Background. Attention-deficit/hyperactivity disorder (ADHD) is one of the most prevalent neuropsychiatric disorders in childhood. Besides inattention and hyperactivity, impulsivity is the third core symptom leading to diverse and serious problems. However, the neuronal mechanisms underlying impulsivity in ADHD are still not fully understood. This is all the more the case when patients with the ADHD combined subtype (ADHD-C) are considered who are characterized by both symptoms of inattention and hyperactivity/impulsivity. Method. Combining high-density electroencephalography (EEG) recordings with source localization analyses, we examined what information processing stages are dysfunctional in ADHD-C (n = 20) compared with controls (n = 18). Results. Patients with ADHD-C made more impulsive errors in a Go/No-go task than healthy controls. Neurophysiologically, different subprocesses from perceptual gating to attentional selection, resource allocation and response selection processes are altered in this patient group. Perceptual gating, stimulus-driven attention selection and resource allocation processes were more pronounced in ADHD-C, are related to activation differences in parieto-occipital networks and suggest attentional filtering deficits. However, only response selection processes, associated with medial prefrontal networks, predicted impulsive errors in ADHD-C. Conclusions. Although the clinical picture of ADHD-C is complex and a multitude of processing steps are altered, only a subset of processes seems to directly modulate impulsive behaviour. The present findings improve the understanding of mechanisms underlying impulsivity in patients with ADHD-C and might help to refine treatment algorithms focusing on impulsivity.

The diagnostic process of attention deficit hyperactivity disorder (ADHD) is complex and relies on criteria sensitive to subjective biases. This may cause significant delays in appropriate treatment initiation. An automated analysis relying on subjective and objective measures might not only simplify the diagnostic process and reduce the time to diagnosis, but also improve reproducibility. While recent machine learning studies have succeeded at distinguishing ADHD from healthy controls, the clinical process requires differentiating among other or multiple psychiatric conditions. We trained a linear support vector machine (SVM) classifier to detect participants with ADHD in a population showing a broad spectrum of psychiatric conditions using anonymized data from clinical records (N = 299 participants). We differentiated children and adolescents with ADHD from those not having the condition with an accuracy of 66.1%. SVM using single features showed slight differences between features and overlapping standard deviations of the achieved accuracies. An automated feature selection achieved the best performance using a combination 19 features. Real-world clinical data from medical records can be used to automatically identify individuals with ADHD among help-seeking individuals using machine learning. The relevant diagnostic information can be reduced using an automated feature selection without loss of performance. A broad combination of symptoms across different domains, rather than specific domains, seems to indicate an ADHD diagnosis.

No description available.

No description available.

Autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD) are two frequently co-occurring neurodevelopmental conditions that share certain symptomatology, including social difficulties. This presents practitioners with challenging (differential) diagnostic considerations, particularly in clinically more complex cases with co-occurring ASD and ADHD. Therefore, the primary aim of the current study was to apply a data-driven machine learning approach (support vector machine) to determine whether and which items from the best-practice clinical instruments for diagnosing ASD (ADOS, ADI-R) would best differentiate between four groups of individuals referred to specialized ASD clinics (i.e., ASD, ADHD, ASD + ADHD, ND = no diagnosis). We found that a subset of five features from both ADOS (clinical observation) and ADI-R (parental interview) reliably differentiated between ASD groups (ASD & ASD + ADHD) and non-ASD groups (ADHD & ND), and these features corresponded to the social-communication but also restrictive and repetitive behavior domains. In conclusion, the results of the current study support the idea that detecting ASD in individuals with suspected signs of the diagnosis, including those with co-occurring ADHD, is possible with considerably fewer items relative to the original ADOS/2 and ADI-R algorithms (i.e., 92% item reduction) while preserving relatively high diagnostic accuracy. Clinical implications and study limitations are discussed.