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Available from: Katya Rubia
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ABSTRACT: The diagnosis of Attention Deficit Hyperactivity Disorder (ADHD) is based on subjective measures despite evidence for multisystemic structural and functional deficits. ADHD patients have consistent neurofunctional deficits in motor response inhibition. The aim of this study was to apply pattern classification to task-based functional magnetic resonance imaging (fMRI) of inhibition, to accurately predict the diagnostic status of ADHD. Thirty adolescent ADHD and thirty age-matched healthy boys underwent fMRI while performing a Stop task. fMRI data were analyzed with Gaussian process classifiers (GPC), a machine learning approach, to predict individual ADHD diagnosis based on task-based activation patterns. Traditional univariate case-control analyses were also performed to replicate previous findings in a relatively large dataset. The pattern of brain activation correctly classified up to 90% of patients and 63% of controls, achieving an overall classification accuracy of 77%. The regions of the discriminative network most predictive of controls included later developing lateral prefrontal, striatal, and temporo-parietal areas that mediate inhibition, while regions most predictive of ADHD were in earlier developing ventromedial fronto-limbic regions, which furthermore correlated with symptom severity. Univariate analysis showed reduced activation in ADHD in bilateral ventrolateral prefrontal, striatal, and temporo-parietal regions that overlapped with areas predictive of controls, suggesting the latter are dysfunctional areas in ADHD. We show that significant individual classification of ADHD patients of 77% can be achieved using whole brain pattern analysis of task-based fMRI inhibition data, suggesting that multivariate pattern recognition analyses of inhibition networks can provide objective diagnostic neuroimaging biomarkers of ADHD. Hum Brain Mapp, 2013. © 2013 Wiley Periodicals, Inc.
Available from: Tom A Mcadams
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ABSTRACT: Depression is commonly co-morbid with obsessive-compulsive disorder (OCD). However, it is unknown whether depression is a functional consequence of OCD or whether these disorders share a common genetic aetiology. This longitudinal twin study compared these two hypotheses. Method Data were drawn from a longitudinal sample of adolescent twins and siblings (n = 2651; Genesis 12-19 study) and from a cross-sectional sample of adult twins (n = 4920). The longitudinal phenotypic associations between OCD symptoms (OCS) and depressive symptoms were examined using a cross-lag model. Multivariate twin analyses were performed to explore the genetic and environmental contributions to the cross-sectional and longitudinal relationship between OCS and depressive symptoms.
In the longitudinal phenotypic analyses, OCS at time 1 (wave 2 of the Genesis 12-19 study) predicted depressive symptoms at time 2 (wave 3 of the Genesis 12-19 study) to a similar extent to which depressive symptoms at time 1 predicted OCS at time 2. Cross-sectional twin analyses in both samples indicated that common genetic factors explained 52-65% of the phenotypic correlation between OCS and depressive symptoms. The proportion of the phenotypic correlation due to common non-shared environmental factors was considerably smaller (35%). In the adolescent sample, the longitudinal association between OCS at time 1 and subsequent depressive symptoms was accounted for by the genetic association between OCS and depressive symptoms at time 1. There was no significant environmental association between OCS and later depressive symptoms.
The present findings show that OCS and depressive symptoms co-occur primarily due to shared genetic factors and suggest that genetic, rather than environmental, effects account for the longitudinal relationship between OCS and depressive symptoms.
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