currently no predictors are available for these and that brain mechanisms are likely to be better predictors of disease progression or medication response than behavioural measures. There is hence a potential that these methods could revolutionise clinical practice and personalised medicine.
An exciting new avenue is therapeutic neuroimaging. EEG-based neurofeedback has been shown to be very promising in reducing ADHD behaviours with similar effect sizes to stimulant medication and similar efficacy when compared directly against stimulant medication (Arns et al., 2014). fMRI-Neurofeedback (fMRI-NF) has a better spatial resolution and can target key ADHD regions such as IFC and basal ganglia. Other neurotherapies such as regional electrical stimulation using repetitive transcranial magnetic stimulation (rTMS) and transcranial direct current stimulation (tDCS) have found successful applications to other psychiatric disorders and are promising for ADHD.
In conclusion, the last decades of neuroimaging have significantly broadened our understanding of the underlying neurobiology of ADHD. They have shown that ADHD is most prominently associated with the dysmorphology, dysfunction and underconnectivity of multiple fronto-striatal, fronto-parietal and fronto-cerebellar networks that mediate “cool” EF, including the ventral fronto-ACC/SMA-striato-thalamic cognitive control system, the dorsal and ventral fronto-striato-thalamo-parietal attention systems and ventral fronto-parieto-cerebellar timing networks. In addition, ADHD children have structural, functional and connectivity deficits in DMN systems that appear to be poorly deactivated during task-performance and hence intruding upon task-positive cognitive systems. Both the poor activation of task-relevant networks and the poor deactivation of the DMN likely underlie their poor performance on EF. However, more studies are needed to integrate different imaging modalities to assess longitudinal trajectories of the disorder to understand the association between abnormal and potentially delayed development of brain structure, brain function and structural and functional connectivity in ADHD. The next decade will likely focus on using neuroimaging techniques in a more clinically applied fashion either in the form of individual diagnosis, prognosis of disease progression and of treatment success or as a neurotherapy to normalise abnormally functioning brain regions.
Arns M, Heinrich H, Strehl U. Neurofeedback in ADHD: The long and winding road. Biol Psychiatry. 2014; 95:108-15.
Chantiluke K, Barrett N, Giampietro V, Brammer M, Simmons A, Murphy D, et al. Inverse effect of Fluoxetine on Medial Prefrontal Cortex Activation during Reward Reversal in ADHD and Autism. Cerebral Cortex. 2013;in press.
Christakou A, Murphy C, Chantiluke C, Cubillo A, Smith A, Giampietro V, et al. Disorder-specific functional abnormalities during sustained attention in youth with Attention Deficit Hyperactivity Disorder (ADHD) and with Autism. Molecular Psychiatry. 2013;18(2):236-44.
Fusar-Poli P, Rubia K, Rossi G, Sartori G, Ballotin U. Dopamine transporter alterations in ADHD: pathophysiology or adaptation to psychostimulants? a meta-analysis. American Journal of Psychiatry. 2012;169:264 -72.
Hart H, Chantiluke K, Cubillo A, Smith A, Simmons A, Marquand A, et al. Pattern classification of response inhibition in ADHD: toward the development of neurobiological markers for ADHD. Human Brain Mapping, 2014, in press.
Hart H, Radua J, Mataix D, Rubia K. Meta-analysis of fMRI studies of timing functions in ADHD. Neuroscience Biobehavioural Review. 2012;36(10):2248-56.
Hart H, Radua J, Mataix D, Rubia K. Meta-analysis of fMRI studies of inhibition and attention in ADHD: exploring task-specific, stimulant medication and age effects. JAMA Psychiatry. 2013;70(2):185-98.
Hart H, Smith A, Cubillo A, Simmons A, Marquand A, Rubia K. Predictive neurofunctional markers of ADHD based on pattern classification of temporal processing. J Abn Child Adol Psych, 2014b, in press.
Lim L, Cubillo A, Smith A, Chantiluke K, Marquand A, Simmons A, 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(5):e63660.
Nakao T, Radua C, Rubia K, Mataix-Cols D. Gray matter volume abnormalities in ADHD and the effects of stimulant medication: Voxel-based meta-analysis. American Journal of Psychiatry. 2011;168(11):1154-63.
Rubia K. “Cool” inferior fronto-striatal dysfunction in Attention Deficit Hyperactivity Disorder (ADHD) versus “hot” ventromedial orbitofronto-limbic dysfunction in conduct disorder: a review. Biological Psychiatry. 2011;69:e69-e87.
Valera EM, Faraone SV, Murray KE, Seidman LJ. Meta-analysis of structural imaging findings in attention-deficit/hyperactivity disorder. Biological Psychiatry. 2007;61(12):1361-9.