Dataset Information

Disorder-specific predictive classification of adolescents with attention deficit hyperactivity disorder (ADHD) relative to autism using structural magnetic resonance imaging.

ABSTRACT:

Objective

Attention Deficit Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder, but diagnosed by subjective clinical and rating measures. The study's aim was to apply Gaussian process classification (GPC) to grey matter (GM) volumetric data, to assess whether individual ADHD adolescents can be accurately differentiated from healthy controls based on objective, brain structure measures and whether this is disorder-specific relative to autism spectrum disorder (ASD).

Method

Twenty-nine adolescent ADHD boys and 29 age-matched healthy and 19 boys with ASD were scanned. GPC was applied to make disorder-specific predictions of ADHD diagnostic status based on individual brain structure patterns. In addition, voxel-based morphometry (VBM) analysis tested for traditional univariate group level differences in GM.

Results

The pattern of GM correctly classified 75.9% of patients and 82.8% of controls, achieving an overall classification accuracy of 79.3%. Furthermore, classification was disorder-specific relative to ASD. The discriminating GM patterns showed higher classification weights for ADHD in earlier developing ventrolateral/premotor fronto-temporo-limbic and stronger classification weights for healthy controls in later developing dorsolateral fronto-striato-parieto-cerebellar networks. Several regions were also decreased in GM in ADHD relative to healthy controls in the univariate VBM analysis, suggesting they are GM deficit areas.

Conclusions

The study provides evidence that pattern recognition analysis can provide significant individual diagnostic classification of ADHD patients and healthy controls based on distributed GM patterns with 79.3% accuracy and that this is disorder-specific relative to ASD. Findings are a promising first step towards finding an objective differential diagnostic tool based on brain imaging measures to aid with the subjective clinical diagnosis of ADHD.

SUBMITTER: Lim L

PROVIDER: S-EPMC3656087 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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Publications

Disorder-specific predictive classification of adolescents with attention deficit hyperactivity disorder (ADHD) relative to autism using structural magnetic resonance imaging.

Lim Lena L Marquand Andre A Cubillo Ana A AA Smith Anna B AB Chantiluke Kaylita K Simmons Andrew A Mehta Mitul M Rubia Katya K

PloS one 20130516 5

<h4>Objective</h4>Attention Deficit Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder, but diagnosed by subjective clinical and rating measures. The study's aim was to apply Gaussian process classification (GPC) to grey matter (GM) volumetric data, to assess whether individual ADHD adolescents can be accurately differentiated from healthy controls based on objective, brain structure measures and whether this is disorder-specific relative to autism spectrum disorder (ASD).<h4>Method< ...[more]

PMID: 23696841

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Project description:While psychiatric comorbidities of attention-deficit/hyperactivity disorder (ADHD) have been extensively explored, less attention has been paid to somatic conditions possibly associated with this disorder. However, mounting evidence in the last decade pointed to a possible significant association between ADHD and certain somatic conditions, including obesity. This papers provides an update of a previous systematic review on the relationship between obesity and ADHD (Cortese and Vincenzi, Curr Top Behav Neurosci 9:199-218, 2012), focusing on pertinent peer-reviewed empirical papers published since 2012. We conducted a systematic search in PubMed, Ovid, and Web of Knowledge databases (search dates: from January 1st, 2012, to July 16th, 2016). We retained a total of 41 studies, providing information on the prevalence of obesity in individuals with ADHD, focusing on the rates of ADHD in individuals with obesity, or reporting data useful to gain insight into possible mechanisms underlying the putative association between ADHD and obesity. Overall, over the past 4 years, an increasing number of studies have assessed the prevalence of obesity in individuals with ADHD or the rates of ADHD in patients with obesity. Although findings are mixed across individual studies, meta-analytic evidence shows a significant association between ADHD and obesity, regardless of possible confounding factors such as psychiatric comorbidities. An increasing number of studies have also addressed possible mechanisms underlying the link between ADHD and obesity, highlighting the role, among others, of abnormal eating patterns, sedentary lifestyle, and possible common genetic alterations. Importantly, recent longitudinal studies support a causal role of ADHD in contributing to weight gain. The next generation of studies in the field should explore if and to which extent the treatment of comorbid ADHD in individuals with obesity may lead to long-term weight loss, ultimately improving their overall well-being and quality of life.

Project description:Attention Deficit Hyperactivity Disorder (ADHD) and Autism Spectrum Disorders (ASD) are two of the most represented neurodevelopmental conditions in childhood. The diagnostic shift introduced by the DSM-5, allowing a combined diagnosis of ADHD and ASD, poses different clinical challenges related to diagnostic overshadowing, accuracy of clinical judgment and potential delay in an ASD diagnosis in children presenting with ADHD. Here we tried to disentangle the clinical phenotype and specificity of the two co-occurring conditions in relation to autism traits and empathy, by comparing children with ASD with and without comorbid ADHD with children presenting ADHD only and children with typical development. The child versions of the Autism Quotient (C-AQ) and Empathy Quotient (C-EQ) were administered to a total sample of 198 male children between 6 and 14 years old with age appropriate language skills and normal intelligence. Univariate analysis demonstrated no significant differences in the C-AQ total and subscale scores as well as the C-EQ between children with ASD and children with ASD + ADHD, while children with ADHD alone presented an intermediate phenotype between ASD and TD. Furthermore, a receiver operating characteristic (ROC) analysis was applied to discriminate among the different phenotypes. We found that the C-AQ and C-EQ were accurate at distinguishing with satisfactory reliability between: (a) ASD vs. non- ASD (N-ASD) groups comprising both ADHD and TD children (Area Under the Curve AUC 88% for C-AQ and 81% for C-EQ); (b) ASD and TD (AUC 92% for C-AQ and 95% for C-EQ); (c) ASD and ADHD (AUC 80% for C-AQ and 68% for C-EQ). Our data confirm the reliability of the C-AQ and C-EQ as behavioral markers to differentiate ASD (regardless of comorbid ADHD) from an ADHD condition and TD. Interestingly, in our sample an ADHD condition does not increase the severity of the clinical phenotype in terms of autism traits distribution and empathy, suggesting that the psychological measures detected by the two quantitative instruments are independent of ADHD traits. This evidence will contribute to the translational efforts in developing better tailored treatments and preventive strategies.

Dataset Information

Disorder-specific predictive classification of adolescents with attention deficit hyperactivity disorder (ADHD) relative to autism using structural magnetic resonance imaging.

Objective

Method

Results

Conclusions

Publications

Disorder-specific predictive classification of adolescents with attention deficit hyperactivity disorder (ADHD) relative to autism using structural magnetic resonance imaging.

Similar Datasets

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